BackgroundThe continued ‘evolution’ of Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the emergence of the Omicron variant after the Delta variant, resulting in a significant increase in the number of people with COVID-19. This increase in the number of cases continues to have a significant impact on lives. Therefore, a more detailed understanding of the clinical characteristics of Omicron infection is essential.MethodsUsing medical charts, we extracted clinical information for 384 patients infected with the Omicron variant in Anyang City, Henan Province, China. Epidemiology and clinical characteristics were compared with a cohort of people infected with the Delta variant in Zhengzhou in 2021.FindingsCommon initial symptoms at onset of illness were cough [240 (63%)], expectoration [112 (29%)], fever [96 (25%)], nasal congestion [96 (25%)] and myalgia or fatigue [30 (6%)]. In patients with the Omicron variant, levels of total cholesterol, low-density lipoprotein and creatinine increased in 52 (14%), 36 (9%) and 58 (15%) patients, respectively, compared with patients with the Delta variant [one (1%), one (1%) and two (2%)]. Levels of triglyceride and high-density lipoprotein also increased. In patients with the Omicron variant, the levels of specific gravity and the erythrocyte sedimentation rate were increased in 115 (30%) and 81 (21%) patients, and serum levels of complement 3 decreased in 93 (41%).ResultsCompared with patients infected with Delta, no major differences in initial clinical symptoms were identified in patients infected with Omicron. However, dyslipidemia and kidney injury were much more severe in patients with the Omicron variant, and the erythrocyte sedimentation rate was increased. Due to decreased levels of complement 3, the immunity of patients with the Omicron variant was weak.

Background. A more accurate prediction of liver metastasis (LM) in pancreatic cancer (PC) would help improve clinical therapeutic effects and follow-up strategies for the management of this disease. This study was to assess various prediction models to evaluate the risk of LM based on machine learning algorithms. Methods. We retrospectively reviewed clinicopathological characteristics of PC patients from the Surveillance, Epidemiology, and End Results database from 2010 to 2018. The logistic regression, extreme gradient boosting, support vector, random forest (RF), and deep neural network machine algorithms were used to establish models to predict the risk of LM in PC patients. Specificity, sensitivity, and receiver operating characteristic (ROC) curves were used to determine the discriminatory capacity of the prediction models. Results. A total of 47,919 PC patients were identified; 15,909 (33.2%) of which developed LM. After iterative filtering, a total of nine features were included to establish the risk model for LM based on machine learning. The RF showed the most promising results in the prediction of complications among the models (ROC 0.871 for training and 0.832 for test sets). In risk stratification analysis, the LM rate and 5-year cancer-specific survival (CSS) in the high-risk group were worse than those in the intermediate- and low-risk groups. Surgery, radiotherapy, and chemotherapy were found to significantly improve the CSS in the high- and intermediate-risk groups. Conclusion. In this study, the RF model constructed could accurately predict the risk of LM in PC patients, which has the potential to provide clinicians with more personalized clinical decision-making recommendations.

Background Gastric cancer (GC) remains one of the most common digestive malignancies worldwide and ranked third causes of cancer-related death. Mounting evidence has revealed that miRNAs exert critical regulatory roles in GC development. Methods Immunohistochemistry (IHC) and western blot assay were performed to determine the protein expression levels of neuropilin-1 (NRP1) and mRNA levels were confirmed by quantitative RT-PCR (qRT-PCR) in GC tissues. Kaplan–Meier analysis was performed to evaluate the prognostic value of NRP1 in GC. Knockdown of NRP1 was conducted to analyse its function in vitro and vivo. Luciferase reporter assay, western blot and qRT-qPCR were employed to identify the miRNAs which directly targeted NRP1. Furthermore, Bioinformatics analysis and experimental verification were used to explore the potential molecular mechanism and signaling pathway. Results In the current study, we revealed that neuropilin-1 (NRP1) was highly expressed in GC tumor tissues and was associated with poor prognosis in GC patients. NRP1 knockdown inhibited GC cell growth, migration and invasion in vitro, while suppressed GC xenograft tumor development in vivo. Bioinformatics analysis predicted that miR-19b-3p down-regulated NRP1 expression by targeting its 3’-UTR. Functional assay demonstrated that miR-19b-3p inhibited GC cell growth, migration and invasion via negatively regulating NRP1. Overexpression NRP1 partially reversed the regulatory effect of miR-19b-3p. Moreover, we showed that miR-19b-3p/NRP1 axis regulated the epithelial-to-mesenchymal transition and focal adhesion in GC, which might contribute the GC development and progression. Conclusions Taken together, our findings suggest a regulatory network of miR-19b-3p/NRP1 in GC development. The miR-19b-3p/NRP1 axis might be further explored as a potential diagnostic and therapeutic target in GC.

The oral bacteriome, gut bacteriome, and gut mycobiome are associated with coronavirus disease 2019 (COVID‐19). However, the oral fungal microbiota in COVID‐19 remains unclear. This article aims to characterize the oral mycobiome in COVID‐19 and recovered patients. Tongue coating specimens of 71 COVID‐19 patients, 36 suspected cases (SCs), 22 recovered COVID‐19 patients, 36 SCs who recovered, and 132 controls from Henan are collected and analyzed using internal transcribed spacer sequencing. The richness of oral fungi is increased in COVID‐19 versus controls, and beta diversity analysis reveals separate fungal communities for COVID‐19 and control. The ratio of Ascomycota and Basidiomycota is higher in COVID‐19, and the opportunistic pathogens, including the genera Candida, Saccharomyces, and Simplicillium, are increased in COVID‐19. The classifier based on two fungal biomarkers is constructed and can distinguish COVID‐19 patients from controls in the training, testing, and independent cohorts. Importantly, the classifier successfully diagnoses SCs with positive specific severe acute respiratory syndrome coronavirus 2 immunoglobulin G antibodies as COVID‐19 patients. The correlation between distinct fungi and bacteria in COVID‐19 and control groups is depicted. These data suggest that the oral mycobiome may play a role in COVID‐19.

The length of stay (LOS) in hospital varied considerably in different patients with COVID-19 caused by SARS-CoV-2 Omicron variant. The study aimed to explore the clinical characteristics of Omicron patients, identify prognostic factors, and develop a prognostic model to predict the LOS of Omicron patients. This was a single center retrospective study in a secondary medical institution in China. A total of 384 Omicron patients in China were enrolled. According to the analyzed data, we employed LASSO to select the primitive predictors. The predictive model was constructed by fitting a linear regression model using the predictors selected by LASSO. Bootstrap validation was used to test performance and eventually we obtained the actual model. Among these patients, 222 (57.8%) were female, the median age of patients was 18 years and 349 (90.9%) completed two doses of vaccination. Patients on admission diagnosed as mild were 363 (94.5%). Five variables were selected by LASSO and a linear model, and those with P < 0.05 were integrated into the analysis. It shows that if Omicron patients receive immunotherapy or heparin, the LOS increases by 36% or 16.1%. If Omicron patients developed rhinorrhea or occur familial cluster, the LOS increased by 10.4% or 12.3%, respectively. Moreover, if Omicron patients’ APTT increased by one unit, the LOS increased by 0.38%. Five variables were identified, including immunotherapy, heparin, familial cluster, rhinorrhea, and APTT. A simple model was developed and evaluated to predict the LOS of Omicron patients. The formula is as follows: Predictive LOS = exp(1*2.66263 + 0.30778* Immunotherapy + 0.1158*Familiar cluster + 0.1496*Heparin + 0.0989*Rhinorrhea + 0.0036*APTT ).