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Application of failure model and effect analysis in nursing care for patients who have undergone endoscopic sub-mucosal dissection.
BMC Nurs
January 2025
Nursing Department, Zhengzhou Central Hospital Affiliated to Zhengzhou University, Zhengzhou, Henan, 450000, China.
Objective: The objective of this study is to investigate the effect after the application of Failure Model and Effect Analysis (FMEA) in nursing care for patients who have undergone endoscopic submucosal dissection (ESD).
Methods: A cohort of 40 patients who underwent ESD between July and September 2023 were selected as the control group, while 42 patients who underwent ESD between October 2023 and June 2024 after implementing FMEA were selected as the observation group. A multidisciplinary team was established based on the FMEA model to analyze and create a nursing flowchart.
Integrating the ADS Score with 24-Hour ASPECTS and red cell distribution width for enhanced prediction of stroke-associated pneumonia following intravenous thrombolysis: model development and internal validation.
Eur J Med Res
January 2025
Division of Radiology, Saraburi Hospital, Saraburi, Thailand.
Introduction: Stroke-associated pneumonia (SAP) is a major cause of mortality during the acute phase of stroke. The ADS score is widely used to predict SAP risk but does not include 24-h non-contrast computed tomography-Alberta Stroke Program Early CT Score (NCCT-ASPECTS) or red cell distribution width (RDW). We aim to evaluate the added prognostic value of incorporating 24-h NCCT-ASPECTS and RDW into the ADS score and to develop a novel prediction model for SAP following thrombolysis.
View Article and Find Full Text PDFCorrecting mitochondrial loss mitigates NOTCH1-related aortopathy in mice.
Nat Cardiovasc Res
January 2025
Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
Loss-of-function mutations in NOTCH1 were previously linked to thoracic aortopathy, a condition for which non-surgical treatment options are limited. Based on clinical proteome analysis, we hypothesized that mitochondrial fusion and biogenesis in aortic smooth muscle cells (SMCs) are crucial for regulating the progression of NOTCH1-related aortopathy. Here we demonstrate that SMC-specific Notch1 knockout mice develop aortic pathology, including stiffening, dilation and focal dissection.
View Article and Find Full Text PDFPretrained Deep Neural Network Kin-SiM for Single-Molecule FRET Trace Idealization.
J Phys Chem B
January 2025
Single Molecule Analysis Group, Department of Chemistry, The University of Michigan, Ann Arbor, Michigan 48109, United States.
Single-molecule fluorescence resonance energy transfer (smFRET) has emerged as a pivotal technique for probing biomolecular dynamics over time at nanometer scales. Quantitative analyses of smFRET time traces remain challenging due to confounding factors such as low signal-to-noise ratios, photophysical effects such as bleaching and blinking, and the complexity of modeling the underlying biomolecular states and kinetics. The dynamic distance information shaping the smFRET trace powerfully uncovers even transient conformational changes in single biomolecules both at or far from equilibrium, relying on trace idealization to identify specific interconverting states.
View Article and Find Full Text PDFConditional Generative Models for Synthetic Tabular Data: Applications for Precision Medicine and Diverse Representations.
Annu Rev Biomed Data Sci
January 2025
2Departments of Bioengineering and Genetics, Stanford University, Stanford, California, USA.
Tabular medical datasets, like electronic health records (EHRs), biobanks, and structured clinical trial data, are rich sources of information with the potential to advance precision medicine and optimize patient care. However, real-world medical datasets have limited patient diversity and cannot simulate hypothetical outcomes, both of which are necessary for equitable and effective medical research. Fueled by recent advancements in machine learning, generative models offer a promising solution to these data limitations by generating enhanced synthetic data.
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