clinicaltrials.gov Identifier: NCT01784172.

Background SARS-CoV-2 has caused millions of deaths, and, since Aug 11, 2020, 20 intramuscular COVID-19 vaccines have been approved for use. We aimed to evaluate the safety and immunogenicity of an aerosolised adenovirus type-5 vector-based COVID-19 vaccine (Ad5-nCoV) in adults without COVID-19 from China. Method This was a randomised, single-centre, open-label, phase 1 trial done in Zhongnan Hospital (Wuhan, China), to evaluate the safety and immunogenicity of the Ad5-nCoV vaccine by aerosol inhalation in adults (≥18 years) seronegative for SARS-CoV-2. Breastfeeding or pregnant women and people with major chronic illnesses or history of allergies were excluded. Participants were enrolled and randomly assigned (1:1:1:1:1) into five groups to be vaccinated via intramuscular injection, aerosol inhalation, or both. Randomisation was stratified by sex and age (18–55 years or ≥56 years) using computer-generated randomisation sequences (block sizes of five). Only laboratory staff were masked to group assignment. The participants in the two aerosol groups received an initial high dose (2 × 10 10 viral particles; HDmu group) or low dose (1 × 10 10 viral particles; LDmu group) of Ad5-nCoV vaccine on day 0, followed by a booster on day 28. The mixed vaccination group received an initial intramuscular (5 × 10 10 viral particles) vaccine on day 0, followed by an aerosolised booster (2 × 10 10 viral particles) vaccine on day 28 (MIX group). The intramuscular groups received one dose (5 × 10 10 viral particles; 1Dim group) or two doses (10 × 10 10 viral particles; 2Dim group) of Ad5-nCoV on day 0. The primary safety outcome was adverse events 7 days after each vaccination, and the primary immunogenicity outcome was anti-SARS-CoV-2 spike receptor IgG antibody and SARS-CoV-2 neutralising antibody geometric mean titres at day 28 after last vaccination. This trial is registered with ClinicalTrials.gov, number NCT04552366. Findings Between Sept 28, 2020, and Sept 30, 2020, 230 individuals were screened for inclusion, of whom 130 (56%) participants were enrolled into the trial and randomly assigned into one of the five groups (26 participants per group). Within 7 days after vaccination, adverse events occurred in 18 (69%) in the HDmu group, 19 (73%) in the LDmu group, 19 (73%) in the MIX group, 19 (73%) in the 1Dim group, and 15 (58%) in the 2Dim group. The most common adverse events reported 7 days after the first or booster vaccine were fever (62 [48%] of 130 participants), fatigue (40 [31%] participants), and headache (46 [35%] participants). More adverse events were reported in participants who received intramuscular vaccination, including participants in the MIX group (49 [63%] of 78 participants), than those who received aerosol vaccine (13 [25%] of 52 participants) after the first vaccine va...

Interleukin 35 (IL-35) is a novel member of the IL-12 family, consisting of an EBV-induced gene 3 (EBI3) subunit and a P35 subunit. IL-35 is an immune-suppressive cytokine mainly produced by regulatory T cells. However, the role of IL-35 in cancer metastasis and progression is not well understood. Here we demonstrate that IL-35 is overexpressed in human pancreatic ductal adenocarcinoma (PDAC) tissues, and that IL-35 overexpression is associated with poor prognosis in PDAC patients. IL-35 has critical roles in PDAC cell extravasation and metastasis by facilitating the adhesion to endothelial cells and transendothelial extravasation. Mechanistically, IL-35 promotes ICAM1 overexpression through a GP130-STAT1 signalling pathway, which facilitates endothelial adhesion and transendothelial migration via an ICAM1–fibrinogen–ICAM1 bridge. In an orthotopic xenograft model, IL-35 promotes spontaneous pancreatic cancer metastasis in an ICAM1-dependent manner. Together, our results indicate additional functions of IL-35 in promoting PDAC metastasis through mediating ICAM1 expression.

Gliomas are the most common primary brain malignancies. Accurate and robust tumor segmentation and prediction of patients' overall survival are important for diagnosis, treatment planning and risk factor identification. Here we present a deep learning-based framework for brain tumor segmentation and survival prediction in glioma, using multimodal MRI scans. For tumor segmentation, we use ensembles of three different 3D CNN architectures for robust performance through a majority rule. This approach can effectively reduce model bias and boost performance. For survival prediction, we extract 4,524 radiomic features from segmented tumor regions, then, a decision tree and cross validation are used to select potent features. Finally, a random forest model is trained to predict the overall survival of patients. The 2018 MICCAI Multimodal Brain Tumor Segmentation Challenge (BraTS), ranks our method at 2nd and 5th place out of 60+ participating teams for survival prediction tasks and segmentation tasks respectively, achieving a promising 61.0% accuracy on the classification of short-survivors, mid-survivors and long-survivors.