Microarray Analysis of the Infrapatellar Fat Pad in Knee Osteoarthritis: Relationship with Joint Inflammation
Bibliographic record
Abstract
OBJECTIVE: To examine differences in genes involved in fat metabolism, energy homeostasis, adipogenesis, and inflammation between endstage and early-stage knee osteoarthritis (OA) infrapatellar fat pads (IFP). METHODS: Twenty-nine endstage and 5 early-stage primary OA IFP were harvested at knee surgery. Total RNA was extracted, labeled, and hybridized to whole-genome expression arrays. Unsupervised analysis of all samples using principal components analysis or 2-way hierarchical clustering showed groupings based on tissue source and disease. Statistical testing identified sets of genes that displayed differences between the 2 fat types. Western blot analysis was performed for protein expression of an identified gene of interest. RESULTS: The 29 IFP demonstrated an elevation in the expression of adipokines such as adiponectin and leptin. A statistically significant increased expression was seen for genes of adipogenesis, such as peroxisome proliferator-activated receptor-γ (PPAR-γ), diacylglycerol acyltransferase 2 (DGAT2), cluster of differentiation (CD36), and thyroid hormone responsive spot (THRSP) in the severe OA fat pads as compared to the controls. A subset of 5 patients in the endstage OA group were consistently similar in gene expression to early OA tissue. Protein expression of PPAR-γ2 was 5.4-fold and PPAR-γ1 was 1.4-fold greater in endstage versus early OA tissue. CONCLUSION: Endstage OA fat pads demonstrated a significant upregulation of genes for fat metabolism and energy homeostasis and a mixed result for inflammatory cytokines.
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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 | 0.001 |
| 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".