Uniform data collection in routine clinical practice in cardiovascular patients for optimal care, quality control and research: The Utrecht Cardiovascular Cohort

Background: Cardiovascular disease remains the major contributor to morbidity and mortality. In routine care for patients with an elevated cardiovascular risk or with symptomatic cardiovascular disease information is mostly collected in an unstructured manner, making the data of limited use for structural feedback, quality control, learning and scientific research. / Objective: The Utrecht Cardiovascular Cohort (UCC) initiative aims to create an infrastructure for uniform registration of cardiovascular information in routine clinical practice for patients referred for cardiovascular care at the University Medical Center Utrecht, the Netherlands. This infrastructure will promote optimal care according to guidelines, continuous quality control in a learning healthcare system and creation of a research database. / Methods: The UCC comprises three parts. UCC-1 comprises enrolment of all eligible cardiovascular patients in whom the same information will be collected, based on the Dutch cardiovascular management guideline. A sample of UCC-1 will be invited for UCC-2. UCC-2 involves an enrichment through extensive clinical measurements with emphasis on heart failure, cerebral ischaemia, arterial aneurysms, diabetes mellitus and elevated blood pressure. UCC-3 comprises on-top studies, with in-depth measurements in smaller groups of participants typically based on dedicated project grants. All participants are followed up for morbidity and mortality through linkage with national registries. / Conclusion: In a multidisciplinary effort with physicians, patients and researchers the UCC sets a benchmark for a learning cardiovascular healthcare system. UCC offers an invaluable resource for future high quality care as well as for first-class research for investigators

Serum macrophage migration inhibitory factor reflects adrenal function in the hypothalamo-pituitary-adrenal axis of septic patients: an observational study

<p>Abstract</p> <p>Background</p> <p>The hypothalamo-pituitary-adrenal (HPA) axis modulates the inflammatory response during sepsis. Macrophage migration inhibitory factor (MIF), which counteracts the anti-inflammatory activity of glucocorticoid (GC), is one of the mediators of the development of inflammation. An inflammatory imbalance involving GC and MIF might be the cause or result of adrenal insufficiency. Our objective was to clarify the relationship between serum MIF and adrenal function in the HPA axis of sepsis patients using the adrenocorticotropic hormone (ACTH) stimulation test.</p> <p>Methods</p> <p>An observational study was performed in a university intensive care unit over a two-year period. Of 64 consecutive sepsis patients, 41 were enrolled. The enrolled patients underwent an ACTH stimulation test within 24 h of the diagnosis of severe sepsis or septic shock. Clinical and laboratory parameters, including serum MIF and cortisol, were measured.</p> <p>Results</p> <p>Based on their responses to the ACTH stimulation test, the patients were divided into a normal adrenal response (NAR) group (n = 22) and an adrenal insufficiency (AI) group (n = 19). The AI group had significantly more septic shock patients and higher prothrombin time ratios, serum MIF, and baseline cortisol than did the NAR group (<it>P </it>< 0.05). Serum MIF correlated significantly with the SOFA (Sequential Organ Failure Assessment) score, prothrombin time ratio, and delta max cortisol, which is maximum increment of serum cortisol concentration after ACTH stimulation test (rs = 0.414, 0.355, and -0.49, respectively, <it>P </it>< 0.05). Serum MIF also correlated significantly with the delta max cortisol/albumin ratio (rs = -0.501, <it>P </it>= 0.001). Receiver operating characteristic curve analysis identified the threshold serum MIF concentration (19.5 ng/mL, <it>P </it>= 0.01) that segregated patients into the NAR and AI groups.</p> <p>Conclusions</p> <p>The inverse correlation between serum MIF and delta max cortisol or the delta max cortisol/albumin ratio suggests that high serum MIF reflects an insufficient adrenal response in the HPA axis. Serum MIF could be a valuable clinical marker of adrenal insufficiency in sepsis patients.</p

A computerised decision support system for cardiovascular risk management ‘live’ in the electronic health record environment: development, validation and implementation—the Utrecht Cardiovascular Cohort Initiative

PURPOSE: We set out to develop a real-time computerised decision support system (CDSS) embedded in the electronic health record (EHR) with information on risk factors, estimated risk, and guideline-based advice on treatment strategy in order to improve adherence to cardiovascular risk management (CVRM) guidelines with the ultimate aim of improving patient healthcare. METHODS: We defined a project plan including the scope and requirements, infrastructure and interface, data quality and study population, validation and evaluation of the CDSS. RESULTS: In collaboration with clinicians, data scientists, epidemiologists, ICT architects, and user experience and interface designers we developed a CDSS that provides ‘live’ information on CVRM within the environment of the EHR. The CDSS provides information on cardiovascular risk factors (age, sex, medical and family history, smoking, blood pressure, lipids, kidney function, and glucose intolerance measurements), estimated 10-year cardiovascular risk, guideline-compliant suggestions for both pharmacological and non-pharmacological treatment to optimise risk factors, and an estimate on the change in 10-year risk of cardiovascular disease if treatment goals are adhered to. Our pilot study identified a number of issues that needed to be addressed, such as missing data, rules and regulations, privacy, and patient participation. CONCLUSION: Development of a CDSS is complex and requires a multidisciplinary approach. We identified opportunities and challenges in our project developing a CDSS aimed at improving adherence to CVRM guidelines. The regulatory environment, including guidance on scientific evaluation, legislation, and privacy issues needs to evolve within this emerging field of eHealth

