Mesenchymal stem cells (MSCs) have been used in therapies owing to their regenerative potential, paracrine regulatory effects, and immunomodulatory activity. To foster commercialization and implementation of stem cells treatments, researchers have recently derived MSCs from human induced pluripotent stem cells (iMSCs). For therapeutic applications, human iMSCs must be produced in xeno-free culture conditions and following procedures that are compatible with the principles of Good Manufacturing Practice.
  Mitochondrial dysfunction is central to sepsis-induced multi-organ dysfunction. Thiamine deficiency may contribute to mitochondrial dysfunction and thus high mortality. Study was planned to assess thiamine status in children with septic shock in comparison to healthy controls from a developing country and to study the effect of thiamine levels on its outcome.
 
  A prospective case-control study (April 2017 to May 2018) enrolling consecutive children with septic shock as 'cases' (n = 76), their healthy siblings (n = 51) and apparently healthy children from immunization clinic (n = 35) as 'controls'. Whole blood total thiamine (WBTT) level was measured on days 1, 10 and 1-month post-discharge. Outcome parameters were acute care area free days on days 14 and 28, and mortality.
 
  WBTT [nMol/l; median (interquartile range, IQR)] was significantly lower on day 1 in cases compared with sibling controls [23.1 (21.8-26.3) vs. 36.9 (33.6-40.5); p < 0.001]. It fell further on day 10 [20.8 (18.1-21.1); p < 0.02]. n days 1, 10 and 1 month after hospital discharge. Seventy-six children were enrolled as cases, 51 children as sibling controls and 35 children as immunization clinic controls. WBTT was significantly lower on day 1 in cases as compared with their sibling controls.  https://www.selleckchem.com/products/quinine-dihydrochloride.html  It fell further on day 10. The level rose significantly after a month of discharge and became comparable to sibling controls. Immunization clinic controls also had lower WBTT but was significantly higher compared with sibling controls and cases at 1-month post-discharge. Survivors and non-survivors of septic shock had similar WBTT levels. Observed severe deficiency might have precluded any further association of thiamine levels with septic shock outcome.
  Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted.
 
  The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes.
 
  The code for the framework will be available at http//www.alphaminers.net/.
 
  Supplementary data are available at Bioinformatics online.
 Supplementary data are available at Bioinformatics online.
  Horizontal gene transfer (HGT) is a major source of variability in prokaryotic genomes. Regions of genome plasticity (RGPs) are clusters of genes located in highly variable genomic regions. Most of them arise from HGT and correspond to genomic islands (GIs). The study of those regions at the species level has become increasingly difficult with the data deluge of genomes. To date, no methods are available to identify GIs using hundreds of genomes to explore their diversity.
 
  We present here the panRGP method that predicts RGPs using pangenome graphs made of all available genomes for a given species. It allows the study of thousands of genomes in order to access the diversity of RGPs and to predict spots of insertions. It gave the best predictions when benchmarked along other GI detection tools against a reference dataset. In addition, we illustrated its use on metagenome assembled genomes by redefining the borders of the leuX tRNA hotspot, a well-studied spot of insertion in Escherichia coli. panRPG is a scalable and reliable tool to predict GIs and spots making it an ideal approach for large comparative studies.
 
  The methods presented in the current work are available through the following software https//github.com/labgem/PPanGGOLiN. Detailed results and scripts to compute the benchmark metrics are available at https//github.com/axbazin/panrgp_supdata.
 The methods presented in the current work are available through the following software https//github.com/labgem/PPanGGOLiN. Detailed results and scripts to compute the benchmark metrics are available at https//github.com/axbazin/panrgp_supdata.
  The discovery of protein-ligand-binding sites is a major step for elucidating protein function and for investigating new functional roles. Detecting protein-ligand-binding sites experimentally is time-consuming and expensive. Thus, a variety of in silico methods to detect and predict binding sites was proposed as they can be scalable, fast and present low cost.
 
  We proposed Graph-based Residue neighborhood Strategy to Predict binding sites (GRaSP), a novel residue centric and scalable method to predict ligand-binding site residues. It is based on a supervised learning strategy that models the residue environment as a graph at the atomic level. Results show that GRaSP made compatible or superior predictions when compared with methods described in the literature. GRaSP outperformed six other residue-centric methods, including the one considered as state-of-the-art. Also, our method achieved better results than the method from CAMEO independent assessment. GRaSP ranked second when compared with five state-of-the-art pocket-centric methods, which we consider a significant result, as it was not devised to predict pockets. Finally, our method proved scalable as it took 10-20 s on average to predict the binding site for a protein complex whereas the state-of-the-art residue-centric method takes 2-5 h on average.
 
  The source code and datasets are available at https//github.com/charles-abreu/GRaSP.
 
  Supplementary data are available at Bioinformatics online.
 Supplementary data are available at Bioinformatics online.