Effectiveness of Light-Emitting Diode Epilation on Different Skin Types: A Pilot Study
Bibliographic record
Abstract
Objective:This study analyzed the histological and immunohistochemical changes in hair follicles submitted to epilation with light-emitting diode (LED). Background:The use of specific wavelengths of LED leads to the absorption of photons by chromophore tissues, enabling different photophysical and photochemical events, bringing therapeutic benefits such as removing body hair. Methods:The sample included five participants, with phototypes II–V, divided into two groups. The volunteers received a session of epilation with the Holonyak® device on the pubic region and right groin, whereas the contralateral side was kept as a control. An energy of 10 J and a cooling temperature of −5°C were used, after which the pain provoked by the equipment was questioned using the analogue pain scale. After 45 days, the punching procedure was performed in the region where skin samples were taken for histological and immunohistochemical analysis. Results:For all phototypes, in the treated area, the follicles and sebaceous glands were in a stage of involution, showing perifollicular inflammatory infiltrate with changes suggestive of apoptosis. The apoptosis process was confirmed by the increase in markers cytokeratin-18 and cleaved caspase 3, in addition to the reduced expression of Blc-2, and the lower cell proliferation (Ki67), reinforcing the action of LED based on the definite involution and resorption of the follicle, through macrophages (CD68) triggered by the inflammatory process. Conclusions:The preliminary results of this study found relevant histological changes and immunohistochemical markers in the epilation process, which may indicate the efficacy of LED in permanent hair removal.
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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 | 0.001 |
| 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) | 0.004 | 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".