NEUTRALIZATION OF TUMOR NECROSIS FACTOR IN PRECLINICAL MODELS OF SEPSIS
Bibliographic record
Abstract
Tumor necrosis factor (TNFalpha), a cardinal early mediator of the innate host inflammatory response, has been an attractive target for therapeutic intervention in human sepsis. However, pooled data from 12 completed randomized controlled trials show only a very modest impact on mortality in a highly heterogeneous population of patients. To gain insight into the preclinical in vivo biology of TNFalpha that might aid in better identifying appropriate patient populations for therapeutic intervention, we undertook a systematic review of published reports of preclinical studies assessing the consequences of neutralization of TNFalpha in models of acute infection or inflammation. We identified 143 reports incorporating 484 unique experimental comparisons in seven different animal species. The effects of neutralization of TNFalpha in these were quite variable. Neutralization of TNFalpha was beneficial in endotoxemia, or after systemic challenge with gram-negative organisms, Staphylococcus aureus, or Group B streptococci. On the other hand, neutralization was detrimental in infections caused by Streptococcus pneumoniae, Candida spp., or intracellular pathogens such as Listeria and Mycobacterium tuberculosis, and in models of pneumonia. Treatment was more efficacious when delivered before infectious challenge, and the therapeutic signal increased as the baseline mortality in the placebo group increased. Evidence of neutralization of TNFalpha bioactivity, and of attenuation of inflammation, was typically accompanied by evidence of impairment of antimicrobial defenses. Multiple specific and nonspecific therapeutic strategies were identified. We conclude that the beneficial effects of TNF in systemic inflammation occur at the cost of impaired antimicrobial defenses, and that a better understanding of the consequences of neutralization of TNFalpha in vivo could aid in better defining optimal patient populations for therapeutic intervention.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".