The National Institute of Ayurveda (abbreviated as NIA) is a research institute of Ayurvedic medicine, located in Jaipur, Rajasthan, India..
Charmadala, described under Kshudra Kustha in Ayurvedic literature and is characterized by Kandu (itching), Raga (erythema), Tvak Rukshata (dryness), and Tvak Patana (scaling) due to Pitta–Kapha vitiation. It closely resembles chronic eczematous dermatitis and atopic dermatitis. A 52-year-old male presented with recurrent erythematous lesions associated with severe itching, burning, watery discharge, swelling, and scaling for 3–4 years. Previous allopathic treatment provided only temporary relief with frequent recurrence. Based on clinical features and Ayurvedic assessment, the condition was diagnosed as Pitta–Kapha predominant Charmadala. The patient was initially managed with Shamana Chikitsa for 45 days, which included Panchatikta Guggulu Ghrita, Haridra Khanda, Panchnimba Churna, Ashtamurti Rasayana, Sheetapittabhanjana Rasa, Trivanga Bhasma, Kamadudha Rasa, Arogyavardhini Vati, Godanti Bhasma, Phalatrikadi Kwatha and Jwarahara Kashaya, Erandabhrishta Haritaki and Kutaki Churna along with external applications of Dadrughnalepa mixed with Gomutra and Jeevantyadi Yamaka were also prescribed. Partial improvement was observed in erythema, itching, burning sensation, discharge, swelling, and scaling. Considering the chronic and recurrent nature of the disease, Shodhana Chikitsa was planned. The patient underwent Deepana-Pachana with Panchakola Churna, followed by Snehapana, Sarvanga Abhyanga, Swedana, and Virechana Karma. All major symptoms improved significantly after treatment, with no recurrence during follow-up. This case highlights the effectiveness of a combined Shamana and Shodhana approach in the Ayurvedic management of Charmadala.
Background: Ayurvedic classical literature preserves complex scientific knowledge through concise and aphoristic expressions, the interpretation of which frequently requires contextual reasoning and identification of representative features. Śṛṅgagrahika Nyāya, an interpretative principle employed by Chakrapāṇi Datta in the Āyurveda Dīpikā commentary on the Charaka Saṃhitā, illustrates the identification of a particular entity through a distinguishing or representative characteristic. Contemporary Artificial Intelligence (AI) similarly employs selective information processing, feature identification, pattern recognition, contextual interpretation, and explainable decision-making. Objective: To explore the conceptual relationship between Śṛṅgagrahika Nyāya and contemporary AI approaches and to propose a conceptual framework for its potential application in Ayurveda-oriented AI systems. Materials and Methods: A narrative conceptual review was undertaken through analysis of classical Ayurvedic literature, particularly the Charaka Saṃhitā with Chakrapāṇi Datta’s Āyurveda Dīpikā commentary, along with contemporary literature related to feature selection, pattern recognition, attention mechanisms, knowledge representation, Natural Language Processing, clinical decision support systems and Explainable Artificial Intelligence. Classical applications of Śṛṅgagrahika Nyāya were examined and conceptually mapped with relevant AI methodologies. Results: Analysis of twelve classical applications demonstrated four major epistemological dimensions of Śṛṅgagrahika Nyāya: representative knowledge representation, knowledge compression with contextual inference, feature-based identification and prioritization and adaptive or individualized interpretation. These dimensions demonstrated conceptual correspondence with feature selection, pattern recognition, attention mechanisms, knowledge representation, context-aware processing and Explainable Artificial Intelligence. Based on these correspondences, potential applications were identified in AI-assisted interpretation of Ayurvedic classical texts, Ayurveda knowledge graphs and ontologies, clinical decision-support systems, Ayurvedic pharmacological knowledge analysis, and explainable Ayurveda-oriented AI. Conclusion: Śṛṅgagrahika Nyāya may be understood as a classical Ayurvedic epistemological framework emphasizing selective identification, representative reasoning, contextual interpretation, and logical explanation. Its conceptual correspondence with contemporary AI provides a novel interdisciplinary perspective for developing transparent, context-sensitive, and explainable AI applications in Ayurveda. However, the proposed relationship is conceptual rather than historical or technological, and computational implementation and empirical validation are required to establish its practical applicability. Keywords: Śṛṅgagrahika Nyāya, Ayurveda, Artificial Intelligence, Feature Selection, Pattern Recognition, Explainable Artificial Intelligence, Knowledge Representation, Ayurvedic Epistemology
Background:Noncommunicable diseases, particularly diabetes, pose a growing global burden, with India disproportionately affected. India also has a rich repository of traditional medical systems-Ayurveda, yoga and naturopathy, Unani, Siddha, Sowa Rigpa, and homeopathy (AYUSH)-collectively governed under the Ministry of Ayush. These systems adopt a personalized and integrative approach to diabetes management, addressing glycemic control alongside metabolic and lifestyle factors. Despite growing use and evidence for AYUSH interventions, standardized evaluation methods remain limited. Objective:This study aims to quantitatively evaluate the evidence status for AYUSH interventions for the management of prediabetes and type 2 diabetes mellitus and establish a road map of evidence through research for better outcomes in the future. Methods:The systematic review will be conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and