Volume 14 | Issue 3 |

 Abstract

It is becoming more widely acknowledged that chronic diabetes mellitus (DM) increases the incidence of neurodegenerative illnesses, including Parkinson's disease (PD). Oxidative stress as well as neuroinflammation stand highlighted as important, interconnected drivers for dopaminergic neurodegeneration among the major mechanisms connecting these two disorders. Diabetes-related chronic hyper glycemia results in a compromised antioxidant defense system, increased generation in reactive oxygen molecules (ROS) and compromised mitochondrial activity. Due to their great susceptibility to oxidative insults, dopamine-producing neurons of the substantia nigra are especially affected by the prolonged oxidative stress caused by these alterations. Furthermore, neuroprotective pathways may be disrupted by poor insulin transmission in the brain, making neurons even more susceptible to oxidative & inflammatory assaults. Oxidative stress & neuroinflammation are key and perhaps changeable pathways in diabetes-induced neurodegeneration, even though other elements like mitochondrial malfunction and protein aggregation (like ?-synuclein) also contribute to Parkinson's disease pathogenesis. ?-synuclein aggregation & dopaminergic cell death are two classic PD diseases that are caused by the synergistic combination of oxidative stress and neuroinflammation, which upsets neuronal homeostasis. These results highlight how crucial it is to target oxidative & inflammatory pathways in diabetic populations help prevent or delay the emergence of Parkinson's disease. One promising approach to risk reduction and therapeutic intervention is to address these pathways.

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 Keywords

Diabetes, Oxidative stress Neuroinflammation, Parkinson

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Artificial Intelligence (AI) is influencing personal lifestyles and professional environments across the globe, with tech-forward cities like Hyderabad at the forefront. This study presents findings from a qualitative statistical survey that explores AI's impact on daily routines and workplace practices in Hyderabad. Drawing on responses from 250 participants from various sectors, the study reveals the dual nature of AI's role: enhancing convenience and productivity on one side while raising concerns around job security and privacy on the other the paper concludes with insights into how individuals and organizations can adapt to AI's continuing evolution.

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 Keywords

Artificial Intelligence, Daily Life, Workplace Automation, Hyderabad, Human-AI Interaction, Job Displacement, AI Ethics, Technology Adoption, Smart Devices, AI Integration.

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 Abstract

Diabetes mellitus is a complex metabolic disease characterized by persistent hyperglycemia arising from defects in insulin secretion, insulin responsiveness, or both. Despite the availability of numerous therapeutic options, diabetes continues to impose a substantial clinical and socioeconomic burden due to its progressive nature and associated complications. Therapeutic paradigms have evolved significantly, shifting from conventional glucose-lowering approaches to multidimensional strategies that address cardiovascular, renal, and metabolic risks. This review critically evaluates established and emerging treatments for diabetes mellitus, including lifestyle-based interventions, traditional and novel pharmacological agents, advanced insulin formulations, and innovative therapeutic platforms. Particular emphasis is placed on mechanistic diversity, clinical outcomes, safety considerations, and future directions aimed at achieving durable glycemic control and ?-cell preservation.

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 Keywords

Diabetes mellitus, antidiabetic therapy, insulin analogues, incretin system, SGLT-2 inhibitors, emerging treatments.

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K R Meera's Yudasinte Suvishesham offers a radical reinterpretation of the biblical narrative of Judas Iscriot, portraying him not as a simple traitor but as a complex, morally ambiguous human being. This paper examines K R Mera's Yudasinte Suvishesham through the twin lenses of existentialism and psychoanalysis to explore the interwoven themes of betrayal, guilt, and the human condition. Through an existential lens, Judas's internal conflicts and choices are analysed as reflections of freedom, ethical dilemmas, and the burden of personal responsibility. Simultaneously, a psychoanalytic approach highlights the psychological dimensions of guilt, repression, and unconscious drives that influence his behaviour and interactions with others. By combining these perspectives, the study analyses how Meera challenges traditional moral binaries, situating betrayal as both a deeply human condition and a site of ethical and psychological complexity. The paper ultimately argues that Yudasinte Suvishesham not only reconstructs biblical narratives but also offers profound insights into human fallibility, conscience, and the existential struggles inherent in moral decision-making.

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 Keywords

Judas Iscariot; Existentialism; Psychoanalysis; Betrayal; Guilt; Human Condition; Malayalam Literature; Biblical Rewriting

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Agricultural marketing systems often involve intermediaries that significantly reduce farmers profit margins and limit their direct access to consumers. The lack of an integrated, real-time digital marketplace frequently causes pricing inefficiencies, delayed transactions, and restricted visibility of agricultural products. In order to collect, manage, and present farm product data from multiple sellers through a unified platform, this study proposes a Mobile App for Direct Market Access for Farmers. The system uses cloud-based database technologies and secure authentication mechanisms to manage product listings, inventory updates, and order processing efficiently. The proposed application provides category-based browsing, real-time synchronization, and seamless cart management to assist both farmers and buyers. The technology enhances pricing transparency, improves direct communication between stakeholders, and accelerates decision-making in agricultural trade.

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 Keywords

Direct Market Access, Mobile Marketplace, Cloud Computing, Smart Farming, E-Commerce in Agriculture, Real-Time Database, Flutter, Firebase

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Bipolar disorder is a chronic and debilitating psychiatric illness characterized by recurrent episodes of mania and depression, significantly affecting an individual's emotional stability, cognitive functioning, and quality of life. Accurate diagnosis remains challenging due to symptom overlap with other mood disorders and reliance on subjective clinical assessments. This study presents a clinically driven deep learning framework for the automated prediction of bipolar disorder using a structured, symptom-based dataset reflecting common psychiatric indicators. Three deep learning models--Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM)--were designed and evaluated to analyze behavioral and clinical symptom patterns. Standard preprocessing techniques, including normalization and train-test splitting, were applied to ensure reliable model performance. The predictive capability of each model was assessed using classification accuracy and confusion matrix analysis. Results demonstrate that deep learning approaches effectively model complex clinical relationships within psychiatric data. The ANN model achieved the highest predictive accuracy, indicating its suitability for tabular clinical datasets. This work emphasizes the potential of deep learning as a supportive clinical decision-making tool for early bipolar disorder screening and improved mental health assessment.

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Artificial Intelligence, Deep learning, Psychiatric Illness, Bipolar Disorder, Mental health.

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S??khya's Philosophy, Yogic Path

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This paper is about cross-domain sentiment analysis for product reviews. People write reviews for products like electronics, books, and clothes on websites like Amazon. Sentiment analysis helps to find whether these reviews are positive, negative, or neutral. But sometimes, there are not enough labelled reviews in one domain to train the system. So, cross-domain sentiment analysis uses data from one domain to help another domain. In this paper, a web based system is implemented that collects reviews, processes text using NLP techniques, and classifies the sentiment using machine learning models. This helps to understand customer opinions and improve decision-making for products across different domains. Additionally, the system integrates an AI-powered chatbot that interacts with users in real time, answers queries about products, provides sentiment summaries, and guides customers in exploring product feedback efficiently.

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AI Chatbot interaction, Cross-domain sentiment analysis, Machine learning models, Natural language processing

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Creative Commons Attribution 4.0 and The Open Definition

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Cinnarizine, Dimenhydrinate, Orodispersible, SSG, Crospovidone

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 Abstract

Social Media Ke Prabhav Se Hindi Media Ki Bhasha Me Badlav

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Social Media Ke Prabhav Se Hindi Media Ki Bhasha Me Badlav

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