Extracellular matrix composition of connective tissues: a systematic review and meta-analysis
Bibliographic record
Abstract
The function of connective tissues depends on the physical and biochemical properties of their extracellular matrix (ECM), which are in turn dictated by ECM protein composition. With the primary objective of obtaining quantitative estimates for absolute and relative amounts of ECM proteins, we performed a systematic review of papers reporting protein composition of human connective tissues. Articles were included in meta-analysis if they contained absolute or relative quantification of proteins found in the ECM of human bone, adipose tissue, tendon, ligament, cartilage and skeletal muscle. We generated absolute quantitative estimates for collagen in articular cartilage, intervertebral disk (IVD), skeletal muscle, tendon, and adipose tissue. In addition, sulfated glycosaminoglycans were quantified in articular cartilage, tendon and skeletal muscle; total proteoglycans in IVD and articular cartilage, fibronectin in tendon, ligament and articular cartilage, and elastin in tendon and IVD cartilage. We identified significant increases in collagen content in the annulus fibrosus of degenerating IVD and osteoarthritic articular cartilage, and in elastin content in degenerating disc. In contrast, collagen content was decreased in the scoliotic IVD. Finally, we built quantitative whole-tissue component breakdowns. Quantitative estimates improve our understanding of composition of human connective tissues, providing insights into their function in physiology and pathology.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".