The epidemiological impact of the NHS COVID-19 app.

The COVID-19 pandemic has seen the emergence of digital contact tracing to help to prevent the spread of the disease. A mobile phone app records proximity events between app users, and when a user tests positive for COVID-19, their recent contacts can be notified instantly. Theoretical evidence has supported this new public health intervention1-6, but its epidemiological impact has remained uncertain7. Here we investigate the impact of the National Health Service (NHS) COVID-19 app for England and Wales, from its launch on 24 September 2020 to the end of December 2020. It was used regularly by approximately 16.5 million users (28% of the total population), and sent approximately 1.7 million exposure notifications: 4.2 per index case consenting to contact tracing. We estimated that the fraction of individuals notified by the app who subsequently showed symptoms and tested positive (the secondary attack rate (SAR)) was 6%, similar to the SAR for manually traced close contacts. We estimated the number of cases averted by the app using two complementary approaches: modelling based on the notifications and SAR gave an estimate of 284,000 (central 95% range of sensitivity analyses 108,000-450,000), and statistical comparison of matched neighbouring local authorities gave an estimate of 594,000 (95% confidence interval 317,000-914,000). Approximately one case was averted for each case consenting to notification of their contacts. We estimated that for every percentage point increase in app uptake, the number of cases could be reduced by 0.8% (using modelling) or 2.3% (using statistical analysis). These findings support the continued development and deployment of such apps in populations that are awaiting full protection from vaccines.

Multivariate phenotype analysis enables genome-wide inference of mammalian gene function.

The function of the majority of genes in the human and mouse genomes is unknown. Investigating and illuminating this dark genome is a major challenge for the biomedical sciences. The International Mouse Phenotyping Consortium (IMPC) is addressing this through the generation and broad-based phenotyping of a knockout (KO) mouse line for every protein-coding gene, producing a multidimensional data set that underlies a genome-wide annotation map from genes to phenotypes. Here, we develop a multivariate (MV) statistical approach and apply it to IMPC data comprising 148 phenotypes measured across 4,548 KO lines. There are 4,256 (1.4% of 302,997 observed data measurements) hits called by the univariate (UV) model analysing each phenotype separately, compared to 31,843 (10.5%) hits in the observed data results of the MV model, corresponding to an estimated 7.5-fold increase in power of the MV model relative to the UV model. One key property of the data set is its 55.0% rate of missingness, resulting from quality control filters and incomplete measurement of some KO lines. This raises the question of whether it is possible to infer perturbations at phenotype-gene pairs at which data are not available, i.e., to infer some in vivo effects using statistical analysis rather than experimentation. We demonstrate that, even at missing phenotypes, the MV model can detect perturbations with power comparable to the single-phenotype analysis, thereby filling in the complete gene-phenotype map with good sensitivity. A factor analysis of the MV model's fitted covariance structure identifies 20 clusters of phenotypes, with each cluster tending to be perturbed collectively. These factors cumulatively explain 75% of the KO-induced variation in the data and facilitate biological interpretation of perturbations. We also demonstrate that the MV approach strengthens the correspondence between IMPC phenotypes and existing gene annotation databases. Analysis of a subset of KO lines measured in replicate across multiple laboratories confirms that the MV model increases power with high replicability.

Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID-19.

From early in the coronavirus disease 2019 (COVID-19) pandemic, there was interest in using machine learning methods to predict COVID-19 infection status based on vocal audio signals, for example, cough recordings. However, early studies had limitations in terms of data collection and of how the performances of the proposed predictive models were assessed. This article describes how these limitations have been overcome in a study carried out by the Turing-RSS Health Data Laboratory and the UK Health Security Agency. As part of the study, the UK Health Security Agency collected a dataset of acoustic recordings, SARS-CoV-2 infection status and extensive study participant meta-data. This allowed us to rigorously assess state-of-the-art machine learning techniques to predict SARS-CoV-2 infection status based on vocal audio signals. The lessons learned from this project should inform future studies on statistical evaluation methods to assess the performance of machine learning techniques for public health tasks.

High-throughput mouse phenomics for characterizing mammalian gene function.

