Hypertonic Saline Resuscitation Attenuates Neutrophil Lung Sequestration and Transmigration by Diminishing Leukocyte-Endothelial Interactions in a Two-Hit Model of Hemorrhagic Shock and Infection
Bibliographic record
Abstract
BACKGROUND: Hypertonic saline (HTS) attenuates polymorphonuclear neutrophil (PMN)-mediated tissue injury after hemorrhagic shock. We hypothesized that HTS resuscitation reduces early in vivo endothelial cell (EC)-PMN interactions and late lung PMN sequestration in a two-hit model of hemorrhagic shock followed by mimicked infection. METHODS: Thirty-two mice were hemorrhaged (40 mm Hg) for 60 minutes and then given intratracheal lipopolysaccharide (10 microg) 1 hour after resuscitation with shed blood and either HTS (4 mL/kg 7.5% NaCl) or Ringer's lactate (RL) (twice shed blood volume). Eleven controls were not manipulated. Cremaster intravital microscopy quantified 5-hour EC-PMN adherence, myeloperoxidase assay assessed lung PMN content (2 1/2 and 24 hours), and lung histology determined 24-hour PMN transmigration. RESULTS: Compared with RL, HTS animals displayed 55% less 5-hour EC-PMN adherence (p = 0.01), 61% lower 24-hour lung myeloperoxidase ( p= 0.007), and 57% lower mean 24-hour lung histologic score ( p= 0.027). CONCLUSION: Compared with RL, HTS resuscitation attenuates early EC-PMN adhesion and late lung PMN accumulation in hemorrhagic shock followed by inflammation. HTS resuscitation may attenuate PMN-mediated organ damage.
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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) | 0.001 | 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 | 0.001 |
| 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".