Ayurvedic Therapies to Target the Microbiome: Evidence and Possibilities.
Bibliographic record
Abstract
Context: The microbiome is a constantly evolving entity, being influenced by diet, lifestyle, age, genetics, medication, and environment; keeping the microbiome in good health is a step toward better health for the body. Ayurveda emphasizes a healthy internal milieu that synchronizes with the circadian and seasonal rhythms, in addition to reacting to other stressors. Objective: The current review intended to provide an overview of Ayurvedic principles related to health and disease and their management and to briefly discuss the current understanding of the human microbiome and explore Ayurvedic herbs and therapies that have been studied for their effects on the microbiome. Design: The team included researchers in India and Canada. A Pubmed search was performed using the keywords Ayurveda therapies, Ayurvedic therapies, Gut microbiome, Panchakarma, Therapeutic purgation, Therapeutic emesis, medicated enema. Results: Research connecting Ayurvedic interventions and the gut microbiome is yet in a nascent stage. Several Ayurvedic herbs have been researched for their potential in altering the gut microbiome. Among the Ayurvedic therapies, virechana (therapeutic purgation) and basti (medicated enema) have been studied for their gut microbiome altering effects. However, the limited number of such studies prevents from drawing categorical conclusions currently, about the effects of Ayurvedic Panchakarma therapy on the human microbiome. Conclusions: Studying where and how the Ayurvedic herbs and therapies can exert their influence on the human microbiome provides a challenging yet novel opportunity and can help address multiple health and disease conditions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".