Bibliographic record
Abstract
Schizophrenia is a severe psychiatric disorder primarily involving symptoms of psychosis. Antipsychotic administration is primarily relied upon by clinicians to treat and manage symptoms of schizophrenia. Pharmacogenetic studies have already uncovered DNA sequence variants that influence antipsychotic treatment response. However, several lines of evidence also suggest that antipsychotics alter epigenetic modifications such as DNA methylation. Our objectives was to investigate the effect of antipsychotic dosage on genome-wide methylation and explore differences in genome-wide methylation between various antipsychotics. From a well-characterized sample of 137 schizophrenia patients prescribed antipsychotics we measured DNA methylation in leukocytes. Results showed that there is no genome-wide significant association between DNA methylation in leukocytes and antipsychotic dosage, although paired sample analyses between antipsychotic cohorts and non-psychiatric controls revealed differentially methylated positions and regions. The present study encourages further research on antipsychotic induced methylation patterns and its potential clinical translatability to predict antipsychotic treatment response and/or side-effects.
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) | 0.004 | 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".