Chick chorioallantoic membrane (CAM) angiogenesis assay has been widely used for finding drugs targeting new blood vessel development in cancer research. In addition to the setup materials and protocols, laboratory findings depend on the quantification and analysis of microscopic blood vessel images. However, it is still a challenging problem because of the high complexity of blood vessel branching structures. We applied preprocessing on CAM microscopic images by keeping the integrity of minor branches in the vessel structure. We then proposed an efficient way to automatically extract blood vessel centerlines based on vector tracing starting from detected seed points. Finally, all branches were coded to construct an abstract model of the branching structure, which enabled more accurate modeling for in-depth analysis. The framework was applied in quantifying Icaritin (ICT) inhibition effects on angiogenesis in a CAM model. Experimental results showed the high accuracy in blood vessel quantification and modeling compared with semimanual measurements. Meanwhile, a set of blood vessel growth indicators were extracted to provide fully automated analysis for angiogenesis assays. Further analysis proved that ICT took effect in a dose-dependent manner which could be applied in suppressing tumor blood vessel growth.
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http://dx.doi.org/10.1117/1.JBO.19.10.106005 | DOI Listing |
Eur J Med Res
January 2025
Medical Big Data Research Center, Medical Innovation Research Division, Chinese PLA General Hospital, 28 Fuxing RD., Beijing, 100853, China.
Background: Chronic kidney disease (CKD) carries the highest population attributable risk for mortality among all comorbidities in chronic heart failure (CHF). No studies about the association between inferior vena cava (IVC) diameter and all-cause mortality in patients with the comorbidity of CKD and CHF has been published.
Methods: In this retrospective cohort study, a total of 1327 patients with CHF and CKD were included.
BMC Public Health
January 2025
Department of Medical Ultrasonics, First Affiliated Hospital of Medical College, Xi'an Jiao tong University, No.277 Yanta West Road, Xi'an, 710061, Shaanxi, China.
Background: Abdominal aortic calcification (AAC) is considered as a strong predictor of cardiovascular disease (CVD) events. Our study aimed to investigate whether the predicted risk for cardiac death with the Framingham risk score (FRS) could be further improved with the addition of AAC score in general population aged ≥ 40 years.
Methods: A total of 2971 participants aged ≥ 40 years in the National Health and Nutrition Examination Surveys (NHANES) 2013-2014 were followed up.
BMC Cardiovasc Disord
January 2025
Department of Internal Medicine, Collage of Medicine and Health Science, Debre Markos University, Debre Markos, Ethiopia.
Background: In developing countries evidences regarding pulmonary hypertension (PH) in rheumatic heart disease (RHD) patients are lacking, despite being responsible for significant morbidity and mortality. As a result, identifying the factors that influence PH is crucial to improve the quality of care.
Objective: To determine prevalence of pulmonary hypertension and its associated factors among rheumatic heart disease patients at the public hospitals of Bahir Dar city, Ethiopia.
J Imaging Inform Med
January 2025
Department of Ophthalmology, The Affiliated Hospital of Guilin Medical University, Guilin, China.
Optical coherence tomography angiography (OCTA) is an emerging, non-invasive technique increasingly utilized for retinal vasculature imaging. Analysis of OCTA images can effectively diagnose retinal diseases, unfortunately, complex vascular structures within OCTA images possess significant challenges for automated segmentation. A novel, fully convolutional dense connected residual network is proposed to effectively segment the vascular regions within OCTA images.
View Article and Find Full Text PDFNat Biotechnol
January 2025
Institute for Intelligent Biotechnologies (iBIO), Helmholtz Center Munich, Neuherberg, Germany.
Efficient and accurate nanocarrier development for targeted drug delivery is hindered by a lack of methods to analyze its cell-level biodistribution across whole organisms. Here we present Single Cell Precision Nanocarrier Identification (SCP-Nano), an integrated experimental and deep learning pipeline to comprehensively quantify the targeting of nanocarriers throughout the whole mouse body at single-cell resolution. SCP-Nano reveals the tissue distribution patterns of lipid nanoparticles (LNPs) after different injection routes at doses as low as 0.
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