2026-06-25T11:40:28Z https://healthsynapse.org/index.php/files/oai

oai:ojs2.healthsynapse.edutechy.xyz:article/1 2026-01-08T07:22:00Z files:ART

oai:ojs2.healthsynapse.edutechy.xyz:article/3 2026-01-21T11:03:04Z files:ART

oai:ojs2.healthsynapse.edutechy.xyz:article/4 2026-01-22T10:31:18Z files:ART

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oai:ojs2.healthsynapse.edutechy.xyz:article/6 2026-01-22T10:31:23Z files:ART

oai:ojs2.healthsynapse.edutechy.xyz:article/7 2026-01-17T11:45:49Z files:ART

oai:ojs2.healthsynapse.edutechy.xyz:article/10 2026-01-22T10:31:28Z files:ART

oai:ojs2.healthsynapse.edutechy.xyz:article/12 2026-01-22T10:31:13Z files:ART

oai:ojs.healthsynapse.org:article/15 2026-06-25T10:16:17Z files:ART

Turmeric: The Golden Shield of Nature that can Treat Cancers Khushi Pandey Mansi Negi Awijeet Tiwari Sanjay Kumar Tiwari Himani Sharma Turmeric, Curcumin, Cancer prevention, Anticancer mechanisms, Antioxidant activity, Bioavailability enhancement, Complementary and integrative cancer therapy The potential health advantages of turmeric, a golden-yellow root that has a long history of use in South Asian cuisine and traditional medicine, have attracted significant scientific interest. The main ingredient in turmeric, curcumin, has antiinflammatory, anti-free radical, and maybe cancer-preventive effects. Based on studies conducted in labs, curcumin has the potential to impact various pathways that lead to cancer progression. These pathways include halting cell proliferation, killing cancer cells, stopping the formation of new blood vessels that supply tumors, and changing specific proteins that control gene function, like NF-κB and STAT3. Laboratory studies have shown that curcumin enhances the effectiveness and decreases the side effects of conventional cancer treatments, such as radiation and chemotherapy, suggesting that it could be utilized as an adjuvant treatment. Unfortunately, curcumin has a poor oral bioavailability, finding the optimal dosage is challenging, and there aren't enough large-scale trials to support its efficacy, making its application in actual medical contexts challenging. This study compiles recent research on curcumin and turmeric as they pertain to cancer treatment, discusses potential future applications, and suggests strategies to enhance their current use. Journal of Health Synapse 2026-01-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://healthsynapse.org/index.php/files/article/view/15 Journal of Health Synapse; JHS: Vol 1, Issue 1, January-March 2026; 1-8 3139-3683 en https://healthsynapse.org/index.php/files/article/view/15/8

oai:ojs.healthsynapse.org:article/16 2026-06-25T10:17:20Z files:ART

Ayurvedic Perspectives on Alzheimer’s Disease: A Conceptual Review Based on Charak Samhita Rahul Sharma Trisha Sonam Chauhan Sanjay Kumar Tiwari Himani Sharma Prachi Khandelwal Alzheimer’s, Neurodegenerative disease, Smriti Bhramsha, Manas Roga, Medhya Rasayan, Vatavyadhi, Charak Samhita. Alzheimer’s disease (AD) is a progressive neurodegenerative disease marked by cognitive impairment, memory loss, and behavioral symptoms, and is currently the most common cause of dementia. Although Alzheimer’s disease has been extensively documented in contemporary medical literature, a clear analogy to Alzheimer’s disease can certainly exist in classical writings on Ayurveda, most specifically in the Charak Samhita text. This article discusses ancient texts of Ayurveda, mentioning cognitive impairments like Smriti Bhramsha (memory loss), Buddhi Vibhrama (Intellectual dysfunction), and imbalances of Manas Dosha, and correlates them with contemporary scientific understanding of Alzheimer's disease. The concepts of derangement of Vata Dosha, aging or Jara, improper dieting, lifestyle, stress, and nutritional deficiency, as documented in ancient texts of Ayurveda, almost correspond with contemporary risk factors documented for Alzheimer’s disease. This article also gives a brief insight into preventive and curative measures documented in ancient texts of Ayurveda through concepts like Medhya Rasayana drugs (including Brahmi, Ashwagandha, Shankhapushpi), monitoring dieting, lifestyle modification, and mental control. Journal of Health Synapse 2026-01-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://healthsynapse.org/index.php/files/article/view/16 Journal of Health Synapse; JHS: Vol 1, Issue 1, January-March 2026; 9-14 3139-3683 en https://healthsynapse.org/index.php/files/article/view/16/9

oai:ojs.healthsynapse.org:article/17 2026-06-25T10:18:18Z files:ART

Artificial Intelligence in Forensic Toxicology: A Systematic Review of Emerging Trends, Analytical Techniques, and Future Directions Himani Raj Sneha Sagar Mansi Negi Artificial Intelligence, Machine Learning, Forensic Toxicology, Novel Psychoactive Substances, Predictive Modeling, Digital Forensics, Legal Admissibility. An essential component of medico-legal investigations, forensic toxicology is being revolutionised by AI. Complex data generated by sophisticated analytical tools like LC-MS/MS and the ongoing appearance of new psychoactive substance (NPS) are two of the many obstacles that the sector must overcome. Machine learning (ML) and artificial intelligence (AI) are changing the face of toxicology by solving problems in areas like predictive toxicology, deconvolution of complicated datasets, AI-assisted spectral library curation, and the integration of multi-omics methods for thorough toxicological profiling. Artificial intelligence (AI)-powered plant toxin detection, postmortem drug redistribution modelling, pesticide categorisation, and NPS monitoring are some concrete examples. There are still a number of problems that need fixing with AI, such as the "black box" problem with algorithmic decision-making, limits on data quality and standardisation, and ethical and legal worries about the admissibility of evidence obtained from AI in court. Personalised toxicology, cloud-based platforms to increase accessibility, and federated learning for collaborative model creation are some of the promising new advancements in the near future. While artificial intelligence (AI) cannot fully replace forensic toxicologists, it is a valuable tool that can greatly improve their analytical accuracy, efficiency, and prediction powers. This, in turn, strengthens the credibility and value of forensic toxicology in the judicial system. Journal of Health Synapse 2026-01-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://healthsynapse.org/index.php/files/article/view/17 Journal of Health Synapse; JHS: Vol 1, Issue 1, January-March 2026; 15-22 3139-3683 en https://healthsynapse.org/index.php/files/article/view/17/10

