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The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p–Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The Transition Radiation Detector (TRD) was designed and built to enhance the capabilities of the ALICE detector at the Large Hadron Collider (LHC). While aimed at providing electron identification and triggering, the TRD also contributes significantly to the track reconstruction and calibration in the central barrel of ALICE. In this paper the design, construction, operation, and performance of this detector are discussed. A pion rejection factor of up to 410 is achieved at a momentum of 1 GeV/c in p-Pb collisions and the resolution at high transverse momentum improves by about 40% when including the TRD information in track reconstruction. The triggering capability is demonstrated both for jet, light nuclei, and electron selection.
The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3,4,5,6,7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
Objective: Pancreatic ductal adenocarcinoma (PDAC) still carries a dismal prognosis with an overall 5-year survival rate of 9%. Conventional combination chemotherapies are a clear advance in the treatment of PDAC; however, subtypes of the disease exist, which exhibit extensive resistance to such therapies. Genomic MYC amplifications represent a distinct subset of PDAC with an aggressive tumour biology. It is clear that hyperactivation of MYC generates dependencies that can be exploited therapeutically. The aim of the study was to find and to target MYC-associated dependencies.
Design: We analysed human PDAC gene expression datasets. Results were corroborated by the analysis of the small ubiquitin-like modifier (SUMO) pathway in a large PDAC cohort using immunohistochemistry. A SUMO inhibitor was used and characterised using human and murine two-dimensional, organoid and in vivo models of PDAC.
Results: We observed that MYC is connected to the SUMOylation machinery in PDAC. Components of the SUMO pathway characterise a PDAC subtype with a dismal prognosis and we provide evidence that hyperactivation of MYC is connected to an increased sensitivity to pharmacological SUMO inhibition.
Conclusion: SUMO inhibitor-based therapies should be further developed for an aggressive PDAC subtype.
Two methods for the fast, fragment-based combinatorial molecule assembly were developed. The software COLIBREE® (Combinatorial Library Breeding) generates candidate structures from scratch, based on stochastic optimization [1]. Result structures of a COLIBREE design run are based on a fixed scaffold and variable linkers and side-chains. Linkers representing virtual chemical reactions and side-chain building blocks obtained from pseudo-retrosynthetic dissection of large compound databases are exchanged during optimization. The process of molecule design employs a discrete version of Particle Swarm Optimization (PSO) [2]. Assembled compounds are scored according to their similarity to known reference ligands. Distance to reference molecules is computed in the space of the topological pharmacophore descriptor CATS [3]. In a case study, the approach was applied to the de novo design of potential peroxisome proliferator-activated receptor (PPAR gamma) selective agonists. In a second approach, we developed the formal grammar Reaction-MQL [4] for the in silico representation and application of chemical reactions. Chemical transformation schemes are defined by functional groups participating in known organic reactions. The substructures are specified by the linear Molecular Query Language (MQL) [5]. The developed software package contains a parser for Reaction-MQL-expressions and enables users to design, test and virtually apply chemical reactions. The program has already been used to create combinatorial libraries for virtual screening studies. It was also applied in fragmentation studies with different sets of retrosynthetic reactions and various compound libraries.
DNA methylation was shown previously to be a crucial mechanism responsible for transcriptional deregulation in the pathogenesis of classical Hodgkin lymphoma (cHL). To identify epigenetically inactivated miRNAs in cHL, we have analyzed the set of miRNAs downregulated in cHL cell lines using bisulfite pyrosequencing. We focused on miRNAs with promoter regions located within or <1000 bp from a CpG island. Most promising candidate miRNAs were further studied in primary Hodgkin and Reed-Sternberg (HRS) cells obtained by laser capture microdissection. Last, to evaluate the function of identified miRNAs, we performed a luciferase reporter assay to confirm miRNA: mRNA interactions and therefore established cHL cell lines with stable overexpression of selected miRNAs for proliferation tests. We found a significant reverse correlation between DNA methylation and expression levels of mir-339-3p, mir-148a-3p, mir-148a-5p and mir-193a-5 demonstrating epigenetic regulation of these miRNAs in cHL cell lines. Moreover, we demonstrated direct interaction between miR-148a-3p and IL15 and HOMER1 transcripts as well as between mir-148a-5p and SUB1 and SERPINH1 transcripts. Furthermore, mir-148a overexpression resulted in reduced cell proliferation in the KM-H2 cell line. In summary, we report that mir-148a is a novel tumor suppressor inactivated in cHL and that epigenetic silencing of miRNAs is a common phenomenon in cHL.
