Characterizing gut dysbiosis and the gut derived immune response to ischemic stroke
Bibliographic record
Abstract
Ischemic stroke is a devastating neurological disease caused by a reduction in blood flow to the brain that results in neuronal cell death. Subsequent neuroinflammation is mediated by central nervous system resident immune cells and peripheral immune cells. Evidence suggests that stroke-induced gut dysbiosis results in priming of pro-inflammatory immune cells in the intestine that migrate to the stroke lesion and contribute to neuroinflammation. Currently, it is unknown whether gut dysbiosis and the migration of gut-derived immune cells to the stroke lesion persist in the chronic phase following injury. In this study, we aimed to characterize; (I) stroke induced gut dysbiosis and (II) the contribution of gut-derived immune cells to neuroinflammation, over time in a rodent model of ischemic stroke. Using 16s rRNA sequencing we observed that gut dysbiosis is observed at D4 following injury in both males and female animals. Interestingly, females exhibit a return to baseline microbial composition at D20 while males continue to exhibit gut dysbiosis. In addition, we injected a lipophilic cell-tracking dye (Cm-DiI) into peyer's patches at various timepoints following injury to observe whether gut derived immune cells migrate to the brain beyond D3. Using immunohistochemical analyses, we observed Cm-DiI+/CD45+/DAPI+ cells in the stroke injured hemisphere at D4 and D9 following ischemic stroke induction. These findings suggest that stroke induced gut dysbiosis and the migration of gut-derived immune cells to the stroke lesion are phenomena that persist beyond the acute phase of injury.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".