<i>Schistosoma japonicum</i> ‐Derived Peptide <scp>SJMHE1</scp> Attenuates Osteoarthritis by Promoting Synovial <scp>M2</scp> Macrophage Polarisation
Bibliographic record
Abstract
Osteoarthritis (OA), a degenerative condition, severely impacts the quality of life in elderly individuals. Current clinical treatment options for OA remain limited. The polarisation of synovial macrophages plays a pivotal role in OA progression. SJMHE1, a peptide derived from Schistosoma japonicum (S. japonicum), has demonstrated the ability to inhibit inflammatory responses such as those seen in asthma and enteritis. Herein, SJMHE1 was administered via intra-articular injection to rats with anterior cruciate ligament transection (ACLT)-induced OA. Its effects on synovial inflammation, cartilage degradation and macrophage polarisation were assessed. Additionally, SJMHE1-stimulated macrophages were co-cultured with chondrocytes to examine chondrocyte degradation and apoptosis. The effect of the peptide on the expression of inflammatory cytokines in peripheral blood mononuclear cells (PBMCs) derived from patients with OA was also evaluated. SJMHE1 treatment delayed OA progression in elderly rats. It inhibited M1 macrophage polarisation, promoted M2 macrophage polarisation, reduced synovial inflammation and alleviated cartilage degradation. In the synovium, SJMHE1 downregulated proinflammatory cytokine IL-6 and upregulated the anti-inflammatory cytokine IL-10. In vitro, SJMHE1-treated macrophages preserved chondrogenic properties and inhibited chondrocyte apoptosis. Furthermore, SJMHE1 suppressed inflammatory cytokine production in PBMCs from patients with OA. The results suggest that SJMHE1 could represent a potential therapeutic approach for managing OA.
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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) | 0.001 | 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".