Shreemati Nathibai Damodar Thackersey Women's University

Abstract Recent evidence suggests a growing popularity of gluten-free diets (GFDs) beyond clinically indicated contexts in low middle income countries. However, a comprehensive investigation of public perceptions, motivations, and professional challenges related to both medically and non-medically indicated GFD adoption remains limited. Therefore, we conducted a mixed method study to explore the perspectives of dietetic professionals and assess the knowledge, attitudes, and practices (KAP) related to gluten and GFDs among adults aged 18–45 years in India. The clinical and nutritional considerations for GFD were examined through in-depth interviews (n = 9) and an online survey was conducted among adults (n = 285) to assess their KAP. Thematic analyses revealed fragmented public understanding, symptom-led self-diagnosis, influence of “anti-inflammatory” narratives, concerns regarding nutritional adequacy of GFDs, and the need to leverage traditional food systems. Data highlighted limited knowledge regarding gluten (50.9%), and GFDs (38.6%). Reasons for GFD adoption included perceived health benefits (19.8%), weight loss (26.4%) and no reasons (32.1%). Consumption of naturally gluten-free staples such as rice (58.9%), lentils (55.8%) and millets (30.9%) was higher than that of oats (7.7%), barley/ quinoa (2.8%), and speciality flours (2.1%). The study provides timely insights into the drivers of the GFD trend within India’s rapidly evolving urban nutrition landscape.

By enabling continuous, individualized, and predictive care, the incorporation of artificial intelligence (AI) into telemedicine has completely transformed the management of metabolic health. To treat metabolic disorders such as diabetes, obesity, and cardiovascular disease, a revolutionary model that integrates gut microbiome analysis with AI-powered telemedicine is examined. Real-time physiological and microbial data are recorded, analysed, and converted into valuable insights by utilizing wearable sensors, mobile health platforms, and cloud-based analytics. Risk stratification, dietary customization, and early intervention are made possible by artificial intelligence (AI) techniques like machine learning, deep learning, and natural language processing. In parallel, functional biomarkers of disease progression and treatment response are revealed by microbiome profiling. The chapter discusses ethical, legal, and technical challenges while presenting successful case studies. This convergence facilitates closed-loop, adaptive systems that maximise results and empower patients.

Assistive technology (AT) for individuals with visual impairment has emerged as a critical driver of independence, accessibility, and inclusion. With advancements in digital innovation, individuals with partial or total vision loss can now navigate, communicate, learn, and participate in society more equitably than ever before (World Health Organization [WHO], 2023). This chapter provides a comprehensive overview of assistive technologies tailored for persons with visual impairment, discussing their classification, functions, and practical applications across various life domains such as education, mobility, communication, and employment. The chapter begins by framing the concept of visual impairment within the broader context of disability and the social model of inclusion, which views disability as a product of environmental and social barriers rather than individual limitations (Oliver, 1990). It then explores a wide range of assistive tools, from traditional tactile aids to cutting-edge artificial intelligence-powered devices (WIPO, 2020). The significance of accessible educational tools like screen readers and Braille notetakers is examined alongside mobility-enabling tools such as smart canes and GPS-based navigation systems (Miele, 2020). The chapter also highlights key international and national policy frameworks that facilitate access to assistive technologies, including the United Nations Convention on the Rights of Persons with Disabilities (UNCRPD, 2006) and the Rights of Persons with Disabilities Act in India (2016). Despite significant progress, challenges such as high costs, limited awareness, inadequate training, and infrastructural barriers persist (WHO, 2023). The chapter critically evaluates these issues while presenting future directions that emphasize universal design, affordability, and inclusive digital ecosystems. Ultimately, this chapter advocates for a rights-based, user-centric approach to assistive technology, aiming to bridge the accessibility divide and empower people with visual impairment to lead fuller, more autonomous lives.

