Sero-Epidemiological Study of Hepatitis E Virus among Thalassemia as High Risk Patients: A Cross-Sectional Survey in Jahrom, Southern, Iran
Bibliographic record
Abstract
Hepatitis E virus (HEV) could be cause of viral hepatitis in the developing countries and cause severe epidemics. According to other studies, blood transfusion as a probable route of HEV infection has been suggested. An infection with hepatitis agents such as HEV causes active liver failure in multi-transfusion patients in particular thalassemia. The purpose of this study determines the seropositivity of anti-HEV antibodies in thalassemia individuals in Jahrom. In a cross-sectional study, sera from 110 thalassemia were collected between 2013 and 2014. Enzyme-linked immunosorbent assay (ELISA) method was performed to detection of anti-HEV antibodies. Individuals' data were collected such as, demographic and clinical, for statistical analysis. Our results show that 10% and 1.8% of the enrolled patients were HEV Ig-G and Ig-M positive antibodies respectively. In addition, there was statiscally significant difference in age groups for prevalence of anti-HEV Ig-G (P = 0.01). Also the serum levels of liver enzymes such as ALT and AST in the HEV Ig-G and Ig-M positive samples were significantly higher than anti-HEV negative samples. But there were no significant difference between sex and splenectomy with anti-HEV positive samples. The results indicate more study are needed to assess HEV screening of blood products to these patients that those have a probably risk of exposure to HEV especially in higher years old.
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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".