@Research Paper
<#LINE#>Corrosion restriction and adsorption characteristics of Nitrogen–containing ligands on Aluminium in different concentrations of Hydrochloric acid<#LINE#>Suresh @Sahu <#LINE#>1-7<#LINE#>1.ISCA-RJRS-2026-004.pdf<#LINE#>Government Engineering College, Ajmer, Rajasthan, India<#LINE#>4/4/2026<#LINE#>14/5/2026<#LINE#>In the present paper, we are presenting mass loss to analyse the corrosion inhibition of aluminium in 0.5N HCl, 1.0N HCl, and 2.0N HCl solutions by three novel nitrogen-containing ligands (Schiff’s bases) viz, N(vanillidine)-4-methyl-1-phenylimine (SB1), N(vanillidine)-4-methoxy-1-phenylimine (SB2), N(anisidine)-1-napthylimine (SB3).  The efficiencies have been compared with those of parent aldehyde and amines from which Schiff's base has been derived. Results of inhibition efficiencies observed from these two methods are in good agreement and have been found to depend on the concentrations of inhibitors as well as those of acids. The inhibitory efficacy of all inhibitors increases with increasing inhibitor concentration. Efficacy also increases with increasing concentration of acids. Adsorption of the inhibition molecule on the aluminium surface was consistent with the Langmuir isotherm.<#LINE#>Serpil, Ş., Duran, B., Yurt, A. and Türkoğlu, G. (2012).@Schiff bases as corrosion inhibitor for aluminium in HCl solution.@Corrosion Science, 54, 251-259.@Yes$Rajalakshmi, K.and Jayendran, T. (2010).@Inhibition of Corrosion of Aluminium in 1N Sodium Hydroxide by Salicylic Acid in Conjunction with Calcium Acetate.@Asian J. Research Chem., 3(2), 351-354.@Yes$Mercier, D. and Barthés, L.M.G. (2009).@The role of chelating agents on the corrosion mechanisms of aluminium in alkaline aqueous solutions.@Corrosion Science, 51(2), 339-34.@Yes$Srikanth, B., Kumar, S. B. S.and Radhakrishna, S. L. (2019).@Mechanical Behavior of Nickel Addition on Aluminium   Alloy Al-7175.@Research J. Engineering and Tech., 10 (3), 125-130.@No$Yadav, M., Kumar, S., Nasar, A. and Kumar, S. (2010).@Inhibition of Corrosion of Copper by 4-Amino-3-       Phenyl-5-Mercapto-1, 2, 4-Triazole in 3.5% Sodium Chloride Solution.@Asian J. Research Chem., 3(4), 938-942.@Yes$Amaal, S. S. and Entesar, O. A.T. (2021).@Synthesis and Characterization of New poly β-Lactam from poly acrolein and Study Corrosion Inhibition for Stainless steel in Hydrochloric Acid Solution.@Research Journal of Pharmacy and Technology, 14 (6), 3039-3044.@Yes$Beccaria, A. M. and Chiaruttini, L. (1999).@The inhibitive action of metacryloxypropylmethoxysilane (MAOS) on aluminium corrosion in NaCl solutions.@Corrosion science, 41(5), 885-899.@No$Zheludkevich, M.L., Yasakau, K.A., Poznyak, S.K. and Ferreira, M.G.S. (2005).@Triazole and thiazole derivatives as corrosion inhibitors for AA2024 aluminum alloy.@Corrosion Science, 47(12), 3368-3383.@No$Bhawsar, J. and Jain, P. (2018).@Investigation of Mentha spicata extract as Green Corrosion Inhibitor for Mild Steel in 2M Sulphuric Acid Medium.@Research J. Pharm. and Tech, 11(10), 4627-4634.@Yes$Jain, T., Chaudhary, R. and Mathur, S. P. (2006).@Electrochemical behavior of aluminium in acdic media.@Material and corrosion,57(5), 1-5.@Yes$Sharma, M. K., Kumar, S., Ratnani, R. and Mathur, S. P. (2006).@Corrosion inhibition of aluminium by extracts of Prosopis cineraria in acdic media.@Bulletin of Electrochemistry, 22(2), 69-73.@Yes$Arora, P., Kumar, S., Sharma, M. K., & Mathur, S. P. (2007).@Corrosion inhibition of aluminium by Capparis deciduas in acidic media.@Journal of Chemistry, 4(4), 450-456.@Yes$Sethi,T., Chaturvedi, A., Upadhyay, R. K. and Mathur, S. P. (2008).@Inhibition effect of nitrogen containing ligands on corrosion of aluminium in acid media with and without KCl.@Polish J. Chem, 82, 591-598.@Yes$Khandelwal, R., Sahu, S. and Arora, S. K. (2018).@Comparative Study of Schiff’s Bases and Plant Extract as Corrosion Inhibitors.@Advanced science Engg. and Medicine, 10, 1023-1028.@Yes
<#LINE#>Alkaline Desilication–Induced Matrix Densification and Rare Earth Element Retention in Coal Fly Ash<#LINE#>Vinod @Kumar,Akanksha @Aggarwal,Amit @Kulshershtha,Manish @Kumar,Shanta @Kumar,Raj Kumar @Tripathi,Yashwant Kumar @Saini,Shyam Manohar @Gupta <#LINE#>8-15<#LINE#>2.ISCA-RJRS-2026-008.pdf<#LINE#>NTPC Energy Technology Research Alliance (NETRA), Plot No. E-3, Ecotech-II, Udyog Vihar, Greater Noida -201306, Uttar Pradesh, India@NTPC Energy Technology Research Alliance (NETRA), Plot No. E-3, Ecotech-II, Udyog Vihar, Greater Noida -201306, Uttar Pradesh, India@NTPC Energy Technology Research Alliance (NETRA), Plot No. E-3, Ecotech-II, Udyog Vihar, Greater Noida -201306, Uttar Pradesh, India@NTPC Energy Technology Research Alliance (NETRA), Plot No. E-3, Ecotech-II, Udyog Vihar, Greater Noida -201306, Uttar Pradesh, India@NTPC Energy Technology Research Alliance (NETRA), Plot No. E-3, Ecotech-II, Udyog Vihar, Greater Noida -201306, Uttar Pradesh, India@NTPC Energy Technology Research Alliance (NETRA), Plot No. E-3, Ecotech-II, Udyog Vihar, Greater Noida -201306, Uttar Pradesh, India@NTPC Energy Technology Research Alliance (NETRA), Plot No. E-3, Ecotech-II, Udyog Vihar, Greater Noida -201306, Uttar Pradesh, India@NTPC Energy Technology Research Alliance (NETRA), Plot No. E-3, Ecotech-II, Udyog Vihar, Greater Noida -201306, Uttar Pradesh, India<#LINE#>9/4/2026<#LINE#>20/5/2026<#LINE#>Coal fly ash (CFA), an aluminosilicate-rich by-product obtained after coal combustion, is a potential secondary resource for rare earth elements (REEs). This study evaluates a matrix-engineering approach based on alkaline desilication–induced densification to restructure the host framework while retaining REEs in the solid phase. Magnetic separation followed by controlled treatment with 4 M NaOH (solid-to-liquid ratio 1:5) was applied to CFA from an Indian thermal power plant. Major oxides were analyzed by ED-XRF and 18 REEs were quantified using ICP–MS. Magnetic separation removed only 3.68% of the total mass, indicating limited association of REEs with strongly magnetic iron phases. Alkaline treatment achieved >92% silica removal, reducing SiO₂ from 58.55 wt.% to 4.41 wt.% and enriching Al₂O₃ to 85.00 wt.% in the bottom-settled fraction. The slurry stratified into density-driven layers, with the alumina-rich residue representing a densified structural phase. Despite extensive desilication, REEs remained predominantly in the solid phase. The abundance pattern (Ce > La > Nd > Y > Sc > Dy) was preserved across treated fractions, and critical elements such as Nd and Dy were retained in the densified residue. Observed decreases in ppm values reflect matrix mass redistribution rather than significant alkaline solubilization. The process is best described as alkaline desilication–induced matrix densification with stratified REE retention. This acid-minimizing strategy upgrades CFA into an alumina-enriched precursor suitable for downstream targeted REE recovery, supporting sustainable resource utilization.<#LINE#>Stoy, L., Diaz, V. & Huang, C. H. (2021).@Preferential recovery of rare-earth elements from coal fly ash using a recyclable ionic liquid.@Environmental Science & Technology, 55(13), 9209–9220.@Yes$Liu, P., Zhao, S., Xie, N., Yang, L., Wang, Q., Wen, Y., Chen, H., & Tang, Y. (2023).@Green approach for rare earth element (REE) recovery from coal fly ash.@Environmental Science & Technology, 57(13), 5414–5423.@Yes$Kim, G. M., Park, S., Choi, J., Park, S., & Kim, J. (2024).@Effects of alkaline extraction on behavior of rare earth elements in coal ashes.@Environmental Science and Pollution Research, 31(54), 63210-63224.@Yes$Zhang, W., Noble, A., Yang, X., & Honaker, R. (2020).@A comprehensive review of rare earth elements recovery from coal-related materials.@Minerals, 10(5), 451.@Yes$Tian, X., Guo, Z., Zhu, D., Pan, J., Yang, C. & Li, S. (2025).@Recovery of valuable elements from coal fly ash: A review.@Environmental Research, 282, 121928.@Yes$Franus, W., Wiatros-Motyka, M. M., & Wdowin, M. (2015).@Coal fly ash as a resource for rare earth elements.@Environmental Science and Pollution Research, 22, 9464–9474.@Yes$Ajayi, L. O., Lejeune, B., Struppe, J., Guo, J., & Daramola, D. A. (2025).@Alkali treatment implications for microwave-assisted rare earth elements extraction from coal mine tailings.@Environmental Science & Technology, 59, 25044–25055.@Yes$Tripathi, R. K., Saini, R. K., Aggarwal, A., Kumar, A. L., Pranay. & Das, A. K. (2025).@A case study for assessing coal fly ash in NTPC power stations as a potential source of rare earth elements.@International Journal of Engineering Research & Technology, 14(4).@Yes$Pan, J., Hassas, B. V., Rezaee, M., Zhou, C., & Pisupati, S. V. (2020).@Recovery of rare earth elements from coal fly ash through sequential chemical roasting, water leaching, and acid leaching processes.@Journal of Cleaner Production, 284, 124725.@Yes$Bisen, S. V., Sharma, S., & Chattopadhyay, S. (2025).@Rare earth elements occurrences in coal fly ash and methods of extraction: A review.@Journal of the Geological Society of India, 101(5), 581–590.@Yes$Thomas, B. S., Dimitriadis, P., Kundu, C., Vuppaladadiyam, S. S. V., & Bhattacharya, S. (2024).@Extraction and separation of rare earth elements from coal and coal fly ash: A review on engineering advancements.@Journal of Environmental Chemical Engineering, 12(3), 112769.@Yes$Tajayani, I. D., Sutijan, S., & Petrus, H. T. B. M. (2023).@Precipitation of rare earth element from Indonesian coal fly ash using sodium sulphate.@Materials Science Forum, 1093, 105–110.@Yes$Kim, G. M., Park, S., Choi, J., Park, S., & Kim, J. (2024).@Effects of alkaline extraction on behavior of rare earth elements in coal ashes.@Environmental Science and Pollution Research, 31(54), 63210–63224.@Yes$Zhang, L., Chen, H., Pan, J., Yang, F., Long, X., Yang, Y., & Zhou, C. (2025).@Rare earth elements recovery and mechanisms from coal fly ash by column leaching using citric acid.@Separation and Purification Technology, 362, 128471.@Yes$Wen, Z., Zhou, C., Pan, J., Cao, S., Hu, T., & Ji, W. (2020).@Recovery of rare-earth elements from coal fly ash via optimized leaching.@Journal of Environmental Management, 270, 110912.@Yes$Zhang, W., Noble, A., Yang, X., & Honaker, R. Q. (2020).@A comprehensive review of rare earth elements recovery from coal-related materials.@Minerals, 10(5), 451.@Yes$Tian, X., Guo, Z., Zhu, D., Pan, J., Yang, C., & Li, S. (2025).@Recovery of valuable elements from coal fly ash: A review.@Environmental Research, 282, 121928.@Yes$Franus, W., Wiatros-Motyka, M. M., & Wdowin, M. (2015).@Coal fly ash as a resource for rare earth elements.@Environmental Science and Pollution Research, 22(12), 9464–9474.@Yes$Saquib, M. F. U. R., Ameer, S. K., Munjial, P., & Aly, O. (2026).@Sustainability assessment of hydrogen fuel cell vehicles versus fossil fuel vehicles: A Canadian perspective.@15(1), 11–15.@Yes$Aggarwal, A., Tripathi, R. K., Kumar, A. L., Sharma, P., & Das, A. K. (2025).@Harnessing Indian coal fly ashes for rare earth elements recovery.@NTPC Transactions on Energy Research (NTER 2025) (pp. 221–235).@Yes$American Society for Testing and Materials. (2008).@Standard specification for coal fly ash and raw or calcined natural pozzolan for use in concrete.@ASTM International.@No$Gambogi, J. (2020).@Mineral commodity summaries: Rare earths.@US Geological Survey, Washington, DC https://www. usgs. gov/centers/nmic/rare-earths-statistics-and-information. Accessed, 14.@Yes$Together, S. S. (2013).@International Energy Agency.@International Energy Agency: Paris, France.@Yes$Hussain, Z., Dwivedi, D., & Kwon, I. (2024).@Recovery of rare earth elements from low-grade coal fly ash using recyclable biosorbents.@Frontiers in Bioengineering and Biotechnology, 12, 1385845.@Yes
<#LINE#>Efflux Pumps of Mycobacterium tuberculosis: Expression analysis and in-silico characterization<#LINE#>Anshu Beulah @Ram,Gurshish @Kour <#LINE#>16-28<#LINE#>3.ISCA-RJRS-2026-010.pdf<#LINE#>Department of Biotechnology, Nrupathunga University, Nrupathunga Road, Bangalore, Karnataka 560001, India@Clinical Microbiology Division, Indian Institute of Integrative Medicine (CSIR), Canal Road, Jammu 180 001, India<#LINE#>10/4/2026<#LINE#>20/5/2026<#LINE#>Multidrug resistant tuberculosis, a global threat to the world community, involves high cost and long duration of treatment. Emergence of drug resistance is attributed to activity of efflux pumps as one of the earliest cause in mycobacterium. Here we show the real time expression profiles of 20 probable efflux pumps of Mycobacterium tuberculosis under the two most important drugs of antiTB regime – rifampin and isoniazid. Under rifampin pressure 10 efflux pump genes were over expressed (Rv1258c, Rv1410c, Rv1819c, Rv1145, Rv2936, Rv2937, Rv0849, Rv1877, Rv2209, and Rv0783). Under isoniazid pressure 4 efflux pump genes were induced (Rv1258c, Rv1819c, Rv2938, and Rv3065).  The three dimensional structure of Rv1410c was modelled that shares significant structural similarity with MFS transporter proteins. Docking with rifampin revealed the drug’s binding pattern. It is hoped that current study will provide significant information regarding efflux mediated drug resistance of mycobacterium and can facilitate the development of new potent efflux pump inhibitors against tuberculosis.<#LINE#>World Health Organization. (2023).@Global tuberculosis report 2023.@World health organization.@Yes$Datta, D., Jamwal, S., Jyoti, N., Patnaik, S., & Kumar, D. (2024).@Actionable mechanisms of drug tolerance and resistance in Mycobacterium tuberculosis.@The FEBS journal, 291(20), 4433-4452.@Yes$Udwadia Z.F., Amale, R. A., Ajbani, K. K., & Rodrigues, C. (2012).@Totally drug-resistant tuberculosis in India.@Clinical Infectious Diseases, 54(4), 579–581.@Yes$Nikaido, H. (2001).@Preventing drug access to targets: cell surface permeability barriers and active efflux in bacteria.@Seminar in Cell and Developmental Biology, 12, 215–223@Yes$Heym, B., Alzari, P.M., Honore, N.  & Cole, S.T. (1995).@Missense mutations in the catalase-peroxidase gene, katG, are associated with isoniazid resistance in Mycobacterium tuberculosis.@Molecular Microbiology, 15, 235–245.@Yes$Heym, B., Saint-Joanis, B. & Cole, S.T. (1999).@The molecular basis of isoniazid resistance in Mycobacterium tuberculosis.@Tubercle and Lung Disease, 79, 267–271.@Yes$Ramaswamy, S. V., Reich, R., Dou, S. J., Jasperse, L., Pan, X., Wanger, A., ... & Graviss, E. A. (2003).