Benchmarking Materials Property Prediction Methods: The Matbench Test Set and Automatminer Reference Algorithm

We present a benchmark test suite and an automated machine learning procedure for evaluating supervised machine learning (ML) models for predicting properties of inorganic bulk materials. The test suite, Matbench, is a set of 13 ML tasks that range in size from 312 to 132k samples and contain data from 10 density functional theory-derived and experimental sources. Tasks include predicting optical, thermal, electronic, thermodynamic, tensile, and elastic properties given a materials composition and/or crystal structure. The reference algorithm, Automatminer, is a highly-extensible, fully-automated ML pipeline for predicting materials properties from materials primitives (such as composition and crystal structure) without user intervention or hyperparameter tuning. We test Automatminer on the Matbench test suite and compare its predictive power with state-of-the-art crystal graph neural networks and a traditional descriptor-based Random Forest model. We find Automatminer achieves the best performance on 8 of 13 tasks in the benchmark. We also show our test suite is capable of exposing predictive advantages of each algorithm - namely, that crystal graph methods appear to outperform traditional machine learning methods given ~10^4 or greater data points. The pre-processed, ready-to-use Matbench tasks and the Automatminer source code are open source and available online (http://hackingmaterials.lbl.gov/automatminer/). We encourage evaluating new materials ML algorithms on the MatBench benchmark and comparing them against the latest version of Automatminer.Comment: Main text, supplemental inf

Perceptions and Practices of Stimulating Children’s Cognitive Development Among Moroccan Immigrant Mothers

We explored the perceptions of children’s cognitive development among Moroccan Arabic and Berber immigrant mothers who cannot read, who are less educated, middle educated or highly educated in the Netherlands. A series of in-depth interviews was conducted with 22 mothers with young children (mean age = 5 years and 6 months). Qualitative data analyses revealed five major themes that are of significant importance to these mothers: moral attitudes, social values and religiousness; conversation, reading and playing as stimulating activities; importance attached to education; parental expectations; attributions of school success. The parental perceptions about the cognitive development of young children differed according to their own educational level. Mothers who cannot read and mothers with less education emphasized the development of moral, social and religious values for strengthening the cultural identity of their children. This sense of identity would enable them to function within their own cultural group and help them to perform well at school. School success was attributed in large part to a combination of the efforts of the child and the school. Middle and highly educated mothers, on the other hand, valued scholastic development and attributed school success to their own efforts and to the kind of support the child received. The ethnic background of the parents, whether Arabic or Berber, did not make a difference in the perceptions

Sustained Action of Developmental Ethanol Exposure on the Cortisol Response to Stress in Zebrafish Larvae and Adults

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedThis study was supported by the National Centre for the replacement, refinement and reduction of animals in research

Database-driven High-Throughput Calculations and Machine Learning Models for Materials Design

This paper reviews past and ongoing efforts in using high-throughput ab-inito calculations in combination with machine learning models for materials design. The primary focus is on bulk materials, i.e., materials with fixed, ordered, crystal structures, although the methods naturally extend into more complicated configurations. Efficient and robust computational methods, computational power, and reliable methods for automated database-driven high-throughput computation are combined to produce high-quality data sets. This data can be used to train machine learning models for predicting the stability of bulk materials and their properties. The underlying computational methods and the tools for automated calculations are discussed in some detail. Various machine learning models and, in particular, descriptors for general use in materials design are also covered.Comment: 19 pages, 2 figure

The Four types of Tregs in malignant lymphomas

Regulatory T cells (Tregs) are a specialized subpopulation of CD4+ T cells, which act to suppress the activation of other immune cells. Tregs represent important modulators for the interaction between lymphomas and host microenvironment. Lymphomas are a group of serious and frequently fatal malignant diseases of lymphocytes. Recent studies revealed that some lymphoma T cells might adopt a Treg profile. Assessment of Treg phenotypes and genotypes in patients may offer prediction of outcome in many types of lymphomas including diffuse large B-cell lymphoma, follicular lymphoma, cutaneous T cell lymphoma, and Hodgkin's lymphoma. Based on characterized roles of Tregs in lymphomas, we can categorize the various roles into four groups: (a) suppressor Tregs; (b) malignant Tregs; (c) direct tumor-killing Tregs; and (d) incompetent Tregs. The classification into four groups is significant in predicting prognosis and designing Tregs-based immunotherapies for treating lymphomas. In patients with lymphomas where Tregs serve either as suppressor Tregs or malignant Tregs, anti-tumor cytotoxicity is suppressed thus decreased numbers of Tregs are associated with a good prognosis. In contrast, in patients with lymphomas where Tregs serve as tumor-killing Tregs and incompetent Tregs, anti-tumor cytotoxicity is enhanced or anti-autoimmune Tregs activities are weakened thus increased numbers of Tregs are associated with a good prognosis and reduced numbers of Tregs are associated with a poor prognosis. However, the mechanisms underlying the various roles of Tregs in patients with lymphomas remain unknown. Therefore, further research is needed in this regard as well as the utility of Tregs as prognostic factors and therapy strategies in different lymphomas