Enterovirus D68 (EV-D68) is an emerging pathogen in humans. EV-D68 causes a wide range of respiratory symptoms in children and has the propensity to cause severe complications. EV-D68 outbreaks are rarely investigated in mainland China. Therefore, in this study, we aimed to investigate the prevalence of EV-D68 in children and to describe the clinical manifestations as well as the phylogeny of EV-D68 in Guangdong Province from 2014 to 2018. Nasopharyngeal swabs were collected from hospitalized children with respiratory symptoms and screened for respiratory pathogens by fluorescence quantitative PCR and culture. The EV-positive samples were subsequently typed by sequencing the 5'-untranslated region and EV-D68-specific VP1 capsid gene. A phylogenetic tree was constructed by the maximum-likelihood method based on the VP1 gene using ClustalW. A total of 1,498 (59.8%) out of 2,503 children were screened positive for ≥1 virus species. Among the 158 (6.31%) EV-positive samples, 17 (0.68%) were identified as EV-D68. Most EV-D68 cases (n = 14) were diagnosed with pneumonia and bronchial pneumonia. No deaths were found in EV-D68 cases. Wheezing occurred in EV-D68 cases more frequently (70.59% vs. 43.26%, P = 0.040) than that of other EVs. All the EV-D68 were of clade B3, which were highly similar to the strains circulating in China. EV-D68 was the predominant enterovirus type in hospitalized children with respiratory symptoms in Guangdong Province. All the EV-D68 strains belong to clade B3. The development of diagnostic tools is warranted in order to monitor EV-D68 infections in China.

Background: The prevalence of primary aldosteronism (PA) varies from 5% to 20% in patients with hypertension but is largely underdiagnosed. Expanding screening for PA to all patients with hypertension to improve diagnostic efficiency is needed. A novel and portable prediction tool that can expand screening for PA is highly desirable.Methods: Clinical characteristics and laboratory data of 1,314 patients with hypertension were collected for modeling and randomly divided into a training cohort (919 of 1,314, 70%) and an internal validation cohort (395 of 1,314, 30%). Additionally, an external dataset (n = 285) was used for model validation. Machine learning algorithms were applied to develop a discriminant model. Sensitivity, specificity, and accuracy were used to evaluate the performance of the model.Results: Seven independent risk factors for predicting PA were identified, including age, sex, hypokalemia, serum sodium, serum sodium-to-potassium ratio, anion gap, and alkaline urine. The prediction model showed sufficient predictive accuracy, with area under the curve (AUC) values of 0.839 (95% CI: 0.81–0.87), 0.814 (95% CI: 0.77–0.86), and 0.839 (95% CI: 0.79–0.89) in the training set, internal validation, and external validation set, respectively. The calibration curves exhibited good agreement between the predictive risk of the model and the actual risk. An online prediction model was developed to make the model more portable to use.Conclusion: The online prediction model we constructed using conventional clinical characteristics and laboratory tests is portable and reliable. This allowed it to be widely used not only in the hospital but also in community health service centers and may help to improve the diagnostic efficiency of PA.

Background: A third mRNA vaccine booster is recommended to improve immunity against SARS-CoV-2 in kidney transplant recipients (KTRs). However, the immunity against SARS-CoV-2 Ancestral strain and Delta and Omicron variants elicited by the third dose of inactivated booster vaccine in KTRs remains unknown.Methods: The blood parameters related to blood cells count, hepatic function, kidney function, heart injury and immunity were explored clinically from laboratory examinations. SARS-CoV-2 specific antibody IgG titer was detected using an enzyme-linked immunosorbent assay. Cellular immunity was analyzed using interferon-γ enzyme-linked immunospot assay.Results: The results showed that there were no severe adverse effects and apparent changes of clinical laboratory biomarkers in KTRs and healthy volunteers (HVs) after homologous inactivated vaccine booster. A third dose of inactivated vaccine booster significantly increased anti-Ancestral-spike-trimer-IgG and anti-Ancestral-receptor binding domain (RBD)-IgG titers in KTRs and HVs compared with the second vaccination. However, the anti-Delta-RBD-IgG and anti-Omicron-RBD-IgG titers were significantly lower than anti-Ancestral-RBD-IgG titer in KTRs and HVs after the third dose. Notably, only 25.6% (10/39) and 10.3% (4/39) of KTRs had seropositivity for anti-Delta-RBD-IgG and anti-Omicron-RBD-IgG after booster, which were significantly lower than HVs (anti-Delta-RBD-IgG: 100%, anti-Omicron-RBD-IgG: 77.8%). Ancestral strain nucleocapsid protein and spike specific T cell frequency after booster was not significantly increased in KTRs compared with the second dose, significantly lower than that in HVs. Moreover, 33.3% (12/36), 14.3% (3/21) and 14.3% (3/21) of KTRs were positive for the Ancestral strain and Delta and Omicron spike-specific T cells, which were significantly lower than HVs (Ancestral: 80.8%, Delta: 53.8%, and Omicron: 57.7%).Conclusions: A third dose of inactivated booster vaccine may significantly increase humoral immunity against the Ancestral strain in KTRs, while humoral and cellular immunity against the Delta and Omicron variants were still poor in KTRs.