Coatless modification of 3D-printed Ti6Al4V implants through tailored Cu ion implantation combined with UV photofunctionalization to enhance cell attachment, osteogenesis and angiogenesis.

The three-dimensional-printed Ti6Al4V implant (3DTi) has been widely accepted for the reconstruction of massive bone defects in orthopedics owing to several advantages, such as its tailored shape design, avoiding bone graft and superior bone-implant interlock. However, the osteoinduction activity of 3DTi is inadequate when applied clinically even though it exhibits osteoconduction. This study developes a comprehensive coatless strategy for the surface improvement of 3DTi through copper (Cu) ion implantation and ultraviolet (UV) photofunctionalization to enhance osteoinductivity. The newly constructed functional 3DTi (UV/Ti-Cu) achieved stable and controllable Cu doping, sustained Cu2+ releasing, and increased surface hydrophilicity. By performing cellular experiments, we determined that the safe dose range of Cu ion implantation was less than 5×1016 ions/cm2. The implanted Cu2+ enhanced the ALP activity and the apatite formation ability of bone marrow stromal cells (BMSCs) while slightly decreasing proliferation ability. When combined with UV photofunctionalization, cell adhesion and proliferation were significantly promoted and bone mineralization was further increased. Meanwhile, UV/Ti-Cu was conducive to the migration and angiogenesis of human umbilical vein endothelial cells (HUVECs) in vitro, theoretically facilitating vascular coupling osteogenesis. In conclusion, UV/Ti-Cu is a novel attempt to apply two coatless techniques for the surface modification of 3DTi. In addition, it is considered a potential bone substrate for repairing bone defects.

A pig model of symptomatic spinal epidural hematoma.

PURPOSE: The purpose of this study was to establish an animal model capable of simulating the development and decompression process of symptomatic spinal epidural hematoma (SSEH). METHODS: A total of 16 male Bama miniature pigs were included in this study and randomly allocated into four groups: Group A (4 h 20 mmHg hematoma compression), Group B (4 h 24 mmHg hematoma compression), Group C (4 h 28 mmHg hematoma compression), and Group Sham (control). Real-time intra-wound hematoma compression values were obtained using the principle of connectors. Electrophysiological analyses, including the latency and amplitude of somatosensory evoked potentials (SSEP) and motor evoked potentials (MEP), along with behavioral observations (Tarlov score), were performed to assess this model. RESULTS: ANOVA tests demonstrated significant differences in the latency and relative amplitude of SSEP and MEP between Groups C and Sham after 4 h of hematoma compression and one month after surgery (P < 0.01). Behavioral assessments 8 h after surgery indicated that animals subjected to 28 mmHg hematoma compression suffered the most severe spinal cord injury. Pearson correlation coefficient test suggested a negative correlation between the epidural pressure and Tarlov score (r = -0.700, p < 0.001). With the progression of compression and the escalation of epidural pressure, the latency of SSEP and MEP gradually increased, while the relative amplitude gradually decreased. CONCLUSIONS: When the epidural pressure reaches approximately 24 mmHg, the spinal cord function occurs progressive dysfunction. Monitoring epidural pressure would be an effective approach to assist to identify the occurrence of postoperative SSEH.

Investigating the use of a two-stage attention-aware convolutional neural network for the automated diagnosis of otitis media from tympanic membrane images: a prediction model development and validation study.

OBJECTIVES: This study investigated the usefulness and performance of a two-stage attention-aware convolutional neural network (CNN) for the automated diagnosis of otitis media from tympanic membrane (TM) images. DESIGN: A classification model development and validation study in ears with otitis media based on otoscopic TM images. Two commonly used CNNs were trained and evaluated on the dataset. On the basis of a Class Activation Map (CAM), a two-stage classification pipeline was developed to improve accuracy and reliability, and simulate an expert reading the TM images. SETTING AND PARTICIPANTS: This is a retrospective study using otoendoscopic images obtained from the Department of Otorhinolaryngology in China. A dataset was generated with 6066 otoscopic images from 2022 participants comprising four kinds of TM images, that is, normal eardrum, otitis media with effusion (OME) and two stages of chronic suppurative otitis media (CSOM). RESULTS: The proposed method achieved an overall accuracy of 93.4% using ResNet50 as the backbone network in a threefold cross-validation. The F1 Score of classification for normal images was 94.3%, and 96.8% for OME. There was a small difference between the active and inactive status of CSOM, achieving 91.7% and 82.4% F1 scores, respectively. The results demonstrate a classification performance equivalent to the diagnosis level of an associate professor in otolaryngology. CONCLUSIONS: CNNs provide a useful and effective tool for the automated classification of TM images. In addition, having a weakly supervised method such as CAM can help the network focus on discriminative parts of the image and improve performance with a relatively small database. This two-stage method is beneficial to improve the accuracy of diagnosis of otitis media for junior otolaryngologists and physicians in other disciplines.