Neuroimaging assessment of memory‐related brain structures in a rat model of acute space‐like radiation
Bibliographic record
Abstract
PURPOSE: To investigate the acute effects on the central nervous system (CNS) of (56)Fe radiation, a component of high-energy charged particles (HZE) in space radiation, using quantitative magnetic resonance imaging (MRI) noninvasively. MATERIALS AND METHODS: Sprague-Dawley rats were exposed to whole-brain (56)Fe (0, 1, 2, and 4 Gy). At 1 week postirradiation, MRI scans were made using T2-weighted (T2WI), diffusion-weighted (DWI), and contrast enhanced T1-(CET1) imaging. T2 relaxation time and apparent diffusion coefficient (ADC) values were obtained from memory-related brain regions of interest (ROIs). Histopathology was correlated using ex vivo tissues. RESULTS: No overt abnormalities were visualized using T2WI and DWI at 1 week postradiation. CET1 values did not differ significantly between the irradiated and control animals. Compared to 0 Gy, there were significant prolongations in T2 values and reductions in ADC after irradiation. In the absence of evident neuronal pathology, immunohistochemistry revealed astrocytic activation in 4 Gy animals. CONCLUSION: At 1 week after whole-brain (56)Fe exposure, T2 and ADC values can differentiate radiosensitivity in regions critical for hippocampal-related memory. MRI may provide noninvasive assessment of the initial molecular/cellular disturbances in vivo after HZE irradiation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".