Computed tomographic findings may be useful for differentiating small intestinal adenocarcinomas,lymphomas, and spindle cell sarcomas in dogs

An improved understanding of the CT characteristics for histologically confirmed primary intestinal tumors would be helpful for guiding prognosis and treatment plans in affected dogs. This retrospective, multi-center, analytical study aimed to evaluate the CT characteristics for the differentiation of adenocarcinoma, lymphoma, and spindle cell sarcoma (SCS) in dogs. Thirty-seven dogs who underwent contrast CT and histopathological examinations were included (adenocarcinomas, n = 11; lymphomas, n = 12; SCS, n = 14). Quantitative and qualitative CT parameters, including tumor morphology, contrast enhancement pattern, Hounsfield unit (HU) value, and presence or absence of intraabdominal lymphadenopathy, were evaluated for each included small intestine tumor CT case. Adenocarcinomas tended to show endophytic growth, intestinal obstruction, and a heterogeneous enhancement pattern. Lymphomas tended to show exophytic growth, contrast enhancement of the intestinal tumor mucosal layer, a homogeneous enhancement pattern, and the presence of lymphadenopathies in the abdominal cavity. SCSs tended to show lobulated growth, a large cystic portion within the tumor, a heterogeneous enhancement pattern, a large size with fat stranding sign, and lower HU values in postcontrast images. Cut-off values of the minimum diameter/fifth lumbar vertebral mid-body height (≥5.80; area under the curve [AUC] = 0.97, P < 0.001) and minimum HU value/HU value of the aorta (≤0.26; AUC = 0.96, P < 0.001) were derived to discriminate SCS from the two other tumor types. In conclusion, contrast CT characteristics may be useful in differentiating small intestinal adenocarcinomas, lymphomas, and SCSs in dogs.     

Deep learning-based diagnosis of stifle joint diseases in dogs

In this retrospective, analytical study, we developed a deep learning-based diagnostic model that can be applied to canine stifle joint diseases and compared its accuracy with that achieved by veterinarians to verify its potential as a reliable diagnostic method. A total of 2382 radiographs of the canine stifle joint from cooperative animal hospitals were included in a dataset. Stifle joint regions were extracted from the original images using the faster region-based convolutional neural network (R-CNN) model, and the object detection accuracy was evaluated. Four radiographic findings: patellar deviation, drawer sign, osteophyte formation, and joint effusion, were observed in the stifle joint and used to train a residual network (ResNet) classification model. Implant and growth plate groups were analyzed to compare the classification accuracy against the total dataset. All deep learning-based classification models achieved target accuracies exceeding 80%, which is comparable to or slightly less than those achieved by veterinarians. However, in the case of drawer signs, further research is necessary to improve the low sensitivity of the model. When the implant group was excluded, the classification accuracy significantly improved, indicating that the implant acted as a distraction. These results indicate that deep learning-based diagnoses can be expected to become useful diagnostic models in veterinary medicine.     

Single-Crystal Epitaxial Thin Films of the Isotropic Metallic Oxides Sr1-xCaxRuO3 (0 le x le 1)

Single-crystal epitaxial thin films of the isotropic metallic oxides Sr1-xCaxRuO(3) (0 相似文献   

Positron emission tomography/computed tomography imaging features of renal cell carcinoma and pulmonary metastases in a dog

A 9-year-old spayed female cocker spaniel dog was referred for hematuria. A large abdominal mass and multiple pulmonary nodules were identified radiographically. A whole-body 2-deoxy-2-[¹⁸F]fluoro-d-glucose positron emission tomography/computed tomography (PET/CT) scan revealed intensely increased uptake in a renal mass and the pulmonary nodules. Renal cell carcinoma was diagnosed on histological examination.     

Effect of Harderian adenectomy on the statistical analyses of mouse brain imaging using positron emission tomography

Positron emission tomography (PET) using 2-deoxy-2-[¹⁸F] fluoro-D-glucose (FDG) as a radioactive tracer is a useful technique for in vivo brain imaging. However, the anatomical and physiological features of the Harderian gland limit the use of FDG-PET imaging in the mouse brain. The gland shows strong FDG uptake, which in turn results in distorted PET images of the frontal brain region. The purpose of this study was to determine if a simple surgical procedure to remove the Harderian gland prior to PET imaging of mouse brains could reduce or eliminate FDG uptake. Measurement of FDG uptake in unilaterally adenectomized mice showed that the radioactive signal emitted from the intact Harderian gland distorts frontal brain region images. Spatial parametric measurement analysis demonstrated that the presence of the Harderian gland could prevent accurate assessment of brain PET imaging. Bilateral Harderian adenectomy efficiently eliminated unwanted radioactive signal spillover into the frontal brain region beginning on postoperative Day 10. Harderian adenectomy did not cause any post-operative complications during the experimental period. These findings demonstrate the benefits of performing a Harderian adenectomy prior to PET imaging of mouse brains.     

Domain dynamics during ferroelectric switching

The utility of ferroelectric materials stems from the ability to nucleate and move polarized domains using an electric field. To understand the mechanisms of polarization switching, structural characterization at the nanoscale is required. We used aberration-corrected transmission electron microscopy to follow the kinetics and dynamics of ferroelectric switching at millisecond temporal and subangstrom spatial resolution in an epitaxial bilayer of an antiferromagnetic ferroelectric (BiFeO(3)) on a ferromagnetic electrode (La(0.7)Sr(0.3)MnO(3)). We observed localized nucleation events at the electrode interface, domain wall pinning on point defects, and the formation of ferroelectric domains localized to the ferroelectric and ferromagnetic interface. These results show how defects and interfaces impede full ferroelectric switching of a thin film.