Lysyl Oxidase-Like 1 (LOXL1) Up-Regulation in Chondrocytes Promotes M1 Macrophage Activation in Osteoarthritis via NF-κB and STAT3 Signaling
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it