This research investigated the effects of ultrasound application (192 ± 6 W/L) on the microstructure of vegetables/fruits with different porosities, cell sizes and patterns (eggplants, beetroots, and apples), submitted to an immersion treatment in different liquids: distilled water, citric acid (1% w/v), and the vegetable/fruit juice, at 25 °C during 5 min. The ultrasound application did not significantly (p > 0.05) affect the size of the cells of the most porous material (eggplant) compared to the samples immersed without ultrasound assistance. The apple samples (with a middle-high porosity and the largest cells) were the most affected by ultrasound application. The median cell areas of samples treated with ultrasound in water and apple juice were 26 and 20% larger than those of samples treated without ultrasound, mainly because of cell wall disruption which caused the cells to merge into bigger clusters, but no effect was observed with the citric acid. Ultrasound application significantly (p < 0.05) increased the median cell area of the less porous raw matter (beetroot) only when the treatment was carried out in the vegetable juice (cells were 26% larger after treatment assisted with ultrasound than without it). Thus, the effects of ultrasound differ in materials with initially different characteristics.
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http://dx.doi.org/10.1016/j.ultsonch.2022.106087 | DOI Listing |
Sci Rep
January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
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January 2025
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District 100070, Beijing, China.
Deep vein thrombosis (DVT) in patients undergoing endoscopic endonasal surgery remains underexplored, despite its potential impact on postoperative recovery. This study aimed to develop and validate a predictive nomogram for assessing the risk of lower-limb DVT in such patients without chemoprophylaxis. A retrospective analysis was conducted on 935 patients with postoperative lower-limb vein ultrasonography.
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January 2025
Ministry of Higher Education, Mataria Technical College, Cairo, 11718, Egypt.
The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.
View Article and Find Full Text PDFZhonghua Nei Ke Za Zhi
February 2025
Department of Rheumatology and Immunology, the First Medical Center of Chinese PLA General Hospital, Beijing100853, China.
Radiography (Lond)
January 2025
Radiotherapy, Leeds Cancer Centre, Leeds Teaching Hospitals NHS Trust, UK; Leeds Institute of Medical Research, University of Leeds, Leeds, UK; NIHR Leeds Biomedical Research Centre, Leeds, UK.
Introduction: Using non-medicinal oral contrast agents may aid safe delivery of magnetic resonance image-guided (MR-guided) radiotherapy by improving the ability to visualise and avoid excessive radiation dose to adjacent bowel/stomach. This scoping review aims to map the literature on non-medicinal oral contrasts used in upper-abdominal diagnostic or therapeutic magnetic resonance imaging (MRI) to find potential candidates for employing in MR-guided radiotherapy and identify gaps in knowledge for further study.
Methods: A scoping review of non-medicinal oral contrast used in upper-abdominal MRI research followed a pre-defined protocol based on Arksey and O'Malley's framework.
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