Goa Medical College

Purpose:Dyslexia is a prevalent neurodevelopmental disorder that impairs a children's ability to reading, writing, and language processing despite normal cognitive skills. Early identification is vital for timely support and interventions in children with dyslexia. This study aimed to develop an efficient EEG-based pipeline for dyslexia detection using deep learning techniques, while providing a consistent evaluation protocol for fair comparison across models and prior approaches. Methods:EEG recordings were acquired from 51 participants (26: dyslexic and 25: non-dyslexic), aged 5-10 years, during cognitive task performance. These signals were processed, segmented, and decomposed into standard frequency bands (alpha, beta, delta, and theta) using the discrete wavelet transform to capture discriminative neural patterns. Filter-based feature selection techniques were applied before classification to optimize performance and reduce redundancy to identify the most informative features. These ranked and individual band-wise features were systematically evaluated with classical machine learning baselines (Decision Trees, SVM, k-NN, and ensemble learners) alongside the proposed deep neural networks. In addition, we benchmarked end-to-end raw-EEG deep learning baselines (1D-CNN, LSTM, and EEGNet) and re-implemented representative existing pipelines, all evaluated on our dataset using the same evaluation protocol. Results:The proposed compact deep neural network with four hidden layers achieved the best performance, reaching classification accuracy of 98.85%, outperforming all baseline models, raw-EEG deep learning baselines, and re-implemented approaches. Conclusion:These findings support the feasibility of DWT-driven EEG analysis combined with deep learning for more accurate and early dyslexia detection. The proposed approach holds promise as a non-invasive screening tool to support improved educational outcomes through early diagnosis and targeted intervention.

Obesity is a global health crisis affecting developing nations, including India. The management of obesity continues to evolve with newer drugs, metabolic and bariatric surgery and endoscopic interventions, requiring family physicians and specialists to adapt their clinical practice accordingly. There is an urgent need for a standardized algorithm to diagnose, stage, and treat obesity. The Endocrine Society of India (ESI) and the Obesity Surgeons Society of India (OSSI) appointed a steering committee to develop an evidence-based algorithm for managing patients with obesity in India. This was put to vote by 80 specialists (38 from OSSI and 42 from ESI) in a physical meeting. A proposed stage-wise algorithm based on Edmonton Obesity Staging System, Asian definition of obesity, and resources in India, received 100

Plant-based extracellular vesicles (Ph-EXs) represent an innovative new approach for targeting cancer, as they combine natural anti-cancer activity with an advanced ability to deliver drugs. Ph-EXs have direct killing activity against cancer cells through different mechanisms, including the initiation of apoptotic pathways, inhibition of cell growth signals, alteration of the metabolism of cancer cells, and overcoming the effects of drug resistance. Ph-EXs have the ability to alter immune cells' programming, modify cancer-associated fibroblasts, and change the composition of the microbiome, impacting the tumor microenvironment as well as contributing to tumor formation. Additionally, Ph-EXs can cross biological barriers (specifically across the blood-brain barrier) and, due to their lower costs in comparison with other anti-cancer drug delivery methods, present a feasible alternative to synthetics. Ph-EXs have been developed with many engineering efforts, including novel ways to load therapeutic agents onto exosomes as well as surface functionalization. There are several challenges associated with translating Ph-EXs into the clinical setting; these include: potential variability in effect depending on the botanicals they were extracted from, standardization of extraction and characterization methods, and regulatory obstacles for clinical implementation. This review identifies critical knowledge gaps and proposes priority research directions to advance Ph-EX-based platforms toward clinical use.

Extracorporeal carbon dioxide removal (ECCO₂R) has emerged as a supportive therapy for patients with acute respiratory failure, particularly when refractory hypercapnia and respiratory acidosis persist despite standard management. This narrative review summarizes the physiological rationale, current evidence, and clinical applications of ECCO₂R in modern critical care practice. ECCO₂R removes carbon dioxide directly from the bloodstream, thereby facilitating lung-protective ventilation strategies and potentially reducing ventilator-induced lung injury. The technique has been primarily evaluated in patients with acute respiratory distress syndrome (ARDS) and acute exacerbations of chronic obstructive pulmonary disease (AECOPD). In these settings, ECCO₂R may enable the use of lower tidal volumes and reduced airway pressures, and in selected patients, it may decrease the need for invasive mechanical ventilation. Although ECCO₂R has been shown to improve carbon dioxide clearance, correct respiratory acidosis, and optimize ventilatory parameters, randomized trials have not demonstrated a definitive mortality benefit. Its clinical application is also limited by device-related complications, including bleeding, thrombosis, and hemolysis. Furthermore, outcomes are influenced by technical factors such as cannula configuration, blood flow rate, membrane efficiency, and anticoagulation strategy. Ongoing technological advancements and well-designed clinical trials are needed to better define the role of ECCO₂R in contemporary critical care practice.

Background: Tetanus is a life-threatening, yet vaccine-preventable disease that continues to contribute in morbidity and mortality, especially in low- and middle-income countries. Despite the availability of an effective vaccine, and inclusion of booster doses at 10 and 16 years under the UIP, gaps in awareness and vaccination practices persist. Objective of the study was to assess the knowledge, attitude and practice (KAP) regarding tetanus vaccination among first-year MBBS and nursing/paramedical students of Government Medical College (GMC), Anantnag. Methods: A cross-sectional analytical study was conducted among 235 first-year MBBS and nursing/paramedical students at GMC Anantnag from June to July 2025. Data was collected using a structured, self-administered questionnaire assessing KAP related to tetanus vaccination. Data was analyzed using descriptive and inferential statistics. Results: The overall mean KAP score was 47.7%, with knowledge (42.5%) and practice (43.0%) scores being suboptimal despite a relatively higher attitude score (63.0%). Female participants demonstrated marginally higher knowledge and attitude scores, whereas males had significantly higher practice scores (p=0.02). No significant gender difference was observed in overall KAP scores. Conclusions: The study revealed inadequate knowledge and practices regarding tetanus vaccination with slightly better attitude scores among first-year students. Strengthening school-based and adolescent health programs, along with routine verification of immunization status during primary care visits, can help improve booster coverage and prevent gaps in protection against tetanus.