Assessing risk of vector transmission of Chagas disease through blood source analysis using LC-MS/MS for hemoglobin sequence identification
Bibliographic record
Abstract
Chagas disease is mainly transmitted by triatomine insect vectors that feed on vertebrate blood. The disease has complex domiciliary infestation patterns and parasite transmission dynamics, influenced by biological, ecological, and socioeconomic factors. In this context, feeding patterns have been used to understand vector movement and transmission risk. Recently, a new technique using Liquid chromatography tandem mass spectrometry (LC-MS/MS) targeting hemoglobin peptides has showed excellent results for understanding triatomines' feeding patterns. The aim of this study was to further develop the automated computational analysis pipeline for peptide sequence taxonomic identification, enhancing the ability to analyze large datasets data. We then used the enhanced pipeline to evaluate the feeding patterns of Triatoma dimidiata, along with domiciliary infestation risk variables, such as unkempt piles of firewood or construction material, cracks in bajareque and adobe walls and intradomiciliary animals. Our new python scripts were able to detect blood meal sources in 100% of the bugs analyzed and identified nine different species of blood meal sources. Human, chicken, and dog were the main blood sources found in 78.7%, 50.4% and 44.8% of the bugs, respectively. In addition, 14% of the bugs feeding on chicken and 15% of those feeding on dogs were captured in houses with no evidence of those animals being present. This suggests a high mobility among ecotopes and houses. Two of the three main blood sources, dog and chicken, were significantly (p < 0.05) affected by domiciliary infestation risk variables, including cracks in walls, construction material and birds sleeping in the intradomicile. This suggests that these variables are important for maintaining reproducing Triatoma dimidiata populations and that it is critical to mitigate these variables in all the houses of a village for effective control of these mobile vectors.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".