This review outlines a new, personalized approach for the classification and management of airway diseases. The current approach to airways disease is, we believe, no longer fit for purpose. It is impractical, overgeneralizes complex and heterogeneous conditions and results in management that is imprecise and outcomes that are worse than they could be. Importantly, the assumptions we make when applying a diagnostic label have impeded new drug discovery and will continue to do so unless we change our approach. This review suggests a new mechanism-based approach where the emphasis is on identification of key causal mechanisms and targeted intervention with treatment based on possession of the relevant mechanism rather than an arbitrary label. We highlight several treatable traits and suggest how they can be identified and managed in different healthcare settings.
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http://dx.doi.org/10.1042/CS20160028 | DOI Listing |
Infect Dis (Lond)
January 2025
Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, GA, USA.
Background: Whether a detected virus or bacteria is a pathogen that may require treatment, or is merely a commensal 'passenger', remains confusing for many infections. This confusion is likely to increase with the wider use of multi-pathogen PCR.
Objectives: To propose a new statistical procedure to analyse and present data from case-control studies clarifying the probability of causality.
Sci Rep
January 2025
Department of Electrical Electronical Engineering, Yaşar University, Bornova, İzmir, Turkey.
We aimed to build a robust classifier for the MGMT methylation status of glioblastoma in multiparametric MRI. We focused on multi-habitat deep image descriptors as our basic focus. A subset of the BRATS 2021 MGMT methylation dataset containing both MGMT class labels and segmentation masks was used.
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January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
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January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
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January 2025
Faculty of Engineering, Université de Moncton, Moncton, NB, E1A3E9, Canada.
Diabetes is a growing health concern in developing countries, causing considerable mortality rates. While machine learning (ML) approaches have been widely used to improve early detection and treatment, several studies have shown low classification accuracies due to overfitting, underfitting, and data noise. This research employs parallel and sequential ensemble ML approaches paired with feature selection techniques to boost classification accuracy.
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