Best Presentation - Parasitic Zoonoses - XXXI Congresso nazionale della società italiana di parassitologia
Bibliographic record
Abstract
INTRODUCTION. Early detection of pathogens in arthropod vectors is highly important in prevention of vectorborne diseases and has the potential of providing the timely indicator of pathogens circulation before reaching \nhumans. Here we propose a BG Sentinel trapping method based on the maintenance of mosquitoes alive \nthrough a sugar delivery system that avoids the need of cool chain for their preservation. This approach might \nbe particularly useful in arbovirus surveillance and can be a possible alternative to CDC traps used for WNV \nsurveillance in Italy. \nMATERIALS AND METHODS. BG-sentinel trap baited with BG-lure and CO2, modified to collect mosquitoes in a \nchamber containing a honey-based solution as a feeding source (Timmins et al., 2018 J Med Entomol. 55: 1638- \n41), was compared to CDC-CO2 trap in 10 sites of Veneto region characterized by both high vector densities and \nviral circulation of WNV and USUV. Overall, 4 captures per site have been carried out on alternate weeks from \nJuly to August 2019. BG traps worked for two consecutive days while CDC were active for one day. Mosquitoes \nwere identified and RNA was extracted from pools and screened for the presence of Flavivirus using a one-step \nSYBR Green-Based rRT-PCR and sequencing (Scaramozzino et al., 2001 J Clin Microb. 39: 1922–27). Data were \nstatistically analysed through GLM models. \nRESULTS AND CONCLUSIONS. In total, 39,313 mosquitoes were collected. The BG caught higher species \ndiversity (Shannon H, BG:0.98 CDC:0.40) with better identification rates (BG:0.02%, CDC:3.2%) but with lower \n1-day median trapped mosquitoes (BG:169, CDC:267), comparable only at low density. Median abundances of \nCulex pipiens were higher for CDC (BG:96, CDC:189) but no differences were observed at 2-days sampling. Equal \nor greater abundance of secondary vectors was observed in BGat1-day sampling (Ochlerotatus caspius BG:8.5, \nCDC:10.5; Aedes albopictus BG:7.5, CDC:1). Flavivirus prevalences were equivalent at 2-days sampling (WNV, \nBG: 0.027% CDC: 0.03%; USUV, BG: 0.054%, CDC: 0.06%). In conclusion, this modified BG shows useful \nperformance in arbovirus surveillance: i) working for consecutive days without the need of cool chain; ii) \ncollecting a higher number of species -and potentially viruses- than CDC ensuring an increase in species \nidentification.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.093 | 0.038 |
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".