Ultrafast Hypothermia Selectively Mitigates the Early Humoral Response After Cardiac Arrest
Bibliographic record
Abstract
Background Total liquid ventilation (TLV) has been shown to prevent neurological damage though ultrafast cooling in animal models of cardiac arrest. We investigated whether its neuroprotective effect could be explained by mitigation of early inflammatory events. Methods and Results Rabbits were submitted to 10 minutes of ventricular fibrillation. After resuscitation, they underwent normothermic follow-up (control) or ultrafast cooling by TLV and hypothermia maintenance for 3 hours (TLV). Immune response, survival, and neurological dysfunction were assessed for 3 days. TLV improved neurological recovery and reduced cerebral lesions and leukocyte infiltration as compared with control (eg, neurological dysfunction score=34±6 versus 66±6% at day 1, respectively). TLV also significantly reduced interleukin-6 blood levels during the hypothermic episode (298±303 versus 991±471 pg/mL in TLV versus control at 3 hours after resuscitation, respectively), but not after rewarming (752±563 versus 741±219 pg/mL in TLV versus control at 6 hours after resuscitation, respectively). In vitro assays confirmed the high temperature sensitivity of interleukin-6 secretion. Conversely, TLV did not modify circulating high-mobility group box 1 levels or immune cell recruitment into the peripheral circulation. The link between interleukin-6 early transcripts (<8 hours) and neurological outcome in a subpopulation of the previously described Epo-ACR-02 (High Dose of Erythropoietin Analogue After Cardiac Arrest) trial confirmed the importance of this cytokine at the early stages as compared with delayed stages (>8 hours). Conclusions The neuroprotective effect of hypothermic TLV was associated with a mitigation of humoral interleukin-6 response. A temperature-dependent attenuation of immune cell reactivity during the early phase of the post-cardiac arrest syndrome could explain the potent effect of rapid hypothermia. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT00999583.
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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".