Finding Out Egyptian Gods’ Secret Using Analytical Chemistry: Biomedical Properties of Egyptian Black Makeup Revealed by Amperometry at Single Cells
Bibliographic record
Abstract
Lead-based compounds were used during antiquity as both pigments and medicines in the formulation of makeup materials. Chemical analysis of cosmetics samples found in Egyptians tombs and the reconstitution of ancient recipes as reported by Greco-Roman authors have shown that two non-natural lead chlorides (laurionite Pb(OH)Cl and phosgenite Pb(2)Cl(2)CO(3)) were purposely synthesized and were used as fine powders in makeup and eye lotions. According to ancient Egyptian manuscripts, these were essential remedies for treating eye illness and skin ailments. This conclusion seems amazing because today we focus only on the well-recognized toxicity of lead salts. Here, using ultramicroelectrodes, we obtain new insights into the biochemical interactions between lead(II) ions and cells, which support the ancient medical use of sparingly soluble lead compounds. Submicromolar concentrations of Pb(2+) ions are shown to be sufficient for eliciting specific oxidative stress responses of keratinocytes. These consist essentially of an overproduction of nitrogen monoxide (NO degrees ). Owing to the biological role of NO degrees in stimulating nonspecific immunological defenses, one may argue that these lead compounds were deliberately manufactured and used in ancient Egyptian formulations to prevent and treat eye illnesses by promoting the action of immune cells.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".