Immunogenicity and protective efficacy of inactivated coxsackievirus B4 viral particles.

Coxsackievirus B4 (CVB4) is associated with a range of acute and chronic diseases such as hand, foot, and mouth disease, myocarditis, meningitis, pancreatitis, and type 1 diabetes, affecting millions of young children annually around the world. However, no vaccine is currently available for preventing CVB4 infection. Here, we report the development of inactivated viral particle vaccines for CVB4.

Fusion extracted features from deep learning for identification of multiple positioning errors in dental panoramic imaging.

Background: Dental panoramic imaging plays a pivotal role in dentistry for diagnosis and treatment planning. However, correctly positioning patients can be challenging for technicians due to the complexity of the imaging equipment and variations in patient anatomy, leading to positioning errors. These errors can compromise image quality and potentially result in misdiagnoses.

Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models.

This study focuses on overcoming challenges in classifying eye diseases using color fundus photographs by leveraging deep learning techniques, aiming to enhance early detection and diagnosis accuracy. We utilized a dataset of 6392 color fundus photographs across eight disease categories, which was later augmented to 17,766 images. Five well-known convolutional neural networks (CNNs)-efficientnetb0, mobilenetv2, shufflenet, resnet50, and resnet101-and a custom-built CNN were integrated and trained on this dataset.

Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography.

According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator's technique, the cooperation of the subjects, and the subjective interpretation by the physician.

Identifying the Posture of Young Adults in Walking Videos by Using a Fusion Artificial Intelligent Method.

Many neurological and musculoskeletal disorders are associated with problems related to postural movement. Noninvasive tracking devices are used to record, analyze, measure, and detect the postural control of the body, which may indicate health problems in real time. A total of 35 young adults without any health problems were recruited for this study to participate in a walking experiment.

Efficient Strike Artifact Reduction Based on 3D-Morphological Structure Operators from Filtered Back-Projection PET Images.

Positron emission tomography (PET) can provide functional images and identify abnormal metabolic regions of the whole-body to effectively detect tumor presence and distribution. The filtered back-projection (FBP) algorithm is one of the most common images reconstruction methods. However, it will generate strike artifacts on the reconstructed image and affect the clinical diagnosis of lesions.

Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks.

Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient's condition and give appropriate medications. However, it is not easy to interpret the ECG signals.

Detection of Postural Control in Young and Elderly Adults Using Deep and Machine Learning Methods with Joint-Node Plots.

Postural control decreases with aging. Thus, an efficient and accurate method of detecting postural control is needed. We enrolled 35 elderly adults (aged 82.

Fully automated determination of arch angle on weight-bearing foot radiograph.

Background And Objective: Flatfeet can be evaluated by measuring the calcaneal-fifth metatarsal angle on a weight-bearing lateral foot radiograph. This study aimed to develop an automated method for determining the calcaneal-fifth metatarsal angle on weight-bearing lateral foot radiograph.

Method: The proposed method comprises four processing steps: (1) identification of the regions including the calcaneus and fifth metatarsal bones in a foot image; (2) delineation of the contours of the calcaneus and the fifth metatarsal; (3) determination of the tangential lines of the two bones from the contours; and (4) determination of the calcaneal-fifth metatarsal angle between the two tangential lines as arch angle.