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Biology subjects

Fariha, K. A.

Publications and source records attributed to Fariha, K. A..

2 recordsLinked to original sources

Defining Operational UV-C Dose Requirements for Autonomous Disinfection of Clinically Relevant Pathogens Across Healthcare and High-Touch Surfaces

Background: Autonomous ultraviolet-C (UV-C) disinfection systems are increasingly used to supplement manual environmental cleaning, yet evidence-based guidance defining pathogen-specific UV-C dose requirements across representative surfaces remains limited. Aim: To characterize operational UV-C dose requirements for clinically relevant pathogens across diverse high-touch and healthcare surfaces and determine how experimentally derived microbial inactivation can inform operational exposure parameters. Methods: SARS-CoV-2, adenovirus, Pseudomonas aeruginosa, Staphylococcus aureus, Klebsiella pneumoniae, Enterococcus faecalis, Candida auris, and Clostridioides difficile spores were exposed to defined UV-C doses on representative high-touch materials or stainless steel under standardized conditions, including a 10% fetal bovine serum organic soil challenge. Microbial inactivation was quantified by viable recovery. Dose-response analysis and operational modelling were used where supported by the experimental data. Findings: UV-C exposure significantly reduced viable recovery of all pathogens, with substantial differences in the exposure conditions associated with microbial inactivation. SARS-CoV-2 exhibited substantial inactivation at doses as low as 2.6 mJ/cm2, whereas the highest evaluated doses were 1,800 mJ/cm2 for C. difficile spores and 3600 mJ/cm2 for C. auris. For C. auris, multi-dose data estimated that approximately 1,410 mJ/cm2 was associated with a 2-log10 reference reduction, enabling distance-dependent exposure-time predictions. Conclusion: Experimentally quantified UV-C exposures produced substantial microbial inactivation across diverse pathogen classes and surfaces. Integrating delivered dose with microbial reduction provides a quantitative framework for translating laboratory efficacy into operational parameters for autonomous UV-C disinfection.

microbiology↗

SiRNA Molecules as Potential RNAi Therapeutics to Silence RdRP Region and N-Gene of SARS-CoV-2: An In Silico Approach

COVID-19 pandemic keeps pressing onward and effective treatment option against it is still far-off. Since the onslaught in 2020, 13 different variants of SARS-CoV-2 have been surfaced including 05 different variants of concern. Success in faster pandemic handling in the future largely depends on reinforcing therapeutics along with vaccines. As a part of RNAi therapeutics, here we developed a computational approach for predicting siRNAs, which are presumed to be intrinsically active against two crucial mRNAs of SARS-CoV-2, the RNA-dependent RNA polymerase (RdRp), and the nucleocapsid phosphoprotein gene (N gene). Sequence conservancy among the alpha, beta, gamma, and delta variants of SARS-CoV-2 was integrated in the analyses that warrants the potential of these siRNAs against multiple variants. We preliminary found 13 RdRP-targeting and 7 N gene-targeting siRNAs using the siDirect V.2.0. These siRNAs were subsequently filtered through different parameters at optimum condition including macromolecular docking studies. As a result, we selected 4 siRNAs against the RdRP and 3 siRNAs against the N-gene as RNAi candidates. Development of these potential siRNA therapeutics can significantly synergize COVID-19 mitigation by lessening the efforts, furthermore, can lay a rudimentary base for the in silico design of RNAi therapeutics for future emergencies.

bioinformatics↗