Fluorescent light energy in wound healing: when is a photon something more?
Bibliographic record
Abstract
Fluorescent Light Energy (FLE) is a unique form of photobiomodulation that stimulates healing, reduces inflammation, and alleviates pain. The system works by exciting a chromophore in a topical substrate, which emits FLE with a broad spectral range (~400-700 nm) that is delivered to the target tissue below. Results from in vivo and in vitro studies have shown FLE modulates inflammation via down-regulation of pro-inflammatory cytokines such as IL-6 and TNF-α and stimulates mitochondria biogenesis1. A recent study showed FLE-stimulated cells responded more potently compared to cells treated with light from an LED light source (“Mimicking Lamp”) designed to generate the same emission spectra and power intensity profile as FLE2. FLE-treated human dermal fibroblasts (HDF) experienced up-regulated collagen production, while a minor and nonsignificant effect was observed for the Mimicking Lamp-treated HDFs. These results suggest that photons generated by FLE either penetrate tissue differently or are absorbed differently compared to photons from a LED light source. Photonic properties of FLE that could impact tissue penetration or absorption may include polarity or coherency, leading to different cellular responses. To investigate if light polarity may influence cellular responses to FLE stimulation, the present study applied linear and circular-polarizing filters to investigate the influence of FLE’s polarity on immune parameters. The data suggest that FLE polarity contributes to its impact on biological systems. Furthermore, the immunemodulatory impact of FLE was investgated in a pilot study on a human ex vivo skin model suggesting that central myeloid immune surface markes are modulated by FLE.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".