Vector competence and innate immune responses to dengue virus infection in selected laboratory and field‐collected <i>Stegomyia aegypti</i> (= <i>Aedes aegypti</i> )
Bibliographic record
Abstract
Control of dengue virus (DenV) transmission, primarily based on strategies to reduce populations of the principle vector Stegomya aegypti (= Aedes aegypti) (Diptera: Culicidae), is difficult to sustain over time. Other potential strategies aim to manipulate characteristics such as vector competence (VC), the innate capacity of the vector to transmit the virus. Previous studies have identified genetic factors, including differential expression of apoptosis-related genes, associated with the refractory and susceptible phenotypes in selected strains of S. aegypti from Cali, Colombia. The present study was designed to evaluate the variability of VC in selected strains against different DenV serotypes and to determine whether field-collected mosquitoes respond similarly to selected laboratory strains in terms of enhanced or reduced expression of apoptosis-related genes. Vector competence differed between strains, but did not differ in response to different DenV serotypes. Differences in VC were observed among mosquitoes collected from different localities in Cali. The overexpression of the pro-apoptosis genes, caspase 16 and Aedronc, was conserved in field-collected refractory mosquitoes and the selected laboratory refractory strain. The results suggest that the apoptosis response is conserved among all refractory mosquitoes to inhibit the development of all DenV serotypes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".