Complement factor D as a predictor of Achilles tendon healing and long‐term patient outcomes
Bibliographic record
Abstract
Dense connective tissue healing, such as tendon, is protracted leading to highly variable and unsatisfactory patient outcomes. Biomarkers prognostic of long-term clinical outcomes is, however, unknown. The present study was designed to investigate the proteomic profile of healing, identify potential biomarkers, and assess their association with the patient's long-term outcomes after ATR. Quantitative mass spectrometry analysis demonstrated 1423 proteins in healing and contralateral healthy Achilles tendons of 28 ATR patients. Comparing healing at 2 weeks and healthy protein profiles, we identified 821 overlapping, 390 upregulated, and 17 downregulated proteins. Upregulated proteins are related mainly to extracellular matrix organization and metabolism, while downregulated pathways were associated with exocytosis in immune modulation and thrombosis formation. Further proteomic profiling in relation to validated patient outcomes revealed the downregulated pro-inflammatory complement factor D (CFD) as the most reliable predictive biomarker of successful tendon healing. Our finding showed a comprehensive proteomic landscape and bioinformatics on human connective tissue, indicating subtype-specific and shared biological processes and proteins in healing and healthy Achilles tendons, as well as in tendons related to good and poor patient outcomes. Inflammatory protein CFD and serpin family B member 1 were finally identified as potential predictive biomarkers of effective healing outcomes when combined the proteomic profiles with a validated clinical database. Following the future elucidation of the mechanisms associated with the identified biomarkers as predictors of good outcomes, our findings could lead to improved prognostic accuracy and development of targeted treatments, thus improving the long-term healing outcomes for all patients.
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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 | 0.002 |
| 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) | 0.001 | 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".