Truncated lubricin glycans in osteoarthritis stimulate the synoviocyte secretion of VEGFA, IL-8, and MIP-1α: Interplay between O-linked glycosylation and inflammatory cytokines
Bibliographic record
Abstract
-linked Tn antigen (GalNAcα1-Ser/Thr) glycan on SF lubricin. Inflammatory markers in plasma and SF in OA patients and controls were measured using a 44-multiplex immunoassay. We found consistently 29 markers detected in both plasma and SF. The difference in their concentration and the low correlation when comparing SF and plasma suggests an independent inflammatory environment in the two biofluids. Only plasma MCP-4 and TARC increased in our patient cohort compared to control plasma. To address the second task, we concluded that plasma markers were irrelevant for a direct connection with SF glycosylation. Hence, we correlated the SF-inflammatory marker concentrations with the level of altered glycosylation of SF-lubricin. We found that the level of SF-IL-8 and SF-MIP-1α and SF-VEGFA in OA patients displayed a positive correlation with the altered lubricin glycosylation. Furthermore, when exposing fibroblast-like synoviocytes from both controls and OA patients to glycovariants of recombinant lubricin, the secretion of IL-8 and MIP-1α and VEGFA were elevated using lubricin with Tn antigens, while lubricin with sialylated and nonsialylated T antigens had less or no measurable effect. These data suggest that truncated glycans of lubricin, as found in OA, promote synovial proinflammatory cytokine production and exacerbate local synovial inflammation.
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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) | 0.001 | 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".