We are entering a new era of mouse phenomics, driven by large-scale and economical generation of mouse mutants coupled with increasingly sophisticated and comprehensive phenotyping. These studies are generating large, multidimensional gene-phenotype data sets, which are shedding new light on the mammalian genome landscape and revealing many hitherto unknown features of mammalian gene function. Moreover, these phenome resources provide a wealth of disease models and can be integrated with human genomics data as a powerful approach for the interpretation of human genetic variation and its relationship to disease. In the future, the development of novel phenotyping platforms allied to improved computational approaches, including machine learning, for the analysis of phenotype data will continue to enhance our ability to develop a comprehensive and powerful model of mammalian gene-phenotype space.

The impact of age on genetic risk for common diseases.

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Previous work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the longitudinal relationship between genetic relative risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic relative risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their relative risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying relative risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic relative risk. The predominant pattern shows genetic risk factors having the greatest relative impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants, although the magnitude and form of the decrease varies among diseases. As a consequence, for diseases where genetic relative risk decreases over age, genetic risk factors have stronger explanatory power among younger populations, compared to older ones. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction.

Statistical inference with exchangeability and martingales.

In this paper, we start by reviewing exchangeability and its relevance to the Bayesian approach. We highlight the predictive nature of Bayesian models and the symmetry assumptions implied by beliefs of an underlying exchangeable sequence of observations. By taking a closer look at the Bayesian bootstrap, the parametric bootstrap of Efron and a version of Bayesian thinking about inference uncovered by Doob based on martingales, we introduce a parametric Bayesian bootstrap. Martingales play a fundamental role. Illustrations are presented as is the relevant theory. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Testing for dependence on tree structures.

Tree structures, showing hierarchical relationships and the latent structures between samples, are ubiquitous in genomic and biomedical sciences. A common question in many studies is whether there is an association between a response variable measured on each sample and the latent group structure represented by some given tree. Currently, this is addressed on an ad hoc basis, usually requiring the user to decide on an appropriate number of clusters to prune out of the tree to be tested against the response variable. Here, we present a statistical method with statistical guarantees that tests for association between the response variable and a fixed tree structure across all levels of the tree hierarchy with high power while accounting for the overall false positive error rate. This enhances the robustness and reproducibility of such findings.

A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices.

Genetic surveillance of malaria parasites supports malaria control programmes, treatment guidelines and elimination strategies. Surveillance studies often pose questions about malaria parasite ancestry (e.g. how antimalarial resistance has spread) and employ statistical methods that characterise parasite population structure. Many of the methods used to characterise structure are unsupervised machine learning algorithms which depend on a genetic distance matrix, notably principal coordinates analysis (PCoA) and hierarchical agglomerative clustering (HAC). PCoA and HAC are sensitive to both the definition of genetic distance and algorithmic specification. Importantly, neither algorithm infers malaria parasite ancestry. As such, PCoA and HAC can inform (e.g. via exploratory data visualisation and hypothesis generation), but not answer comprehensively, key questions about malaria parasite ancestry. We illustrate the sensitivity of PCoA and HAC using 393 Plasmodium falciparum whole genome sequences collected from Cambodia and neighbouring regions (where antimalarial resistance has emerged and spread recently) and we provide tentative guidance for the use and interpretation of PCoA and HAC in malaria parasite genetic epidemiology. This guidance includes a call for fully transparent and reproducible analysis pipelines that feature (i) a clearly outlined scientific question; (ii) a clear justification of analytical methods used to answer the scientific question along with discussion of any inferential limitations; (iii) publicly available genetic distance matrices when downstream analyses depend on them; and (iv) sensitivity analyses. To bridge the inferential disconnect between the output of non-inferential unsupervised learning algorithms and the scientific questions of interest, tailor-made statistical models are needed to infer malaria parasite ancestry. In the absence of such models speculative reasoning should feature only as discussion but not as results.

Direct Oral Anticoagulants vs Low-Molecular-Weight Heparin and Recurrent VTE in Patients With Cancer: A Randomized Clinical Trial.

