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Record W2923665108 · doi:10.1186/s40543-018-0162-0

Assessment of some heavy metals in selected cosmetics commonly used in Bangladesh and human health risk

2019· article· en· W2923665108 on OpenAlexaboutno aff
Md. Ferdous Alam, Mohammad Salim Akhter, B. Mazumder, A K M Abdur Rahman Ferdous, Md Delwar Hossain, Nirmal Chandra Dafader, Farah Tasneem Ahmed, Sukalyan Kumar Kundu, Talal A Taheri, A. K. M. Atique Ullah

Bibliographic record

VenueJournal of Analytical Science & Technology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsnot available
Fundersnot available
KeywordsCadmiumMercury (programming language)Hazard quotientCosmeticsChromiumEnvironmental healthHealth risk assessmentHeavy metalsToxicologyEnvironmental scienceHealth riskRisk assessmentMedicineEnvironmental chemistryChemistryMetallurgyMaterials scienceBiologyComputer sciencePathology

Abstract

fetched live from OpenAlex

In Bangladesh, use of beauty cream is very much popular and a common daily task for the people. The current study is focusing on the assessment of some toxic heavy metals such as lead, cadmium, chromium, and mercury contamination in some beauty creams commonly used by Bangladeshi people. The results indicate that the concentrations of lead, cadmium, and chromium exceed the maximum allowable concentrations, whereas mercury is found below the acceptable limit set by WHO/EU. However, according to Health Canada, the concentrations of cadmium and chromium for all the selected samples (except cadmium in sample coded A) was within the permissible limit. Health risks associated with these metal intakes via dermal exposure route are evaluated in terms of chronic daily intake (CDI) and hazard quotient (HQ). The carcinogenic risk (CR) estimated for chromium indicates that it is within the acceptable range. The cancer and non-cancer risk results indicate that although the chances of cancer risk and non-cancer risk resulting from the use of these cosmetic products were unlikely, buildup of these toxic heavy metals overtime on continuous usage could be detrimental for Bangladeshi people.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.316
Teacher spread0.302 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations118
Published2019
Admission routes1
Has abstractyes

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