Ultrafast Cooling With Total Liquid Ventilation Mitigates Early Inflammatory Response and Offers Neuroprotection in a Porcine Model of Cardiac Arrest
Bibliographic record
Abstract
BACKGROUND: Brain injury is one of the most serious complications after cardiac arrest (CA). To prevent this phenomenon, rapid cooling with total liquid ventilation (TLV) has been proposed in small animal models of CA (rabbits and piglets). Here, we aimed to determine whether hypothermic TLV can also offer neuroprotection and mitigate cerebral inflammatory response in large animals. METHODS AND RESULTS: Anesthetized pigs were subjected to 14 minutes of ventricular fibrillation followed by cardiopulmonary resuscitation. After return of spontaneous circulation, animals were randomly subjected to normothermia (control group, n=8) or ultrafast cooling with TLV (TLV group, n=8). In the latter group, TLV was initiated within a window of 15 minutes after return of spontaneous circulation and allowed to reduce tympanic, esophageal, and bladder temperature to the 32 to 34 °C range within 30 minutes. After 45 minutes of TLV, gas ventilation was resumed, and hypothermia was maintained externally until 3 hours after CA, before rewarming using heat pads (0.5 °C-1 °C/h). After an additional period of progressive rewarming for 3 hours, animals were euthanized for brain withdrawal and histological analysis. At the end of the follow-up (ie, 6 hours after CA), histology showed reduced brain injury as witnessed by the reduced number of Fluroro-Jade C-positive cerebral degenerating neurons in TLV versus control. IL (interleukin)-1ra and IL-8 levels were also significantly reduced in the cerebrospinal fluid in TLV versus control along with cerebral infiltration by CD3+ cells. Conversely, circulating levels of cytokines were not different among groups, suggesting a discrepancy between local and systemic inflammatory levels. CONCLUSIONS: Ultrafast cooling with TLV mitigates neuroinflammation and attenuates acute brain lesions in the early phase following resuscitation in large animals subjected to CA.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".