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Genetic surveillance of the parasite shows great promise for helping National Malaria Control Programs (NMCPs) assess parasite transmission. Genetic metrics such as the frequency of polygenomic (multiple strain) infections, genetic clones, and the complexity of infection (COI, number of strains per infection) are correlated with transmission intensity. However, despite these correlations, it is unclear whether genetic metrics alone are sufficient to estimate clinical incidence. Here, we examined parasites from 3,147 clinical infections sampled between the years 2012-2020 through passive case detection (PCD) across 16 clinic sites spread throughout Senegal. Samples were genotyped with a 24 single nucleotide polymorphism (SNP) molecular barcode that detects parasite strains, distinguishes polygenomic (multiple strain) from monogenomic (single strain) infections, and identifies clonal infections. To determine whether genetic signals can predict incidence, we constructed a series of Poisson generalized linear mixed-effects models to predict the incidence level at each clinical site from a set of genetic metrics designed to measure parasite clonality, superinfection, and co-transmission rates. We compared the model-predicted incidence with the reported standard incidence data determined by the NMCP for each clinic and found that parasite genetic metrics generally correlated with reported incidence, with departures from expected values at very low annual incidence (<10/1000/annual [‰]). When transmission is greater than 10 cases per 1000 annual parasite incidence (annual incidence >10 ‰), parasite genetics can be used to accurately infer incidence and is consistent with superinfection-based hypotheses of malaria transmission. When transmission was <10 ‰, we found that many of the correlations between parasite genetics and incidence were reversed, which we hypothesize reflects the disproportionate impact of importation and focal transmission on parasite genetics when local transmission levels are low.
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http://dx.doi.org/10.21203/rs.3.rs-3516287/v1 | DOI Listing |
Front Nucl Med
August 2025
School of Health Sciences and Social Work, Griffith University, Brisbane/Gold Coast, QLD, Australia.
Background: Animal models of nerve compression have revealed neuroinflammation not only at the entrapment site, but also remotely at the spinal cord. However, there is limited information on the presence of neuroinflammation in human compression neuropathies. The objectives of this study were to: (1) assess which tracer kinetic model most optimally quantified [C]DPA713 uptake in the spinal cord and neuroforamina in patients with painful cervical radiculopathy, (2) evaluate the performance of linearized methods (e.
View Article and Find Full Text PDFNAR Genom Bioinform
September 2025
Centre for Integrative Biology and Systems Medicine (IBSE), Wadhwani School of Data Science and AI, Indian Institute of Technology (IIT) Madras, Chennai 600036, India.
Genome graphs provide a powerful reference structure for representing genetic diversity. Their structure emphasizes the polymorphic regions in a collection of genomes, enabling network-based comparisons of population-level variation. However, current tools are limited in their ability to quantify and compare structural features across large genome graphs.
View Article and Find Full Text PDFNAR Genom Bioinform
September 2025
BGI Research, Shenzhen 518083, China.
Next-generation sequencing has greatly advanced genomics, enabling large-scale studies of population genetics and complex traits. Genomic DNA (gDNA) from white blood cells has traditionally been the main data source, but cell-free DNA (cfDNA), found in bodily fluids as fragmented DNA, is increasingly recognized as a valuable biomarker in clinical and genetic studies. However, a direct comparison between cfDNA and gDNA has not been fully explored.
View Article and Find Full Text PDFNat Ment Health
August 2025
Department of Psychiatry, Washington University School of Medicine.
Psychotic-like experiences (PLEs) may arise from genetic and environmental risk leading to worsening cognitive and morphometry metrics over time, which in turn lead to worsening PLEs. Analyses used three waves of unique longitudinal Adolescent Brain Cognitive Development Study data (ages 9-13) to test whether changes in cognition and global morphometry metrics attenuate associations between genetic and environmental risk with persistent distressing PLEs. Multigroup univariate latent growth models examined three waves of cognitive metrics and global morphometry separately for three PLE groups: persistent distressing PLEs (n=356), transient distressing PLEs (n=408), and low-level PLEs (n=7901).
View Article and Find Full Text PDFBioinform Biol Insights
September 2025
School of Computer Science and Mathematics, Kingston University, London, UK.
Interpreting the effects of variants within the human genome and proteome is essential for analysing disease risk, predicting medication response, and developing personalised health interventions. Due to the intrinsic similarities between the structure of natural languages and genetic sequences, natural language processing techniques have demonstrated great applicability in computational variant effect prediction. In particular, the advent of the Transformer has led to significant advancements in the field.
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