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Polycythemia vera (PV) is characterized by and essential thrombocythemia (ET) by , calreticulin (), and myeloproliferative leukemia virus oncogene () mutations; we describe the occurrence and prognostic relevance of DNA sequence variants/mutations other than //. A myeloid neoplasm-relevant 27-gene panel was used for next-generation sequencing of bone marrow or whole blood DNA and conventional tools were used for analysis. "Adverse variants/mutations" were identified by age-adjusted multivariable analysis of impact on overall, leukemia-free, or myelofibrosis-free survival. Fifty-three percent of 133 Mayo Clinic patients with PV and 53% of 183 with ET harbored 1 or more sequence variants/mutations other than /; the most frequent were and . "Adverse variants/mutations" in PV included , , and and in ET , , , , , and ; combined prevalence was 15% and 15%, respectively. Adverse variants/mutations were associated with inferior survival in both PV (median, 7.7 vs 16.9 years) and ET (median, 9 vs 22 years) and the effect was independent of conventional prognostic models with respective hazard ratio (95% confidence interval) of 2.8 (1.5-5.1) and 2.6 (1.4-4.8); these observations were validated in 215 Italian patients with PV and 174 with ET. In both Mayo Clinic and Italian cohorts, leukemic or fibrotic progression was also predicted by adverse variants/mutations. Number of mutations did not provide additional prognostic information. We conclude that targeted deep sequencing in PV and ET allows for genetic risk stratification that is independent of clinically derived prognostic models.
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http://dx.doi.org/10.1182/bloodadvances.2016000216 | DOI Listing |
Comput Biol Med
September 2025
Department of Electrical and Computer Engineering and the Institute of Biomedical Engineering, University of New Brunswick, Fredericton, E3B 5A3, NB, Canada.
Pattern recognition-based myoelectric control is traditionally trained with static or ramp contractions, but this fails to capture the dynamic nature of real-world movements. This study investigated the benefits of training classifiers with continuous dynamic data, encompassing transitions between various movement classes. We employed both conventional (LDA) and deep learning (LSTM) classifiers, comparing their performance when trained with ramp data, continuous dynamic data, and an LSTM pre-trained with a self-supervised learning technique (VICReg).
View Article and Find Full Text PDFPLoS One
September 2025
Symbiosis Institute of Technology, Symbiosis International University, Pune, India.
With the rapid development of industrial automation and intelligent manufacturing, defect detection of electronic products has become crucial in the production process. Traditional defect detection methods often face the problems of insufficient accuracy and inefficiency when dealing with complex backgrounds, tiny defects, and multiple defect types. To overcome these problems, this paper proposes Y-MaskNet, a multi-task joint learning framework based on YOLOv5 and Mask R-CNN, which aims to improve the accuracy and efficiency of defect detection and segmentation in electronic products.
View Article and Find Full Text PDFCell Rep Methods
July 2025
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, P.R. China; Key Laboratory of Smart Farming for Agricultural Animals, Ministry of Agriculture and Rural Affairs, Beijing, P.R. China; College of Informatics, Huazhong Agricult
We introduce a cell-free DNA (cfDNA) fragmentation pattern: the fragment dispersity index (FDI), which integrates information on the distribution of cfDNA fragment ends with the variation in fragment coverage, enabling precise characterization of chromatin accessibility in specific regions. The FDI shows a strong correlation with chromatin accessibility and gene expression, and regions with high FDI are enriched in active regulatory elements. Using whole-genome cfDNA data from five datasets, we developed and validated the FDI-oncology model, which demonstrates robust performance in early cancer diagnosis, subtyping, and prognosis.
View Article and Find Full Text PDFIEEE J Biomed Health Inform
September 2025
Significant progress has been made in applying deep learning for the automatic diagnosis of skin lesions. However, most models remain unexplainable, which severely hinders their application in clinical settings. Concept-based ante-hoc interpretable models have the potential to clarify the decision-making process of diagnosis by learning high-level, human-understandable concepts, while they can only provide numerical values of conceptual contributions.
View Article and Find Full Text PDFCrit Rev Food Sci Nutr
September 2025
Hunan Key Laboratory of Deep Processing and Quality Control of Cereals and Oils, State Key Laboratory of Utilization of Woody Oil Resource, College of Food Science and Engineering, Central South University of Forestry and Technology, Changsha, Hunan, China.
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a condition that results from metabolic disorders. In addition to genetic factors, irregular and high-energy diets may also significantly contribute to its pathogenesis. Dietary habits can profoundly alter the composition of gut microbiota and metabolites.
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