@Single nucleotide polymorphisms in genes associated with isoniazid resistance in Mycobacterium tuberculosis.@Antimicrobial agents and chemotherapy, 47(4), 1241-1250.@Yes$Baulard, A. R., Betts, J.C., Engohang-Ndong, J., Quan, S., McAdam, R.A., Brennan, P.J., Locht, C.  and Besra, G.S.  (2000).@Activation of the pro-drug ethionamide is regulated in mycobacteria.@Journal of Biological Chemistry. 275, 28326–28331.@Yes$Morlock, G.P., Metchock, B., Sikes, D., Crawford, J.T. and Cooksey, R.C. (2003).@Eth A, inh A, and kat Gloci of ethionamide-resistant clinical Mycobacterium tuberculosis isolates.@Antimicrobial agents and Chemotherapy 47, 3799–3805.@Yes$Zainuddin, Z.F. and Dale, J.W. (1990).@Does Mycobacterium tuberculosis have plasmids?@Tubercle 71:43–49.@Yes$Telenti, A. (1997).@Genetics of drug resistance in tuberculosis.@Clinics in Chest Medicine, 18, 55–64.@Yes$Mitchison, D. A. (1979).@Basic mechanisms of chemotherapy.@Chest, 76(6), 771-776.@Yes$Musser J. M. (1995).@Antimicrobial agent resistance in mycobacteria: molecular genetics insights.@Clinical Microbiology Reviews, 8, 496–514.@Yes$Hazbon, M.H., Brimacombe, M., Bobadilla Del Valle, M., Cavatore, M., Guerrero, M.I., Varma-Basil, M., Billman-Jacobe, H., Lavender, C., Fyfe, J., García-García, L. & León, C.I., (2006).@Population genetics study of isoniazid resistance mutations and evolution of multidrug-resistant Mycobacterium tuberculosis.@Antimicrobial agents and Chemotherapy, 50, 2640–2649.@Yes$Basso L.A., Zheng R., Musser J.M., Jacobs Jr, W.R., & Blanchard J.S. (1998).@Mechanisms of isoniazid resistance in Mycobacterium tuberculosis: enzymatic characterization of enoyl reductase mutants identified in isoniazid-resistant clinical isolates.@Journal of Infectious Diseases, 178, 769–775.@Yes$Miesel L., Weisbrod T.R., Marcinkeviciene J.A., Bittman, R., & Jacobs, W.R.Jr. (1998).@NADH dehydrogenase defects confer isoniazid resistance and conditional lethality in Mycobacterium smegmatis.@Journal of Bacteriology, 180, 2459–67.@Yes$Larsen, M.H., Vilchèze, C., Kremer, L., Besra, G.S., Parsons, L., Salfinger, M., Heifets, L., Hazbon, M.H., Alland, D., Sacchettini, J.C. & Jacobs, W.R. Jr. (2002).@Over expression of inhA, but not kasA, confers resistance to isoniazid and ethionamide in Mycobacterium smegmatis, M. bovis BCG and M. tuberculosis.@Molecular Microbiology, 46, 453–466.@Yes$Dalla Costa, E.R., Ribeiro, M.O., Silva, M.S., Arnold, L.S., Rostirolla, D.C., Cafrune, P.I., Espinoza, R.C., Palaci, M., Telles, M.A., Ritacco, V., Suffys, P.N., Lopes, M.L., Campelo, C.L., Miranda, S.S., Kremer, K., da Silva, P.E., Fonseca, Lde S., Ho, J.L., Kritski, A.L. & Rossetti, M.L. (2009).@Correlations of mutations in katG, oxyR-ahpC and inhA genes and in vitro susceptibility in Mycobacterium tuberculosis clinical strains segregated by spoligotype families from tuberculosis prevalent countries in South America.@BMC Microbiology, 9, 39.@Yes$Ando H., Kitao T., Miyoshi-Akiyama T., Kato, S., Mori, T., &Kirikae, T. (2011).@Downregulation of katG expression is associated with isoniazid resistance in Mycobacterium tuberculosis.@Molecular Microbiology, 79: 1615–28.@Yes$Louw, G.E., Warren, R.M., Gey van Pittius, N.C., McEvoy, C.R.E., Van Helden, P.D. & Victor, T.C. (2009).@A Balancing Act: Efflux/Influx in Mycobacterial Drug Resistance.@Antimicrobial agents and Chemotherapy, 53(8), 3181-3189.@Yes$Dhamdhere, G. & Zgurskaya, H.I. (2010).@Metabolic shutdown in Escherichia coli cells lacking the outer membrane channel TolC.@Molecular Microbiology, 77, 743–754.@Yes$Lau, S.Y. & Zgurskaya, H.I. (2005).@Cell division defects in Escherichia coli deficient in the multidrug efflux transporter AcrEF-TolC.@Journal of Bacteriology, 187, 7815–7825.@Yes$Krulwich, T.A., Lewinson. O., Padan, E., & Bibi, E. (2005).@Do physiological roles foster persistence of drug/multidrug-efflux transporters? A case study.@Nature Reviews Microbiology, 3, 566 –572.@Yes$De Rossi, E., Aı́nsa, J. A, & Riccardi, G. (2006).@Role of mycobacterial efflux transporters in drug resistance: an unresolved question.@FEMS Microbiology Reviews, 30, 36–52.@Yes$Pasipanodya, J.G. & Gumbo, T. (2011).@A new evolutionary and pharmacokinetic-pharmacodynamic scenario for rapid emergence of resistance to single and multiple anti-tuberculosis drugs.@Current Opinion in Pharmacology, 11(5), 457–463.@Yes$Adams, K. N., Takaki, K., Connolly, L. E., Wiedenhoft, H., Winglee, K., Humbert, O., Edelstein, P. H., Cosma, C. L., & Ramakrishnan, L. (2011).@Drug Tolerance in Replicating Mycobacteria Mediated by a Macrophage-Induced Efflux Mechanism.@Cell, 145, 39–53.@Yes$Cole, S.T., Brosch, R., Parkhill, J., Garnier, T., Churcher, C., Harris, D., Gordon, S.V., Eiglmeier, K., Gas, S., Barry, C.E. III, Tekaia, F., Badcock, K., Basham, D., Brown, D., Chillingworth, T., Connor, R., Davies, R., Devlin, K., Feltwell, T., Gentles, S., Hamlin, N., Holroyd, S., Hornsby, T., Jagels, K. & Barrell, B.G. (1998).@Deciphering the biology of Mycobacterium tuberculosis from the complete genome sequence.@Nature, 393, 537–544.@Yes$MA, W. (2006).@Methods for dilution antimicrobial susceptibility tests for bacteria that grow aerobically: approved standard.@Clsi (Nccls), 26, M7-A7.@Yes$Eliopoulos, G.M. & Wennersten, C.B. (2002).@Antimicrobial activity of quinupristin–dalfopristin combined with other antibiotics against vancomycin-resistant enterococci.@Antimicrobial agents and Chemotherapy, 46, 1319–24.@Yes$Alvireaz-Freits, E.J., Carter, J.L. & Cynamon, M.H. (2002)@In vitro and in vivo activity of gatifloxacin against Mycobacterium tuberculosis.@Antimicrobial agents and Chemotherapy, 46, 1022–5.@Yes$Sanger, G., & Goldstein, C. (2001).@Principles, workflows and advantages of the new Light Cycler Relative Quantification Softwar.@Biochemical, 3, 15-17@Yes$Altschul, S. F., Gish, W., Miller, W., Myers, E. W., & Lipman, D. J. (1990).@Basic local alignment search tool.@Journal of molecular biology, 215(3), 403-410.@Yes$Jiang, D., Zhao, Y., Wang, X., Fan, J., Heng, J., Liu, X., Feng, W., Kang, X., Huang, B., Liu, J., & Zhang, X.C. (2013).@Structure of the YajR transporter suggests a transport mechanism based on the conserved motif A.@Proceedings of the National Academy of Sciences of the United States of America, 110, 14664-14669.@Yes$Sali, A., & Blundell, T.L. (1993).@Comparative protein modelling by satisfaction of spatial restraints.@Journal of Molecular Biology, (3), 779–815@Yes$Wiederstein M. & Sippl M.J. (2007).@ProSA-web: interactive web service for the recognition of errors in three-dimensional structures of proteins.@Nucleic Acids Research, 35, W407–W410.@Yes$Glaser, F., Pupko, T., Paz, I., Bell, R.E., Bechor, D., Martz, E. & Ben-Tal, N. (2003).@ConSurf: Identification of Functional Regions in Proteins by Surface-Mapping of Phylogenetic Information.@Bioinformatics, 19, 163-164.@Yes$Laurie, A.T. & Jackson, R.M. (2005).@Q-SiteFinder: an energy-based method for the prediction of protein-ligand binding sites.@Bioinformatics, 21, 1908-1916@Yes$Morris, G.M., Huey, R., Lindstrom, W., Sanner, M.F., Belew, R.K., Goodsell, D.S. & Olson, A. J. (2009).@Autodock4 and AutoDockTools4: automated docking with selective receptor flexibility.@Journal of Computational Chemistry, 16, 2785-91@Yes$Hete´nyi, C. & van der Spoel, D. (2002).@Efficient docking of peptides to proteins without prior knowledge of the binding site.@Protein Science, 11, 1729–1737.@Yes$Wallace, A.C., Laskowski, R.A. & Thornton, J.M. (1995).@LIGPLOT: a program to generate schematic diagrams of protein-ligand interactions.@Protein Engineering, Design and Selection, 8, 127–134@Yes$Khan, I.A., Mirza, Z.M., Kumar, A., Verma, V., & Qazi, G.N. (2006).@Piperine, a phytochemical potentiator of ciprofloxacin against Staphylococcus aureus.@Antimicrobial Agents and Chemotherapy, 50, 810-812@Yes$Zhang, J.R., Li, G.L., Zhao, X.Q., Wan, K.L. &Lü, J.X. (2013).@A primary investigation on the isoniazid-induced alterations in efflux gene expression among the isoniazid resistant Mycobacteriumtuberculosis clinical isolates.@Zhonghua Liu Xing Bing Xue Za Zhi, 34(4), 379-84@Yes$Jiang, X., Zhang, W., Zhang, Y., Gao, F., Lu, C., Zhang, X., & Wang, H. (2008).@Assessment of efflux pump gene expression in a clinical isolate Mycobacteriumtuberculosis by real-time reverse transcription PCR.@Microbial drug resistance, 14(1), 7-11.@Yes$Gupta, A.K., Katoch, V.M., Chauhan, D.S., Sharma, R., Singh, M., Venkatesan, K. & Sharma, V.D. (2010).@Microarray analysis of efflux pump genes in multidrug-resistant Mycobacteriumtuberculosis during stress induced by common anti-tuberculous drugs.@Microbial drug resistance, 16(1), 21-8.@Yes$Machado, D., Couto, I., Perdigão, J., Rodrigues, L., Portugal, I., Baptista, P., Veigas, B., Amaral, L., & Viveiros, M. (2012).@Contribution of efflux to the emergence of isoniazid and multidrug resistance in Mycobacterium tuberculosis.@PLOSone, 7(4), e34538@Yes$Rodrigues, L., Villellas, C., Bailo, R., Viveiros, M. &Aínsa, J.A. (2013).@Role of the Mmr efflux pump in drug resistance in Mycobacterium tuberculosis.@Antimicrobial Agents and Chemotherapy, 57(2), 751-7.@Yes$Ghajavand, H., Kargarpour Kamakoli, M., Khanipour, S., Pourazar Dizaji, S., Masoumi, M., Rahimi Jamnani, F., ... & Vaziri, F. (2019).@Scrutinizing the drug resistance mechanism of multi-and extensively-drug resistant Mycobacterium tuberculosis: mutations versus efflux pumps.@Antimicrobial Resistance & Infection Control, 8(1), 70.@Yes$Gupta, A.K., Chauhan, D.S., Srivastava, K., Das, R., Batra, S., Mittal, M., Goswami, P., Singhal, N., Sharma, V.D., Venkatesan, K., Hasnain, S.E. & Katoch, V.M. (2006).@Estimation of efflux mediated multi-drug resistance and its correlation with expression levels of two major efflux pumps in mycobacteria.@Journal of Communicable Diseases, 38(3), 246-54.@Yes$Pang, Y., Lu, J., Wang, Y., Song, Y., Wang, S., & Zhao, Y. (2013).@Study of the Rifampin monoresistance mechanism in Mycobacterium tuberculosis.@Antimicrobial Agents and Chemotherapy, 57, 893-900@Yes$Silva, P.E., Bigi, F., Santangelo, M.P., Romano, M.I., Martín, C., Cataldi, A. & Aínsa, J.A. (2001).@Characterization of P55, a multidrug efflux pump in Mycobacteriumbovis and Mycobacterium tuberculosis.@Antimicrobial Agents and Chemotherapy, 45(3), 800-4.@Yes$Rodrigues, L., Machado, D., Couto, I., Amaral, L. & Viveiros, M. (2012).@Contribution of efflux activity to isoniazid resistance in the Mycobacterium tuberculosis complex.@Infection, Genetics and Evolution, (4), 695-700.@Yes$Kaatz, G.W., Thyagarajan, R.V. & Seo, S.M. (2005).@Effect of promoter region mutations and mgrAoverexpression on transcription of norA, which encodes a Staphylococcus aureus multidrug efflux transporter.@Antimicrobial Agents and Chemotherapy, 49, 161–169.@Yes$Piddock, L.J.V. (2006).@Clinically relevant chromosomally encoded multidrug resistance efflux pumps in bacteria.@Clinical microbiology reviews, 19(2), 382-402.@Yes$Yan, N. (2013).@Structural advances for the major facilitator superfamily (MFS) transporters.@Trends in Biochemical Sciences, 38(3), 151-159@Yes$Sun, L., Zeng, X., Yan, C., Sun, X., Gong, X., Rao, Y., & Yan, N. (2012).@Crystal structure of a bacterial homologue of glucose transporters GLUT1–4.@Nature, 490(7420), 361-366.@Yes$Pao, S.S., Paulsen, I.T. & Saier, M.H. Jr. (1998).@Major facilitator superfamily.@Microbiology and Molecular Biology Reviews, 62, 1–34@Yes$Marger, M.D. & Saier, M.H., Jr (1993).@A major superfamily of transmembrane facilitators that catalyse uniport, symport and antiport.@Trends in Biochemical Sciences, 18, 13–20@Yes$Reddy, V. S., Shlykov, M. A., Castillo, R., Sun, E. I., & Saier Jr, M. H. (2012).@The major facilitator superfamily (MFS) revisited.@The FEBS journal, 279(11), 2022-2035.@Yes$Saidijam, M., Benedetti, G.,  Ren, Q.,  Xu, Z.,  Hoyle, C.J.,  Palmer, S.L.,  Ward, A.,  Bettaney, K.E.,  Szakonyi, G.,  Meuller, J.,  Morrison, S.,  Pos, M.K.,  Butaye, P.,  Walravens, K.,  Langton, K.,  Herbert, R.B.,  Skurray, R.A.,  Paulsen, I.T.,  O’reilly, J., Rutherford, N.G.,  Brown, M.H.,  Bill, R.M.  & Henderson, P.J.. (2006).@Microbial drug efflux proteins of the major facilitator superfamily.@Current Drug Targets, 7, 793–811.@Yes$Schlessinger, A., Khuri, N., Giacomini, K. M., & Sali, A. (2013).@Molecular Modeling and Ligand Docking for Solute Carrier (SLC) Transporters.@Current Topics in Medicinal Chemistry, 13, 843-856.@Yes
