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Record W4383554846 · doi:10.1097/gh9.0000000000000173

Forever chemicals: the ugly side of cosmetics

2023· article· en· W4383554846 on OpenAlexaboutno aff
Omer Ahmed Shaikh, Areej Fatima, Manahil Shekha, Lakshya Kumar, Vadodarya Jaykumar, Resham, Irfan Ullah, Muhammad Sohaib Asghar

Bibliographic record

VenueInternational Journal of Surgery Global Health · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPer- and polyfluoroalkyl substances research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineToxicologyEnvironmental health

Abstract

fetched live from OpenAlex

Perfluoroalkyl and polyfluoroalkyl substances, also known as PFAS, are aliphatic carbon chains, on which all the hydrogen substituents have been replaced by fluorine atoms such that they contain the perfluoroalkyl CnF2n+1 moiety1. They are a large, intricate, and constantly developing class of synthetic chemicals utilized in a variety of everyday products. For instance, they may make clothes and carpets stain-resistant, stop food from sticking to cookware, and increase the efficiency of firefighting foam. PFAS are also used by the military, automotive, aerospace, building, and electronics sectors (https://www.niehs.nih.gov/health/topics/agents/pfc/index.cfm). Human exposure to PFAS has been primarily attributed to contaminated food and drinking water2. It is a widespread occurrence, with numerous exposures and growing numbers, as much as 4700 PFAS existing, and are posing concerns about the public health impact. Some long-term studies have shown a link between exposure to PFAS and increased risk of type 2 diabetes in women, altered immune and thyroid function, liver disease, lipid and insulin dysregulation, kidney disease, adverse reproductive and developmental outcomes, and cancer (https://www.niehs.nih.gov/health/topics/agents/pfc/index.cfm3). Multiple studies conducted globally have revealed that PFAS has also “silently” maneuvered its way into the cosmetic industry. The prolific usage of PFAS in makeup and skin care products is due to its hydrophobic chemical property and film-forming ability, which are believed to increase product durability, spreadability, and wear. In addition, benefits like increased skin absorption of the product and better skin texture have also been reported4. However, the association of PFAS exposure with the various organ function derangements, as aforementioned, is a conspicuous testimony of its cons outweighing the pros. A recent study by Whitehead et al5 evaluating the occurrence of fluorinated ingredients in 231 cosmetic products, across 8 categories, of well-known brands in North America and Canada revealed that high total fluorine ≥0.384 μg F/cm2 was found in several of these with the highest proportion of products being foundations (65%), mascaras (47%), and lip products (55%). These products also happen to be most commonly used by women. Moreover, the ingredient lists of most products tested did not reveal the presence of fluorinated compounds in the product packaging, indicative of a loophole in United States and Canadian labeling laws5. Pakistan is among the major importers of cosmetics and personal care products, largely posing a threat to its consumer base: women who form 48.5% of its population. By means of this study, we aim to draw the attention of the concerned authorities towards strictly regulating and monitoring the use of such chemicals in cosmetics, and the manufacturers to use human-friendly ingredients with their associated risks mentioned on the product to ensure complete transparency. The use of cosmetics on a daily basis should also be discouraged, especially those containing high levels of PFAS as aforementioned. Lastly, further study is required on available alternative PFAS and their level of safety measures. Ethical approval Not applicable. Sources of funding None. Author contributions O.A.S., A.F., conceived the idea, M.S., L.K., V.J. performed a literature review and wrote the manuscript. R., M.S.A., I.U., reviewed the manuscript and critically revised it to the final form. All authors approved the final version of the manuscript. Conflict of interest disclosures The authors declare that they have no financial conflict of interest with regards to the content of this report. Research registration unique identifying number (UIN) N/A. Guarantor Muhammad Sohaib Asghar.

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)0.002
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 score0.368
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.060
GPT teacher head0.395
Teacher spread0.336 · 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

Citations5
Published2023
Admission routes1
Has abstractyes

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