Exploring the therapeutic effect of human recombinant IL11 on lesioned OA human osteochondral explants
Bibliographic record
Abstract
OBJECTIVE: To explore IL11 co-expression profiles in our previously reported RNA-sequencing dataset of OA articular cartilage, in interaction with IL6, and to investigate the effects of hrIL11 administration as potential therapeutic strategy for OA articular cartilage using our biomimetic aged human osteochondral explant model of OA. METHODS: We used RNA-sequencing datasets of macroscopically preserved and lesioned OA articular cartilage (N = 35 patients). Spearman correlations were calculated between IL11 and IL6 expression levels and genes expressed in cartilage (N = 20048 genes). Osteochondral explants were isolated from macroscopically preserved and lesioned areas of the joint and were kept in culture for two weeks, with or without exposure to 200ng/ml hrIL11. RESULTS: We found no overlap in correlating genes between IL11 and IL6, indicating their distinct roles in articular cartilage. Moreover, we identified more genes being correlated to IL11 in the lesioned compared to preserved articular cartilage (N = 203 and 106 genes, respectively). Upon treatment of ex vivo OA articular cartilage with hrIL11, we overall observed unbeneficial effects on chondrocyte phenotype, as illustrated by upregulation of MMP13, EPAS1, RUNX2, and POSTN. We did not observe significant differences in Mankin scores upon addition of hrIL11. CONCLUSION: The current study showed that treatment of OA articular cartilage with hrIL11 is unlikely to be beneficial despite previous indications of hrIL11 as potential druggable target. These findings underscore the importance of functionally investigating OA risk genes. Better understanding of IL11 signaling and the underlying pathways is necessary towards the development of OA treatment strategy.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".