Importance: In patients with cancer who have venous thromboembolism (VTE) events, long-term anticoagulation with low-molecular-weight heparin (LMWH) is recommended to prevent recurrent VTE. The effectiveness of a direct oral anticoagulant (DOAC) compared with LMWH for preventing recurrent VTE in patients with cancer is uncertain. Objective: To evaluate DOACs, compared with LMWH, for preventing recurrent VTE and for rates of bleeding in patients with cancer following an initial VTE event. Design, Setting, and Participants: Unblinded, comparative effectiveness, noninferiority randomized clinical trial conducted at 67 oncology practices in the US that enrolled 671 patients with cancer (any invasive solid tumor, lymphoma, multiple myeloma, or chronic lymphocytic leukemia) who had a new clinical or radiological diagnosis of VTE. Enrollment occurred from December 2016 to April 2020. Final follow-up was in November 2020. Intervention: Participants were randomized in a 1:1 ratio to either a DOAC (n = 335) or LMWH (n = 336) and were followed up for 6 months or until death. Physicians and patients selected any DOAC or any LMWH (or fondaparinux) and physicians selected drug doses. Main Outcomes and Measures: The primary outcome was the recurrent VTE rate at 6 months. Noninferiority of anticoagulation with a DOAC vs LMWH was defined by the upper limit of the 1-sided 95% CI for the difference of a DOAC relative to LMWH of less than 3% in the randomized cohort that received at least 1 dose of assigned treatment. The 6 prespecified secondary outcomes included major bleeding, which was assessed using a 2.5% noninferiority margin. Results: Between December 2016 and April 2020, 671 participants were randomized and 638 (95%) completed the trial (median age, 64 years; 353 women [55%]). Among those randomized to a DOAC, 330 received at least 1 dose. Among those randomized to LMWH, 308 received at least 1 dose. Rates of recurrent VTE were 6.1% in the DOAC group and 8.8% in the LMWH group (difference, -2.7%; 1-sided 95% CI, -100% to 0.7%) consistent with the prespecified noninferiority criterion. Of 6 prespecified secondary outcomes, none were statistically significant. Major bleeding occurred in 5.2% of participants in the DOAC group and 5.6% in the LMWH group (difference, -0.4%; 1-sided 95% CI, -100% to 2.5%) and did not meet the noninferiority criterion. Severe adverse events occurred in 33.8% of participants in the DOAC group and 35.1% in the LMWH group. The most common serious adverse events were anemia and death. Conclusions and Relevance: Among adults with cancer and VTE, DOACs were noninferior to LMWH for preventing recurrent VTE over 6-month follow-up. These findings support use of a DOAC to prevent recurrent VTE in patients with cancer. Trial Registration: ClinicalTrials.gov Identifier: NCT02744092.

Interoperability of statistical models in pandemic preparedness: principles and reality.

We present interoperability as a guiding framework for statistical modelling to assist policy makers asking multiple questions using diverse datasets in the face of an evolving pandemic response. Interoperability provides an important set of principles for future pandemic preparedness, through the joint design and deployment of adaptable systems of statistical models for disease surveillance using probabilistic reasoning. We illustrate this through case studies for inferring and characterising spatial-temporal prevalence and reproduction numbers of SARS-CoV-2 infections in England.

Safety of apixaban for venous thromboembolic primary prophylaxis in patients with newly diagnosed malignant glioma.

The cumulative incidence of symptomatic venous thromboembolism (VTE) among patients with malignant gliomas (MG) is estimated to be as high as 36% during the course of therapy. Development of VTE is associated with an increased risk of hospitalization, delays in cancer treatment, and an increased risk of complications including intracranial hemorrhage as well as VTE specific symptoms. Despite the high risk of VTE and associated morbidity, there is no standard recommendations regarding long term outpatient VTE prophylaxis in patients with MG due to the lack of clinical trial evidence in this patient population. In this study, we treated ten patients with newly diagnosed MG with apixaban, 2.5 mg twice daily beginning 2-21 days after craniotomy and continuing for up to 6 months. Unacceptable toxicity was defined by ≥ grade 2 CNS or non-CNS hemorrhage, a thromboembolic event (i.e. stroke) or cardiovascular event requiring anticoagulation or anti-platelet therapy. There were no unacceptable toxicities to report and no treatment-related adverse events. None of the patients on the study were diagnosed with a VTE while receiving apixaban. We conclude that apixaban can be given safely to patients with primary MG shortly after craniotomy and should be considered for VTE prevention in these high-risk patients.

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.

BACKGROUND: The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. METHODS: In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. FINDINGS: We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41-4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0. INTERPRETATION: We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality. FUNDING: National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.

Advancing data science in drug development through an innovative computational framework for data sharing and statistical analysis.

