Caribbean Spiny Lobster Fishery Is Underpinned by Trophic Subsidies from Chemosynthetic Primary Production

Data files are deposited with Figshare and are available at: https://dx.doi.org/10.6084/ m9.figshare.4225334

Utility of three anthropometric indices in assessing the cardiometabolic risk profile in children

Objectives: To evaluate the ability of BMI, WC and WHtR to identify increased cardiometabolic risk in pre-adolescents. Methods: This is a cross-sectional study involving 192 children (10.92 ± 0.58 years, 56% female) from the United Kingdom between 2010 and 2013. Receiver operating characteristic curves determined the discriminatory ability of BMI, WC and WHtR to identify individuals with increased cardiometabolic risk (increased clustered triglycerides, HDL-cholesterol, systolic blood pressure, cardiorespiratory fitness and glucose). Results: A WHtR ≥ 0.5 increased the odds by 5.2 (95% confidence interval 2.6, 10.3) of having increased cardiometabolic risk. Similar associations were observed for BMI and WC. Both BMI-z and WHtR were fair predictors of increased cardiometabolic risk although BMI-z demonstrated the best trade-off between sensitivity and specificity, 76.1% and 63.6%, compared to 68.1% and 65.5% for WHtR. Cross-validation analysis revealed that BMI-z and WHtR correctly classified 84% of individuals (kappa score = 0.671, 95% CI 0.55, 0.79). The sensitivity of the cut-points suggests that 89.3% of individuals were correctly classified as being at risk with only 10.7% misdiagnosed whereas the specificity of the cut-points indicated that 77.8% of individuals were correctly identified as being healthy with 22.2% of individuals incorrectly diagnosed as being at risk. Conclusions: Findings suggest that WHtR provides similar cardiometabolic risk estimates to age and sex adjusted BMI

Physicians' migration in Europe: an overview of the current situation

<p>Abstract</p> <p>Background</p> <p>The migration of medical professionals as a result of the expansion of the European Union is cause for concern. But there is a significant lack of information available about this phenomenon.</p> <p>Methods</p> <p>Search of secondary databases about the presence of european doctors working abroad, through two search engines in the Internet (Google and Pubmed) and a survey of professional organisations and regulators in countries of the European Union.</p> <p>Results</p> <p>The United Kingdom has more foreign doctors than all other European countries for which figures are available (Ireland, France, Germany, Norway, Portugal, Italy, Austria and Poland). Some 74,031 foreign doctors are registered in the UK, 30.94% of the total. European countries with the highest percentage of doctors working abroad are Ireland (47.5%, or 10,065 doctors) and Malta (23.1%, 376 doctors). The data obtained from Norway, France and Germany do not indicate an increase in the migration of professionals from countries recently incorporated into the EU.</p> <p>Conclusion</p> <p>There is significant mobility and heterogeneous distribution of doctors within the EEA and it should be cause for concern among health care authorities. However, there is no evidence about a possible increase in this phenomenon after the recent expansion of the EU.</p

Coaggregation of RNA-Binding Proteins in a Model of TDP-43 Proteinopathy with Selective RGG Motif Methylation and a Role for RRM1 Ubiquitination

TAR DNA-binding protein 43 (TDP-43) is a major component within ubiquitin-positive inclusions of a number of neurodegenerative diseases that increasingly are considered as TDP-43 proteinopathies. Identities of other inclusion proteins associated with TDP-43 aggregation remain poorly defined. In this study, we identify and quantitate 35 co-aggregating proteins in the detergent-resistant fraction of HEK-293 cells in which TDP-43 or a particularly aggregate prone variant, TDP-S6, were enriched following overexpression, using stable isotope-labeled (SILAC) internal standards and liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). We also searched for differential post-translational modification (PTM) sites of ubiquitination. Four sites of ubiquitin conjugation to TDP-43 or TDP-S6 were confirmed by dialkylated GST-TDP-43 external reference peptides, occurring on or near RNA binding motif (RRM) 1. RRM-containing proteins co-enriched in cytoplasmic granular structures in HEK-293 cells and primary motor neurons with insoluble TDP-S6, including cytoplasmic stress granule associated proteins G3BP, PABPC1, and eIF4A1. Proteomic evidence for TDP-43 co-aggregation with paraspeckle markers RBM14, PSF and NonO was also validated by western blot and by immunocytochemistry in HEK-293 cells. An increase in peptides from methylated arginine-glycine-glycine (RGG) RNA-binding motifs of FUS/TLS and hnRNPs was found in the detergent-insoluble fraction of TDP-overexpressing cells. Finally, TDP-43 and TDP-S6 detergent-insoluble species were reduced by mutagenesis of the identified ubiquitination sites, even following oxidative or proteolytic stress. Together, these findings define some of the aggregation partners of TDP-43, and suggest that TDP-43 ubiquitination influences TDP-43 oligomerization

