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A 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 PDFPredicting the risk of bone metastases in patients with newly diagnosed prostate cancer - Development and validation of a nomogram model.
PLoS One
January 2025
Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, Jiangsu, PR. China.
Objectives: The aim of this study was to develop and validate a nomogram model that predicts the risk of bone metastasis (BM) in a prostate cancer (PCa) population.
Methods: We retrospectively collected and analyzed the clinical data of patients with pathologic diagnosis of PCa from January 1, 2013 to December 31, 2022 in two hospitals in Yangzhou, China. Patients from the Affiliated Hospital of Yangzhou University were divided into a training set and patients from the Affiliated Clinical College of Traditional Chinese Medicine of Yangzhou University were divided into a validation set.
KaMLs for Predicting Protein p Values and Ionization States: Are Trees All You Need?
J Chem Theory Comput
January 2025
Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, Maryland 21201, United States.
Despite its importance in understanding biology and computer-aided drug discovery, the accurate prediction of protein ionization states remains a formidable challenge. Physics-based approaches struggle to capture the small, competing contributions in the complex protein environment, while machine learning (ML) is hampered by the scarcity of experimental data. Here, we report the development of p ML (KaML) models based on decision trees and graph attention networks (GAT), exploiting physicochemical understanding and a new experiment p database (PKAD-3) enriched with highly shifted p's.
View Article and Find Full Text PDFCyber anti-intellectualism and science communication during the COVID-19 pandemic: a cross-sectional study.
Front Public Health
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School of Journalism and New Media, Xi'an Jiaotong University, Xi'an, China.
Background: During the COVID-19 pandemic, science communication played a crucial role in disseminating accurate information and promoting scientific literacy among the public. However, the rise of anti-intellectualism on social media platforms has posed significant challenges to science, scientists, and science communication, hindering effective public engagement with scientific affairs. This study aims to explore the mechanisms through which anti-intellectualism impacts science communication on social media platforms from the perspective of communication effect theory.
View Article and Find Full Text PDFThe impact of teacher emotional support on learning engagement among college students mediated by academic self-efficacy and academic resilience.
Sci Rep
January 2025
Department of Public Courses, Shandong University of Science and Technology, Taian, China.
College students' learning engagement not only significantly influences their academic performance but also plays a vital role in their future career development. Ensuring that students maintain high levels of engagement is essential for society's goal of cultivating high-quality talent. Therefore, understanding the key factors that drive student engagement is critical for educators as they develop effective strategies to foster this engagement.
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