Biomarkers for bipolar disorder: current insights
Bibliographic record
Abstract
Angela Duong,1 Bushra Syed,1 Gustavo Scola2,3 1Department of Pharmacology and Toxicology, 2Department of Psychiatry, University of Toronto, 3Centre for Addiction and Mental Health, Toronto, ON, Canada Abstract: Currently, there exists a lack of definitive diagnostic tools for neuropsychiatric disorders, particularly molecular markers that could help assess the illness and develop more personalized treatments for different disorders. Understanding of the neurobiology and potential novel treatments for bipolar disorder (BD), one of the most complex psychiatric illnesses, remains poor. This review aims to compile the most reproducible findings regarding the molecular, genetic, and structural changes that occur in BD. Neuroimaging studies have indicated alterations in neural circuits, disrupted white matter integrity, alterations in reward activation, and decreased gray matter (GM) volume. Genetic studies have identified variations in a number of genes that confer risk for BD development. Studies involving peripheral biomarkers include alterations in the levels of oxidative stress, inflammation, and neurotrophins. These potential molecular markers could be used as tools for diagnosis, to assess illness progression, and to help with the improvement of more specific and personalized treatments for patients with BD. Identification of biologically relevant markers could improve the quality of life of patients with BD and revolutionize public health. Keywords: biomarkers, neuroimaging, neural activation, gene regulation, microRNAs, oxidative stress, inflammation
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".