Biology subjects

Shahab, M.

Publications and source records attributed to Shahab, M..

3 recordsLinked to original sources

bioRxiv2025

Noninvasive detection of bacterial biofilms using an insect olfactory brain-based gas sensor

Bacteria emit volatile organic compounds (VOCs) that can be targeted for disease detection. Biological olfactory systems have keen senses of smell, can detect VOCs at low concentrations, and are naturally adapted to classifying mixtures of VOCs as odors. Here, we employed locust (Schistocerca americana) olfactory neural circuitry to differentiate biofilm and planktonic cultures of Pseudomonas aeruginosa and Staphylococcus aureus using their odors. In vivo extracellular neural recordings were taken from the second-order olfactory processing center (antennal lobe) of locusts. The VOCs from biofilm cultures evoked distinct spiking responses compared to the planktonic cultures for both bacterial species. By analyzing the population neuronal responses, we classified individual bacterial biofilm vs. planktonic odors with up to 96% accuracy. The neural responses were highly discriminatory within the first couple of seconds of odor presentation and our analysis was conducted on less than five seconds of data, highlighting the potential of our biological sensor for real-time biofilm detection.

neuroscience↗

bioRxiv2025

Synergizing Network Pharmacology and Pan-Cancer Analysis in TCM Repositioning for Tumor Therapy

BackgroundRepositioning Traditional Chinese Medicine (TCM) for cancer treatment, addressing heterogeneity through synergistic effects, aligns with the evolving trend of combination therapy. However, the complexity of TCM and the lack of methodology hinders the elucidation of TCM treatment mechanisms and potential indications. The research aims to construct develop a comprehensive method combining network pharmacology and pan-cancer analysis (NetPharm-PanCan) for TCM repositioning, exemplified by the Astragali Radix-Curcumae Rhizoma (ARCR) herb pair. MethodThe TCM-component-gene network was constructed using Cytoscape3.7.2 with gene screening based on components from TCMSP and targets predicted via the Swiss Target Prediction database. The core gene set of the ARCR (CGSARCR) was identified by analyzing network via the dual algorithm, namely Degree and MCODE followed by GO and KEGG enrichment analyses. The pan-cancer analysis encompassing Gene Set Variation Analysis (GSVA), immune infiltration correlation, cancer pathway analysis, and Gene Set Enrichment Analysis (GSEA) was unveiled to identify the potential indications. Multivariate cox regression analysis was employed for identifying prognostic genes, followed by the modeling for potential therapeutic indications using the R software. The Metescape database was harnessed to reveal similarities and differences in the mechanisms for prognostic genes associated with potential indications. The XGBoost algorithm was used to construct an indication prediction system according to the analysis results above. ResultThe CGSARCR comprises 28 genes targeting all ARCR components. The pan-cancer analysis revealed that the CGSARCR showed significantly higher tumor scores than normal tissue scores across nine different types of cancer, including THCA, KIRC, LUAD, COAD, BRCA, STAD, ESCA, and others. The CGSARCR correlated strongly with cancer-related pathways and the immune microenvironment. Prognostic models evaluate the potential of these indications as follows: THCA> KIRC> LUAD> COAD> BRCA> STAD> ESCA, with enrichment analysis suggesting KIRC and LUAD as the most potential indications of ARCR. The XGBoost-based system achieved high predictive accuracy (training AUC: 0.985, testing AUC: 0.96, training logloss: 0.21, testing logloss: 0.25). ConclusionThe NetPharm-PanCan method provides a robust, network-based pan-cancer analysis framework for TCM repositioning in cancer research. It provides theoretical foundations and practical tools for TCM-based drug development, with potential applicability to broader drug repurposing efforts.

bioinformatics↗

bioRxiv2024

Prevalence of Key Spike Protein Mutations and Their Limited Effect on COVID-19 Clinical Manifestations in Sylhet, Bangladesh

SARS-CoV-2 is the virus responsible for the COVID-19 pandemic, which has spread rapidly around the world and had a significant impact on public health and the economy worldwide. This study investigated the correlation between SARS-CoV-2 spike protein mutations, clinical outcomes and patient demographics in the Sylhet region of Bangladesh. We looked at the full genome sequences of 37 SARS-CoV-2 samples that were collected between January and June 2020. Specifically, we looked at five major spike protein mutations: D614G, A570D, D1118H, A222V, and P681R. The D614G mutation was the most prevalent (94.6%), followed by A570D and D1118H (both 32.4%), A222V (29.7%), and P681R (13.5%). Despite their high prevalence, we found no statistically significant associations between these mutations and clinical outcomes or demographic variables, except for possible trends for the P681R mutation. We found that age played a decisive role in recovery from COVID-19, with older patients exhibiting slower recovery rates. In terms of predictors of outcome, gender differences were observed: clinical symptoms and viral genetic mutations were more influential for men, while age and disease progression were more important for women. Common nucleotide substitutions (A23403G, C3037T, and C14408T) associated with European strains were identified, suggesting possible routes of transmission. This study contributes to our understanding of the genetics, clinical manifestations and epidemiology of SARS-CoV-2 in the Sylhet region and emphasizes the need for continuous genomic surveillance and adaptive public health strategies.

microbiology↗