BACKGROUND: Novartis and the University of Oxford's Big Data Institute (BDI) have established a research alliance with the aim to improve health care and drug development by making it more efficient and targeted. Using a combination of the latest statistical machine learning technology with an innovative IT platform developed to manage large volumes of anonymised data from numerous data sources and types we plan to identify novel patterns with clinical relevance which cannot be detected by humans alone to identify phenotypes and early predictors of patient disease activity and progression. METHOD: The collaboration focuses on highly complex autoimmune diseases and develops a computational framework to assemble a research-ready dataset across numerous modalities. For the Multiple Sclerosis (MS) project, the collaboration has anonymised and integrated phase II to phase IV clinical and imaging trial data from ≈35,000 patients across all clinical phenotypes and collected in more than 2200 centres worldwide. For the "IL-17" project, the collaboration has anonymised and integrated clinical and imaging data from over 30 phase II and III Cosentyx clinical trials including more than 15,000 patients, suffering from four autoimmune disorders (Psoriasis, Axial Spondyloarthritis, Psoriatic arthritis (PsA) and Rheumatoid arthritis (RA)). RESULTS: A fundamental component of successful data analysis and the collaborative development of novel machine learning methods on these rich data sets has been the construction of a research informatics framework that can capture the data at regular intervals where images could be anonymised and integrated with the de-identified clinical data, quality controlled and compiled into a research-ready relational database which would then be available to multi-disciplinary analysts. The collaborative development from a group of software developers, data wranglers, statisticians, clinicians, and domain scientists across both organisations has been key. This framework is innovative, as it facilitates collaborative data management and makes a complicated clinical trial data set from a pharmaceutical company available to academic researchers who become associated with the project. CONCLUSIONS: An informatics framework has been developed to capture clinical trial data into a pipeline of anonymisation, quality control, data exploration, and subsequent integration into a database. Establishing this framework has been integral to the development of analytical tools.

Prospective evaluation of drug-drug interactions in ambulatory cancer patients initiated on prophylactic anticoagulation.

INTRODUCTION: Drug options for VTE prophylaxis are increasing for ambulatory cancer patients and data regarding anticoagulant-drug interactions and their relationship to VTE and bleeding are needed to improve care. METHODS: Over one year, 108 cancer patients with high VTE risk were prospectively identified. Potential anticoagulant-drug interactions were ascertained by chart review and graded on need for intervention. Providers selected anticoagulant prophylaxis based on potential drug interactions and patient-provider discussion. A cross-sectional analysis was performed thereafter to evaluate VTE and bleeding endpoints within one year of anticoagulant initiation. RESULTS: The average number of potential drug interactions per patient was higher for warfarin than others (3.04 vs. 1.28 (apixaban), 1.02 (rivaroxaban), and 0.98 (LMWH)). The severity of the interactions based on grade was, for apixaban: 1.6% grade X, 50.8% grade D, and 47.5% grade C; for rivaroxaban: 2.1% grade X, 64.9% grade D, 33.0% grade C; for LMWH, 0% grade X, 66.7% grade D, 33.3% grade C; and for warfarin, 0% grade X, 29.4% grade D, 70.6% grade C. At the end of the investigational period, 11 bleeds and 7 VTEs were reported. Drug combinations significantly associated with an increased bleeding risk were crizotinib with apixaban or rivaroxaban and PPIs with warfarin. The use of sulfamethoxazole-trimethoprim with warfarin was associated with an increased VTE risk. CONCLUSIONS: DOACs had fewer DDIs than warfarin, although interaction severity differed between anticoagulants. Some anticoagulant-drug interactions were associated with bleeding or VTE. Although not powered for analysis, DDI severity did not affect bleeding rates and inversely correlated with VTE risk.

Too noisy to sleep safely?-An observational study of noise levels and distractions during critical phases of pediatric anesthesia.

BACKGROUND: Noise and distraction are recognized as contributing factors in critical incidents associated with surgery and anesthesia. In addition, excessive noise exposure can have negative effects on patients and staff members in these environments. AIMS: We aimed to quantify noise exposure of the anesthesiology team and patients in pediatric operating theaters, comparing them to adult studies, internationally recognized standards, and identifying factors contributing to noise and/or distraction. METHODS: We measured noise levels during three phases of anesthesia (pre-induction, induction, and postinduction/maintenance) and recorded additional environmental sources of distraction and noise. RESULTS: We included 49 theater cases. The median and interquartile range (IQR) of mean noise levels during pre-induction, induction, and maintenance were 61 (59.7-63.5), 61.9 (60.6-63.3), and 61.6 (60.4-63.7) dB, respectively, while the median (IQR) of maximal noise levels was 81.4 (77.3-86.8), 83.7 (78.5-87.4), and 86.3 (84.6-90.4), respectively. There was an average of 8.3 people present in the room during induction. In 23 cases, there were additional unnecessary conversations occurring within the room at induction. We noted the maintenance phase was the noisiest phase of anesthesia. We recorded frequent potential sources of distraction during pre-induction and induction, though no significant correlations between these distractions and recorded noise levels. CONCLUSIONS: We documented noise levels well above recommended levels during all phases of pediatric anesthesia. We highlighted factors with potential to contribute to noise and distraction but found no statistically significant correlation with noise levels.

