miRNAs and their role in the correlation between schizophrenia and cancer
Bibliographic record
Abstract
Schizophrenia (SZ) and cancer (Ca) have a broad spectrum of clinical phenotypes and a complex biological background, implicating a large number of genetic and epigenetic factors. SZ is a chronic neurodevelopmental disorder signified by an increase in the expression of apoptotic molecular signals, whereas Ca is conversely characterized by an increase in appropriate molecular signaling that stimulates uncontrolled cell proliferation. The rather low risk of developing Ca in patients suffering from SZ is a hypothesis that is still under debate. Recent evidence has indicated that microRNAs (miRNAs or miRs), a large group of small non‑coding oligonoucleotides, may play a significant role in the development of Ca and major psychiatric disorders, such as SZ, bipolar disorder, autism spectrum disorders, suicidality and depression, through their interference with the expression of multiple genes. For instance, the possible role of let‑7, miR‑98 and miR‑183 as biomarkers for Ca and SZ was investigated in our previous research studies. Therefore, further investigations on the expression profiles of these regulatory, small RNA molecules and the molecular pathways through which they exert their control may provide a plausible explanation as to whether there is a correlation between psychiatric disorders and low risk of developing Ca.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".