ABSTRACT
Background: Cervical cancer, endometrial cancer, and ovarian cancer are among the top 10 most common cancers in women, with ovarian cancer in particular being considered a "silent killer". Therefore, early detection, diagnosis, and treatment constitute important means of care for women's health. This study investigated the clinical value of the quantitative analysis of contrast-enhanced ultrasonography (CEUS) in the differential diagnosis of benign and malignant pelvic tumors. Methods: CEUS was performed on 151 patients with pelvic masses. Subsequently, a qualitative diagnosis was completed using the image enhancement features and tumor parameters. A multiparametric analysis of CEUS images was performed, which included the following parameters: arrival time (AT), time to peak (TTP), peak intensity (PI), and ascent slope (AS). In addition, the qualitative diagnostic efficiency of CEUS was assessed in a multiparametric analysis, and the results were compared with pathological findings. Results: The patients in the malignant group were older (P=0.001) and had larger lesion PI values (P<0.01) than those in the benign group. The PI difference (PId) and the AS difference (ASd) showed statistical differences (P<0.01) between the myometrium and lesion tissues in the same patient. Moreover, the PId and ASd showed the largest receiver operating characteristic (ROC) curve and area under the ROC curve (AUC), with sensitivities of 90.9% and 91.7% and specificities of 86.4% and 72.5%, respectively. Conclusions: The quantitative analysis of CEUS provides a new, simpler, and more accurate method for the differential diagnosis of benign and malignant pelvic masses in clinical practice. The sensitivities and specificities of PId and ASd were higher compared to other parameters from the same patient.
ABSTRACT
Cardiomegaly is the main imaging finding for canine heart diseases. There are many advances in the field of medical diagnosing based on imaging with deep learning for human being. However there are also increasing realization of the potential of using deep learning in veterinary medicine. We reported a clinically applicable assisted platform for diagnosing the canine cardiomegaly with deep learning. VHS (vertebral heart score) is a measuring method used for the heart size of a dog. The concrete value of VHS is calculated with the relative position of 16 key points detected by the system, and this result is then combined with VHS reference range of all dog breeds to assist in the evaluation of the canine cardiomegaly. We adopted HRNet (high resolution network) to detect 16 key points (12 and four key points located on vertebra and heart respectively) in 2274 lateral X-ray images (training and validation datasets) of dogs, the model was then used to detect the key points in external testing dataset (396 images), the AP (average performance) for key point detection reach 86.4 %. Then we applied an additional post processing procedure to correct the output of HRNets so that the AP reaches 90.9 %. This result signifies that this system can effectively assist the evaluation of canine cardiomegaly in a real clinical scenario.
Subject(s)
Cardiomegaly/veterinary , Deep Learning , Dog Diseases , Animals , Cardiomegaly/diagnostic imaging , Dog Diseases/diagnostic imaging , Dogs , Heart , Reference ValuesABSTRACT
Transition-metal sulfide is pursued for replacing scare platinum-group metals for oxygen electrocatalysis and is of great importance in developing low-cost, high-performance rechargeable metal-air batteries. We report herein a facile cationic-doping strategy for preparing nickel-doped cobalt sulfide embedded into a mesopore-rich hydrangea-like carbon nanoflower. Nickel cations are introduced to induce the formation of Co3+-active species and more oxygen vacancies due to higher electronegativity and smaller ionic radius, thereby strengthening the intrinsic activity for oxygen electrocatalysis. Moreover, hydrangea-like superstructure composed of interconnected carbon cages provides abundant accessible active sites and hierarchical porosity. As a result, it shows excellent catalytic performance with a superior mass activity for the oxygen reduction reaction to the state-of-the-art Pt/C catalyst and a low overpotential of 314 mV at 10 mA cm-2 for the oxygen evolution reaction. When used as an air cathode for the rechargeable Zn-air battery, it delivers a peak power density of 96.3 mW cm-2 and stably operates over 214 h. This work highlights the importance of cationic doping in strengthening the electrocatalytic performance of 3d-transition-metal chalcogenides.
ABSTRACT
We report a self-template and facile pyrolysis method to synthesize Fe/Fe3C-decorated metal-nitrogen-carbon mesoporous nanospheres, of which preserved plum-like and hollow structures can be simply engineered via controlling the thickness of the outermost polydopamine layer in the precursors. The preserved plum-like structure is demonstrated to show a large electrochemically active surface area and facilitate fast charge transfer, in comparison with the hollow one. The catalytic activities of metal-nitrogen-carbon and nitrogen-doped carbon active sites in the outer carbon layer toward oxygen reduction are improved under the activation of the encased Fe species. Hence, preserved plum-like structures exhibit excellent catalytic kinetics toward the oxygen reduction reaction in alkaline media. The mass activity of 21.0 mA mgcatalyst-1 at 0.9 V vs RHE is achieved and the half-wave potential is 50 mV more positive than that of the Pt/C catalyst with the same mass loading. Moreover, the outer carbon layer endows the tolerance of strong acidic and alkaline environments, resulting in good durability. Our study proposes a simple strategy for the rational design of novel transition metal carbide-based catalysts, making it a promising candidate for replacing platinum-group metal catalysts in low-temperature fuel cells.
