NLRX1 is not involved in the host defense against Escherichia coli induced pyelonephritis
Bibliographic record
Abstract
Background: Urinary tract infections (UTIs) caused by uropathogenic Escherichia coli (E. coli) are one of the most prominent infections that have serious impact on kidney functioning and the development of chronic kidney disease. NOD-like receptor (NLR)X1 is an innate immune receptor that is important for immune metabolism and regulation, with as yet an unknown role in UTI and the pathophysiology of pyelonephritis. Methods: Wild-type (WT) and NLRX1 Knock-out (KO) female mice were subjected to UTI by intravesically inoculation of uropathogenic E. coli and sacrificed at 24h and 48h after infection after which bacterial burden and the inflammatory response in the bladder and kidney were studied. Ex vivo we studied the role of NLRX1 during the LPS induced pro-inflammatory cytokine response and phagocytosis of E. coli by granulocytes and monocytes. Results: Here, we report that during early experimental UTI NLRX1 absence reduces bacterial clearance in the bladder and dampens the inflammatory cytokine response, whereas in the kidney NLRX1 does not affect bacterial burden or cytokine response. In addition, we found that NLRX1 is not essential for the pro-inflammatory cytokine secretion by granulocytes and monocytes in response to LPS nor for bacterial phagocytosis . Conclusion: Together, we report that NLRX1 is important in enhancing the early host defense against uropathogenic E. coli in the bladder but does not affect the development of pyelonephritis.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".