<#LINE#>Green synthesis of Copper Nanoparticles from Coleus amboinicus stem extract and its Characterization, Antibacterial potential study against Staphylococcus aureus<#LINE#>Priya @P.,Druthi @P.R.,Jeevitha @M.,Sarina P. @Kabhade,Bhupanapadu Sunkesula Mary @Stella <#LINE#>29-35<#LINE#>4.ISCA-RJRS-2026-013.pdf<#LINE#>Department of PG studies in Biotechnology, Nrupathunga University, Bengaluru, Karnataka, India@Department of PG studies in Biotechnology, Nrupathunga University, Bengaluru, Karnataka, India@Department of PG studies in Biotechnology, Nrupathunga University, Bengaluru, Karnataka, India@Department of PG studies in Biotechnology, Nrupathunga University, Bengaluru, Karnataka, India@Insearch Laboratory, Bengaluru, Karnataka, India<#LINE#>12/4/2026<#LINE#>25/5/2026<#LINE#>The green synthesis of nanoparticles is widely preferred due to their versatile nature, and this approach enabled the successful synthesis of nanoparticles in the laboratory. The plant extract from Coleus amboinicus possesses antibacterial properties, and copper, a natural antimicrobial element, has been used since ancient times. Combining them as nanoparticles enhanced their antibacterial activity, effectively inhibiting the growth of Staphylococcus aureus, which was confirmed by agar well diffusion, showing the largest zone of inhibition at the maximum dose of 40 μl, and nutrient broth assay, exhibiting 88% inhibition at the same dose. Biofilm disruption was demonstrated using the Congo red agar method, linked to reactive oxygen species generation. 3.8% SOD activity was shown by Superoxide dismutase assay, indicating oxidative stress and confirming ROS presence. These findings validate the antibacterial potential of Mexican mint–copper nanoparticles, highlighting their applicability in biomedical and environmental disciplines.<#LINE#>Joudeh, N., & Linke, D. (2022).@Nanoparticle classification, physicochemical properties, characterization, and applications: a comprehensive review for biologists.@Journal of nanobiotechnology, 20(1), 262.@Yes$Radulescu, D. M., Surdu, V. A., Ficai, A., Ficai, D., Grumezescu, A. M., & Andronescu, E. (2023).@Green synthesis of metal and metal oxide nanoparticles: a review of the principles and biomedical applications.@International journal of molecular sciences, 24(20), 15397.@Yes$Bhardwaj, B., Singh, P., Kumar, A., Kumar, S., &Budhwar, V. (2020).@Eco-friendly greener synthesis of nanoparticles.@Advanced pharmaceutical bulletin, 10(4), 566.@Yes$Jadoun, S., Arif, R., Jangid, N. K., & Meena, R. K. (2021).@Green synthesis of nanoparticles using plant extracts: A review.@Environmental Chemistry Letters, 19(1), 355-374.@Yes$Kulkarni, D., Sherkar, R., Shirsathe, C., Sonwane, R., Varpe, N., Shelke, S., ... & Dyawanapelly, S. (2023).@Biofabrication of nanoparticles: sources, synthesis, and biomedical applications.@Frontiers in bioengineering and biotechnology, 11, 1159193.@Yes$Vijayaram, S., Razafindralambo, H., Sun, Y. Z., Vasantharaj, S., Ghafarifarsani, H., Hoseinifar, S. H., & Raeeszadeh, M. (2023).@Applications of green synthesized metal nanoparticles—a review.@Biological trace element research, 1.@Yes$Labaran, A. N., Zango, Z. U., Tailor, G., Alsadig, A., Usman, F., Mukhtar, M. T., ... & Aldaghri, O. A. (2024).@Biosynthesis of copper nanoparticles using Alstoniascholaris leaves and its antimicrobial studies.@Scientific reports, 14(1), 5589.h@Yes$Paul, K., Gowda, B. H., Hani, U., Chandan, R. S., Mohanto, S., Ahmed, M. G., ... & Kesharwani, P. (2024).@Traditional uses, phytochemistry, and pharmacological activities of Coleus amboinicus: a comprehensive review.@Current Pharmaceutical Design, 30(7), 519-535.@Yes$Gurgel, A. P. A. D., da SILVA, J. G., Grangeiro, A. R., Xavier, H. S., Oliveira, R. A., Pereira, M. S., & de SOUZA, I. A. (2009).@Antibacterial effects of Plectranthus amboinicus (Lour.) spreng (Lamiaceae) in methicillin resistant Staphylococcus aureus (MRSA).@Latin American Journal of Pharmacy, 28(3), 460-464.@Yes$Nazliniwaty, N., & Laila, L. (2019).@Formulation and antibacterial activity of Plectranthus amboinicus (Lour.) Spreng leaves ethanolic extract as herbal mouthwash against halitosis caused bacteria.@Open access Macedonian journal of medical sciences, 7(22), 3900.@Yes$Taylor, T. A., Tobin, E. H., & Unakal, C. G. (2025).@Staphylococcus aureus infection. In Statpearls [internet].@StatPearls Publishing.@Yes$Pasieczna-Patkowska, S., Cichy, M., & Flieger, J. (2025).@Application of Fourier transform infrared (FTIR) spectroscopy in characterization of green synthesized nanoparticles.@Molecules, 30(3), 684.h@Yes$Thirumoorthy, G., Balasubramanian, B., George, J. A., Nizam, A., Nagella, P., Srinatha, N., ... & Veerappa Lakshmaiah, V. (2024).@Phytofabricated bimetallic synthesis of silver-copper nanoparticles using Aerva lanata extract to evaluate their potential cytotoxic and antimicrobial activities.@Scientific reports, 14(1), 1270.@Yes$Abd-Elhamed, W., Mohamed, A. A., Saad, Z. H., Hassanien, S. E. S. I., Salem, M. Z., & El-Hefny, M. (2025).@Green synthesis of silver nanoparticles mediated by Solanum nigrum leaf extract and their antifungal activity against pine pathogens.@Scientific Reports, 15(1), 35025.@Yes$Alagesan, V., & Venugopal, S. (2019).@Green synthesis of selenium nanoparticle using leaves extract of With aniasomnifera and its biological applications and photocatalytic activities.@Bionanoscience, 9(1), 105-116.@Yes$Das, K., Tiwari, R. K. S., & Shrivastava, D. K. (2010).@Techniques for evaluation of medicinal plant products as antimicrobial agent: Current methods and future trends.@Journal of medicinal plants research, 4(2), 104-111.@Yes$Salehi, B., Mehrabian, S., & Ahmadi, M. (2014).@Investigation of antibacterial effect of Cadmium Oxide nanoparticles on Staphylococcus Aureus bacteria.@Journal of nanobiotechnology, 12(1), 26.h@Yes$Ansari, M. A., Khan, H. M., Khan, A. A., Cameotra, S. S., & Pal, R. (2014).@Antibiofilm efficacy of silver nanoparticles against biofilm of extended spectrum β-lactamase isolates of Escherichia coli and Klebsiella pneumoniae.@Applied Nanoscience, 4(7), 859-868.@Yes$Mesa-Herrera, F., Quinto-Alemany, D., & Díaz, M. (2019).@A sensitive, accurate, and versatile method for the quantification of superoxide dismutase activities in biological preparations.@React. Oxyg. Species, 7(19), 10-20.@Yes$Libertext, C. (2014).@Infrared spectroscopy absorption table.@Chemistry LibreTexts.@Yes$Ślusarczyk, S., Cieślak, A., Yanza, Y. R., Szumacher-Strabel, M., Varadyova, Z., Stafiniak, M., ... & Matkowski, A. (2021).@Phytochemical profile and antioxidant activities of Coleus amboinicus Lour. cultivated in Indonesia and Poland.@Molecules, 26(10), 2915.@Yes
<#LINE#>Green synthesis of Titanium Dioxide nanoparticles from Capsicum annum extract and its Characterization and Evaluation of antioxidant activity<#LINE#>Jeevitha @M.,Druthi @P.R.,Priya @P.,Sarina P @Kabhade,Bhupanapadu Sunkesula Mary @Stella <#LINE#>36-41<#LINE#>5.ISCA-RJRS-2026-014.pdf<#LINE#>Department of Biotechnology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Biotechnology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Biotechnology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Biotechnology, Nrupathunga University, Bengaluru, Karnataka, India@Insearch Laboratory, Bengaluru, Karnataka, India<#LINE#>13/4/2026<#LINE#>5/5/2026<#LINE#>The green synthesis approach for producing nanoparticles has significantly increased due to their adaptable and flexible nature. Titanium dioxide nanoparticles (TiO₂ NPs) were synthesised using Capsicum annuum extract through an eco-friendly green synthesis approach. Plant-derived phytochemicals acted as natural reducing and stabilising agents, enhancing nanoparticle bioactivity. The antioxidant potential of the synthesized TiO₂ nanoparticles was evaluated using DPPH, Ferrozine, and FRAP assays. The nanoparticles exhibited significant free-radical scavenging activity with a clear dose-dependent response, strong ferric-reducing capacity, and surface accessible ferrous ion reactivity, indicating functional antioxidant behaviour. Structural and chemical characterisation using UV–Visible spectroscopy and FTIR spectroscopy confirmed formation of nanoparticles and phytochemical functionalization. The results demonstrate that Capsicum annuum-mediated TiO₂ nanoparticles are structurally stable, functionally active, and biologically relevant, highlighting their potential for biomedical and pharmaceutical applications involving oxidative stress modulation.<#LINE#>Khan, Y., Sadia, H., Ali Shah, S. Z., Khan, M. N., Shah, A. A., Ullah, N., ... & Khan, M. I. (2022).@Classification, synthetic, and characterization approaches to nanoparticles, and their applications in various fields of nanotechnology: a review.@Catalysts, 12(11), 1386.@Yes$Eker, F., Duman, H., Akdaşçi, E., Bolat, E., Sarıtaş, S., Karav, S., & Witkowska, A. M. (2024).@A comprehensive review of nanoparticles: from classification to application and toxicity.@Molecules, 29(15), 3482.@Yes$Asghar, N., Hussain, A., Nguyen, D. A., Ali, S., Hussain, I., Junejo, A., & Ali, A. (2024).@Advancement in nanomaterials for environmental pollutants remediation: a systematic review on bibliometrics analysis, material types, synthesis pathways, and related mechanisms.@Journal of Nanobiotechnology, 22(1), 26.@Yes$Abid, N., Khan, A. M., Shujait, S., Chaudhary, K., Ikram, M., Imran, M., ... & Maqbool, M. (2022).@Synthesis of nanomaterials using various top-down and bottom-up approaches, influencing factors, advantages, and disadvantages: A review.@Advances in colloid and interface science, 300, 102597.@Yes$Kumari, S., Raturi, S., Kulshrestha, S., Chauhan, K., Dhingra, S., András, K., ... & Singh, T. (2023).@A comprehensive review on various techniques used for synthesizing nanoparticles.@Journal of Materials Research and Technology, 27, 1739-1763.@Yes$Gour, A., & Jain, N. K. (2019).@Advances in green synthesis of nanoparticles.@Artificial cells, nanomedicine, and biotechnology, 47(1), 844-851.@Yes$Abuzeid, H. M., Julien, C. M., Zhu, L., & Hashem, A. M. (2023).@Green synthesis of nanoparticles and their energy storage, environmental, and biomedical applications.@Crystals, 13(11), 1576.@Yes$Jassal, P. S., Kaur, D., Prasad, R., & Singh, J. (2022).@Green synthesis of titanium dioxide nanoparticles: development and applications.@Journal of Agriculture and Food Research, 10, 100361.@Yes$Singh, S. (2025).@S. Green Synthesis of Nanoparticles Using Medicinal Plants: Mechanisms, Applications, and Future Prospects.@Int. J. Multidiscip. Res, 3, 166-184.@Yes$Tripodi, P., & Kumar, S. (2019).@The Capsicum crop: an introduction.@In The capsicum genome (pp. 1-8). Cham: Springer International Publishing.@Yes$Bhalabhai, J. G., Rajhans, S., Pandya, H., Mankad, A., & Patel, C. (2021).@A comprehensive review on Capsicum spp.@International Journal of Research and Analytical Reviews, 8(4), 581-599.@Yes$Thirumoorthy, G., Balasubramanian, B., George, J. A., Nizam, A., Nagella, P., Srinatha, N., ... &Veerappa Lakshmaiah, V. (2024).@Phytofabricated bimetallic synthesis of silver-copper nanoparticles using Aerva lanata extract to evaluate their potential cytotoxic and antimicrobial activities.@Scientific reports, 14(1), 1270.@Yes$Abd-Elhamed, W., Mohamed, A. A., Saad, Z. H., Hassanien, S. E. S. I., Salem, M. Z., & El-Hefny, M. (2025).@Green synthesis of silver nanoparticles mediated by Solanum nigrum leaf extract and their antifungal activity against pine pathogens.@Scientific Reports, 15(1), 35025.@Yes$Alagesan, V., & Venugopal, S. (2019).@Green synthesis of selenium nanoparticle using leaves extract of Withaniasomnifera and its biological applications and photocatalytic activities.@Bionanoscience, 9(1), 105-116.@Yes$Kedare, S. B., & Singh, R. P. (2011).@Genesis and development of DPPH method of antioxidant assay.@Journal of food science and technology, 48(4), 412-422.@Yes$Halboup, A., & Alkubati, S. A. (2025).@Protocols for Antioxidant Testing: A Mini Review of Common Assays and Approaches.@AUIQ Complementary Biological System, 2(3), 71-80.@Yes$Baiwa, F. I., Mudi, S. Y., Hausa, S. S. K., & Gumel, S. A. (2025).@Antioxidant activities of isolated endophytic fungi from Vitex doniana using DPPH and FRAP techniques.@GAS Journal of Clinical Medicine and Medical Research, 2(7), 9-13.@Yes$Libertext, C. (2014).@Infrared spectroscopy absorption table.@Chemistry LibreTexts.@Yes$Rahman, A., Akter, S., Rafsan, A., Praptia, B. B. R., Hossain, M. I., Chouhan, C. S., ... & Siddique, M. P. (2025).@Green Synthesis of Antibacterial Titanium Dioxide Nanoparticles for Controlling Environmental Spread of Multi-Drug Resistance Clostridium Perfringens.@Environmental Technology & innovation, 39@Yes$Narh, D., Sampson, B., Ocrah Junior, S., Pokuaa Manu, G., Agyei-Tuffour, B., Nyankson, E., & Kwame Efavi, J. (2024).@Green synthesis of Citrus sinensis peel extract‐mediated Ag‐TiO2 and its application as a photocatalyst for organic molecules and antimicrobial agent.@Journal of Nanotechnology, 2024(1), 9169241.@Yes$Joseph, S., Nallaswamy, D., Rajeshkumar, S., Dathan, P. C., Rasheed, N., Tharani, M., ... & Jose, L. (2025).@An in vitro evaluation of anti-oxidant properties of novel nano-composite material containing titanium oxide, zinc oxide and green tea extract.@Med J Malaysia, 80, 52-8.@Yes
<#LINE#>Microbial safety evaluation of Terminalia bellirica (Gaertn.) Roxb. Fruit raw material: Bacterial diversity, Fungal incidence and Ergosterol analysis<#LINE#>Rajeshwari @P.,K.A. @Raveesha <#LINE#>42-46<#LINE#>6.ISCA-RJRS-2026-023.pdf<#LINE#>Government Science College, Chitradurga, Karnataka, India@Life Sciences, JSS Academy of Higher Education and Research (JSSAHER) Mysuru, Karnataka, India<#LINE#>10/4/2026<#LINE#>18/5/2026<#LINE#>The microbiological safety of crude herbal drugs significantly influences the safety, efficacy and shelf life of herbal formulations. Terminalia bellirica (Combretaceae), an important medicinal fruit and a key component of Triphala, is widely used in traditional medicine and nutraceutical preparations. The present investigation aimed to evaluate the microbial status of T. bellirica fruits collected from retail herbal markets of Mysuru, Karnataka. Microbial enumeration was carried out using the serial dilution technique, followed by isolation and characterization of bacterial and fungal contaminants. Detection of indicator pathogens such as Escherichia coli and Salmonella was performed using selective media and biochemical tests. Fungal contamination was further confirmed by estimation of ergosterol using TLC and LCMS methods. Results revealed substantial microbial contamination with average bacterial and fungal loads of 1.66×10⁷ CFU/g and 1.55×10⁵ CFU/g, respectively. A total of 27 morphologically distinct bacterial isolates were obtained, of which 70% were Gram-positive. Six out of eighteen samples were contaminated with E. coli. Nineteen fungal species belonging to ten genera were identified, with Aspergillusniger and A. flavus as predominant contaminants. Ergosterol(184.49 µg/kg) was detected in 15 samples, confirming potential fungal incidence. The study highlights the need for stringent microbial quality control measures for T. bellirica fruit raw materials used in herbal drug formulations.<#LINE#>WHO (2007).@WHO guidelines for assessing quality of herbal medicines with reference to contaminants and residues.@World Health Organization, Geneva.@Yes$De Smet, P. A. G. M. (2004).@Health risks of herbal remedies.@Drug Safety, 27(5), 311–327.@Yes$Sharma, A., Shanker, C., Tyagi, L. K., Singh, M., and Rao, C. V. (2014).@Herbal medicine contamination: Microbial contamination and its control.@International Journal of Pharmaceutical Sciences Review and Research, 24(2), 1–6.@Yes$Kirtikar, K. R., and Basu, B. D. (2005).@Indian medicinal plants (Vol. II).@Lalit Mohan Basu, Allahabad.@Yes$Nadkarni, K. M. (2007).@Indian materiamedica (Vol. I).@Popular Prakashan, Mumbai.@Yes$Pitt, J. I., and Hocking, A. D. (2009).@Fungi and food spoilage (3rd ed.).@Springer, New York.@Yes$Samson, R. A., Houbraken, J., Thrane, U., Frisvad, J. C., and Andersen, B. (2011).@Food and indoor fungi.@CBS-KNAW Fungal Biodiversity Centre, Utrecht.@Yes$Seitz, L. M., Mohr, H. E., Burroughs, R., and Sauer, D. B. (1977).@Ergosterol as a measure of fungal growth.@Journal of Agricultural and Food Chemistry, 25(6), 1356–1359.@Yes$Jambunathan, R., Kherdekar, B. M., &Vaidya, P. (1991).@Ergosterol concentration in mould-susceptible and mould-resistant sorghum at different stages of grain development and relationship to flavan-4-ols.@Journal of Agricultural and Food Chemistry, 39(10), 1866–1870.@Yes$Parsi, Z., and Górecki, T. (2006).@Determination of ergosterol as an indicator of fungal contamination in food products.@Food Chemistry, 97(4), 684–689.@Yes$AOAC International. (2019). Fat in meat by-products (Method 996.06). In Official methods of analysis of AOAC INTERNATIONAL (21st ed.). AOAC International.@undefined@undefined@Yes$Monograph, T. (2017). European pharmacopoeia. European Directorate for the Quality of Medicine & Health Care of the Council of Europe (EDQM), edn, 9, 3104-5.@undefined@undefined@Yes$Samson, R. A., Houbraken, J., Thrane, U., Frisvad, J. C., & Andersen, B. (2010).@Food and indoor fungi: CBS-KNAW fungal biodiversity centre.@CBS-KNAW Fungal Biodiversity Centre, Utrecht, the Netherlands.@Yes$Deacon, J. W. (2006). Fungal biology (4th ed.) Fungal Biology. Blackwell Publishing.@undefined@undefined@Yes$Sharma, R., Sil, J., & Sharma, M. (2014).@Assessment of bacteriological quality of ready to eat food vended in Silchar city, Assam, India.@Indian Journal of Medical Microbiology, 32(2), 169–171.@Yes$Zin, N. M., Chit, Y. M. C., & Bakar, N. F. A. (2013).@Commercial herbal slimming products: Concern for the presence of heavy metals and bacteria.@Pakistan Journal of Biological Sciences, 16(23), 1763–1768.@Yes$Aneja, K. R. (2003).@Experiments in microbiology, plant pathology and biotechnology (4th ed.).@New Age International Publishers, New Delhi.@Yes$Ellis, M. B., Ellis, J. P., and Ellis, P. (2007).@Microfungi on land plants: An identification handbook.@Richmond Publishing, Slough, UK.@Yes$Mohana, D. C., Raveesha, K. A., and Lokesh, S. (2016).@Ergosterol estimation as an indicator of fungal contamination in stored agricultural commodities.@Journal of Food Science and Technology, 53(6), 2873–2880.@Yes
