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Bera, D.

Publications and source records attributed to Bera, D..

2 recordsLinked to original sources

Evidence of residual Brugia malayi infection in TAS-III cleared areas of coastal Odisha: Findings from Molecular Xenomonitoring

BackgroundLymphatic filariasis (LF), caused by Wuchereria (W) bancrofti, Brugia malayi and Brugia (B) timori, remains a major public health problem known for its disfiguring and debilitating effects despite notable progress under the Global Program to Eliminate Lymphatic Filariasis (GPELF). Following recent national directives (2024) restructuring implementation units (IUs) to the block level, all TAS-cleared blocks now require re-evaluation using night blood surveys (NBS) or filarial test strip (FTS) kits. Molecular Xenomonitoring (MX) is recommended as a post-validation surveillance method for detecting active LF transmission in vectors. Therefore, an exploratory study was conducted in two coastal districts of Odisha, namely Jagatsinghpur and Puri, which cleared TAS-III, to investigate the presence of B. malayi and W. bancrofti infections in filaria-transmitting vectors. Methodology/Principal FindingsAdult female mosquitoes were collected from three villages across Jagatsinghpur and Puri districts during March-April 2025. Species were identified morphologically, segregated and pooled. The pooled samples were screened for the presence of filarial DNA using species-specific PCR assays. Of 1,518 collected adult female mosquitoes, 22.6% were Culex quinquefasciatus, 18.3% were Mansonia (Mn) annulifera, and 4.3% were Mansonia uniformis. Among 33 pools screened, six pools were positive for B. malayi DNA (18.18% pool positivity; infection rate 2.88%, 95% CI: 1.23-5.74), in Mn. annulifera, whereas W. bancrofti was not detected. Conclusions/SignificanceThe study provides the first molecular confirmation of B. malayi infection in Mn. annulifera, indicating a persistent Brugian transmission in Odisha even after the implementation of mass drug administration. The Study highlights MX as a sensitive, non-invasive surveillance tool for detecting residual infection in post-MDA and post-TAS settings. The study underscores the need for a standardized MX protocol and integration with entomological monitoring to sustain national LF elimination targets by 2027. Author SummaryLymphatic filariasis is a mosquito-borne disease that can cause severe, disfiguring swelling of the limbs and other lasting disabilities, and it remains a serious public health problem in India. National programmes in India are actively working to eliminate it by giving preventive medicines to communities, conducting surveys to confirm that transmission has ceased, and once an area passes, this treatment is withdrawn. However, it is not always clear whether the parasite has truly disappeared or is still lingering at very low levels. In this study, we searched for the parasite directly inside mosquitoes, an approach that does not require collecting blood from people. We sampled mosquitoes from two coastal districts of Odisha, India, that had already passed the required surveys and stopped treatment. We found the parasite Brugia malayi in Mansonia mosquitoes, the first time this has been shown using this molecular method in the region, although the more common filarial parasite was not detected. Our findings show that transmission can smoulder on even after an area is judged free of disease. Because India has no available rapid field test for this particular parasite, we argue that examining mosquitoes should become a routine safeguard for the countrys elimination effort.

zoology↗

Supervised learning of protein thermal stability using sequence mining and distribution statistics of network centrality

MotivationIt is expected that the difference in the thermal stability of mesophilic and thermophilic proteins arises, in part at least, from the differences in their molecular structures and amino acid compositions. Existing machine learning approaches for supervised classification of proteins rely on the features derived from the structural networks and the amino acid sequences. However, the network features used leave out several important network centrality values, the statistic used is a simple average and the sequence features used are hand-picked leading to an accuracy of 90%.\n\nResultsWe show that discriminating sub-sequences of the amino acid sequences can significantly improve classification accuracy compared to the existing approaches of counting amino acids, di-peptide or even tri-peptide bonds. We identify notions of network centrality, specifically that depends on the distances between C atoms, that appears to correlate better with thermal stability compared to the existing network features. We also show how to generate better statistics from the node- and edge-wise centrality values that more accurately captures the variations in their values for different types of proteins. These improved feature selection techniques make it possible to classify between thermophilic and mesophilic proteins with 96% accuracy and 99% area under ROC.\n\nAvailabilityThe dataset and source code used are available at https://github.com/ankits0207/Protein_Classification_BIO699\n\nContactdbera@iiitd.ac.in\n\nonline.

bioinformatics↗