Imaging oxidative stress in brains of chronic methamphetamine users: A combined 1H-magnetic resonance spectroscopy and peripheral blood biomarker study
Bibliographic record
Abstract
Introduction Preclinical data suggest methamphetamine (MA), a widely used stimulant drug, can harm the brain by causing oxidative stress and inflammation, but only limited information is available in humans. We tested the hypothesis that levels of glutathione (GSH), a major antioxidant, would be lower in the brains of chronic human MA preferring polysubstance users. We also explored if concentrations of peripheral immunoinflammatory blood biomarkers were related with brain GSH concentrations. Methods 20 healthy controls (HC) (33 years; 11 M) and 14 MA users (40 years; 9 M) completed a magnetic resonance spectroscopy (MRS) scan, with GSH spectra obtained by the interleaved J-difference editing MEGA-PRESS method in anterior cingulate cortex (ACC) and left dorsolateral prefrontal cortex (DLPFC). Peripheral blood samples were drawn for measurements of immunoinflammatory biomarkers. Independent samples t-tests evaluated MA vs. HC differences in GSH. Results GSH levels did not differ between HC and MA users (ACC p = 0.30; DLPFC p = 0.85). A total of 17 of 25 immunoinflammatory biomarkers were significantly elevated in MA users and matrix metalloproteinase (MMP)-2 (r = 0.577, p = 0.039), myeloperoxidase (MPO) (r = –0.556, p = 0.049), and MMP-9 (r = 0.660, p = 0.038) were correlated with brain levels of GSH. Conclusion Normal brain GSH in living brain of chronic MA users is consistent with our previous postmortem brain finding and suggests that any oxidative stress caused by MA, at the doses used by our participants, might not be sufficient to cause either a compensatory increase in, or substantial overutilization of, this antioxidant. Additionally, more research is required to understand how oxidative stress and inflammatory processes are related and potentially dysregulated in MA use.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".