Sudden cardiac death (SCD) in adolescents and young adults may be the first manifestation of an inherited arrhythmic syndrome. Thus identification of a genetic origin in sudden death cases deemed inconclusive after a comprehensive autopsy and may help to reduce the risk of lethal episodes in the remaining family. Using next-generation sequencing (NGS), a large number of variants of unknown significance (VUS) are detected. In the majority of cases, there is insufficient evidence of pathogenicity, representing a huge dilemma in current genetic investigations. Misinterpretation of such variants may lead to inaccurate genetic diagnoses and/or the adoption of unnecessary and/or inappropriate therapeutic approaches. In our study, we applied current (ACMG) recommendations for variant classification in post-mortem genetic screening of a cohort of 56 SCD victims. We identified a total 53 rare protein-altering variants (MAF < 0.2%) classified as VUS or worse. Twelve percent of the cases exhibited a clinically actionable variant (pathogenic, likely pathogenic or VUS – potentially pathogenic) that would warrant cascade genetic screening in relatives. Most of the variants detected by means of the post-mortem genetic investigations were VUS. Thus, genetic testing by itself might be fairly meaningless without supporting background data. This data reinforces the need for an experienced multidisciplinary team for obtaining reliable and accountable interpretations of variant significance for elucidating potential causes for SCDs in the young. This enables the early identification of relatives at risk or excludes family members as genetic carriers. Also, development of adequate forensic guidelines to enable appropriate interpretation of rare genetic variants is fundamental.
We investigate what statistical properties drive risk-taking in a large set of observational panel data on online poker games (n=4,450,585). Each observation refers to a choice between a safe 'insurance' option and a binary lottery of winning or losing the game. Our setting offers a real-world choice situation with substantial incentives where probability distributions are simple, transparent, and known to the individuals. We find that individuals reveal a strong and robust preference for skewness. The effect of skewness is most pronounced among experienced and losing players but remains highly significant for winning players, in contrast to the variance effect.
Background: About 2000 children and adolescents under the age of 18 are diagnosed with cancer each year in Germany. Because of current medical treatment methods, a high survival rate can be reached for many types of the disease. Nevertheless, patients face a number of long-term effects related to the treatment. As a result, physical and psychological consequences have increasingly become the focus of research in recent years. Social dimensions of health have received little attention in health services research in oncology so far. Yet, there are no robust results that allow an estimation of whether and to what extent the disease and treatment impair the participation of children and adolescents and which factors mediate this effect. Social participation is of great importance especially because interactions with peers and experiences in different areas of life are essential for the development of children and adolescents.
Methods: Data are collected in a longitudinal, prospective, observational multicenter study. For this purpose, all patients and their parents who are being treated for cancer in one of the participating clinics throughout Germany will be interviewed within the first month after diagnosis (t1), after completion of intensive treatment (t2) and half a year after the end of intensive treatment (t3) using standardized questionnaires. Analysis will be done by descriptive and multivariate methods.
Discussion: The results can be used to identify children and adolescents in high-risk situations at an early stage in order to be able to initiate interventions tailored to the needs. Such tailored interventions will finally reduce the risk of impairments in the participation of children and adolescents and increase quality of life.
Trial registration: ClinicalTrials.gov: NCT04101123.
CXCL12-CXCR4 signaling controls multiple physiological processes and its dysregulation is associated with cancers and inflammatory diseases. To discover as-yet-unknown endogenous ligands of CXCR4, we screened a blood-derived peptide library for inhibitors of CXCR4-tropic HIV-1 strains. This approach identified a 16 amino acid fragment of serum albumin as an effective and highly specific CXCR4 antagonist. The endogenous peptide, termed EPI-X4, is evolutionarily conserved and generated from the highly abundant albumin precursor by pH-regulated proteases. EPI-X4 forms an unusual lasso-like structure and antagonizes CXCL12-induced tumor cell migration, mobilizes stem cells, and suppresses inflammatory responses in mice. Furthermore, the peptide is abundant in the urine of patients with inflammatory kidney diseases and may serve as a biomarker. Our results identify EPI-X4 as a key regulator of CXCR4 signaling and introduce proteolysis of an abundant precursor protein as an alternative concept for chemokine receptor regulation.
