MR Imaging Depicts Oxidative Stress Induced by Methemoglobin
Bibliographic record
Abstract
PURPOSE: To correlate the effect of red blood cell hemoglobin on signal generation during magnetic resonance (MR) imaging and local oxidation of low-density lipoprotein (LDL). MATERIALS AND METHODS: Informed consent was obtained from all volunteers participating in this study, which was approved by the research ethics board. T1 relaxometry of blood samples from six volunteers was performed. Lipid peroxidation was assayed by using thiobarbituric acid reactive species (TBARS) and fluorescence quenching of cis-parinaric acid. Two-tailed Student t tests were used to detect differences between means. A Pearson correlation coefficient was calculated to determine the linearity of the data. RESULTS: Lipid oxidation was significantly enhanced after addition of blood, according to results of the TBARS assay; greater oxidation occurred with ferric than with ferrous blood. The cis-parinaric acid assay demonstrated increased oxidative stress caused by extracellular as compared with intracellular ferric hemoglobin. MR imaging measures showed a T1 relaxivity that was 10 times higher for ferric than for ferrous forms of hemoglobin. CONCLUSION: Extracellular ferric hemoglobin is significantly more pro-oxidant and has higher T1 relaxivity than its ferrous counterparts. These results support the hypothesis that ferric methemoglobin-generated T1 high signal intensity reflects a pro-oxidant environment that, in the setting of vessel wall disease, might be proatherogenic.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".