<#LINE#>Harnessing Microbes for Power: Evaluating Voltage Yield across Various Bio-waste Substrates<#LINE#>Kavitha @B.,Chandana R @Shetty,Pavithra @R.,Vanaja @V.,Vidhya @Varshini <#LINE#>47-54<#LINE#>7.ISCA-RJRS-2026-025.pdf<#LINE#>Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India<#LINE#>10/4/2026<#LINE#>3/5/2026<#LINE#>The demand for sustainable energy drives the exploration of organic waste for power generation via Microbial Fuel Cells (MFCs). This study explores the potential of microbial systems to generate electrical energy using different bio-wastes as substrates. Voltage outputs were compared across diverse organic wastes, to identify substrates that maximize energy production. Substrates used for the study include banana peel, sugarcane bagasse, rhizosphere soil and cattle manure. A dual-chambered MFC setup consistently utilized aluminum anodes, while cathode configurations varied viz., water with an aluminum electrode for banana peel, potassium ferricyanide with copper electrodes for sugarcane bagasse and rhizosphere soil and potassium permanganate with a copper electrode for cattle manure. Voltage and pH monitoring revealed significant variations in performance with cattle manure yielding the highest output of 1255 mV and banana peel the lowest at 252 mV. The identification of Rhizobium in rhizosphere soil emphasizes the critical role of specific microbial communities in driving the electrochemical process. The findings indicate that MFCs can effectively utilize organic residues for electricity generation while also helping waste utilization and renewable energy production. It also highlights the potential of MFC technology for environmental challenges and energy self-sufficiency, while offering insights into effective waste-to-energy conversion strategies.<#LINE#>Logan, B. E., Hamelers, B., Rozendal, R., Schröder, U., Keller, J., Freguia, S., Aelterman, P., Verstraete, W., & Rabaey, K. (2006).@Microbial fuel cells: Methodology and technology.@Environmental Science & Technology, 40(17), 5181–5192.@Yes$Moqsud, M. A., Omine, K., Yasufuku, N., Hyodo, M., & Nakata, Y. (2013).@Microbial fuel cell (MFC) for bioelectricity generation from organic wastes.@Waste Management, 33(11), 2465–2469.@Yes$Prabhu, N., Abitha, M., Ilakkiya, E., Parvatham, V., Thovarne, P., & Karthick, P. J. (2019).@Electricity production from Rhizobium sp. biofertilizer enriched soil using microbial fuel cells.@European Journal of Biotechnology and Bioscience,@Yes$Arulmani, S. R. B., Sathiyanarayanan, G., Prakash, S., Kumar, C. S., & Mathivanan, K. (2021). Sustainable bioelectricity production from Amaranthus viridis and Triticum aestivum mediated plant microbial fuel cells with efficient electrogenic bacteria selections.@Process Biochemistry, 107, 27–37.@undefined@Yes$Singh, S., Kiran, B. R., & Mohan, S. V. (2024).@Carbon farming: A circular framework to augment CO₂ sinks and to combat climate change.@Environmental Science: Advances, 3(4), 522–542.@Yes$Torres, C. R. A. G., Espiritu, R. C., Doydoy, N. J. Z., Deocares, J. A. S., Saldo, I. J. P., & Dandoy, M. J. P. (2023).@Electric generation capacities of three varieties of banana peel using microbial fuel cell.@World Journal of Agricultural Research, 11(2), 39–43.@Yes$Žalnėravičius, R., Paškevičius, A., Samukaitė-Bubnienė, U., Ramanavičius, S., Vilkienė, M., Mockevičienė, I., & Ramanavičius, A. (2022).@Microbial fuel cell based on nitrogen-fixing Rhizobium anhuiense bacteria.@Biosensors, 12(2), Article 113.@Yes$Christwardana, M., Yoshi, L. A., & Joelianingsih, J. (2021).@Energy harvesting from sugarcane bagasse juice using yeast microbial fuel cell technology.@Reaktor, 21(2), 52–58.@Yes$Kumar, S., Maiti, P. K., & Ala-Nissila, T. (2015).@Role of interchain interactions in the stabilization of helical DNA.@Physical Review E, 91(3), Article 032701.@Yes$Saba, B., Christy, A. D., Yu, Z., & Co, A. C. (2017).@Sustainable power generation from bacterio-algal microbial fuel cells: An overview.@Renewable and Sustainable Energy Reviews, 73, 75–84.@Yes$Maddalwar, S. R., & Shanware, A. S. (2018).@Growth curve analysis of Rhizobium leguminosarum using voltage produced by microbial fuel cell.@International Journal of Life-Sciences Scientific Research, 4(6), 2111–2115.@Yes$Zhang, X., Li, X., Zhao, X., & Li, Y. (2019).@Factors affecting the efficiency of a bioelectrochemical system: A review.@RSC Advances, 9(34), 19748–19761.@Yes$Logan, B. E., Rossi, R., Ragab, A., & Saikaly, P. E. (2019).@Electroactive microorganisms in bioelectrochemical systems.@Nature Reviews Microbiology, 17(5), 307–319.@Yes$Pant, D., Van Bogaert, G., Diels, L., & Vanbroekhoven, K. (2010).@A review of the substrates used in microbial fuel cells (MFCs) for sustainable energy production.@Bioresource Technology, 101(6), 1533–1543.@Yes$Rabaey, K., & Verstraete, W. (2005).@Microbial fuel cells: Novel biotechnology for energy generation.@Trends in Biotechnology, 23(6), 291–298.@Yes$Liu, Y., Balkwill, D. L., Aldrich, H. C., Drake, G. R., & Boone, D. R. (2008).@Characterization of the anaerobic microbial community in cattle manure.@Applied and Environmental Microbiology, 74(13), 4142–4150.@Yes$Hook, S. E., Wright, A. D. G. & McBride, B. W. (2010).@Methanogens: Methane producers of the rumen and mitigation strategies.@Archaea, 2010, Article 945785.@Yes$Microbial Fuel Cells. Logan, B. E. (2008).@Microbial fuel cells.@Wiley. ISBN978-0470258590@Yes$Verma, M., & Mishra, V. (2023).@Bioelectricity generation by microbial degradation of banana peel waste biomass in a dual-chamber Saccharomyces cerevisiae-based microbial fuel cell.@Biomass and Bioenergy, 168, 106677.@Yes$Arifani, A. F., Mulyono, T. & Misto. (2025).@Utilization of banana peel waste as a sustainable substrate in microbial fuel cell systems for renewable energy development.@Computational and Experimental Research in Materials and Renewable Energy, 8(2), 126–139.@Yes$Reinikovaite, V., Žukauskas, Š., Žalnėravičius, R., Ratautaite, V., Ramanavičius, S., Bucinskas, V., Vilkienė, M., Ramanavičius, A., & Samukaitė-Bubnienė, U. (2023).@Assessment of Rhizobium anhuiense bacteria as a potential biocatalyst for microbial biofuel cell design.@Biosensors, 13(1), 66.@Yes$Madigan, M. T., Bender, K. S., Buckley, D. H., Sattley, W. M., & Stahl, D. A. (2018).@Brock biology of microorganisms (15th ed.).@Pearson. ISBN-10: 1292235101@Yes$Musa, H. A., Yusuf, I., Aliyu, A., Fardami, A. Y., Bukar, U. A., Bichi, Y. H., & Abbas, R. (2024).@Isolation and identification of bacteria associated with bioelectricity generation from fruit wastes and gutter sludge using microbial fuel cells (MFC) in Kano metropolis, Kano State, Nigeria.@Nigerian Journal of Science and Engineering Infrastructure, 2, 10–26.@Yes$Hegazy, G. E., Soliman, N. A., Abdel-Fattah, Y. R., & Taha, T. H. (2026).@Enhanced voltage generation in microbial fuel cells (MFCs) using bacterial isolates from seawater and industrial wastewater.@Microbial Cell Factories, 25, 21.@Yes
<#LINE#>Production, Optimization and Characterization of L-Glutaminase from Soil-Derived Aspergillus Species under Solid-State Fermentation<#LINE#>Anuroopa @N.,Prajwal @R.,Kishore Rao @M.,Deepika @C.,Jayashree @M.,Rashmi @P. <#LINE#>55-60<#LINE#>8.ISCA-RJRS-2026-027.pdf<#LINE#>Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India@Department of Microbiology, Nrupathunga University, Bengaluru, Karnataka, India<#LINE#>11/4/2026<#LINE#>5/5/2026<#LINE#>L-glutaminase (EC 3.5.1.2) is an amidohydrolase enzyme of significant biomedical and industrial importance due to its potential therapeutic application in cancer treatment and its role in food biotechnology. The present study aimed to isolate, produce, purify, and characterize L-glutaminase from Aspergillus species obtained from soil samples. Fungal isolates were screened using a modified Czapek’s medium supplemented with L-glutamine and Congo red dye indicator. Positive isolates were further confirmed by thin layer chromatography for enzymatic activity. Quantitative estimation of enzyme activity was carried out using Nessler’s reagent by measuring ammonia released from L-glutamine hydrolysis. Optimization studies were performed to evaluate the influence of physicochemical parameters such as pH, temperature, moisture content, inoculum size, and incubation period on enzyme production. Among the screened isolates, strain S3 showed the highest enzyme activity and was identified as Aspergillus species through microscopic examination. Maximum enzyme activity of 15.4 µmol/ml/min was observed at pH 5.0, 30°C, and 60% moisture content under solid-state fermentation. The enzyme exhibited high specificity toward L-glutamine and demonstrated stability across a range of environmental conditions. The results indicate that Aspergillus species can serve as a promising microbial source for L-glutaminase production with potential applications in oncology, biosensor development, and food fermentation industries.<#LINE#>Abdallah, A. N., Amer, K. S., & Habeeb, K. M. (2013).@Production, purification and characterization of L-glutaminase enzyme from Streptomyces avermitilis.@African Journal of Microbiology Research, 7(14), 1184–1190.@Yes$Anita, S., Namita, S., &Narsi, R. B. (2009).@Production of cellulases by Aspergillus heteromorphus from wheat straw under submerged fermentation.@International Journal of Civil and Environmental Engineering, 1(1), 23–26.@Yes$Balagurunathan, R., Radhakrishnan, M., & Somasundaram, S. T. (2010).@L-glutaminase producing actinomycetes from marine sediments: Selective isolation, semi-quantitative assay and characterization of potential strain.@Australian Journal of Basic and Applied Sciences, 4, 698–705.@Yes$Chitanand, M. P., & Shete, H. G. (2012).@Condition optimization and production of extracellular L-glutaminase from Pseudomonas fluorescens.@International Journal of Pharmacy and Biological Sciences, 3(3), 155–162.@Yes$Divya Teja, D., Sri Devi, V., Harsha, N., Satya Vishala, S., & Santhosha Lakshmi, P. K. (2014).@Production of L-glutaminase from marine ecosystems and optimal conditions for maximal production by actinomycetes.@International Journal of Advanced Research, 2(1), 485–490.@Yes$Cooper, G. M. (2000).@The cell: A molecular approach (2nd ed.).@ASM Press.@Yes$Garcia-Kirchner, O., Muñoz-Aguilar, M., Pérez-Villalva, M., & Huitrón-Vargas, C. (2002).@Mixed submerged fermentation with two filamentous fungi for cellulolytic and xylanolytic enzyme production.@Applied Biochemistry and Biotechnology, 98(1–9), 1105–1114.@Yes$Lonsane, B. K., Ghildyal, N. P., Budiatman, S., & Ramakrishna, S. V. (1985).@Engineering aspects of solid-state fermentation.@Enzyme and Microbial Technology, 7(6), 258–265.@Yes$Huerta-Saquero, A., Calderón, J., Arreguín, R., Calderón-Flores, A., & Durán, S. (2001).@Over expression and purification of Rhizobium etli glutaminase A by recombinant and conventional procedures: A comparative study of enzymatic properties.@Protein Expression and Purification, 21(3), 432–437.@Yes$Iyer, P., & Singhal, R. (2010).@Isolation, screening, and selection of an L-glutaminase producer from soil and media optimization using a statistical approach.@Biotechnology and Bioprocess Engineering, 15, 975–983.@Yes$Iyer, P., & Singhal, R. S. (2009).@Screening and selection of marine isolate for L-glutaminase production and media optimization using response surface methodology.@Applied Biochemistry and Biotechnology, 159(1), 233@Yes$Jeon, J. M., Lee, H. L., & Han, S. H. (2009).@Partial purification and characterization of glutaminase from Lactobacillus reuteri KCTC3594. Applied Biochemistry and Biotechnology.@undefined@Yes$Kashyap, P., Sabu, A., Pandey, A., Szakacs, G., &Soccol, C. R. (2002).@Extracellular L-glutaminase production by Zygosaccharomyces rouxii under solid-state fermentation.@Process Biochemistry, 38(3), 307–312.@Yes$Bobbarala, V., Prabhakar, T. P., & Guntuku, G. S. (2009).@Screening of L-glutaminase-producing marine bacterial cultures for extracellular production of L-glutaminase.@International Journal of Chemical Technology Research.@Yes$Kozolov, T. A., Tsvetkova, T., et al. (1982).@Photosensitized oxidation of asparagine-glutamine deamidase from Pseudomonas fluorescens.@Bulletin of Experimental Biology and Medicine, 94(3), 1209–1212.@Yes$Kusaykin, M. I., et al. (2003).@Distribution of O-glycosylhydrolases in marine invertebrates: Enzymes of the marine mollusk Littorina kurila that catalyze fucoidan transformation.@Biochemistry (Moscow), 68(3), 317–324.@Yes$Papaspyridi, L. M., Aligiannis, N., Topakas, E., Christakopoulos, P., Skaltsounis, A. L., & Fokialakis, N. (2012).@Submerged fermentation of the edible mushroom Pleurotusostreatus in a batch stirred tank bioreactor as a promising alternative for the effective production of bioactive metabolites.@Molecules, 17(3), 2714–2724.@Yes$Levine, A. J., & Puzio-Kuter, A. M. (2010).@The control of the metabolic switch in cancers by oncogenes and tumor suppressor genes.@Science, 330(6009), 1340–1344.@Yes$Liu, S. L., Shi, D. Y., Shen, Z. H., & Wu, Y.D. (2000).@Effects of glutamine on tumor growth and apoptosis of hepatoma cells.@Acta Pharmacologica Sinica, 21, 668–672.@Yes$Lokendrakumar, Balvinder, S., Dilipkumar, A., Joydeep, M., & Debashish, G. (2012).@A temperature- and salt-tolerant L-glutaminase from the Gangotri region of Uttarakhand Himalaya: Enzyme purification and characterization.@Applied Biochemistry and Biotechnology, 166, 1723–1735.@Yes$Prabhakar, A., Krishnaiah, K., Janaun, J., & Bono, A. (2005).@An overview of engineering aspects of solid-state fermentation.@Malaysian Journal of Microbiology, 1(2), 10–16.@Yes$Sivakumar, K., Sahu, M. K., Manivel, P. R., & Kannan, L. (2006).@Optimum conditions for L-glutaminase production by an actinomycete strain isolated from the estuarine fish Chanoschanos (Forsskål, 1775).@Indian Journal of Experimental Biology, 44(3), 256–260.@Yes$Wakayama, M., Yamagata, T., Kamemura, A., Bootim, N., Yano, S., Tachiki, T., Yoshimune, K., & Moriguchi, M. (2005).@Characterization of salt-tolerant glutaminase from Stenotrophomonas maltophilia NYW-81 and its application in Japanese soy sauce fermentation.@Journal of Industrial Microbiology & Biotechnology, 32(9), 383–389.@Yes