During the measurement campaign FROST 2 (FReezing Of duST 2), the Leipzig Aerosol Cloud Interaction Simulator (LACIS) was used to investigate the influence of various surface modifications on the ice nucleating ability of Arizona Test Dust (ATD) particles in the immersion freezing mode. The dust particles were exposed to sulfuric acid vapor, to water vapor with and without the addition of ammonia gas, and heat using a thermodenuder operating at 250 °C. Size selected, quasi monodisperse particles with a mobility diameter of 300 nm were fed into LACIS and droplets grew on these particles such that each droplet contained a single particle. Temperature dependent frozen fractions of these droplets were determined in a temperature range between −40 °C ≤T≤−28 °C. The pure ATD particles nucleated ice over a broad temperature range with their freezing behavior being separated into two freezing branches characterized through different slopes in the frozen fraction vs. temperature curves. Coating the ATD particles with sulfuric acid resulted in the particles' IN potential significantly decreasing in the first freezing branch (T>−35 °C) and a slight increase in the second branch (T≤−35 °C). The addition of water vapor after the sulfuric acid coating caused the disappearance of the first freezing branch and a strong reduction of the IN ability in the second freezing branch. The presence of ammonia gas during water vapor exposure had a negligible effect on the particles' IN ability compared to the effect of water vapor. Heating in the thermodenuder led to a decreased IN ability of the sulfuric acid coated particles for both branches but the additional heat did not or only slightly change the IN ability of the pure ATD and the water vapor exposed sulfuric acid coated particles. In other words, the combination of both sulfuric acid and water vapor being present is a main cause for the ice active surface features of the ATD particles being destroyed. A possible explanation could be the chemical transformation of ice active metal silicates to metal sulfates. The strongly enhanced reaction between sulfuric acid and dust in the presence of water vapor and the resulting significant reductions in IN potential are of importance for atmospheric ice cloud formation. Our findings suggest that the IN concentration can decrease by up to one order of magnitude for the conditions investigated.
During the measurement campaign FROST 2 (FReezing Of duST 2), the Leipzig Aerosol Cloud Interaction Simulator (LACIS) was used to investigate the influences of various surface modifications on the immersion freezing behavior of Arizona Test Dust (ATD) particles. The dust particles were exposed to sulfuric acid vapor, to water vapor with and without the addition of ammonia gas, and heat using a thermodenuder operating at 250 °C. Size selected, quasi monodisperse particles with a mobility diameter of 300 nm were fed into LACIS and droplets grew on these particles such that each droplet contained a single particle. Temperature dependent frozen fractions of these droplets were determined in a temperature range between −40 °C ≤ T ≤ −28 °C. The pure ATD particles nucleated ice over a~broad temperature range with their freezing behavior being separated into two freezing branches characterized through different slopes in the frozen fraction vs. temperature curves. Coating the ATD particles with sulfuric acid resulted in the particles' IN potential significantly decreasing in the first freezing branch (T > −35 °C) and a slight increase in the second branch (T≤ −35 °C). The addition of water vapor after the sulfuric acid coating caused the disappearance of the first freezing branch and a strong reduction of the IN ability in the second freezing branch. The presence of ammonia gas during water vapor exposure had a negligible effect on the particles' IN ability compared to the effect of water vapor. Heating in the thermodenuder led to a decreased IN ability of the sulfuric acid coated particles for both branches but the additional heat did not or only slightly change the IN ability of the pure ATD and the water vapor exposed sulfuric acid coated particles. In other words, the combination of both sulfuric acid and water vapor being present is a main cause for the ice active surface features of the ATD particles being destroyed. A possible explanation could be the chemical transformation of ice active metal silicates to metal sulfates. From an atmospheric point of view, and here specifically the influences of atmospheric aging on the IN ability of dust particles, the strongly enhanced reaction between sulfuric acid and dust in the presence of water vapor, and the resulting significant reductions in IN potential, are certainly very interesting.
