Neuroinflammation and the immune system in hypoxic ischaemic brain injury pathophysiology after cardiac arrest
Bibliographic record
Abstract
Hypoxic ischaemic brain injury after resuscitation from cardiac arrest is associated with dismal clinical outcomes. To date, most clinical interventions have been geared towards the restoration of cerebral oxygen delivery after resuscitation; however, outcomes in clinical trials are disappointing. Therefore, alternative disease mechanism(s) are likely to be at play, of which the response of the innate immune system to sterile injured tissue in vivo after reperfusion has garnered significant interest. The innate immune system is composed of three pillars: (i) cytokines and signalling molecules; (ii) leucocyte migration and activation; and (iii) the complement cascade. In animal models of hypoxic ischaemic brain injury, pro-inflammatory cytokines are central to propagation of the response of the innate immune system to cerebral ischaemia-reperfusion. In particular, interleukin-1 beta and downstream signalling can result in direct neural injury that culminates in cell death, termed pyroptosis. Leucocyte chemotaxis and activation are central to the in vivo response to cerebral ischaemia-reperfusion. Both parenchymal microglial activation and possible infiltration of peripherally circulating monocytes might account for exacerbation of an immunopathological response in humans. Finally, activation of the complement cascade intersects with multiple aspects of the innate immune response by facilitating leucocyte activation, further cytokine release and endothelial activation. To date, large studies of immunomodulatory therapies have not been conducted; however, lessons learned from historical studies using therapeutic hypothermia in humans suggest that quelling an immunopathological response might be efficacious. Future work should delineate the precise pathways involved in vivo in humans to target specific signalling molecules.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".