<#LINE#>Next generation Molecular tools for tracking Harmful algal bloom and Microbial sources<#LINE#>Hithyshi @N.,Shashikanth @B.C.,Uday Kumar @D.A.,Mohan Kumar @H.M. <#LINE#>61-67<#LINE#>9.ISCA-RJRS-2026-029.pdf<#LINE#>Department of Post-Graduate Studies in Zoology, Nrupathunga University, Bengaluru, India@Department of Post-Graduate Studies in Zoology, Nrupathunga University, Bengaluru, India@Department of Post-Graduate Studies in Zoology, Nrupathunga University, Bengaluru, India@Department of Post-Graduate Studies in Zoology, Nrupathunga University, Bengaluru, India<#LINE#>12/4/2026<#LINE#>25/5/2026<#LINE#>In the recent decade’s escalated anthropogenic pressure and climatic change has led to severe destruction of ecosystem along with the formation of harmful algal blooms. Substantial implications of these harmful algal blooms which constitute one of the major threats to aquatic ecosystem include considerable impact on ecological balance, aquatic biodiversity, human health and blue economy. In order to monitor, detect and mitigate the effects of harmful algal blooms, emphasis has to be given for early detection using molecular tools. In the present study water quality parameters analyzed from different lakes establish a clear link between environmental conditions and quality as well as quantity of living beings present. Highest colony counts reflected on nutrient media were in consistent with the eutrophic conditions. Prevalence of gram negative bacteria, in Lakes B and D were due to their ability to degrade organic matter, contributing to bloom dynamics. Sequencing of purified PCR products from cultured isolates identified dominant genera, Pseudomonas, Vibrio, Aeromonas and Bacillus in Lakes B and D. it is in alignment with the highest read counts in metagenomic analysis. Higher GC content in Lakes B and D is likely due to the dominance of cyanobacteria. Microcystin biosynthesis genes (mcy gene) were most abundant in Lakes B and D correlating with Microcystis abundance.<#LINE#>Durham, W. M., & Stocker, R. (2012).@Thin phytoplankton layers: characteristics, mechanisms, and consequences.@Annual review of marine science, 4, 177-207.@Yes$Behrenfeld, M. J., & Boss, E. S. (2014).@Resurrecting the ecological underpinnings of ocean plankton blooms.@Annual review of marine science, 6(1), 167-194.@Yes$Beardall, J., Ihnken, S., & Quigg, A. (2009).@Gross and net primary production: closing the gap between concepts and measurements.@Aquatic Microbial Ecology, 56, 113-122.@Yes$Nayak, A. R., Kolluru, S., Kumar, A., & Bhadury, P. (2025).@Revisiting harmful algal blooms in India through a global lens: An integrated framework for enhanced research and monitoring.@Iscience, 28(2).@No$Hallegraeff, G., Enevoldsen, H., & Zingone, A. (2021).@Global harmful algal bloom status reporting.@Harmful Algae, 102, 101992.@No$Masoomi, S. R., Ganji, M, Annuk A., Mahmood A., Eftekhari, M.,  Gheibi, M. and Moezzi R. (2026).@Harmful Algal Blooms as Emerging Marine Pollutants: A Review of Monitoring, Risk Assessment, and Management with a Mexican Case Study.@Pollutants, 6(4).@Yes$Feng, L.; Wang, Y.; Hou, X.; Qin, B.; Kutser, T.; Qu, F.; Chen, N.; Paerl, H.W.; Zheng, C. (2024).@Harmful Algal Blooms in Inland Waters.@Nat. Rev. Earth Environ., 5, 631–644.@Yes$Huang, H., Xu, Q., Gibson, K., Chen, Y., & Chen, N. (2021).@Molecular characterization of harmful algal blooms in the Bohai Sea using metabarcoding analysis.@Harmful Algae, 106, 102066.@No$Diaz, M. R., Jacobson, J. W., Goodwin, K.D., Dunbar, S.A., and Fell, J.W. (2010).@Molecular detection of harmful algal blooms (HABs) using locked nucleic acids and bead array technology.@Limnol. Oceanogr. Methods, 8, 269–284.@Yes$Perini, F., Bastianini, M., Capellacci, S., Pugliese, L., DiPoi, E., Cabrini, M., ... & Penna, A. (2019).@Molecular methods for cost-efficient monitoring of HAB (harmful algal bloom) dinoflagellate resting cysts.@Marine pollution bulletin, 147, 209-218.@No$Smith, K.F., Stuart, J., and Rhodes, L.L. (2024).@Molecular approaches and challenges for monitoring marine harmful algal blooms in a changing world.@Front. Protistol. 1, 1305634.@Yes
<#LINE#>A Quantitative analysis of Machine Learning approaches to missing value imputation<#LINE#>Priya @S. <#LINE#>68-71<#LINE#>10.ISCA-RJRS-2026-030.pdf<#LINE#>Department of Computer Science, Government First Grade College, Domlur Shanthi Nagar, Bengaluru, India<#LINE#>14/4/2026<#LINE#>18/5/2026<#LINE#>Missing data is a persistent challenge in real-world datasets and can significantly reduce the reliability and predictive performance of machine learning models. Accurate imputation strategies are therefore essential for effective data pre processing. This study presents a quantitative evaluation of machine learning–based imputation techniques, with a focused comparative analysis of Naïve Bayes and K-Nearest Neighbor (KNN) methods. The algorithms are assessed using accuracy, root mean square error (RMSE), and computational efficiency across datasets with varying proportions of missing values. Experimental observations indicate that KNN achieves superior estimation accuracy, while Naïve Bayes demonstrates faster execution and better scalability in high-dimensional environments.<#LINE#>Lin, W. C., & Tsai, C. F. (2020).@Missing value imputation: A review and analysis of the literature (2006–2017).@Artificial Intelligence Review, 53(2), 1487–1509.@Yes$Emmanuel, T., Maupong, T., Mpoeleng, D., Semong, T., Mphago, B., & Tabona, O. (2021).@A survey on missing data in machine learning.@Journal of Big data, 8(1), 140.@Yes$Hasan, M. K., Alam, M. A., Roy, S., Dutta, A., Jawad, M. T., & Das, S. (2021).@Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021).@Informatics in Medicine Unlocked, 27, 100799.@Yes$Liu, M., Li, S., Yuan, H., Ong, M. E. H., Ning, Y., Xie, F., Saffari, S. E., Shang, Y., Volovici, V., Chakraborty, B., & Liu, N. (2023).@Handling missing values in healthcare data: A systematic review of deep learning-based imputation techniques.@Artificial Intelligence in Medicine, 142, 102587.@Yes$Rahman, M. G., Islam, M. Z., Islam, M. M., & Uddin, J. (2021).@A systematic review of machine learning-based missing value imputation techniques.@Data Technologies and Applications, 55(4), 558–585.@Yes$Twala, B. E., Cartwright, M., & Shepperd, M. (2011).@Missing data imputation using the EM algorithm and machine learning techniques.@Applied Artificial Intelligence, 25(5), 373–393.@Yes$Acuña, E., & Rodriguez, C. (2004).@The treatment of missing values and its effect on classifier accuracy.@Classification, Clustering, and Data Mining Applications, 639–647.@Yes$García-Laencina, P. J., Sancho-Gómez, J. L., & Figueiras-Vidal, A. R. (2010).@Pattern classification with missing data: A review.@Neural Computing and Applications, 19(2), 263–282.@Yes$García-Laencina, P. J., Sancho-Gómez, J. L., & Figueiras-Vidal, A. R. (2010).@Missing value imputation on missing completely at random data using multilayer perceptrons.@Neural Networks, 24(1), 121–129.@Yes$Liu, W., Luo, L., & Zhou, L. (2023).@Online missing value imputation for high-dimensional mixed-type data via generalized factor models.@Computational Statistics & Data Analysis, 187, 107822.@Yes$Little, R. J. A., & Rubin, D. B. (2019).@Statistical analysis with missing data (3rd ed.).@Wiley.@Yes$Rubin, D. B. (1987).@Multiple imputation for nonresponse in surveys.@Wiley.@Yes$Schafer, J. L. (1997).@Analysis of incomplete multivariate data.@Chapman & Hall/CRC.@Yes$Van Buuren, S. (2018).@Flexible imputation of missing data (2nd ed.).@Chapman & Hall/CRC.@Yes$Stekhoven, D. J., & Bühlmann, P. (2012).@MissForest—Non-parametric missing value imputation for mixed-type data.@Bioinformatics, 28(1), 112–118.@Yes
<#LINE#>QSPR Study of Anti-Hepatitis Drugs using Degree-Based Topological Indices<#LINE#>Bharathi Shettahalli Nagaraje @Urs <#LINE#>72-82<#LINE#>11.ISCA-RJRS-2026-032.pdf<#LINE#>Department of Mathematics, Government Science College, Chitradurga, Karnataka, India<#LINE#>15/4/2026<#LINE#>19/5/2026<#LINE#>Topological indices provide an effective approach to relate molecular structure with the physicochemical properties of pharmaceutical compounds. In this study, selected anti-hepatitis drugs are modelled as molecular graphs, where atoms and bonds are characterized as vertices and edges, respectively. Anti-hepatitis drugs are widely used in the treatment and management of viral hepatitis, and understanding their structural behaviour is important for predicting their physicochemical characteristics. Five degree-based topological descriptors are computed and utilized for quantitative structure–property relationship (QSPR) analysis. The correlations between these indices and selected physicochemical properties, including boiling point (BP), surface tension (ST), index of refraction (IR), molar refractivity (MR), and polar surface area (PSA), are examined. The results indicate that three of the five descriptors exhibit strong positive correlations with BP, MR, and PSA, whereas the remaining two descriptors show comparatively weaker relationships with these physicochemical properties. These findings demonstrate the effectiveness of selected topological descriptors in predicting physicochemical behavior and highlight the applicability of graph-theoretical methods in pharmaceutical research.<#LINE#>Mahboob, A., Rasheed, M.W., Dhia, A.M., Hanif, I. and Amin, L. (2024).@On quantitative structure-property relationship (QSPR) analysis of physicochemical properties and anti-hepatitis prescription drugs using a linear regression model.@Heliyon, 10, e25908.@Yes$Harary, F. (1969).@Graph Theory.@Reading (MA): Addison-Wesley.@Yes$Wiener, H. (1947).@Structural determination of paraffin boiling points.@Journal of the American Chemical Society, 69(1), 17–20.@Yes$Gutman, I. and Polansky, O. (1986).@Mathematical Concepts in Organic Chemistry.@Berlin: Springer-Verlag.@Yes$Trinajstić, N. (2018).@Chemical Graph Theory (2nd ed.).@Boca Raton (FL): CRC Press.@Yes$Basak, S.C., Grunwald, B.D., Niemi, G.J. and Veith, D.J. (1987).@Topological indices: Their nature, mutual relatedness, and applications.@Mathematical Modelling, 8, 1–12.@Yes$Katritzky, A.R. and Gordeeva, E.V. (1993).@Quantitative structure-property relationship correlation and prediction of boiling points.@Journal of Chemical Information and Computer Sciences, 33, 835–857.@Yes$Lučić, B. and Trinajstić, N. (1997).@QSPR modeling by molecular connectivity indices.@SAR and QSAR in Environmental Research, 7, 77–95.@Yes$Lepović, M. and Gutman, I. (1998).@On structure-property modeling with distance-based descriptors.@Journal of Chemical Information and Computer Sciences, 38, 823–828.@Yes$Estrada, E. and Rodríguez, L. (1999).@Graph theoretical descriptors in structure-activity modeling.@Journal of Chemical Information and Computer Sciences, 39, 986–991.@Yes$Ali, A., Rehman, M.A. and Akhter, S. (2021).@On neighborhood degree-based topological indices of dendrimer nanostars.@Journal of Molecular Structure, 1245, 131093.@No$Abbas, G., Ibrahim, M., Ahmad, A., Azeem, M. and Elahi, K. (2023).@Application of M-polynomial-based topological indices in drug property prediction.@Polycyclic Aromatic Compounds.@No$Hakeem, A., Ullah, A., Zaman, S. and Jabeen, S. (2023).@QSPR analysis of drugs using degree-based topological indices and regression models.@Polycyclic Aromatic Compounds.@No$Hakami, K.H., Khan, A.R. and Ali, M. (2024).@Mathematical modeling and QSPR analysis of hepatitis treatment drugs through connection indices: An innovative approach.@Heliyon, 10, e17265.@Yes$Rasheed, M.W., Shafiq, M. and Imran, M. (2024).@Uses of degree-based topological indices in QSPR analysis of drug molecules.@Frontiers in Physics, 12, 1381887.@Yes$Urs, B.S.N., Krishnamurthy, S., Kamalakaran, A.S. and Togarichetu, D. (2025).@Evaluating degree-based topological indices in QSPR modeling of anticancer drugs using linear and multilinear regression.@International Journal of Quantum Chemistry, 125, e70123.@Yes$Wei, G.F., Farahani, M.R. and Gao, W. (2021).@Topological indices and QSPR analysis of antiviral drugs used for COVID-19 treatment.@Polycyclic Aromatic Compounds, 42(5), 1987–2002.@Yes$Ravi, V. and Reddy, G.S. (2024).@QSPR analysis of drugs used for treatment of hepatitis via reduced reverse degree-based topological indices.@Physica Scripta, 99, 105236.@Yes$Raza, A., Ismaeel, M. &Tolasa, F.T. (2024).@Valency based novel quantitative structure-property relationship (QSPR) approach for predicting physical properties of polycyclic chemical compounds.@Scientific Reports, 14, 7080.@Yes$Arora, P.K., Patil, V.M. and Gupta, S.P. (2010).@A QSAR study on some series of anti-hepatitis B virus (HBV) agents.@Bioinformation, 4(9), 417–420.@Yes$Sharma, A., Gupta, S.P. and Siddiqui, A.A. (2013).@A QSAR study on a series of thiourea derivatives acting as anti-hepatitis C virus agents.@Indian Journal of Biochemistry and Biophysics, 50(4), 278–283.@Yes$Asghar, A. (2025).@QSPR analysis of anti-hepatitis prescription drugs using degree-based topological indices through M-polynomial and NM-polynomial.@Chimica Techno Acta, 12(2), Article 06.@Yes$Urs, B.S.N. and Kuntal, R.S. (2024).@Lower bounds and upper bounds for the topological indices of ξ-graph.@AIP Conference Proceedings, 3149, 140033.@Yes$Urs, B.S.N. and Krishnamurthy, S. (2025).@Figuring of some degree-based topological indices of graphene.@AIP Conference Proceedings, 3258, 020002.@Yes$Urs, B.S.N., Krishnamurthy, S. and Maji, S. (2026).@Topological indices of certain derived graphs of triglycerides.@Boletim da Sociedade Paranaense de Matemática, 44, 1–11.@Yes$Deepika, T. (2021).@VL index and bounds for the tensor products of F-sum graphs.@TWMS Journal of Applied and Engineering Mathematics, 11, 374–385.@Yes
