"COMPARATIVE ANALYTICAL STUDY OF MUSTADI KWATHA AND MUSTADI GHANA, TWO DOSAGE FORMS OF A CLASSICAL AYURVEDIC FORMULATION"
Bibliographic record
Abstract
Background: Kwatha Kalpana (Decoction), despite of its uniqueness and potency in ayurvedic pharmacopaeia, beholds certain drawbacks related to preparation, palatability, transportation etc. Practice of refrigerating the Kwatha is adopted considering the difficulty in fresh preparation. Ghana is one such dosage form wherein the intactness of the constituents can be expected with a longer shelf life. Methods: Mustadi Kwatha (MK) and Mustadi Ghana (MG) were prepared following the classical methods. MK was kept under two different temperatures, i.e. under room temperature (MKRT) and in the refrigerator (MKRF) for different durations, MKRT1 (24 hours) MKRT2 (48 hours), MKRF1 (24 hours), MKRF2 (48 hours), MKRF3 (96 hours) and MKRF4 (160 hours). Organoleptic characters were determined, phytochemical analysis including tests for alkaloids, flavonoids, phenolic compounds, glycosides, proteins and carbohydrates was carried out. HPTLC fingerprinting was performed on all the samples of MK and MG. Results: Yield percentage of MG was 2.86%. Organoleptic characters of MKRT changed by the end of 160 hours. MG was brownish black in color, had characteristic odor, astringent in taste, was hard and sticky. All the tested phytochemicals were present in both MK and MG. In HPTLC of MKRT and MKRF samples, Rf ranged from 0.321 to 0.416. The common Rf was noted to be 0.353, corresponded to gallic acid. Fluctuations were noticed in Area Under Curve (AUC) in MKRF samples suggesting changes in the constituents. Conclusion: Mustadi Kwatha is best used within 24 hours of preparation. Refrigeration can preserve Mustadi Kwatha longer, but potential chemical interactions may affect its constituents.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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 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".