eEF2 improves dense connective tissue repair and healing outcome by regulating cellular death, autophagy, apoptosis, proliferation and migration
Bibliographic record
Abstract
Outcomes following human dense connective tissue (DCT) repair are often variable and suboptimal, resulting in compromised function and development of chronic painful degenerative diseases. Moreover, biomarkers and mechanisms that guide good clinical outcomes after DCT injuries are mostly unknown. Here, we characterize the proteomic landscape of DCT repair following human Achilles tendon rupture and its association with long-term patient-reported outcomes. Moreover, the potential regulatory mechanisms of relevant biomarkers were assessed partly by gene silencing experiments. A mass-spectrometry based proteomic approach quantified a large number (769) of proteins, including 51 differentially expressed proteins among 20 good versus 20 poor outcome patients. A novel biomarker, elongation factor-2 (eEF2) was identified as being strongly prognostic of the 1-year clinical outcome. Further bioinformatic and experimental investigation revealed that eEF2 positively regulated autophagy, cell proliferation and migration, as well as reduced cell death and apoptosis, leading to improved DCT repair and outcomes. Findings of eEF2 as novel prognostic biomarker could pave the way for new targeted treatments to improve healing outcomes after DCT injuries.Trial registration: NCT02318472 registered 17 December 2014 and NCT01317160 registered 17 March 2011, with URL http://clinicaltrials.gov/ct2/show/NCT02318472 and http://clinicaltrials.gov/ct2/show/study/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 | 0.000 | 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".