Pathways of Microvascular Permeability in the Synovium of Normal and Diseased Human Knees
Bibliographic record
Abstract
OBJECTIVE: Our study uses the entire proteomes of serum and synovial fluid (SF) to characterize the avenues of microvascular egress of plasma proteins, and quantifies that traffic in normal and diseased human knees. METHODS: Paired aliquots of serum and SF were collected from 17 knees of 11 subjects who died without evident joint disease and 16 patients with clinical effusions, fractionated by gel filtration chromatography and analyzed as continuous plots of the SF/serum concentration ratio versus molecular radius from 1 to 12 nanometers (nm). Curve-stripping methodology, a 3-pore model, and known protein kinetics were then applied to estimate the dimensions of and the net outflow through fenestral, "small," and "large" apertures in the microvascular endothelium. RESULTS: The 3-pore model correlated highly with the observed data (r = 0.992 in normal and 0.980 in arthritis), yielding the following mean values: for the fenestra, the normal radius (nm) was 1.75 and the effused 3.5, and the normal flow (μl/min) was 1.74 and the arthritic 22.0; for the small pore, the normal radius was 8.6 and the effused 8.5, and the normal flow was 1.5 and the arthritic flow 9.1; for the large pore, the normal radius was 40 and the effused 36, and the normal flow was 0.24 and the arthritic flow 15.5. CONCLUSION: These findings provide the first functional definition of synovial, endothelial fenestrae; reveal that the "increased vascular permeability" of inflammation is not limited to interendothelial gaps; present evidence suggesting that glycocalyceal damage and aquaporin upregulation may affect permeability in arthritic synovium; and define a straightforward methodology for interpretation of biomarker concentrations in arthritic SF.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".