Expression of human endogenous retrovirus HERV-K18 is associated with clinical severity in osteoarthritis patients
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the involvement of human endogenous retrovirus K18 (HERV-K18) in osteoarthritis (OA), by genotyping the HERV-K18 env locus in OA patients and controls, and analysing HERV-K18 RNA expression and its association with OA risk and clinical variables. METHOD: We recruited 558 patients with symptomatic OA and 600 controls. We performed the genotyping by TaqMan assays and the analysis of expression by quantitative real-time polymerase chain reaction (qRT-PCR). Scores on the Western Ontario and McMasters Universities Osteoarthritis Index (WOMAC), the Lequesne index, and the Stanford Health Assessment Questionnaire (HAQ) were analysed with regard to the expression levels of HERV-K18. RESULTS: The 18.3 haplotype tended towards an association with OA risk and concordantly this haplotype was associated with a higher HERV-K18 expression (p = 0.05). We found statistically significant differences when we compared the scores on the WOMAC, the Lequesne index for knee and hip, and the HAQ between OA patients with higher expression [normalization ratio (NR) > 10] and OA patients without HERV-K18 expression (p = 0.0003, 0.0005, 0.002, and 0.05, respectively), and also when the comparison was made between OA patients with higher expression (NR > 10) and OA patients with low expression of HERV-K18 (NR = 1) for the WOMAC and the Lequesne index for knee and hip (p = 0.002, 0.013, and 0.006, respectively). CONCLUSIONS: We found an association between health status measurement systems and severity index for OA and the levels of expression of HERV-K18. These results suggest the possible involvement of HERV-K18 in the aetiopathogenesis of the disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".