Characterization of the Molecular Mechanisms Underlying the Chronic Phase of Stroke in a Cynomolgus Monkey Model of Induced Cerebral Ischemia
Bibliographic record
Abstract
Stroke is one of the main causes of mortality and long-term disability worldwide. The pathophysiological mechanisms underlying this disease are not well understood, particularly in the chronic phase after the initial ischemic episode. In this study, a Macaca fascicularis stroke model consisting of two sample groups, as determined by MRI-quantified infarct volumes as a measure of the stroke severity 28 days after the ischemic episode, was evaluated using qualitative and quantitative proteomics analyses. By using multiple online multidimensional liquid chromatography platforms, 8790 nonredundant proteins were identified that condensed to 5223 protein groups at 1% global false discovery rate (FDR). After the application of a conservative criterion (5% local FDR), 4906 protein groups were identified from the analysis of cerebral cortex. Of the 2068 quantified proteins, differential proteomic analyses revealed that 31 and 23 were dysregulated in the elevated- and low-infarct-volume groups, respectively. Neurogenesis, synaptogenesis, and inflammation featured prominently as the cellular processes associated with these dysregulated proteins. Protein interaction network analysis revealed that the dysregulated proteins for inflammation and neurogenesis were highly connected, suggesting potential cross-talk between these processes in modulating the cytoskeletal structure and dynamics in the chronic phase poststroke. Elucidating the long-term consequences of brain tissue injuries from a cellular prospective, as well as the molecular mechanisms that are involved, would provide a basis for the development of new potentially neurorestorative therapies.
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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 | 0.002 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".