The objective of this study was to evaluate a novel automated test based on ultrasound cervical texture analysis to predict spontaneous Preterm Birth (sPTB) alone and in combination with Cervical Length (CL). General population singleton pregnancies between 18 + 0 and 24 + 6 weeks' gestation were assessed prospectively at two centers. Cervical ultrasound images were evaluated and the occurrence of sPTB before weeks 37 + 0 and 34 + 0 were recorded. CL was measured on-site. The automated texture analysis test was applied offline to all images. Their performance to predict the occurrence of sPTB before 37 + 0 and 34 + 0 weeks was evaluated separately and in combination on 633 recruited patients. AUC for sPTB prediction before weeks 37 and 34 respectively were as follows: 55.5% and 65.3% for CL, 63.4% and 66.3% for texture analysis, 67.5% and 76.7% when combined. The new test improved detection rates of CL at similar low FPR. Combining the two increased detection rate compared to CL alone from 13.0 to 30.4% for sPTB < 37 and from 14.3 to 42.9% sPTB < 34. Texture analysis of cervical ultrasound improved sPTB detection rate compared to cervical length for similar FPR, and the two combined together increased significantly prediction performance. This results should be confirmed in larger cohorts.
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http://dx.doi.org/10.1038/s41598-021-86906-8 | DOI Listing |
In Vitro Model
December 2024
Laboratório de Biologia Básica de Células-Tronco, FIOCRUZ, Rua Professor Algacyr Munhoz Mader, 3775, Instituto Carlos Chagas, Curitiba, Paraná PR 81350-010 Brazil.
Obesity is associated with several comorbidities that cause high mortality rates worldwide. Thus, the study of adipose tissue (AT) has become a target of high interest because of its crucial contribution to many metabolic diseases and metabolizing potential. However, many AT-related physiological, pathophysiological, and toxicological mechanisms in humans are still poorly understood, mainly due to the use of non-human animal models.
View Article and Find Full Text PDFJDS Commun
January 2025
Department of Food Science, STELA Dairy Science and Technology Research Center, Institute of Nutrition and Functional Foods (INAF), Laval University, Québec City, QC, Canada G1V 0A6.
This work aims to evaluate the potential and limits of adhesiveness measurement using a texturometer to assess the ropiness of acid dairy gels for starter selection. Commercial yogurts of various formulations and textures were used to assess the ability of adhesiveness to detect ropiness and to compare performance of different probes. Chemically acidified gels using different concentrations of glucono-delta-lactone (GDL) were tested to determine the effect of pH on adhesiveness.
View Article and Find Full Text PDFJ Dent Sci
January 2025
Department of Fragrance and Cosmetic Science, College of Pharmacy, Kaohsiung Medical University, Kaohsiung, Taiwan.
Background/purpose: Various pulp-covering materials offer advantages in regenerative root canal treatment, but each has limitations, highlighting the need for more effective antibacterial strategies for pulp repair and regeneration. Mesoporous bioactive glasses (MBG) show significant biological activity, making them valuable in tissue/dental repair. Silver-incorporated MBG exhibits promising antibacterial effects against various bacteria; copper ions are crucial in regulating angiogenesis signals.
View Article and Find Full Text PDFFood Sci Nutr
January 2025
Traditionally fermented sufu is popular because of its flavor, abundance of nutrients, and long shelf life. However, traditional sufu is difficult to produce via industrial processes because of dominant microorganism attenuation during fermentation. Herein, specific protease-producing strains were isolated from traditional sufu.
View Article and Find Full Text PDFDigit Biomark
December 2024
Electrical and Computer Engineering, Western Michigan University, Kalamazoo, MI, USA.
Introduction: This research is focused on early detection of Alzheimer's disease (AD) using a multiscale feature fusion framework, combining biomarkers from memory, vision, and speech regions extracted from magnetic resonance imaging and positron emission tomography images.
Methods: Using 2D gray level co-occurrence matrix (2D-GLCM) texture features, volume, standardized uptake value ratios (SUVR), and obesity from different neuroimaging modalities, the study applies various classifiers, demonstrating a feature importance analysis in each region of interest. The research employs four classifiers, namely linear support vector machine, linear discriminant analysis, logistic regression (LR), and logistic regression with stochastic gradient descent (LRSGD) classifiers, to determine feature importance, leading to subsequent validation using a probabilistic neural network classifier.
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