Regional variation and cost implications of prescribed extended half-life factor concentrates among U.S. Haemophilia Treatment Centres for patients with moderate and severe haemophilia.

BACKGROUND: Extended half-life (EHL) factor VIII (FVIII) and IX (FIX) products are intended to decrease the burden of prophylaxis for patients with haemophilia A or B. Whether these newer concentrates have led to meaningful clinical practice change remains vague. AIM: To characterize the longitudinal use of standard (SHL) and EHL factor concentrates at haemophilia treatment centres (HTCs), using the ATHNdataset, a US database of 138 ATHN-affiliated HTCs. METHODS: Factor concentrate use among moderate and severe haemophilia A and B patients without inhibitors was analysed at three time points over 18 months. RESULTS: Use of EHL concentrates rose from 10% of patients to 22% during this study. EHL FVIII prophylaxis is prescribed to the minority of patients, 28%; EHL FIX now predominates for prophylaxis, 52%. Rates of prescribed EHL products varied significantly by age group and HTC region. Median prescribed prophylaxis for SHL compared to EHL products was FVIII 6240 and 5200 and FIX 6968 and FIX 3900 IU/kg/y, respectively. On-demand EHL use has grown but has minimal contribution to overall usage (2%). CONCLUSION: Haemophilia treatment centre region and patient age impact the rate of adoption of EHL products; however, EHL prescribing continues to rise nationally, particularly for EHL FIX. Careful attention to annual cost of prophylaxis is imperative as the decrease in median EHL prophylaxis consumption is not offset by the higher unit cost of these products. It is unclear how further growth in use of EHLs will be impacted by emerging non-factor replacement and gene therapies.

Saving sevoflurane: Automated gas control can reduce consumption of anesthetic vapor by one-third in pediatric anesthesia.

BACKGROUND: Many modern anesthetic machines offer automated control of anesthetic vapor. The user simply sets a desired end-tidal concentration and the machine will manipulate the vaporizer and gas flow rates to obtain and maintain the preset target. Greater efficiency, and more accurate delivery of anesthetic vapor have been documented across multiple machines within the adult setting however, there is little evidence for their use in children. AIMS: The aim of this study was to compare the consumption of sevoflurane using the Maquet Flow-i anesthesia machine (Maquet, Solna, Sweden) in automatic gas control mode vs manual mode in pediatric anesthesia. The primary outcome measure is rate of sevoflurane use. METHOD: Data logs were collected from our three Maquet Flow-i anesthesia machines over a 4-week period. We compared the rate of sevoflurane use when in manual mode vs cases where the automatic gas control mode was used. We also examined each automatic gas control case to determine whether percentage of anesthesia time in this mode correlated significantly with average rate of sevoflurane consumption. RESULTS: Sevoflurane was the primary anesthetic used in 220 cases, comprising over 230 hours of anesthesia time. Of these, 36 cases were identified as automatic gas control cases and 184 as manual cases. Consumption of sevoflurane liquid in mL/min was significantly lower in automatic gas control cases (median 0.46, IQR 0.32-0.72 mL/min for automatic gas control; median 0.82, IQR 0.62-1.17 mL/min for manual; P < 0.001 by Wilcoxon Rank Sum test). For a case of median duration (49 minutes), average rate of sevoflurane liquid consumption was 0.54 mL/min for automatic gas control cases vs 0.81 mL/min for manual cases, a reduction of 33% (bootstrapped 95% CI 0.21-0.61 mL/min, P < 0.001). CONCLUSION: Maquet's Flow-i automatic gas control mode reduced use of sevoflurane an average of one-third in a pediatric anesthesia setting.

STAC3 variants cause a congenital myopathy with distinctive dysmorphic features and malignant hyperthermia susceptibility.

