Relationships between plasma expression levels of microRNA-146a and microRNA-132 in epileptic patients and their cognitive, mental and psychological disorders
Bibliographic record
Abstract
We aimed to explore the relationships between the plasma expression levels of microRNA (miR)-146a and miR-132 in epileptic patients and cognitive, mental and psychological disorders. Eighty epileptic patients and seventy healthy subjects as controls were evaluated with Montreal Cognitive Assessment (MoCA), Hamilton Anxiety Rating (HAMA) and Hamilton Depression Rating (HAMD) scales, and plasma samples were collected. MiR-146a and miR-132 levels were detected by real-time quantitative PCR. The total incidence rate of cognitive dysfunction, anxiety and depression in epilepsy group was 62.5%. Cognitive dysfunction was correlated positively with educational level, but negatively with disease course, duration and type of administration. The frequency and duration of seizures were positively correlated with anxiety. Depression was correlated negatively with educational level, whereas positively with course of disease and number of used drugs. Epileptic patients had significantly higher miR-146a and miR-132 levels than those of healthy controls. The miR-146a and miR-132 levels of patients with complications were significantly higher than those of cases without complications. Their expressions were correlated negatively with total MoCA scale score, but positively with type of complications. MiR-132 expression was positively correlated with the total scores of HAMA and HAMD scales. Plasma miR-146a and miR-132 expressions increased in epileptic patients, and miR-132 expression reflected the severity of epilepsy and predicted the risks of complications.
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 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 | 0.000 | 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) | 0.001 | 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".