MiR-124 Regulates IQGAP1 and Participates in the Relationship Between Morphine Dependence Susceptibility and Cognition
Bibliographic record
Abstract
Background: Long-term excessive use of morphine leads to addictive diseases and affects cognitive function. Cognitive performance is associated with genetic characteristics.MiR-124 plays a critical regulatory role in neurogenesis, synaptic development, brain plasticity, and the use of addictive substances. As a scaffold protein, IQGAP1 affects learning and memory dose-dependent. However, the role of miR-124 and its target protein as potential addiction biomarkers and the impact on cognitive function have not been fully explored. Method: A total of 40 patients with morphine dependence and 40 cases of healthy people were recruited. We collected basic and clinical information about the two groups. The Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire-9(PHQ-9), Montreal Cognition Assessment Scale (MoCA), Pittsburgh Sleep Quality Index (PSQI) were used to assess the severity of depression, anxiety, depressive symptoms, cognitive dysfunction, and sleep quality. Results: Compared to the control group, the morphine-dependent group had higher GAD-7, PHQ-9, PSQI scores, and more elevated miR-124 levels but lower MOCA scores and IQGAP1 levels. MiR-124, IQGAP1, the average intake last year were related to OASI scores.MiR-124, IQGAP1, PHQ-9 were associated with MOCA scores. In the multiple regression model, the levels of miR-124 and IQGAP1 were independent factors influencing the severity of morphine dependence. The level of miR-124 was an independent factor influencing the severity of cognitive impairment in patients with morphine dependence. In addition, the luciferase report confirmed that IQGAP1 mRNA is the direct target of miR-124. Conclusion: MiR-124 and its target protein IQGAP1 are involved in the regulation of addiction and cognitive function in patients with morphine dependence.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".