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A π-Extended AIE Platform for Pattern Recognition of Glycosaminoglycans.
Anal Chem
January 2025
Department of Chemistry, College of Sciences, Nanjing Agricultural University, Nanjing, Jiangsu 210095, People's Republic of China.
Accurate discrimination of complicated glycosaminoglycans is a challenging but meaningful task for ensuring their safe use in clinics. With the purpose of reducing the production cost of sensor arrays for glycosaminoglycans, three fluorescence turn-on sensors named , , and were readily synthesized by simple alkylation of the pyridyl units of the π-extended AIEgen, namely, tetra-(4-pyridylphenyl) ethylene. The designed sensors are cross-reactive toward tested glycosaminoglycans including heparin, chondroitin sulfate, hyaluronic acid, and dextran sulfate, whose mechanism could be ascribed to the multivalent electrostatic, CH···π, and hydrophobic interactions between the sensors and different glycosaminoglycans to form corresponding fluorescent aggregates.
View Article and Find Full Text PDFIdentification 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.
Comparison of 3 Aging Metrics in Dual Declines to Capture All-Cause Dementia and Mortality Risk: Cohort Study.
JMIR Aging
January 2025
Department of Geriatrics, Guangdong Provincial Geriatrics Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Southern Medical University, No. 106, Zhongshan 2nd Road, Yuexiu District, Guangzhou, China, 0898-66571684.
Background: The utility of aging metrics that incorporate cognitive and physical function is not fully understood.
Objective: We aim to compare the predictive capacities of 3 distinct aging metrics-motoric cognitive risk syndrome (MCR), physio-cognitive decline syndrome (PCDS), and cognitive frailty (CF)-for incident dementia and all-cause mortality among community-dwelling older adults.
Methods: We used longitudinal data from waves 10-15 of the Health and Retirement Study.
A comparative analysis of CNNs and LSTMs for ECG-based diagnosis of arrythmia and congestive heart failure.
Comput Methods Biomech Biomed Engin
January 2025
Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
Cardiac arrhythmias are major global health concern and their early detection is critical for diagnosis. This study comprehensively evaluates the effectiveness of CNNs and LSTMs for the classification of cardiac arrhythmias, considering three PhysioNet datasets. ECG records are segmented to accommodate around ∼10s of ECG data.
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