In vitro collagen biomarkers in mechanically stimulated human tendon cells: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to comprehensively examine and summarize the available in vitro evidence regarding the relationship between mechanical stimulation and biomarkers of collagen synthesis in human-derived tendon cells. METHODS: Systematic review with narrative analyses and risk of bias assessment guided by the Health Assessment and Translation tool. The electronic databases MEDLINE (Ovid), EMBASE (Ovid), CENTRAL (Ovid) and COMPENDEX (Engineering Village) were systematically searched from inception to 3 August 2023. Inclusion criteria encompassed English language, original experimental, or quasi-experimental in vitro publications that subjected human tendon cells to mechanical stimulation, with collagen synthesis (total collagen, type I, III, V, XI, XII, and XIV) and related biomarkers (matrix metalloproteinases, transforming growth factor β, scleraxis, basic fibroblast growth factor) as outcomes. RESULTS: Twenty-one publications were included. A pervasive definite high risk of bias was evident in all included studies. Owing to incomplete outcome reporting and heterogeneity in mechanical stimulation protocols, planned meta-analyses were unfeasible. Reviewed data suggested that human tendon cells respond to mechanical stimulation with increased synthesis of collagen (e.g., COL1A1, procollagen, total soluble collagen, etc.), scleraxis and several matrix metalloproteinases. Results also indicate that mechanical stimulation dose magnitude may influence synthesis in several biomarkers. CONCLUSIONS: A limited number of studies, unfortunately characterized by a definite high risk of bias, suggest that in vitro mechanical stimulation primarily increases type I collagen synthesis by human tendon cells. Findings from this systematic review provide researchers and clinicians with biological evidence concerning the possible beneficial influence of exercise and loading on cellular-level tendon adaptation.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".