Background: Calodium hepaticum (syn. Capillaria hepatica) is a worldwide helminth parasite of which several aspects of transmission still remain unclear. In the Amazon region, the mechanism of transmission based on the ingestion of eggs present in the liver of wild mammals has been suggested as the cause of the spurious infections described. We performed an epidemiological investigation to determine the incidence, risk of spurious infection and the dynamics of transmission of C. hepaticum in a community of the Brazilian Amazon.
Methodology/principal Findings: Stool samples of 135 individuals, two dog feces and liver tissue from a peccary (captured and eaten by the residents) were analyzed by conventional microscopy. Dog feces were collected from the gardens of households presenting human cases of spurious C. hepaticum infections. Community practices and feeding habits related to the transmission of the parasite were investigated. The individual incidence of spurious infection was 6.7% (95% CI: 2.08-11.24). Cases of spurious infection were observed in 7.5% of the families and the household incidence was from 50% to 83.3%. The risk of spurious infection was 10-fold greater in persons consuming the liver of wild mammals (p = 0.02). The liver tissue of a peccary and one feces sample of a dog presented eggs of C. hepaticum. The consumption of the infected liver was the cause of the spurious infections reported in one household.
Conclusions/significance: This is the first identification of a source of spurious infection by C. hepaticum in humans and we describe a high rate of incidence in household clusters related to game liver alimentary habits. The finding of a dog feces contaminating peridomiciliary ground suggests the risk of new infections. We conclude that the mechanism of transmission based on the ingestion of liver is important for the dynamics of transmission of C. hepaticum in the studied area.
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http://dx.doi.org/10.1371/journal.pntd.0001943 | DOI Listing |
J Assoc Physicians India
December 2024
Professor, The Wellcome Trust Research Laboratory, Division of Gastrointestinal Sciences, Christian Medical College, Vellore, Tamil Nadu, India.
Background: Comprehensive reviews on the use and utility of point-of-care tests (POCs) in public health programs in relation to infectious disease and nutrition are limited. Point-of-care technologies have potential to improve the management of infectious diseases particularly in settings where healthcare infrastructure and timely access to quality medical care are limited.
Methods: We aim to describe POC tests currently used or under evaluation in the Indian national programs for communicable diseases and nutrition, and to identify the barriers and facilitators.
Cell Syst
December 2024
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02142, USA. Electronic address:
Analysis of multi-modal datasets can identify multi-scale interactions underlying biological systems but can be beset by spurious connections due to indirect impacts propagating through an unmapped biological network. For example, studies in macaques have shown that Bacillus Calmette-Guerin (BCG) vaccination by an intravenous route protects against tuberculosis, correlating with changes across various immune data modes. To eliminate spurious correlations and identify critical immune interactions in a public multi-modal dataset (systems serology, cytokines, and cytometry) of vaccinated macaques, we applied Markov fields (MFs), a data-driven approach that explains vaccine efficacy and immune correlations via multivariate network paths, without requiring large numbers of samples (i.
View Article and Find Full Text PDFJ Virol
December 2024
The Pirbright Institute, Pirbright, Woking, United Kingdom.
Understanding the origin and evolution of mutations in SARS-CoV-2 variants of concern (VOCs) is a critical area of research. B. Cao, X.
View Article and Find Full Text PDFBMJ Case Rep
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Critical Care Medicine, Northeast Georgia Medical Center and Health System, Gainesville, Georgia, USA.
Brief Bioinform
September 2024
Medical Imaging Research Center, University Hospitals Leuven, Herestraat 49, 3000 Leuven, Belgium.
Unsupervised learning, particularly clustering, plays a pivotal role in disease subtyping and patient stratification, especially with the abundance of large-scale multi-omics data. Deep learning models, such as variational autoencoders (VAEs), can enhance clustering algorithms by leveraging inter-individual heterogeneity. However, the impact of confounders-external factors unrelated to the condition, e.
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