Genomics, Transcriptomics and Methylomics: Alternative Approaches for the Analysis of Serotonin System and Antipsychotic Response
Bibliographic record
Abstract
Serotonin receptors blockade is the major basis for the action of atypical antipsychotic drugs. Genetic factors affecting the density and/or function of serotonergic receptors, transporters and enzymes may therefore affect antipsychotic response. This exploratory study investigates the effect of ten polymorphisms from HTR1A, HTR1D, HTR2A, HTR3A, HTR3B, HTR4, HTR6, SLC6A4, TPH1, TPH2 genes on antipsychotic response in a sample of 289 patients with DSM-diagnosis of schizophrenia. Clinical Response was assessed using Brief Psychiatric Rating Scale (BPRS). Response was determined as 20% reduction improvement of BPRS compared to baseline. Selection of the biological relevant interactions, regardless the phenotype was performed using different statistics strategies regardless the phenotype to investigate epistasis within the serotonin system. the test for relevant interaction selection showed that 5HT4 and 5HT6 can be in epistatic relationship. the single locus analysis of these two receptor polymorphisms showed no significant results and the logistic regression model incorporating both genes, the clinical and demographic variables was not significant. Even this result is not significant, this strategy aimed to investigate the epistatic effect among genes could be useful for finding relevant biological interaction among genetic variants. Furthermore we are currently analyzing the methylation level of HTR2A in responders and non-responders, this epigenetic analysis will be very valuable in adding more information to the classic pharmacogenetic studies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".