Comparisons of infected and uninfected patients exposed to their own COVID-19-positive caregivers.

Comparisons of infected and uninfected patients exposed to their own COVID-19-positive caregivers.</p

Characteristics of caregivers infected with COVID-19.

Characteristics of caregivers infected with COVID-19.</p

The compositional distribution of 54 COVID-19-confirmed cases between the patients and their own caregivers.

a One patient transmitted COVID-19 to two caregivers. He has one family caregiver and one professional caregiver. b Six patients infected with COVID-19 from the caregiver and three non-infected patients were compared in Table 2. c The caregiver’s symptoms developed at least 5 days after discharge and could not be transmitted to the patient. d Three caregivers were family members of one patient.</p

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(DOCX)</p

Transmission map.

Transmission map.</p

Strategy for caregivers to prevent COVID-19 transmission.

Strategy for caregivers to prevent COVID-19 transmission.</p

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(XLSX)</p

Significance of EZH2 Expression in Canine Mammary Tumors

Datasets related to the article, "Significance of EZH2 Expression in Canine Mammary Tumors"

Automated detection and classification of the proximal humerus fracture by using deep learning algorithm

<p>Background and purpose — We aimed to evaluate the ability of artificial intelligence (a deep learning algorithm) to detect and classify proximal humerus fractures using plain anteroposterior shoulder radiographs.</p> <p>Patients and methods — 1,891 images (1 image per person) of normal shoulders (n = 515) and 4 proximal humerus fracture types (greater tuberosity, 346; surgical neck, 514; 3-part, 269; 4-part, 247) classified by 3 specialists were evaluated. We trained a deep convolutional neural network (CNN) after augmentation of a training dataset. The ability of the CNN, as measured by top-1 accuracy, area under receiver operating characteristics curve (AUC), sensitivity/specificity, and Youden index, in comparison with humans (28 general physicians, 11 general orthopedists, and 19 orthopedists specialized in the shoulder) to detect and classify proximal humerus fractures was evaluated.</p> <p>Results — The CNN showed a high performance of 96% top-1 accuracy, 1.00 AUC, 0.99/0.97 sensitivity/specificity, and 0.97 Youden index for distinguishing normal shoulders from proximal humerus fractures. In addition, the CNN showed promising results with 65–86% top-1 accuracy, 0.90–0.98 AUC, 0.88/0.83–0.97/0.94 sensitivity/specificity, and 0.71–0.90 Youden index for classifying fracture type. When compared with the human groups, the CNN showed superior performance to that of general physicians and orthopedists, similar performance to orthopedists specialized in the shoulder, and the superior performance of the CNN was more marked in complex 3- and 4-part fractures.</p> <p>Interpretation — The use of artificial intelligence can accurately detect and classify proximal humerus fractures on plain shoulder AP radiographs. Further studies are necessary to determine the feasibility of applying artificial intelligence in the clinic and whether its use could improve care and outcomes compared with current orthopedic assessments.</p

Clinical characteristics of patients with tuberculosis after allogeneic hematopoietic stem cell transplantation.

<p>Clinical characteristics of patients with tuberculosis after allogeneic hematopoietic stem cell transplantation.</p