Project description:BackgroundCardiovascular disease (CVD) risk prediction models are often used to identify individuals at high risk of CVD events. Providing preventive treatment to these individuals may then reduce the CVD burden at population level. However, different prediction models may predict different (sets of) CVD outcomes which may lead to variation in selection of high risk individuals. Here, it is investigated if the use of different prediction models may actually lead to different treatment recommendations in clinical practice.MethodThe exact definition of and the event types included in the predicted outcomes of four widely used CVD risk prediction models (ATP-III, Framingham (FRS), Pooled Cohort Equations (PCE) and SCORE) was determined according to ICD-10 codes. The models were applied to a Dutch population cohort (n = 18,137) to predict the 10-year CVD risks. Finally, treatment recommendations, based on predicted risks and the treatment threshold associated with each model, were investigated and compared across models.ResultsDue to the different definitions of predicted outcomes, the predicted risks varied widely, with an average 10-year CVD risk of 1.2% (ATP), 5.2% (FRS), 1.9% (PCE), and 0.7% (SCORE). Given the variation in predicted risks and recommended treatment thresholds, preventive drugs would be prescribed for 0.2%, 14.9%, 4.4%, and 2.0% of all individuals when using ATP, FRS, PCE and SCORE, respectively.ConclusionWidely used CVD prediction models vary substantially regarding their outcomes and associated absolute risk estimates. Consequently, absolute predicted 10-year risks from different prediction models cannot be compared directly. Furthermore, treatment decisions often depend on which prediction model is applied and its recommended risk threshold, introducing unwanted practice variation into risk-based preventive strategies for CVD.
Project description:Objective:In children many antiepileptic drugs (AEDs) are prescribed off-label due to a lack of well-designed randomized controlled trials (RCTs). We conducted a multicenter RCT in the Netherlands to compare levetiracetam and valproic acid as monotherapy in children with newly diagnosed epilepsy. After 2 years, we had to stop this investigator-initiated trial prematurely because the inclusion rate was too low. We analyzed the reasons for this failure, assessed the various issues involved in performing RCTs in children, and now give recommendations for future studies. Methods:A questionnaire was completed by all investigators involved in the study. It included questions about the motivation to participate and the perceived reasons for recruitment failure. We also studied literature about financial, logistic, legal, and ethical aspects of RCTs in children. Results:Main reasons for recruitment failure were overestimation of the number of eligible AED-naive children referred by general pediatricians; personal preferences of investigators for specific antiepileptic drugs; and the extensive administrative load due to extra regulations and guidelines for children. Fundraising for investigator-initiated trials is difficult and the majority of RCTs concerning AEDs are sponsored by pharmaceutical companies. Involving children requires balancing between protection and participation; the randomization procedure and obtaining informed consent are complex for both children and parents. Significance:Performing RCTs with AEDs in children is important but complicated by logistic, regulatory, legal, and ethical restrictions. Based on our recent experience, our advice to colleagues who are planning a similar trial would be to perform a feasibility pilot study; to set up intensive collaboration with referring pediatricians; to arrange support of a clinical trials unit and a local research nurse during the complete trial period; and to incorporate the possibility of extending the recruitment period. Major investments, both financially from governmental organizations and in time, are imperative for independent RCTs in children.
Project description:Antisense oligonucleotides can regulate gene expression in living cells. As such, they regulate cell function and division, and can modulate cellular responses to internal and external stresses and stimuli. Although encouraging results from preclinical and clinical studies have been obtained and significant progress has been made in developing these agents as drugs, they are not yet recognized as effective therapeutics. Several major hurdles remain to be overcome, including problems with efficacy, off-target effects, delivery and side effects. The lessons learned from antisense drug development can help in the development of other oligonucleotide-based therapeutics such as CpG oligonucleotides, RNAi and miRNA.
