Analysis of Sebum Lipid Composition and the Development of Acneiform Rash before and after Administration of egfr Inhibitor
Bibliographic record
Abstract
Treatment with an epidermal growth factor receptor inhibitor (egfri) in patients having non-small-cell lung cancer can cause frequent and diverse skin toxicities, an acneiform rash being one of the commonest. Although the exact pathophysiology of this rash and its development mechanisms remain unknown, investigators have noted that egfri-induced skin toxicity might be partly associated with sebaceous gland function. Sebum is composed mainly of the lipids squalene (sq), wax ester (we), triglyceride, free fatty acid, and cholesterol, which are secreted mostly from the sebaceous glands and by keratinocytes. We therefore investigated the lipid composition of sebum before and after administration of egfri and whether sebum composition was associated with the development of acneiform rash. To investigate any associated changes in sebum gland activity, we focused especially on alterations in the amounts of sq and we, which are secreted solely from the sebaceous glands. In contrast to our expectations, we observed no substantial changes in the lipid composition of sebum before and after administration of egfri. Composition varies with the individual; however, the proportion of sq and we derived from the sebaceous glands was significantly lower in regions that did not develop acneiform rash than in regions that did. Our results suggest that development of an acneiform rash after administration of egfri could be related to sebaceous gland activity. Measurement of the lipid composition of sebum before therapy with egfri might predict which patients will be prone to acneiform rash.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".