Biotemplated Syntheses of Macroporous Materials for Bone Tissue Engineering Scaffolds and Experiments in Vitro and Vivo

The macroporous materials were prepared from the transformation of cuttlebone as biotemplates under hydrothermal reactions and characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric/differential thermal analyses (TG-DTA), and scanning electron microscopy (SEM). Cell experimental results showed that the prepared materials as bone tissue engineering scaffolds or fillers had fine biocompatibility suitable for adhesion and proliferation of the hMSCs (human marrow mesenchymal stem cells). Histological analyses were carried out by implanting the scaffolds into a rabbit femur, where the bioresorption, degradation, and biological activity of the scaffolds were observed in the animal body. The prepared scaffolds kept the original three-dimensional frameworks with the ordered porous structures, which made for blood circulation, nutrition supply, and the cells implantation. The biotemplated syntheses could provide a new effective approach to prepare the bone tissue engineering scaffold materials

Data_Sheet_1_Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm.xlsx

The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions.</p