Formulation and clinical evaluation of topical dosage forms of Indian Penny Wort, walnut and turmeric in eczema.
Bibliographic record
Abstract
Eczema is characterized by itching, lichenification, scaling, oedema and erythema. Current management strategies include corticosteroids, which are limited due to side effects. Many herbal remedies are used traditionally but unfortunately have not been validated in controlled clinical trials. Three popular traditional treatments of eczema include Indian pennywort, Walnut and Turmeric. In this study three topical formulations (micro emulsion, gel and ointment) were prepared from extracts of Indian pennywort, Walnut and Turmeric. These formulations were monitored for stability for a period of three months. Controlled clinical trials were conducted on 360 eczema patients. Clinical parameters observed were degree of erythema, oedema, scaling, itching and lichenification. Effects of each formulation on these clinical parameters were compared with placebo formulations. Micro emulsion formulations in all cases proved to be more effective in reducing semi quantitative scores of erythema and oedema. Itching was relieved more by gel formulation. The ointment showed more efficacy towards scaling and lichenification. Comparison of the effects of placebo and the specific formulations was performed by chi-square statistics and found to be highly significant. In summary it is concluded that all the formulations could be used as promising source for treatment of eczema.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".