D.Y. Patil Education Society

Dysphagia, a severe complication of neurological disorders, is now managed through advanced diagnostics and treatments that leverage technological innovation. Modern approaches utilize high-resolution manometry and endoscopic imaging for precise biomechanical assessment, moving beyond traditional strategies. The therapeutic focus has shifted towards harnessing neuroplasticity with interventions like pharyngeal electrical stimulation and biofeedback-aided exercises. These innovations enable targeted, mechanism-based rehabilitation that actively restores swallow function, significantly improving safety, reducing aspiration risk, and enhancing quality of life for patients. This integration of technology represents a new era in dysphagia care.

Spinal cord injury (SCI) is a devastating condition with limited therapeutic options owing to poor intrinsic regeneration and the formation of glial scars. Platelet-rich plasma (PRP)-based biomaterials have emerged as promising candidates for neural repair; however, their application in complete SCI models with rigorous multimodal validation has not yet been investigated. This study aimed to extend the current knowledge on SCI-PRP based study,by developing and comprehensively validating a clinically translatable PRP-derived fibrin scaffold for spinal cord regeneration. PRP-fibrin scaffolds were synthesized from donor-derived plasma and extensively characterized in terms of their physicochemical properties, degradation profiles, protein release dynamics, and cellular compatibility. Its regenerative potential was evaluated using an integrative pipeline that included in ovo chorioallantoic membrane (CAM) assays, a complete spinal cord transection rat model, and multimodal outcome measures, including magnetic resonance imaging (MRI), retrograde neuronal tracttracing, electrophysiological recordings, and gene expression analyses. The scaffold exhibited favorable structural and biochemical characteristics, supported angiogenesis in the CAM assay, and promoted tissue integration in vivo. In an SCI model, the scaffold significantly enhanced neovascularization, reduced glial scarring, and facilitated axonal regeneration. Functional improvements were observed 30days' post-implantation. In silico docking further demonstrated stable interactions between scaffold proteins and key neuroregenerative signaling molecules. This study provides multimodal validation of the derived fibrin scaffold, establishing it as a robust ECM-mimetic platform for spinal cord repair. These findings lay the groundwork for future clinical translation of SCI therapeutics.

Airports need to provide a secure environment and handle the highly dynamic passenger demand, where operator fatigue, cluttered X-ray/CT images and increased throughput requirements are challenging the manual baggage screening. Here we introduce an AI-based bag screening framework that integrates image pre-processing, deep learning-based threat detection/classification and explainability to help ensure a fast and confident decision-making at contemporary lanes. The proposed architecture takes X-ray/CT scans as input, and normalizes and augments the data for introducing robustness to CNN models before it is used for localizing suspicious objects and assigning threat labels such as weapon, explosive or contraband. An interpretation module extracts visual evidence to assist screeners in verifying the alarms and trust enhancement. We also address close-to-production deployment considerations, such as class imbalance caused by the low occurrence rate of threat samples, target domain mismatches between scanners and airports, runtime latency constraints, and the requirement to manage false alarm rates to prevent operational bottlenecks. The paper brings together methods and challenges towards a deployment-centric perspective, as well as provides motivations to address cross-site generalization, disruption-aware evaluation, or human-in-the-loop learning to achieve stable performance under realistic operating conditions.

Abstract—Distillation remains one of the most widely employed separation processes in chemical and petrochemical industries due to its reliability and effectiveness in separating liquid mixtures based on volatility differences. Despite the availability of advanced simulation and optimization tools, classical design methodologies continue to play a crucial role in building fundamental understanding and supporting preliminary design decisions. This paper presents the theoretical design and parametric analysis of a binary distillation column for the separation of a benzene–toluene mixture using the McCabe–Thiele graphical method. Vapor– liquid equilibrium data are utilized to construct equilibrium and operating lines, enabling the estimation of minimum reflux ratio, number of theoretical stages, and feed tray location. A detailed parametric study is conducted to evaluate the influence of reflux ratio and feed condition on column performance, separation efficiency, and energy considerations. The results demonstrate that while higher reflux ratios reduce the required number of stages, they lead to increased energy consumption. The study further discusses the relevance of classical design techniques in modern engineering practice, particularly in the context of energy optimization and sustainable process design. This work is intended for undergraduate academic study and serves as a foundation for further simulation-based analysis. Keywords—Binary distillation, McCabe–Thiele method, reflux ratio, vapor–liquid equilibrium, energy optimization, column design, sustainability

Introduction: Artificial Intelligence (AI) is rapidly transforming the pharmaceutical marketing landscape by enabling more data-driven, personalized, and efficient strategies. Traditionally cautious in adopting new marketing technologies, the pharmaceutical industry is now embracing AI to enhance decision-making and customer engagement. Background: Pharmaceutical marketing has historically relied on conventional methods, but the advent of AI technologies such as natural language processing, machine learning, and predictive analytics is reshaping how companies approach customer relationship management, brand positioning, and market forecasting. These tools allow for deeper insights into customer behavior, sentiment analysis, and omnichannel engagement, which are critical in a highly regulated and competitive environment. Methodology: This review synthesizes current literature and case studies from leading pharmaceutical companies to evaluate the applications and impact of AI in marketing. It examines AI-driven tools used to optimize sales operations, improve medical representative efficiency, and ensure regulatory compliance. Challenges such as data privacy, ethical concerns, and regulatory constraints are also critically analyzed. Results: Findings indicate that AI adoption leads to measurable benefits including increased return on investment (ROI), enhanced market segmentation, and improved drug adoption rates. AI facilitates more personalized communication strategies and streamlines marketing workflows, contributing to cost efficiency and patient-centric approaches. Conclusion: AI integration marks a pivotal shift toward smarter, ethical, and more effective pharmaceutical marketing. Future trends such as generative AI, conversational analytics, and digital twins promise to further revolutionize the sector, driving sustainable digital growth.

This chapter explores the potential of traditional NiX (X = Te, Se, and S) materials in the field of electrochemical supercapacitors (SCs). A wide range of nanostructured electrode materials have been investigated to improve the efficiency and electrochemical performance of SCs. Recent research highlights that transition metal chalcogenides, especially NiX, stand out as promising candidates for SC applications due to their notable properties, including mechanical robustness, excellent cycling stability, remarkable intrinsic conductivity analyzed to transition metal oxides, and thermal stability. Significant efforts have been devoted to the development of advanced materials as an electrode for SCs, aiming to achieve steep specific capacitance via fast Faradaic redox reactions while maintaining enormous cyclic stability. This chapter also provides an overview of the supercapacitive characterization of traditional NiX nanomaterials, addressing both their benefits and the challenges they present in future energy storage applications.