Network proteomic analysis identifies inter-alpha-trypsin inhibitor heavy chain 4 during early human Achilles tendon healing as a prognostic biomarker of good long-term outcomes
Bibliographic record
Abstract
The suboptimal or protracted regeneration of injured connective tissues often results in significant dysfunction, pain, and functional disability. Despite the prevalence of the condition, few studies have been conducted which focused on biomarkers or key molecules involved in processes governing healing outcomes. To gain insight into injured connective tissue repair, and using the Achilles tendon as a model system, we utilized quantitative proteomic and weighted co-expression network analysis of tissues acquired from Achilles tendon rupture (ATR) patients with different outcomes at 1-year postoperatively. Two modules were detected to be associated with prognosis. The initial analysis identified inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4) as a biomarker or hub protein positively associated with better healing outcomes. Additional analysis identified the beneficial role of ITIH4 in inflammation, cell viability, apoptosis, proliferation, wound healing, and for the synthesis of type I collagen in cultured fibroblasts. Functionally, the effects of ITIH4 were found to be mediated by peroxisome proliferator-activated receptor gamma (PPARγ) signaling pathways. Taken together, these findings suggest that ITIH4 plays an important role in processes of connective tissue repair and advocate for the potential of ITIH4 as a therapeutic target for injured connective tissue repair. Trial registration http://clinicaltrials.gov , identifiers NCT02318472, NCT01317160.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".