OBJECTIVE: To examine the plasma adiponectin concentration in coronary heart disease (CHD) patients combined with abnormal glucose metabolism, and to explore the clinical significance of adiponectin. METHODS: Eighty-seven hospitalized CHD patients confirmed by coronary angiography from August 2009 to April 2010 at Xiangya Hospital were enrolled and divided into 3 groups according to their glucose metabolic state: 31 patients were selected as a simple CHD group, 28 were selected as a CHD combined with impaired glucose tolerance group (CHD+IGT group), and the other 28 as a CHD combined with diabetes mellitus group (CHD+DM group). The 31 healthy subjects who got health checkup at the same time were enrolled as a normal control group (NC group). Plasma adiponectin was measured by enzyme linked immunosorbent assay. The height, weight,waistline and blood pressure of all the subjects were checked, and the fasting blood glucose (FBG), insulin, lipids, high-sensitivity C-reactive protein (hs-CRP), free fatty acids (FFA), the liver function and the renal function were checked as well. The body mass index and the homeostasis model were assessed for insulin resistance. RESULTS: 1) Plasma adiponectin in the CHD group, the CHD+IGT group, and the CHD+DM group was all lower than that in the NC group (P<0.05); 2) Compared with the CHD group, the plasma adiponectin in the CHD+DM group was the lowest, followed by the CHD+IGT group, and there was significant difference in the 3 groups (P<0.05); 3) Plasma adiponectin level was positively related with the high density lipoprotein cholesterol-C (HDL-C) (r=0.483, P<0.01), while it was negatively related with the hs-CRP and Gensini score (r=-0.489, P<0.05;r=-0.252, P<0.05). CONCLUSION Plasma adiponectin concentration is reduced in the CHD patients, and significantly reduced in CHD patients combined with abnormal glucose metabolism. Plasma adiponectin concentration decreases significantly with the severity of abnormal glucose metabolism. CHD and the abnormal glucose metabolism are important influence factors for plasma adiponectin. That plasma adiponectin level significantly decreases may be the superimposed results of CHD and abnormal glucose metabolism. Plasma adiponectin combined with HDL-C, hs-CRP and Gensini score may provide the reference in the judgement of the severity of CHD patients with abnormal glucose metabolism.
Adiponectin ; blood ; Aged ; Coronary Disease ; blood ; complications ; metabolism ; Diabetes Mellitus, Type 2 ; blood ; complications ; Female ; Humans ; Insulin Resistance ; physiology ; Male ; Middle Aged

Objective:To develop a remote training system for CT simulation positioning of radiotherapy using virtual reality technology, and to explore a new method of medical training.Methods:The 3DMax and Maya were employed to establish the 3D model. The unity3D engine was adopted to develop 3D virtual operation and interaction system. Java spring MVC architecture was utilized as the system background service. MySQL was used as the background database system. The users were assigned into two roles: teacher and student, and the modes were divided into teaching and assessment modes.Results:The function of the system covered the whole process of CT simulation positioning, mainly including modules of patient information management, CT simulation positioning machine cognition, body position fixation technology, CT positioning scanning, and emergency handling, etc. Since it was put into use in 2018, the system has been running stably, with 14 920 pages views and an 86.66% pass rate. Compared with the traditional training, the training efficiency has been significantly improved and has received unanimous recognition.Conclusions:The remote training system can effectively improve the clinical practice ability and humanistic care ability of the trainees, which has good autonomy, sharing, and innovation. At present, the system has been put online and has strong popularization with prospects for broad application.

Objective:To improve the quality assurance (QA) skills of radiotherapy personnel and medical students and reduce the radiation risk of training by developing a remote training system for QA of medical electronic linear accelerators.Methods:This training system was built based on radiotherapy technology and quality control contents of medical electronic linear accelerators, and a virtual reality interactive software was developed using extended reality (XR) technology Unity 3D. A remote control module of multi-terminal platform was also developed. A multi-perspective evaluation system was adopted and a questionnaire was designed to analyze the application value of this system.Results:The training system reproduced the live environment and physical objects of medical electronic linear accelerator treatment room. It built a multi-terminal virtual simulation training system with radiotherapy technology as well as QA knowledge system. This system could provide 5G remote control of medical electronic linear accelerator for off-site quality control demonstration and guidance. By March 1, 2022, a total number of 133 people had been trained using this system, 76 valid questionnaires had been taken, of which 90.79% (69/76) of the respondents trusted the experimental results shown by the system and 88.16% (67/76) of the respondents considered the training system necessary.Conclusions:The training effect of this system is widely recognized. It fundamentally reduces the training radiation hazard and provides reference for the reform and progress of QA training mode of medical electronic linear accelerators.

BACKGROUND: Hypertrophic cardiomyopathy (HCM) is an underdiagnosed genetic heart disease worldwide. The management and prognosis of obstructive HCM (HOCM) and non-obstructive HCM (HNCM) are quite different, but it also remains challenging to discriminate these two subtypes. HCM is characterized by dysmetabolism, and myocardial amino acid (AA) metabolism is robustly changed. The present study aimed to delineate plasma AA and derivatives profiles, and identify potential biomarkers for HCM. METHODS: Plasma samples from 166 participants, including 57 cases of HOCM, 52 cases of HNCM, and 57 normal controls (NCs), who first visited the International Cooperation Center for HCM, Xijing Hospital between December 2019 and September 2020, were collected and analyzed by high-performance liquid chromatography-mass spectrometry based on targeted AA metabolomics. Three separate classification algorithms, including random forest, support vector machine, and logistic regression, were applied for the identification of specific AA and derivatives compositions for HCM and the development of screening models to discriminate HCM from NC as well as HOCM from HNCM. RESULTS: The univariate analysis showed that the serine, glycine, proline, citrulline, glutamine, cystine, creatinine, cysteine, choline, and aminoadipic acid levels in the HCM group were significantly different from those in the NC group. Four AAs and derivatives (Panel A; proline, glycine, cysteine, and choline) were screened out by multiple feature selection algorithms for discriminating HCM patients from NCs. The receiver operating characteristic (ROC) analysis in Panel A yielded an area under the ROC curve (AUC) of 0.83 (0.75-0.91) in the training set and 0.79 (0.65-0.94) in the validation set. Moreover, among 10 AAs and derivatives (arginine, phenylalanine, tyrosine, proline, alanine, asparagine, creatine, tryptophan, ornithine, and choline) with statistical significance between HOCM and HNCM, 3 AAs (Panel B; arginine, proline, and ornithine) were selected to differentiate the two subgroups. The AUC values in the training and validation sets for Panel B were 0.83 (0.74-0.93) and 0.82 (0.66-0.98), respectively. CONCLUSIONS The plasma AA and derivatives profiles were distinct between the HCM and NC groups. Based on the differential profiles, the two established screening models have potential value in assisting HCM screening and identifying whether it is obstructive.
Humans ; Amino Acids ; Cysteine ; Cardiomyopathy, Hypertrophic/diagnosis* ; Biomarkers ; Proline ; Arginine ; Ornithine ; Glycine ; Choline