Allele-Specific HLA Loss and Immune Escape in Lung Cancer Evolution

Immune evasion is a hallmark of cancer. Losing the ability to present neoantigens through human leukocyte antigen (HLA) loss may facilitate immune evasion. However, the polymorphic nature of the locus has precluded accurate HLA copy-number analysis. Here, we present loss of heterozygosity in human leukocyte antigen (LOHHLA), a computational tool to determine HLA allele-specific copy number from sequencing data. Using LOHHLA, we find that HLA LOH occurs in 40% of non-small-cell lung cancers (NSCLCs) and is associated with a high subclonal neoantigen burden, APOBEC-mediated mutagenesis, upregulation of cytolytic activity, and PD-L1 positivity. The focal nature of HLA LOH alterations, their subclonal frequencies, enrichment in metastatic sites, and occurrence as parallel events suggests that HLA LOH is an immune escape mechanism that is subject to strong microenvironmental selection pressures later in tumor evolution. Characterizing HLA LOH with LOHHLA refines neoantigen prediction and may have implications for our understanding of resistance mechanisms and immunotherapeutic approaches targeting neoantigens. Video Abstract [Figure presented] Development of the bioinformatics tool LOHHLA allows precise measurement of allele-specific HLA copy number, improves the accuracy in neoantigen prediction, and uncovers insights into how immune escape contributes to tumor evolution in non-small-cell lung cancer

The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy

Background: The ability to accurately predict operative duration has the potential to optimise theatre efficiency and utilisation, thus reducing costs and increasing staff and patient satisfaction. With laparoscopic cholecystectomy being one of the most commonly performed procedures worldwide, a tool to predict operative duration could be extremely beneficial to healthcare organisations. Methods: Data collected from the CholeS study on patients undergoing cholecystectomy in UK and Irish hospitals between 04/2014 and 05/2014 were used to study operative duration. A multivariable binary logistic regression model was produced in order to identify significant independent predictors of long (> 90 min) operations. The resulting model was converted to a risk score, which was subsequently validated on second cohort of patients using ROC curves. Results: After exclusions, data were available for 7227 patients in the derivation (CholeS) cohort. The median operative duration was 60 min (interquartile range 45–85), with 17.7% of operations lasting longer than 90 min. Ten factors were found to be significant independent predictors of operative durations > 90 min, including ASA, age, previous surgical admissions, BMI, gallbladder wall thickness and CBD diameter. A risk score was then produced from these factors, and applied to a cohort of 2405 patients from a tertiary centre for external validation. This returned an area under the ROC curve of 0.708 (SE = 0.013, p  90 min increasing more than eightfold from 5.1 to 41.8% in the extremes of the score. Conclusion: The scoring tool produced in this study was found to be significantly predictive of long operative durations on validation in an external cohort. As such, the tool may have the potential to enable organisations to better organise theatre lists and deliver greater efficiencies in care

Fc-Optimized Anti-CD25 Depletes Tumor-Infiltrating Regulatory T Cells and Synergizes with PD-1 Blockade to Eradicate Established Tumors

CD25 is expressed at high levels on regulatory T (Treg) cells and was initially proposed as a target for cancer immunotherapy. However, anti-CD25 antibodies have displayed limited activity against established tumors. We demonstrated that CD25 expression is largely restricted to tumor-infiltrating Treg cells in mice and humans. While existing anti-CD25 antibodies were observed to deplete Treg cells in the periphery, upregulation of the inhibitory Fc gamma receptor (FcγR) IIb at the tumor site prevented intra-tumoral Treg cell depletion, which may underlie the lack of anti-tumor activity previously observed in pre-clinical models. Use of an anti-CD25 antibody with enhanced binding to activating FcγRs led to effective depletion of tumor-infiltrating Treg cells, increased effector to Treg cell ratios, and improved control of established tumors. Combination with anti-programmed cell death protein-1 antibodies promoted complete tumor rejection, demonstrating the relevance of CD25 as a therapeutic target and promising substrate for future combination approaches in immune-oncology

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

Funder: Science and Technology Development Fund; doi: https://doi.org/10.13039/Funder: Al-Alfi FoundationFunder: Magdi Yacoub Heart FoundationFunder: Rosetrees and Stoneygate Imperial College Research FellowshipFunder: National Health and Medical Research Council (Australia)Abstract: Purpose: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. Methods: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. Results: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. Conclusions: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions

Funder: Science and Technology Development Fund; doi: https://doi.org/10.13039/Funder: Al-Alfi FoundationFunder: Magdi Yacoub Heart FoundationFunder: Rosetrees and Stoneygate Imperial College Research FellowshipFunder: National Health and Medical Research Council (Australia)Abstract: Purpose: Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. Methods: We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. Results: CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. Conclusions: A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity