Transvenous Diaphragm Neurostimulation Mitigates Ventilation-associated Brain Injury
Bibliographic record
Abstract
Abstract Rationale Mechanical ventilation (MV) is associated with hippocampal apoptosis and inflammation, and it is important to study strategies to mitigate them. Objectives To explore whether temporary transvenous diaphragm neurostimulation (TTDN) in association with MV mitigates hippocampal apoptosis and inflammation after 50 hours of MV. Methods Normal-lung porcine study comparing apoptotic index, inflammatory markers, and neurological-damage serum markers between never-ventilated subjects, subjects undergoing 50 hours of MV plus either TTDN every other breath or every breath, and subjects undergoing 50 hours of MV (MV group). MV settings in volume control were Vt of 8 ml/kg, and positive end-expiratory pressure of 5 cm H2O. Measurements and Main Results Apoptotic indices, microglia percentages, and reactive astrocyte percentages were greater in the MV group in comparison with the other groups (P < 0.05). Transpulmonary pressure at baseline and at study end were both lower in the group receiving TTDN every breath, but lung injury scores and systemic inflammatory markers were not different between the groups. Serum concentrations of four neurological-damage markers were lower in the group receiving TTDN every breath than in the MV group (P < 0.05). Heart rate variability declined significantly in the MV group and increased significantly in both TTDN groups over the course of the experiments. Conclusions Our study found that mechanical ventilation is associated with hippocampal apoptosis and inflammation, independent of lung injury and systemic inflammation. Also, in a porcine model, TTDN results in neuroprotection after 50 hours, and the degree of neuroprotection increases with greater exposure to TTDN.
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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".