Safe usage of cosmetics in Bangladesh: a quality perspective based on microbiological attributes
Bibliographic record
Abstract
The present review attempted to emphasize on the microbiological quality of the commonly used cosmetics item by the majority of the Bangladeshi community. The abundance of contaminating microorganisms has been quantitatively discussed and the possible health risk has been focused upon usage of these items. Only a very few research efforts have been conducted on the cosmetic items in Bangladesh so far. The microbiological contamination aspects have been portrayed in this review using the information collected from a substantial number of cosmetic items which were earlier subjected to extensive microbiological and biochemical analyses. The prevalence of bacteria, fungi and the specific pathogenic microorganisms has been discussed based on research so far locally conducted on the finished items sold in markets, especially within the Dhaka metropolis. The laboratory scale experiments revealed the presence of enormous number of bacteria, actinomycetes and fungi within the commonly used cosmetics. Conversely, the anti-bacterial activity was noticed in some of the products which might be in favor of the user safety. The prevalence of pathogenic microorganisms in the cosmetic items certainly raises a substantial public health issue. The necessity of the routine microbiological testing of the commonly used cosmetic items as well as the legislative measures to mitigate the contamination problem is thus of great significance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".