Illuminating the Effects of Stroke on the Diabetic Brain: Insights From Imaging Neural and Vascular Networks in Experimental Animal Models
Bibliographic record
Abstract
Type 1 diabetes is known to cause circulatory problems in the eyes, heart, and limbs, and the brain is no exception. Because of the insidious effects of diabetes on brain circulation, patients with diabetes are two to four times more likely to have an ischemic stroke and are less likely to regain functions that are lost. To provide a more mechanistic understanding of this clinically significant problem, imaging studies have focused on how stroke affects neural and vascular networks in experimental models of type 1 diabetes. The emerging picture is that diabetes leads to maladaptive changes in the cerebrovascular system that ultimately limit neuronal rewiring and recovery of functions after stroke. At the cellular and systems level, diabetes is associated with abnormal cerebral blood flow in surviving brain regions and greater disruption of the blood-brain barrier. The abnormal vascular responses to stroke can be partly attributed to aberrant vascular endothelial growth factor (VEGF) signaling because genetic or pharmacological inhibition of VEGF signaling can mitigate vascular dysfunction and improve stroke recovery in diabetic animals. These experimental studies offer new insights and strategies for optimizing stroke recovery in diabetic populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".