The economic evaluation of stratified breast cancer screening gains momentum, but produces also very diverse results. Systematic reviews so far focused on modeling techniques and epidemiologic ...assumptions. However, cost and utility parameters received only little attention. This systematic review assesses simulation models for stratified breast cancer screening based on their cost and utility parameters in each phase of breast cancer screening and care.
A literature review was conducted to compare economic evaluations with simulation models of personalized breast cancer screening. Study quality was assessed using reporting guidelines. Cost and utility inputs were extracted, standardized and structured using a care delivery framework. Studies were then clustered according to their study aim and parameters were compared within the clusters.
Eighteen studies were identified within three study clusters. Reporting quality was very diverse in all three clusters. Only two studies in cluster 1, four studies in cluster 2 and one study in cluster 3 scored high in the quality appraisal. In addition to the quality appraisal, this review assessed if the simulation models were consistent in integrating all relevant phases of care, if utility parameters were consistent and methodological sound and if cost were compatible and consistent in the actual parameters used for screening, diagnostic work up and treatment. Of 18 studies, only three studies did not show signs of potential bias.
This systematic review shows that a closer look into the cost and utility parameter can help to identify potential bias. Future simulation models should focus on integrating all relevant phases of care, using methodologically sound utility parameters and avoiding inconsistent cost parameters.
Academic research about digital non-Latin script (hereafter: NLS) research data can pose a number of challenges just because the material is from a region where the Latin alphabet was not used. Not ...all of them are easy to spot. In this paper, I introduce two use cases to demonstrate different aspects of the complex tasks that may be related to NLS material. The first use case focuses on metadata standards used to describe NLS material. Taking the VRA Core 4 XML as example, I will show where we found limitations for NLS material and how we were able to overcome them by expanding the standard. In the second use case, I look at the research data itself. Although the full-text digitization of western newspapers from the 20th century usually is not problematic anymore, this is not the case for Chinese newspapers from the Republican era (1912–1949). A major obstacle here is the dense and complex layout of the pages, which prevents OCR solutions from getting to the character recognition part. In our approach, we are combining different manual and computational methods like crowdsourcing, pattern recognition, and neural networks to be able to process the material in a more efficient way. The two use cases illustrate that data standards or processing methods that are established and stable for Latin script material may not always be easily adopted to non-Latin script research data.Des recherches académiques sur les recherches de textes numériques qui ne sont pas en alphabet latin (désormais NLS) peuvent poser plusieurs défis, car le matériel vient d’une région où l’alphabet latin n’était pas utilisé. Ils ne sont pas tous faciles à trouver. Dans cet article, je vais présenter deux cas d’utilisation pour démontrer les différents aspects des tâches complexes qui pourraient être reliées au matériel NLS. Le premier cas d’utilisation focus sur les standards de métadonnées utilisés pour décrire le matériel NLS. En prenant comme exemple le VRA Core 4 XML, je montre où se trouvent les limitations pour le matériel NLS et comment nous sommes capables de les surmonter en augmentant les standards. Pour le deuxième cas d’utilisation, je regarde les données de recherches elles-mêmes. Même si la numérisation de textes complets de journaux occidentaux du 20e siècle n’est plus problématique, ce n’est pas le cas pour les journaux chinois de l’ère républicaine (1912-1949). Un obstacle majeur est la densité et la complexité de la mise en page, ce qui empêche les solutions OCR (reconnaissance optique de caractères) de se rendre à la partie de reconnaissance des caractères. Dans notre approche, nous avons combiné des méthodes manuelles et computationnelles différentes comme l’externalisation ouverte (crowdsourcing), la reconnaissance de motifs, et le réseau neuronal pour procéder au matériel de manière plus efficace. Les deux cas d’utilisations démontrent que les données standards ou les méthodes de traitement qui sont établies et stables pour le matériel en alphabet latin ne peuvent être utilisées facilement pour des données qui ne sont pas en alphabet latin.
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), ...and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
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•Multi-omics studies can unravel the complex molecular underpinnings of diseases.•Data availability and study aims influence the selection of the integration strategy.•Knowledge-based integration can enhance the biological interpretability of results.•Data-driven integration can infer relationships between uncharacterized molecules.•Network-based, hybrid integration strategies combine the strengths of both.
