Biology subjects

Aktar, S.

Publications and source records attributed to Aktar, S..

3 recordsLinked to original sources

bioRxiv2026

Characterization of Chromium-Resistant and Reducing Bacteria from Poultry Litter Ecosystems

Compact poultry raising has turned poultry litter into an environmental problem, as it may all be packed with heavy metals and drug-resistant germs. Of all the metals, chromium contamination not only disturbs the general environment but is also a source of concern for public health. Poultry litters were taken from 14 farms in different places, and the bacteria characters from different places were tested for their capacity to tolerate Cr(VI). A total of 31 bacterial isolates were initially screened, and three of them (AH-2, AZ-1, and AMF-3) appeared to be very resistant to chromium. The isolates were able to survive at the highest concentration, 800 mg/L of the Cr(VI); however, AH-2 was the most resistant one (MIC: 900 mg/L; MBC: 1000 mg/L). Chromium reduction tests showed that AMF-3 at high concentration showed the maximum chromium reduction, while AH-2 achieved higher chromium reduction at medium concentration. Phenotypic and biochemical analysis showed that the isolates were Staphylococcus spp., which was confirmed by 16S rRNA gene sequencing as S. cohnii, S. saprophyticus, and S. gallinarum. Moreover, chromium was detected at higher levels in poultry litter compared to the feed, with the highest accumulation in AZ farm litter (4464.0 g/kg). The highlighting feature of our article is the presence of chromium-tolerant and reducing bacteria in poultry environments. Besides that, the level of chromium in poultry litter is really high, and it points to the need for better waste management.

microbiology↗

bioRxiv2026

Distribution-Aware Federated Learning for Diabetes Prediction Using Tabular Clinical Data Under Non-IID and Class-Imbalanced Settings

Federated learning (FL) enables collaborative clinical model training without centralized data sharing, yet its deployment is hindered by statistical heterogeneity (non-IID data) and inherent class imbalance across healthcare institutions. Conventional aggregation strategies such as FedAvg and FedProx weight client updates solely by dataset size, ignoring class distributions and thereby biasing the global model toward the majority class. To address this, we propose Distribution-Aware Federated Learning (DA-FL), which introduces a minority-class amplification factor{phi} k computed as the ratio of a clients local positive class rate to the global positive class rate. Combined with class-weighted cross-entropy loss at the client level, DA-FL forms a two-level correction mechanism that mitigates imbalance without additional data sharing. Experiments on the CDC BRFSS 2021 diabetes dataset (236,378 records across five simulated clients under three non-IID levels) show that DA-FL improves F1-Macro by 18.2% and G-Mean by 26.7% over FedAvg under moderate non-IID conditions, while achieving 31-fold greater F1-Macro stability across 30 communication rounds. These findings demonstrate that DA-FL is an effective and practically deployable solution for federated clinical prediction under realistic non-IID and class-imbalanced settings.

developmental biology↗

bioRxiv2025

Mutation profiling of KRAS and BRAF in primary tumors and circulating tumor cells of colorectal cancer patients using PNA-LNA molecular switch

Identifying KRAS and BRAF mutation status is essential for guiding targeted therapies and enhancing treatment outcomes in colorectal cancer (CRC). This study employs the "PNA-LNA molecular switch" to detect mutations in KRAS codon 12 (c.35G>T/G12V) and BRAF codon 600 (c.1799T>A/V600E) from primary tumours and circulating tumour cells (CTCs) in CRC patients, correlating mutation status with clinicopathological parameters. DNA was isolated from 71 primary tumours and 37 CTC samples. Mutation profiles were generated using the PNA-LNA molecular switch. KRAS mutations were detected in 26 primary tumours (36.6%) and 13 CTCs (26.8%), while BRAF mutations were observed in 19 primary tumours (26.8%) and 7 CTCs (19%). No significant correlation was observed between mutation status and clinicopathological parameters in primary tumours. However, KRAS G12V mutations in CTCs significantly correlated with lymph node metastasis (p=0.002), overall pathological stage (p=0.005), and lymphovascular invasion (p=0.034). BRAF V600E mutation status showed no significant clinicopathological associations. Validation of the PNA-LNA molecular switch against Next-Generation Sequencing (NGS) showed 89% concordance with p-values < 0.001 for both genes. This method is highly comparable to NGS for detecting KRAS and BRAF mutations and shows promise as a point-of-care diagnostic tool. Larger patient cohorts are required to confirm its clinical utility.

cancer biology↗