IMPROVEMENT OF ENZYME IMMUNODETECTION IN THE LABORATORY DIAGNOSIS OF HEPATITIS E VIRUS
Bibliographic record
Abstract
Abstract Hepatitis E is an RNA virus causing chronic diseases with detrimental effects, such as liver cancer and cirrhosis. Various biochemical tests exist for its diagnosis, but low sensitivity and cross-reactivity led the focus toward improved serological methods: immunoglobulin-based and enzyme immunodetection. This study aims to determine the effectiveness of improved enzyme immunodetection methods in hepatitis E virus laboratory diagnosis compared with other diagnostic methods. A systematic literature review was conducted over secondary source databases: Google Scholar, Web of Science, Springer, ScienceDirect, and PubMed. The study followed a Preferred Reporting of Items for Systematic Reviews and Meta-Analysis checklist for conducting the systematic review. Abstracted and fulltext peer-reviewed articles were selected, published in English in 2015-2022. The study employed keywords and set publication dates to search for the most relevant articles. However, these studies were assessed using the risk of bias tools: the Cochrane Risk of Bias for randomized controlled design and the Newcastle-Ottawa Scale for non-randomized controlled design. Critical Appraisal Skills Programme checklist was also used to assess the quality of the studies that cannot be assessed by the Cochrane risk of bias tool and Newcastle-Ottawa Scale. After the selection, the data were synthesized using a qualitative approach to present the results. About 10 articles were identified, including randomized, cohort, qualitative, and diagnostic studies. They found the specificity of an immunoassay to achieve a significantly high specificity (98.3%) and sensitivity (89.5%) for immunoglobulin G-based hepatitis E virus detection. However, they reported that immunoglobulin G and immunoglobulin M detection in the suspected hepatitis E virus patients and exposed groups gave potential results for detecting hepatitis E virus. The method was found as efficient as can be designed as non-invasive and with low risks and challenges. The results showed that the improved enzyme immune-detective method can assist in providing a reliable, easily accessible, and error-free hepatitis E virus diagnostic method. Thus, future research must focus on exploiting these methods and strategies.
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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 | 0.026 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".