In-vitro study on type I collagen synthesis in low-level laser therapy on the early ligament fibroblasts’ healing process
Bibliographic record
Abstract
BACKGROUND: Low-level Laser Therapy (LLLT) has demonstrated its potential in promoting fiber matrix maturation, collagen synthesis, and fibroblast proliferation, contributing to tissue regeneration. Our study aimed to investigate the impact of LLLT on collagen type I synthesis, cell proliferation, and viability in human ligament fibroblasts derived from the Anterior Cruciate Ligament (ACL). METHODS: Tissue samples were obtained from individuals undergoing arthroscopic ACL reconstruction surgery. Primary human fibroblasts were isolated, and immunohistochemical assays confirmed their characteristics. LLLT at 850 nm was administered in three groups: Low dose (1.0 J/cm²), High dose (5.0 J/cm²), and Control (0.0 J/cm²). Cell viability was calculated using a membrane integrity assay, proliferation was determined by automated counting, and collagen type I concentration in cell culture was measured using an immunoassay. RESULTS: Fibroblasts showed decreased viability after low and high doses of LLLT, increased proliferation at the low dose, and increased collagen synthesis at the high dose on day 10 for both sexes after treatment. CONCLUSION: Our study demonstrated that LLLT may improve the early ligament healing process by increasing cell proliferation at the low dose and enhancing collagen type I synthesis at the high dose in human ligament fibroblasts.
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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".