Preparation and In Vitro Bioactivity Study of a Novel Hollow Mesoporous Bioactive Glass Nanofiber Scaffold.

In this study, a novel three-dimensional hollow mesoporous bioactive glass nanofiber scaffold has been synthesized with a template-assisted sol-gel method using bacterial cellulose (BC) as a template and nonionic triblock copolymer (P123) as a pore-directing agent, ethyl orthosilicate (TEOS), calcium nitrate tetrahydrate (CN), and triethyl phosphate (TEP) as glass precursors. Scanning and transmission electron microscopies, X-ray diffraction, nitrogen adsorption-desorption, and nuclear magnetic resonance method were applied to characterize the morphology, crystal structure, and chemical structure of the mesoporous bioactive glass nanofiber scaffold. Furthermore, the in vitro bioactivity and biocompatibility were also explored. The obtained scaffold depicted nanofiber-like morphology and interconnected three-dimensional network structure that replicated the BC template. The scaffold showed a large specific surface area (230.0 cm2 g-1) and pore volume (0.2 m3 g-1). More importantly, the scaffold exhibited excellent apatite-forming ability and cellular biocompatibility. We believe that the hollow mesoporous bioactive glass nanofiber scaffold has great potential application in bone tissue regeneration.

Strength and deformation characteristics of waste mud-solidified soil.

The treatment, disposal, and resource utilization of waste mud are challenges for engineering construction. This study investigates the road performance of waste mud-solidified soil and explains how solidifying materials influence the strength and deformation characteristics of waste mud. Unconfined compressive strength tests, consolidated undrained triaxial shear tests, resonant column tests, and consolidation compression tests were conducted to evaluate the solidification effect. The test results show that with an increase in cement content from 5 to 9%, the unconfined compressive strength of the waste mud-solidified soil increased by over 100%, the curing time was extended from 3 to 28 days, and the unconfined compressive strength increased by approximately 70%. However, an increase in initial water content from 40 to 60% reduced the unconfined compressive strength by 50%. With the increase of cement content from 5 to 9%, the cohesion and friction angles increased by approximately 78% and 24%, respectively. The initial shear modulus under dynamic shear increased by approximately 38% and the shear strain corresponding to a damping ratio decay to 70% of the initial shear modulus decreased by nearly 11%. The compression coefficient decreased by approximately 55%. Scanning electron microscopy and X-ray diffraction tests showed that a higher cement content led to the formation of more hydration reaction products, especially an increase in the content of AlPO4, which can effectively fill the pores between soil particles, enhance the bonding between soil particles, and form a skeleton with soil particles to improve compactness. Consequently, the strength of the waste mud-solidified soil increased significantly while its compressibility decreased. This study can provide data support for dynamic characteristics of waste mud solidified soil subgrade.

Deep learning based ultrasonic visualization of distal humeral cartilage for image-guided therapy: a pilot validation study.

Background: Ultrasound is widely used for image-guided therapy (IGT) in many surgical fields, thanks to its various advantages, such as portability, lack of radiation and real-time imaging. This article presents the first attempt to utilize multiple deep learning algorithms in distal humeral cartilage segmentation for dynamic, volumetric ultrasound images employed in minimally invasive surgery. Methods: The dataset, consisting 5,321 ultrasound images were collected from 12 healthy volunteers. These images were randomly split into training and validation sets in an 8:2 ratio. Based on deep learning algorithms, 9 semantic segmentation networks were developed and trained using our dataset at Southern University of Science and Technology Hospital in September 2022. The performance of the networks was evaluated based on their segmenting accuracy and processing efficiency. Furthermore, these networks were implemented in an IGT system to assess their feasibility in 3-dimentional imaging precision. Results: In 2D segmentation, Medical Transformer (MedT) showed the highest accuracy result with a Dice score of 89.4%, however, the efficiency in processing images was relatively lower at 2.6 frames per second (FPS). In 3D imaging, the average root mean square (RMS) between ultrasound (US)-generated models based on the networks and magnetic resonance imaging (MRI)-generated models was no more than 1.12 mm. Conclusions: The findings of this study indicate the technological feasibility of a novel method for real-time visualization of distal humeral cartilage. The increased precision of ultrasound calibration and segmentation are both important approaches to improve the accuracy of 3D imaging.