Highly pathogenic coronaviruses and the kidney.

Since the end of 2019, the outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has triggered a pneumonia epidemic, posing a significant public health challenge in 236 countries, territories, and regions worldwide. Clinically, in addition to the symptoms of pulmonary infection, many patients with SARS-CoV-2 infections, especially those with a critical illness, eventually develop multiple organ failure in which damage to the kidney function is common, ultimately leading to severe consequences such as increased mortality and morbidity. To date, three coronaviruses have set off major global public health security incidents: Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV), Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and SARS-CoV-2. Among the diseases caused by the coronaviruses, the coronavirus disease 2019 (COVID-19) has been the most impactful and harmful. Similar to with SARS-CoV-2 infections, previous studies have shown that kidney injury is also common and prominent in patients with the two other highly pathogenic coronaviruses. Therefore, in this review, we aimed to comprehensively summarize the epidemiological and clinical characteristics of these three pandemic-level infections, provide a deep analysis of the potential mechanism of COVID-19 in various types of kidney diseases, and explore the causes of secondary kidney diseases of SARS-CoV-2, so as to provide a reference for further research and the clinical prevention of kidney damage caused by coronaviruses.

Predicting medication nonadherence risk in the Chinese type 2 diabetes mellitus population - establishment of a new risk nomogram model: a retrospective study.

OBJECTIVE: To investigate the risk factors of medication nonadherence in patients with type 2 diabetes mellitus (T2DM) and to establish a risk nomogram model. METHODS: This retrospective study enrolled patients with T2DM, which were divided into two groups based on their scores on the Morisky Medication Adherence scale. Univariate and multivariate logistic regression analyses were used to screen for independent risk factors for medication nonadherence. A risk model was then established using a nomogram. The accuracy of the prediction model was evaluated using centrality measurement index and receiver operating characteristic curves. Internal verification was evaluated using bootstrapping validation. RESULTS: A total of 338 patients with T2DM who included in the analysis. Logistic regression analysis showed that the educational level, monthly per capita income, drug affordability, the number of drugs used, daily doses of drugs and the time spent taking medicine were all independent risk factors for medication nonadherence. Based on these six risk factors, a nomogram model was established to predict the risk of medication nonadherence, which was shown to be very reliable. Bootstrapping validated the nonadherence nomogram model for patients with T2DM. CONCLUSIONS: This nomogram model could be used to evaluate the risks of drug nonadherence in patients with T2DM.