Quantitative T2 MRI is predictive of neurodegeneration following organophosphate exposure in a rat model
Bibliographic record
Abstract
Abstract Organophosphorus compounds, such as chemical warfare nerve agents and pesticides, are known to cause neurological damage. This study measured nerve agent-related neuropathology and determined whether quantitative T2 MRI could be used as a biomarker of neurodegeneration. Quantitative T2 MRI was performed using a 9.4 T MRI on rats prior to and following soman exposure. T2 images were taken at least 24 h prior, 1 h and 18–24 h after soman exposure. Rats were pre- and post-treated with HI-6 dimethanesulfonate and atropine methyl nitrate. A multicomponent T2 acquisition and analysis was performed. Brains were stained with Fluoro-Jade C to assess neurodegeneration. Rats exposed to soman developed behavioral expression of electrographic seizures. At 18–24 h after soman exposure, significant increases in T2, a possible marker of edema, were found in multiple regions. The largest changes were in the piriform cortex (before: 47.7 ± 1.4 ms; 18–24 h: 82.3 ± 13.4 ms). Fluoro-Jade C staining showed significant neurodegeneration 18–24 h post exposure. The piriform cortex had the strongest correlation between the change in relaxation rate and percent neurodegeneration (r = 0.96, p < 0.001). These findings indicate there is regionally specific neurodegeneration 24 h after exposure to soman. The high correlation between T2 relaxivity and histopathology supports the use of T2 as a marker of injury.
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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) | 0.000 | 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".