Acinetobacter baumannii
(
A. baumannii
) stands as a critical priority pathogen, rapidly developing resistance against the currently available antibiotic treatments, including last‐resort antibiotics. This nosocomial pathogen is responsible for alarmingly high mortality rates across the world. The drug repurposing approach may aid effectively in this crucial situation as the pharmacokinetic profile and safety details of currently available drugs are already well known, thereby taking comparatively less time than the traditional drug development process. In this study, the Food and Drug Administration (FDA) sanctioned drugs with structural similarity to ampicillin were screened, followed by pharmacokinetic and antimicrobial activity evaluations via in silico analysis. Further, the binding affinity of the drugs toward penicillin‐binding protein 3 (PBP3) and its prevalent mutants was evaluated via molecular docking and simulation studies. Based on results, the antidiabetic drug canagliflozin has been found to possess good binding affinity with wild type penicillin‐binding protein 3 (PBP3
WT)
(−8.01 kcal/mol), as well as its clinically prevalent mutants, PBP3
A515V
(−7.76 kcal/mol), PBP3
T526S
(−7.26 kcal/mol). According to our results, the drug possesses stable molecular dynamics interactions with PBP3
WT
, as well as its mutants, PBP3
A515V
and PBP3
T526S
. Based on our observations, we suggest canagliflozin as a potent PBP3‐binding drug against the
A. baumannii
pathogen.
Srujal Kacha, A. Anbarasu· ChemistrySelect· 0 citations
Background Carbapenem resistant Acinetobacter baumannii (CRAB) is recognized as one of the most critical priority pathogens by the World Health Organization due to its persistence in nosocomial settings, extensive antimicrobial resistance, and increasing dissemination at the global level. Despite the escalating availability of genomic data, genotype–phenotype integrated studies exploring the genetic determinants associated with carbapenem resistance remain limited. Methods In this study, a comprehensive comparative genomics was performed using publicly available 395 clinical A. baumannii genomes, comprising of 267 CRAB and 128 carbapenem susceptible A. baumannii (CSAB). Comparative genomic analyses included sequence types (STs), virulence factors (VFs), antimicrobial resistance genes (ARGs), and mobile genetic elements (MGEs) characterization. Pangenome-wide association study (PanGWAS) was performed to test the associations between genotypes and carbapenem resistance phenotype. Results CRAB genome subset demonstrated higher abundance of ARGs (acquired carbapenemases in particular), plasmids, carbapenem resistance-associated insertion sequences, and integrons than CSAB genomes. PanGWAS identified six positively associated genes (relE, umuC, hphA, hsmA, hphR, and fecI) significantly enriched in CRAB population. Core SNP phylogeny integrated with STs and acquired carbapenemase genes exhibited heterogeneous distribution of resistance genes across lineages, indicating potential role of both clonal dissemination and horizontal gene transfer. Conclusion This study provides an overall genomic architecture of CRAB integrating comparative genomics, PanGWAS, and phylogenomics approaches. The findings underscore the complex interplay between ARGs, VFs, and MGEs in the genomic evolution of CRAB, expanding current understanding of CRAB adaptation and may contribute toward enhanced surveillance, antimicrobial stewardship, and exploration of alternative therapeutic targets.
Sara Pearl, A. Anbarasu· Frontiers in Cellular and In...· 0 citations
Cancer remains a major global health burden, with approximately 20 million new cases and 9.7 million cancer-related deaths reported globally in 2022. While advances in radiological imaging, molecular profiling, and clinical data have enhanced the interpretation of disease progression, the availability of multiple such modalities still does not meet the needs of a large patient population. This narrative review focuses on the role of multimodal artificial intelligence and machine learning in bridging the gap in interpreting heterogeneous modalities to improve risk prediction, prognostic assessment, and treatment decision-making in precision oncology. Multimodal frameworks such as Pathomic Fusion illustrate how complementary histopathological and genomic information can be integrated for cancer diagnosis and prognostic modeling. Multimodal models have demonstrated potential in virtual biopsy, cancer screening, prognostic prediction, radiotherapy planning, intraoperative guidance, and clinical-trial design using digital twins and synthetic control arms. The major limitations of incorporating multimodal artificial intelligence and machine learning in oncology include data heterogeneity, demographic or institutional biases, and reproducibility challenges that hinder translation. Accordingly, appropriate data-governance strategies, fairness audits, and privacy-preserving approaches such as federated learning should be considered where appropriate. Future progress will depend on the development of standardized benchmarking datasets, robust external validation, seamless integration with electronic health records and picture archiving and communication systems, and the implementation of explainable, secure, and clinically validated multimodal artificial intelligence frameworks that support precision oncology in routine clinical practice.
