A study protocol for assessing the efficacy of Guduchyadi Ghrita in mild neurocognitive disorder: a placebo-controlled trial with brain-derived neurotrophic factor evaluation
Bibliographic record
Abstract
Background: Mild neurocognitive disorder (mNCD), a preclinical stage of dementia, is becoming a significant public health concern, especially among the aging population. Brain-derived neurotrophic factor (BDNF) has emerged as a key biomarker for cognitive health. Guduchyadi Ghrita, an Ayurvedic formulation rich in nootropic, Rasayana, and antioxidant herbs, has shown promise in improving cognitive functions, including memory and attention, in preliminary studies. Pratimarsha Nasya, known for its brain-targeted therapeutic effects, may enhance the bioavailability of these compounds. This study aims to evaluate the efficacy of Guduchyadi Ghrita with Pratimarsha Nasya in improving cognition and BDNF levels in individuals with mNCD. Methods: This randomized, placebo-controlled, open-label, single-centre clinical trial will involve 80 participants diagnosed with mNCD according to DSM-V criteria. Group A will receive 12 gm/day of Guduchyadi Ghrita orally and 2 drops per nostril of Pratimarsha Nasya for 60 days, following Deepana-Pachana and Mridu Virechana. Group B will receive a placebo (roasted semolina capsules) and cognitive stimulation activities for 60 days. Primary outcomes will include improvements in cognition, assessed using the Montreal cognitive assessment (MoCA). Secondary outcomes will involve changes in plasma BDNF levels, quality of life (QOL-AD), and daily functioning (Bristol ADL scale). Conclusions: This study will assess the potential of Guduchyadi Ghrita and Pratimarsha Nasya in enhancing cognitive function and promoting neuroplasticity in individuals with mNCD, with a focus on BDNF as a biomarker. Trial registration: The trial is registered with the Clinical Trials Registry of India (CTRI) (REF/2025/04104935).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".