Objective: Nonobstructive general angiography (NOGA) is a novel modality to detect and sample spontaneous ruptured aortic plaques (SRAPs). We aimed to establish novel methods to detect cholesterol crystals (CCs) in sampled SRAPs.
Methods: Blood specimens containing SRAPs were obtained from patients using NOGA. Blood was instantly frozen on a glass slide and subsequently thawed for quantitative analysis and spread onto a filter paper that was rinsed using distilled water. Qualitative analysis was performed for the rinsed water using polarized light microscopy, and the filter paper was embedded in paraffin for histologic analysis.
Results: The CCs were clearly observed after hemolysis using the instant freeze-thaw method. The filter paper rinse method indicated free CCs of varying shapes under polarized light microscopy without erythrocytes. On the filter paper, sampled SRAPs showed Lamé-like small particles. Histopathology revealed various atheromatous components.
Conclusion: A set of novel methods for detecting CCs from sampled blood was established.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1093/labmed/lmab078 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Environment and management jointly shape the spatial patterns of plant species diversity of moist grasslands in the mountains of northeastern Yunnan.
Plant Divers
November 2024
College of Urban and Environmental Sciences, MOE Key Laboratory of Earth Surface Processes, Peking University, Beijing 100871, China.
Grasslands account for about a quarter of the Earth's land area and are one of the major terrestrial ecosystems, with significant ecological and economic values. The influence of environmental factors and management types on grassland biodiversity has garnered considerable attention. This study investigated how patterns of species richness are influenced by geographical distance, environmental gradients, and management type in the moist mountain grasslands of northeastern Yunnan, China.
View Article and Find Full Text PDFDetection of functional diversity gradients and their geoclimatic filters is sensitive to data types (occurrence vs. abundance) and spatial scales (sites vs. regions).
Plant Divers
November 2024
Laboratório de Ciências Ambientais, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, Rio de Janeiro, Brazil.
Functional diversity (FD) reflects within- and between-site variation of species traits (α- and β-FD, respectively). Understanding how much data types (occurrence-based vs. abundance-weighted) and spatial scales (sites vs.
View Article and Find Full Text PDFEnhancing glaucoma diagnosis: Generative adversarial networks in synthesized imagery and classification with pretrained MobileNetV2.
MethodsX
June 2025
Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Tamil Nadu 600062, India.
The disease affects the optic nerve and represents the principle reasons of irreversible vision loss, mostly asymptomatic and uncontrolled. Consequently, early and accurate diagnosis is critical to prevent or reduce its effect, however, conventional diagnostic techniques often fail to provide concrete results. In this regard, we present a new approach built on Generative Adversarial Networks (GAN) and MobileNetV2 pretrained architecture for diagnosing glaucoma.
View Article and Find Full Text PDFThe construction of a digital dissemination platform for the intangible cultural heritage using convolutional neural network models.
Heliyon
January 2025
School of International Tourism and Culture, Guizhou Normal University, Guiyang, 550025, China.
In order to promote the digital dissemination and preservation of Chinese intangible cultural heritage, this work constructs a digital platform for its transmission. The platform integrates a range of advanced technologies, including the Densely Connected Convolutional Networks - Bottleneck and Compression model, a notable convolutional neural network, along with natural language processing algorithms, generative adversarial network algorithms, and neural collaborative filtering algorithms. The platform is validated with 224,055 publicly archived valid data records, ensuring its effectiveness and reliability.
View Article and Find Full Text PDFFluid volume status detection model for patients with heart failure based on machine learning methods.
Heliyon
January 2025
Chest Clinical College of Tianjin Medical University, Tianjin, 300270, China.
Backgroud: Fluid volume abnormalities are a major cause of exacerbations in heart failure patients. However, there is few efficient, rapid, or cost-effective clinical approach for determining volume status, resulting in inadequate or unsatisfactory treatment. The aim was to develop an early fluid volume detection model for heart failure patients utilizing a machine learning stratification.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!