Relevant Allelic Frequency of Gene Polymorphism and Genetic Predisposition of Human Papillomavirus in Patients with Cervical Cancer
Bibliographic record
Abstract
Cervical Cancer Human Papillomavirus Amplification-refractory mutation system p53 gene Single Nucleotide Polymorphisms Background and Objectives: The present study investigated the correlation between p53 gene codon 72 polymorphism and 6 other genetic single nucleotide polymorphisms (SNPs) in patients with cervical cancer infected by HPV.Methods: 450 patients with cervical cancer (280 Squamous cell carcinoma and 170 Adenocarcinoma) were followed at hospitals in Iran from Dec. 2014 to Apr. 2015.Moreover, 100 age/sex-matched was used as the control group.HPV was detected by LINEAR ARRAY® HPV Genotyping Test.Allelic frequency of 6 gene polymorphisms was detected by the amplification-refractory mutation system (ARMS).Results: From 450 patients, 408 cases (90.66%) were positive for HPV.Four genotypes were observed as single infections (16, 18, 31, and 45).The most common genotypes were HPV-16 (73.52%),HPV-18 (23.28%),HPV-31 and 45 (3.17%), respectively.306 samples were arginine-arginine homozygous (70.6% and 71.4% of adenocarcinoma and squamous cell carcinoma, respectively), 70 cases were arginine-proline heterozygous (17.6% of adenocarcinoma and 23.8% of squamous cell carcinoma), and 20 cases were as proline-proline homozygous (11.8% and 4.8% of adenocarcinoma and squamous cell carcinoma, respectively). Conclusion:The prevalence of HPV was 84% and that was the estimation of the Global Burden among Iranian patients with cervical cancer (85% -99%).There was no correlation between mutations in the p53 allele and the size/type of tumors, while we found a correlation between mutations in p53 alleles and age.Therefore, XRCC1 G399A SNP and TP53 G72C SNP were significantly correlated with the 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".