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The majority of genomic sequencing and microarray results are clinically uninformative, meaning that they do not suggest a need for any behavioral action or medical intervention. Prior studies have shown that recipients of uninformative genomic testing results ("uninformative results" hereafter) may incorrectly interpret them to imply a lowered risk of disease or false reassurance about future health risks. Few studies have examined how patients understand uninformative results when they are returned in a research setting, where there is wide variation in analytical specifications of testing, interpretation and reporting practices, and resources to support the return of results. We conducted cross-sectional interviews (N = 17) to explore how a subset of research participants in one genomics study at the National Institutes of Health reacted to and understood their uninformative test results, which were returned to them via a patient portal without genetic counseling. We found that most participants did not remember the details of the informed consent process, including the distinction between "primary" and "secondary" findings. Participants had questions about what genes were tested for and, in most cases, requested a list of the genes covered. Several participants incorrectly assumed that autosomal recessive carrier results would have been reported to them if detected. Some participants interpreted their uninformative results to mean that they could forgo prenatal testing, and participants had mixed expectations about whether their results might be reinterpreted in the future. These themes suggest that there are specific challenges to returning uninformative results in research settings. Educational supplements to uninformative test reports may be most useful if they contextualize results in relation to other types of clinical genetic or genomic testing that may be made available to research participants in their lifetimes.
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http://dx.doi.org/10.1002/jgc4.1772 | DOI Listing |
Acta Psychiatr Scand
September 2025
Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
Introduction: Machine learning studies sometimes include a high number of predictors relative to the number of training cases. This increases the risk of overfitting and poor generalizability. A recent study hypothesized that between-trial heterogeneity precluded generalizable outcome prediction in schizophrenia from being achieved.
View Article and Find Full Text PDFJ Am Chem Soc
September 2025
School of Chemistry and RNA Institute, University of New South Wales, 2052 Sydney, Australia.
The diversity of noncovalent interactions makes the design space of multicomponent molecular systems highly complex. To efficiently explore supramolecular design space, data-driven strategies are needed. Here, we demonstrate a methodological framework for the targeted design of multicomponent molecular systems with noncovalent interactions using Bayesian optimization.
View Article and Find Full Text PDFSci Rep
September 2025
Department of Anthropology, University of Delhi, Delhi, 110007, India.
Substance use is a major public health concern, particularly among college students. Adverse Childhood Experiences (ACEs) have been shown to increase the risk of substance use in adulthood. Therefore, the present study aims to understand the impact of cumulative and domain-specific ACEs on alcohol and tobacco use, and associated addiction risks among college-going students in the Delhi-NCR, India.
View Article and Find Full Text PDFR Soc Open Sci
July 2025
Big Data Institute, Nuffield Department of Population Health, Oxford, UK.
The global burden of multimorbidity is increasing yet poorly understood, owing to insufficient methods for modelling complex systems of conditions. In particular, hepatosplenic multimorbidity has been inadequately investigated. From 17 January to 16 February 2023, we examined 3186 individuals aged 5-92 years from 52 villages across Uganda within the SchistoTrack Cohort.
View Article and Find Full Text PDFBioengineering (Basel)
August 2025
Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong SAR 999077, China.
Modeling drug-induced transcriptional responses at the single-cell level is essential for advancing human healthcare, particularly in understanding disease mechanisms, assessing therapeutic efficacy, and anticipating adverse effects. However, existing approaches often impose a rigid constraint by enforcing pointwise alignment of latent representations to a standard normal prior, which limits expressiveness and results in biologically uninformative embeddings, especially in complex biological systems. Additionally, many methods inadequately address the challenges of unpaired data, typically relying on naive averaging strategies that ignore cell-type specificity and intercellular heterogeneity.
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