Diabetes Augments<i>In Vivo</i>Microvascular Blood Flow Dynamics after Stroke
Bibliographic record
Abstract
Stroke usually affects people with underlying medical conditions. In particular, diabetics are significantly more likely to have a stroke and the prognosis for recovery is poor. Because diabetes is associated with degenerative changes in the vasculature of many organs, we sought to determine how hyperglycemia affects blood flow dynamics after an ischemic stroke. Longitudinal in vivo two-photon imaging was used to track microvessels before and after photothrombotic stroke in a diabetic mouse model. Chronic hyperglycemia exacerbated acute (3-7 d) ischemia-induced increases in blood flow velocity, vessel lumen diameter, and red blood cell flux in peri-infarct regions. These changes in blood flow dynamics were most evident in superficial blood vessels within 500 μm from the infarct, rather than deeper or more distant cortical regions. Long-term imaging of diabetic mice not subjected to stroke indicated that these acute stroke-related changes in vascular function could not be attributed to complications from hyperglycemia alone. Treating diabetic mice with insulin immediately after stroke resulted in less severe alterations in blood flow within the first 7 d of recovery, but had more variable results at later time points. Analysis of microvessel branching patterns revealed that stroke led to a pruning of microvessels in peri-infarct cortex, with very few instances of sprouting. These results indicate that chronic hyperglycemia significantly affects the vascular response to ischemic stroke and that insulin only partially mitigates these changes. The combination of these acute and chronic alterations in blood flow dynamics could underlie diabetes-related deficits in cortical plasticity and stroke recovery.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".