Current prevention and potential treatment options for dengue infection
Bibliographic record
Abstract
Currently, treatments for dengue infection are only symptomatic as no antiviral agents nor vaccines are available to combat this virus. Despite challenges faced by researchers, many efforts are ongoing to reduce cases of dengue infection either by targeting the vector or the virus. Vector population is monitored and reduced by using mechanical, chemical and biological controls. Chemical control is achieved either by using synthetic or natural insecticides where the latter is more preferable. In biological control, bacteria, fungi and larvivorous fish are utilised to reduce the vector population. Moreover, genes of mosquitoes are also explored to produce progenies which are sterile with low survival ability. Vaccines are among the most effective ways to prevent viral infection. Various approaches have been used and are still being explored towards producing vaccines for dengue. These include live attenuated, inactivated, recombinant subunit, nucleic acid and virus-like particles vaccines. The aim is to produce a vaccine which can target all the four serotypes of the virus. Monoclonal antibodies are widely researched on to equip the host defense mechanism against the dengue virus. Deeper understanding of the virus replication cycle warrants the development of antiviral agents which target viral proteins vital for the replication process. Bioactive compounds are also utilised in the development of antiviral agents. The importance of surveillance and supportive therapy are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".