Objective To investigate the application effect of remote Teach-back method on the precise management of elderly patients with osteoporosis (OP). Methods From June to September 2016, the elderly outpatients with OP were selected and randomly divided into the conventional treatment group and the remote Teach-back comprehensive treatment group. The remote Teach-back comprehensive treatment group was given the mobile Internet (WeChat) on the basis of conventional treatment to guide patients with remote OP treatment. The OP knowledge, OP self-efficacy, activities of daily living (ADL), bone mineral density (BMD) and other indicators were compared between the two groups after 12 months of treatment. Results A total of 80 elderly patients with OP were included, with 40 patients in each group. The comparison of the improvement values of the two groups showed that the remote Teach-back comprehensive treatment group was superior to the conventional treatment group in terms of exercise knowledge, calcium knowledge, exercise performance, ADL and BMD (P<0.05). Among them, the improvement in exercise knowledge intervention in the remote Teach-back group and the conventional treatment group were 1.870 (1.098, 2.870) and 0.670 (0.043, 1.283); the improvement values of calcium knowledge intervention in the two groups were 2.495 (1.860, 3.455) and 0.250 (–0.810, 0.705); the improvement values after exercise intervention in the two groups were 15.015 (10.490, 26.175) and 6.045 (1.405, 13.545); the improvement of ADL in the two groups were 13.565 (4.053, 23.768) and 2.245 (–4.953, 7.872); the improvement of lumbar 1-waist 4 BMD in the two groups were 0.155 (0.010, 0.313) g/cm2 and 0.045 (–0.095, 0.160) g/cm2; the hip BMD improvement values of the two groups were 0.130 (–0.023, 0.245) g/cm2 and 0.035 (–0.043, 0.165) g/cm2. There was no significant difference in OP knowledge between the two groups (P>0.05). Conclusions Remote Teach-back online education makes OP management more effective. Relying on the characteristics of rehabilitation education and rehabilitation training, we will establish an advantageous management model to prevent the occurrence of OP and fragility fractures and improve the quality of life.
How to accurately identify factors of cancer occurrence and to provide intervention early are the key issues that urgently need to be addressed in cancer prevention and treatment. Mendelian randomization (MR) analysis uses genetic variants as instrument variables for exposures of interest, which compensates the shortcomings of traditional observational studies and clinical trials. This review introduced the current application status of MR analysis in cancer etiology and treatment researches in details, including assessment of cancer risk factors, exploration of cancer treatment targets, and evaluation of drug efficiency and adverse reactions. The scopes and dimensions of cancer etiology and treatment researches are greatly expanded because of various MR designs and abundant high-level omics data. As well, it provides a practical and feasible method for constructing cancer etiology networks and drug targeted databases, which are promising for supporting the development of precision cancer prevention and treatment.
In recent years, deep learning has provided a new method for cancer prognosis analysis. The literatures related to the application of deep learning in the prognosis of cancer are summarized and their advantages and disadvantages are analyzed, which can be provided for in-depth research. Based on this, this paper systematically reviewed the latest research progress of deep learning in the construction of cancer prognosis model, and made an analysis on the strengths and weaknesses of relevant methods. Firstly, the construction idea and performance evaluation index of deep learning cancer prognosis model were clarified. Secondly, the basic network structure was introduced, and the data type, data amount, and specific network structures and their merits and demerits were discussed. Then, the mainstream method of establishing deep learning cancer prognosis model was verified and the experimental results were analyzed. Finally, the challenges and future research directions in this field were summarized and expected. Compared with the previous models, the deep learning cancer prognosis model can better improve the prognosis prediction ability of cancer patients. In the future, we should continue to explore the research of deep learning in cancer recurrence rate, cancer treatment program and drug efficacy evaluation, and fully explore the application value and potential of deep learning in cancer prognosis model, so as to establish an efficient and accurate cancer prognosis model and realize the goal of precision medicine.
