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The V122I variant in transthyretin (TTR) is the most common amyloidogenic mutation worldwide. The aim of this study is to describe the cardiac phenotype and risk for adverse cardiovascular outcomes of young V122I TTR carriers in the general population.TTR genotypes were extracted from whole-exome sequence data in participants of the Dallas Heart Study. Participants with African ancestry, available V122I TTR genotypes (N = 1818) and either cardiac magnetic resonance imaging (n = 1364) or long-term follow-up (n = 1532) were included. The prevalence of V122I TTR carriers (45 ± 10 years) was 3.2% (n/N = 59/1818). The V122I TTR carriers had higher baseline left ventricular wall thickness (8.52 ± 1.82 vs 8.21 ± 1.62 mm, adjusted P = .038) than noncarriers, but no differences in other cardiac magnetic resonance imaging measures (P.05 for all). Although carrier status was not associated with amino terminal pro-B-type natriuretic peptide (NT-proBNP) at baseline (P = .79), V122I TTR carriers had a greater increase in NT-proBNP on follow-up than noncarriers (median 28.5 pg/mL, interquartile range 11.4-104.1 pg/mL vs median 15.9 pg/mL, interquartile range 0.0-43.0 pg/mL, adjusted P = .018). V122I TTR carriers were at a higher adjusted risk of heart failure (hazard ratio 3.82, 95% confidence interval 1.80-8.13, P.001), cardiovascular death (hazard ratio 2.65, 95% confidence interval 1.14-6.15, P = .023), and all-cause mortality (hazard ratio 1.95, 95% confidence interval 1.08-3.51, P = .026) in comparison with noncarriers.V122I TTR carrier status was associated with a greater increase in NT-proBNP, slightly greater left ventricular wall thickness, and a higher risk for heart failure, cardiovascular death, and all-cause mortality. These findings suggest the need to develop amyloidosis screening strategies for V122I TTR carriers.

It is evident that the technological growth of the last few decades has signaled the development of several application domains. One application domain that has expanded massively in recent years is robotics. The usage and spread of robotic systems in commercial and non-commercial environments resulted in increased productivity, efficiency, and higher quality of life. Many researchers have developed systems that improve many aspects of people’s lives, based on robotics. Most of the engineers use high-cost robotic arms, which are usually out of the reach of typical consumers. We fill this gap by presenting a low-cost and high-accuracy project to be used as a robotic assistant for every consumer. Our project aims to further improve people’s quality of life, and more specifically people with physical and mobility impairments. The robotic system is based on the Niryo-One robotic arm, equipped with a USB (Universal Serial Bus) HD (High Definition) camera on the end-effector. To achieve high accuracy, we modified the YOLO algorithm by adding novel features and additional computations to be used in the kinematic model. We evaluated the proposed system by conducting experiments using PhD students of our laboratory and demonstrated its effectiveness. The experimental results indicate that the robotic arm can detect and deliver the requested object in a timely manner with a 96.66% accuracy.

It is evident that over the last years, the usage of robotics in warehouses has been rapidly increasing. The usage of robot vehicles in storage facilities has resulted in increased efficiency and improved productivity levels. The robots, however, are only as efficient as the algorithms that govern them. Many researchers have attempted to improve the efficiency of industrial robots by improving on the internal routing of a warehouse, or by finding the best locations for charging power stations. Because of the popularity of the problem, many research works can be found in the literature regarding warehouse routing. The majority of these algorithms found in the literature, however, are statically designed and cannot handle multi-robot situations, especially when robots have different characteristics. The proposed algorithm of this paper attempts to give the following solution to this issue: utilizing more than one robot simultaneously to allocate tasks and tailor the navigation path of each robot based on its characteristics, such as its speed, type and current location within the warehouse so as to minimize the task delivery timing. Moreover, the algorithm finds the optimal location for the placement of power stations. We evaluated the proposed methodology in a synthetic realistic environment and demonstrated that the algorithm is capable of finding an improved solution within a realistic time frame.

Understanding mechanisms of hepatocellular damage may lead to new treatments for liver disease, and genome-wide association studies (GWAS) of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) serum activities have proven useful for investigating liver biology. Here we report 100 loci associating with both enzymes, using GWAS across 411,048 subjects in the UK Biobank. The rare missense variant SLC30A10 Thr95Ile (rs188273166) associates with the largest elevation of both enzymes, and this association replicates in the DiscovEHR study. SLC30A10 excretes manganese from the liver to the bile duct, and rare homozygous loss of function causes the syndrome hypermanganesemia with dystonia-1 (HMNDYT1) which involves cirrhosis. Consistent with hematological symptoms of hypermanganesemia, SLC30A10 Thr95Ile carriers have increased hematocrit and risk of iron deficiency anemia. Carriers also have increased risk of extrahepatic bile duct cancer. These results suggest that genetic variation in SLC30A10 adversely affects more individuals than patients with diagnosed HMNDYT1., Circulating liver enzymes, like alanine aminotransferase (ALT) and aspartate aminotransferase (AST), are highly heritable and predictive of disease. Here, the authors perform a genome-wide association study on ALT and AST, revealing a rare variant in SLC30A10 associated with elevated ALT and AST.

Serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are biomarkers for liver health. Here we report the largest genome‐wide association analysis to date of serum ALT and AST levels in over 388k people of European ancestry from UK biobank and DiscovEHR. Eleven million imputed markers with a minor allele frequency (MAF) ≥ 0.5% were analyzed. Overall, 300 ALT and 336 AST independent genome‐wide significant associations were identified. Among them, 81 ALT and 61 AST associations are reported for the first time. Genome‐wide interaction study identified 9 ALT and 12 AST independent associations significantly modified by body mass index (BMI), including several previously reported potential liver disease therapeutic targets, for example, PNPLA3, HSD17B13, and MARC1. While further work is necessary to understand the effect of ALT and AST‐associated variants on liver disease, the weighted burden of significant BMI‐modified signals is significantly associated with liver disease outcomes. In summary, this study identifies genetic associations which offer an important step forward in understanding the genetic architecture of serum ALT and AST levels. Significant interactions between BMI and genetic loci not only highlight the important role of adiposity in liver damage but also shed light on the genetic etiology of liver disease in obese individuals.

Genome-wide association analysis of cohorts with thousands of phenotypes is computationally expensive, particularly when accounting for sample relatedness or population structure. Here we present a novel machine-learning method called REGENIE for fitting a whole-genome regression model for quantitative and binary phenotypes that is substantially faster than alternatives in multi-trait analyses while maintaining statistical efficiency. The method naturally accommodates parallel analysis of multiple phenotypes and requires only local segments of the genotype matrix to be loaded in memory, in contrast to existing alternatives, which must load genome-wide matrices into memory. This results in substantial savings in compute time and memory usage. We introduce a fast, approximate Firth logistic regression test for unbalanced case–control phenotypes. The method is ideally suited to take advantage of distributed computing frameworks. We demonstrate the accuracy and computational benefits of this approach using the UK Biobank dataset with up to 407,746 individuals. REGENIE is a whole-genome regression method based on ridge regression that enables highly parallelized analysis of quantitative and binary traits in biobank-scale data with reduced computational requirements.

