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Intraventricular Hemorrhage and Survival, Multimorbidity, and Neurodevelopment.
JAMA Netw Open
January 2025
Population Policy and Practice, Great Ormond Street UCL Institute of Child Health, London, United Kingdom.
Importance: Intraventricular hemorrhage (IVH) has proven to be a challenging and enduring complication of prematurity. However, its association with neurodevelopment across the spectrum of IVH severity, independent of prematurity, and in the context of contemporary care remains uncertain.
Objective: To evaluate national trends in IVH diagnosis and the association with survival and neurodevelopmental outcomes at 2 years of age.
PRONAME: a user-friendly pipeline to process long-read nanopore metabarcoding data by generating high-quality consensus sequences.
Front Bioinform
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Bioengineering Unit, Life Sciences Department, Walloon Agricultural Research Centre, Gembloux, Belgium.
Background: The study of sample taxonomic composition has evolved from direct observations and labor-intensive morphological studies to different DNA sequencing methodologies. Most of these studies leverage the metabarcoding approach, which involves the amplification of a small taxonomically-informative portion of the genome and its subsequent high-throughput sequencing. Recent advances in sequencing technology brought by Oxford Nanopore Technologies have revolutionized the field, enabling portability, affordable cost and long-read sequencing, therefore leading to a significant increase in taxonomic resolution.
View Article and Find Full Text PDFEnhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images.
Curr Med Imaging
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Department of Electrical Engineering, Government College University, Lahore, Pakistan.
Background: Colon diseases are major global health issues that often require early detection and correct diagnosis to be effectively treated. Deep learning approaches and recent developments in medical imaging have demonstrated promise in increasing diagnostic accuracy.
Objective: This work suggests that a Convolutional Neural Network (CNN) model paired with other models can detect different gastrointestinal (GI) abnormalities or diseases from endoscopic images via the fusion of residual blocks, including alpha dropouts (αDO) and auxiliary fusing layers.
Efficiency and safety of automated label cleaning on multimodal retinal images.
NPJ Digit Med
January 2025
Joint Shantou International Eye Center, Shantou University and the Chinese University of Hong Kong, Shantou, Guangdong, 515041, China.
Label noise is a common and important issue that would affect the model's performance in artificial intelligence. This study assessed the effectiveness and potential risks of automated label cleaning using an open-source framework, Cleanlab, in multi-category datasets of fundus photography and optical coherence tomography, with intentionally introduced label noise ranging from 0 to 70%. After six cycles of automatic cleaning, significant improvements are achieved in label accuracies (3.
View Article and Find Full Text PDFEvaluation of ST-segment and T-wave changes associated with NSTE-ACS in patients with RBBB: a nested case-control study design.
Intern Emerg Med
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Faculty of Medicine, Department of Emergency Medicine, Akdeniz University, Antalya, Turkey.
Patients presenting with suspected acute coronary syndrome (ACS) in the emergency department (ED) require rapid and accurate electrocardiographic (ECG) evaluation. This study aims to assess conventional ECG markers for diagnosing non-ST-elevation ACS (NSTE-ACS) in patients with chest discomfort and right bundle branch block (RBBB). A nested case-control design was employed to compare patients with RBBB admitted to the ED for suspected cardiac ischemia, focusing on those who developed NSTE-ACS versus those who did not.
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