Boosting with an aerosolized Ad5-nCoV elicited robust immune responses in inactivated COVID-19 vaccines recipients.

Introduction: The SARS-CoV-2 Omicron variant has become the dominant SARS-CoV-2 variant and exhibits immune escape to current COVID-19 vaccines, the further boosting strategies are required. Methods: We have conducted a non-randomized, open-label and parallel-controlled phase 4 trial to evaluate the magnitude and longevity of immune responses to booster vaccination with intramuscular adenovirus vectored vaccine (Ad5-nCoV), aerosolized Ad5-nCoV, a recombinant protein subunit vaccine (ZF2001) or homologous inactivated vaccine (CoronaVac) in those who received two doses of inactivated COVID-19 vaccines. Results: The aerosolized Ad5-nCoV induced the most robust and long-lasting neutralizing activity against Omicron variant and IFNg T-cell response among all the boosters, with a distinct mucosal immune response. SARS-CoV-2-specific mucosal IgA response was substantially generated in subjects boosted with the aerosolized Ad5-nCoV at day 14 post-vaccination. At month 6, participants boosted with the aerosolized Ad5-nCoV had remarkably higher median titer and seroconversion of the Omicron BA.4/5-specific neutralizing antibody than those who received other boosters. Discussion: Our findings suggest that aerosolized Ad5-nCoV may provide an efficient alternative in response to the spread of the Omicron BA.4/5 variant. Clinical trial registration: https://www.chictr.org.cn/showproj.html?proj=152729, identifier ChiCTR2200057278.

LncRNA CCAT2 promotes malignant progression of metastatic gastric cancer through regulating CD44 alternative splicing.

OBJECTIVE: Gastric cancer (GC) is one of the most malignant tumors worldwide. Thus, it is necessary to explore the underlying mechanisms of GC progression and develop novel therapeutic regimens. Long non-coding RNAs (lncRNAs) have been demonstrated to be abnormally expressed and regulate the malignant behaviors of cancer cells. Our previous research demonstrated that lncRNA colon cancer-associated transcript 2 (CCAT2) has potential value for GC diagnosis and discrimination. However, the functional mechanisms of lncRNA CCAT2 in GC development remain to be explored. METHODS: GC and normal adjacent tissues were collected to detect the expression of lncRNA CCAT2, ESRP1 and CD44 in clinical specimens and their clinical significance for GC patients. Cell counting kit-8, wound healing and transwell assays were conducted to investigate the malignant behaviors in vitro. The generation of nude mouse xenografts by subcutaneous, intraperitoneal and tail vein injection was performed to examine GC growth and metastasis in vivo. Co-immunoprecipitation, RNA-binding protein pull-down assay and fluorescence in situ hybridization were performed to reveal the binding relationships between ESRP1 and CD44. RESULTS: In the present study, lncRNA CCAT2 was overexpressed in GC tissues compared to adjacent normal tissues and correlated with short survival time of patients. lncRNA CCAT2 promoted the proliferation, migration and invasion of GC cells. Its overexpression modulates alternative splicing of Cluster of differentiation 44 (CD44) variants and facilitates the conversion from the standard form to variable CD44 isoform 6 (CD44v6). Mechanistically, lncRNA CCAT2 upregulated CD44v6 expression by binding to epithelial splicing regulatory protein 1 (ESRP1), which subsequently mediates CD44 alternative splicing. The oncogenic role of the lncRNA CCAT2/ESRP1/CD44 axis in the promotion of malignant behaviors was verified by both in vivo and in vitro experiments. CONCLUSIONS: Our findings identified a novel mechanism by which lncRNA CCAT2, as a type of protein-binding RNA, regulates alternative splicing of CD44 and promotes GC progression. This axis may become an effective target for clinical diagnosis and treatment.

Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.

Current machine learning techniques provide the opportunity to develop noninvasive and automated glioma grading tools, by utilizing quantitative parameters derived from multi-modal magnetic resonance imaging (MRI) data. However, the efficacies of different machine learning methods in glioma grading have not been investigated.A comprehensive comparison of varied machine learning methods in differentiating low-grade gliomas (LGGs) and high-grade gliomas (HGGs) as well as WHO grade II, III and IV gliomas based on multi-parametric MRI images was proposed in the current study. The parametric histogram and image texture attributes of 120 glioma patients were extracted from the perfusion, diffusion and permeability parametric maps of preoperative MRI. Then, 25 commonly used machine learning classifiers combined with 8 independent attribute selection methods were applied and evaluated using leave-one-out cross validation (LOOCV) strategy. Besides, the influences of parameter selection on the classifying performances were investigated. We found that support vector machine (SVM) exhibited superior performance to other classifiers. By combining all tumor attributes with synthetic minority over-sampling technique (SMOTE), the highest classifying accuracy of 0.945 or 0.961 for LGG and HGG or grade II, III and IV gliomas was achieved. Application of Recursive Feature Elimination (RFE) attribute selection strategy further improved the classifying accuracies. Besides, the performances of LibSVM, SMO, IBk classifiers were influenced by some key parameters such as kernel type, c, gama, K, etc. SVM is a promising tool in developing automated preoperative glioma grading system, especially when being combined with RFE strategy. Model parameters should be considered in glioma grading model optimization.