Ambedkar University Delhi

Elevated levels of the gut-derived metabolite trimethylamine N-oxide (TMAO) are associated with atherosclerosis and thrombotic disorders, yet the molecular basis of its contribution to these pathologies remains poorly understood. Atherosclerosis initiates with endothelial damage and progresses through lipid deposition, foam cell formation, plaque development, rupture, and ultimately fibrin-rich thrombus formation, which may embolize. In this study, we investigate how TMAO modulates fibrinogen structure and consequently influences fibrin clot formation and stability. Using biophysical experiments with in-silico docking and molecular dynamics simulations, we show that TMAO binds specifically to the β1 calcium-binding site of fibrinogen. This interaction alters fibrinogen's dynamics and enhances its propensity to form fibrin clots. Clots generated in the presence of TMAO are more resistant to proteolytic degradation, indicating stability. Our analyses show that β-nodules of native fibrinogen may exist in two conformational states: one harboring high-affinity holes and another containing low-affinity holes. TMAO binding appears to shift this equilibrium toward the high-affinity state, promoting protofibril assembly. Structurally similar N-oxides from antidepressants showed comparable fibrinogen binding and accelerated fibrin assembly. Overall, our study identifies the β1 calcium-binding site as a promising therapeutic target for regulating clot stability and mitigating cardiovascular complications driven by gut metabolites and drug-derived N-oxides.

This study investigates how student awareness of library-provided e-resources at IGDTUW influences their usage across academic levels. Using a structured questionnaire distributed to 333 students (with 264 valid responses), the research applied Chi-Square tests, Pearson’s correlation, and regression analysis to examine patterns in the use of e-books, e-journals, and databases. Results show that postgraduate and PhD students demonstrate higher awareness and usage compared to undergraduates. A strong positive correlation (r=0.815) was found between awareness and usage, indicating that students who are more informed are significantly more likely to use digital resources. The findings highlight the need for targeted training programs and greater promotion of mobile access, particularly for undergraduates.

This paper examines Amitav Ghosh’s Gun Island (2019) as a captivating literary intervention into contemporary discourse on environmental crisis and forced mobility. Though the existing scholarship on climate-induced migration has largely focused on human displacement, the novel largely expands this framework by foregrounding the forced movement and habitat loss of non-human species. The paper argues that Ghosh challenges anthropocentric narratives of migration by situating human refugeehood within a broader multispecies ecology of displacement. Through myth, folklore, and contemporary realism, the novel expose how environmental crises destabilise traditional livelihoods, disrupt migratory routes of animals, and blur the boundaries between natural and political causes of mobility. By reading human and non-human displacements as intertwined, the study highlights Ghosh’s vision of planetary environmentalism, which calls for an ethical rethinking of responsibility, coexistence, and survival in the Anthropocene.

India has strengthened its clinical research infrastructure and regulatory frameworks to support new drug development and early-phase clinical trials. However, historical concerns related to participant safety, ethics, and research capacity have limited the conduct of first-in-human (FIH) studies for products developed outside India. A multi-stakeholder panel comprising representatives from regulatory authorities, academia, industry, and funding agencies examined regulatory, ethical, infrastructural, and innovator perspectives relevant to conducting such FIH trials in India.

Parkinson's disease (PD) is marked by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc). Adenosine receptors (A2ARs) modulate the striatopallidal non-dopaminergic pathway to alleviate PD symptoms. In the present study, the neuroprotective mechanism of the selective A2AR antagonist exhibiting a non-purine scaffold, IDPU (Ki = 0.0038nM), was explored using the primary mid-brain neuronal (PMDN) cells isolated from P0/P1 rat pups that differentiated to form dopaminergic neurons, as validated using tyrosine hydroxylase. PMDN cells, when treated with IDPU (0-10μM) alone, showed insignificant toxicity. However, they exhibited < 60% cell viability when treated with 6-OHDA (150μM) after 24h. Cell viability improved to > 80%, and dopamine levels were restored in 6-OHDA- (150μM) induced PMDN cells when 3h post-treated with IDPU (0.7μM, 1μM), including the depletion in ROS generation and [Ca2+]i levels observed after 24h. IDPU treatment further impacts the mitochondrial control by attenuating both mitochondrial SOD production and its membrane potential loss in PD-like conditions. To investigate the mechanism of A2AR intervention on governing the mitochondrial-associated signalling cascades, the proteins were isolated from 6-OHDA (150μM) induced PMDN cells 3h post-treated with IDPU (0.7μM, 1μM) and ZM241385 (1μM) for western blot analysis. Our results exhibited that the phosphorylation of both DRP1 (Ser616) (78kDa) and p38MAPK (Tyr182) (41kDa) proteins was enhanced when exposed to 6-OHDA; however, the protein levels reduced post-treatment with both A2AR antagonists. In contrast, 6-OHDA toxicity alleviated the levels of both Parkin (58kDa) and DJ-1(23kDa), while exposure to A2AR antagonists (IDPU and ZM241385) improved their protein levels. This suggests the possible involvement of A2AR blockade in regulating mitochondrial dynamics, thus promoting survival. These findings present the first evidence that IDPU demonstrates neuroprotection in PDlike conditions via the p38MAPK/DRP1/Parkin signalling, offering a potential therapeutic mechanism for targeting mitochondrial dynamics through A2AR antagonism.

Access to precise visual information is vital for digital accessibility, particularly for blind and visuall y impaired individuals. Existing image captioning models still generate correct captions that make sense only when trained on tidy curated datasets, which is a constraint for real applications. The variability in this is problematic for assistive technolog ies and thus, consistent and reliable generation of captions is of critical importance. To overcome these issues, we used and compared three deep learning models VGG16, ResNet50 and EfficientNetB2 combined with an LSTM language model for automatic image captioning. From these, the EfficientNetB2 + LSTM outperformed the rest as it had the best optimized features and was the most efficient architecture that provided the best answer to caption reliability problem. They report BLEU-4 and ROUGE scores of 0.26 and 0.59, respectively, as well as a CIDEr score of 0.89 on the Flickr30k dataset. This demonstrates the objective performance of the model. On a subjective level, the captions produced were logical and contextually appropriate, and showed great promise as a means to improve digital access. Our findings indicate that EfficientNetB2 + LSTM can be successfully deployed in assistive technologies as it is the most efficient and effective model we have tested.