Combining redox-proteomics and epigenomics to explain the involvement of oxidative stress in psychiatric disorders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it