HLA-DRB1 and HLA-DQB1 genetic diversity modulates response to lithium in bipolar affective disorders
(2021)
Bipolar affective disorder (BD) is a severe psychiatric illness, for which lithium (Li) is the gold standard for acute and maintenance therapies. The therapeutic response to Li in BD is heterogeneous and reliable biomarkers allowing patients stratification are still needed. A GWAS performed by the International Consortium on Lithium Genetics (ConLiGen) has recently identified genetic markers associated with treatment responses to Li in the human leukocyte antigens (HLA) region. To better understand the molecular mechanisms underlying this association, we have genetically imputed the classical alleles of the HLA region in the European patients of the ConLiGen cohort. We found our best signal for amino-acid variants belonging to the HLA-DRB1*11:01 classical allele, associated with a better response to Li (p < 1 × 10−3; FDR < 0.09 in the recessive model). Alanine or Leucine at position 74 of the HLA-DRB1 heavy chain was associated with a good response while Arginine or Glutamic acid with a poor response. As these variants have been implicated in common inflammatory/autoimmune processes, our findings strongly suggest that HLA-mediated low inflammatory background may contribute to the efficient response to Li in BD patients, while an inflammatory status overriding Li anti-inflammatory properties would favor a weak response.
Congenital lower urinary-tract obstruction (LUTO) is caused by anatomical blockage of the bladder outflow tract or by functional impairment of urinary voiding. About three out of 10,000 pregnancies are affected. Although several monogenic causes of functional obstruction have been defined, it is unknown whether congenital LUTO caused by anatomical blockage has a monogenic cause. Exome sequencing in a family with four affected individuals with anatomical blockage of the urethra identified a rare nonsense variant (c.2557C>T [p.Arg853∗]) in BNC2, encoding basonuclin 2, tracking with LUTO over three generations. Re-sequencing BNC2 in 697 individuals with LUTO revealed three further independent missense variants in three unrelated families. In human and mouse embryogenesis, basonuclin 2 was detected in lower urinary-tract rudiments. In zebrafish embryos, bnc2 was expressed in the pronephric duct and cloaca, analogs of the mammalian lower urinary tract. Experimental knockdown of Bnc2 in zebrafish caused pronephric-outlet obstruction and cloacal dilatation, phenocopying human congenital LUTO. Collectively, these results support the conclusion that variants in BNC2 are strongly implicated in LUTO etiology as a result of anatomical blockage.
We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.
The project focuses on the efficiency of combined technologies to reduce the release of micropollutants and bacteria into surface waters via sewage treatment plants of different size and via stormwater overflow basins of different types. As a model river in a highly populated catchment area, the river Schussen and, as a control, the river Argen, two tributaries of Lake Constance, Southern Germany, are under investigation in this project. The efficiency of the different cleaning technologies is monitored by a wide range of exposure and effect analyses including chemical and microbiological techniques as well as effect studies ranging from molecules to communities.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Introduction: The German PID-NET registry was founded in 2009, serving as the first national registry of patients with primary immunodeficiencies (PID) in Germany. It is part of the European Society for Immunodeficiencies (ESID) registry. The primary purpose of the registry is to gather data on the epidemiology, diagnostic delay, diagnosis, and treatment of PIDs.
Methods: Clinical and laboratory data was collected from 2,453 patients from 36 German PID centres in an online registry. Data was analysed with the software Stata® and Excel.
Results: The minimum prevalence of PID in Germany is 2.72 per 100,000 inhabitants. Among patients aged 1–25, there was a clear predominance of males. The median age of living patients ranged between 7 and 40 years, depending on the respective PID. Predominantly antibody disorders were the most prevalent group with 57% of all 2,453 PID patients (including 728 CVID patients). A gene defect was identified in 36% of patients. Familial cases were observed in 21% of patients. The age of onset for presenting symptoms ranged from birth to late adulthood (range 0–88 years). Presenting symptoms comprised infections (74%) and immune dysregulation (22%). Ninety-three patients were diagnosed without prior clinical symptoms. Regarding the general and clinical diagnostic delay, no PID had undergone a slight decrease within the last decade. However, both, SCID and hyper IgE- syndrome showed a substantial improvement in shortening the time between onset of symptoms and genetic diagnosis. Regarding treatment, 49% of all patients received immunoglobulin G (IgG) substitution (70%—subcutaneous; 29%—intravenous; 1%—unknown). Three-hundred patients underwent at least one hematopoietic stem cell transplantation (HSCT). Five patients had gene therapy.
