Zika virus infection: a review of available techniques towards early detection
Bibliographic record
Abstract
Zika virus belongs to the family Flaviviridae as do other viruses like Dengue, West Nile and Yellow Fever.They are arboviruses transmitted by the Aedes species of mosquito.Zika virus was first isolated in rhesus monkeys in Uganda in 1947.Human infections of the virus were found between the 1960s and 1980s in Africa, the Americas, Asia and the Pacific.The similarity in clinical presentation in Zika-infected patients compared with Dengue caused infections to be previously misdiagnosed as Dengue infection.The Zika virus pandemic in 2015 created a lot of concern globally because of little information about available techniques, samples as well as no available antiviral and vaccines for treatment and vaccination against infec-tion.In addition, the vectors identified for transmission, Aedes aegypti and Aedes albopictus, were of great concern due to their ability to survive both temperate and tropical climatic conditions, hence indicating the possible global spread of Zika virus infection.Almost two years after the report of infection in pregnant women in Brazil resulting in microcephalic babies, Zika virus was identified as a public health problem.Thus, a lot of research into early detection and prevention has been conducted to control the spread of the virus.This review paper highlights available information on techniques currently available for diagnosis of infection caused by Zika virus.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".