Bibliographic record
Abstract
Mosquito-borne viral infections, such as dengue, Zika, and chikungunya, pose significant global public health challenges due to their widespread prevalence and economic burden. While vector control remains a cornerstone of prevention, its limitations, coupled with the lack of vaccines for many mosquito-borne diseases, underscore the urgent need for effective antiviral drugs to mitigate morbidity and mortality. This study examines existing and emerging antiviral strategies, with a focus on the mechanisms of virus entry, replication, and immune evasion, and explores drug development methods targeting these pathways. It evaluates broad-spectrum antiviral drugs, immune enhancers, and drugs with virus specific effects and challenges such as drug resistance, pharmacological limitations, and economic barriers. A case study on the development of dengue antiviral drugs emphasizes real-world applications and cross applicability to other mosquito borne infections. Technological advances, including artificial intelligence, multi omics integration, and structural biology, have the potential to revolutionize drug discovery and provide recommendations for future research. Efforts are ongoing to develop and distribute antiviral drugs to address the evolving threat of mosquito borne virus infections. Global cooperation initiatives and innovative policy frameworks are crucial to ensuring fair access to these advances.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".