Infliximab alleviates the mortality, mesenteric hypoperfusion, aortic dysfunction, and multiple organ damage in septic rats
Bibliographic record
Abstract
Tumor necrosis factor-alpha (TNF-α) is a pivotal mediator that triggers inflammatory process, oxidative stress, and multiple organ injury in sepsis. We investigated the effects of infliximab on survival, mesenteric artery blood flow (MBF), vascular reactivity, and oxidative and inflammatory injuries in cecal ligation and puncture (CLP)-induced sepsis. Wistar rats were divided into Sham, CLP, Sham+infliximab, and CLP+infliximab subgroups. Twenty-four hours before the operations, rats were injected intraperitoneally with infliximab (7 mg/kg) or vehicle (saline; 1 mL/kg). Twenty hours after the operations, MBF and phenylephrine responses of isolated aortic rings were measured. Tissue damages were examined biochemically and histopathologically. Furthermore, survival rates were monitored throughout 96 h. Infliximab improved survival, mesenteric perfusion, and aortic function after CLP. Increases of serum AST, ALT, LDH, BUN, Cr, and inflammatory cytokines (tumor necrosis factor-alpha, interleukin-1 beta, and interleukin-6) induced by CLP were blocked by infliximab. Infliximab prevented malondialdehyde elevations in septic liver, lung, spleen, and kidney tissues, as well as glutathione reductions in septic liver, spleen, and kidney tissues. Protective effects of infliximab on multiple organ damage were also observed histopathologically. Infliximab showed protective effects in sepsis due to its improvement effects on mesenteric perfusion, aortic function, and its anti-inflammatory and antioxidative effects.
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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 | 0.001 | 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".