oai:ojs2.healthsynapse.edutechy.xyz:article/18 2026-02-13T07:08:43Z files:ART

oai:ojs.healthsynapse.org:article/19 2026-06-25T10:20:25Z files:ART

AI–Ayurveda Convergence: Reimagining Traditional Wisdom Through Ethical Digital Intelligence Prachi Khandelwal Jonah Sandrepogu Sanjay Kumar Tiwari Sonam Chauhan Ragini Bairwa Himani Sharma Vidhi Jain Rupesh Gangurde Ayurveda, Artifical Intelligence, AYUSH, Machine learning While Ayurveda offers a tried-and-true, person-centred understanding of health and illness, artificial intelligence provides previously unheard-of tools for decision support, data interpretation, and predictive modelling in the healthcare industry. An ethical and evidence-based integrative digital health model in India can be developed thanks to the intersection of these paradigms. The conceptual and methodological frameworks that connect AI applications to Ayurvedic concepts like Prakriti, Dosha balance, and customised therapies are described in this narrative review. It critically looks at projects like Ayurgenomics and the Ayush Grid, which show how machine learning can improve data organisation, drug discovery, and decision-making within AYUSH frameworks. It is discussed that ethical aspects such as algorithmic transparency, cultural sensitivity, and patient consent are necessary prerequisites for sustainable innovation. In order to guarantee that digital transformation enhances rather than replaces conventional clinical reasoning, the review emphasises the significance of human oversight, community involvement, and cross-disciplinary capacity building. India can lead the world in developing a participatory, equitable, and contextually adaptive healthcare model that combines advanced analytics and civilisational wisdom. This can be achieved by fusing Ayurveda's holistic vision with advanced analytics. Journal of Health Synapse 2026-01-22 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://healthsynapse.org/index.php/files/article/view/19 Journal of Health Synapse; JHS: Vol 1, Issue 1, January-March 2026; 35-45 3139-3683 en https://healthsynapse.org/index.php/files/article/view/19/12

oai:ojs.healthsynapse.org:article/20 2026-06-25T10:19:29Z files:ART

Ayurveda and Artificial Intelligence: A Review of Applications in Diagnosis, Prakriti Analysis, and Personalized Therapeutics Sanjay Kumar Tiwari Raja Ram Mahto Himani Sharma Prachi Khandelwal Sonam Chauhan Ayurveda, Artificial Intelligence, Prakriti Analysis, Nadi Pariksha, Machine Learning, Personalized Medicine Background: Ayurveda, the Indian system of traditional personalized medicine, focuses on constitution (Prakriti) and imbalance-based (Vikriti) diagnosis and treatment. Artificial Intelligence (AI) capabilities in pattern recognition, multimodal data fusion, and prediction can uniquely contribute to digitizing and scaling Ayurvedic modalities. Objective: This review integrates evidence regarding AI applications for Ayurvedic diagnosis, Prakriti assessment, and individualized therapeutics, with opportunities, challenges, and future research directions. Methods: A narrative review was conducted by searching PubMed, Scopus, Web of Science, and AYUSH-specific journals (2010–2025) using term Ayurveda, Artificial Intelligence, machine learning, digital diagnosis, and Prakriti. Peer-reviewed studies, technical reports, and conceptual models were included. Grey literature (e.g., apps, websites) was excluded unless directly relevant to clinical practice. Results: Applications of AI in Ayurveda are - Diagnosis: Digital Nadi Pariksha with pulse sensors and Machine Learning (ML)-based methods (up to 85% accuracy), tongue imaging with Convolutional Neural Network (CNN)-based methods for disease categorization, and hybrid Artificial Intelligence (AI) models incorporating Darshana (inspection/visual examination), Sparshana (palpation/tactile examination), and Prashna (interrogation/patient history taking). Prakriti Analysis: Automated questionnaires, ML-based classification, and Ayurgenomics integration enable scalable, objective constitution typing. Personalized Therapeutics: AI-powered treatment suggestion systems, predictive Panchakarma protocols, and NLP-based selection models of drugs boost individualized care. Limitations: Challenges include lack of standardized datasets, the interpretability of AI models, epistemological mismatch, and unresolved ethical/regulatory frameworks. Conclusion: AI can make Ayurveda into an evidence-based, scalable, and worldwide applicable system of personalized medicine. Success involves data standardization, interdisciplinary teamwork, and culturally appropriate regulation to provide assurance of safety, trustworthiness, and clinical translation. Journal of Health Synapse 2026-01-17 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article application/pdf https://healthsynapse.org/index.php/files/article/view/20 Journal of Health Synapse; JHS: Vol 1, Issue 1, January-March 2026; 23-34 3139-3683 en https://healthsynapse.org/index.php/files/article/view/20/13