Evidence map›Paper›PMID 41838738›Full record

ArticlePLoS neglected tropical diseases2026

Unveiling the ecology and spatial dynamics of Trypanosoma cruzi, its DTUs and Triatoma vitticeps in the Atlantic Forest of south-eastern Espírito Santo State, Brazil.

Raphael Testai, Felipe de Oliveira, Maria Augusta Dario, Ane Luíse Quinze Dias de Faro de Oliveira, Flávio Luis de Mello, Ana Maria Jansen, Samanta Cristina das Chagas Xavier

Abstract read
In one paragraph

Article in PLoS neglected tropical diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Raphael TestaiLaboratório de Biologia de Tripanosomatídeos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Rio de Janeiro, Brasil.
Felipe de OliveiraLaboratório de Biologia de Tripanosomatídeos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Rio de Janeiro, Brasil.
Maria Augusta DarioLaboratório de Biologia de Tripanosomatídeos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Rio de Janeiro, Brasil.ORCID https://orcid.org/0000-0002-4951-3013
Ane Luíse Quinze Dias de Faro de OliveiraLaboratório de Biologia de Tripanosomatídeos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Rio de Janeiro, Brasil.
Flávio Luis de MelloDepartamento de Engenheira Eletrônica e Computação, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Rio de Janeiro, Brasil.
Ana Maria JansenLaboratório de Biologia de Tripanosomatídeos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Rio de Janeiro, Brasil.
Samanta Cristina das Chagas XavierLaboratório de Biologia de Tripanosomatídeos, Instituto Oswaldo Cruz, Fiocruz, Rio de Janeiro, Rio de Janeiro, Brasil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The transmission dynamics of Trypanosoma cruzi in natural environments exhibit considerable variation at the micro-locality scale. However, the specific biotic and abiotic factors driving this heterogeneity remain largely unidentified. The Atlantic Forest of the state of Espírito Santo (ES) presents a unique transmission network of T. cruzi, in which Triatoma vitticeps represents the absolute majority among existing triatomines, with high infection rates and diversity of genotypes, frequently invading homes. No infection was found in peridomestic mammals. This study aimed to elucidate the spatial and environmental distribution patterns of T. vitticeps and its infection by T. cruzi DTUs throughout Espírito Santo, quantifying the influence of abiotic variables on both vector occurrence and infection dynamics. Species Distribution Modeling (SDM) of T. cruzi genotypes in T. vitticeps collected in the Atlantic Forest of Espírito Santo was performed using the ModleR package, in the R programming language, with climate and landscape variables (~1km²) selected by Spearman's correlation [-0.7 ≤ ρ ≤ 0.7]. True Skill Statistic (≥ 0.7) was used to evaluate model performance. Decision tree to classify T. vitticeps infection by T. cruzi was created using machine learning algorithms in WEKA 3.8.6 software. The SDMs of T. vitticeps and its infection demonstrated: i. Central and South mesoregions presented better environmental conditions for their occurrence; ii. association with mountainous regions with high altitudes, humid and superhumid, with vegetation density and vigor and high values of topographic diversity; iii. Schoener similarity suggests Z3 is mixed, dominated by TcIV and TcIII in Central-South, with TcIII influence Northwest and North Coast; iv. Infection was explained by wind speed, mammal richness, and temperature, with the decision tree identifying 84% of positives and 29% of negatives. T. vitticeps may originate in high-altitude regions and disperse via wind to lowlands, promoting domiciliary invasion and supporting previously hypothesized long-distance transmission of T. cruzi.

Indexed as

Chagas DiseaseInsect VectorsTriatomaTrypanosoma cruziAnimalsBrazilForestsGenotypeHumans

Identifiers

PMID41838738
PMCPMC13004508

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.