Toll-like Receptor 1 Polymorphisms Affect Innate Immune Responses and Outcomes in Sepsis
Bibliographic record
Abstract
RATIONALE: Polymorphisms affecting Toll-like receptor (TLR)-mediated responses could predispose to excessive inflammation during an infection and contribute to an increased risk for poor outcomes in patients with sepsis. OBJECTIVES: To identify hypermorphic polymorphisms causing elevated TLR-mediated innate immune cytokine and chemokine responses and to test whether these polymorphisms are associated with increased susceptibility to death, organ dysfunction, and infections in patients with sepsis. METHODS: We screened single-nucleotide polymorphisms (SNPs) in 43 TLR-related genes to identify variants affecting TLR-mediated inflammatory responses in blood from healthy volunteers ex vivo. The SNP associated most strongly with hypermorphic responses was tested for associations with death, organ dysfunction, and type of infection in two studies: a nested case-control study in a cohort of intensive care unit patients with sepsis, and a case-control study using patients with sepsis, patients with sepsis-related acute lung injury, and healthy control subjects. MEASUREMENTS AND MAIN RESULTS: The SNP demonstrating the most hypermorphic effect was the G allele of TLR1(-7202A/G) (rs5743551), which associated with elevated TLR1-mediated cytokine production (P < 2 x 10(-20)). TLR1(-7202G) marked a coding SNP that causes higher TLR1-induced NF-kappaB activation and higher cell surface TLR1 expression. In the cohort of patients with sepsis TLR1(-7202G) predicted worse organ dysfunction and death (odds ratio, 1.82; 95% confidence interval, 1.07-3.09). In the case-control study TLR1(-7202G) was associated with sepsis-related acute lung injury (odds ratio, 3.40; 95% confidence interval, 1.59-7.27). TLR1(-7202G) also associated with a higher prevalence of gram-positive cultures in both clinical studies. CONCLUSIONS: Hypermorphic genetic variation in TLR1 is associated with increased susceptibility to organ dysfunction, death, and gram-positive infection in sepsis.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".