Association between Polymorphisms of Glutathione S-Transferase and Progression to Cervical Cancer in Women from Burkina Faso and Mali
Bibliographic record
Abstract
Although persistence of high-risk human papillomavirus infection is the main risk factor, Glutathione S-Transferase highly polymorphic enzyme involved in the metabolism of xenobiotics, is a good candidate gene. The objective of this study was to compare the polymorphisms of Glutathione S-Transferase M1-null in women with cancerous lesions and without lesions. This study consisted of 322 uterine cervix samples of women from Mali and Burkina Faso with Cervical Intra-epithelial Neoplasia 2 and 3, adenocarcinoma and squamous cell carcinoma and 100 women with no lesions. Human Papillomavirus genotyping was performed by Real-time multiplex Polymerase Chain Reaction. Glutathione S-Transferase gene polymorphisms were determined using conventional Polymerase Chain Reaction followed by migration on agarose gel. A statistically significant association with high relative risks of 10.77 for the development of High grade Superficial or Squamous Intra-epithelial Lesion (95% CI = 5.59 - 20.72; p < 0.001), and 13.20 for cancer development (95% CI = 6.79 - 25.63; p < 0.001) was found in women with the null genotype of Glutathione S-Transferase M1 in the study population. In Burkina Faso and Mali, Glutathione S-Transferase M1-null presented relative risks of 9 and 11.05 for high-grade lesions, 15 and 11.40 for cancer. Similarly, significant results had been observed in women with human papillomavirus positive and human papillomavirus negative. The results of the present study support the idea that the deletion of Glutathione S-Transferase M1 plays a crucial role in the progression of high-grade lesions and cervical cancer.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".