Functional polymorphisms of macrophage migration inhibitory factor as predictors of morbidity and mortality of pneumococcal meningitis
Bibliographic record
Abstract
Pneumococcal meningitis is the most frequent and critical type of bacterial meningitis. Because cytokines play an important role in the pathogenesis of bacterial meningitis, we examined whether functional polymorphisms of the proinflammatory cytokine macrophage migration inhibitory factor (MIF) were associated with morbidity and mortality of pneumococcal meningitis. Two functional MIF promoter polymorphisms, a microsatellite (-794 CATT5-8; rs5844572) and a single-nucleotide polymorphism (-173 G/C; rs755622) were genotyped in a prospective, nationwide cohort of 405 patients with pneumococcal meningitis and in 329 controls matched for age, gender, and ethnicity. Carriages of the CATT7 and -173 C high-expression MIF alleles were associated with unfavorable outcome (P= 0.005 and 0.003) and death (P= 0.03 and 0.01). In a multivariate logistic regression model, shock [odds ratio (OR) 26.0, P= 0.02] and carriage of the CATT7 allele (OR 5.12,P= 0.04) were the main predictors of mortality. MIF levels in the cerebrospinal fluid were associated with systemic complications and death (P= 0.0002). Streptococcus pneumoniae strongly up-regulated MIF production in whole blood and transcription activity of high-expression MIF promoter Luciferase reporter constructs in THP-1 monocytes. Consistent with these findings, treatment with anti-MIF immunoglogulin G (IgG) antibodies reduced bacterial loads and improved survival in a mouse model of pneumococcal pneumonia and sepsis. The present study provides strong evidence that carriage of high-expression MIF alleles is a genetic marker of morbidity and mortality of pneumococcal meningitis and also suggests a potential role for MIF as a target of immune-modulating adjunctive therapy.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".