Resistance status of Aedes mosquitoes as dengue vectors and the potential of plant larvicides from Indonesia for biological control: A narrative review
Bibliographic record
Abstract
Dengue fever remains a major public health threat in Indonesia, exacerbated by rising insecticide resistance in Aedes aegypti. Strategies relying on chemical insecticides, while initially effective, have led to widespread resistance in mosquito populations. This resistance is particularly pronounced in areas such as Java, where the extensive use of insecticides, including organophosphates and pyrethroids, has been documented. To address this challenge, one promising alternative is the utilization of biolarvicides derived from local Indonesian plant materials. Biolarvicides are environmentally friendly, safe, and have the potential to mitigate the adverse impacts associated with chemical insecticides. Numerous studies have explored the larvicidal properties of indigenous plants native to Indonesia, demonstrating their efficacy against A. aegypti. The aim of this study was to examine insecticide resistance in Aedes mosquitoes across Indonesia, highlighting geographical variations and underexplored regions, and exploring plant-based biolarvicides as sustainable alternatives. Biolarvicides derived from native Indonesian plants could be eco-friendly alternative for dengue vector management. Their integration into existing control strategies could significantly enhance efforts to control dengue while reducing the environmental and health risks posed by chemical insecticides.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".