Tumor Necrosis Factor Receptor p55-Deficient Mice Respond to Acute<i>Yersinia enterocolitica</i>Infection with Less Apoptosis and More Effective Host Resistance
Bibliographic record
Abstract
Tumor necrosis factor (TNF) has generally been regarded as a protective cytokine in host defense against bacterial infections. In the present study, we evaluated the role of TNF in the acute phase of infection by Yersinia enterocolitica by using mice rendered genetically deficient in TNF receptor p55 (TNFRp55(-/-)). Unexpectedly, TNFRp55(-/-) mice showed more effective resistance to the bacteria, reflected in enhanced bacterial clearance and less tissue damage, than did control C57BL/6 mice. C57BL/6 mice showed evidence of extensive apoptosis in the spleen accompanied by a selective decrease in the CD4(+)-T-cell population of splenocytes, whereas TNFRp55(-/-) mice were spared these changes. The splenocytes from TNFRp55(-/-) mice also maintained a robust gamma interferon IFN-gamma response to mitogenic stimulation, while the comparable response in C57BL/6 mice was impaired. In addition, splenocytes harvested from infected mice demonstrated lower production of interleukin-10 IL-10 in TNFRp55(-/-) mice than in C57BL/6 mice. These findings suggest that Yersinia can induce TNFRp55-mediated apoptosis of splenocytes in the acute phase of the infection and that alteration of T-cell-generated cytokines can dramatically alter the early events in host defense against this pathogen.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".