Conclusion: The German PID-NET registry is a precious tool for physicians, researchers, the pharmaceutical industry, politicians, and ultimately the patients, for whom the outcomes will eventually lead to a more timely diagnosis and better treatment.
Background: Definite diagnosis and therapeutic management of cholangiocarcinoma (CCA) remains a challenge. The aim of the current study was to investigate feasibility and potential impact on clinical management of targeted sequencing of intraductal biopsies.
Methods: Intraductal biopsies with suspicious findings from 16 patients with CCA in later clinical course were analyzed with targeted sequencing including tumor and control benign tissue (n = 55 samples). A CCA-specific sequencing panel containing 41 genes was designed and a dual strand targeted enrichment was applied.
Results: Sequencing was successfully performed for all samples. In total, 79 mutations were identified and a mean of 1.7 mutations per tumor sample (range 0–4) as well as 2.3 per biopsy (0–6) were detected and potentially therapeutically relevant genes were identified in 6/16 cases. In 14/18 (78%) biopsies with dysplasia or inconclusive findings at least one mutation was detected. The majority of mutations were found in both surgical specimen and biopsy (68%), while 28% were only present in biopsies in contrast to 4% being only present in the surgical tumor specimen.
Conclusion: Targeted sequencing from intraductal biopsies is feasible and potentially improves the diagnostic yield. A profound genetic heterogeneity in biliary dysplasia needs to be considered in clinical management and warrants further investigation.
Translational impact: The current study is the first to demonstrate the feasibility of sequencing of intraductal biopsies which holds the potential to impact diagnostic and therapeutical management of patients with biliary dysplasia and neoplasia.
Genetic heterogeneity of primary lesion and metastasis in small intestine neuroendocrine tumors
(2018)
Data on intratumoral heterogeneity of small intestine neuroendocrine tumors (SI-NETs) and related liver metastasis are limited. The aim of this study was to characterize genetic heterogeneity of 5 patients with SI-NETs. Therefore, formalin-fixed, paraffin-embedded tissue samples of primary and metastatic lesions as well as benign liver of five patients with synchronously metastasized, well differentiated SI-NETs were analyzed with whole exome sequencing. For one patient, chip based 850k whole DNA methylome analysis was performed of primary and metastatic tumor tissue as well as control tissue. Thereby, 156 single nucleotide variants (SNVs) in 150 genes were identified and amount of mutations per sample ranged from 9–34 (mean 22). The degree of common (0–94%) and private mutations per sample was strongly varying (6–100%). In all patients, copy number variations (CNV) were found and the degree of intratumoral heterogeneity of CNVs corresponded to SNV analysis. DNA methylation analysis of a patient without common SNVs revealed a large overlap of common methylated CpG sites. In conclusion, SI-NET primary and metastatic lesions show a highly varying degree of intratumoral heterogeneity. Driver events might not be detectable with exome analysis only, and further comprehensive studies including whole genome and epigenetic analyses are warranted.
Immune-modulating therapy is a promising therapy for patients with cholangiocarcinoma (CCA). Microsatellite instability (MSI) might be a favorable predictor for treatment response, but comprehensive data on the prevalence of MSI in CCA are missing. The aim of the current study was to determine the prevalence of MSI in a German tertiary care hospital. Formalin-fixed paraffin-embedded tissue samples, obtained in the study period from 2007 to 2015 from patients with CCA undergoing surgical resection with curative intention at Johann Wolfgang Goethe University hospital, were examined. All samples were investigated immunohistochemically for the presence of MSI (expression of MLH1, PMS2, MSH2, and MSH6) as well as by pentaplex polymerase chain reaction for five quasimonomorphic mononucleotide repeats (BAT-25, BAT-26, NR-21, NR-22, and NR-24). In total, 102 patients were included, presenting intrahepatic (n = 35, 34.3%), perihilar (n = 42, 41.2%), and distal CCA (n = 25, 24.5%). In the immunohistochemical analysis, no loss of expression of DNA repair enzymes was observed. In the PCR-based analysis, one out of 102 patients was found to be MSI-high and one out of 102 was found to be MSI-low. Thus, MSI seems to appear rarely in CCA in Germany. This should be considered when planning immune-modulating therapy trials for patients with CCA.
