C-peptide attenuates acute lung inflammation in a murine model of hemorrhagic shock and resuscitation by reducing gut injury
Bibliographic record
Abstract
BACKGROUND: The study aims to evaluate whether C-peptide can reduce gut injury during hemorrhagic shock (HS) and resuscitation (R) therefore attenuate shock-induced inflammation and subsequent acute lung injury. METHODS: Twelve-week-old male mice (C57/BL6) were hemorrhaged (mean arterial blood pressure maintained at 35 mm Hg for 60 minutes) and then resuscitated with Ringer's lactate, followed by red blood cell transfusion with (HS/R) or without C-peptide (HS/R + C-peptide). Mouse gut permeability, bacterial translocation into the circulatory system and intestinal pathology, circulating HMGB1, and acute lung injury were assessed at different times after R. The mice in the control group underwent sham procedures without HS. RESULTS: Compared to the sham group, the mice in the HS/R group showed increased gut permeability (6.07 ± 3.41 μg of FD4/mL) and bacterial translocation into the circulatory system (10.05 ± 4.92, lipopolysaccharide [LPS] of pg/mL), and increased gut damage; conversely, mice in the HS/R + C-peptide group showed significantly reduced gut permeability (1.59 ± 1.39 μg of FD4/mL; p < 0.05) and bacterial translocation (4.53 ± 1.08 pg of LPS/mL; p < 0.05) with reduced intestine damage. In addition, mice in the HS/R group had increased circulating HMGB1 (21.64 ± 14.17 ng/mL), lung myeloperoxidase) activity (34.4 ± 8.91 mU/g of tissue), and pulmonary protein leakage (2.33 ± 1.16 μg Evans blue/g tissue per minute). Mice in the HS/R + C-peptide group showed decreased HMGB1 (7.27 ± 1.93 ng/mL; p < 0.05), lung myeloperoxidase (23.73 ± 8.39 mU/g of tissue; p < 0.05), and pulmonary protein leakage (1.17 ± 0.42 Evans Blue/g tissue per minute; p < 0.05). CONCLUSION: Our results indicate that C-peptide exerts beneficial effects to attenuate gut injury and dysfunction, therefore diminishing lung inflammation and subsequent injury in mice with HS and R.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".