Background: Heart rate–corrected QT interval (QTc) prolongation, whether secondary to drugs, genetics including congenital long QT syndrome, and/or systemic diseases including SARS-CoV-2–mediated coronavirus disease 2019 (COVID-19), can predispose to ventricular arrhythmias and sudden cardiac death. Currently, QTc assessment and monitoring relies largely on 12-lead electrocardiography. As such, we sought to train and validate an artificial intelligence (AI)–enabled 12-lead ECG algorithm to determine the QTc, and then prospectively test this algorithm on tracings acquired from a mobile ECG (mECG) device in a population enriched for repolarization abnormalities. Methods: Using >1.6 million 12-lead ECGs from 538 200 patients, a deep neural network (DNN) was derived (patients for training, n = 250 767; patients for testing, n = 107 920) and validated (n = 179 513 patients) to predict the QTc using cardiologist-overread QTc values as the “gold standard”. The ability of this DNN to detect clinically-relevant QTc prolongation (eg, QTc ≥500 ms) was then tested prospectively on 686 patients with genetic heart disease (50% with long QT syndrome) with QTc values obtained from both a 12-lead ECG and a prototype mECG device equivalent to the commercially-available AliveCor KardiaMobile 6L. Results: In the validation sample, strong agreement was observed between human over-read and DNN-predicted QTc values (−1.76±23.14 ms). Similarly, within the prospective, genetic heart disease–enriched dataset, the difference between DNN-predicted QTc values derived from mECG tracings and those annotated from 12-lead ECGs by a QT expert (−0.45±24.73 ms) and a commercial core ECG laboratory [10.52±25.64 ms] was nominal. When applied to mECG tracings, the DNN’s ability to detect a QTc value ≥500 ms yielded an area under the curve, sensitivity, and specificity of 0.97, 80.0%, and 94.4%, respectively. Conclusions: Using smartphone-enabled electrodes, an AI DNN can predict accurately the QTc of a standard 12-lead ECG. QTc estimation from an AI-enabled mECG device may provide a cost-effective means of screening for both acquired and congenital long QT syndrome in a variety of clinical settings where standard 12-lead electrocardiography is not accessible or cost-effective.

Background: Standard of care for patients with muscle-invasive bladder cancer (MIBC) includes neoadjuvant cisplatin-based chemotherapy (NAC) followed by consolidative therapy with either chemoradiation or radical cystectomy (RC). Some patients experience robust pathologic responses to NAC, and these have been reported to associate with somatic mutations in specific gene pathways including DNA damage response genes. Objective: To evaluate the ability of post-NAC clinical restaging, with or without tumor sequencing, to predict final RC pathologic staging. Design, setting, and participants: We reviewed our institutional review board–approved institutional database to identify patients with MIBC who underwent NAC followed by RC from 2003 to 2016. Following NAC prior to RC, cystoscopy was performed routinely, with resection of residual visible tumor and/or tumor base (transurethral resection [TUR]). For patients with pre-NAC tumor tissue available, tumor sequencing was performed. Outcome measurements and statistical analysis: Clinical restaging and tumor sequencing were evaluated for their ability to predict the final pathologic stage accurately at RC using chi-square or Fisher’s exact test. Results and limitations: A total of 114 patients underwent restaging TUR following NAC and prior to RC. The diagnostic accuracy of post-NAC clinical restaging including TUR was poor, with 32% of patients being downstaged falsely when compared with their final RC pathology. Forty-nine patients had sequencing of pre-NAC tumor tissue, of whom 32 showed at least one mutation of interest. However, NAC responses and rates of false downstaging did not differ significantly according to tumor mutation status. Conclusions: This study highlights the inaccuracy of post-NAC clinical restaging TUR with or without adjunctive tumor mutation analysis, to assess pathologic residual disease accurately. Caution must be taken when performing post-NAC restaging, especially when considering conservative management strategies such as active surveillance on this basis. Patient summary: Several groups are evaluating whether certain patients, whose bladder cancer responds well to upfront chemotherapy, may be able to forego cystectomy safely. We demonstrate that currently available staging tools and tumor DNA sequencing cannot identify such patients reliably and accurately.

[BACKGROUND & AIMS] Primary biliary cholangitis (PBC) is a chronic liver disease in which autoimmune destruction of the small intra-hepatic bile ducts eventually leads to cirrhosis. Many patients have inadequate response to licensed medications, motivating the search for novel therapies. Previous genome-wide association studies (GWAS) and meta-analyses (GWMA) of PBC have identified numerous risk loci for this condition, providing insight into its aetiology. We undertook the largest GWMA of PBC to date, aiming to identify additional risk loci and prioritise candidate genes for in silico drug efficacy screening. [METHODS] We combined new and existing genotype data for 10, 516 cases and 20, 772 controls from five European and two East Asian cohorts. [RESULTS] We identified 56 genome-wide significant loci (20 novel) including 46 in European, 13 in Asian, and 41 in combined cohorts; and a 57th genome-wide significant locus (also novel) in conditional analysis of the European cohorts. Candidate genes at newly identified loci include FCRL3, INAVA, PRDM1, IRF7, CCR6, CD226, and IL12RB1, each having key roles in immunity. Pathway analysis reiterated the likely importance of pattern recognition receptor and TNF signalling, Jak-STAT signalling, and differentiation of TH1 and TH17 cells in the pathogenesis of this disease. Drug efficacy screening identified several medications predicted to be therapeutic in PBC, some well-established in the treatment of other autoimmune disorders. [CONCLUSIONS] This study has identified additional risk loci for PBC, provided a hierarchy of agents that could be trialled in this condition, and emphasised the value of genetic and genomic approaches to drug discovery in complex disorders. [Lay summary] Primary biliary cholangitis (PBC) is a chronic liver disease that eventually leads to cirrhosis. In this study, we analysed genetic information from 10, 516 people with PBC and 20, 772 healthy individuals recruited in Canada, China, Italy, Japan, UK, or USA. We identified several genetic regions associated with PBC. Each of these regions contains several genes. For each region, we used diverse sources of evidence to help us choose the gene most likely to be involved in causing PBC. We used these ‘candidate genes’ to help us identify medications that are currently used for treatment of other conditions, which might also be useful for treatment of PBC., 原発性胆汁性胆管炎のゲノムワイド関連解析 --国際メタ解析による新規疾患感受性遺伝子と治療薬候補の同定--. 京都大学プレスリリース. 2021-06-28.

When Low Tech Wins Just as people with visual impairment may have heightened use of their other senses, for one physician in a safety-net system, a year of telephone care during the Covid-19 pandem...

Human herpesvirus (HHV)-6A can be inherited and chromosomally integrated (iciHHV-6A), and donor-to-recipient transmission has been reported in solid organ transplant. However, when HHV-6A reactivation happens after transplant, the source of HHV-6A is often not evident and its pathogenicity remains unclear. Here, we present an exhaustive case of donor-to-recipient transmission and reactivation of iciHHV-6A through kidney transplant. The absence of HHV-6A genome from the nails of the recipient excluded a recipient-related iciHHV-6A. Viral loads > 7 log10 copies/106 cells in donor blood samples and similarities of U38, U39, U69, and U100 viral genes between donor, recipient, and previously published iciHHV-6A strains are proof of donor-related transmission. Detection of noncoding HHV-6 snc-RNA14 using fluorescence in situ hybridization analysis and immunofluorescence staining of HHV-6A gp82/gp105 late proteins on kidney biopsies showed evidence of reactivation in the transplanted kidney. Because HHV-6A reactivation can be life threatening in immunocompromised patients, we provide several tools to help during the complete screening and diagnosis.

Purpose: The purpose of this paper is to present numerical modelling results for 3D-printed aluminium components with different variable core infill values. Information published in this paper will guide engineers when designing the components with core infill regions. Design/methodology/approach: During this study 3 different core types (Gyroid, Schwarz P and Schwarz D) and different combinations of their parameters were examined numerically, using FEM by means of the software ANSYS Workbench 2019 R2. Influence of core type as well as its parameters on 3D printed components strength was studied. The “best” core type with the “best” combination of parameters was chosen. Findings: Results obtained from the numerical static compression tests distinctly showed that component strength is highly influenced by the type infill choice selected. Specifically, infill parameters and the coefficient (force reaction/volumetric percentage solid material) were investigated. Resulting total reaction force and percentage of solid material in the component were compared to the fully solid reference model. Research limitations/implications: Based on the Finite Element Analysis carried out in this work, it was found that results highlighted the optimal infill condition defined as the lowest amount of material theoretically used, whilst assuring sufficient mechanical strength. The best results were obtained by Schwarz D core type samples. Practical implications: In the case of the aviation or automotive industry, very high strength of manufactured elements along with a simultaneous reduction of their wight is extremely important. As the viability of additively manufactured parts continues to increase, traditionally manufactured components are continually being replaced with 3D-printed components. The parts produced by additive manufacturing do not have the solid core, they are rather filled with specific geometrical patterns. The reason of such operation is to save the material and, in this way, also weight. Originality/value: The conducted numerical analysis allowed to determine the most favourable parameters for optimal core infill configurations for aluminium 3D printed parts, taking into account the lowest amount of material theoretically used, whilst assuring sufficient mechanical strength.

