Heavy metals contamination in eye shadows sold in Malaysia and user`s potential health risks
Bibliographic record
Abstract
Background: Nowadays, eye shadows have become common cosmetics used by consumers. Previous studies proved that some of the eye shadows used had excessive levels of heavy metals. Objectives: The aims of this study are to (i) quantify the heavy metals concentration of lead and chromium in the eye shadows based on the color categories and types of eye shadows and (ii) assess potential non-carcinogenic health risk due exposure to heavy metals concentrations in eye shadows by using Hazard Quotient (HQ). Methodology: A conventional method using oven heating was applied to extract heavy metals from the samples. The analysis of heavy metals in the samples was performed using the Graphite Furnace Atomic Absorption Spectrophotometer (GF-AAS). The chronic non-carcinogenic health effect was evaluated quantitatively using HQ. Results: Both lead and chromium concentrations were found to be the highest in blue color category with the mean concentration of 161.8 ± 101.6 µg kg−1 and 149.4 ± 53.1 µg kg−1, respectively. The chromium levels were higher in the shimmering shade compared to the matte shade. The lead concentrations in all the samples analyzed were below the standard set by Health Canada (10 mg kg−1) and United States Food and Drug Administration (20 mg kg−1). The HQ values for chromium in all samples were less than 1. Conclusion: Lead concentrations were present within the permitted levels stated by the international standards in cosmetics intended for external use. The HQ values for chromium were less than 1 in all samples, indicating there was no significant chronic non-carcinogenic health risk to eye shadow users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".