Combining redox-proteomics and epigenomics to explain the involvement of oxidative stress in psychiatric disorders
Bibliographic record
Abstract
Psychiatric disorders affect approximately 10% of adults in North-America. The complex nature of these illnesses makes the search for their pathophysiology a challenge. However, studies have consistently shown that mitochondrial dysfunction and oxidative stress are common features across major psychiatric disorders, including bipolar disorder and schizophrenia. Nevertheless, little is known about specific targets of oxidation in the brain. The search for redox sensors (protein targets for oxidation) will offer information about which pathways are regulated by oxidation in psychiatric disorders. Additionally, DNA is also a target for oxidative damage and recently, studies have suggested that oxidation of cytosine and guanosine can serve as an epigenetic modulator by decreasing or preventing further DNA methylation. Therefore, this review aims to discuss how we can use redox-proteomics and epigenomics to help explain the role of oxidative damage in major psychiatric disorders, which may ultimately lead to the identification of targets for development of new medications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".