Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Primary aldosteronism(PA) is one of the most common secondary hypertension, the pathogenesis is still not fully understood. Aldosterone synthase(CYP11B2) was thought to be continuously expressed in the zona glomerulosa of the adrenal cortex. In recent years, it is found that there were discontinuous CYP11B2 positive cell clusters in adrenal cortex via immunohistochemical staining, and proposed the concept of aldosterone-producing cell clusters(APCC). Thenceforwarding a growing body of studies suggest that there may be a potential causal link between APCC and PA. This article summarizes the latest studies on APCC and provide an update on the potential role of APCC in the pathogenesis of PA.

Objective Using Z-score to assess the prevalence of proximal aorta dilatation in middle-aged and aged individuals during routine transthoracic echocardiogram examinations and to identify its risk factors. Methods A total of 823 middle-aged or elderly patients on routine transthoracic echocardiogram examinations were consecutively enrolled. The internal diameters of the sinus of Valsalva (SoV ) and the ascending aorta (AA ) were measured. Z-scores were calculated according to the proposed equation for SoV and AA. A dilated aortic root was defined as a Z-score ≥1.96 or the diameter of SoV or AA ≥ 40 mm. The prevalence of proximal aorta dilatation and associated factors were analyzed. Results The prevalences of proximal aorta dilatation ,SoV dilatation ,and AA dilatation were 26.1%(25/823 ) ,6.0%(49/823 ) ,and 23.7%(195/823 ) , respectively.In the aortic root dilatation group ,age and the proportion of obesity were higher (both P＜0.05) ,and there were more female subjects (30.5% or 117/384 vs.22.3% or 98/439 ,P＜0.01) . The incidences of left atrial dilation ,left ventricular dilation ,left ventricular hypertrophy ,and aortic regurgitation in the aortic root dilatation group were higher than those in the non-aortic root dilatation group(P＜0.05 ) .Logistic regression analysis demonstrated that sex (OR＝ 1.827 ,95% CI :1.248-2.673 ,P＝0.002) ,hypertension (OR＝1.441 ,95% CI :1.000-2.075 ,P＝0.050)and left ventricular hypertrophy (OR＝1.827 ,95% CI :1.248-2.673 ,P＝0.002)were independently correlated with aortic root dilatation. Conclusions The prevalence of proximal aorta dilatation is high in middle-aged and aged individuals. Proximal aorta dilatation is related to sex ,age ,and body size ,and it is often accompanied by structural abnormalities of the heart.

Objective To study the feasibility of using rough set and support vector machines to detect Parkinson disease. Methods The reduction algorithm based on the importance of the attributes in the rough set theory was used to reduce the common diagnosis features in the clinical practice. The support vector machines were applied for classification with the linear, polynomial and RBF kernel, and the Results were compare with that of BP neural network. Results The algorism combined attributes reduction and support vector machines appeared the highest accuracy of 92.71% in the classification, which seemed greater advantage in accuracy and stability than BP neural network. Conclusion Improving the accuracy of the classification as well as saving the resources, rough set and support vector machines are proved to be an effective method to assist the clinical diagnosis of Parkinson disease.