<#LINE#>Deep Attention Models for Identification of Laser Printed Document Origins<#LINE#>Pushpalata @Gonasagi <#LINE#>81-85<#LINE#>12.ISCA-RJRS-2026-036.pdf<#LINE#>Department of Computer Science, Govt. First Grade College Mahagaon Cross, Kalaburagi, Karnataka, India<#LINE#>10/4/2026<#LINE#>13/5/2026<#LINE#>In the field of digital forensic science, artificial intelligence (AI) technologies are being used more and more to settle document-related disputes that have historically required the knowledge of human experts. Finding the precise printer that generated a given document is one of the main goals of intelligent systems based on printer identification. Nevertheless, a lot of current methods rely on text-dependent approaches, which might not work in some forensic situations. These drawbacks have spurred research into text-independent methods using word images from various laser printer models. In this work, laser printer models are classified using grayscale word images. The constructed dataset consists of 100,000-word images collected from five distinct laser printer models. A deep learning–based Convolutional Neural Network (CNN) is employed to identify the source laser printer model. The performance of the proposed CNN architecture is evaluated and compared with recent studies reported in the literature, particularly those based on textural feature analysis. Experimental results demonstrate that the proposed CNN model achieves a high classification accuracy of 98.8%, outperforming several existing methods.<#LINE#>Gebhardt, J., Goldstein, M., Shafait, F., & Dengel, A. (2013).@Document authentication using printing technique features and unsupervised anomaly detection.@In 2013 12th International conference on document analysis and recognition (pp. 479-483). IEEE.@Yes$Khanna, N., Mikkilineni, A. K., Chiu, G. T. C., Allebach, J. P., & Delp, E. J. (2008).@Survey of scanner and printer forensics at purdue university. In International Workshop on Computational Forensics (pp. 22-34).@Berlin, Heidelberg: Springer Berlin Heidelberg.@Yes$Ferreira, A., Navarro, L. C., Pinheiro, G., dos Santos, J. A., & Rocha, A. (2015).@Laser printer attribution: Exploring new features and beyond.@Forensic science international, 247, 105-125.@Yes$Elkasrawi, S., & Shafait, F. (2014).@Printer identification using supervised learning for document forgery detection.@In 2014 11th IAPR International Workshop on Document Analysis Systems (pp. 146-150). IEEE.@Yes$Shang, S., Memon, N., & Kong, X. (2014).@Detecting documents forged by printing and copying.@EURASIP Journal on Advances in Signal Processing, 2014(1), 140.@Yes$Schreyer, M., Schulze, C., Stahl, A., & Effelsberg, W. (2009).@Intelligent Printing Technique Recognition and Photocopy Detection for Forensic Document Examination.@In Informatiktage (Vol. 8, pp. 39-42).@Yes$Lampert, C. H., Mei, L., & Breuel, T. M. (2006).@Printing technique classification for document counterfeit detection.@In 2006 International Conference on Computational Intelligence and Security (Vol. 1, pp. 639-644). IEEE.@Yes$Wu, Y., Kong, X., You, X. G., & Guo, Y. (2009).@Printer forensics based on page document@In 2009 16th IEEE International Conference on Image Processing (ICIP) (pp. 2909-2912). IEEE.@Yes$Devi, M. U., Rao, C. R., & Jayaram, M. (2014).@Statistical measures for differentiation of photocopy from print technology forensic perspective.@International Journal of Computer Applications, 105(15).@Yes$Gonasagi, P., Rumma, S. S., & Hangarge, M. (2023).@Text-independent source identification of printed documents using texture features and CNN model.@In First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022) (pp. 250-261). Atlantis Press.@Yes$Ferreira, A., Bondi, L., Baroffio, L., Bestagini, P., Huang, J., Dos Santos, J. A., ... & Rocha, A. (2017).@Data-driven feature characterization techniques for laser printer attribution.@IEEE Transactions on Information Forensics and Security, 12(8), 1860-1873.@Yes$Jain, H., Joshi, S., Gupta, G., & Khanna, N. (2020).@Passive classification of source printer using text-line-level geometric distortion signatures from scanned images of printed documents.@Multimedia Tools and Applications, 79(11), 7377-7400.@Yes$Joshi, S., Saxena, S., & Khanna, N. (2020).@Source printer identification from document images acquired using smartphone.@arXiv preprint arXiv:2003.12602.@Yes$Bibi, M., Hamid, A., Moetesum, M., & Siddiqi, I. (2019).@Document forgery detection using printer source identification—a text-independent approach.@In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW) (Vol. 8, pp. 7-12). IEEE.@Yes$Darwish, S. M., & ELgohary, H. M. (2021).@Building an expert system for printer forensics: A new printer identification model based on niching genetic algorithm.@Expert Systems, 38(2), e12624.@Yes$Otsu, N. (1979).@A threshold selection method from gray-level histograms.@Automatica, 11(285-296).@Yes$Hangarge, M., Santosh, K. C., Doddamani, S., & Pardeshi, R. (2013).@Statistical texture features based handwritten and printed text classification in south Indian documents.@arXiv preprint arXiv:1303.3087.@Yes$Gonasagi, P., Rumma, S. S., & Hangarge, M. (2020).@Classification of historical documents based on LBP and LPQ techniques.@Int J Innov Technol Exploring Eng , 9(3).@Yes$Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012).@Imagenet classification with deep convolutional neural networks.@Advances in neural information processing systems, 25.@Yes$Ioffe, S., & Szegedy, C. (2015).@Batch normalization: Accelerating deep network training by reducing internal covariate shift.@In International conference on machine learning (pp. 448-456). pmlr.@Yes$Fix, E. (1985).@Discriminatory analysis: nonparametric discrimination, consistency properties (Vol. 1).@USAF school of Aviation Medicine.@Yes$Gonasagi, P., Rumma, S. S., & Hangarge, M. (2023, August).@Text-independent source identification of printed documents using texture features and CNN model.@In First International Conference on Advances in Computer Vision and Artificial Intelligence Technologies (ACVAIT 2022) (pp. 250-261). Atlantis Press.@Yes
<#LINE#>A Web-based multi-disease Prediction framework using Machine learning approaches<#LINE#>Priyanka @L.A.,Subramani @C. <#LINE#>86-90<#LINE#>13.ISCA-RJRS-2026-037.pdf<#LINE#>St Pauls College, Bangalore, Karnataka, India@GFGC and PG centre Shankaranarayana, Udupi, Karnataka, India<#LINE#>12/4/2026<#LINE#>3/5/2026<#LINE#>The increasing reliance on data-driven technologies in healthcare has created new opportunities for developing intelligent systems capable of supporting early disease identification. Rather than relying solely on conventional diagnostic procedures, machine learning techniques enable the analysis of complex medical datasets to uncover latent patterns associated with disease progression. In this work, a web-based multi-disease prediction framework is developed to estimate the likelihood of diabetes, heart disease, and Parkinson’s disease using supervised machine learning algorithms. The system is implemented in Python and deployed via the Streamlit framework to ensure accessibility and ease of use. Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest classifiers are trained and evaluated using standard medical datasets. Model performance is assessed using accuracy, precision, recall, and F1-score metrics. Experimental observations indicate that ensemble-based approaches provide more consistent and robust predictions across datasets. The developed framework demonstrates the practical feasibility of integrating machine learning models into lightweight web applications for preliminary disease risk assessment and clinical decision support.<#LINE#>Topol, E. J. (2019).@High-performance medicine: the convergence of human and artificial intelligence.@Nature medicine, 25(1), 44-56.@Yes$Ristevski, B., & Chen, M. (2018).@Big data analytics in medicine and healthcare.@Journal of integrative bioinformatics, 15(3), 20170030.@Yes$Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019).@A guide to deep learning in healthcare.3 Nature medicine, 25(1), 24-29.@undefined@Yes$Beam, A. L., & Kohane, I. S. (2018).@Big data and machine learning in health care.@Jama, 319(13), 1317-1318..@Yes$World Health Organization (2025).@Global strategy on digital health 2020-2027.@World Health Organization.@Yes$Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2017).@Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis.@IEEE journal of biomedical and health informatics, 22(5), 1589-1604.@Yes$Rajkomar, A., Dean, J., & Kohane, I. (2019).@Machine learning in medicine.@New England Journal of Medicine, 380(14), 1347-1358.@Yes$Nayan Reddy Challa, K., Sasank Pagolu, V., Panda, G., & Majhi, B. (2016).@An Improved Approach for Prediction of Parkinson@arXiv e-prints, arXiv-1610.@Yes$Hasan, M., & Yasmin, F. (2025).@Predicting diabetes using machine learning: A comparative study of classifiers.@arXiv preprint arXiv:2505.07036.@Yes$Fregoso-Aparicio, L., Noguez, J., Montesinos, L., & García-García, J. A. (2021).@Machine learning and deep learning predictive models for type 2 diabetes: a systematic review.@Diabetology & metabolic syndrome, 13(1), 148.@Yes$Chowdhury, E. (2025).@Risk Prediction of Cardiovascular Disease for Diabetic Patients with Machine Learning and Deep Learning Techniques.@arXiv preprint arXiv:2511. 04971.@Yes$Al-Alshaikh, H. A., P, P., Poonia, R. C., Saudagar, A. K. J., Yadav, M., AlSagri, H. S., & AlSanad, A. A. (2024).@Comprehensive evaluation and performance analysis of machine learning in heart disease prediction.@Scientific Reports, 14(1), 7819.@Yes$Kumar, R., Garg, S., Kaur, R., Johar, M. G. M., Singh, S., Menon, S. V., ... & Lozanović, J. (2025).@A comprehensive review of machine learning for heart disease prediction: challenges, trends, ethical considerations, and future directions.@Frontiers in artificial intelligence, 8, 1583459.@Yes$Azmi, J., Arif, M., Nafis, M. T., Alam, M. A., Tanweer, S., & Wang, G. (2022).@A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data.@Medical engineering & physics, 105(1), 103825.@Yes$Park, D. J., Park, M. W., Lee, H., Kim, Y. J., Kim, Y., & Park, Y. H. (2021).@Development of machine learning model for diagnostic disease prediction based on laboratory tests.@Scientific reports, 11(1), 7567..@Yes$Arumugam, K., Naved, M., Shinde, P. P., Leiva-Chauca, O., Huaman-Osorio, A., & Gonzales-Yanac, T. (2023).@Multiple disease prediction using Machine learning algorithms.@Materials today: proceedings, 80, 3682-3685..@Yes$Zannat, R., Al Shafi, A., & Muntakim, A. (2025).@Bridging the gap in bangla healthcare: Machine learning based disease prediction using a symptoms-disease dataset.@In 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE) (pp. 1-6). IEEE.@Yes$Gopisetti, L. D., Kummera, S. K. L., Pattamsetti, S. R., Kuna, S., Parsi, N., & Kodali, H. P. (2023).@Multiple disease prediction system using machine learning and streamlit.@In 2023 5th International conference on smart systems and inventive technology (ICSSIT) (pp. 923-931). IEEE.@Yes$Gandhi, K., Mittal, M., Gupta, N., & Dhall, S. (2020).@Disease prediction using machine learning.@International journal for research in applied science and engineering technology, 8(6).@Yes$Yadav, A., Dwivedi, S., Dwivedi, A., Thakur, U., & Akhtar, D. N. (2025).@Intelligent Disease Diagnosis: A Multi-Disease Prediction Approach Using Machine Learning.@International Journal of Scientific Research in Science, Engineering and Technology, 12(3), 98-109..@Yes$Farooqui, M. E., & Ahmad, J. (2020).@A detailed review on disease prediction models that uses machine learning.@International Journal of Innovative Research in Computer Science & Technology, 8(4).@Yes$Park, Y. H., Suh, J. H., Kim, Y. W., Kang, D. R., Shin, J., Yang, S. N., & Yoon, S. Y. (2022).@Machine learning based risk prediction for Parkinson@Scientific reports, 12(1), 19499.@Yes
