Diversity of KIR Genes and Their Association with Human Papillomavirus (HPV) Infection in West Africa
Bibliographic record
Abstract
Background: Cervix cancer is the leading cause of cancer death in women in many developing countries, and most commonly the result of human papillomavirus (HPV) infection. Most often, this infection does not lead to injury or cancer - there is viral clearance. We therefore proposed to study the polymorphism of certain genes such as KIR, which seems to have a strong association with HPV infection. We aimed to contribute to the fight against HPV virus infection, through better knowledge of circulating high risk-HPV (HR-HPV) genotype, and the involvement of KIR genes in the progression to cervical cancer. Methods: A total of 1419 samples, taken from the endocervix, were collected. The women came from 6 countries in West Africa (Burkina Faso, Benin, Côte d’Ivoire, Niger, Nigeria, and Togo). These samples were analyzed by multiplex real-time PCR to search for fourteen HR-HPV genotypes, and the KIR genes were characterized using the SSP-PCR method. Results: The results showed that 35.94% (510/1419) of women were infected with HR-HPV. KIR genes were significantly more common in HPV-infected subjects than in controls. The inhibitory KIR genes were, KIR 2DL3 [OR = 2.24, CI (95%) (1.48 - 3.39) p KIR 2DL4 [OR = 3.82, CI (95%) (1.77 - 8.23) p KIR 2DL5A [OR = 1.50, CI (95%) (1.05 - 2.15) p = 0.02], KIR 2DL5B [OR = 1.52, CI (95%) (1.07 - 2.17) p = 0.02], KIR 3DL2 [OR = 3.30, CI (95%) (1.29 - 8.47) p = 0.01] and the pseudogene KIR 2DP1 [OR = 1.97, CI (95%) (1.36 - 2.85) p Conclusion: This study showed that the KIR genes 3DL3, 2DL4, 2DL5, 3DL2 and 2DP1 seems to be associated with the risk of developing HPV infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".