Bio Integration最新文献

{"title":"Review on Anti-diabetic Research on Two Important Spices: <i>Trachyspermum ammi</i> and <i>Pimpinella anisum</i>","authors":"Amar Godavari, Manicka Moorthi, Arvindganth Rajasekar","doi":"10.15212/bioi-2023-0010","DOIUrl":"https://doi.org/10.15212/bioi-2023-0010","url":null,"abstract":"Diabetes mellitus (DM) arises from a cascade of factors, primarily stemming from defective insulin secretion by the pancreas and emergence of insulin resistance. These alterations disrupt lipid and protein metabolism, which may lay the foundation for hyperglycemia. The efficacy and safety of spice herbs from traditional medicine have long been regarded for the potential to treat this condition. Remarkably, many of the drugs we rely on today have origins, either directly or indirectly, in the realm of plant sources. The exploration of hypoglycemic potential extends beyond the boundaries of herbs and spices, embracing a diverse tapestry of food extracts. Among the spices, Trachyspermum ammi and Pimpinella anisum are plants in the Umbelliferae family, and their fruits are used traditionally as carminatives, aromatics, disinfectants, and galactogogues. In this comprehensive review the published scientific articles related to antidiabetic properties of both seeds are discussed.","PeriodicalId":488774,"journal":{"name":"Bio Integration","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135653258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-Cell RNA-Sequencing Integration Analysis Revealed Immune Cell Heterogeneity in Five Human Autoimmune Diseases","authors":"Siweier Luo, Le Wang, Yi Xiao, Chunwei Cao, Qinghua Liu, Yiming Zhou","doi":"10.15212/bioi-2023-0012","DOIUrl":"https://doi.org/10.15212/bioi-2023-0012","url":null,"abstract":"Background: Autoimmune diseases are a group of diseases caused by abnormal immune responses to functional body parts. Single-cell RNA-sequencing (scRNA-seq) technology provides transcriptomic information at the single-cell resolution, thus offering a new way to study autoimmune diseases. Most single-cell RNA-seq studies, however, have often focused on one type of autoimmune disease. Methods: We integrated scRNA-seq data from peripheral blood cells of five different autoimmune diseases (IgA nephropathy [IgAN], Kawasaki disease [KD], multiple sclerosis [MS], Sjogren&amp;#x2019;s syndrome [SS], and systemic lupus erythematosus [SLE]). We performed dimensionality clustering, cellular communication analysis, re-clustering analysis of monocytes, NK cell populations, differential gene expression analysis, and functional enrichment for all immune cells in these data. Results: We integrated the scRNA-seq results of peripheral blood cells from five different autoimmune diseases (IgAN, KD, MS, SS, and SLE). We showed that all samples contained 18 different immune cell subsets, although the cell cluster populations were different among the 5 diseases. Through intercellular communication network analysis, we determined that the signals of classical and non-classical monocytes were significantly enhanced in patients with IgAN and SLE. The signals of na&amp;#x00EF;ve B cells were increased in patients KD. Interestingly, the signals of NK and NK-T cells were enhanced in patients with SS, but reduced in patients with IgAN and SLE. Transcriptomic analysis of classical and non-classical monocyte subsets further revealed that pro-inflammatory cytokines and interferon-related genes, including CCL3, IL1B, ISG15, and IFI6, were specifically increased in patients with IgAN and SLE. Unlike monocytes, the number and NK marker genes were decreased in patients with IgAN and KD, but increased in patients with SS. Meanwhile, two NK-T cell subsets were exclusively found in SS. Conclusions: In summary, based on an integration of the single-cell RNA-seq results, we demonstrated changes in the immune cell landscape of five different autoimmune diseases with respect to immune cell subsets, populations, differentially-expressed genes, and the cell-to-cell communication network. Our data provide new insight to further explore the heterogeneity and similarity among different autoimmune diseases.","PeriodicalId":488774,"journal":{"name":"Bio Integration","volume":"30 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135550411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Embedding R inside the PhysPK Bio-simulation Software for Pharmacokinetics Population Analysis","authors":"Sergio Sánchez-Herrero, Fernando Carbonero Martínez, Jenifer Serna, Marina Cuquerella-Gilabert, Almudena Rueda-Ferreiro, Angel A. Juan, Laura Calvet","doi":"10.15212/bioi-2023-0008","DOIUrl":"https://doi.org/10.15212/bioi-2023-0008","url":null,"abstract":"Background: PhysPK stands as a flexible and robust bio-simulation and modeling software designed for analysis of population pharmacokinetics (PK) and pharmacodynamics (PD) systems. PhysPK equips users with standard diagnostic plots for pre- and post-analysis to delineate PK and PD within population-based frameworks. Furthermore, PhysPK facilitates the establishment of mathematical models that elucidate the intricate interplay between exposure, safety, and efficacy. Methods: Enhancing simulation modeling capabilities necessitates seamless integration between commercial discrete-event PK and PD simulation tools and external software. This synergy can be amplified by incorporating open-source solutions, like R, which boasts a rich array of comprehensive packages tailored for diverse tasks, including data analysis (ggplot2), scientific computation (stats), application development (shiny), back-end web development (dplyr), and machine learning (CARAT). The integration of R within PhysPK holds the potential to efficiently interpret and analyze PK/PD output and routines using R packages. Results: This article presents a tutorial that highlights the incorporation of R code within PhysPK and the rendering of R scripts within the PhysPK monitor. The tutorial utilizes a two-compartment model for comparison against the analysis developed by Hosseini et al. in 2018 within the context of the gPKPDSim application and WinNonlin &amp;#x00AE; software. The illustrative example that is provided and discussed demonstrate estimated and simulated plots, revealing negligible differences in the significance for C L and C Ld (6.89 &amp;#x00B1; 0.2 and 45.5 &amp;#x00B1; 17.4 [reference], and 7.06 &amp;#x00B1; 0.32 and 49.04 &amp;#x00B1; 9.2 [PhysPK], respectively), as well as volumes V 1 and V 2 (49.15 &amp;#x00B1; 3.8 and 34.61 &amp;#x00B1; 5.2 [reference], and 48.8 &amp;#x00B1; 3.66, and 33.2 &amp;#x00B1; 3.95 [PhysPK], respectively). Conclusions: Our study underscores the potential of integrating open-source software, replete with an array of innovative packages, to elevate predictive capabilities and streamline analyses in PK methods. This integration ushers in new avenues for an advanced intelligent simulation modeling within the realm of PK, thus holding significant promise for the advancement of drug research and development.","PeriodicalId":488774,"journal":{"name":"Bio Integration","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135403514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}