gbtools: Interactive Visualization of Metagenome Bins in R.

Improvements in DNA sequencing technology have increased the amount and quality of sequences that can be obtained from metagenomic samples, making it practical to extract individual microbial genomes from metagenomic assemblies ("binning"). However, while many tools and methods exist for unsupervised binning with various statistical algorithms, there are few options for visualizing the results, even though visualization is vital to exploratory data analysis. We have developed gbtools, a software package that allows users to visualize metagenomic assemblies by plotting coverage (sequencing depth) and GC values of contigs, and also to annotate the plots with taxonomic information. Different sets of annotations, including taxonomic assignments from conserved marker genes or SSU rRNA genes, can be imported simultaneously; users can choose which annotations to plot. Bins can be manually defined from plots, or be imported from third-party binning tools and overlaid onto plots, such that results from different methods can be compared side-by-side. gbtools reports summary statistics of bins including marker gene completeness, and allows the user to add or subtract bins with each other. We illustrate some of the functions available in gbtools with two examples: the metagenome of Olavius algarvensis, a marine oligochaete worm that has up to five bacterial symbionts, and the metagenome of a synthetic mock community comprising 64 bacterial and archaeal strains. We show how instances of poor automated binning, sequencer GC% bias, and variation between samples can be quickly diagnosed by visualization, and demonstrate how the results from different binning tools can be combined and refined to yield manually curated bins with higher completeness. gbtools is open-source and written in R. The software package, documentation, and example data are available freely online at https://github.com/kbseah/genome-bin-tools

Determination of Abundant Metabolite Matrix Adducts Illuminates the Dark Metabolome of MALDI-Mass Spectrometry Imaging Datasets

: Spatial metabolomics using mass spectrometry imaging (MSI) is a powerful tool to map hundreds to thousands of metabolites in biological systems. One major challenge in MSI is the annotation of m/z values, which is substantially complicated by background ions introduced throughout the chemicals and equipment used during experimental procedures. Among many factors, the formation of adducts with sodium or potassium ions, or in case of matrix-assisted laser desorption ionization (MALDI)- MSI, the presence of abundant matrix clusters strongly increases total m/z peak counts. Currently, there is a limitation to identify the chemistry of the many unknown peaks to interpret their biological function. We took advantage of the co-localization of adducts with their parent ions and the accuracy of high mass resolution to estimate adduct abundance in 20 datasets from different vendors of mass spectrometers. Metabolites ranging from lipids to amines and amino acids form matrix adducts with the commonly used 2,5-dihydroxybenzoic acid (DHB) matrix like [M + (DHB-H2O) + H]+ and [M + DHB + Na]+ . Current data analyses neglect those matrix adducts and overestimate total metabolite numbers, thereby expanding the number of unidentified peaks. Our study demonstrates that MALDI-MSI data are strongly influenced by adduct formation across different sample types and vendor platforms and reveals a major influence of so far unrecognized metabolite−matrix adducts on total peak counts (up to one third). We developed a software package, mass2adduct, for the community for an automated putative assignment and quantification of metabolite−matrix adducts enabling users to ultimately focus on the biologically relevant portion of the MSI data

Virtual Simulation to Enhance Clinical Reasoning in Nursing: A Systematic Review and Meta-analysis.

Background: The COVID-19 pandemic has given rise to more virtual simulation training. This study aimed to review the effectiveness of virtual simulations and their design features in developing clinical reasoning skills among nurses and nursing students. Method: A systematic search in CINAHL, PubMed, Cochrane Library, Embase, ProQuest, PsycINFO, and Scopus was conducted. The PRISMA guidelines, Cochrane's risk of bias, and GRADE was used to assess the articles. Meta-analyses and random-effects meta-regression were performed. Results: The search retrieved 11,105 articles, and 12 randomized controlled trials (RCTs) were included. Meta-analysis demonstrated a significant improvement in clinical reasoning based on applied knowledge and clinical performance among learners in the virtual simulation group compared with the control group. Meta-regression did not identify any significant covariates. Subgroup analyses revealed that virtual simulations with patient management contents, using multiple scenarios with nonimmersive experiences, conducted more than 30-minutes and postscenario feedback were more effective. Conclusions: Virtual simulations can improve clinical reasoning skill. This study may inform nurse educators on how virtual simulation should be designed to optimize the development of clinical reasoning

Chest radiographs and machine learning - Past, present and future.

Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well-tested machine learning algorithms will be a revolution akin to early advances in X-ray technology. Current use cases, strengths, limitations and applications of chest X-ray machine learning systems are discussed