Shruthi Suresh, A. S. Parvathy, Megha Raj et al.· Frontiers in Digital Health· 0 citations
Introduction Brucella spp. are Gram-negative bacteria accountable for brucellosis in immunocompromised individuals and livestock. Due to the slow-growing latent phenotype, current antibiotics are insufficient to treat the infection. The lack of an approved vaccine for human use against this pathogen represents a significant public health concern and indicates the urgent need for novel prophylactic interventions. Methodology In this study, the reverse vaccinology method was combined with pan-genome analysis to identify potential vaccine targets. Proteins have been screened for antigenicity, solubility, immunogenicity, and subcellular localization. B cell and T cell epitopes exhibiting high immunogenicity and solubility have been identified. Multi-epitope vaccine constructs have been evaluated and further analyzed depending on their physicochemical properties. Molecular docking, conformational dynamics, in silico cloning, and immune simulations were conducted to identify the optimal vaccine candidate. Results Four proteins, trigger factor, outer membrane protein assembly factor BamA, urease subunit beta (UreB), and urease subunit alpha (UreC1) were considered for potential vaccine targets. A total of 26 B cell and 97 T cell epitopes with notable immunogenicity and solubility have been shortlisted. Twelve multi-epitope vaccine constructs were generated, among which Vc7 has been chosen based on structural and physicochemical properties. Molecular docking analysis revealed a good correlation with 2FSE and 2Z65, which were further analyzed to reveal that Vc7 exhibited stronger binding affinity (−135.24 kcal/mol) towards 2FSE, mediated by hydrophobic contacts, salt bridges, and intermolecular hydrogen bonds, making it the ideal vaccine complex and validated through a 150 ns molecular dynamics simulation. In silico cloning established construct compatibility, and immune simulation confirmed Vc7’s potential to elicit T cell, B cell, antibody, and cytokine-mediated responses. Conclusion Vc7 has been identified as a structurally stable and highly immunogenic construct, suggesting its potential as a universal multi-epitope vaccine candidate for the prevention of brucellosis.
Rhitam Biswas, Swapno Surabhi Sinha, Aditi Roy et al.· Frontiers in Bioinformatics· 0 citations
Crohn's disease is a long-term inflammatory disorder arising from the interaction of genetic risk factors, immune system dysfunction, and alterations in gut microbiota. Variability in clinical phenotypes and lack of biomarker specificity hinder the efficiency of current traditional diagnostic and treatment approaches. This review aims to assess how AI- and ML-driven multi-omics offer comprehensive insights into pathogenicity, thereby enhancing diagnostic techniques and personalized therapeutic approaches in CD. Current studies employ integration of multi-omics like genomics, proteomics, transcriptomics, metabolomics, and microbiome analysis in CD with AI and ML for significant advancement of biomarker discovery and clinical applications. Emerging evidence reveals that CD is a multi-factorial disorder involving host genetics, immune dysfunction, and microbiome shifts. Integration of advanced AI/ML models with multi-omics data can predict disease-specific biomarkers for easy diagnosis and facilitate precision medicine to enhance therapies. For a successful clinical implementation of an AI/ML model with multi-omics in CD, a standardized data framework and large-scale validation are needed. Additionally, future research should focus on developing interpretable AI models, real-time monitoring systems, and theranostic platforms to enhance precision healthcare delivery.