Keratinocyte Motility Is Affected by UVA Radiation—A Comparison between Normal and Dysplastic Cells
Bibliographic record
Abstract
UVA radiation induces multiple and complex changes in the skin, affecting epidermal cell behavior. This study reports the effects of UVA exposure on normal (HaCaT) and dysplastic (DOK) keratinocytes. The adherence, spreading and proliferation were investigated by time-lapse measurement of cell layer impedance on different matrix proteins. Prior to UVA exposure, the time required for adherence and spreading did not differ significantly for HaCaT and DOK cells, while spreading areas were larger for HaCaT cells. Under UVA exposure, HaCaT and DOK cells behavior differed in terms of movement and proliferation. The cells' ability to cover the denuded surface and individual cell trajectories were recorded by time-lapse videomicroscopy, during wound healing experiments. Dysplastic keratinocytes showed more sensitivity to UVA, exhibiting transient deficiencies in directionality of movement and a delay in re-coating the denuded area. The actin cytoskeleton displayed a cortical organization immediately after irradiation, in both cell lines, similar to mock-irradiated cells. Post-irradiation, DOK cells displayed a better organization of stress fibers, persistent filopodia, and new, stronger focal contacts. In conclusion, after UVA exposure HaCaT and DOK cells showed a different behavior in terms of adherence, spreading, motility, proliferation, and actin cytoskeleton dynamics, with the dyplastic keratinocytes being more sensitive.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".