A flexible, and wireless LED therapy patch for skin wound photomedicine with IoT-connected healthcare application
Bibliographic record
Abstract
Abstract Low-level laser therapy (LLLT), also known as photobiomodulation, is a safe and noninvasive method for various dermatological applications. However, most LLLT devices have limitations, such as low flexibility, high energy consumption, and huge equipment size, limiting their usage in daily life and clinical treatment. This study presents a flexible and wireless light emitting diode (LED) patch with an internet of thing (IoT) healthcare platform for wound healing applications. The flexible LED patch was designed with a high-efficiency performance of thermal stability, device uniformity, and mechanical durability for skin-attachable phototherapies application and clinical use. The application of a smartphone app with an IoT-connected healthcare platform for the flexible LED patch opens tremendous opportunities for the development of a remote healthcare system with cost-effectiveness in the future. In wound healing test on normal human fibroblasts, the LED light was proven to have no cytotoxic effect with high fibroblast proliferation and fibroblast migration (over 16% compared to control) under various light irradiations. Furthermore, a high association between wavelengths and exposure duration with biologic responses and migration effects was indicated in the study. The cell proliferation and migration experiments show the necessity of optimizing LED wavelength, radiation doses for better clinical assessment. Based on the results, the flexible LED patch is expected to be a suitable photomedical device for various types of dermatology applications.
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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) | 0.002 | 0.001 |
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".