KIR2DL2, KIR2DL5A and KIR2DL5B genes induce susceptibility to dengue virus infection while KIR3DL3 and KIR2DS5 confers protection
Bibliographic record
Abstract
Background/objective: Dengue, an emerging and re-emerging viral disease, is a major public health problem. The aim of this study was to investigate the influence of KIRs genes polymorphism and KIRs genotypes in susceptibility of dengue virus infection and disease severity in a population from Burkina Faso through a case-control study.Methods: KIRs genes determination were performed using PCR-SSP in 50 patients infected by dengue virus (DENV) and 54 Healthy controls (HC) subjects which never having infected by DENV.Results: Data analysis showed significant association between frequencies of three KIR genes and dengue virus infection (DF): KIR2DL2 (OR: 7.32; IC: 2.87-18.65; P < 0.001); KIR2DL5A (OR: 15.00, IC: 5.68-39.59; P < 0.001) and KIR2DL5B (OR: 11.43; IC: 4.42-29; P < 0.001). While, KIR3DL3 (OR: 0.13, IC: 0.052-0.32; P < 0.001) and KIR2DS5 (OR: 0.12; IC: 0.04-0.30; P < 0.001) were associated with protection against DF. KIR2DL4 (OR: 9.75; IC95%: 1.33-70.97; p: 0.03) and KIRD3DL1 (OR: 12.00; IC95%: 1.60-90.13; p: 0.02) were associated with an increased risk in the development of secondary dengue infection (SDI).Conclusion: The results suggest a contribution of KIR2DL2, KIR2DL5A and KIR2DL5B genes in the susceptibility of DF development. While, KIR3DL3 and KIR2DS5 were found to be associated with protection against DF development by enhancing both the innate and acquired immune responses.
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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) | 0.004 | 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".