👤 Rohit Bhowmick

With less than two years remaining from 2027-the year which the government has targeted to achieve zero Indigenous cases, we map the malaria indicators across the 700 + districts for five years between 2019 and 2023 using spatiotemporal maps and also assess the potential drivers of malaria transmission in different regions. We used the annual district-wise malaria data from the National Center for Vector Borne Disease Control Programme (NCVBDC) and the cross-sectional socio-economic data from the National Family Health Survey. We also collated the meteorological and land-use land-cover data from the MERRA-2 and Sentinel-LPA satellites, respectively. We then developed region-specific ensembles of spatiotemporal models that allowed us to identify the associated covariates while the regions were identified using the Getis-Ord Gi* statistics. With 0.33 million malaria cases in 2019, the COVID-19 pandemic led to a significant reduction in reported cases. The P. falciparum affected regions are widespread in North-eastern and Central India. However, after the pandemic, an emerging geographical expansion into the north-eastern parts is observed for the P. vivax, which is evident from the clusters and the spatiotemporal ensemble models. Population belonging to scheduled castes and scheduled tribes and those economically marginalised are among the most vulnerable, but lifestyle habits such as drinking water practices, maternal education, and healthcare accessibility are associated with malaria transmission. We also developed a digital dashboard that allows the general public and the stakeholders to track the malaria indicators for each district and the corresponding year. Show less

Familial hypercholesterolemia (FH) is a frequently underdiagnosed genetic disorder characterized by elevated low-density lipoprotein (LDL) levels. Genetic testing of LDLR, APOB, and PCSK9 genes can identify variants in up to 80% of clinically diagnosed patients. However, limitations in time, scalability, and cost have hindered effective next-generation sequencing of these genes. Additionally, pharmacogenomic variants are associated with statin-induced adverse effects in FH patients. To address these challenges, we developed a multiplex primer-based amplicon sequencing approach for FH genetic testing. Multiplex primers were designed for the exons of the LDLR, APOB, and PCSK9 genes, as well as for pharmacogenomic variants rs4149056 (SLCO1B1:c.521T > A), rs2306283 (SLCO1B1:c.388A > G), and rs2231142 (ABCG2:c.421C > A). Analytical validation using samples with known pathogenic variants and clinical validation with 12 FH-suspected probands were conducted. Library preparation was based on a bead-based tagmentation method, and sequencing was conducted on the NovaSeq 6000 platform. Our approach ensured no amplicon dropouts, with over 100× coverage on each amplicon. Known variants in 2 samples were successfully detected. Further, we identified one heterozygous LDLR (p.Glu228Ter) variant and 2 homozygous cases of LDLR (p.Lys294Ter) and LDLR (p.Ser177Leu) variants in patients. Pharmacogenomic analysis revealed that overall 3 patients may require reduced statin doses. Our approach offered reduced library preparation time (approximately 3 h), greater scalability, and lower costs (under $50) for FH genetic testing. Our method effectively sequences LDLR, APOB, and PCSK9 genes including pharmacogenomic variants that will guide appropriate screening and statin dosing, thus increasing both efficiency and affordability. Show less

The exact molecular mechanism underlying the heterogeneous drug response against breast carcinoma remains to be fully understood. It is urgently required to identify key genes that are intricately associated with varied clinical response of standard anti-cancer drugs, clinically used to treat breast cancer patients. In the present study, the utility of transcriptomic data of breast cancer patients in discerning the clinical drug response using machine learning-based approaches were evaluated. Here, a computational framework has been developed which can be used to identify key genes that can be linked with clinical drug response and progression of cancer, offering an immense opportunity to predict potential prognostic biomarkers and therapeutic targets. The framework concerned utilizes DeSeq2, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape, and machine learning techniques to find these crucial genes. Total RNA extraction and qRT-PCR were performed to quantify relative expression of few hub genes selected from the networks. In our study, we have experimentally checked the expression of few key hub genes like APOA2, DLX5, APOC3, CAMK2B, and PAK6 that were predicted to play an immense role in breast cancer tumorigenesis and progression in response to anti-cancer drug Paclitaxel. However, further experimental validations will be required to get mechanistic insights of these genes in regulating the drug response and cancer progression which will likely to play pivotal role in cancer treatment and precision oncology. Show less