Anthropogenic activities have increasingly led to habitat fragmentation and biodiversity loss across ecosystems worldwide, severely impacting species such as Lepidopterans, which act as ecological indicators. Among them, the Tropical Tasar Silkmoth (Antheraea mylitta Drury), a unique species endemic to India, which has been deeply woven into the lives and traditions of Scheduled Tribe and Scheduled Caste communities, has suffered heavy population losses as its natural habitats continue to shrink. This study investigates the distribution and genetic diversity of A. mylitta ecoraces across Odisha, where seven historical ecoraces have dwindled to five, with signs of local extinction in some regions. Through Cytochrome b as a mitochondrial molecular marker, phylogenetic analyses revealed the gene flow patterns consistent with geographical proximity but also uncovered unexpected genetic similarities, suggesting historical population mixing. Habitat degradation, primarily from mining and deforestation, has severely impacted tasar host plant availability, leading to a decline in the tasar silk production and threatening the wild ecorace survival. The findings highlight the critical need for conservation strategies focusing on habitat protection, genetic monitoring, and sustainable management to preserve A. mylitta populations and the forest ecosystems they symbolize.

In the face of escalating environmental crises, the imperative for education systems to embrace sustainability has never been more pressing. Teaching sustainability involves integrating ecological, social, and economic dimensions into the learning process, aiming to equip students with the knowledge, skills, and values needed to foster a sustainable future. This educational approach transcends traditional disciplinary boundaries, promoting an interdisciplinary perspective that emphasizes critical thinking, collaboration, and problem-solving. The principles of sustainability education focus on fostering a holistic understanding of interconnected systems, encouraging learners to recognize their role within these systems, and inspiring them to act responsibly and ethically in their communities. One of the core principles of teaching sustainability is the emphasis on systems thinking. This approach allows students to grasp the complexity of ecological and social interdependencies.

Efficient comprehension of the neural substrates of language processing that characterize the discipline of cognitive science is important. However, it should be acknowledged there are some limitations with the current analytical techniques employed for examining signals stemming from PET based labelling of language processing in real-time; often producing outcomes that may be inaccurate and/or costly in terms of computational resources. The propose a hybrid deep learning model containing LSTM networks and Bidirectional Encoder Representations from Transformers (BERT). BERT has the advantage of pulling contextual embeddings from texts while LSTM retains sequential dependencies; a hybrid model is therefore ideal for process extraction from PET signals. With a composite loss function, BERT embeddings fine-tuned with domain-correct data, dropout regularization optimization, the model demonstrated enhanced stability and robustness. A PET signal dataset, annotated for process was used to train and evaluate the model. Results from the experiment writ-ten using Python demonstrated a BERT-LSTM hybrid model achieved an accuracy of 22% growing accuracy above the customary analyst led approaches used for examining PET signals. The improvement suggests candidate model has the potential to assist with increasing the accuracy of PET signal analyses during language comprehending tasks. The contribution will add to our understanding of the neural processes of language and offers exciting new conceptualizations to contemporaneously analyze language.

Microencapsulation is a process used in the pharmaceutical, cosmetic, and food industries to encapsulate solids, liquids, or gases within polymeric coatings. It offers benefits like protection from adverse changes and the controlled release of core materials. In the pharmaceutical industry, it improves drug delivery systems, enhances drug taste masking, and increases patient compliance. The cosmetic industry encapsulates active ingredients for topical application, ensuring gradual release and improved product quality. In the food sector, it protects food components and enhances nutritional content.

Microencapsulation is a process used in the pharmaceutical, cosmetic, and food industries to encapsulate solids, liquids, or gases within polymeric coatings. It is a vital technology in the pharmaceutical industry that helps with precise drug release, and stability and makes unpleasant drugs more tolerable. Through various microencapsulation technologies, it also offers a wide range of benefits in the food and cosmetics industries. Various microencapsulation techniques are utilized in different sectors based on their respective principles. This article provides a review of different microencapsulation techniques, their advantages and disadvantages, and the various core and coating materials used.