Linking genes and functional information to genetic variants identified by association studies remains difficult. Resources containing extensive genomic annotations are available but often not fully ...utilized due to heterogeneous data formats. To enhance their accessibility, we integrated many annotation datasets into a user-friendly webserver.
http://www.snipa.org/
g.kastenmueller@helmholtz-muenchen.de
Supplementary data are available at Bioinformatics online.
Mapping the proteo-genomic convergence of human diseases Pietzner, Maik; Wheeler, Eleanor; Carrasco-Zanini, Julia ...
Science (American Association for the Advancement of Science),
2021-Nov-12, 2021-11-12, 20211112, Volume:
374, Issue:
6569
Journal Article
Peer reviewed
Open access
Characterization of the genetic regulation of proteins is essential for understanding disease etiology and developing therapies. We identified 10,674 genetic associations for 3892 plasma proteins to ...create a cis-anchored gene-protein-disease map of 1859 connections that highlights strong cross-disease biological convergence. This proteo-genomic map provides a framework to connect etiologically related diseases, to provide biological context for new or emerging disorders, and to integrate different biological domains to establish mechanisms for known gene-disease links. Our results identify proteo-genomic connections within and between diseases and establish the value of cis-protein variants for annotation of likely causal disease genes at loci identified in genome-wide association studies, thereby addressing a major barrier to experimental validation and clinical translation of genetic discoveries.
Conventional explanations for the post-1991 growth of India's manufacturing sector focus on goods trade liberalisation and industrial delicensing. We demonstrate the powerful contribution of a ...neglected factor: India's policy reforms in services. The link between these reforms and the productivity of manufacturing firms is examined using panel data for about 4,000 Indian firms for the period 1993–2005. We find that banking, telecommunications, insurance and transport reforms all had significant positive effects on the productivity of manufacturing firms. Services reforms benefited both foreign and locally owned manufacturing firms, but the effects on foreign firms tended to be stronger.
The metabolic basis of Alzheimer disease (AD) is poorly understood, and the relationships between systemic abnormalities in metabolism and AD pathogenesis are unclear. Understanding how global ...perturbations in metabolism are related to severity of AD neuropathology and the eventual expression of AD symptoms in at-risk individuals is critical to developing effective disease-modifying treatments. In this study, we undertook parallel metabolomics analyses in both the brain and blood to identify systemic correlates of neuropathology and their associations with prodromal and preclinical measures of AD progression.
Quantitative and targeted metabolomics (Biocrates AbsoluteIDQ identification and quantification p180) assays were performed on brain tissue samples from the autopsy cohort of the Baltimore Longitudinal Study of Aging (BLSA) (N = 44, mean age = 81.33, % female = 36.36) from AD (N = 15), control (CN; N = 14), and "asymptomatic Alzheimer's disease" (ASYMAD, i.e., individuals with significant AD pathology but no cognitive impairment during life; N = 15) participants. Using machine-learning methods, we identified a panel of 26 metabolites from two main classes-sphingolipids and glycerophospholipids-that discriminated AD and CN samples with accuracy, sensitivity, and specificity of 83.33%, 86.67%, and 80%, respectively. We then assayed these 26 metabolites in serum samples from two well-characterized longitudinal cohorts representing prodromal (Alzheimer's Disease Neuroimaging Initiative ADNI, N = 767, mean age = 75.19, % female = 42.63) and preclinical (BLSA) (N = 207, mean age = 78.68, % female = 42.63) AD, in which we tested their associations with magnetic resonance imaging (MRI) measures of AD-related brain atrophy, cerebrospinal fluid (CSF) biomarkers of AD pathology, risk of conversion to incident AD, and trajectories of cognitive performance. We developed an integrated blood and brain endophenotype score that summarized the relative importance of each metabolite to severity of AD pathology and disease progression (Endophenotype Association Score in Early Alzheimer's Disease EASE-AD). Finally, we mapped the main metabolite classes emerging from our analyses to key biological pathways implicated in AD pathogenesis. We found that distinct sphingolipid species including sphingomyelin (SM) with acyl residue sums C16:0, C18:1, and C16:1 (SM C16:0, SM C18:1, SM C16:1) and hydroxysphingomyelin with acyl residue sum C14:1 (SM (OH) C14:1) were consistently associated with severity of AD pathology at autopsy and AD progression across prodromal and preclinical stages. Higher log-transformed blood concentrations of all four sphingolipids in cognitively normal individuals were significantly associated with increased risk of future conversion to incident AD: SM C16:0 (hazard ratio HR = 4.430, 95% confidence interval CI = 1.703-11.520, p = 0.002), SM C16:1 (HR = 3.455, 95% CI = 1.516-7.873, p = 0.003), SM (OH) C14:1 (HR = 3.539, 95% CI = 1.373-9.122, p = 0.009), and SM C18:1 (HR = 2.255, 95% CI = 1.047-4.855, p = 0.038). The sphingolipid species identified map to several biologically relevant pathways implicated in AD, including tau phosphorylation, amyloid-β (Aβ) metabolism, calcium homeostasis, acetylcholine biosynthesis, and apoptosis. Our study has limitations: the relatively small number of brain tissue samples may have limited our power to detect significant associations, control for heterogeneity between groups, and replicate our findings in independent, autopsy-derived brain samples.
