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Comparison of clinical and virological features in pediatric and adult dengue cases at Insein General Hospital during Myanmar's 2022 dengue season.
Trop Med Health
January 2025
Department of Medical Research, Ministry of Health, No.5, Ziwaka Road, Dagon Township, Yangon, 11191, Myanmar.
Background: Myanmar is one of the countries in Southeast Asia where serious dengue outbreaks occur and Yangon is among the regions with the highest number of cases in the country. Many infections including dengue are common in Yangon during the rainy season, and co-infections may also occur. Adults are more likely than children to experience co-infections of dengue and other diseases.
View Article and Find Full Text PDFscSMD: a deep learning method for accurate clustering of single cells based on auto-encoder.
BMC Bioinformatics
January 2025
Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Background: Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology has become increasingly vital. This study explores the application of deep learning to single-cell data clustering, with a particular focus on managing sparse, high-dimensional data.
View Article and Find Full Text PDFLangerhans cell histiocytosis in children: the value of ultrasound in diagnosis and follow-up.
BMC Med Imaging
January 2025
Department of Ultrasound Medicine, First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, China.
Background: Langerhans cell histiocytosis (LCH) is a rare disease, most prevalent in children. Ultrasound is a noninvasive, cheap, and widely available technique. However, systematic elucidation of sonographic features of LCH and treatment related follow-up are relatively few, resulting in overall underestimation of the clinical value of ultrasound in diagnosing and monitoring LCH.
View Article and Find Full Text PDFAI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.
BMC Pregnancy Childbirth
January 2025
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, University of Utah Health, 30 N. Mario Capecchi Dr., Level 5 South, Salt Lake City, UT, 84132, USA.
Background: Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR.
Methods: Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
Fully automated segmentation and classification of renal tumors on CT scans via machine learning.
BMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
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