Translating knowledge of genetics and pharmacology into improving everyday practice
Bibliographic record
Abstract
Bipolar disorder is associated with significant morbidity and mortality; however, many aspects of this disorder remain poorly understood. It is likely that rapid advances in molecular genetics and neuroimaging will play a major role in advancing our understanding of bipolar disorder in future. Molecular genetics studies have already identified some candidate genes; for example, the BDNF, G72 and XBP1 genes, and chromosomal 'hot spots', which may confer a predisposition to development of bipolar disorder. Such advances may facilitate earlier, easier and more accurate diagnosis and provide novel targets for the treatment of this condition. Brain imaging studies using positron emission tomography and single photon emission computed tomography have shown that reduction in brain 5-hydroxytryptamine type 2 (5-HT(2)) receptors may be associated with prevention of or relief from depressive symptoms. Similarly, other imaging studies suggest that increased dopamine levels in the synapse mediate the symptoms of bipolar mania and that reduction in dopamine transmission through reduction in dopamine synthesis or blockade of dopamine D(2) receptors may be associated with antimanic effects. The ability of atypical antipsychotics to block both 5-HT(2) and D(2) receptors and downregulate 5-HT(2) receptors may explain how these drugs treat both the depressive and manic symptoms of bipolar disorder. Although molecular genetics and imaging techniques are not yet used as clinical tools for bipolar disorder, they provide valuable data to improve the understanding of the pathophysiology of bipolar disorder and should lead to new treatments and potentially episode prevention.
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 | 0.017 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".