Analysis of circulating microRNAs aberrantly expressed in alcohol-induced osteonecrosis of femoral head
Bibliographic record
Abstract
Serum miRNAs are potential biomarkers for predicting the progress of bone diseases, but little is known about miRNAs in alcohol-induced osteonecrosis of femoral head (AIONFH). This study evaluated disease-prevention value of specific serum miRNA expression profiles in AIONFH. MiRNA PCR Panel was taken to explore specific miRNAs in serum of AIONFH cases. The top differentially miRNAs were further validated by RT-qPCR assay in serum and bone tissues of two independent cohorts. Their biofunction and target genes were predicted by bioinformatics databases. Target genes related with angiogenesis and osteogenesis were quantified by RT-qPCR in necrotic bone tissue. Our findings demonstrated that multiple miRNAs were evaluated to be differentially expressed with high dignostic values. MiR-127-3p, miR-628-3p, and miR-1 were downregulated, whereas miR-885-5p, miR-483-3p, and miR-483-5p were upregulated in serum and bone samples from the AIONFH patients compared to those from the normal control individuals (p < 0.01). The predicted target genes of the indicated miRNAs quantified by qRT-PCR, including IGF2, PDGFA, RUNX2, PTEN, and VEGF, were presumed to be altered in necrotic bone tissue of AIONFH patients. The presence of five altered miRNAs in AIONFH patients may serve as non-invasive biomarkers and potential therapeutic targets for the early diagnosis of AIONFH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".