Background Monogenic diseases are individually rare but collectively common, and are likely underdiagnosed. Objectives The purpose of this study was to estimate the prevalence of monogenic cardiovascular diseases (MCVDs) and potentially missed diagnoses in a cardiovascular cohort. Methods Exomes from 8,574 individuals referred for cardiac catheterization were analyzed. Pathogenic/likely pathogenic (P/LP) variants associated with MCVD (cardiomyopathies, arrhythmias, connective tissue disorders, and familial hypercholesterolemia were identified. Electronic health records (EHRs) were reviewed for individuals harboring P/LP variants who were predicted to develop disease (G+). G+ individuals who did not have a documented relevant diagnosis were classified into groups of whether they may represent missed diagnoses (unknown, unlikely, possible, probable, or definite) based on relevant diagnostic criteria/features for that disease. Results In total, 159 P/LP variants were identified; 2,361 individuals harbored at least 1 P/LP variant, of whom 389 G+ individuals (4.5% of total cohort) were predicted to have at least 1 MCVD. EHR review of 342 G+ individuals predicted to have 1 MCVD with sufficient EHR data revealed that 52 had been given the relevant clinical diagnosis. The remaining 290 individuals were classified as potentially having an MCVD as follows: 193 unlikely (66.6%), 50 possible (17.2%), 30 probable (10.3%), and 17 definite (5.9%). Grouping possible, probable, definite, and known diagnoses, 149 were considered to have an MCVD. Novel MCVD pathogenic variants were identified in 16 individuals. Conclusions Overall, 149 individuals (1.7% of cohort) had MCVDs, but only 35% were diagnosed. These patients represents a “missed opportunity,” which could be addressed by greater use of genetic testing of patients seen by cardiologists.

We found that FOXO1-shRNA sublines or FOXO1-positive cells co-treated with a FOXO1 inhibitor were significantly more resistant to cisplatin treatment at pharmacological concentrations, compared with respective control sublines or those with mock treatment. Western blot demonstrated considerable increases in the expression levels of a phosphorylated inactive form of FOXO1 (p-FOXO1) in cisplatin-resistant sublines established by long-term culture with low/increasing doses of cisplatin, compared with respective controls. Immunohistochemistry in surgical specimens from patients with muscle-invasive bladder cancer undergoing cisplatin-based neoadjuvant therapy further showed a strong trend to associate between p-FOXO1 positivity and unfavorable response to chemotherapy.

Polygenic risk scores (PRS) for coronary artery disease (CAD) identify high-risk individuals more likely to benefit from primary prevention statin therapy. Whether polygenic CAD risk is captured by conventional paradigms for assessing clinical cardiovascular risk remains unclear.This study sought to intersect polygenic risk with guideline-based recommendations and management patterns for CAD primary prevention.A genome-wide CAD PRS was applied to 47,108 individuals across 3 U.S. health care systems. The authors then assessed whether primary prevention patients at high polygenic risk might be distinguished on the basis of greater guideline-recommended statin eligibility and higher rates of statin therapy.Of 47,108 study participants, the mean age was 60 years, and 11,020 (23.4%) had CAD. The CAD PRS strongly associated with prevalent CAD (odds ratio: 1.4 per SD increase in PRS; p 0.0001). High polygenic risk (top 20% of PRS) conferred 1.9-fold odds of developing CAD (p 0.0001). However, among primary prevention patients (n = 33,251), high polygenic risk did not correspond with increased recommendations for statin therapy per the American College of Cardiology/American Heart Association (46.2% for those with high PRS vs. 46.8% for all others, p = 0.54) or U.S. Preventive Services Task Force (43.7% vs. 43.7%, p = 0.99) or higher rates of statin prescriptions (25.0% vs. 23.8%, p = 0.04). An additional 4.1% of primary prevention patients may be recommended for statin therapy if high CAD PRS were considered a guideline-based risk-enhancing factor.Current paradigms for primary cardiovascular prevention incompletely capture a polygenic susceptibility to CAD. An opportunity may exist to improve CAD prevention efforts by integrating both genetic and clinical risk.

Background: Alirocumab, an antibody that blocks PCSK9 (proprotein convertase subtilisin/kexin type 9), was associated with reduced major adverse cardiovascular events (MACE) and death in the ODYSSEY OUTCOMES trial (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab). In this study, higher baseline levels of low-density lipoprotein cholesterol (LDL-C) predicted greater benefit from alirocumab treatment. Recent studies indicate high polygenic risk scores (PRS) for coronary artery disease (CAD) identify individuals at higher risk who derive increased benefit from statins. We performed post hoc analyses to determine whether high PRS for CAD identifies higher-risk individuals, independent of baseline LDL-C and other known risk factors, who might derive greater benefit from alirocumab treatment. Methods: ODYSSEY OUTCOMES was a randomized, double-blind, placebo-controlled trial comparing alirocumab or placebo in 18 924 patients with acute coronary syndrome and elevated atherogenic lipoproteins despite optimized statin treatment. The primary endpoint (MACE) comprised death of CAD, nonfatal myocardial infarction, ischemic stroke, or unstable angina requiring hospitalization. A genome-wide PRS for CAD comprising 6 579 025 genetic variants was evaluated in 11 953 patients with available DNA samples. Analysis of MACE risk was performed in placebo-treated patients, whereas treatment benefit analysis was performed in all patients. Results: The incidence of MACE in the placebo group was related to PRS for CAD: 17.0% for high PRS patients (>90th percentile) and 11.4% for lower PRS patients (≤90th percentile; P P =0.004) versus a 13% reduction in the low PRS group (hazard ratio, 0.87 [95% CI, 0.78–0.98]; P =0.022; interaction P =0.04). Conclusions: A high PRS for CAD is associated with elevated risk for recurrent MACE after acute coronary syndrome and a larger absolute and relative risk reduction with alirocumab treatment, providing an independent tool for risk stratification and precision medicine.

Purpose: To characterize immune cell expression among patients with non–muscle invasive bladder cancer (NMIBC) treated with Bacillus Calmette-Guerin (BCG). Experimental Design: Patients with NMIBC treated with intravesical BCG (2008–2015) were identified, and a tissue microarray was constructed using paired pre- and post-BCG bladder samples. Among patients undergoing BCG, cystoscopic evaluation began 3 months after initiating BCG treatment to determine therapeutic response. IHC was performed for CD8, CD4, FoxP3, PD-L1 (SP-142 and 22C3), and PD-1. A full slide review of PD-L1+ staining tumors was performed to characterize PD-L1 and CD8 colocalization. RNA-seq was performed on cored tumors from available specimens. We compared immune cell populations between BCG responders and nonresponders, and between pretreatment and postreatment tumor samples. Baseline PD-L1 staining in the BCG naïve population was then validated in a separate cohort. Results: The final cohort contained 63 pretreatment NMIBC cases, including 31 BCG responders and 32 BCG nonresponders. No differences in CD4, CD8, or FoxP3 expression were identified between responders and nonresponders. Baseline PD-L1 expression (22C3 and SP-142) was observed in 25% to 28% of nonresponders and 0% to 4% of responders (P < 0.01). PD-L1+ cells in BCG nonresponders colocalized with CD8+ T cells. In addition, BCG therapy did not increase PD-L1 gene expression (RNA-seq) or protein levels (IHC). The number of pretreatment CD4+ T cells was very low among PD-L1+ nonresponders (12%) and high among PD-L1− nonresponders (50%, P < 0.01). In a separate cohort of 57 patients with NMIBC undergoing BCG, baseline PD-L1 (22C3) staining was similar (26%). Conclusions: One mechanism of BCG failure may be adaptive immune resistance. Baseline tumor PD-L1 expression predicts an unfavorable response to BCG and if validated, could be used to guide therapeutic decisions.

