Targeting inflammatory monocytes in sepsis-associated encephalopathy and long-term cognitive impairment
Bibliographic record
Abstract
Sepsis-associated encephalopathy manifesting as delirium is a common problem in critical care medicine. In this study, patients that had delirium due to sepsis had significant cognitive impairments at 12-18 months after hospital discharge when compared with controls and Cambridge Neuropsychological Automated Test Battery-standardized scores in spatial recognition memory, pattern recognition memory, and delayed-matching-to-sample tests but not other cognitive functions. A mouse model of S. pneumoniae pneumonia-induced sepsis, which modeled numerous aspects of the human sepsis-associated multiorgan dysfunction, including encephalopathy, also revealed similar deficits in spatial memory but not new task learning. Both humans and mice had large increases in chemokines for myeloid cell recruitment. Intravital imaging of the brains of septic mice revealed increased neutrophil and CCR2+ inflammatory monocyte recruitment (the latter being far more robust), accompanied by subtle microglial activation. Prevention of CCR2+ inflammatory monocyte recruitment, but not neutrophil recruitment, reduced microglial activation and other signs of neuroinflammation and prevented all signs of cognitive impairment after infection. Therefore, therapeutically targeting CCR2+ inflammatory monocytes at the time of sepsis may provide a novel neuroprotective clinical intervention to prevent the development of persistent cognitive impairments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".