Lysyl Oxidase-Like 1 (LOXL1) Up-Regulation in Chondrocytes Promotes M1 Macrophage Activation in Osteoarthritis via NF-κB and STAT3 Signaling
Bibliographic record
Abstract
Purpose: Osteoarthritis (OA) constitutes a widespread degenerative joint disease predominantly affecting the elderly, leading to disability. There is still a lack of biomarkers for OA, so it cannot be intervened in time. Methods: OA biomarkers were identified from human cartilage datasets using LASSO and SVM-RFE, followed by ROC analysis. LOXL1 was prioritized for further research due to its high expression in OA cartilage and robust predictive performance. Anterior cruciate ligament transection (ACLT) surgery-induced OA rats were used to explore the correlation between LOXL1 and inflammatory factors and macrophages. Macrophage markers and cytokine secretion were detected from macrophages treated with LOXL1, or co-cultured with chondrocytes after LOXL1 siRNA silencing. Results: Five hub biomarkers with OA-specific expression were identified. Elevated LOXL1 correlated with IL-6 and IL-8 in patients and increased M1 macrophages in OA rats. LOXL1-stimulated macrophages upregulated CD86 and inflammatory cytokines. Silencing LOXL1 in chondrocytes reduced CD86, inflammatory cytokines, and NF-κB p65 and p-STAT3 expression in co-cultured macrophages, mitigating MMP13 and chondrocyte apoptosis. STAT3 and NF-κB signal inhibition reduces p-STAT3, p-p65, CD86, IL-6 and IL-1β expression in LOXL1-stimulated macrophages. Conclusion: This study underscores the pivotal role of LOXL1 in activating M1 macrophages through NF-κB and STAT3 signaling, thereby promoting pro-inflammatory cytokine secretion and contributing to OA pathogenesis. LOXL1 holds promise as a potential marker for early diagnosis of OA inflammation and as a novel therapeutic target.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".