The high incidence of bacterial genes that confer resistance to last-resort antibiotics, such as colistin, caused by mobilized colistin resistance (mcr) genes, poses an unprecedented threat to human health. Understanding the spread, evolution, and distribution of such genes among human populations will help in the development of strategies to diminish their occurrence. To tackle this problem, we investigated the distribution and prevalence of potential mcr genes in the human gut microbiome using a set of bioinformatics tools to screen the Unified Human Gastrointestinal Genome (UHGG) collection for the presence, synteny and phylogeny of putative mcr genes, and co-located antibiotic resistance genes. A total of 2079 antibiotic resistance genes (ARGs) were classified as mcr genes in 2046 metagenome assembled genomes (MAGs), distributed across 1596 individuals from 41 countries, of which 215 were identified in plasmidial contigs. The genera that presented the largest number of mcr-like genes were Suterella anrsity of mcr-like genes in the human gut microbiome. We demonstrated the cosmopolitan distribution of these genes in individuals worldwide and the co-presence of other antibiotic resistance genes, including Extended-spectrum Beta-Lactamases (ESBL). Also, we described mcr-like genes fused to a PAP2-like domain in S. wadsworthensis. These novel sequences increase our knowledge about the diversity and evolution of mcr-like genes. Future research should focus on activity, genetic mobility and a potential colistin resistance in the corresponding strains to experimentally validate those findings. Extracellular vesicles (EVs) derived from tumor cells are implicated in the progression of malignancies through the transfer of molecular cargo microRNAs (miRNAs or miRs). We aimed to explore the role of EVs derived from breast cancer cells carrying miR-182-5p in the occurrence and development of breast cancer. Differentially expressed miRNAs and their downstream target genes related to breast cancer were screened through GEO and TCGA databases. miR-182-5p expression was examined in cancer tissues and adjacent normal tissues from patients with breast cancer. EVs were isolated from breast cancer cell line MDA-MB-231 cells and identified. The gain- and loss-of function approaches of miR-182-5p and CKLF-like MARVEL transmembrane domain-containing 7 (CMTM7) were performed in MDA-MB-231 cells and the isolated EVs. Human umbilical vein endothelial cells (HUVECs) were subjected to co-culture with MDA-MB-231 cell-derived EVs and biological behaviors were detected by CCK-8 assay, flow cytometry, immunohistochemicaAKT signaling pathway.Taken altogether, our findings demonstrates that EVs secreted by breast cancer cells could carry miR-182-5p to aggravate breast cancer through downregulating CMTM7 expression and activating the EGFR/AKT signaling pathway. Due to the complexity of microbial communities, de novo assembly on next generation sequencing data is commonly unable to produce complete microbial genomes. Metagenome assembly binning becomes an essential step that could group the fragmented contigs into clusters to represent microbial genomes based on contigs' nucleotide compositions and read depths. These features work well on the long contigs, but are not stable for the short ones. Contigs can be linked by sequence overlap (assembly graph) or by the paired-end reads aligned to them (PE graph), where the linked contigs have high chance to be derived from the same clusters. We developed METAMVGL, a multi-view graph-based metagenomic contig binning algorithm by integrating both assembly and PE graphs. It could strikingly rescue the short contigs and correct the binning errors from dead ends. METAMVGL learns the two graphs' weights automatically and predicts the contig labels in a uniform multi-view label propagation framework. Piperlongumine In experiments, we observed METAMVGL made use of significantly more high-confidence edges from the combined graph and linked dead ends to the main graph. It also outperformed many state-of-the-art contig binning algorithms, including MaxBin2, MetaBAT2, MyCC, CONCOCT, SolidBin and GraphBin on the metagenomic sequencing data from simulation, two mock communities and Sharon infant fecal samples. Our findings demonstrate METAMVGL outstandingly improves the short contig binning and outperforms the other existing contig binning tools on the metagenomic sequencing data from simulation, mock communities and infant fecal samples.Our findings demonstrate METAMVGL outstandingly improves the short contig binning and outperforms the other existing contig binning tools on the metagenomic sequencing data from simulation, mock communities and infant fecal samples. There is a paucity of published literature describing electrical storm after the correction of uncomplicated atrial septal defect (ASD) in an adult. We present a 49-year-old woman with a congenital ASD combined with mild tricuspid regurgitation who denied any history of arrhythmia or other medical history. She suffered from electrical storm (≥ 3 episodes of ventricular tachycardias or ventricular fibrillations) in the early stage after ASD repair with combined tricuspid valvuloplasty. During electrical storm, her electrolytes were within normal ranges and no ischemic electrocardiographic changes were detected, which suggested that retained air embolism or acute coronary thrombosis were unlikely. Additionally, echocardiographic findings and her central venous pressure (5-8mmHg during the interval between attacks) failed to support the diagnosis of pericardial tamponade. After a thorough discussion, the surgeons conducted an emergent re-exploration and repeated closure of the ASD with combined DeVega's annud assess the benefit-risk ratio when taking this unconventional measure. Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy.