Increased TLR4 Expression Aggravates Sepsis by Promoting IFN-<i>γ</i>Expression in CD38<sup>−/−</sup>Mice
Bibliographic record
Abstract
Gram-negative bacterial sepsis accounts for up to 50% worldwide sepsis that causes hospital mortality. Acute kidney injury (AKI), a common complication of Gram-negative bacterial sepsis, is caused by Toll-like receptor 4 (TLR4) activation. Lipopolysaccharide (LPS) is an endotoxin in Gram-negative bacteria and is recognized specifically by TLR4, which initiates innate immune response. Also, TLR4 signaling pathway activation is essential in response to LPS infection. CD38 is one of the well-known regulators of innate immunity, whose dysregulation contributes to sepsis. Many studies have proven that an attenuated Gram-positive bacterium induces sepsis in a CD38-blocking model. However, the pathogenesis of Gram-negative bacteria-induced sepsis in a CD38−/−mouse model remains unclear. The aim of this study is to investigate whether kidney injury is still attenuated in a LPS-induced CD38−/−sepsis model and identify the potential mechanism. We assess the severity of kidney injury related to proinflammatory cytokine expressions (IFN-γ, TNF-α, IL-1β, and IL-6) in WT and CD38−/−mice. Our results showed more aggravated kidney damage in CD38−/−mice than in WT mice, accompanied with an increase of proinflammatory cytokine expression. In addition, compared with CD38−/−TLR4mutmice, we found an increase of TLR4 expression and mRNA expression of these cytokines in the kidney of CD38−/−mice, although only increased IFN-γlevel was detected in the serum. Taken together, these results demonstrated that an increased TLR4 expression in CD38−/−mice could contribute to the aggravation of AKI through boosting of the production of IFN-γ.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".