Multimodal imaging of hemorrhagic transformation biomarkers in an ischemic stroke model.
Bibliographic record
Abstract
Hemorrhagic transformation of ischemic stroke has devastating consequences, with high mortality and poor functional outcomes. Animal models of ischemic stroke also demonstrate the potential for hemorrhagic transformation, which complicates biochemical characterization, treatment studies, and hinders poststroke functional outcomes in affected subjects. The incidence of hemorrhagic transformation of ischemic stroke in animal model research is not commonly reported. The postmortem brain of such cases presents a complex milieu of biomarkers due to the presence of healthy cells, regions of varying degrees of ischemia, dead and dying cells, dysregulated metabolites, and blood components (especially reactive Fe species released from lysed erythrocytes). To improve the characterization of hemorrhage biomarkers on an ischemic stroke background, we have employed a combination of histology, X-ray fluorescence imaging (XFI), and Fourier transform infrared (FTIR) spectroscopic imaging to assess 122 photothrombotic (ischemic) stroke brains. Rapid freezing preserves brain biomarkers in situ and minimizes metabolic artifacts due to postmortem ischemia. Analysis revealed that 25% of the photothrombotic models had clear signs of hemorrhagic transformation. The XFI and FTIR metabolites provided a quantitative method to differentiate key metabolic regions in these models. Across all hemorrhage cases, it was possible to consistently differentiate otherwise healthy tissue from other metabolically distinct regions, including the ischemic infarct, the ischemic penumbra, blood vessels, sites of hemorrhage, and a region surrounding the hemorrhage core that contained elevated lipid oxidation. Chemical speciation of deposited Fe demonstrates the presence of heme-Fe and accumulation of ferritin.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".