Systematic Review of Relevant Biomarkers for Human Connective Tissue Repair and Healing Outcome: Implications for Understanding Healing Processes and Design of Healing Interventions
Bibliographic record
Abstract
OBJECTIVE: The healing process following connective tissue (CT) injuries is complex, resulting in variable and often suboptimal outcomes. Patients undergoing CT repair frequently experience healing failures, compromised function, and chronic degenerative diseases. The identification of biomarkers to guide improved clinical outcomes after CT injuries remains an emerging but promising field. [Figure: see text] [Figure: see text] Design:Systematic review. DATA SOURCES: Databases, including PubMed, MEDLINE Ovid, Web of Science, and Google Scholar, were searched up to August 2024. ELIGIBILITY CRITERIA: To achieve the research objective, randomized control trials, cohort studies, and case-control studies on biomarkers associated with CT repair and healing outcomes were selected. The present analysis was confined to clinical and preclinical models, excluding imaging studies. The entire process of this systematic review adhered strictly to the guidelines outlined in the Preferred Reporting Items for Systematic Review and Meta-Analyses protocol checklist. RESULTS: A total of 1,815 studies on biomarkers of CT repair were initially identified, with 75 studies meeting eligibility criteria and 55 passing quality assessments. For biomarkers associated with CT healing outcomes, 281 studies were considered, with 30 studies meeting eligibility criteria and 24 passing quality assessments. Twenty-one overlapping studies investigated the effects of biomarkers on both CT repair and healing outcomes. Specific biomarkers identified, and ranked from highest to lowest quality, include complement factor D, eukaryotic elongation factor-2, procollagen type I N-terminal propetide, procollagen type III N-terminal propetide, lactate, pyruvate, platelet-derived growth factor-BB, tissue inhibitor of metalloproteinase-3 (TIMP-3), cysteine-rich protein-1, plastin-3, periostin, protein S100-A11, vimentin, matrix metalloproteinases (MMP-2, MMP-7, and MMP-9), hepatocyte growth factor, interferon-γ, interleukins (IL-6, IL-8, and IL-10), MMP-1, MMP-3, tumor necrosis factor-α, fibroblast growth factor-2, IL-1α, chondroitin-6-sulfate, inter-alpha-trypsin inhibitor heavy chain-4, transforming growth factor-beta 1, vascular endothelial growth factor, C-C chemokine receptor 7, C-C chemokine ligand 19, IL-1β, IL-1Ra, IL-12p40, granulocyte-macrophage colony-stimulating factor (GM-CSF), and TIMP-1. CONCLUSIONS: All of the 37 identified potential biomarkers demonstrated regulatory effects on CT repair and mediated healing outcomes. Notably, the identified biomarkers from human studies can potentially play an essential role in the development of targeted treatment protocols to counteract compromised healing and can also serve as predictors for detecting CT healing processes and long-term outcomes.
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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 | 0.028 | 0.111 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".