We present a novel framework to identify biologically relevant brain and blood metabolites associated with disease pathology and progression during the prodromal and preclinical stages of AD. Our results show that perturbations in sphingolipid metabolism are consistently associated with endophenotypes across preclinical and prodromal AD, as well as with AD pathology at autopsy. Sphingolipids may be biologically relevant biomarkers for the early detection of AD, and correcting perturbations in sphingolipid metabolism may be a plausible and novel therapeutic strategy in AD.
Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into ...clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.
Evidence suggests interplay among the three major risk factors for Alzheimer’s disease (AD): age, APOE genotype, and sex. Here, we present comprehensive datasets and analyses of brain transcriptomes ...and blood metabolomes from human apoE2-, apoE3-, and apoE4-targeted replacement mice across young, middle, and old ages with both sexes. We found that age had the greatest impact on brain transcriptomes highlighted by an immune module led by Trem2 and Tyrobp, whereas APOE4 was associated with upregulation of multiple Serpina3 genes. Importantly, these networks and gene expression changes were mostly conserved in human brains. Finally, we observed a significant interaction between age, APOE genotype, and sex on unfolded protein response pathway. In the periphery, APOE2 drove distinct blood metabolome profile highlighted by the upregulation of lipid metabolites. Our work identifies unique and interactive molecular pathways underlying AD risk factors providing valuable resources for discovery and validation research in model systems and humans.
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•Aging leads to the most profound changes in brain gene expression networks•Immune module led by Alzheimer’s risk genes Trem2/Tyrobp is upregulated with aging•Alzheimer’s risk allele APOE4 increases the expression of Serpina3 family genes•Alzheimer’s protective allele APOE2 drives unique serum metabolome profiles
Zhao et al. present comprehensive datasets and analyses of brain transcriptomes and blood metabolomes from human apoE2-, apoE3-, and apoE4-targeted replacement mice across young, middle, and old ages with both sexes. The study provides critical insight on the molecular pathways underlying three major Alzheimer’s risk factors age, APOE, and sex.
This paper introduces a novel nothing-on-road (NOR) bridge weigh-in-motion (BWIM) approach with deep learning (DL) and non-invasive ground-based radar (GBR) time-series data. BWIMs allow ...site-specific structural health monitoring (SHM) but are usually difficult to attach and maintain. GBR measures the bridge deflection contactless. In this study, GBR and an unmanned aerial vehicle (UAV) monitor a two-span bridge in Germany to gather ground-truth data. Based on the UAV data, we determine vehicle type, lane, locus, speed, axle count, and axle spacing for single-presence vehicle crossings. Since displacement is a global response, using peak detection like conventional strain-based BWIMs is challenging. Therefore, we investigate data-driven machine learning approaches to extract the vehicle configurations directly from the displacement data. Despite a small and imbalanced real-world dataset, the proposed approaches classify, e.g., the axle count for trucks with a balanced accuracy of 76.7% satisfyingly. Additionally, we demonstrate that, for the selected bridge, high-frequency vibrations can coincide with axles crossing the junction between the street and the bridge. We evaluate whether filtering approaches via bandpass filtering or wavelet transform can be exploited for axle count and axle spacing identification. Overall, we can show that GBR is a serious contender for BWIM systems.
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