Molecular Detection and Genotyping of Dengue Viruses: Current Techniques and Future Prospect
Bibliographic record
Abstract
The Dengue virus is a prevalent mosquito-borne disease, and its global incidence has been steadily increasing due to the favorable environmental conditions that promote mosquito breeding, primarily influenced by rising temperatures. Bangladesh has been particularly hard-hit by an intense and ongoing outbreak, resulting in a surge of cases and fatalities. Effective management of this disease necessitates the implementation of robust public health measures, including rigorous surveillance and early diagnosis. While serological tests are commonly employed in clinical diagnosis, molecular methods hold a critical role in identifying the specific Dengue virus strain, thereby contributing to a more comprehensive understanding of disease severity. This article serves as an extensive review, delving into various molecular testing techniques employed for both surveillance and clinical diagnosis. It offers valuable insights for research and clinical laboratories engaged in the detection of Dengue virus RNA in mosquitoes, environmental samples, and clinical specimens. The methods covered encompass a spectrum of approaches, including conventional PCR, isothermal amplification, real-time RT-PCR, Sanger sequencing, and whole-genome sequencing, providing a holistic overview of the available techniques. These methods play pivotal roles in clinical diagnosis, outbreak analysis, vector surveillance, and vaccine development. Furthermore, the article underscores the importance of integrating these techniques into existing healthcare systems, emphasizing their significance in ensuring precise dengue diagnostics to enhance the efficiency of disease management. These molecular methods are indispensable tools that contribute to accurate diagnosis, enable effective outbreak investigation, facilitate vector surveillance, and support vaccine preparation, thereby enhancing the overall management and control of dengue, ultimately working toward mitigating its impact on public health. Bangladesh J Microbiol, Volume 40, Number 1, June 2023, pp 41-49
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".