Standardization of Polyherbal Extract for Type-2 Diabetes
Bibliographic record
Abstract
Aims: This study was aimed to standardize the polyherbal extract containing Annona squamosa, Phyllanthus emblica, Berberis aristata and Curcuma longa for the management of type-2 diabetes. The standardization of polyherbal formulation is indispensable in order to achieve the quality, purity, safety and efficacy of drugs. Study Design: Physico-chemical investigations, Physical characteristics, Qualitative phytochemical analyses, fluorescence analysis and HPLC analysis. Materials and Methods: The Standardization of polyherbal extract was based on systematic organoleptic evaluation, physico-chemical investigation, physical characteristics, heavy metal analysis, fluorescence analysis, phytochemical screening, total alkaloid content, determination of viscosity, surface tension, density and HPLC analysis were carried out by official method. Results: Organoleptic evaluation resulted that it was yellowish green in colour with characteristic odour, bitter, pungent taste and fine texture. All the applied Physico-chemical parameters like total ash, acid insoluble, water soluble ash, extractive values, observed pH, moisture content, crude fibre, foaming index were found to be within limit. The limits obtained from physical and other parameters could be used as reference in quality control. The phytochemical analysis indicated the presence of alkaloids, carbohydrates, flavonoids, volatile oils, tannins, saponins, phytosterols and mucilage. Absence of detectable levels of heavy metal confirmed that extract was non-toxic in nature. HPLC studies confirm the presence of marker compounds in each extract. Conclusion: On the basis of observations and experimental results, the study can be used as reference standard for the further quality control research as it significantly ensures the use of genuine and uniform material and well-designed methodologies for standardization and development of poly herbal extract.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".