Importance: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimaging data, yet nobody compared them in this context.
Objective: Multi-class prediction is key for building computational aid systems for differential diagnosis. We compared support vector machine, random forest, gradient boosting, and deep feed-forward neural networks for the classification of different neurodegenerative syndromes based on structural magnetic resonance imaging.
Design, setting, and participants: Atlas-based volumetry was performed on multi-centric T1-weighted MRI data from 940 subjects, i.e., 124 healthy controls and 816 patients with ten different neurodegenerative diseases, leading to a multi-diagnostic multi-class classification task with eleven different classes.
Interventions: N.A.
Main outcomes and measures: Cohen’s kappa, accuracy, and F1-score to assess model performance.
Results: Overall, the neural network produced both the best performance measures and the most robust results. The smaller classes however were better classified by either the ensemble learning methods or the support vector machine, while performance measures for small classes were comparatively low, as expected. Diseases with regionally specific and pronounced atrophy patterns were generally better classified than diseases with widespread and rather weak atrophy.
Conclusions and relevance: Our study furthermore underlines the necessity of larger data sets but also calls for a careful consideration of different machine learning methods that can handle the type of data and the classification task best.
Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 22 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, YWHAE, DPH1, GSDMB, MED24, THRA, EEF1A2, and KCNQ2 in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance and transferability of BD polygenic risk scores across ancestrally diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).
Highlights
• A panel of 20 biomarkers was identified capable of differentiating BD patients from controls.
• Excellent discrimination between established BD patients and controls.
• Good to excellent discrimination between misdiagnosed BD patients and first onset MDD patients.
• Fair to good discrimination between pre-diagnostic BD patients and controls.
• Study demonstrates the potential utility of a protein biomarker panel as a diagnostic test for BD.
Abstract
Background: Bipolar disorder (BD) is a costly, devastating and life shortening mental disorder that is often misdiagnosed, especially on initial presentation. Misdiagnosis frequently results in ineffective treatment. We investigated the utility of a biomarker panel as a diagnostic test for BD.
Methods and findings: We performed a meta-analysis of eight case-control studies to define a diagnostic biomarker panel for BD. After validating the panel on established BD patients, we applied it to undiagnosed BD patients. We analysed 249 BD, 122 pre-diagnostic BD, 75 pre-diagnostic schizophrenia and 90 first onset major depression disorder (MDD) patients and 371 controls. The biomarker panel was identified using ten-fold cross-validation with lasso regression applied to the 87 analytes available across the meta-analysis studies.
We identified 20 protein analytes with excellent predictive performance [area under the curve (AUC) ⩾ 0.90]. Importantly, the panel had a good predictive performance (AUC 0.84) to differentiate 12 misdiagnosed BD patients from 90 first onset MDD patients, and a fair to good predictive performance (AUC 0.79) to differentiate between 110 pre-diagnostic BD patients and 184 controls. We also demonstrated the disease specificity of the panel.
Conclusions: An early and accurate diagnosis has the potential to delay or even prevent the onset of BD. This study demonstrates the potential utility of a biomarker panel as a diagnostic test for BD.
