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| http://dx.doi.org/10.1007/s43441-023-00546-w | DOI Listing |
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Machine learning-based interpretation of non-contrast feature tracking strain analysis and T1/T2 mapping for assessing myocardial viability.
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January 2025
Cardiovascular Research Center, Rajaie Cardiovascular, Medical, and Research Center, University of Medical Sciences, Tehran, Iran.
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.
View Article and Find Full Text PDFPointer meters recognition method in the wild based on innovative deep learning techniques.
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College of Computer and Data Science, Minjiang University, Fuzhou, 350018, China.
This study presents a novel approach to identifying meters and their pointers in modern industrial scenarios using deep learning. We developed a neural network model that can detect gauges and one or more of their pointers on low-quality images. We use an encoder network, jump connections, and a modified Convolutional Block Attention Module (CBAM) to detect gauge panels and pointer keypoints in images.
View Article and Find Full Text PDFImproved ovalbumin accurate quantitative performance in processed foods by full-length isotope-labeled protein.
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National Key Laboratory of Veterinary Public Health Security, College of Veterinary Medicine, China Agricultural University, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, and Beijing Laboratory for Food Quality and Safety, Beijing 100193, China; Beijing Key Laboratory of Diagnostic and Traceability Technologies for Food Poisoning, Beijing Center for Disease Prevention and Control, Beijing 100013, China. Electronic address:
Ovalbumin (OVA) is a high-risk allergen with complex tertiary structure in food samples. Here, we developed an accurate UPLC-MS/MS-based assay to improve OVA quantitative performance in processed foods. Full-length isotope-labeled OVA proteins (OVA-I) were synthesized using stable isotope labeling by amino acids in cell culture (SILAC) technique and employed as functional internal standards to ensure similar cleavage sites between internal standards and analytes.
View Article and Find Full Text PDFIncreased parietal operculum functional connectivity following vestibular rehabilitation in benign paroxysmal positional vertigo patients with residual dizziness: a randomized controlled resting-state fMRI study.
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Department of Neurology, Second Affiliated Hospital of Xuzhou Medical University, No. 32, Meijian Road, Quanshan District, Xuzhou, 221006, Jiangsu, China.
Introduction: Residual dizziness (RD) is common in patients with benign paroxysmal positional vertigo (BPPV) after successful canalith repositioning procedures. This study aimed to investigate the therapeutic effects of vestibular rehabilitation (VR) on BPPV patients experiencing RD, and to explore the impact of VR on functional connectivity (FC), specifically focusing on the bilateral parietal operculum (OP) cortex.
Methods: Seventy patients with RD were randomly assigned to either a four-week VR group or a control group that received no treatment.
Development of machine learning models for diagnostic biomarker identification and immune cell infiltration analysis in PCOS.
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Reproductive Medicine Center, Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South University, Changsha, Hunan, China.
Background: Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. It is characterized by symptoms such as hyperandrogenemia, oligo or anovulation and polycystic ovarian, significantly impacting quality of life. However, the practical implementation of machine learning (ML) in PCOS diagnosis is hindered by the limitations related to data size and algorithmic models.
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