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Background Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19. Methods In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance. Results The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846–.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790–.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit. Conclusions Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.

Older age; higher LDH, CRP, RDW, DBIL, BUN; lower ALB on admission correlated with higher odds of severe COVID-19. An effective prognostic nomogram composed of 7 features could allow early identification of patients at risk of exacerbation to severe COVID-19. Abstract BackgroundDue to no reliable risk stratification tool for severe corona virus disease 2019 patients at admission, we aimed to construct an effective model for early identifying cases at high risk of progression to severe COVID-19. MethodsIn this retrospective three-centers study, 372 non-severe COVID-19 patients during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. Based on baseline data of the two groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluate its performance. ResultsThe train cohort consisted of 189 patients, while the two independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.35%) patients developed severe COVID-19. We found that old age, and higher serum lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood cell distribution width, blood urea nitrogen, direct bilirubin, lower albumin, are associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the train cohort (AUC 0.912 [95% CI 0.846-0.978], sensitivity 85.71%, specificity 87.58%); in validation cohort (0.853 [0.790-0.916], 77.5%, 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that nomogram conferred high clinical net benefit. ConclusionOur nomogram could help clinicians to early identify patients who will exacerbate to severe COVID-19, which will enable better centralized management and early treatment of severe patients.

Background: Circular RNAs (circRNAs) are a class of non-coding RNAs (ncRNAs) broadly expressed in cells of various species. However, the molecular mechanisms that link circRNAs with laryngeal squamous cell carcinoma (LSCC) are not well understood. In the present study, we attempted to provide novel basis for targeted therapy for LSCC from the aspect of circRNA–microRNA (miRNA)–mRNA interaction. Methods: We investigated the expression of circRNAs in three paired LSCC tissues and adjacent non-tumor tissues by microarray analysis. Differentially expressed circRNAs were identified between LSCC tissues and non-cancerous matched tissues, including 527 up-regulated circRNAs and 414 down-regulated circRNAs. We focused on hsa_circ_0059354, which is located on chromosome 20 and derived from RASSF2, and thus we named it circRASSF2. Results: circRASSF2 was found to be significantly up-regulated in LSCC tissues and LSCC cell lines compared with paired adjacent non-tumorous tissues and normal cells. Moreover, knockdown of circRASSF2 significantly inhibited cell proliferation and migration in vitro, which was blocked by miR-302b-3p inhibitor. Bioinformatics analysis predicted that there is a circRASSF2/miR-302b-3p/ insulin-like growth factor 1 receptor (IGF-1R) axis in LSCC progression. Dual-luciferase reporter system validated the direct interaction of circRASSF2, miR-302b-3p, and IGF-1R. Western blot verified that inhibition of circRASSF2 decreased IGF-1R expression. Furthermore, silencing circRASSF2 suppressed LSCC growth in vivo. Importantly, we demonstrated that circRASSF2 was up-regulated in serum exosomes from LSCC patients. Altogether, silencing circRASSF2 suppresses progression of LSCC by interacting with miR-302b-3p and decreasing inhibiting IGF-1R expression. Conclusion: In conclusion, these data suggest that circRASSF2 is a central component linking circRNAs to progression of LSCC via an miR-302b-3p/IGF-1R axis.

Background: Activated hepatic stellate cells (aHSCs) regulate the function of immune cells during liver fibrosis. As major innate cells in the liver, macrophages have inducible plasticity. Nevertheless, the mechanisms through which aHSCs regulate macrophages' phenotype and function during liver fibrosis and cirrhosis remain unclear. In this study, we examined the immunoregulatory function of aHSCs during liver fibrosis and explored their role in regulating macrophage phenotype and function.Methods: A total of 96 patients with different stages of chronic hepatitis B-related liver fibrosis were recruited in the study. Metavir score system was used to evaluate the degree of fibrosis. The expression of hepatic CCL2 and M2 phenotype macrophage marker CD163 were detected by immunohistochemistry, and the relationship among hepatic CD163, CCL2, and fibrosis scores were also explored. In the in vitro model, the aHSCs isolated from human liver tissues and THP-1-derived M0-type macrophages (M0MΦ) were co-cultured to observe whether and how aHSCs regulate the phenotype and function of macrophages. To explore whether CCL2/CCR2 axis has a crucial role in macrophage phenotypic changes during liver fibrosis, we treated the M0MΦ with recombinant human CCL2 or its specific receptor antagonist INCB-3284. Furthermore, we used LX2 and TGF-β-activated LX2 to mimic the different activation statuses of aHSCs to further confirm our results.Results: In patients, the infiltration of M2 macrophages increased during the progression of liver fibrosis. Intriguingly, as a key molecule for aHSC chemotactic macrophage aggregation, CCL2 markedly up-regulated the expression of CD163 and CD206 on the macrophages, which was further confirmed by adding the CCR2 antagonist (INCB 3284) into the cell culture system. In addition, the TGF-β stimulated LX2 further confirmed that aHSCs up-regulate the expression of CD163 and CD206 on macrophages. LX2 stimulated with TGF-β could produce more CCL2 and up-regulate other M2 phenotype macrophage-specific markers, including IL-10, ARG-1, and CCR2 besides CD163 and CD206 at the gene level, indicating that the different activation status of aHSCs might affect the final phenotype and function of macrophages.Conclusions: The expression of the M2 macrophage marker increases during liver fibrosis progression and is associated with fibrosis severity. AHSCs can recruit macrophages through the CCL2/CCR2 pathway and induce M2 phenotypic transformation.