Mixed reality technology is new digital holographic imaging technology that generates three-dimensional simulation images through computers and anchors the virtual images to the real world. Compared with traditional imaging diagnosis and treatment methods, mixed reality technology is more conducive to the advantages of precision medicine, helps to promote the development of medical clinical application, teaching and scientific research in the field of orthopedics, and will further promote the progress of clinical orthopedics toward standardization, digitization and precision. This article briefly introduces the mixed reality technology, reviews its application in the perioperative period, teaching and diagnosis and treatment standardization and dataization in the field of orthopedics, and discusses its technical advantages, aiming to provide a reference for the better use of mixed reality technology in orthopedics.
In 2022, there were 367.7 thousands new cases and 316.5 thousands deaths of primary liver cancer in China. Radiofrequency ablation (RFA) is one of the radical treatments for liver cancer. It has the characteristics of definite curative effect, minimal invasion and low cost. In patients with early liver cancer, the curative effect is similar to surgical resection. The concept and practice of precision surgery provide new ideas for improving the efficacy of RFA. Based on the RFA treatment experience of more than ten thousand cases, the author’s team has carried out research on the precise ablation treatment of primary liver cancer. Now, the author will combine the existing literature and our team’s experience to discuss the application and prospect of the precise surgery concept in RFA treatment.
Exercise intervention is an important non-pharmacological intervention for various diseases, and establishing precise exercise load assessment techniques can improve the quality of exercise intervention and the efficiency of disease prevention and control. Based on data collection from wearable devices, this study conducts nonlinear optimization and empirical verification of the original "Fitness-Fatigue Model". By constructing a time-varying attenuation function and specific coefficients, this study develops an optimized mathematical model that reflects the nonlinear characteristics of training responses. Thirteen participants underwent 12 weeks of moderate-intensity continuous cycling, three times per week. For each training session, external load (actual work done) and internal load (heart rate variability index) data were collected for each individual to conduct a performance comparison between the optimized model and the original model. The results show that the optimized model demonstrates a significantly improved overall goodness of fit and superior predictive ability. In summary, the findings of this study can support dynamic adjustments to participants' training programs and aid in the prevention and control of chronic diseases.
ObjectiveTo investigate the role of 3D visualization technology in the laparoscopic precise hepatectomy. MethodsTo retrieve the literatures about the application of 3D visualization technology in laparoscopic precise hepatectomy, and summarize and analyzed them. ResultsThe application of 3D visualization in laparoscopic precise hepatectomy could effectively reduce the operative time, blood loss, blood transfusion rate, and total complication rate. The application of 3D visualization in preoperative evaluation of the resection surface and residual liver volume had been relatively mature. Although many organizations try to use 3D visualization in laparoscopic hepatectomy, such as laser registration and real-time intraoperative navigation, it had not been widely used in clinic because of technical limitations. ConclusionsExisting research results show that, the application of 3D visualization technology in laparoscopic precise hepatectomy can improve the resectability of lesions, increase perioperative safety, but intraoperative navigation is still need to be further developed before it is expected to be widely used in clinical practice. Existing evidence of increased benefit from laparoscopic precise hepatectomy with 3D reconstruction remains limited, and more rigorous randomized controlled trials of large cases are needed to confirm this.
Non-small cell lung cancer is one of the cancers with the highest incidence and mortality rate in the world, and precise prognostic models can guide clinical treatment plans. With the continuous upgrading of computer technology, deep learning as a breakthrough technology of artificial intelligence has shown good performance and great potential in the application of non-small cell lung cancer prognosis model. The research on the application of deep learning in survival and recurrence prediction, efficacy prediction, distant metastasis prediction, and complication prediction of non-small cell lung cancer has made some progress, and it shows a trend of multi-omics and multi-modal joint, but there are still shortcomings, which should be further explored in the future to strengthen model verification and solve practical problems in clinical practice.