Development and validation of a prognostic model for assessing long COVID risk following Omicron wave-a large population-based cohort study.

BACKGROUND: Long coronavirus disease (COVID) after COVID-19 infection is continuously threatening the health of people all over the world. Early prediction of the risk of Long COVID in hospitalized patients will help clinical management of COVID-19, but there is still no reliable and effective prediction model. METHODS: A total of 1905 hospitalized patients with COVID-19 infection were included in this study, and their Long COVID status was followed up 4-8 weeks after discharge. Univariable and multivariable logistic regression analysis were used to determine the risk factors for Long COVID. Patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and factors for constructing the model were screened using Lasso regression in the training cohort. Visualize the Long COVID risk prediction model using nomogram. Evaluate the performance of the model in the training and validation cohort using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 657 patients (34.5%) reported that they had symptoms of long COVID. The most common symptoms were fatigue or muscle weakness (16.8%), followed by sleep difficulties (11.1%) and cough (9.5%). The risk prediction nomogram of age, diabetes, chronic kidney disease, vaccination status, procalcitonin, leukocytes, lymphocytes, interleukin-6 and D-dimer were included for early identification of high-risk patients with Long COVID. AUCs of the model in the training cohort and validation cohort are 0.762 and 0.713, respectively, demonstrating relatively high discrimination of the model. The calibration curve further substantiated the proximity of the nomogram's predicted outcomes to the ideal curve, the consistency between the predicted outcomes and the actual outcomes, and the potential benefits for all patients as indicated by DCA. This observation was further validated in the validation cohort. CONCLUSIONS: We established a nomogram model to predict the long COVID risk of hospitalized patients with COVID-19, and proved its relatively good predictive performance. This model is helpful for the clinical management of long COVID.

Changes in the nasopharyngeal and oropharyngeal microbiota in pediatric obstructive sleep apnea before and after surgery: a prospective study.

OBJECTIVE: To explore the changes and potential mechanisms of microbiome in different parts of the upper airway in the development of pediatric OSA and observe the impact of surgical intervention on oral microbiome for pediatric OSA. METHODS: Before adeno-tonsillectomy, we collected throat swab samples from different parts of the oropharynx and nasopharynx of 30 OSA patients and 10 non-OSA patients and collected throat swab samples from the oropharynx of the above patients one month after the adeno-tonsillectomy. The 16 S rRNA V3-V4 region was sequenced to identify the microbial communities. The correlation analysis was conducted based on clinical characteristics. RESULTS: There was a significant difference of alpha diversity in different parts of the upper airway of pediatric OSA, but this difference was not found in children with non-OSA. Beta diversity was significantly different between non-OSA and pediatric OSA. At the genus level, the composition of flora in different parts is different between non-OSA and pediatric OSA. The correlation analysis revealed that the relative abundance of Neisseria was significantly correlated with obstructive apnea hypopnea index. Furthermore, the functional prediction revealed that pathways related to cell proliferation and material metabolism were significantly different between non-OSA and pediatric OSA. Besides, the adeno-tonsillectomy has minimal impact on oral microbiota composition in short term. CONCLUSION: The changes in upper airway microbiome are highly associated with pediatric OSA. The relative abundance of some bacteria was significantly different between OSA and non-OSA. These bacteria have the potential to become new diagnostic and early warning biomarkers.

Causal relationship between psychiatric disorders and sensorineural hearing loss: A bidirectional two-sample mendelian randomization analysis.

OBJECTIVE: This study employed bidirectional two-sample Mendelian randomization (MR) to investigate the causal links between psychiatric disorders and sensorineural hearing loss (SNHL). METHODS: Instrumental variables were chosen from genome-wide association studies of schizophrenia (SCH, N = 127,906), bipolar disorder (BD, N = 51,710), major depressive disorder (MDD, N = 500,199), and SNHL (N = 212,544). In the univariable MR analysis, the inverse-variance weighted method (IVW) was conducted as the primary analysis, complemented by various sensitivity analyses to ensure result robustness. RESULTS: SCH exhibited a decreased the risk of SNHL (OR = 0.949, P = 0.005), whereas BD showed an increased incidence of SNHL (OR = 1.145, P = 0.005). No causal association was found for MDD on SNHL (OR = 1.088, P = 0.246). Multivariable MR validated these results. In the reverse direction, genetically predicted SNHL was linked to a decreased risk of SCH with suggestive significance (OR = 0.912, P = 0.023). No reverse causal relationships were observed for SNHL influencing BD or MDD. These findings remained consistent across various MR methods and sensitivity analyses. CONCLUSION: This study demonstrated that the causal relationships between diverse psychiatric disorders with SNHL were heterogeneous. Specifically, SCH was inversely associated with SNHL susceptibility, and similarly, a reduced risk of SNHL was observed in schizophrenia patients. In contrast, BD exhibited an increased incidence of SNHL, although SNHL did not influence the prevalence of BD. No causal association between MDD and SNHL was found.

Adjusting the Valence State of Vanadium in VO2 (B) by Extracting Oxygen Anions for High-Performance Aqueous Zinc-Ion Batteries.

VO2 generally has a higher theoretical capacity and layered structure suitable for the intercalation/extraction of zinc ions. However, Zn2+ ions with high charge density interact with the crystal lattice and limit further improvement in electrochemical performance. Defect engineering is a potential modification method with very promising application prospects, but the established procedures for preparing defects are complicated. In this study, VO2-x (B) with oxygen deficiency is prepared by a simple solution reaction with NaBH4 . The presence of oxygen deficiencies is confirmed by positron annihilation lifetime spectroscopy, UV/Vis absorbance spectroscopy and others. Owing to the presence of oxygen defects, the aqueous Zn/VO2-x (B) battery exhibits improved specific capacity, excellent reversibility, and structural stability. Ex situ characterization techniques are employed to demonstrate the reversible insertion-extraction mechanism of Zn2+ ions from and into the host material. In addition, the Zn/VO2-x (B) batteries still exhibit considerable electrochemical performance, even with high-loading electrodes (about 4 mg cm-2 ).