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Identification of Intracranial Germ Cell Tumors Based on Facial Photos: Exploratory Study on the Use of Deep Learning for Software Development.
J Med Internet Res
January 2025
Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background: Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.
View Article and Find Full Text PDFRisk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study.
J Med Internet Res
January 2025
Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
State-Level Influenza Hospitalization Burden in the United States, 2022-2023.
Am J Public Health
January 2025
Alexia Couture, A. Danielle Iuliano, Ryan Threlkel, Matthew Gilmer, Alissa O'Halloran, Dawud Ujamaa, Matthew Biggerstaff, and Carrie Reed are with the National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA. Howard H. Chang is with the Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.
To develop a method leveraging hospital-based surveillance to estimate influenza-related hospitalizations by state, age, and month as a means of enhancing current US influenza burden estimation efforts. Using data from the Influenza Hospitalization Surveillance Network (FluSurv-NET), we extrapolated monthly FluSurv-NET hospitalization rates after adjusting for testing practices and diagnostic test sensitivities to non-FluSurv-NET states. We used a Poisson zero-inflated model with an overdispersion parameter within the Bayesian hierarchical framework and accounted for uncertainty and variability between states and across time.
View Article and Find Full Text PDFA Quantitative First Passage Time Model for Tubular Microfluidic Immunoassays.
ACS Sens
January 2025
Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Solid-phase immunosorbent reactions, such as ELISA, are widely used for detecting, identifying, and quantifying protein markers. However, traditional centimeter scale well-based immunoreactors suffer from low surface-to-volume (S/V) ratios, leading to large sample consumption and a long assay time. Microfluidic technologies, particularly tubular microfluidic immunoreactors, have emerged as promising alternatives due to their high S/V ratios.
View Article and Find Full Text PDFErrors are robustly tamed in cumulative knowledge processes.
Proc Natl Acad Sci U S A
February 2025
Computer Science, School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134.
As knowledge accumulates in science and society in a distributed fashion, erroneous derivations can be introduced into the corpus of knowledge. Such derivations can compromise the validity of any units of knowledge that rely on them in the future. Can societal knowledge maintain some level of integrity given simple distributed error-checking mechanisms? In this paper, we investigate the following formulation of the question: assuming that a constant fraction of the new derivations is wrong, is it possible for simple error-checking mechanisms that apply when a new unit of knowledge is derived to maintain the integrity of the corpus of knowledge? This question was introduced by Ben-Eliezer et al.
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