Biology subjects

Zhai, S.

Publications and source records attributed to Zhai, S..

21 records · Page 2Linked to original sources

bioRxiv2021

The Genome Sequence Archive Family: Towards Explosive Data Growth and Diverse Data Types

The Genome Sequence Archive (GSA) is a data repository for archiving raw sequence data, which provides data storing and sharing services for worldwide scientific communities. Considering explosive data growth with diverse data types, here we present the GSA family by expanding into a set of resources for raw data archive with different purposes, namely, GSA (https://ngdc.cncb.ac.cn/gsa/), GSA for Human (GSA-Human, https://ngdc.cncb.ac.cn/gsa-human/), and Open Archive for Miscellaneous Data (OMIX, https://ngdc.cncb.ac.cn/omix/). Compared with the 2017 version, GSA has been significantly updated in data model, online functionalities, and web interfaces. GSA-Human, as a new partner of GSA, is a data repository specialized in human genetics-related data with controlled access and security. OMIX, as a critical complement to the two resources mentioned above, is an open archive for miscellaneous data. Together, all these resources form a family of resources dedicated to archiving explosive data with diverse types, accept data submissions from all over the world and provide free open access to all publicly available data in support of worldwide research activities.

bioinformatics↗

bioRxiv2020

Menthol in Electronic Cigarettes: A Contributor to Respiratory Disease?

Menthol is widely used in tobacco products. This study compared the effects of menthol on human bronchial epithelium using submerged cultures, a VITROCELL(R) cloud chamber that provides air liquid interface (ALI) exposure without solvents or heating, and a Cultex ALI system that delivers aerosol equivalent to that inhaled during vaping. In submerged culture, menthol significantly increased calcium influx and mitochondrial reactive oxygen species (ROS) via the TRPM8 receptor, responses that were inhibited by a TRPM8 antagonist. VITROCELL(R) cloud chamber exposure of BEAS-2B monolayers increased mitochondrial protein oxidation, expression of the antioxidant enzyme SOD2, activation of NF-{kappa}B, and secretion of inflammatory cytokines (IL-6 and IL-8). Proteomics data collected following ALI exposure of 3D EpiAirway tissue in the Cultex showed upregulation of NRF-2-mediated oxidative stress, oxidative phosphorylation, and IL-8 signaling. Across the three platforms, menthol adversely effected human bronchial epithelium in a manner that could lead to respiratory disease. O_FIG O_LINKSMALLFIG WIDTH=115 HEIGHT=200 SRC="FIGDIR/small/988006v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@b86c96org.highwire.dtl.DTLVardef@1e12bd2org.highwire.dtl.DTLVardef@4008corg.highwire.dtl.DTLVardef@c01cad_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract. Mechanism of action of menthol on human bronchial epithelium.Three in vitro platforms were used to study the effect of menthol on bronchial epithelium. In submerged culture (using BEAS-2B cells), menthol produced rapid calcium influx followed by an increase in oxidative stress and inflammatory cytokines. ALI exposure of BEAS-2B cells to unheated menthol in a cloud chamber caused activation of an inflammatory transcription factor (NF-{kappa}B) and oxidative stress. Proteomics analysis of human EpiAirway tissues exposed at the ALI to heated menthol EC aerosols identified changes in the expression of proteins involved in oxidative stress and in an inflammatory response. C_FIG

cell biology↗

bioRxiv2019

Machine learning prediction of emesis and gastrointestinal state in ferrets

Although electrogastrography (EGG) could be a critical tool in the diagnosis and treatment of patients with gastrointestinal (GI) disease, it remains under-utilized. The lack of spatial and temporal resolution using current EGG methods presents a significant roadblock to more widespread usage. Human and preclinical studies have shown that GI myoelectric electrodes can record signals containing significantly more information than can be derived abdominal surface electrodes. The current study sought to assess the efficacy of multi-electrode arrays, surgically implanted on the serosal surface of the GI tract, from gastric fundus to duodenum, in recording myoelectric signals. It also examines the potential for machine learning algorithms to predict functional states, such as retching and emesis, from GI signal features. Studies were performed using ferrets, a gold standard model for emesis testing. Our results include simultaneous recordings from up to six GI recording sites in both anesthetized and chronically implanted free-moving ferrets. Testing conditions to produce different gastric states included gastric distension, intragastric infusion of emetine (a prototypical emetic agent), and feeding. Despite the observed variability in GI signals, machine learning algorithms, including k nearest neighbors and support vector machines, were able to detect the state of the stomach with high overall accuracy (>80%). The present study is the first demonstration of machine learning algorithms to detect the physiological state of the stomach and onset of retching and could provide insight into methodologies to treat GI diseases and control symptoms such as nausea and vomiting.

neuroscience↗