Objective To investigate the value of Q-analysis real-time elasticity in differentiating between benign and malignant thyroid nodules. Methods Eighty-six thyroid nodules in 62 patients with pathologic diagnosis were included in this study and were examined using Q-analysis real-time elasticity. The real-time elasticity features were observed and the quantitative index including the whole elasticity rate and the local elasticity rate were compared between benign and malignant nodules. Results There were 51 benign and 35 malignant nodules according to histopathological examination. The Q-analysis curve of real-time elasticity of benign nodules was smoother and with lower peak, compared with that of malignant nodules. The whole elasticity rate of malignant nodules were significantly higher than that of benign nodules (3.59±0.84 vs 2.32±0.56, P=0.000). And the local elasticity rate of malignant nodules were significantly higher than that of benign nodules (3.96±1.32 vs 2.39±0.58, P=0.000). The cutoff point of whole elasticity rate for the differential diagnosis was 3.25 with sensitivity, specificity and diagnostic accuracy as 71.4%, 96.1% and 86.0% respectively. The cutoff point of local elasticity rate for the differential diagnosis was 3.45 with sensitivity, specificity and diagnostic accuracy as 68.6%, 96.1% and 84.9% respectively. The diagnostic efficiency of whole elasticity rate and local elasticity rate had no significant difference (P=0.591).Conclusions Q-analysis real-time elasticity could provide the real-time elasticity features of thyroid nodules. The whole and local elasticity rate as the quantitative index contributed to the differential diagnosis of benign and malignant thyroid nodules.

Objective To optimize the extraction technology of total flavonoids from Lycii Fructus by response surface methodology (RSM).Methods On the basis of single-factor tests, enzyme concentration, enzymolysis time, and enzyme solution temperature were selected as influencing factors during extraction to conduct three-factor three-level center combination design. The effects of three factors on the yield of total flavonoids from Lycii Fructus were analyzed by RSM.Results The optimum conditions for extraction technology of total flavonoids from Lycii Fructus were enzyme concentration 0.30%, enzymolysis time 1.0 h, enzyme solution temperature 60℃. The predicted extraction yield of total flavonoids was 0.9492%.ConclusionUsing RSM to optimize the extraction technology of total flavonoids from Lycii Fructus is stable, feasible, and simple, which can provide references for further study and application.

The integration of artificial intelligence into the medical field is developing rapidly and has achieved ground-breaking advances in the diagnosis, treatment, and efficacy evaluation of imaging medicine. This article reviews the research advances in artificial intelligence in imaging diagnosis of hepatocellular carcinoma and its performance in evaluating treatment outcome and predicting prognosis in combination with clinical features and looks forward to how artificial intelligence can be better used in the practice of hepatocellular carcinoma imaging in the era of growing clinical needs and rapid advances in diagnosis and treatment techniques.