Review: Molecular Mechanism of Ultraviolet-Induced Keratinocyte Apoptosis
Bibliographic record
Abstract
This article reviews advances in the study of the molecular mechanisms for ultraviolet (UV)-induced keratinocyte apoptosis, with particular reference to the cytokines tumor necrosis factor-alpha (TNF-alpha) and Fas ligand (FasL). TNF-alpha and FasL induce their respective receptors and then activate caspase enzymes that are critically involved in the apoptotic process. This activation is further amplified by intracellular mitochondria-associated mechanisms. Using gene-targeted knockout mice lacking either the TNF-Rp55 or the TNF-Rp75, we have shown that TNF-alpha plays an important role in UV-induced keratinocyte apoptosis via TNF-Rp55. TNF-Rp55 shares homology with Fas and contains an intracellular death domain. UV seems to directly stimulate cross-linking of Fas, resulting in the engagement of the death machinery. Fas-associated death domain protein (FADD) acts as an adapter protein in both the TNF-Rp55 and Fas death-inducing cascades and is responsible for downstream signal transduction by recruiting caspases. Moreover, signaling of p53 contributes to the induction of apoptosis by regulating Bcl-2 family expression and increasing surface Fas expression. In addition to induction mechanisms of apoptosis, there are numerous inhibitory molecules that play a role in restricting the apoptotic pathway. Thus, the ultimate determination of whether or not a cell undergoes apoptosis after UV radiation is based on the balance between agonist and antagonist pathways.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.008 |
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".