Objective To evaluate the value of low-dose spectral CT imaging combined with ASIR recon-struction in differenting peripheral lung cancer from inflammatory mass. Methods We retrospectively analyzed the imaging data of 147 cases with 165 pulmonary nodules or masses underwent enhanced CT scans with spectral imag-ing mode from June 2015 to May 2016. Spectral curves,normalized slope rates,iodine-water concentration were measured on the lesions in arteral phase images. The differences of the spectral characteristic parameters were eval-uated statistically by indepengdent samples t test. Results Among 165 pulmonary nodules or masses,including 68 inflammatory mass and 97 peripheral lung cancer. In the arterial phase,the iodine concentration,water concentra-tion and normalized spectral curves rates of peripheral lung cancer were 10.93 ± 5.12,1033.96 ± 9.74,0.998 ± 0.66,which was obviously higher than those in inflammatory mass in 5.29 ± 0.96,1028.85 ± 9.31,0.620 ± 0.16. There were significant difference between peripheral lung cancer and inflammatory mass(0.000 ,0.008 ,0.001 in P values). Conclusion The iodine concentration,water concentration and normalized spectral curves rates has high value for differential diagnosis of peripheral pung cancer and pneumonia mass in the arterial phase using low-dose spectral CT imaging combined with ASIR reconstruction.

Human embryonic stem cells (hESCs) are pluripotent cells that have the ability of unlimited self-renewal and can be differentiated into different cell lineages, including neural stem (NS) cells. Diverse regulatory signaling pathways of neural stem cells differentiation have been discovered, and this will be of great benefit to uncover the mechanisms of neuronal differentiation in vivo and in vitro. However, the limitations of hESCs resource along with the religious and ethical concerns impede the progress of ESCs application. Therefore, the induced pluripotent stem cells (iPSCs) via somatic cell reprogramming have opened up another new territory for regenerative medicine. iPSCs now can be derived from a number of lineages of cells, and are able to differentiate into certain cell types, including neurons. Patient-specifi c iPSCs are being used in human neurodegenerative disease modeling and drug screening. Furthermore, with the development of somatic direct reprogramming or lineage reprogramming technique, a more effective approach for regenerative medicine could become a complement for iPSCs.
Cell Differentiation ; Cell Lineage ; Cell Transdifferentiation ; Cellular Reprogramming ; drug effects ; Embryonic Stem Cells ; cytology ; Humans ; Induced Pluripotent Stem Cells ; cytology ; transplantation ; Neural Stem Cells ; cytology ; transplantation ; Neurodegenerative Diseases ; therapy ; Regenerative Medicine ; Transcription Factors ; genetics ; metabolism

Artificial Intelligence (AI) is an interdisciplinary subject developed on the basis of computer technology, cybernetics, mathematics, philosophy and brain science. The purpose of AI is to study new ways to extend the intelligence of human brain in various fields. In recent years, the rapid development of AI technology has brought innovation to medical science and health care. During the pandemic of coronavirus disease 2019 (COVID-19) AI has been widely used in epidemiological investigation and outbreak prediction, clinical diagnosis and treatment, hospital management, research and development of new drugs and vaccines. The application of AI has reduced the clinical workload and the consumption of medical resources, greatly assisted the battle against COVID-19.This article introduces the progresses on the applications of AI technology to provide information for its further application in the fighting against COVID-19.

In recent years, artificial intelligence-related technologies have been deeply combined with many medical fields, and the intersection of medicine and engineering has become a hot topic. There are problems with heavy data and difficulty making decisions in orthopedic disease diagnosis and treatment. Machine learning is an important method of artificial intelligence. Since it can automatically analyze and predict medical big data, it is widely used in the field of orthopedics. It also assists physicians in completing disease diagnosis and treatment efficiently. In this review paper, the application and progress of machine learning in preoperative, intraoperative, and postoperative diagnosis and treatment in orthopedics are reviewed, providing new ways for exploring more rational diagnosis and treatment strategies.

