Preventing cardiovascular disease after hypertensive disorders of pregnancy: Searching for the how and when

Background: Women with a history of a hypertensive disorder during pregnancy (HDP) have an increased risk of cardiovascular events. Guidelines recommend assessment of cardiovascular risk factors in these women later in life, but provide limited advice on how this follow-up should be organized. Design: Systematic review and meta-regression analysis. Methods: The aim of our study was to provide an overview of existing knowledge on the changes over time in three major modifiable components of cardiovascular risk assessment after HDP: blood pressure, glucose homeostasis and lipid levels. Data from 44 studies and up to 6904 women with a history of a HDP were compared with risk factor levels reported for women of corresponding age in the National Health And Nutrition Examination Survey, Estudio Epidemiólogico de la Insuficiencia Renal en España and Hong Kong cohorts (N = 27,803). Results: Compared with the reference cohort, women with a HDP presented with higher mean blood pressure. Hypertension was present in a higher rate among women with a previous HDP from 15 years postpartum onwards. At 15 years postpartum (±age 45), one in five women with a history of a HDP suffer from hypertension. No differences in glucose homeostasis parameters or lipid levels were observed. Conclusions: Based on our analysis, it is not possible to point out a time point to commence screening for cardiovascular risk factors in women after a HDP. We recommend redirection of future research towards the development of a stepwise approach identifying the women with the highest cardiovascular risk

Discontinuation of reimbursement of benzodiazepines in the Netherlands: does it make a difference?

BACKGROUND: In an attempt to control chronic benzodiazepine use and its costs in the Netherlands, health care insurance reimbursement of this medication was stopped on January 1(st) 2009. This study investigates whether benzodiazepine prescriptions issued by general practitioners changed during the first two years following implementation of this regulation. METHODS: Registry study based on data from all benzodiazepine users derived from the Registration Network Groningen. This general practice-based research network collects longitudinal data on the primary care administered to about 30,000 patients. Based on the number of quarterly accumulated prescription days, a comparison was made of benzodiazepine prescriptions issued between 2007/2008 and 2009/2010. Also investigated was which type of user (i.e. short-term or long-term) showed the most change. RESULTS: Information on benzodiazepine prescriptions among 5,200 patients from 16 consecutive trimesters between 2007 and 2010 was available for analysis. A significant reduction in prescription days was observed between 2007/2008 and 2009/2010. Overall, an estimated 1.73 (CI:-1.94 to -1.53; p<0.001) days were less prescribed per trimester after the termination of reimbursement. In particular, short-term users experienced a reduction in prescription days in 2009 and 2010. The number of long-term users decreased by 2.3%, while the number of individuals that did not use increased by 4.2%. CONCLUSIONS: A total reduction of almost 14 prescription days was observed over eight trimesters after implementation of the regulation to terminate the reimbursement of benzodiazepines. Short-term users were mainly responsible for this reduction in prescription days in 2009 and 2010. Although long-term users did not alter their benzodiazepine use in 2009 and 2010, the number of long-term users decreased slightly

Hydrogen Bonding Controls Excited-State Decay of the Photoactive Yellow Protein Chromophore

International audienceWe have performed excited-state dynamics simulations of a Photoactive Yellow Protein chromophore analogue in water. The results of the simulations demonstrate that in water the chromophore predominantly undergoes single-bond photoisomerization, rather than double-bond photoisomerization. Despite opposite charge distributions in the chromophore, excited-state decay takes place very efficiently from both single- and double-bond twisted minima in water. Radiationless decay is facilitated by ultrafast solvent reorganization, which stabilizes both minima by specific hydrogen bond interactions. Changing the solvent to the slightly more viscous D(2)O leads to an increase of the excited-state lifetime. Together with previous simulations, the present results provide a complete picture of the effect of the protein on the photoisomerization of the chromophore in PYP: the positive guanidinium group of Arg52 favors double-bond isomerization over single-bond isomerization by lowering the barrier for double-bond isomerization, while the hydrogen bonds with Tyr42 and Glu46 enhance deactivation from the double-bond twisted minimum

Chromophore Protonation State Controls Photoswitching of the Fluoroprotein asFP595

Fluorescent proteins have been widely used as genetically encodable fusion tags for biological imaging. Recently, a new class of fluorescent proteins was discovered that can be reversibly light-switched between a fluorescent and a non-fluorescent state. Such proteins can not only provide nanoscale resolution in far-field fluorescence optical microscopy much below the diffraction limit, but also hold promise for other nanotechnological applications, such as optical data storage. To systematically exploit the potential of such photoswitchable proteins and to enable rational improvements to their properties requires a detailed understanding of the molecular switching mechanism, which is currently unknown. Here, we have studied the photoswitching mechanism of the reversibly switchable fluoroprotein asFP595 at the atomic level by multiconfigurational ab initio (CASSCF) calculations and QM/MM excited state molecular dynamics simulations with explicit surface hopping. Our simulations explain measured quantum yields and excited state lifetimes, and also predict the structures of the hitherto unknown intermediates and of the irreversibly fluorescent state. Further, we find that the proton distribution in the active site of the asFP595 controls the photochemical conversion pathways of the chromophore in the protein matrix. Accordingly, changes in the protonation state of the chromophore and some proximal amino acids lead to different photochemical states, which all turn out to be essential for the photoswitching mechanism. These photochemical states are (i) a neutral chromophore, which can trans-cis photoisomerize, (ii) an anionic chromophore, which rapidly undergoes radiationless decay after excitation, and (iii) a putative fluorescent zwitterionic chromophore. The overall stability of the different protonation states is controlled by the isomeric state of the chromophore. We finally propose that radiation-induced decarboxylation of the glutamic acid Glu215 blocks the proton transfer pathways that enable the deactivation of the zwitterionic chromophore and thus leads to irreversible fluorescence. We have identified the tight coupling of trans-cis isomerization and proton transfers in photoswitchable proteins to be essential for their function and propose a detailed underlying mechanism, which provides a comprehensive picture that explains the available experimental data. The structural similarity between asFP595 and other fluoroproteins of interest for imaging suggests that this coupling is a quite general mechanism for photoswitchable proteins. These insights can guide the rational design and optimization of photoswitchable proteins