Bipolar disorder (BD) is a genetically complex mental illness characterized by severe oscillations of mood and behavior. Genome-wide association studies (GWAS) have identified several risk loci that together account for a small portion of the heritability. To identify additional risk loci, we performed a two-stage meta-analysis of >9 million genetic variants in 9,784 bipolar disorder patients and 30,471 controls, the largest GWAS of BD to date. In this study, to increase power we used ~2,000 lithium-treated cases with a long-term diagnosis of BD from the Consortium on Lithium Genetics, excess controls, and analytic methods optimized for markers on the Xchromosome. In addition to four known loci, results revealed genome-wide significant associations at two novel loci: an intergenic region on 9p21.3 (rs12553324, p = 5.87×10-9; odds ratio = 1.12) and markers within ERBB2 (rs2517959, p = 4.53×10-9; odds ratio = 1.13). No significant X-chromosome associations were detected and X-linked markers explained very little BD heritability. The results add to a growing list of common autosomal variants involved in BD and illustrate the power of comparing well-characterized cases to an excess of controls in GWAS.
Inhibitors against the NS3-4A protease of hepatitis C virus (HCV) have proven to be useful drugs in the treatment of HCV infection. Although variants have been identified with mutations that confer resistance to these inhibitors, the mutations do not restore replicative fitness and no secondary mutations that rescue fitness have been found. To gain insight into the molecular mechanisms underlying the lack of fitness compensation, we screened known resistance mutations in infectious HCV cell culture with different genomic backgrounds. We observed that the Q41R mutation of NS3-4A efficiently rescues the replicative fitness in cell culture for virus variants containing mutations at NS3-Asp168. To understand how the Q41R mutation rescues activity, we performed protease activity assays complemented by molecular dynamics simulations, which showed that protease-peptide interactions far outside the targeted peptide cleavage sites mediate substrate recognition by NS3-4A and support protease cleavage kinetics. These interactions shed new light on the mechanisms by which NS3-4A cleaves its substrates, viral polyproteins and a prime cellular antiviral adaptor protein, the mitochondrial antiviral signaling protein MAVS. Peptide binding is mediated by an extended hydrogen-bond network in NS3-4A that was effectively optimized for protease-MAVS binding in Asp168 variants with rescued replicative fitness from NS3-Q41R. In the protease harboring NS3-Q41R, the N-terminal cleavage products of MAVS retained high affinity to the active site, rendering the protease susceptible for potential product inhibition. Our findings reveal delicately balanced protease-peptide interactions in viral replication and immune escape that likely restrict the protease adaptive capability and narrow the virus evolutionary space.
An iridium(III/IV/V) redox series featuring a terminal imido complex with triplet ground state
(2018)
The iridium(III/IV/V) imido redox series [Ir(NtBu){N(CHCHPtBu2)2}]0/+/2+ was synthesized and examined spectroscopically, magnetically, crystallographically and computationally. The monocationic iridium(IV) imide exhibits an electronic doublet ground state with considerable ‘imidyl’ character as a result of covalent Ir–NtBu bonding. Reduction gives the neutral imide [Ir(NtBu){N(CHCHPtBu2)2}] as the first example of an iridium complex with a triplet ground state. Its reactivity with respect to nitrene transfer to selected electrophiles (CO2) and nucleophiles (PMe3), respectively, is reported.
An integrative correlation of myopathology, phenotype and genotype in late onset Pompe disease
(2019)
Aims: Pompe disease is caused by pathogenic mutations in the alpha 1,4‐glucosidase (GAA) gene and in patients with late onset Pome disease (LOPD), genotype–phenotype correlations are unpredictable. Skeletal muscle pathology includes glycogen accumulation and altered autophagy of various degrees. A correlation of the muscle morphology with clinical features and the genetic background in GAA may contribute to the understanding of the phenotypic variability.
Methods: Muscle biopsies taken before enzyme replacement therapy were analysed from 53 patients with LOPD. On resin sections, glycogen accumulation, fibrosis, autophagic vacuoles and the degree of muscle damage (morphology‐score) were analysed and the results were compared with clinical findings. Additional autophagy markers microtubule‐associated protein 1A/1B‐light chain 3, p62 and Bcl2‐associated athanogene 3 were analysed on cryosections from 22 LOPD biopsies.
Results: The myopathology showed a high variability with, in most patients, a moderate glycogen accumulation and a low morphology‐score. High morphology‐scores were associated with increased fibrosis and autophagy highlighting the role of autophagy in severe stages of skeletal muscle damage. The morphology‐score did not correlate with the patient's age at biopsy, disease duration, nor with the residual GAA enzyme activity or creatine‐kinase levels. In 37 patients with LOPD, genetic analysis identified the most frequent mutation, c.‐32‐13T>G, in 95%, most commonly in combination with c.525delT (19%). No significant correlation was found between the different GAA genotypes and muscle morphology type.
