Abstract WP478: Microglia Knockdown Reduces Inflammation and Improves Post-Stroke Cognitive Impairment in Diabetic Animals
Bibliographic record
Abstract
Unfortunately, over 40% of stroke victims have pre-existing diabetes which not only increases their risk of stroke up to 2-6 fold, but also worsens both functional recovery and the severity of cognitive impairment. Our lab has recently linked the chronic inflammation that persists in diabetic animals to their poor functional outcomes and exacerbated cognitive impairment, also known as post-stroke cognitive impairment (PSCI). Although we have shown that the development of PSCI in diabetes is associated with the upregulation and activation of pro-inflammatory microglia, we have not established a direct causation between the two. We tested the hypothesis that microglia depletion in the post-stroke recovery period prevents sustained inflammation and attenuates PSCI in diabetes. Methods: Diabetes was induced by a high fat diet (HFD) and low dose streptozotocin (STZ) combination. At 13 weeks of age, diabetic animals received bilateral intracerebroventricular (ICV) injections of short hairpin RNA (shRNA) lentiviral particles targeted at the colony stimulating factor 1 receptor (CSF1R), a key factor for microglia survival. After 14 days, animals were subjected to 60 min middle cerebral artery occlusion (MCAO) or sham surgery. Novel object recognition (NOR), and 2-trial Y-maze were utilized to evaluate cognitive function. Brains were analyzed by flow cytometry (B-D slice containing the prefrontal cortex through the hippocampus) and immunohistochemistry (B slice) 3 weeks post-MCAO. Results: CSF1R silencing resulted in a drastic 94% knockdown of residential microglia to relieve inflammation and decrease the macrophage infiltration by 74%. This also led to improved myelination of white matter in the brain and improved cognition in diabetic animals. Conclusion: Neuroinflammation, through microglial and macrophage polarization, is largely responsible for the development of PSCI in diabetes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".