Investigation of extracellular microRNAs in oral squamous cell carcinoma, rheumatoid arthritis and mesenchymal stem cell differentiation
Bibliographic record
Abstract
Extracellular microRNAs (miRNAs) refer to cell-free miRNAs that are protected by extracellular vesicles (EVs) and protein complexes from degradation. Extracellular miRNAs are also known as circulating miRNAs that can circulate in bodily fluids. Studies have reported that extracellular miRNAs can serve as biomarkers for human diseases and can also act as mediators in cell-cell communication. In cancer, the abnormal expression of miRNAs in plasma has been observed. However, there is no report on the association of plasma miRNA expression with oral squamous cell carcinoma (OSCC) recurrence after surgery to date. In the first project, miR-486-5p, miR-375 and miR-92b-3p were validated to be highly associated with OSCC recurrence using next generation sequencing (NGS) and qRT-PCR. In cell-cell communication, bioactive information, including miRNAs, can be transferred by EVs. Studies have shown that the secretion and content of EVs are associated with human diseases. In rheumatoid arthritis (RA), the dysregulation of miRNAs in EVs secreted by peripheral blood mononuclear cells (PBMCs) and the association of miRNAs in EVs with T cell exhaustion have not been reported. In the second project, miRNAs in EVs derived from RA PBMCs were dysregulated compared to healthy control using NGS. Moreover, EVs derived from RA synovial fluid mononuclear cells (SFMCs) and RA PBMCs contained miRNA information positively associated with T cell exhaustion. Studies have shown that EVs derived from osteoblastic differentiated MSCs contained differentially regulated miRNAs. However, the change in EV miRNA derived from BMSCs and ASCs during osteogenesis has not been characterized by NGS. In the third project, it was found that osteogenic differentiation regulated miRNA expression in BMSCs and ASCs, as well as their EV miRNAs, using NGS. Some miRNAs were regulated in both two types of MSCs, while some miRNAs showed distinct changes in each cell type, which was also shown in EVs. The projects supported the important role of extracellular miRNAs in OSCC, RA and MSC differentiation.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".