The Influence of Single Nucleotide Polymorphisms of NOD2 or CD14 on Susceptibility to Tuberculosis: A Systematic Review
Bibliographic record
Abstract
Abstract Background: Tuberculosis (TB) is still one of the leading causes of death worldwide. Genetic studies have pointed to the relevance of the NOD2 and CD14 polymorphic alleles in association with susceptibility or resistance to TB. Methods: A systematic review was performed on search platforms to examine the association between single nucleotide polymorphisms (SNP) and TB risk. Study quality was evaluated using the Newcastle-Ottawa Quality Scale (NOQS) Results: Thirteen studies matched the selection criteria. Of those, 9 investigated CD14 SNPs, and 6 reported a significant association between the T allele and TT genotypes of the rs2569190 SNP and increased TB risk. In contrast, the genotype CC was found to be protective against the disease. Furthermore, in two studies, rs2569191 SNP of the CD14, G allele was described to be significantly associated with increased TB risk. Four studies reported data uncovering the relationship between NOD2 SNPs and TB risk, with two of them reporting significant associations of rs1861759 and rs7194886 and higher TB risk in a Chinese Han population. Paradoxically, minor allele carriers (CG or GG) of rs2066842 and rs2066844 NOD2 SNPs were associated with lower TB risk in African Americans. Conclusions: The CD14 rs2569190 and rs2569191 polymorphisms influence TB risk depending on the allele. Furthermore, there is significant association between NOD2 SNPs rs1861759 and rs7194886 and augmented risk of TB, especially in persons with Chinese ethnicity. The referred polymorphisms of CD14 and NOD2 genes likely play an important role in TB susceptibility and physiopathology; such effect may be affected by ethnicity.Systematic review registration: CRD42020186523
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".