MicroRNAs as Biomarkers in Pituitary Tumors
Bibliographic record
Abstract
The use of extracellular microRNAs (miRNAs) as circulating biomarkers is currently leading to relevant advances in the diagnosis and assessment of prognosis of several diseases. Specific miRNAs have also been shown to play a role in the pathophysiology of many neoplastic and non-neoplastic diseases. A number of studies have demonstrated that miRNAs show differential expression in various tumors, such as in the prostate, ovary, lung, breast, brain, and pituitary. Recent findings have built connections between miRNAs that are deregulated within the tumor and their presence in peripheral blood. MiRNAs have been shown to be stable in the blood where they are present in either free and/or uncomplexed form, as well as packed in microvesicles, exosomes, and apoptotic bodies, or bound to different proteins. Because the pituitary is a highly vascularized organ that releases hormones into the circulation, miRNAs would be useful biomarkers for the diagnosis of pituitary tumors, as well as for predicting or detecting recurrence after surgery. Here we review the biological significance of miRNAs in pituitary tumors and the potential value of circulating miRNAs as biomarkers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".