SH3 and cysteine-rich domain-containing protein 3 (STAC3) is an essential component of the skeletal muscle excitation-contraction coupling (ECC) machinery, though its role and function are not yet completely understood. Here, we report 18 patients carrying a homozygous p.(Trp284Ser) STAC3 variant in addition to a patient compound heterozygous for the p.(Trp284Ser) and a novel splice site change (c.997-1G > T). Clinical severity ranged from prenatal onset with severe features at birth, to a milder and slowly progressive congenital myopathy phenotype. A malignant hyperthermia (MH)-like reaction had occurred in several patients. The functional analysis demonstrated impaired ECC. In particular, KCl-induced membrane depolarization resulted in significantly reduced sarcoplasmic reticulum Ca2+ release. Co-immunoprecipitation of STAC3 with CaV 1.1 in patients and control muscle samples showed that the protein interaction between STAC3 and CaV 1.1 was not significantly affected by the STAC3 variants. This study demonstrates that STAC3 gene analysis should be included in the diagnostic work up of patients of any ethnicity presenting with congenital myopathy, in particular if a history of MH-like episodes is reported. While the precise pathomechanism remains to be elucidated, our functional characterization of STAC3 variants revealed that defective ECC is not a result of CaV 1.1 sarcolemma mislocalization or impaired STAC3-CaV 1.1 interaction.

Characterization of purine-rich element binding protein B as a novel biomarker in acute myelogenous leukemia prognostication.

Acute myelogenous leukemia (AML) is an aggressive hematologic cancer characterized by infiltration of proliferative, clonal, abnormally differentiated cells of myeloid lineage in the bone marrow and blood. Malignant cells in AML often exhibit chromosomal and other genetic or epigenetic abnormalities that are useful in prognostic risk assessment. In this study, the relative expression and novel single-stranded DNA (ssDNA) binding function of purine-rich element binding proteins A and B (Purα and Purß) were systematically evaluated in established leukemia cell lines and in lineage committed myeloid cells isolated from patients diagnosed with a hematologic malignancy. Western blotting revealed that Purα and Purß are markedly elevated in CD33+ /CD66b+ cells from AML patients compared to healthy subjects and to patients with other types of myeloid cell disorders. Results of in silico database analysis of PURA and PURB mRNA expression during hematopoiesis in conjunction with the quantitative immunoassay of the ssDNA-binding activities of Purα and Purß in transformed leukocyte cell lines pointed to Purß as the more distinguishing biomarker of myeloid cell differentiation status. Purß ssDNA-binding activity was significantly increased in myeloid cells from AML patients but not from individuals with other myeloid-related diseases. The highest levels of Purß activity were detected in myeloid cells from primary AML patients and from AML patients displaying other risk factors forecasting a poor prognosis. Collectively, these findings suggest that the enhanced ssDNA-binding activity of Purß in transformed myeloid cells may serve as a unique and measurable phenotypic trait for improving prognostic risk stratification in AML.

Principles of Bayesian Inference Using General Divergence Criteria.

When it is acknowledged that all candidate parameterised statistical models are misspecified relative to the data generating process, the decision maker (DM) must currently concern themselves with inference for the parameter value minimising the Kullback-Leibler (KL)-divergence between the model and this process (Walker, 2013). However, it has long been known that minimising the KL-divergence places a large weight on correctly capturing the tails of the sample distribution. As a result, the DM is required to worry about the robustness of their model to tail misspecifications if they want to conduct principled inference. In this paper we alleviate these concerns for the DM. We advance recent methodological developments in general Bayesian updating (Bissiri, Holmes & Walker, 2016) to propose a statistically well principled Bayesian updating of beliefs targeting the minimisation of more general divergence criteria. We improve both the motivation and the statistical foundations of existing Bayesian minimum divergence estimation (Hooker & Vidyashankar, 2014; Ghosh & Basu, 2016), allowing the well principled Bayesian to target predictions from the model that are close to the genuine model in terms of some alternative divergence measure to the KL-divergence. Our principled formulation allows us to consider a broader range of divergences than have previously been considered. In fact, we argue defining the divergence measure forms an important, subjective part of any statistical analysis, and aim to provide some decision theoretic rational for this selection. We illustrate how targeting alternative divergence measures can impact the conclusions of simple inference tasks, and discuss then how our methods might apply to more complicated, high dimensional models.