Objective To assess the differences in carotid artery stiffness properties and endothelium-independent dilation (EID)between elderly and young patients,and evaluate the echotracking (ET)system for vascular stiffness at different ages.Methods A total of 79 outpatients with multiple cardiovascular risks were recruited.Clinical data including medical history,height,weight,blood pressure,fasting blood glucose and blood lipid were collected.We evaluated the arterial stiffness parameters of carotid artery and EID using an ultrasonic ET system in 46 elderly subjects,compared with 33 sex-matched non-elderly subjects.The impaired EID function was defined as brachial artery nitroglycerin mediated dilation (NMD)below 4％.Results All stiffness parameters including pressure-strain elasticity modulus stiffness index β (Ep),pulse wave velocity β (PWVβ)and augmentation index (AI)were significantly increased in elderly group compared with the non-elderly group [(138.9±64.7)kPa vs.(100.6±30.8)kPa,(10.9±4.7)vs.(8.2±2.3),and (6.9±1.4)m/s vs.(6.1±0.9)m/s,P＜0.05 respectively],while the exception of arterial compliance (AC)was reduced (0.9±0.3)mm2/kPa vs.(1.0±0.5)mm2/kPa(P＜0.05).The incidence of impaired EID in elderly group was higher than in non-elderly group [56.5％ (26 cases) vs.33.3％ (11 cases),P＜0.05].ET parameters including Ep,stiffness index β,PWVβ,AC and AI were related to age (r=-0.44,-0.45,-0.40,-0.40,0.34,all P＜0.01)； Ep,stiffness index β,PWVβ and AC were also related to impaired EDI (r=-0.38,-0.40,-0.34,-0.29,all P＜0.01).Conclusions Arterial stiffness properties and EID measured by ET system was more serious in elderly with multiple cardiovascular risks than in non-elderly subjects.As a convenient and accurate assessment of stiffness parameters,ET system is optimal option for measuring arterial stiffness and EID in elderly people.

Proton is formed after hydrogen atom loses an electron with a positive charge of particle (H+ ). After the proton is accelerated, it possesses significant advantages in terms of the distribution of physical dose compared with the photon. Currently, proton radiation has captivated extensive attention and has been actively applied in clinical practice. Nevertheless, due to the small amount of proton facilities and lack of clinical trials, the proton therapy, especially the radiobiological characteristics and biological effect of photon radiotherapy has been poorly understood. In this article, these issues were summarized as below.

Objective:To explore the value of salivary gland imaging based on deep learning and Delta radiomics in assessing salivary gland injury after 131I treatment in post-thyroidectomy thyroid cancer patients. Methods:A retrospective analysis on 223 patients (46 males, 177 females, age(47.7±14.0) years ) with papillary thyroid cancer, who underwent total thyroidectomy and 131I treatment in Affiliated Hospital of Guilin Medical University between December 2019 and January 2022, was conducted. All patients underwent salivary gland 99Tc mO 4- imaging before and after 131I therapy. The patients were categorized according to salivary gland function based on 99Tc mO 4- imaging results (normal salivary gland vs salivary gland injury), and divided into training and test sets in a ratio of 7∶3. A ResNet-34 neural network model was trained using images at the time of maximum salivary gland radioactivity and those based on background radioactivity counts for structured image feature data. The Delta radiomics approach was then used to subtract the image feature values of the two periods, followed by feature selection through t-test, correlation analysis, and the least absolute shrinkage and selection operator( LASSO) algorithm, to develop logistic regression (LR), support vector machine (SVM), and K-nearest neighbor (KNN) predictive models. The diagnostic performance of 3 models for salivary gland function on the test set was compared with that of the manual interpretation. The AUCs of the 3 models on the test set were compared (Delong test). Results:Among the 67 cases of the test set, the diagnostic accuracy of 3 physicians were 89.6%(60/67), 83.6%(56/67), and 82.1%(55/67) respectively, with the time required for diagnosis of 56, 74 and 55 min, respectively. The accuracies of LR, SVM, and KNN models were 91.0%(61/67), 86.6%(58/67), and 82.1%(55/67), with the required times of 12.5, 15.3 and 17.9 s, respectively. All 3 radiomics models demonstrated good classification and predictive capabilities, with AUC values for the training set of 0.972, 0.965, and 0.943, and for the test set of 0.954, 0.913, and 0.791, respectively. There were no significant differences among the AUC values for the test set ( z values: 0.72, 1.18, 1.82, all P>0.05). Conclusion:The models based on deep learning and Delta radiomics possess high predictive value in assessing salivary gland injury following 131I treatment after surgery in patients with thyroid cancer.