Conclusions: Muscle morphology in LOPD patients shows a high variability with, in most cases, moderate pathology. Increased pathology is associated with more fibrosis and autophagy.
Poster presentation from Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011. In statistical spike train analysis, stochastic point process models usually assume stationarity, in particular that the underlying spike train shows a constant firing rate (e.g. [1]). However, such models can lead to misinterpretation of the associated tests if the assumption of rate stationarity is not met (e.g. [2]). Therefore, the analysis of nonstationary data requires that rate changes can be located as precisely as possible. However, present statistical methods focus on rejecting the null hypothesis of stationarity without explicitly locating the change point(s) (e.g. [3]). We propose a test for stationarity of a given spike train that can also be used to estimate the change points in the firing rate. Assuming a Poisson process with piecewise constant firing rate, we propose a Step-Filter-Test (SFT) which can work simultaneously in different time scales, accounting for the high variety of firing patterns in experimental spike trains. Formally, we compare the numbers N1=N1(t,h) and N2=N2(t,h) of spikes in the time intervals (t-h,t] and (h,t+h]. By varying t within a fine time lattice and simultaneously varying the interval length h, we obtain a multivariate statistic D(h,t):=(N1-N2)/V(N1+N2), for which we prove asymptotic multivariate normality under homogeneity. From this a practical, graphical device to spot changes of the firing rate is constructed. Our graphical representation of D(h,t) (Figure 1A) visualizes the changes in the firing rate. For the statistical test, a threshold K is chosen such that under homogeneity, |D(h,t)|<K holds for all investigated h and t with probability 0.95. This threshold can indicate potential change points in order to estimate the inhomogeneous rate profile (Figure 1B). The SFT is applied to a sample data set of spontaneous single unit activity recorded from the substantia nigra of anesthetized mice. In this data set, multiple rate changes are identified which agree closely with visual inspection. In contrast to approaches choosing one fixed kernel width [4], our method has advantages in the flexibility of h.
Poster presentation: Introduction The ability of neurons to emit different firing patterns is considered relevant for neuronal information processing. In dopaminergic neurons, prominent patterns include highly regular pacemakers with separate spikes and stereotyped intervals, processes with repetitive bursts and partial regularity, and irregular spike trains with nonstationary properties. In order to model and quantify these processes and the variability of their patterns with respect to pharmacological and cellular properties, we aim to describe the two dimensions of burstiness and regularity in a single model framework. Methods We present a stochastic spike train model in which the degree of burstiness and the regularity of the oscillation are described independently and with two simple parameters. In this model, a background oscillation with independent and normally distributed intervals gives rise to Poissonian spike packets with a Gaussian firing intensity. The variability of inter-burst intervals and the average number of spikes in each burst indicate regularity and burstiness, respectively. These parameters can be estimated by fitting the model to the autocorrelograms. This allows to assign every spike train a position in the two-dimensional space described by regularity and burstiness and thus, to investigate the dependence of the firing patterns on different experimental conditions. Finally, burst detection in single spike trains is possible within the model because the parameter estimates determine the appropriate bandwidth that should be used for burst identification. Results and Discussion We applied the model to a sample data set obtained from dopaminergic substantia nigra and ventral tegmental area neurons recorded extracellularly in vivo and studied differences between the firing activity of dopaminergic neurons in wildtype and K-ATP channel knock-out mice. The model is able to represent a variety of discharge patterns and to describe changes induced pharmacologically. It provides a simple and objective classification scheme for the observed spike trains into pacemaker, irregular and bursty processes. In addition to the simple classification, changes in the parameters can be studied quantitatively, also including the properties related to bursting behavior. Interestingly, the proposed algorithm for burst detection may be applicable also to spike trains with nonstationary firing rates if the remaining parameters are unaffected. Thus, the proposed model and its burst detection algorithm can be useful for the description and investigation of neuronal firing patterns and their variability with cellular and experimental conditions.