<#LINE#>AI-Assisted Assessment of Programming Assignments: A Comparative Study of Automated and Human Evaluation Methods<#LINE#>Asha @N.,Sinchana @S. <#LINE#>91-95<#LINE#>14.ISCA-RJRS-2026-042.pdf<#LINE#>Department of Computer Science, Nrupathunga University (Formerly Government Science College), Bangalore, Karnataka, India@Department of CSE, BGS College of Engineering and Technology, Bangalore, Karnataka, India<#LINE#>13/4/2026<#LINE#>17/5/2026<#LINE#>Assessment of programming assignments is an essential part of teaching computer science. Conventional manual grading takes a lot of time and is frequently inconsistent. Using automated code analysis and artificial intelligence methods, the study suggests an Artificial Intelligence (AI) -assisted assessment system for programming assignments. Logical correctness, language syntax, readability, documentation, and code standards are all assessed by the proposed framework. The findings show that while retaining a high degree of agreement with instructor ratings, AI-assisted assessment can drastically cut down on grading time. According to the results, AI can be a useful tool for decision-making to assess a large number of programming assignments to save time.<#LINE#>Bernik, A., Radošević, D., & Čep, A. (2025).@A comparative study of large language models in programming education: Accuracy, efficiency, and feedback in student assignment grading.@Applied Sciences, 15(18), 10055.@Yes$Jukiewicz, M. (2026).@A systematic comparison of large language models for automated assignment assessment in programming education: Exploring the importance of architecture and vendor.@Computers and Education Open, 10, 100364.@Yes$Mohamed, K., Yousef, M., Medhat, W., Mohamed, E. H., Khoriba, G., & Arafa, T. (2025).@Hands-on analysis of using large language models for the auto evaluation of programming assignments.@Information Systems, 128, 102473.@Yes$Ala-Mutka, K. M. (2005).@A survey of automated assessment approaches for programming assignments.@Computer Science Education, 15(2), 83–102.@Yes$Kiesler, N., & Schiffner, D. (2023).@Large language models in introductory programming education: ChatGPT@Cornell University.@Yes$Yousef, M., Mohamed, K., Medhat, W., Mohamed, E. H., Khoriba, G., & Arafa, T. (2025).@BeGrading: large language models for enhanced feedback in programming education.@Neural Computing and Applications, 37(2), 1027-1040.@Yes$Raihan, N., Goswami, D., Puspo, S. S. C., Siddiq, M. L., Newman, C., Ranasinghe, T., ... & Zampieri, M. (2026).@On the performance of large language models on introductory programming assignments.@Journal of Intelligent Information Systems, 64(1), 239-263.@Yes$Fan, G., Liu, D., Zhang, R., & Pan, L. (2025).@The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: A comparative study with traditional pair programming and individual approaches.@International Journal of STEM Education, 12(1), 16.@Yes$Cohen, J. (1960).@A coefficient of agreement for nominal scales.@Educational and Psychological Measurement, 20(1), 37–46.@Yes$Zhang, D. W., Boey, M., Tan, Y. Y., & Jia, A. H. S. (2024).@Evaluating large language models for criterion-based grading from agreement to consistency.@npj Science of Learning, 9(1), 79.@Yes
@Research Article
<#LINE#>Diversity of Orchid Mycorrhizal Fungi in selected terrestrial orchids from Western Ghats, Karnataka, India<#LINE#>Jyothsna @B.S. <#LINE#>96-100<#LINE#>15.ISCA-RJRS-2026-018.pdf<#LINE#>Department of Botany, Government Science College, N.T. Road, Bangalore-01, Karnataka, India<#LINE#>12/4/2026<#LINE#>19/5/2026<#LINE#>Orchids form mutualistic associations with a specialized group of fungi known as orchid mycorrhiza, particularly during early stages of development. This study investigates mycorrhizal associations in selected terrestrial orchid species. Transmission electron microscopy was used to examine fungal structures, measure hyphal and peloton dimensions, and assess the pattern of colonization within root tissues. Phylogenetic analysis was conducted to identify the associated fungal species. Fungal colonization was observed in 88% of the root cortex, indicating a strong symbiotic relationship. Entry of the fungus occurred mainly through root hairs, followed by the formation of tightly coiled hyphal structures, known as pelotons, within cortical cells. The spread of the fungus involved cell-to-cell penetration, and both intact and degraded pelotons were observed within the cortex. Fungal genera such as Tulasnella and Rhizoctonia were identified from root samples. Seed germination experiments using the isolated fungi demonstrated their role in promoting germination. These findings contribute to understanding the processes of colonization, isolation, and characterization of orchid mycorrhizal fungi, as well as the establishment of symbiotic relationships. Such knowledge may support conservation efforts and help address ecological challenges associated with orchid species.<#LINE#>Rasmussen, H. N. (1995).@Terrestrial orchids: From seed to mycotrophic plant.@Cambridge University Press, 1(1), 1-444.@Yes$Smith, S. E., & Read, D. J. (2008).@Mycorrhizal symbiosis.@Academic Press, 3(1), 1-800.@Yes$Peterson, R. L., Massicotte, H. B., & Melville, L. H. (2004).@Mycorrhizas: Anatomy and cell biology.@NRC Research Press, 1(1), 1-173.@Yes$Dearnaley, J. D. W. (2007).@Further advances in orchid mycorrhizal research.@Mycorrhiza, 17(6), 475-486.@Yes$Selosse, M. A., Weiß, M., Jany, J. L., & Tillier, A. (2002).@Communities and populations of sebacinoid basidiomycetes associated with orchids.@New Phytologist, 154(1), 1-12.@Yes$McCormick, M. K., Whigham, D. F., & O@Mycorrhizal diversity in photosynthetic terrestrial orchids.@New Phytologist, 163(2), 425-438.@Yes$Myers, N., Mittermeier, R. A., Mittermeier, C. G., Fonseca, G. A., & Kent, J. (2000).@Biodiversity hotspots for conservation priorities.@Nature, 403(6772), 853-858.@Yes$Swarts, N. D., & Dixon, K. W. (2009).@Terrestrial orchid conservation in the age of extinction.@Annals of Botany, 104(3), 543-556.@Yes$Taylor, D. L., & McCormick, M. K. (2008).@Internal transcribed spacer primers and sequences for improved characterization of basidiomycetous orchid mycorrhizas.@New Phytologist, 177(4), 1020-1033.@Yes$Yokoya, K., Zettler, L., & Sharma, J. (2021).@Molecular identification of orchid mycorrhiza a review.@Plant Biology, 23(4), 567-578.@Yes$Smith, F. A., & Smith, S. E. (2011).@Roles of arbuscular mycorrhizas in plant nutrition and growth: New paradigms from cellular to ecosystem scales.@Plant Physiology, 155(3), 1050-1057.@Yes$Harvais, G., & Hadley, G. (1967).@The development of orchid mycorrhiza.@New Phytologist, 66(1), 217-230.@Yes$Rodriguez, R. J., White, J. F., Arnold, A. E., & Redman, R. S. (2009).@Fungal endophytes diversity and functional roles.@New Phytologist, 182(2), 314-330.@Yes$Li, T., Wang, Y., & Liu, X. (2021).@Progress and prospects of mycorrhizal fungal diversity in orchids.@Frontiers in Plant Science, 12(1), 646340.@Yes$Sathiyadash, K., Muthukumar, T., & Uma, E. (2020).@Orchid root associated fungal diversity and the waiting room hypothesis.@Annals of Botany, 125(3), 567-580.@Yes$Dearnaley, J. D. W., Martos, F., & Selosse, M. A. (2012).@Orchid mycorrhizas molecular ecology physiology and evolution.@Fungal Diversity, 47(1), 1-26.@Yes
<#LINE#>Steady and Dynamic Analysis of a Porous Land Tapered Slider Bearing Under MHD Couplestress Lubrication<#LINE#>Thippeswamy @Gonchigara <#LINE#>101-109<#LINE#>16.ISCA-RJRS-2026-033.pdf<#LINE#>Department of Mathematics, Government First Grade College, Harapanahalli, Vijayanagara Dt.-583131, Karnataka, India<#LINE#>14/4/2026<#LINE#>23/5/2026<#LINE#>A theoretical investigation is carried out to study the steady and dynamic performance of a porous land tapered slider bearing lubricated with an electrically conducting couple-stress fluid under the influence of an external magnetic field. The governing modified Reynolds equation is formulated by incorporating the effects of porosity, couple-stress fluid behavior, and magnetohydrodynamics. Variations of steady-state pressure distribution, load carrying capacity, dynamic stiffness, and damping coefficient are illustrated graphically. The results indicate that an increase in the permeability parameter leads to a reduction in steady pressure, load capacity, stiffness, and damping characteristics, whereas these performance parameters are significantly enhanced with increasing magnetic field strength and couple-stress fluid parameter.<#LINE#>Morgan, V. T., & Cameron, A. (1957).@Mechanism of lubrication in porous metal bearings. In Proceedings of the Conference on Lubrication and Wear, 89,151–157.@undefined@Yes$Sanni, S. A., & Ayomidele, I. O. (1991).@Hydrodynamic lubrication of a porous slider: Limitations of the simplifying assumption of a small porous facing thickness.@Wear, 147(1), 1–7.@Yes$Prakash, J., & Vij, S. K. (1974).@Squeeze films in porous bearings.@Wear, 27(3), 359–366.@Yes$Shukla, J. B. (1964).@The magnetohydrodynamic composite slider bearing.@Wear, 7(5), 460–465.@Yes$Agrawal, V. K., & Bhatt, S. B. (1980).@Porous pivoted slider bearings lubricated with a micropolar fluid.@Wear, 61(1), 1–8.@Yes$Hughes, W. F. (1963).@The magnetohydrodynamic finite step slider bearing.@Journal of Basic Engineering, 85, 129–136.@Yes$Ramanaiah, G. (1963).@Optimal load carrying capacity of a parallel plate slider bearing.@Japanese Journal of Applied Physics, 6, 797–801.@Yes$Rodkiewicz, C. M., & Anwar, M. I. (1972).@Solution for MHD slider bearing with arbitrary magnetic field.@ASME Journal of Lubrication Technology, 94, 288–290.@Yes$Lin, J. R., Hung, C. R., Hsu, C. H., & Lai, C. (2009).@Dynamic stiffness and damping characteristics of one-dimensional magnetohydrodynamic inclined-plane slider bearings.@Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 223(2), 211–219.@Yes$Naduvinamani, N. B., Siddangouda, A., & Siddharam, P. (2017).@A comparative study of static and dynamic characteristics of parabolic and plane inclined slider bearings lubricated with MHD couple stress fluids.@Tribology Transactions, 60(1), 1–11.@Yes$Stokes, V. K. (1966).@Couple stresses in fluids.@Physics of Fluids, 9, 1709–1715.@Yes$Gupta, R. S., & Sharma, L. G. (1988).@Analysis of couple stress lubricant in hydrostatic thrust bearing.@Wear, 125(3), 257–269.@Yes$Rodkiewicz, C. M., & Anwar, M. I. (1972).@Effects of step-type magnetic field in slider bearing.@Wear, 21(2), 223–229.@Yes$Lin, J. R., & Lu, Y. M. (2004).@Steady-state performance of wide parabolic-shaped slider bearings with a couple stress fluid.@Journal of Marine Science and Technology, 12(4), 2–10.@Yes$Naduvinamani, N. B., Fathima, S. T., & Hanumagowda, B. N. (2011).@Magnetohydrodynamic couple stress squeeze film lubrication of circular stepped plates.@Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology, 225(3), 111–119.@Yes$Naganagowda, H. B. (2016).@Effect of magnetohydrodynamics and couple stress on steady and dynamic characteristics of plane slider bearing.@Tribology Online, 11(1), 40–49.@Yes$Hanumagowda, B. N., Gonchigara, T., Kumar, J. S., & Shivakumar, H. M. (2017).@Steady and dynamic characteristics of MHD land-tapered slider bearing using Stokes’ couple stress model.@International Journal of Pure and Applied Mathematics, 113, 325–333.@Yes$Hanumagowda, B. N., Gonchigara, T., Kumar, J. S., & Shivakumar, H. M. (2018).@Study of effect of magnetohydrodynamics and couple stress on steady and dynamic characteristics of porous exponential slider bearings.@Journal of Physics: Conference Series, 1000(1), Article 012091.@Yes$Hanumagowda, B. N., & Salma, A. (2018).@Study of squeeze film performance with MHD and couple stress between curved annular plates.@International Journal of Research and Analytical Reviews, 5(3), 669–676.@Yes$Ayyappa, G. H., Hanumagowda, B. N., Siddharam, P., & Jagadish, P. (2020).@Influence of magnetic field on a curved circular plate and flat plate lubricated with non-Newtonian fluid.@Journal of Physics: Conference Series, 1473(1), 1–12.@Yes$Naduvinamani, N. B., Hiremath, P. S., & Gurubasawaraj, G. (2001).@Squeeze film lubrication of a short porous journal bearing with a couple stress fluid.@Industrial Lubrication and Tribology, 53(2), 73–77.@Yes$Gonchigara, T. (2025).@Analysis of steady and dynamic MHD performance of circular sine-film thrust bearing with couple stress fluid under surface roughness.@International Journal of Engineering Sciences and Management, 14(4), 119-132.@Yes$Gonchigara, T. (2026).@Effect of roughness on steady and dynamic characteristics of pivot slider bearing with MHD and couple stress fluid.@International Journal of Scientific and Applied Mathematics, 11(2), 24–38.@Yes$Ayyappa, G. H., Hanumagowda, B. N., Siddharam, P., Siddangouda, & Jagadish, P. (2019).@Effect of MHD on pivoted curved slider bearing lubricated with non-Newtonian fluid.@IOSR Journal of Engineering, 9(3).@Yes$Ayyappa, G. H., Hanumagowda, B. N., Siddharam, P., & Jagadish, P. (2019).@Theoretical analysis of MHD effects on curved circular plate and rough flat plate with non-Newtonian fluid.@International Journal of Research and Analytical Reviews, 6(2), 105-114.@Yes
<#LINE#>Study of Photophysical Properties of Coumarin 4-(4-Methoxy-Phenoxymethyl)-5,7-Dimethyl-Chromen-2-one (C19H18O4) and Estimation of Ground-State and Excited-State Dipole Moments<#LINE#>Alageri @Lingappa,S.M. @Hanagodimath <#LINE#>110-114<#LINE#>17.ISCA-RJRS-2026-035.pdf<#LINE#>Government First Grade College, Sandur- 583119, Karnataka, India@Department of PG Studies and Research in Physics, Gulbarga University, Kalaburagi-585 106, Karnataka, India<#LINE#>10/4/2026<#LINE#>14/5/2026<#LINE#>The photophysical properties of coumarin-4-(4-Methoxy-Phenoxymethyl)-5,7-dimethyl-chromen-2-one (C19H18O4) (4MPDC) at room temperature are investigated in pure polar and nonpolar solvents using Gaussian 16W software with the B3LYP/6-31 basis set. The influence of pure solvents on spectral characteristics is investigated by applying theories such as the Lippert-Mataga polarity function, Reichardt's microscopic solvent polarity parameter, and Kamlet and Catalan's multiple linear regression techniques. The main role of solute-solvent interactions in pure solvents depend on particularly dielectric interaction and hydrogen bonding. Hydrogen bonding interactions dominate the contribution of dielectric interactions. The electric dipole moments of both the ground state and excited states have been estimated using the Solvatochromic method. The value of the electric dipole moment of the excited state and the red shifts of emission spectra show that the emitting singlet state has an intramolecular charge transfer (ICT) character. The DFT data showed that λ_max resulted from the support of the highest occupied molecular orbital to the lowest unoccupied molecular orbital transition. We conclude that polar solvents change the fluorescent properties of coumarin.<#LINE#>HR Deepa, S Chandrasekhar and J Thipperudrappa (2022).@Investigation of FRET from organic dyes to silver nanoparticles and structural properties using the DFT/TD-DFT approach.@Chemical Physics Impact, 4 (2022), 1-9,@Yes$Chandrasekhar, S., Deepa, H. R., Melavanki, R. M., Mogurampelly, S., Basanagouda, M. M., Yallappa, S., & Thipperudrappa, J. (2020).@Quantum chemical and solvatochromic studies of biological active 1, 3, 4-thiadiazol coumarin derivatives.@Chemical Data Collections, 29, 100516.@Yes$Tewari, N., Joshi, N. K., Rautela, R., Gahlaut, R., Joshi, H. C., & Pant, S. (2011).@On the ground and excited state dipole moments of dansylamide from solvatochromic shifts of absorption and fluorescence spectra.@Journal of Molecular Liquids, 160(3), 150-153.@Yes$Leela, J. S. P. P., Hemamalini, R., Muthu, S. & Al-Saadi, A. A. (2015).