Lung-protective mechanical ventilation for 50 hours activates hippocampal astrocytes
Bibliographic record
Abstract
Introduction: Mechanical ventilation (MV) can cause injury to the lungs and distal organs. We hypothesize that lung-protective MV causes neural inflammation with activation of hippocampal astrocytes. Astrocytes have an essential function in neuronal connection and brain inflammation. Once activated, these cells assist in propagating the inflammatory cascade in the central nervous system. Preclinically, deficits in cognitive function have been associated with an increased number of reactive astrocytes in the CA1 hippocampal area in rodents undergoing MV. The percentage of reactive astrocytes in the hippocampus has not been quantified after 50 hours of lung-protective MV. This data could be used to the development of cognitive impairment after MV. Methods: Six human-size pigs with non-injured lungs were subjected to lung-protective MV for 50 hours (MV group). Six additional never-ventilated pigs were used as a control group (NV group). Lung-protective MV was defined as: tidal volume 8ml/kg, peak pressure <30 cmH2O and PEEP of 5 mmHg. Three hippocampal regions were analyzed; dentate gyrus (DG), CA3 and CA1. Glial Fibrillary Acid Protein (GFAP) was used to identify reactive astrocytes, and the cells were counted by IMAGEJ. Results: The percentages of reactive astrocytes in the DG, CA3 and CA1 combined were significantly higher in the MV group than NV group (26% (130,571/502,195) vs. 11% (44,686/406,235)) (p=0.002). MV group showed higher percentages of reactive astrocytes in each hippocampal region; DG 25% (MV) vs. 10% (NV) (p=0.17); CA3: 22% (MV) vs. 8% (NV) (p<0.01); CA1: 30% (MV) vs. 13% (NV) (p<0.01). Conclusion: Lung-protective MV for 50 hours in pigs leads to activation of hippocampal astrocytes.
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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) | 0.003 | 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".