is registered in PROSPERO. All primary study designs, including randomized controlled trials, nonrandomized controlled trials, parallel-arm intervention trials, pretest-posttest trials, observational studies (including cross-sectional, case-control, and cohort studies), and case series and case reports will be assessed. Systematic reviews and meta-analyses will be screened for background information and identification of relevant primary studies but will not be included in the evidence synthesis. Studies involving AYUSH interventions either as stand-alone therapies or as an add-on to standard care will be reviewed. Electronic databases along with AYUSH-specific sources, including PubMed, CENTRAL, clinical trial registries, AYUSH Research Portal, MEDLINE, Scopus, Web of Science, Embase, Digital Helpline for Ayurveda Research Articles, and IndMED, will be searched using database-specific search strategies combining AYUSH-related and diabetes-specific keywords with Boolean operators. Outcome measures will include clinical recovery, biochemical parameters, quality of life, and adverse events. Data will be synthesized systematically and represented through an evidence map. Results:Database searches and pilot-testing of strategies are planned to commence in September 2025. Screening of eligible studies, data extraction, and quality assessment are planned for December 2025; data compilation and manuscript preparation will be conducted from July 2026 to October 2026; and the final systematic review and evidence map are anticipated by December 2026. Publication of the results is expected in early 2027. Anticipated findings will include a systematic integration of data relevant to the efficacy and safety profiles of AYUSH interventions for prediabetes and type 2 diabetes accompanied by an evidence map showcasing the allocation and reliability of the current evidence base on different AYUSH modalities. Conclusions:This review seeks to consolidate and evaluate existing data to facilitate evidence-based integration of Indian traditional medicine in diabetes management. The resulting evidence map will serve as a strategic tool for clinical research, health care policy, and future systematic reviews in the field of integrative medicine.
IntroductionAshwagandha (Withania somnifera Dunal) is an Ayurveda medicine that was widely used during the COVID-19 pandemic. In the context of widespread use of herbal formulations alongside COVID-19 vaccination, this study evaluated the safety, tolerability, and exploratory immunological outcomes of Ashwagandha administered following COVISHIELD™ vaccination.Methods1,200 consenting apparently healthy participants (age 18–70 years) with a negative SARS-CoV-2 RNA assay (RT-PCR using oral and nasal swab) were randomized within 7 days of vaccine administration (primary or booster) to receive oral standardized Ashwagandha (500 mg aqueous root extract) or a matching placebo daily. Participants were monitored every 4 weeks till weeks 28 (study completion) which included comprehensive clinical and laboratory assessment, specific viral RNA and IgG antibody assays (including Wuhan and Delta strain neutralization). Statistical analyses were performed using standard methods, with a two-sided p-value of < 0.05 considered statistically significant.ResultsOf the 1,200 randomized participants, 122 (10.2%) were assigned to the SDG (Single Dose Group), 503 (41.9%) to the DDG (Double Dose Group), and 575 (47.9%) to the BDG (Booster Dose Group); overall, 1,032 participants (86%) completed the trial. The safety and tolerability profile of Ashwagandha was comparable to placebo. Adverse events were predominantly gut related and mild, and none caused withdrawal. Following first Covishield™ injection, a persistently higher IgG antibody assay was observed in the Ashwagandha arm (not statistically significant). The cumulative incidence rate of breakthrough infections was 3.9% (95% confidence interval 3.0–5.2%) and did not differ by study intervention. The immunogenic effect of Ashwagandha could not be demonstrated, likely due to a persistently high post-vaccination immune response. In addition, approximately 70% of participants were seropositive for nucleocapsid antibodies at baseline, indicating prior SARS-CoV-2 infection.ConclusionProlonged use and tolerability of Ashwagandha in post-vaccinated healthy participants was shown safe but adjunct immunogenicity could not be discerned. Further research is required.Clinical Trial Registrationhttp://www.clinicaltrials.gov/, identifier CTRI/2021/06/034496.
Background: Age-related macular degeneration (ARMD) is a progressive neurodegenerative disease of the retina. While typically a geriatric condition, early-onset ARMD (before age 50) is an emerging clinical concern with limited therapeutic interventions in conventional medicine for the non-exudative (dry) form. Methods: A 43-year-old male diagnosed with dry ARMD presented with painless, gradual diminution of vision in both eyes. The condition was diagnosed as Pitta Vidagdha Drishti based on Ayurvedic clinical features. Management followed an integrated Ayurvedic protocol comprising Shodhana (systemic purification via Nasya and Matra Basti), Shamana (internal medicine), and Kriyakalpa (targeted ocular therapies including Akshi Tarpana and Anjana). Results: Post-treatment Optical Coherence Tomography (OCT) demonstrated a significant reduction in drusen count (from 4 to 1 in the left eye) and stabilized retinal thickness. Best Corrected Visual Acuity (BCVA) improved from 6/24 to 6/12 in the left eye. Conclusion: Ayurvedic management effectively addresses both the functional and structural aspects of early-onset dry ARMD, providing a promising non-invasive strategy for retinal preservation.