The availability of large datasets from multiple sources [e.g. registries, biobanks, electronic health records (EHRs), claims or billing databases, implantable devices, wearable sensors, and mobile apps], coupled with advances in computing and analytic technologies, have provided new opportunities for conducting innovative health research. Equally, improved digital access to health information has facilitated the conduct of efficient randomized controlled trials (RCTs) upon which clinical management decisions can be based, for instance, by permitting the identification of eligible patients for recruitment and/or linkage for follow-up via their EHRs. Given these advances in cardiovascular data science and the complexities they behold, it is important that health professionals have clarity on the appropriate use and interpretation of observational, so-called ‘real-world’, and randomized data in cardiovascular medicine. The Cardiovascular Roundtable of the European Society of Cardiology (ESC) held a workshop to explore the future of RCTs and the current and emerging opportunities for gathering and exploiting complex observational datasets in cardiovascular research. The aim of this article is to provide a perspective on the appropriate use of randomized and observational data and to outline the ESC plans for supporting the collection and availability of clinical data to monitor and improve the quality of care of patients with cardiovascular disease in Europe and provide an infrastructure for undertaking pragmatic RCTs. Moreover, the ESC continues to campaign for greater engagement amongst regulators, industry, patients, and health professionals in the development and application of a more efficient regulatory framework that is able to take maximal advantage of new opportunities for improving the design and efficiency of observational studies and RCT in patients with cardiovascular disease.

Inflammatory bowel disease (IBD), clinically defined as Crohn’s disease (CD), ulcerative colitis (UC), or IBD-unclassified, results in chronic inflammation of the gastrointestinal tract in genetically susceptible hosts. Pediatric onset IBD represents ≥ 25% of all IBD diagnoses and often presents with intestinal stricturing, perianal disease, and failed response to conventional treatments. NOD2 was the first and is the most replicated locus associated with adult IBD, to date. However, its role in pediatric onset IBD is not well understood. We performed whole-exome sequencing on a cohort of 1,183 patients with pediatric onset IBD (ages 0–18.5 years). We identified 92 probands with biallelic rare and low frequency NOD2 variants accounting for approximately 8% of our cohort, suggesting a Mendelian inheritance pattern of disease. Additionally, we investigated the contribution of recessive inheritance of NOD2 alleles in adult IBD patients from a large clinical population cohort. We found that recessive inheritance of NOD2 variants explains ~ 7% of cases in this adult IBD cohort, including ~ 10% of CD cases, confirming the observations from our pediatric IBD cohort. Exploration of EHR data showed that several of these adult IBD patients obtained their initial IBD diagnosis before 18 years of age, consistent with early onset disease. While it has been previously reported that carriers of more than one NOD2 risk alleles have increased susceptibility to Crohn’s Disease (CD), our data formally demonstrate that recessive inheritance of NOD2 alleles is a mechanistic driver of early onset IBD, specifically CD, likely due to loss of NOD2 protein function. Collectively, our findings show that recessive inheritance of rare and low frequency deleterious NOD2 variants account for 7–10% of CD cases and implicate NOD2 as a Mendelian disease gene for early onset Crohn’s Disease.

Introduction Multiple references to the violent and especially difficult patient have been presented by the international literature. However, there is little literature on the aggressive behaviors of health professionals in their workplaces. The aim of this research is to record and correlate aggression and attachment type data of adult health professionals. Methods The sample includes 192 individuals (43 men and 149 women) health professionals in the private and public sector, aged 20 to 60 years, who were selected by the method of random sampling. The survey was conducted from February 2018 to May 2018. The Greek version of the Aggression Questionnaire and the Greek version of the Revised Experiences in Close Relationships (G-ECR-R) self-report inventory were used and the analysis was performed with the Statistical Package of Social Sciences (SPSS 26) (IBM Corp., Armonk, NY). Results The analysis shows that the dimension of avoidance has a positive correlation with hostility and physical aggression and the dimension of stress has a positive correlation with anger, physical aggression and hostility. It also seems that the stress dimension of the adult attachment contributes significantly positively to the prediction of anger and the stress dimension contributes significantly to the prediction of hostility. The dimension of avoiding adult attachment contributes significantly to the prediction of physical aggression. Conclusions To our knowledge, no studies were found in the literature to examine the relationship between the subscales of aggression and dimensions of attachment. It is important that violence in the workplace is recognized as an underlying occupational risk and not just as a matter of criminal law. Finally, more research is needed to study the phenomenon in order to make it more understandable.

BackgroundThe FLNC gene has recently garnered attention as a likely cause of arrhythmogenic cardiomyopathy (ACM), which is considered an actionable genetic condition. However, the association with disease in an unselected clinical population is unknown. We hypothesized that individuals with loss-of-function variants in FLNC (FLNCLOF) would have increased odds for ACM-associated phenotypes versus variant-negative controls in the Geisinger MyCode cohort.MethodsWe identified rare, putative FLNCLOF among 171,948 individuals with exome sequencing linked to health records. Associations with ACM phenotypes from available diagnoses and cardiac evaluations were investigated.ResultsSixty individuals (0.03%; median age 58 years [47–70 IQR], 43% male) harbored 27 unique FLNCLOF. These individuals had significantly increased odds ratios (OR) for dilated cardiomyopathy (OR:4.9, [95% confidence interval: 2.6–7.6]; pFLNCLOF. Overall, at least 9% of FLNCLOF carriers demonstrated evidence of penetrant disease.ConclusionsFLNCLOF variants are associated with increased odds of ventricular arrhythmia and dysfunction in an unselected clinical population. These findings support genomic screening of FLNC for actionable secondary findings.

It is evident that over the last years the usage of robotics and automated machinery in our society has been rapidly increasing. Specifically, the usage of automated vehicles in industrial and also everyday life has benefited all of humanity greatly. One area that has not been fully explored is the usage of automated robotic vehicles in waste management within cities. It is clear that the rate of urban waste production is constantly increasing as a result of the Earth's rapid population growth and modern lifestyle. People consume more and more, and as a result, they produce more and more waste. In most countries, these waste materials are being thrown into bins in the streets of the cities. Opposed to the traditional waste collection model where a waste collection truck, passes from all smart bins and picks up the waste materials in a pr- defined route, we propose an algorithm to dynamically generate these routes in real time, based from the data received from the installed smart-bin devices. The vehicles, however, are only as efficient as the algorithms that govern them. The majority of the waste collection algorithms found in literature, however, are statically designed and cannot handle multi-robot situations and utilize data received in real time. The proposed algorithm of this paper attempts to give a solution to this issue; utilizing more than one waste collection vehicles simultaneously to allocate waste pick up tasks and tailor the navigation path of each vehicle based on its characteristics, like its type and its current location within the environment so as to minimize the pick up timing. We evaluated the proposed methodology in a synthetic realistic environment and and demonstrated that the algorithm is capable of finding an improved solution within a realistic time frame.

In the past few years, one of the essential elements of most formal and informal education practices is Information & Communications Technology (ICT). Teachers are implementing new technologies in the education process to make learning more fun and fruitful. Educational Robotics, an ICT sub field is one of these technologies and it has triggered many studies regarding its effectiveness. This paper presents our design process of a unique educational humanoid robot that can be used as a teaching aid for wide variety of subjects to children at primary schools.

Highway on-ramp merge junctions remain a major bottleneck in transportation networks. However, with the introduction of Connected Autonomous Vehicles (CAVs) with advanced sensing and communication capabilities modern algorithms can capitalize on the cooperation between vehicles. This paper enhances highway merging efficiency by optimally coordinating CAVs in order to maximize the flow of vehicles while satisfying all safety constraints. Focus is also placed on the effect of varying priorities of different vehicle classes in selecting the best merging sequence. Our algorithm is capable of real time operation through parallel computation, optimized merge sequence generation and management of the diverse needs of heterogeneous (multi-class) traffic. Results are verified through a realistic traffic simulation software.

