Let-7, MiR-98 and MiR-181 as Biomarkers for Cancer and Schizophrenia
Bibliographic record
Abstract
Recent studies support an important role of microRNAs in cancer and major psychiatric disorders, through their regulotory role on the expression of multiple genes. The low incidence of cancer in patients with schizophrenia as a comorbidity status, is an old hypothesis which needs further investigation mainly on microRNAs function, through their oncosupressive or oncogenic activity, in the development of psychiatric disorders. The expression pattern of a variety of different was investigated in a sample of patients suffering from schizophrenia (n=6), in a another sample wit a solid tumor (n=10) and in a sample of patients with both schizophrenia and tumor (n=8). MiRNAs analysis was performed in whole blood samples using the miRCURY LNA TM microRNA Aray technology. A number of 3 microRNAs showed a statistically significant differential expression between the 3 groups. Specifically, significant down-regulation of the let-7p-5p, miR-98-5p and miR-183-5p in the study groups of tumor alone and and tumorand schizophrenia. The results of the present study that let-7, miR-98 and miR-183 might play an important oncosuppressive role through their regulatory impact in gene expression irrespective of the presence of schizophrenia. Further studies are warranted in order to investigate of these and other mico-RNAs in the molecular pathways of schizophrenia and of other major psychiatric disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".