Sulfur-Oxidizing Symbionts without Canonical Genes for Autotrophic CO2 Fixation

Many animals and protists depend on symbiotic sulfur-oxidizing bacteria as their main food source. These bacteria use energy from oxidizing inorganic sulfur compounds to make biomass autotrophically from CO2, serving as primary producers for their hosts. Here we describe a clade of nonautotrophic sulfur-oxidizing symbionts, “Candidatus Kentron,” associated with marine ciliates. They lack genes for known autotrophic pathways and have a carbon stable isotope fingerprint heavier than other symbionts from similar habitats. Instead, they have the potential to oxidize sulfur to fuel the uptake of organic compounds for heterotrophic growth, a metabolic mode called chemolithoheterotrophy that is not found in other symbioses. Although several symbionts have heterotrophic features to supplement primary production, in Kentron they appear to supplant it entirely.Since the discovery of symbioses between sulfur-oxidizing (thiotrophic) bacteria and invertebrates at hydrothermal vents over 40 years ago, it has been assumed that autotrophic fixation of CO2 by the symbionts drives these nutritional associations. In this study, we investigated “Candidatus Kentron,” the clade of symbionts hosted by Kentrophoros, a diverse genus of ciliates which are found in marine coastal sediments around the world. Despite being the main food source for their hosts, Kentron bacteria lack the key canonical genes for any of the known pathways for autotrophic carbon fixation and have a carbon stable isotope fingerprint that is unlike other thiotrophic symbionts from similar habitats. Our genomic and transcriptomic analyses instead found metabolic features consistent with growth on organic carbon, especially organic and amino acids, for which they have abundant uptake transporters. All known thiotrophic symbionts have converged on using reduced sulfur to gain energy lithotrophically, but they are diverse in their carbon sources. Some clades are obligate autotrophs, while many are mixotrophs that can supplement autotrophic carbon fixation with heterotrophic capabilities similar to those in Kentron. Here we show that Kentron bacteria are the only thiotrophic symbionts that appear to be entirely heterotrophic, unlike all other thiotrophic symbionts studied to date, which possess either the Calvin-Benson-Bassham or the reverse tricarboxylic acid cycle for autotrophy

Perioperative adjuvant corticosteroids for post-operative analgesia in knee arthroplasty – A meta-analysis of 1396 knees

Background and purpose — Immediate postoperative pain management offered in knee arthroplasty is suboptimal in up to onethird of patients resulting in high opiate consumption and delayed discharge. In this meta-analysis we investigate the analgesic effect and safety of perioperative adjuvant corticosteroids in knee arthroplasty. Methods — Databases Medline, Embase, and Central were searched for randomized studies comparing the analgesic effect of adjuvant perioperative corticosteroids in knee arthroplasty. Our primary outcome was pain score at 24 hours postoperatively. Secondary outcomes included pain at 12, 48, and 72 hours, opiate consumption, postoperative nausea and vomiting, infection, and discharge time. Systemic (intravenous) and local (intra-articular) corticosteroids were analyzed separately. Results — 14 randomized controlled trials (1,396 knees) were included. Mean corticosteroid dosages were predominantly 50–75mg oral prednisolone equivalents for both systemic and local routes. Systemic corticosteroids demonstrated statistically signifi cant and clinically modest reductions in pain at 12 hours by –1.1 points (95%CI –2.2 to 0.02), 24 hours by –1.3 points (CI –2.3 to –0.26) and 48 hours by –0.4 points (CI –0.67 to –0.04). Local corticosteroids did not reduce pain. Opiate consumption, postoperative nausea and vomiting, infection, or time till discharge were similar between groups. Interpretation — Corticosteroids modestly reduce pain postoperatively at 12 and 24 hours when used systemically without any increase in associated risks for dosages between 50 and 75 mg oral prednisolone equivalents

Performance characteristics of next-generation sequencing for the detection of antimicrobial resistance determinants in Escherichia coli genomes and metagenomes

Short-read sequencing can provide detection of multiple genomic determinants of antimicrobial resistance from single bacterial genomes and metagenomic samples. Despite its increasing application in human, animal, and environmental microbiology, including human clinical trials, the performance of short-read Illumina sequencing for antimicrobial resistance gene (ARG) detection, including resistance-conferring single nucleotide polymorphisms (SNPs), has not been systematically characterized. Using paired-end 2 x 150 bp (base pair) Illumina sequencing and an assembly-based method for ARG prediction, we determined sensitivity, positive predictive value (PPV), and sequencing depths required for ARG detection in an Escherichia coli isolate of sequence type (ST) 38 spiked into a synthetic microbial community at varying abundances. Approximately 300,000 reads or 15x genome coverage was sufficient to detect ARGs in E. coli ST38, with comparable sensitivity and PPV to ~100x genome coverage. Using metagenome assembly of mixed microbial communities, ARG detection at E. coli relative abundances of 1% would require assembly of approximately 30 million reads to achieve 15x target coverage. The minimum sequencing depths were validated using public data sets of 948 E. coli genomes and 10 metagenomic rectal swab samples. A read-based approach using k-mer alignment (KMA) for ARG prediction did not substantially improve minimum sequencing depths for ARG detection compared to assembly of the E. coli ST38 genome or the combined metagenomic samples. Analysis of sequencing depths from recent studies assessing ARG content in metagenomic samples demonstrated that sequencing depths had a median estimated detection frequency of 84% (interquartile range: 30%-92%) for a relative abundance of 1%. IMPORTANCE Systematically determining Illumina sequencing performance characteristics for detection of ARGs in metagenomic samples is essential to inform study design and appraisal of human, animal, and environmental metagenomic antimicrobial resistance studies. In this study, we quantified the performance characteristics of ARG detection in E. coli genomes and metagenomes and established a benchmark of ~15x coverage for ARG detection for E. coli in metagenomes. We demonstrate that for low relative abundances, sequencing depths of ~30 million reads or more may be required for adequate sensitivity for many applications

Dacryoadenitis with multiple unilateral ocular abscesses in disseminated melioidosis

Melioidosis is caused by the gram-negative bacterium Burkholderia pseudomallei and has a wide range of manifestations in many organ systems. Ocular melioidosis is a rare manifestation of Burkholderia pseudomallei that commonly presents with orbital cellulitis rather than dacryoadenitis and multiple unilateral ocular abscesses, which are uncommon. The risk of blindness in ocular melioidosis is high because this bacterium is resistant to many antibiotics, requires a longer period of treatment with antibiotics and the diagnosis is not always straightforward. Here we describe a different presentation of ocular melioidosis manifested with left preseptal abscess, orbital cellulitis with abscess and dacryoadenitis in a 50-year-old man who was treated successfully with surgery and antibiotics, along with a brief review of the literature