Geographical distribution and oncogenic risk association of human papillomavirus type 58 E6 and E7 sequence variations
Bibliographic record
Abstract
Human papillomavirus (HPV) 58 accounts for a notable proportion of cervical cancers in East Asia and parts of Latin America, but it is uncommon elsewhere. The reason for such ethnogeographical predilection is unknown. In our study, nucleotide sequences of E6 and E7 genes of 401 HPV58 isolates collected from 15 countries/cities across four continents were examined. Phylogenetic relationship, geographical distribution and risk association of nucleotide sequence variations were analyzed. We found that the E6 genes of HPV58 variants were more conserved than E7. Thus, E6 is a more appropriate target for type-specific detection, whereas E7 is more appropriate for strain differentiation. The frequency of sequence variation varied geographically. Africa had significantly more isolates with E6-367A (D86E) but significantly less isolates with E6-203G, -245G, -367C (prototype-like) than other regions (p ≤ 0.003). E7-632T, -760A (T20I, G63S) was more frequently found in Asia, and E7-793G (T74A) was more frequent in Africa (p < 0.001). Variants with T20I and G63S substitutions at E7 conferred a significantly higher risk for cervical intraepithelial neoplasia grade III and invasive cervical cancer compared to other HPV58 variants (odds ratio = 4.44, p = 0.007). In conclusion, T20I and/or G63S substitution(s) at E7 of HPV58 is/are associated with a higher risk for cervical neoplasia. These substitutions are more commonly found in Asia and the Americas, which may account for the higher disease attribution of HPV58 in these areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".