Glaucoma is a leading cause of blindness. Current glaucoma medications work by lowering intraocular pressure (IOP), a risk factor for glaucoma, but most treatments do not directly target the pathological changes leading to increased IOP, which can manifest as medication resistance as disease progresses. To identify physiological modulators of IOP, we performed genome- and exome-wide association analysis in >129,000 individuals with IOP measurements and extended these findings to an analysis of glaucoma risk. We report the identification and functional characterization of rare coding variants (including loss-of-function variants) in ANGPTL7 associated with reduction in IOP and glaucoma protection. We validated the human genetics findings in mice by establishing that Angptl7 knockout mice have lower (~2 mmHg) basal IOP compared to wild-type, with a trend towards lower IOP also in heterozygotes. Conversely, increasing mAngptl7 levels via injection into mouse eyes increases the IOP. We also show that acute gene silencing via siRNA knockdown of Angptl7 in adult mice lowers the IOP (~2-4 mmHg), reproducing the observations in knockout mice. Collectively, our data suggest that ANGPTL7 is important for IOP homeostasis and is amenable to therapeutic modulation to help maintain a healthy IOP that can prevent onset or slow the progression of glaucoma.

Introduction and objectives Coronavirus disease 2019 (COVID-19) has affected the quality of life of both general population and the healthcare workers and has increased the psychopathology levels. The objective of this research was to study the personality traits and the quality of life of healthcare professionals during the COVID-19 pandemic, in order to organize and apply interventions for the well-being of the staff. Materials and methods The study sample consisted of 400 healthcare workers, in Thoracic Diseases General Hospital "Sotiria". Participants were asked to provide sociodemographic information and to complete: (1) the WHOQOL-BREF, (2) the NEO-FFI. The questionnaire was administered in person to the hospital staff. The data were collected between May and July 2021, in Athens, Greece. Results Younger healthcare professionals had a better quality of life and with regard to gender differences, males reported higher scores of physical and psychological health compared to females. Also regarding personality traits, neuroticism and extroversion have a statistically significant effect on the quality of life. In contrast, education level, work area and specialty did not appear to affect the quality of life of hospital staff. Conclusions From our research findings, it appears that quality of life has been affected by the COVID-19 pandemic. Interventions are needed to apply mindfulness, increase well-being and form groups with the hospital staff in order to share their experiences.

Summary Characterizing likelihood of response to neoadjuvant chemotherapy (NAC) in muscle-invasive bladder cancer (MIBC) is an important yet unmet challenge. In this study, a machine-learning framework is developed using imaging of biopsy pathology specimens to generate models of likelihood of NAC response. Developed using cross-validation (evaluable N = 66) and an independent validation cohort (evaluable N = 56), our models achieve promising results (65%–73% accuracy). Interestingly, one model—using features derived from hematoxylin and eosin (H&E)-stained tissues in conjunction with clinico-demographic features—is able to stratify the cohort into likely responders in cross-validation and the validation cohort (response rate of 65% for predicted responder compared with the 41% baseline response rate in the validation cohort). The results suggest that computational approaches applied to routine pathology specimens of MIBC can capture differences between responders and non-responders to NAC and should therefore be considered in the future design of precision oncology for MIBC., Graphical abstract, Highlights Using imaging of pathology samples to predict chemotherapy response in bladder cancer Multi-modal integration of cell nuclear and tissue architectural features Models using H&E images and basic clinical features able to enrich for responders Predictive features suggest response-modulating factors in tumor microenvironment, Using multi-modal machine-learning leveraging features from digital pathology, Mi et al. develop models to predict response to chemotherapy in muscle-invasive bladder cancer. Models using handcrafted features derived from conventional H&E TMAs in conjunction with basic clinico-demographic features significantly stratify likelihood of response in both discovery and independent validation cohorts.

Acute Promyelocytic Leukemia (APL) is a subtype of Acute Myeloid Leukemia (AML), classified by a translocation between chromosomes 15 and 17 [t(15;17)], that is notably distinguished clinically by a rapidly progressive and fatal course. Due to the acute nature of its presentation, prompt and accurate diagnosis is required to initiate appropriate therapy that can be curative. However, the gold standard genetic tests can take days to confirm a diagnosis and thus therapy is often initiated on high clinical suspicion based on both clinical presentation as well as direct visualization of the peripheral smear. While there are described cellular morphological features that distinguish APL, there is still considerable difficulty in diagnosing APL from direct visualization of a peripheral smear by a hematopathologist. We hypothesized that deep learning pattern recognition would have greater discriminatory power and consistency compared to humans to distinguish t(15;17) translocation positive APL from t(15;17) translocation negative AML. To best tackle the problem of diagnosing APL rapidly from a peripheral smear, study patients with APL and AML were identified via retrospective chart review from a list of confirmed FISH t(15;17)-positive (n = 34) and -negative (n = 72) patients presenting at The Johns Hopkins Hospital (JHH). Additional inclusion criteria included new disease diagnosis, no prior treatment, and availability of peripheral blood smear image uploaded to CellaVision. Patients were separated into a discovery cohort presenting prior to 1/2019 (APL, n = 22; AML, n=60) and a validation cohort presenting on or after 1/2019 (APL, n = 12; AML, n = 12). A multiple-instance deep learning model employing convolutional layers at the per-cell level (Figure 1A) was trained on the discovery cohort and then tested on the independent prospective validation cohort to assess generalizability of the model. When compared to 10 academic clinicians (denoted with red +) who consisted of leukemia-treating hematologists, oncologists, and hematopathologists, the deep learning model was equivalent or outperformed 9/10 readers (Figure 1B) with an AUC of 0.861. We further looked at the performance of using proportion of promyelocytes (per CellaVision classification) as a biomarker of APL which had an AUC of 0.611. Finally, we applied integrated gradients, a method by which to extract per-pixel importance to the classification probability to identify and understand the morphological features the model was learning and using to distinguish APL (Figure 1C). We noted that the appearance of the chromatin in the non-APL leukemias was more dispersed and focused at the edge of the cell whereas in APL, the chromatin was more condensed and focused at the center of the cell. These morphological features, taught to us by the model, have not been previously reported in the literature as being useful for distinguishing APL from non-APL. Our work presents a deep learning model capable of rapid and accurate diagnosis of APL from universally available peripheral smears. In addition, explainable artificial intelligence is provided for biological insights to facilitate clinical management and reveal morphological concepts previously unappreciated in APL. The deep learning framework we have delineated is applicable to any diagnostic pipeline that can leverage a peripheral blood smear, potentially allowing for efficient diagnosis and early treatment of disease. Figure 1. Disclosures Streiff: Bayer: Consultancy, Speakers Bureau; Dispersol: Consultancy; BristolMyersSquibb: Consultancy; Janssen: Consultancy, Research Funding; Pfizer: Consultancy, Speakers Bureau; Portola: Consultancy; Boehringer-Ingelheim: Research Funding; NHLBI: Research Funding; PCORI: Research Funding; NovoNordisk: Research Funding; Sanofi: Research Funding. Moliterno:Pharmessentia: Consultancy; MPNRF: Research Funding. DeZern:MEI: Consultancy; Abbvie: Consultancy; Astex: Research Funding; Celgene: Consultancy, Honoraria. Levis:Astellas: Honoraria, Research Funding; Menarini: Honoraria; Amgen: Honoraria; FujiFilm: Honoraria, Research Funding; Daiichi-Sankyo: Honoraria.

Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in nonresponding tumors in three immunotherapy treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature and human leukocyte antigen status provided an improved predictor of response to immunotherapy that was independently validated.

This paper proposes an event-triggered add-on safety mechanism to adjust the control parameters for timely braking in a networked vehicular system while maintaining maneuverability. Passenger vehicle maneuverability is significantly affected by the combined slip friction effect, in which larger longitudinal tire slips result in considerable drop in lateral tire forces. This is of higher importance when unexpected dangerous situations occur on the road and immediate actions, such as braking, need to be taken to avoid collision. Harsh braking can lead to high-slip and loss of maneuverability; hence, timely braking is essential to reduce high-slip scenarios. In addition to the vehicles own active safety systems, the proposed event-triggered add-on safety is activated upon being informed about dangers by the road-side infrastructure. The aim is to incorporate the add-on safety feature to adjust the automatic control parameters for smooth and timely braking such that a collision is avoided while vehicle’s maneuverability is maintained. We study two different wireless technologies for communication between the infrastructure and the vehicles, the Long-Term Evolution (LTE) and the fifth generation (5G) schemes. The safety advantages of the proposed framework is validated through high-fidelity software simulations.

