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Record W1844444309 · doi:10.1186/s40709-015-0033-4

Safe usage of cosmetics in Bangladesh: a quality perspective based on microbiological attributes

2015· review· en· W1844444309 on OpenAlexaff
Rashed Noor, Nagma Zerin, Kamal Kanta Das, Luthfun Naher Nitu

Bibliographic record

VenueJournal of Biological Research - Thessaloniki · 2015
Typereview
Languageen
FieldChemistry
TopicAntimicrobial agents and applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCosmeticsQuality (philosophy)Environmental healthHuman healthBiotechnologyBusinessToxicologyMedicineBiologyPathology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.495
GPT teacher head0.522
Teacher spread0.027 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations16
Published2015
Admission routes1
Has abstractyes

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