Inflammation, plasticity and real-time imaging after cerebral ischemia
Bibliographic record
Abstract
With an incidence of approximately 350 in 100,000, stroke is the third leading cause of death and a major cause of disability in industrialized countries. At present, although progress has been made in understanding the molecular pathways that lead to ischemic cell death, the current clinical treatments remain poorly effective. There is mounting evidence that inflammation plays an important role in cerebral ischemia. Experimentally and clinically, brain response to ischemic injury is associated with an acute and prolonged inflammatory process characterized by the activation of resident glial cells, production of inflammatory cytokines as well as leukocyte and monocyte infiltration in the brain, events that may contribute to ischemic brain injury and affect brain recovery and plasticity. However, whether the post-ischemic inflammatory response is deleterious or beneficial to brain recovery is presently a matter of debate and controversies. Here, we summarize the current knowledge on the molecular mechanisms underlying post-ischemic neuronal plasticity and the potential role of inflammation in regenerative processes and functional recovery after stroke. Furthermore, because of the dynamic nature of the brain inflammatory response, we highlight the importance of the development of novel experimental approaches such as real-time imaging. Finally, we discuss the novel transgenic reporter mice models that have allowed us to visualize and to analyze the processes such as neuroinflammation and neuronal repair from the ischemic brains of live animals.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".