Each area fingerprint scanner (sensor) has a unique scanner pattern that can be used to distinguish one scanner from another. The pattern results from imperfections in the conversion of the signal acquired from the object, applied to the scanner, into a digital image. The scanner pattern is a sufficiently unique, persistent, and unalterable intrinsic characteristic of the fingerprint scanners even to those of the same technology, manufacturer, and model. We propose a model of the image acquisition in widely-used capacitive area fingerprint scanners and a method to extract the pattern from a single image and compare it with a similarly extracted pattern from another image. The method is very simple and computationally efficient yet extremely accurate, achieving an equal error rate in the order of 1e-7 for 22 scanners of exactly the same model, and is also robust in a wide range of conditions. The scanner pattern can be used to enhance the security of a biometric system by detecting an attack in which an image containing the fingerprint pattern of the legitimate user and acquired by the authentic fingerprint scanner has been replaced by another image that may still contain the fingerprint pattern of the legitimate user but has been acquired by another, unauthentic fingerprint scanner, i.e., for scanner authentication.

The rate of urban waste production is constantly increasing as a result of the Earth’s rapid population growth and modern lifestyle. Organic and non-organic recyclable waste, which accounts for a significant portion of urban solid waste, has raised environmental concerns. The best way to create a healthy ecosystem is to recycle. According to estimates from the US Environmental Protection Agency, approximately 75% of total waste can be recycled, but we only recycle 25% of it. The majority of the non-recyclable waste is dumped on the ground or incinerated. We can save energy and create a valuable by-product that can be used as a locally generated fertilizer through recycling organic materials through the compost process, whereas non organic materials can be used as industrial materials. Under this study, we suggest a solution: a low-cost and efficient Smart Recycle Bin that aides the recycling process by using modern technologies such as environmental sensors and the LoRaWAN protocol. A centralized Information System gathers data from smart bins that can be placed virtually anywhere and can help with waste collection. The Information System will generate real-time routes for waste collection vehicles. We ran several tests with our smart bin prototype, assessed its performance, and came to the conclusion that it is a viable solution.

PurposeGenome-wide association studies (GWAS) have identified hundreds of single nucleotide polymorphisms (SNPs) significantly associated with several cancers, but the predictive ability of polygenic risk scores (PRS) is unclear, especially among non-Whites.MethodsGenome-wide genotype data was available for 20,079 individuals enrolled in an academic biobank. PRS were derived from significant DNA variants for 15 cancers. We determined the discriminatory accuracy of each cancer-specific PRS in patients of genetically-determined African and European ancestry separately.ResultsAmong individuals of European genetic ancestry, PRS for breast, colon, melanoma, and prostate were significantly associated with their respective cancers (OR 1.25-1.47). Among individuals of African genetic ancestry, PRS for breast, colon, and prostate were significantly associated with their respective cancers. The AUC of a model comprised of age, sex, and principal components was 0.617–0.709 and increased by 1-4% with the PRS in individuals of European genetic ancestry. In individuals of African genetic ancestry, AUC was overall higher in the model without PRS (0.740-0.811) but increased < 1% with the PRS in the majority of cancers.ConclusionPRS constructed from SNPs moderately increased discriminatory ability for cancer status in individuals of European but not African ancestry. Further large-scale studies are needed to identify ancestry-specific genetic factors in non-White populations to incorporate PRS into cancer risk assessment.

Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML), classified by a translocation between chromosomes 15 and 17 [t(15;17)], that is considered a true oncologic emergency though appropriate therapy is considered curative. Therapy is often initiated on clinical suspicion, informed by both clinical presentation as well as direct visualization of the peripheral smear. We hypothesized that genomic imprinting of morphologic features learned by deep learning pattern recognition would have greater discriminatory power and consistency compared to humans, thereby facilitating identification of t(15;17) positive APL. By applying both cell-level and patient-level classification linked to t(15;17) PML/RARA ground-truth, we demonstrate that deep learning is capable of distinguishing APL in both discovery and prospective independent cohort of patients. Furthermore, we extract learned information from the trained network to identify previously undescribed morphological features of APL. The deep learning method we describe herein potentially allows a rapid, explainable, and accurate physician-aid for diagnosing APL at the time of presentation in any resource-poor or -rich medical setting given the universally available peripheral smear.

BACKGROUNDSarilumab (anti-interleukin-6 receptor-α monoclonal antibody) may attenuate the inflammatory response in Covid-19.METHODSWe performed an adaptive, phase 2/3, randomized, double-blind, placebo-controlled trial of intravenous sarilumab 200 mg or 400 mg in adults hospitalized with Covid-19. The phase 3 primary analysis population (cohort 1) was patients with critical Covid-19 receiving mechanical ventilation (MV) randomized to sarilumab 400 mg or placebo. The primary end point for phase 3 was the proportion of patients with ≥1-point improvement in clinical status from baseline to day 22.RESULTSFour-hundred fifty-seven (457) and 1365 patients were randomized and treated in phases 2 and 3, respectively. Among phase 3 critical patients receiving MV (n=289; 34.3% on corticosteroids), the proportion with ≥1-point improvement in clinical status (alive not receiving MV) at day 22 was 43.2% in sarilumab 400 mg and 35.5% in placebo (risk difference [RD] +7.5%; 95% confidence interval [CI], –7.4 to 21.3; P=0.3261), representing a relative risk improvement of 21.7%. Day 29 all-cause mortality was 36.4% in sarilumab 400 mg versus 41.9% in placebo (RD –5.5%; 95% CI, –20.2 to 8.7; relative risk reduction 13.3%). In post hoc analyses pooling phase 2 and 3 critical patients receiving MV, the hazard ratio (HR) for death in sarilumab 400 mg compared with placebo was 0.76 (95% CI, 0.51 to 1.13) overall, improving to 0.49 (95% CI, 0.25 to 0.94) in patients receiving corticosteroids at baseline.CONCLUSIONIn hospitalized patients with Covid-19 receiving MV, numerical benefits with sarilumab did not achieve statistical significance, but benefit may be greater in patients receiving corticosteroids. A larger study is required to confirm this observed numerical benefit.(ClinicalTrials.gov number, NCT04315298)

SARS-CoV-2 infection is characterized by a highly variable clinical course with patients experiencing asymptomatic infection all the way to requiring critical care support. This variation in clinical course has led physicians and scientists to study factors that may predispose certain individuals to more severe clinical presentations in hopes of either identifying these individuals early in their illness or improving their medical management. We sought to understand immunogenomic differences that may result in varied clinical outcomes through analysis of T-cell receptor sequencing (TCR-Seq) data in the open access ImmuneCODE database. We identified two cohorts within the database that had clinical outcomes data reflecting severity of illness and utilized DeepTCR, a multiple-instance deep learning repertoire classifier, to predict patients with severe SARS-CoV-2 infection from their repertoire sequencing. We demonstrate that patients with severe infection have repertoires with higher T-cell responses associated with SARS-CoV-2 epitopes and identify the epitopes that result in these responses. Our results provide evidence that the highly variable clinical course seen in SARS-CoV-2 infection is associated to certain antigen-specific responses.

Slip detection and correction plays a very important role in robotic manipulation tasks, and it has long been a challenging problem in the robotic community. Further, the advantage of using systems engineering tools and framework to approach a solution and/or modeling of robotic tasks is not often pursued. In this paper, we use Model-Based Systems Engineering techniques to verify system requirements and validate stakeholder requirements for the problem of detecting and correcting for object slippage within a dexterous five-fingered robotic hand. We will discuss how the work accomplished in our laboratory was transferred to a simulated environment and how this simulated environment built in CoppeliaSim was connected to a systems engineering software, Cameo Systems Modeler. Measures of effectiveness were created from the stakeholder requirements for the slippage problem which allowed us to validate the robotic simulation that was built. Structural diagrams of the robotic system and environment were built along with behavioral diagrams of the simulation. Further, we used the connection of Cameo Systems Modeler and CoppeliaSim to track the measures of effectiveness for our robotic task which provided us with a complete systems engineering framework for the problem from the requirements phase through the implementation phase. Our main goal is to show the advantages of following a systems engineering framework to complete a robotic task through the connection of Cameo Systems Modeler and CoppeliaSim.

