Management of all three phases of wound healing through the induction of fluorescence biomodulation using fluorescence light energy
Bibliographic record
Abstract
Research on photobiomodulation (PBM) has led to the development of various light-generating devices that can benefit a wide range of clinical indications. A novel approach of inducing PBM is through application of a Fluorescence Biomodulation (FB) System consisting of a blue light (peak wavelength between 440 and 460 nm) which activates topical photoconverter substrates containing specialized chromophores that generate fluorescence light energy (FLE). In clinical trials, FLE has been shown to modulate both healthy and disease-affected skin/soft tissue, providing a unique method for managing inflammatory skin conditions and accelerating healing. To better understand the biological impact of FB-induced FLE, we studied this system in vitro on dermal human fibroblasts (DHFs) and in vivo in canine deep pyoderma. In vitro data from stimulated DHFs exposed to an FB System showed a significant decrease in IL-6 production by 130.14% after 24 hr (p<0.001), compared to control groups. In canines with chronic deep pyoderma, the use of FB plus standard of care (SOC) treatment significantly reduced time to clinical resolution compared to controls that received SOC alone (p<0.001). Biopsies from lesional areas showed enhanced mitochondrial biogenesis in the FB lesions versus the SOC lesions, as supported by a significant increase in the number and size of mitochondria (89.31% and 90.15% respectively; p<0.0001). Significant modulation of inflammatory pathways, epithelialization, and angiogenesis were also demonstrated. These results support the use of FB Systems for skin conditions impacted by inflammation and offer a promising therapeutic solution to support its use in other medical conditions.
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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 | 0.001 |
| 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".