Bone-inspired (GNEC/HAPAAm) hydrogel with fatigue-resistance for use in underwater robots and highly piezoresistive sensors.

A novel bone-inspired fatigue-resistant hydrogel with excellent mechanical and piezoresistive properties was developed, and it exhibited great potential as a load and strain sensor for underwater robotics and daily monitoring. The hydrogel was created by using the high edge density and aspect ratio of graphene nanosheet-embedded carbon (GNEC) nanomaterials to form a three-dimensional conductive network and prevent the expansion of microcracks in the hydrogel system. Multiscale progressive enhancement of the organic hydrogels (micrometer scale) was realized with inorganic graphene nanosheets (nanometer scale). The graphene nanocrystals inside the GNEC film exhibited good electron transport properties, and the increased distances between the graphene nanocrystals inside the GNEC film caused by external forces increased the resistance, so the hydrogel was highly sensitive and suitable for connection to a loop for sensing applications. The hydrogels obtained in this work exhibited excellent mechanical properties, such as tensile properties (strain up to 1685%) and strengths (stresses up to 171 kPa), that make them suitable for use as elastic retraction devices in robotics and provide high sensitivities (150 ms) for daily human monitoring.

Elastography and contrast-enhanced ultrasound to assess the effect of varicocelectomy: A case-controlled study.

To evaluate the changes in testicular stiffness and microcirculation caused by spermatic vein ligation in patients with varicocele, we conducted a case-controlled study. A total of 27 grade III left varicocele cases were enrolled. Testicular stiffness and perfusion were evaluated by shear wave elastography and contrast-enhanced ultrasound during subinguinal microscopic varicocelectomy. The external and the internal parenchyma of bilateral testes were selected to compare the shear wave velocity of bilateral testes during the spermatic vein ligation. We mapped and compared the intensity-time curves following bolus contrast injection three times in the same region of interest. Initially, the shear wave velocity of the left internal parenchyma was higher than the right side (1.10 ± 0.06 m/s vs. 1.00 ± 0.03 m/s). It decreased (1.09 ± 0.06 m/s) (p < 0.05) after ligation. Meanwhile, the left epididymis had the higher agent peak intensity (0.90 × 10E-5 AU), the largest area under the curve (80.20 × 10E-5 AU s), and the longest washout area (54.35 × 10E-5 AU s). In addition, the left internal parenchyma presented a sharper slope (0.18 × 10E-5 AU/s) (p < 0.05). In conclusion, the spermatic vein ligation improved the perfusion of the internal testicular parenchyma, but it could temporally deteriorate the stasis of the epididymis. These changes caused softer testicular parenchyma.

Application of Deep Learning in Quantitative Analysis of 2-Dimensional Ultrasound Imaging of Nonalcoholic Fatty Liver Disease.

OBJECTIVES: To verify the value of deep learning in diagnosing nonalcoholic fatty liver disease (NAFLD) by comparing 3 image-processing techniques. METHODS: A total of 240 participants were recruited and divided into 4 groups (normal, mild, moderate, and severe NAFLD groups), according to the definition and the ultrasound scoring system for NAFLD. Two-dimensional hepatic imaging was analyzed by the envelope signal, grayscale signal, and deep-learning index obtained by 3 image-processing techniques. The values of the 3 methods ranged from 0 to 65,535, 0 to 255, and 0 to 4, respectively. We compared the values among the 4 groups, draw receiver operating characteristic curves, and compared the area under the curve (AUC) values to identify the best image-processing technique. RESULTS: The envelope signal value, grayscale value, and deep-learning index had a significant difference between groups and increased with the severity of NAFLD (P < .05). The 3 methods showed good ability (AUC > 0.7) to identify NAFLD. Meanwhile, the deep-learning index showed the superior diagnostic ability in distinguishing moderate and severe NAFLD (AUC = 0.958). CONCLUSIONS: The envelope signal and grayscale values were vital parameters in the diagnosis of NAFLD. Furthermore, deep learning had the best sensitivity and specificity in assessing the severity of NAFLD.