Detection and genotyping of TT virus in healthy and subjects with HBV or HCV in different populations in the United Arab Emirates
Bibliographic record
Abstract
TT virus (TTV) and TTV-like viruses (TTVLs) have been reported to be associated with non-A-E hepatitis. To determine the rate of infection and genotypic characteristics of TTV in the United Arab Emirates (UAE), a total of 449 serum samples representing different populations in the UAE and comprising healthy as well as patients positive for HBsAg and HCV were screened. National subjects (n = 200) and non-nationals residing in the UAE (n = 249) were tested by PCR. The results obtained showed that the rate of TTV infection in healthy nationals, and those with HBsAg or antibody to HCV were 34.9, 97.9, and 95.7, respectively, compared to 89.1% (115/129), 89.2% (66/74), and 84.8% (39/46), respectively, in non-nationals. Sequence analysis of the untranslated region (UTR) using 71 clones generated from the PCR products of eight serum samples from healthy individuals (four nationals and four non-nationals) showed that 83.1% of the TTV clones were classified into groups 1-4, whereas 16.9% into possibly new genotype(s). The analysis also revealed that healthy national subjects carried multiple viruses. Phylogenetic analysis of representative sequences revealed clustering of clones into at least five major groups. Also, when compared to reference genotypes (from GenBank), two of our clones belonged to two previously identified genotypes. Non-significant gender differences were seen in all ethnic groups studied (P > 0.05). In conclusion, the rate of TTV infection in the UAE nationals is significantly lower (P < 0.05) than that of the non-nationals and several genotypes were isolated with common multi-infections.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".