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| http://dx.doi.org/10.1371/journal.pntd.0004342 | DOI Listing |
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US Veterans' Experiences With VA and Non-VA Health Care.
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Veterans Affairs Center for Clinical Management Research, Ann Arbor, Michigan.
Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.
Eur J Nucl Med Mol Imaging
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Department of Nuclear Medicine, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu, Sichuan, 610041, China.
Purpose: Extranodal natural killer/T-cell lymphoma (ENKTCL) is an hematologic malignancy with prognostic heterogeneity. We aimed to develop and validate DeepENKTCL, an interpretable deep learning prediction system for prognosis risk stratification in ENKTCL.
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Institutes of Physical Science and Information Technology, Anhui University, Hefei, 230601, Anhui, China.
High-throughput sequencing has exponentially increased peptide sequences, necessitating a computational method to identify multi-functional therapeutic peptides (MFTP) from their sequences. However, existing computational methods are challenged by class imbalance, particularly in learning effective sequence representations. To address this, we propose PSCFA, a prototypical supervised contrastive learning with a feature augmentation method for MFTP prediction.
View Article and Find Full Text PDFEnDM-CPP: A Multi-view Explainable Framework Based on Deep Learning and Machine Learning for Identifying Cell-Penetrating Peptides with Transformers and Analyzing Sequence Information.
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School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, 213164, China.
Cell-Penetrating Peptides (CPPs) are a crucial carrier for drug delivery. Since the process of synthesizing new CPPs in the laboratory is both time- and resource-consuming, computational methods to predict potential CPPs can be used to find CPPs to enhance the development of CPPs in therapy. In this study, EnDM-CPP is proposed, which combines machine learning algorithms (SVM and CatBoost) with convolutional neural networks (CNN and TextCNN).
View Article and Find Full Text PDFSuccessful portosystemic shunt embolization resolves hepatic encephalopathy and enhances hepatic function and glycemic control in MASH-related cirrhosis: a case report.
Clin J Gastroenterol
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Department of Medicine, Division of Gastroenterology and Hepatology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan.
We report the case of a 70-year-old woman with advanced hepatic encephalopathy (HE) secondary to metabolic dysfunction-associated steatohepatitis (MASH)-related cirrhosis who exhibited an excellent response to portosystemic shunt embolization. Four years earlier, she was diagnosed as having MASH-related cirrhosis accompanied by multiple mesenteric vein-inferior vena cava shunts. As her condition progressed, she suffered recurrent HE that was unresponsive to oral medication, prompting the decision to proceed with shunt embolization.
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