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.
ObjectiveTo summarize the application of circulating free DNA (cfDNA) in the diagnosis and treatment of hepatocellular carcinoma (HCC). MethodThe relevant literature on the application of cfDNA in the diagnosis and treatment of HCC both domestic and international was reviewed and summarized. ResultsThe cfDNA is an emerging biomarker in recent years. At present, the different detection methods had been reported in a large number of studies to detect abnormal methylation, hot spot mutation, gene copy number variation, quantitative detection of cfDNA concentration, etc. It was found that the cfDNA could be used in the management process of early diagnosis, treatment guidance, and efficacy evaluation of HCC patients. ConclusionscfDNA detection is a good tool in the diagnosis and treatment of HCC, which can help clinicians make-decisions and bring more possibilities for the diagnosis and treatment of HCC, which is of great significance for changing the current diagnosis and treatment of HCC. However, there are still many challenges in cost control, technology optimization, and standardization of evaluation indicators. With the continuous progress of molecular biology technology and artificial intelligence, the application of cfDNA in diagnosis and treatment of HCC will be further expanded, its advantages will be better played, and the related shortcomings will be gradually solved.
This article introduces the exploration and establishment of “grass-roots Party building + targeted poverty alleviation” model by the Party Branch of Emergency Department of West China Hospital of Sichuan University, and discusses how to establish the “trinity mode” of management support, personnel training, and on-site guidance under the leading of grass-roots Party building through a series of the branches combined activities, according to the core idea of “strengthening the Party construction, bringing people closer together, and promoting development”. The aim is to form a long-term mechanism of grass-roots Party building and targeted medical poverty alleviation through continuously implementing this model, which can benefit more people in remote and ethnic minority areas and contribute to “Healthy China 2030”.
With the advancement of thyroid tumor treatment concepts and the progress of standardized treatment processes nationwide, the 5-year survival rate of thyroid tumors in China has risen from 67.5% in 2003 to 84.3% in 2015. As China has been continuously enriching its treatment options for advanced thyroid cancer in recent years, gradually improving the standardized treatment system for early and intermediate thyroid cancer, enhancing multidisciplinary collaboration methods and concepts, and regularizing scientific statistics, the survival rate of thyroid tumors continues to improve. We still need to consider the future development direction and core driving force of China’s thyroid discipline, correctly view the “prosperous” stage of domestic thyroid discipline development, and actively review the future development direction of China’s thyroid discipline.
ObjectiveTo review the progress of radiomics in the field of colorectal cancer in recent years and summarize its value in the imaging diagnosis of colorectal cancer.MethodsEighty English and seven Chinese articles were retrieved through PUBMED, OVID, CNKI, Weipu and Wanfang. The structure and content of these literatures were classified and analyzed.ResultsIn five studies predicting the preoperative stages of colorectal cancer based on CT radiomics, the area under curve (AUC) ranged from 0.736 to 0.817; in two studies predicting the preoperative stages of colorectal cancer based on MRI radiomics, the AUC were 0.87 and 0.827 respectively. In two studies about radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy based on CT, the AUC were 0.79 and 0.72 respectively; in four studies about radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy based on MRI, the AUC ranged from 0.84 to 0.979. In one study evaluating the sensitivity of neoadjuvant chemotherapy based on MRI radiomics, the AUC was 0.79. In one study predicting the postoperative survival rate based on MRI radiomics, the AUC value of the final model was 0.827. In one study, the accuracy of the model based on PET/CT radiomics in 4-year disease-free survival (DSS), progression-free survival (DFS) and overall survival (OS) were 0.87, 0.79 and 0.79 respectively.ConclusionAt present, radiomics has a valuable impact on preoperative staging, neoadjuvant therapy evaluation, and survival analysis of colorectal cancer.
Retinitis pigmentosa (RP) is an inherited retinal disease characterized by degeneration of retinal pigment epithelial cells. Precision medicine is a new medical model that applies modern genetic technology, combining living environment, clinical data of patients, molecular imaging technology and bio-information technology to achieve accurate diagnosis and treatment, and establish personalized disease prevention and treatment model. At present, precise diagnosis of RP is mainly based on next-generation sequencing technology and preimplantation genetic diagnosis, while precise therapy is mainly reflected in gene therapy, stem cell transplantation and gene-stem cell therapy. Although the current research on precision medicine for RP has achieved remarkable results, there are still many problems in the application process that is needed close attention. For instance, the current gene therapy cannot completely treat dominant or advanced genetic diseases, the safety of gene editing technology has not been solved, the cells after stem cell transplantation cannot be effectively integrated with the host, gene sequencing has not been fully popularized, and the big data information platform is imperfect. It is believed that with the in-depth research of gene sequencing technology, regenerative medicine and the successful development of clinical trials, the precision medicine for RP will be gradually improved and is expected to be applied to improve the vision of patients with RP in the future.
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.
Tuberculosis (TB) is one of the major public health concerns worldwide. Since the development of precision medicine, the filed regarding TB control and prevention has been brought into the era of precision medicine. Although great progress has been achieved in the accurate diagnosis, treatment and management of TB patients, we have to face several challenges. We should seize the opportunity, and develop and improve novel measures in TB prevention on the basis of precision medicine. The accurate diagnosis criteria, treatment regimen and management of TB patients should be carried out according to the standard of precision medicine. We aim to improve the treatment of TB patients and prevent the transmission of TB in the community, thereby contributing to the achievement of the End TB Strategy by 2035.
ObjectiveTo categorize and describe stroke-patients based on factors related to patient reported outcomes. MethodsA questionnaire survey was conducted among stroke-patients in nine hospitals and communities in Shanxi Province. The general information questionnaire and stroke-patient reported outcome manual (Stroke-PROM) were completed. Latent profile analysis was used to analyze the scores of Stroke-PROM, and the explicit variables of the model were the final scores of each dimension. ANOVA and correlation analysis were used to measure the correlation between the factors and subtypes. ResultsFour unique stroke-patient profiles emerged, including a low physiological and low social group (9%), a high physiological and middle social group (40%), a middle physiological and middle social group (26%), and a middle physiological and high social group (25%). There were significant differences in scores of four areas among patients with different subtypes (P<0.05). Moreover, there was a correlation between age, payment, exercise and subtypes (P<0.05). ConclusionThere are obvious grouping characteristics for stroke patients. It is necessary to focus on stroke patients who are advanced in age, have a self-funded status and lack exercise, and provide targeted nursing measures to improve their quality of life.