@Spectroscopic investigation (FTIR spectrum), NBO, HOMO–LUMO energies, NLO and thermodynamic properties of 8-Methyl-N-vanillyl-6-nonenamideby DFT methods.@Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 146, 177-186.@Yes$Goudar, R., Gupta, R., Kulkarni, G. U. & Inamdar, S. R. (2015).@Photophysical and fluorescence studies of molecular systems.@Journal of Fluorescence, 25(2015), 1671–1679.@Yes$Presiado I, Y Erez, R Gepshtein and D Huppert, (2009).@Excited-state proton transfer and proton reactions of 6-hydroxyquinoline and 7-hydroxyquinoline in water and ice.@The Journal of Physical Chemistry C, 113(46), 20066–20075,@Yes$Ch Bheema Lingam, K. Ramesh babu, Surya P Tewari and G Vaitheeswaran, (2011).@Structural, electronic, bonding, and elastic properties of NH₃BH₃: A density functional study.@Journal of Computational Chemistry, 32(8), 1734–1742.@Yes$Ch Bheema Lingam, K. Ramesh babu, Surya P Tewari and G Vaitheeswaran, (2011).@Quantum chemical studies on beryllium hydride oligomers.@Computational and Theoretical Chemistry, 963(2–3), 371–377.@Yes$Zhang, X., Shetty, A. S., & Jenekhe, S. A. (1999).@Electroluminescence and photophysical properties of polyquinolines.@Macromolecules, 32(22), 7422-7429.@Yes$Ipate, A. M., Homocianu, M., Hamciuc, C., Airinei, A., & Bruma, M. (2014).@Photophysical behavior of some aromatic poly (1, 3, 4-oxadiazole-ether) s derivatives.@Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 123, 167-175.@Yes$Galina V Loukova, Alexey A Milov, Vasiliev and Vladimir I Minkin (2016).@Dipole moments and solvatochromism of metal complexes: Principle photophysical and theoretical approach.@Physical Chemistry Chemical Physics, 18(27), 17822–17826.@Yes$Kawski A, Bojarski P and Kuklinski B, (2008).@Estimation of the ground- and excited-state dipole moments of Nile Red dye from solvatochromic effect on absorption and fluorescence spectra.@Chemical Physics Letters, 463(4–6), 410–412.@Yes$Pereyra, R. G., Asar, M. L., & Carignano, M. A. (2011).@The role of acetone dipole moment in acetone–water mixture.@Chemical Physics Letters, 507(4-6), 240-243.@Yes$Salman Ahmad Khan, Abdullah M Asiri, Saad H AL-Thaqafy and Hassan Moustafa Faidallah (2014).@Synthesis, characterization and spectroscopic behavior of novel 2-oxo-1,4-disubstituted-1,2,5,6-tetrahydrobenzo [h]quinoline-3-carbonitrile dyes.@Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 133(c), 564–570.@Yes$Isa Sidir and Yadigar Gulsevsn Sidir (2018).@Investigation on the interactions of E-4-methoxycinnamic acid with the solvent: Solvatochromism, electric dipole moment and pH effect.@Journal of Molecular Liquids, 249(2018), 1161–1171.@Yes
<#LINE#>Measurement of volumes in the Gaṇita-Sāra-Saṅgraha of Mahāvīrācārya  (c.850 AD)<#LINE#>Vanaja @V.,Shailaja @M. <#LINE#>115-118<#LINE#>18.ISCA-RJRS-2026-040.pdf<#LINE#>Department of Mathematics, Government First Grade College, Yelahanka, Bangalore-560 064, India@Department of Mathematics, Government First Grade College, Yelahanka, Bangalore-560 064, India<#LINE#>14/4/2026<#LINE#>19/5/2026<#LINE#>Ancient Indian mathematical texts presented detailed problems in arithmetic, algebra, and geometry, which were essential for daily life. Mahāvīrācārya’s treatise, the Gaṇita-Sāra-Saṅgraha (GSS), represents a significant contribution to mathematics. This work comprises nine chapters and approximately 1100 verses. Chapter eight, titled Khātavyavahāraḥ (Calculations regarding excavations), uniquely and thoroughly addresses calculations for the depth of a ditch, the volume of a pyramid and its frustum, the volume of a cone, and the volume of a sphere. He developed formulas for calculating the areas and volumes of both regular and irregular polygons. His contributions represent a significant and distinctive advancement upon the work of earlier mathematicians such as Āryabhaṭa and Bramhagupta. This paper examines issues concerning certain calculations and other Indian mathematical texts, offering modern mathematical interpretations.<#LINE#>Balachandra Rao, S. (1995).@Indian Mathematics and Astronomy-Some Landmarks.@Indian Journal of History of Science, 30, 159-159.@Yes$Padmavathamma. (2000).@The Gaṇita-Sāra-Saṅgraha of Sri Mahāvīrācārya with English transliteration, Kannada translation and notes.@Sri Siddhāntakīrthi Granthamā Sri Hombuja Jain math, Shimoga District, Karnataka.@No$Rangacharya, M. (1912).@Gaṇita-sāra-saṅgraha of Mahāvīrācārya.@The Government of Madras, Madras (now Chennai).@Yes$Kolachana, A., Mahesh, K., & Ramasubramanian, K. (2019).@Studies in Indian Mathematics and Astronomy.@@Yes$Broadbent, T. A. A. (1968).@The history of ancient Indian mathematics by cn srinivasiengar. pp. vii, 157. 36s. 1967. (World press, Calcutta.).@The mathematical gazette, 52(381), 307-308.@Yes$Patwardhan, K. S., Naimpally, S. A. & Singh, S. L. (2001).@Lilavati of Bhaskaracarya. A treatise of mathematics of vedic tradition.@New Delhi: Motilal Banarsidass.@Yes$Datta, B., & Singh, A. N. (1935).@History of Hindu mathematics: A source book (Vol. 2, p. 169).@Bombay: Asia Publishing House.@Yes$Robertson, E. F., & Hadamard, J. (1963).@MacTutor History of Mathematics Archive.@University of St Andrews: St. Andrews, UK.@Yes$Puttaswamy, T. K. (2012).@Mathematical achievements of pre-modern Indian mathematicians.@Newnes.@Yes$Srinivas, K. (1993).@Mathematics in Ancient India.@Publications Division, India.@No$Bag, A.K. (1979).@Mathematics in ancient and medieval India.@Chaukhambha Orientalia, Varanasi.@Yes$Datta, B. (1928).@On Mahavira’s solution of rational triangles and quadrilaterals.@Bulletin of the Calcutta Mathematical Society, 20, 267-294.@Yes$Gupta, R. C. (1993).@Rectification of ellipse from Mahāvīra to Ramanujan.@Ganita Bharati, 15(1-4), 14-40.@Yes$Hayashi, T. (1992).@Mahavira@Ganita Bharati, 14 (1-4), 275-280.@Yes$Jain, B. S. (1977).@On the Ganita-Sara-Samgraha of Mahavira.@Indian Journal of History of Science Calcutta, 12(1), 17-32.@Yes$Gupta, R. C. (1974).@Maha–vi–raca–rya on the Perimeter and Area of an Ellipse.@The Mathematics Education, 8(1), 17-18.@Yes$Plofker, K. (2008).@Mathematics in India.@Princeton University Press.@Yes$Srinivas, M. D. (2005).@Proofs in Indian mathematics. In Contributions to the history of Indian mathematics (pp. 209-248).@Gurgaon: Hindustan Book Agency.@Yes$Bell, E. T. (1946).@Mahavira@Bull. Calcutta Math. Soc, 38.@Yes$Singh, G. (2024).@Two Ninth Century Indian Mathematicians and their works (A brief note).@International Journal of Novel Research and Development, 9(6), b55-b57.@No$Sahu, C. K. (2025).@Mathematical Contributions of Mahaviracharya –A Historical Perspective.@International Journal of Progressive Research in Engineering Management and Science, 5(7), 513-514.@No$Bhinde, R. (2025).@Mathematical Knowledge System in Ancient India.@International Journal of Mathematics and Computer Science, 13(12), 6043-6045.@No
@Review Paper
<#LINE#>Biocorrosivity of metals: A review on Interaction, Causes, Impacts and Recent advancements<#LINE#>Dharani @P.A.,Sarina P. @Khabade <#LINE#>119-123<#LINE#>19.ISCA-RJRS-2026-009.pdf<#LINE#>Department of PG studies in Biotechnology, Nrupathunga University. Bengaluru-560001, Karnataka, India@Department of PG studies in Biotechnology, Nrupathunga University. Bengaluru-560001, Karnataka, India<#LINE#>10/4/2026<#LINE#>16/5/2026<#LINE#>Biocorrosion is the deterioration of metals by the influence of microbial activity. Globally, biocorrosion is a crucial industrial problem that leads to material degradation, loss of structural framework, and economic loss. This review article summarizes the biocorrosion process, microbes involved, the impact of biocorrosion techniques to mitigate biocorrosion, and recent advancements in biocorrosion research. Several articles were surveyed to analyze the process, electrochemical interaction, byproducts of the process, and metabolism of microorganisms. Understanding this process is essential for developing effective and sustainable corrosion control strategies.<#LINE#>Beech, I. B., & Sunner, J. (2004).@Biocorrosion: towards understanding interactions between biofilms and metals.@Current opinion in Biotechnology, 15(3), 181-186.@Yes$Jacobson, G. A. (2007).@Corrosion at Prudhoe Bay—a lesson on the line.@Materials performance, 46(8), 27-34.@Yes$Yao, X., Fu, Q., Song, G. L., & Wang, K. (2025).@The Biocorrosion of a Rare Earth Magnesium Alloy in Artificial Seawater Containing Chlorella vulgaris.@Materials,  18(15), 3698.@Yes$Abramova, E., Shapagina, N., Artemiev, G., & Safonov, A. (2024).@Microbial Corrosion of Copper Under Conditions Simulating Deep Radioactive Waste Disposal.@Biology, 13(12), 1086.@Yes$Drebezghova, V., Cugnet, C., Fernandes, S. C. M., Dupin, J. C., Guignard, M., Ranchou-Peyruse, A., Ranchou-Peyruse, M., & Nardin, C. (2025).@Chitosan-based coatings to prevent Nitratidesulfovibrio vulgaris influenced (bio)corrosion on aluminium alloy.@Colloids and Surfaces. B, Biointerfaces, 253, 114751.@Yes$Huang, S., Bergonzi, C., Smith, S., Hicks, R. E., & Elias, M. H. (2023).@Field testing of an enzymatic quorum quencher coating additive to reduce biocorrosion of steel.@Microbiology Spectrum, 11(5), e05178-22.@Yes$Zhou, E., Zhang, M., Huang, Y., Li, H., Wang, J., Jiang, G., Jiang, C., Xu, D., Wang, Q., & Wang, F. (2022).@Accelerated biocorrosion of stainless steel in marine water via extracellular electron transfer encoding gene phzH of Pseudomonas aeruginosa.@Water research, 220, 118634.@Yes$Rao, T. S., & Feser, R. (2024).@Biofilm formation by sulphate-reducing bacteria on different metals and their prospective role in titanium corrosion.@Environmental technology, 45(13), 2575–2588.@Yes$Zhang, S., Qiu, J., Ren, Y., Yu, W., Zhang, F., & Liu, X. (2016).@Reciprocal interaction between dental alloy biocorrosion and Streptococcus mutans virulent gene expression.@Journal of Materials Science. Materials in medicine, 27(4), 78.@Yes$Koch, G., Varney, J., Thompson, N., Moghissi, O., Gould, M., & Payer, J. (2016).@International measures of prevention, application, and economics of corrosion technologies study.@NACE int, 216(3).@Yes$Wolodko, J., Haile, T., Khan, F., Taylor, C., Eckert, R., Hashemi, S. J., ... & Skovhus, T. L. (2018).@Modeling of microbiologically influenced corrosion (MIC) in the oil and gas industry-past, present and future.@In NACE Corrosion (pp. NACE-2018). NACE.@Yes$Dou, W., Xu, D., & Gu, T. (2021).@Biocorrosion caused by microbial biofilms is ubiquitous around us.@Microbial Biotechnology, 14(3), 803-805.@Yes$Nasser, B., Saito, Y., Alarawi, M., Al-Humam, A. A., Mineta, K., &Gojobori, T. (2021).@Characterization of microbiologically influenced corrosion by comprehensive metagenomic analysis of an inland oil field.@Gene, 774, 145425.@Yes$Shangguan, Y., Liu, M., Liu, X., Mei, K., Shi, B., Chen, H., Liao, R., & Liu, K. (2025).@Sulfate-reducing bacteria-induced corrosion behavior of L415 pipeline steel in Southern Jiangxi, China.@ACS Omega, 10(42), 49591–49601.@Yes$Ivanovich, N., Marsili, E., Shen, X., Messinese, E., Marcos, Rajala, P., & Lauro, F. M. (2025).@Exploring the impact of flow dynamics on corrosive biofilms under simulated deep-sea high-pressure conditions using bio-electrochemostasis.@Frontiers in Microbiology, 16, 1540664.@Yes$Huang, Y., Xu, D., Huang, L. Y., Lou, Y. T., Muhadesi, J. B., Qian, H. C., ... & Jiang, C. Y. (2021).@Responses of soil microbiome to steel corrosion.@npj Biofilms and Microbiomes, 7(1), 6.@Yes$Maji, K., & Lavanya, M. (2024).@Microbiologically influenced corrosion in stainless steel by Pseudomonas aeruginosa: an overview.@Journal of Bio-and Tribo-corrosion, 10(1), 16.@Yes$Floyd, J. G., Stamps, B. W., Goodson, W. J., & Stevenson, B. S. (2021).@Locating and Quantifying Carbon Steel Corrosion Rates Linked to Fungal B20 Biodiesel Degradation.@Applied and environmental microbiology, 87(24), e0117721.@Yes$Zhang, J., Fu, Q., & Song, G. L. (2025).@The Influence of NaClO on the Biocorrosion of Carbon Steel Induced by Chlorella vulgaris in Artificial Seawater.@Molecules,  30(17), 3636.@Yes$Pal, M. K., & Lavanya, M. (2022).@Microbial influenced corrosion: understanding bioadhesion and biofilm formation.@Journal of Bio-and Tribo-Corrosion, 8(3), 76.@Yes$Cai, D., Wu, J., Chai, K., Wang, Y., & Li, X. (2021).@Microbiologically influenced corrosion behavior of carbon steel in the presence of marine bacteria Pseudomonas sp. and Vibrio sp.@ACS Omega, 6(5), 3780–3790.@Yes$Zhao, J., Csetenyi, L., & Gadd, G. M. (2020).@Biocorrosion of copper metal by Aspergillus niger.@International Biodeterioration & Biodegradation,  154, 105081.@Yes$Dong, Y., Song, G. L., Zhang, J., Gao, Y., Wang, Z. M., & Zheng, D. (2022).@Biocorrosion induced by red-tide alga-bacterium symbiosis and the biofouling induced by dissolved iron for carbon steel in marine environment.@Journal of Materials Science & Technology, 128, 107-117.@Yes$Jacobson, M. Z. (2019).@Short-term impacts of the Aliso Canyon natural gas blowout on weather, climate, air quality, and health in California and Los Angeles.@Environmental science & technology, 53(10), 6081-6093.@Yes$Ali, S. I., & Ahmad, S. N. (2025).@Microbiologically influenced corrosion in uncoated and coated mild steel.@Scientific Reports, 15(1), 12629.@Yes$Narenkumar, J., Parthipan, P., Usha Raja Nanthini, A., Benelli, G., Murugan, K., & Rajasekar, A. (2017).@Ginger extract as green biocide to control microbial corrosion of mild steel.@3 Biotech, 7(2), 133.@Yes$Faccioli, Y. E. S., França, I. B., Oliveira, K. W., Roque, B. A. C., Selva Filho, A. A. P., Converti, A., ... & Sarubbo, L. A. (2025).@Microbial biosurfactant as sustainable inhibitor to mitigate biocorrosion in metallic structures used in the offshore energy sector.@Coatings, 15(8), 937.@Yes$Kotu, S. P., Mannan, M. S., & Jayaraman, A. (2019).@Emerging molecular techniques for studying microbial community composition and function in microbiologically influenced corrosion.@International Biodeterioration & Biodegradation, 144, 104722.@Yes$Lv, M., Chen, L., Tang, X., Huang, R., Du, M., Zhang, X., ... & Du, Y. (2026).@Influence of Bacillus subtilis on the corrosion resistance of B30 copper–nickel alloy and the biomass-regulated mineralization mechanism.@Applied and Environmental Microbiology, 92(1), e02286-25.@Yes$Ambepitiya, H., Rathnayaka, S., Perera, Y., Jayathilake, C., Ferdinandez, H., Herath, A., ... & Fernando, E. (2025).@Mitigating Microbiologically Influenced Corrosion of Iron Caused by Sulphate-Reducing Bacteria Using ZnO Nanoparticles.@Processes, 13(10), 3239.@Yes