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity. We present DeepTCR, a suite of unsupervised and supervised deep learning methods able to model highly complex TCR sequencing data by learning a joint representation of a TCR by its CDR3 sequences and V/D/J gene usage. We demonstrate the utility of deep learning to provide an improved 'featurization' of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs and extraction of antigen-specific TCRs from noisy single-cell RNA-Seq and T-cell culture-based assays. Our results highlight the flexibility and capacity for deep neural networks to extract meaningful information from complex immunogenomic data for both descriptive and predictive purposes.

Coronary artery disease (CAD) is a leading cause of death, yet its genetic determinants are not fully elucidated. We report a multi-ethnic genome-wide association study of CAD involving nearly a quarter of a million cases, incorporating the largest cohorts to date of Whites, Blacks, and Hispanics from the Million Veteran Program with existing studies including CARDIoGRAMplusC4D, UK Biobank, and Biobank Japan. We verify substantial and nearly equivalent heritability of CAD across multiple ancestral groups, discover 107 novel loci including the first nine on the X-chromosome, identify the first eight genome-wide significant loci among Blacks and Hispanics, and demonstrate that two common haplotypes are largely responsible for the risk stratification at the well-known 9p21 locus in most populations except those of African origin where both haplotypes are virtually absent. We identify 15 loci for angiographically derived burden of coronary atherosclerosis, which robustly overlap with the strongest and earliest loci reported to date for clinical CAD. Phenome-wide association analyses of novel loci and externally validated polygenic risk scores (PRS) augment signals from the insulin resistance cluster of risk factors and consequences, extend previously established pleiotropic associations of loci with traditional risk factors to include smoking and family history, and confirm a substantially reduced transferability of existing PRS to Blacks. Downstream integrative genomic analyses reinforce the critical role of endothelial, fibroblast, and smooth muscle cells within the coronary vessel wall in CAD susceptibility. Our study highlights the value of a multi-ethnic design in efficiently characterizing the genetic architecture of CAD across all human populations.

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity. We present DeepTCR, a suite of unsupervised and supervised deep learning methods able to model highly complex TCR sequencing data by learning a joint representation of a TCR by its CDR3 sequences and V/D/J gene usage. We demonstrate the utility of deep learning to provide an improved ‘featurization’ of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs and extraction of antigen-specific TCRs from noisy single-cell RNA-Seq and T-cell culture-based assays. Our results highlight the flexibility and capacity for deep neural networks to extract meaningful information from complex immunogenomic data for both descriptive and predictive purposes., The advent of high-throughput T-cell receptor sequencing has allowed for the rapid and thorough characterization of the adaptive immune response. Here the authors show how deep learning can reveal both descriptive and predictive sequence concepts within the immune repertoire.

Summary Background Genetic data implicate IL-33 in asthma susceptibility. Itepekimab, a monoclonal antibody targeting IL-33, demonstrated clinical activity in asthma, with potential in chronic obstructive pulmonary disease (COPD). In this study we first aimed to test the hypothesis that genetic variants in the IL-33 pathway were also associated with COPD. On the basis of the strong association of IL-33 pathway genes with pulmonary diseases like asthma and COPD, we conducted this phase 2a trial to assess the safety and efficacy of itepekimab in patients with moderate-to-severe COPD on a stable regimen of triple-inhaled or double-inhaled background maintenance therapy. Methods In this two-part study, genetic analyses of loss-of-function and gain-of-function variants in the IL-33 pathway, previously associated with asthma risk, were initially characterised for COPD. We then did a double-blind, phase 2a trial comparing itepekimab with placebo in patients with moderate-to-severe COPD despite standard therapy, at 83 study sites in ten countries. Patients aged 40–75 years who were current or former smokers, had been diagnosed with COPD for at least 1 year, and were on a stable regimen of triple-inhaled or double-inhaled background maintenance therapy, were randomly assigned (1:1) to receive itepekimab 300 mg or placebo, administered as two subcutaneous injections every 2 weeks for 24–52 weeks. The primary endpoint of the phase 2a trial was annualised rate of moderate-to-severe acute exacerbations of COPD during the treatment period. The key secondary outcome was change in prebronchodilator FEV1 from baseline to weeks 16–24. Prespecified subgroup analyses were done for each of the endpoints, including by smoking status. Efficacy and safety analyses were done in all participants who received at least one dose of assigned treatment (modified intention-to-treat population). This trial is registered at ClinicalTrials.gov ( NCT03546907 ). Findings Genetic analyses demonstrated association of loss of function in IL33 with reduced COPD risk, and gain of function in IL33 and IL1RL1 variants with increased risk. Subsequent to this, in the phase 2 trial, 343 patients were randomly assigned to placebo (n=171) or itepekimab (n=172) from July 16, 2018, to Feb 19, 2020. Annualised rates of acute exacerbations of COPD were 1·61 (95% CI 1·32–1·97) in the placebo group and 1·30 (1·05–1·61) in the itepekimab group (relative risk [RR] 0·81 [95% CI 0·61–1·07], p=0·13), and least squares mean prebronchodilator FEV1 change from baseline to weeks 16–24 was 0·0 L (SD 0·02) and 0·06 L (0·02; difference 0·06 L [95% CI 0·01–0·10], p=0·024). When analysis was restricted to former smokers, treatment with itepekimab was associated with nominally significant reductions in acute exacerbations of COPD (RR 0·58 [95% CI 0·39–0·85], p=0·0061) and FEV1 improvement (least squares mean difference 0·09 L [0·02–0·15], p=0·0076) compared with placebo. Current smokers treated with itepekimab showed no treatment benefit versus placebo for exacerbations (RR 1·09 [0·74–1·61], p=0·65) or FEV1 (least squares mean difference 0·02 [−0·05 to 0·09], p=0·54). Treatment-emergent adverse events (TEAEs) occurred in 135 (78%) patients in the itepekimab group and 136 (80%) in the placebo group. The most common TEAEs were nasopharyngitis (28 [16%] in the itepekimab group vs 29 [17%] in the placebo group), bronchitis (18 [10%] vs 14 [8%]), headache (14 [8%] vs 23 [13%]), and upper respiratory tract infection (13 [8%] vs 15 [9%]). Interpretation The primary endpoint in the overall population was not met, subgroup analysis showed that itepekimab reduced exacerbation rate and improved lung function in former smokers with COPD. Two phase 3 clinical studies are ongoing to confirm the efficacy and safety profile of itepekimab in former smokers with COPD. Funding Sanofi and Regeneron Pharmaceuticals.

Simple Summary It remains unclear why chemotherapy is often ineffective in patients with bladder cancer. Meanwhile, we previously reported that male sex hormones (i.e., androgens) could considerably reduce the efficacy of cisplatin, an anti-cancer drug used as the first-line treatment against advanced bladder cancer. The present study aimed to investigate how androgen receptor signaling, which is activated by binding of androgenic hormones, modulates sensitivity to cisplatin treatment in bladder cancer, using cell line models and surgical specimens. We found that the expression levels of the androgen receptor and a molecule (BXDC2) were inversely correlated and that loss of BXDC2 was associated with cisplatin resistance. We thus provide evidence to suggest an underlying molecular mechanism responsible for androgen receptor-induced chemoresistance in bladder cancer. Abstract Underlying mechanisms for resistance to cisplatin-based chemotherapy in bladder cancer patients are largely unknown, although androgen receptor (AR) activity, as well as extracellular signal-regulated kinase (ERK) signaling, has been indicated to correlate with chemosensitivity. We also previously showed ERK activation by androgen treatment in AR-positive bladder cancer cells. Because our DNA microarray analysis in control vs. AR-knockdown bladder cancer lines identified BXDC2 as a potential downstream target of AR, we herein assessed its functional role in cisplatin sensitivity, using bladder cancer lines and surgical specimens. BXDC2 protein expression was considerably downregulated in AR-positive or cisplatin-resistant cells. BXDC2-knockdown sublines were significantly more resistant to cisplatin, compared with respective controls. Without cisplatin treatment, BXDC2-knockdown resulted in significant increases/decreases in cell proliferation/apoptosis, respectively. An ERK activator was also found to reduce BXDC2 expression. Immunohistochemistry showed downregulation of BXDC2 expression in tumor (vs. non-neoplastic urothelium), higher grade/stage tumor (vs. lower grade/stage), and AR-positive tumor (vs. AR-negative). Patients with BXDC2-positive/AR-negative muscle-invasive bladder cancer had a significantly lower risk of disease-specific mortality, compared to those with a BXDC2-negative/AR-positive tumor. Additionally, in those undergoing cisplatin-based chemotherapy, BXDC2 positivity alone (p = 0.083) or together with AR negativity (p = 0.047) was associated with favorable response. We identified BXDC2 as a key molecule in enhancing cisplatin sensitivity. AR-ERK activation may thus be associated with chemoresistance via downregulating BXDC2 expression in bladder cancer.