Multiple injuries caused by trauma have high rates of disability and mortality and are difficult to treat, which have a negative impact on the patients, their families and the society. At present, the medical model of trauma treatment is still inadequate, and the treatment of trauma patients faces great challenges. Artificial intelligence (AI) is an intelligent technology based on machine learning, reinforcement learning and deep learning algorithm, and it has been applied to the treatment of patients with trauma. Its efficient and accurate computer vision, planning and decision-making, and big data statistical analysis not only improve the safety and efficiency in the treatment of trauma, but also reduce the workload of clinicians, which makes up for the deficiency of the traditional model of trauma care. After screening the recent studies of AI in trauma care, the authors review its application in emergency triage, diagnosis, treatment and prevention of war trauma, in order to introduce the latest research progress of AI in trauma care and provide references for future developments.

Objective To compare the differences in the long-term survival rate and the tumor shrinkage rate of inoperable hepatocellular carcinoma (HCC) between transcatheter arterial chemoembolization (TACE) combined with percutaneous ablation therapy and simple TACE therapy in order to provide the basis for the clinical treatment of HCC.Methods Randomized controlled trials (RCT) for comparing the difference in survival rates between TACE plus PA and simple TACE for inoperable HCC were searched from medical literature database,from which the relevant data were extracted.According to Cochrane manual standard,the quality of inclusion literature was evaluated.Results A total of 15 RCT papers were included in this study,including 859 HCC patients.The results of meta-analysis showed that all the 1-year,2-year and 3-year survival rates in TACE plus PA group were better than those in simple TACE group (RR=1.454,95％CI=1.333and 1.586,Z=8.56,P＜0.001;RR=1.781,95％CI=1.511 and 2.099,Z=6.88,P＜0.001;RR=2.351,95％CI=1.808 and 3.059,Z=6.37,P＜0.001,respectively).The tumor shrinkage rate in TACE plus PA group was also better than that in simple TACE group (RR=1.314,95％CI=1.190 and 1.452,Z=5.38,P＜0.001).The sensitivity analysis indicated that the results of the differences in survival rate and tumor shrinkage rate between the two groups were reliable.Conclusion The 1-year,2-year and 3-year survival rates as well as the tumor shrinkage rate of TACE plus PA group are higher than those of simple TACE group.

The ABRACL protein, the regulator of actin and cell motility, belongs to the HSPC280 family, and its conserved hydrophobic groove can interact with other proteins to facilitate actin motility and cellular activity. ABRACL is upregulated in tumor tissues and is closely linked with the proliferation and migration of tumor cells. A deeper understanding of the role of ABRACL in tumorigenesis and development may provide new ideas and insights for ABRACL to prevent or reverse tumor progression.

Orthopaedic surgery is complex and delicate. The Orthopaedic Navigation System is developed to provide an augmented reality three-dimensional (3D) visualization environment to improve treatment outcomes by analyzing preoperative, intraoperative and postoperative data. With the rapid development and clinical application of digital technology, artificial intelligence technology has been introduced into orthopaedic intraoperative navigation system. Artificial intelligence, combined with instrumentation devices and imaging technology, enhances the visualization capabilities of orthopaedic surgeons, allowing them to receive real-time feedback and guidance during surgery, which in turn provides optimal clinical decision-making. The application of artificial intelligence to intraoperative orthopaedic navigation also improves the repeatability of procedures and reduces the incidence of human error. This paper reviews the current status of the application of artificial intelligence in orthopaedic intraoperative navigation, and introduces the basic concepts of artificial and the development of image alignment, real-time tracking, and 3D visualization techniques based on artificial intelligence, as well as discusses the current limitations and shortcomings.

OBJECTIVETo understand the knowledge of acupuncture disease spectrum, expectation of acupuncture treatment in Chengdu residents.

METHODSA questionnaire regarding the knowledge of acupuncture disease spectrum and expectation of acupuncture treatment in Chengdu residents was established. By field sampling and internet survey, related data were collected and analyzed.

RESULTSTotally 1 548 valid questionnaires were collected, including 1 041 from field survey and 507 from internet survey. The results indicated the knowledge of acupuncture and its disease spectrum were moderate in Chengdu residents; among the disease spectrum of acupuncture, the disease with the highest cognition was insomnia, accounting for 45.0% in field survey and 75.4% in internet survey; while the disease with the lowest cognition was infertility, accounting for 8.3% in field survey and 34.3% in internet survey.

CONCLUSIONSThe knowledge of acupuncture in Chengdu residents could be further improved, and the promotion of popular science of acupuncture should be strengthened in future.