Micro-RNA-125b and its use as a biomarker of systemic malignancies besides urothelial cancers
Bibliographic record
Abstract
Iread with great interest the recent article by Snowdon and colleagues. 1 MiR-125b may serve as a biomarker in a number of systemic malignancies besides urothelial cancers.For instance, in non-small cell lung carcinomas (NSCLC) miR-125b expression is a significant biomarker.In fact, miR-125b expression in these malignancies is an independent determining factor of prognosis.Patients with NSCLC and high miR-125b levels typically exhibit a poor clinical outcome. 2 Lower levels of miR-125b are seen in those with well differentiated tumours in comparison to those with poorly differentiated tumours.Patients with lung malignancies that do not respond to therapy typically exhibit higher levels.Similarly, miR-125b influences clinical prognosis in colorectal malignancies.Progression of the primary tumour involves a direct involvement of miR-125b.miR-125b acts by attenuating p53 expression in the malignant cells.3 Colorectal carcinoma patients with high miR-125b levels typically have a poor clinical outcome in contrast to patients who express low levels of miR-125b.Individuals with high miR-125b expression develop larger tumours that exhibit greater invasiveness.These findings have been confirmed by Nishida and colleagues in a recent study.4 The above examples clearly illustrate the significance of assessing miR-125b levels in determining the prognosis in a number of systemic malignancies.There is a clear need for further studies to further explore the possible relationship of miR-125b with clinical prognosis in other systemic malignancies.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".