Intraductal carcinoma of the prostate (IDC-P) has been recently recognized by the World Health Organization classification of prostatic tumors as a distinct entity, most often occurring concurrently with invasive prostatic adenocarcinoma (PCa). Whether documented admixed with PCa or in its rare pure form, numerous studies associate this entity with clinical aggressiveness. Despite increasing clinical experience and requirement of IDC-P documentation in protocols for synoptic reporting, the specifics of its potential contribution to assessment of grade group (GG) and cancer quantitation of PCa in both needle biopsies (NBx) and radical prostatectomy (RP) specimens remain unclear. Moreover, there are no standard guidelines for incorporating basal cell marker immunohistochemistry (IHC) in the diagnosis of IDC-P, either alone or as part of a cocktail with AMACR/racemase. An online survey containing 26 questions regarding diagnosis, reporting practices, and IHC resource utilization, focusing on IDC-P, was undertaken by 42 genitourinary subspecialists from 9 countries. The degree of agreement or disagreement regarding approaches to individual questions was classified as significant majority (>75%), majority (51% to 75%), minority (26% to 50%) and significant minority (≤25%). IDC-P with or without invasive cancer is considered a contraindication for active surveillance by the significant majority (95%) of respondents, although a majority (66%) also agreed that the clinical significance/behavior of IDC-P on NBx or RP with PCa required further study. The majority do not upgrade PCa based on comedonecrosis seen only in the intraductal component in NBx (62%) or RP (69%) specimens. Similarly, recognizable IDC-P with GG1 PCa was not a factor in upgrading in NBx (78%) or RP (71%) specimens. The majority (60%) of respondents include readily recognizable IDC-P in assessment of linear extent of PCa at NBx. A significant majority (78%) would use IHC to confirm or exclude intraductal carcinoma if other biopsies showed no PCa, while 60% would use it to confirm IDC-P with invasive PCa in NBx if it would change the overall GG assignment. Nearly half (48%, a minority) would use IHC to confirm IDC-P for accurate Gleason pattern 4 quantitation. A majority (57%) report the percentage of IDC-P when present, in RP specimens. When obvious Gleason pattern 4 or 5 PCa is present in RP or NBx, IHC is rarely to almost never used to confirm the presence of IDC-P by the significant majority (88% and 90%, respectively). Most genitourinary pathologists consider IDC-P to be an adverse prognostic feature independent of the PCa grade, although recommendations for standardization are needed to guide reporting of IDC-P vis a vis tumor quantitation and final GG assessment. The use of IHC varies widely and is performed for a multitude of indications, although it is used most frequently in scenarios where confirmation of IDC-P would impact the GG assigned. Further study and best practices recommendations are needed to provide guidance with regards to the most appropriate indications for IHC use in scenarios regarding IDC-P.

Objectives: PD-L1 is an important axis of immunotolerance in normal pregnancy and this may be perpetuated as a mechanism of immune evasion in the pathobiologic development of gestational trophoblastic neoplasia (GTN). There are early efficacy signals for anti-PD-1 immunotherapy in chemoresistant GTN, however response rates are variable. The characteristics of predictive biomarkers used for triaging anti-PD-1 immunotherapy are not well-known in GTN. The aim of this study was to quantify trophoblast PD-L1 expression in GTN using the FDA approved PD-L1 22C3 immunohistochemical (IHC) companion assay and to profile GTN-infiltrating immune cells. Methods: Choriocarcinoma (CC), epithelioid trophoblastic tumor (ETT) and placental site trophoblastic tumor (PSTT) cases with available tissue blocks in the Johns Hopkins GTN library were used for this investigation. IHC staining for PD-L1 and each immune marker (CD68, CD8, CD4, FOXP3) was performed on paired, fresh cut 4 micrometer tissue sections. Tumor proportion scores (TPS, percentage of viable tumor cells with any PD-L1 membrane staining) were assigned by 2 independent gynecologic pathologists. Discrepancies in scoring were adjudicated by a third gynecologic pathologist. Digital image analyses using QuPath v0.2.3 to average CD68, CD8, CD4 and FOXP3 immune cell density in three 40x hotspots per case was performed. Hotspots were targeted to the tumor-immune cell interface. Descriptive statistics and the Wilcoxon Rank Sum Test were performed for analysis. Results: A large proportion of PSTT (n=6/8,75%) and CC (n=22/32, 69%) and approximately half of ETT (n=9/19, 47%) cases have highpositive PD-L1 expression defined as the clinically relevant TPS cutoff of >50%. These exceed the rates of high PD-L1 positivity observed in all other solid tumor sites, which ranges from 2-31%. PSTT and CC have significantly higher trophoblast PD-L1 expression than ETT; the median TPS for PSTT and CC is 90% and 85%, compared to 35% for ETT (p=0.02). CD68 monocytes were more abundant in CC and PSTT compared to ETT. However, CD8 cytotoxic T (p 5% (Table). Conclusions: PD-L1 expression in GTN is heterogeneous, yet disproportionately high compared to other primary tumor sites. Of the 3 GTN subtypes, ETT has the lowest and broadest range of PD-L1 expression; this is associated with lower CD68 monocyte infiltration and correlates with degree of lymphocytic infiltration. How these expression patterns predict response to anti-PD-L1/PD-1 agents in GTN will be need to be explored. In a tumor type with such lineage inherent PD-L1 expression, characterization of tumor cell PD-L1 expression in conjunction with the tumor immune-microenvironment may provide a unique opportunity to study mechanisms of immune escape.Funding was provided by the Bruce Patsner Research Grant for Innovative Research and Technology through the Foundation for Women's Cancer.

Nonalcoholic fatty liver disease (NAFLD) is a growing cause of chronic liver disease. Using a proxy NAFLD definition of chronic alanine aminotransferase elevation (cALT) without other liver diseases, we performed a trans-ancestry genome-wide association study in the Million Veteran Program including 90,408 cALT cases and 128,187 controls. In the Discovery stage, seventy-seven loci exceeded genome-wide significance – including 25 without prior NAFLD or ALT associations – with one additional locus identified in European-American-only and two in African-American-only analyses (P-8). External replication in cohorts with NAFLD defined by histology (7,397 cases, 56,785 controls) or liver fat extracted from radiologic imaging (n=44,289) validated 17 SNPs (P-4) of which 9 were novel (TRIB1, PPARG, MTTP, SERPINA1, FTO, IL1RN, COBLL1, APOH, and IFI30). Pleiotropy analysis showed that 61 of 77 trans-ancestry and all 17 validated SNPs were jointly associated with metabolic and/or inflammatory traits, revealing a complex model of genetic architecture. Our approach integrating cALT, histology and imaging reveals new insights into genetic liability to NAFLD.

Unmanned Aerial Vehicles (UAVs) have been extensively utilized to provide wireless connectivity in rural and under-developed areas, enhance network capacity and provide support for peaks or unexpected surges in user demand, mainly due to their fast deployment, cost-efficiency and superior communication performance resulting from Line of Sight (LoS)-dominated wireless channels. In order to exploit the benefits of UAVs as base stations or relays in a mobile network, a major challenge is to determine the optimal UAV placement and relocation strategy with respect to the mobility and traffic patterns of the ground network nodes. Moreover, considering that the UAVs form a multi-hop aerial network, capacity and connectivity constraints have significant impacts on the end-to-end network performance. To this end, we formulate the joint UAV placement and routing problem as a Mixed Integer Linear Program (MILP) and propose an approximation that leads to a LP rounding algorithm and achieves a balance between time-complexity and optimality.