Automatic Prediction of Recurrence of Major Cardiovascular Events: A Text Mining Study Using Chest X-Ray Reports

Background and Objective. Electronic health records (EHRs) contain free-text information on symptoms, diagnosis, treatment, and prognosis of diseases. However, this potential goldmine of health information cannot be easily accessed and used unless proper text mining techniques are applied. The aim of this project was to develop and evaluate a text mining pipeline in a multimodal learning architecture to demonstrate the value of medical text classification in chest radiograph reports for cardiovascular risk prediction. We sought to assess the integration of various text representation approaches and clinical structured data with state-of-the-art deep learning methods in the process of medical text mining. Methods. We used EHR data of patients included in the Second Manifestations of ARTerial disease (SMART) study. We propose a deep learning-based multimodal architecture for our text mining pipeline that integrates neural text representation with preprocessed clinical predictors for the prediction of recurrence of major cardiovascular events in cardiovascular patients. Text preprocessing, including cleaning and stemming, was first applied to filter out the unwanted texts from X-ray radiology reports. Thereafter, text representation methods were used to numerically represent unstructured radiology reports with vectors. Subsequently, these text representation methods were added to prediction models to assess their clinical relevance. In this step, we applied logistic regression, support vector machine (SVM), multilayer perceptron neural network, convolutional neural network, long short-term memory (LSTM), and bidirectional LSTM deep neural network (BiLSTM). Results. We performed various experiments to evaluate the added value of the text in the prediction of major cardiovascular events. The two main scenarios were the integration of radiology reports (1) with classical clinical predictors and (2) with only age and sex in the case of unavailable clinical predictors. In total, data of 5603 patients were used with 5-fold cross-validation to train the models. In the first scenario, the multimodal BiLSTM (MI-BiLSTM) model achieved an area under the curve (AUC) of 84.7%, misclassification rate of 14.3%, and F1 score of 83.8%. In this scenario, the SVM model, trained on clinical variables and bag-of-words representation, achieved the lowest misclassification rate of 12.2%. In the case of unavailable clinical predictors, the MI-BiLSTM model trained on radiology reports and demographic (age and sex) variables reached an AUC, F1 score, and misclassification rate of 74.5%, 70.8%, and 20.4%, respectively. Conclusions. Using the case study of routine care chest X-ray radiology reports, we demonstrated the clinical relevance of integrating text features and classical predictors in our text mining pipeline for cardiovascular risk prediction. The MI-BiLSTM model with word embedding representation appeared to have a desirable performance when trained on text data integrated with the clinical variables from the SMART study. Our results mined from chest X-ray reports showed that models using text data in addition to laboratory values outperform those using only known clinical predictors

Cationic and anionic impact on the electronic structure of liquid water.

Hydration shells around ions are crucial for many fundamental biological and chemical processes. Their local physicochemical properties are quite different from those of bulk water and hard to probe experimentally. We address this problem by combining soft X-ray spectroscopy using a liquid jet and molecular dynamics (MD) simulations together with ab initio electronic structure calculations to elucidate the water-ion interaction in a MgCl2 solution at the molecular level. Our results reveal that salt ions mainly affect the electronic properties of water molecules in close vicinity and that the oxygen K-edge X-ray emission spectrum of water molecules in the first solvation shell differs significantly from that of bulk water. Ion-specific effects are identified by fingerprint features in the water X-ray emission spectra. While Mg2+ ions cause a bathochromic shift of the water lone pair orbital, the 3p orbital of the Cl- ions causes an additional peak in the water emission spectrum at around 528 eV

Mortality in patients with Dupuytren’s disease in the first 5 years after diagnosis:a population-based survival analysis

Previous studies suggest that Dupuytren’s disease is associated with increased mortality, but most studies failed to account for important confounders. In this population-based cohort study, general practitioners’ (GP) data were linked to Statistics Netherlands to register all-cause and disease-specific mortality. Patients with Dupuytren’s disease were identified using the corresponding diagnosis code and assessing free-text fields from GP consultations. Multiple imputations were performed to estimate missing values of covariates, followed by 1:7 propensity score matching to balance cases with controls on confounding factors. A frailty proportional hazard model was used to compare mortality between both groups. Out of 209,966 individuals, 2561 patients with Dupuytren’s disease were identified and matched to at least four controls. After a median follow-up of 5 years, mortality was found to be actually reduced in patients with Dupuytren’s disease. There was no difference in mortality secondary to cancer or cardiovascular disease. Future studies with longer average follow-up using longitudinal data should clarify these associations in the longer term. Level of evidence: III.</p