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.

Intermittent Hypoxia Promotes TAM-Induced Glycolysis in Laryngeal Cancer Cells via Regulation of HK1 Expression through Activation of ZBTB10.

Obstructive sleep apnea (OSA), characterized by intermittent hypoxia (IH), may increase the risk of cancer development and a poor cancer prognosis. TAMs of the M2 phenotype, together with the intermittent hypoxic environment within the tumor, drive tumor aggressiveness. However, the mechanism of TAMs in IH remains unclear. In our study, IH induced the recruitment of macrophages, and IH-induced M2-like TAMs promoted glycolysis in laryngeal cancer cells through hexokinase 1. The hexokinase inhibitor 2-deoxy-D-glucose and HK1 shRNA were applied to verify this finding, confirming that M2-like TAMs enhanced glycolysis in laryngeal cancer cells through HK1 under intermittent hypoxic conditions. Comprehensive RNA-seq analysis disclosed a marked elevation in the expression levels of the transcription factor ZBTB10, while evaluation of a laryngeal cancer patient tissue microarray demonstrated a positive correlation between ZBTB10 and HK1 expression in laryngeal carcinoma. Knockdown of ZBTB10 decreased HK1 expression, and overexpression of ZBTB10 increased HK1 expression in both laryngeal cancer cells and 293T cells. The luciferase reporter assay and Chromatin immunoprecipitation assay confirmed that ZBTB10 directly bound to the promoter region of HK1 and regulated the transcriptional activity of HK1. Finally, the CLEC3B level of the M2 supernatant is significantly higher in the IH group and showed a protumor effect on Hep2 cells. As ZBTB10-mediated regulation of HK1 affects glycolysis in laryngeal cancer, our findings may provide new potential therapeutic targets for laryngeal cancer.

Can portable sleep monitors replace polysomnography for diagnosis of pediatric OSA: a systematic review and meta-analysis.

BACKGROUND: Obstructive sleep apnoea (OSA) is an increasing health problem in children. The "gold standard" for OSA diagnosis at the moment is overnight polysomnography (PSG). Some researchers think portable monitors (PMs) are promising methods for diagnosing OSA, which make children more comfortable and lower costs. Compared with PSG, our comprehensively evaluated the diagnostic accuracy of PMs for diagnosing OSA in pediatrics. RESEARCH QUESTION: This study aims to determine whether PMs can replace PSG in pediatric OSA diagnosis. STUDY DESIGN AND METHODS: The PubMed, Embase, Medline databases Scopus, Web of Science, and Cochrane Library databases were searched systematically for studies published up to December 2022, evaluating the ability of PMs to diagnose OSA in children. For estimating the pooled sensitivity and specificity of the PMs in the included studies, we used a random-effects bivariate model. Studies included in this meta-analysis were evaluated systematically according to QUADAS-2 guidelines for assessing diagnostic accuracy studies. Two independent investigators conducted each stage of the review independently. RESULTS: A total of 396 abstracts and 31 full-text articles were screened, and 41 full-text articles were chosen for final review. There were 707 pediatric patients enrolled in these twelve studies, and 9 PMs were evaluated. There was a wide range of diagnostic sensitivity and specificity among PM systems as compared to AHI measured by PSG. The pooled sensitivity and specificity in diagnosing pediatric OSA were, respectively, 0.91 [0.86, 0.94] and 0.76 [0.58, 0.88] for PMs. According to the summary receiver operating characteristic (SROC) curve, the AUC of PMs in diagnosing OSA in pediatric population was 0.93 [0.90, 0.95]. INTERPRETATION: PMs were more sensitive but slightly less specific for pediatric OSA. The combination of PMs and questionnaires appeared to be a reliable tool for the diagnosis of pediatric OSA. This test may be used for screening subjects or populations at high risk of OSA when there is a high demand for PSG, but the quantity is limited. No clinical trial was involved in the current study.

Comparison of prognostic and treatment between T1a and T1b glottic cancer: a propensity score-matched SEER database analysis.

PURPOSE: The prognoses of T1a and T1b glottic cancers are still controversial. This study aimed to compare the prognosis difference between the two groups based on the population-based Surveillance, Epidemiology, and End Results database (SEER). METHODS: Data for patients with T1a or T1b glottic cancers were extracted from the SEER database. The bias between T1a and T1b glottic cancers was minimized with Propensity Score Matching (PSM), and disease-specific survival (DSS) was analyzed using the Kaplan-Meier method, log-rank test, and Cox proportional hazards models. RESULTS: A total of 5,272 patients were extracted from the database, including 847 patients with T1b glottic cancer that were 1:1 propensity score-matched with patients with T1a glottic cancer. After propensity score-matching, there was no statistical difference in disease-specific survival between T1a and T1b patients, whilst survival was impaired by old age. However, on the multivariate analysis, the T1a stage was associated with improved DSS compared with the T1b stage. CONCLUSION: Our analysis showed that T1a glottic cancers didn't have a significantly better prognosis compared with T1b after PSM. However, the DSS of T1a patients is superior to that of T1b patients in multivariate analysis.