Integrative analysis of candidate MicroRNAs and gene targets for OSA management using in silico and in-vitro approach
Bibliographic record
Abstract
MicroRNAs (miRNAs) have been implicated in the pathogenesis of human diseases including sleep disorders. The aim of this study is to address the involvement of miRNAs (miR-21 and miR-29) in the pathophysiology of obstructive sleep apnea (OSA). In this study we have done integrated analysis of miRNAs with their potential gene targets as a strategy for management of OSA. Methods: miRNA expression levels were quantified in healthy control group and obese vs. Non-obese OSA subjects by Quantitative real-time PCR. In-silico analysis of interplay of miRNAs with potential gene targets was done using Schrödinger Release 2023-1. Results: The real time expression analysis revealed a differential expression pattern in miRNAs indicating down-regulation of miR-21 in obese OSA while miR-29 showed upregulation as compared to non-obese OSA and healthy subjects with p values of ≤0.01 and <0.0001respectively. A trend was observed where target genes TGFBR2, NAMPT, and NPPB were significantly increased with p-value of ≤0.0001 and TGFBR3 and INSIG2 showed decreasing trend with p-value of ≤0.0001 between obese and non-obese OSA respectively. MD simulation analysis provided valuable information regarding the stability, flexibility, compactness and solvent exposure of the complexes over time. Conclusion: miR-21 and miR-29 possesses differential expressions in obese OSA subject and exihbits strong molecular interactions with potential target genes, such as TGFBR2, NPPB, NAMPT and INSIG2. Identifying the miRNAs, genes and pathways associated with OSA can help to expand our understanding of the risk factors for the disease as well as provide new avenues for potential treatment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 0.001 |
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".