Health and disease are fundamentally influenced by microbial communities and their genes (the microbiome). An in-depth analysis of microbiome structure that enables the classification of individuals based on their health can be crucial in enhancing diagnostics and treatment strategies to improve the overall well-being of an individual. In this paper, we present a novel semi-supervised methodology known as Randomized Feature Selection based Latent Dirichlet Allocation (RFSLDA) to study the impact of the gut microbiome on a subject's health status. Since the data in our study consists of fuzzy health labels, which are self-reported, traditional supervised learning approaches may not be suitable. As a first step, based on the similarity between documents in text analysis and gut-microbiome data, we employ Latent Dirichlet Allocation (LDA), a topic modeling approach which uses microbiome counts as features to group subjects into relatively homogeneous clusters, without invoking any knowledge of observed health status (labels) of subjects. We then leverage information from the observed health status of subjects to associate these clusters with the most similar health status making it a semi-supervised approach. Finally, a feature selection technique is incorporated into the model to improve the overall classification performance. The proposed method provides a semi-supervised topic modelling approach that can help handle the high dimensionality of the microbiome data in association studies. Our experiments reveal that our semi-supervised classification algorithm is effective and efficient in terms of high classification accuracy compared to popular supervised learning approaches like SVM and multinomial logistic model. The RFSLDA framework is attractive because it (i) enhances clustering accuracy by identifying key bacteria types as indicators of health status, (ii) identifies key bacteria types within each group based on estimates of the proportion of bacteria types within the groups, and (iii) computes a measure of within-group similarity to identify highly similar subjects in terms of their health status.
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http://dx.doi.org/10.1038/s41598-024-59682-4 | DOI Listing |
J Med Microbiol
January 2025
Departamento de Bioqumica e Imunologia, Instituto de Cincias Biolgicas, Universidade Federal de Minas Gerais.
Apolipoprotein E (ApoE), especially the ApoE4 isotype, is suggested to influence the severity of respiratory viral infections; however, this association is still unclear. The presence of allele ε4 impacts the development of flu-like syndromes. This study aimed to evaluate the impact of the Apo E4 isoform on the severity and duration of flu-like syndromes, including the coronavirus disease COVID-19.
View Article and Find Full Text PDFJAMA Netw Open
January 2025
University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, Ohio.
Importance: A substantial number of individuals worldwide experience long COVID, or post-COVID condition. Other postviral and autoimmune conditions have a female predominance, but whether the same is true for long COVID, especially within different subgroups, is uncertain.
Objective: To evaluate sex differences in the risk of developing long COVID among adults with SARS-CoV-2 infection.
JAMA Netw Open
January 2025
Division of Geriatrics, School of Medicine, University of California San Francisco.
Importance: The Walter Index is a widely used prognostic tool for assessing 12-month mortality risk among hospitalized older adults. Developed in the US in 2001, its accuracy in contemporary non-US contexts is unclear.
Objective: To evaluate the external validity of the Walter Index in predicting posthospitalization mortality risk in Brazilian older adult inpatients.
JAMA Netw Open
January 2025
Alzheimer Center Limburg, Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, the Netherlands.
Importance: Baseline cerebral microbleeds (CMBs) and APOE ε4 allele copy number are important risk factors for amyloid-related imaging abnormalities in patients with Alzheimer disease (AD) receiving therapies to lower amyloid-β plaque levels.
Objective: To provide prevalence estimates of any, no more than 4, or fewer than 2 CMBs in association with amyloid status, APOE ε4 copy number, and age.
Design, Setting, And Participants: This cross-sectional study used data included in the Amyloid Biomarker Study data pooling initiative (January 1, 2012, to the present [data collection is ongoing]).
JAMA Netw Open
January 2025
Translational Research Center for TBI and Stress Disorders, Veterans Affairs Boston Healthcare System, Boston, Massachusetts.
Importance: There has been a great deal of interest in mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) and their association with one another, yet their interaction and subsequent associations with long-term outcomes remain poorly understood.
Objective: To compare the long-term outcomes of mTBI that occurred in the context of psychological trauma (peritraumatic context) with mTBI that did not (nonperitraumatic context).
Design, Setting, And Participants: This cohort study of post-9/11 US veterans used data from the Translational Research Center for Traumatic Brain Injury and Stress Disorders (TRACTS) study at the Veterans Affairs Boston Healthcare System, which began in 2009; the current study utilized data from baseline TRACTS visits conducted between 2009 and 2024.
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