Effects of Low-Level Laser Therapy (LLLT) of 810 nm upon <i>in Vitro</i> Growth of Bacteria: Relevance of Irradiance and Radiant Exposure
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate the irradiance-dependency of low-level laser therapy (LLLT) effects on bacterial growth. BACKGROUND: LLLT is applied to open wounds to improve healing; however, its effect on wound bacteria is not well understood. MATERIALS AND METHODS: Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus were irradiated using a wavelength of 810 nm at irradiances of 0.015 W/cm2 (0-50 J/cm2) and 0.03 W/cm2 (0-80 J/cm2). Bacteria were counted after 20 h of incubation. RESULTS: LLLT effects varied significantly with species. P.aeruginosa growth decreased overall dependent on an interaction of irradiance and radiant exposure; greatest inhibition was produced using high irradiance delivering radiant exposures in the range of 1-20 J/cm2 (p = 0.001-0.04). In contrast, E. coli growth increased overall (p = 0.01), regardless of irradiance; greatest effects were produced using low radiant exposures (1-20 J/cm2). There was a main effect for irradiance (p = 0.03) on S. aureus growth; however, growth was not different compared with controls. Additional analysis showed that there were differences in growth of P.aeruginosa when comparing samples that were matched by exposure times (66, 329, 658, 1316, 1974, and 2632 sec) rather than radiant exposure; this suggests that irradiance rather than exposure time was the significant factor in P. aeruginosa inhibition. CONCLUSION: These findings have immediate relevancy in the use of LLLT for infected wounds. Exposure to 810-nm irradiation (0.03 W/cm2) could potentially benefit wounds infected with P. aeruginosa. However, increased E. coli growth could further delay recovery.
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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 | 0.000 |
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".