Targeted ischemic stroke induction and mesoscopic imaging assessment of blood flow and ischemic depolarization in awake mice
Bibliographic record
Abstract
bihemispheric transcranial windows for longitudinal mesoscopic imaging of cortical function to establish a protocol for focal ischemic stroke induction in target brain regions using photothrombosis in awake head-fixed mice. Our protocol does not require any surgical steps at the time of stroke induction or anesthetics during either head fixation or photoactivation. In addition, we performed laser speckle contrast imaging and wide-field calcium imaging to reveal the effect of cortical spreading ischemic depolarization after stroke in both anesthetized and awake animals over a spatial scale encompassing both hemispheres. With our combined approach, we observed ischemic depolarizing waves (3 to [Formula: see text]) propagating across the cortex 1 to 5 min after stroke induction in genetically encoded calcium indicator mice. Measures of blood flow by laser speckle were correlated with neurological impairment and lesion volume, suggesting a metric for reducing experimental variability. The ability to follow brain dynamics immediately after stroke as well as during recovery may provide a valuable guide to develop activity-dependent therapeutic interventions to be performed shortly after stroke induction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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".