Project description:The classical complement pathway is initiated by the large (~800 kDa) and flexible multimeric C1 complex. Its catalytic function is triggered by the proteases hetero-tetramer C1r2s2, which is associated to the C1q sensing unit, a complex assembly of 18 chains built as a hexamer of heterotrimers. Initial pioneering studies gained insights into the main architectural principles of the C1 complex. A dissection strategy then provided the high-resolution structures of its main functional and/or structural building blocks, as well as structural details on some key protein-protein interactions. These past and current discoveries will be briefly summed up in order to address the question of what is still ill-defined. On a functional point of view, the main molecular determinants of C1 activation and its tight control will be delineated. The current perspective remains to decipher how C1 really works and is controlled in vivo, both in normal and pathological settings.
Project description:Early animal embryonic development requires maternal products that drive developmental processes prior to the activation of the zygotic genome at the mid-blastula transition. During and after this transition, maternal products may continue to act within incipient zygotic developmental programs. Mechanisms that control maternally-inherited products to spatially and temporally restrict developmental responses remain poorly understood, but necessarily depend on posttranscriptional regulation. We report the functional analysis and molecular identification of the zebrafish maternal-effect gene mission impossible (mis). Our studies suggest requirements for maternally-derived mis function in events that occur during gastrulation, including cell movement and the activation of some endodermal target genes. Cell transplantation experiments show that the cell movement defect is cell autonomous. Within the endoderm induction pathway, mis is not required for the activation of early zygotic genes, but is essential to implement nodal activity downstream of casanova/sox 32 but upstream of sox17 expression. Activation of nodal signaling in blastoderm explants shows that the requirement for mis function in endoderm gene induction is independent of the underlying yolk cell. Positional cloning of mis, including genetic rescue and complementation analysis, shows that it encodes the DEAH-box RNA helicase Dhx16, shown in other systems to act in RNA regulatory processes such as splicing and translational control. Analysis of a previously identified insertional dhx16 mutation shows that the zygotic component of this gene is also essential for embryonic viability. Our studies provide a striking example of the interweaving of maternal and zygotic genetic functions during the egg-to-embryo transition. Maternal RNA helicases have long been known to be involved in the development of the animal germ line, but our findings add to growing evidence that these factors may also control specific gene expression programs in somatic tissues.
Project description:Fatty acyl-AMP ligases (FAALs) channelize fatty acids towards biosynthesis of virulent lipids in mycobacteria and other pharmaceutically or ecologically important polyketides and lipopeptides in other microbes. They do so by bypassing the ubiquitous coenzyme A-dependent activation and rely on the acyl carrier protein-tethered 4'-phosphopantetheine (holo-ACP). The molecular basis of how FAALs strictly reject chemically identical and abundant acceptors like coenzyme A (CoA) and accept holo-ACP unlike other members of the ANL superfamily remains elusive. We show that FAALs have plugged the promiscuous canonical CoA-binding pockets and utilize highly selective alternative binding sites. These alternative pockets can distinguish adenosine 3',5'-bisphosphate-containing CoA from holo-ACP and thus FAALs can distinguish between CoA and holo-ACP. These exclusive features helped identify the omnipresence of FAAL-like proteins and their emergence in plants, fungi, and animals with unconventional domain organizations. The universal distribution of FAALs suggests that they are parallelly evolved with FACLs for ensuring a CoA-independent activation and redirection of fatty acids towards lipidic metabolites.
Project description:Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease, and is related to fatal and non-fatal liver, metabolic, and cardiovascular complications. Its non-invasive diagnosis and effective treatment remain an unmet clinical need. NAFLD is a heterogeneous disease that is most commonly present in the context of metabolic syndrome and obesity, but not uncommonly, may also be present without metabolic abnormalities and in subjects with normal body mass index. Therefore, a more specific pathophysiology-based subcategorization of fatty liver disease (FLD) is needed to better understand, diagnose, and treat patients with FLD. A precision medicine approach for FLD is expected to improve patient care, decrease long-term disease outcomes, and develop better-targeted, more effective treatments. We present herein a precision medicine approach for FLD based on our recently proposed subcategorization, which includes the metabolic-associated FLD (MAFLD) (i.e., obesity-associated FLD (OAFLD), sarcopenia-associated FLD (SAFLD, and lipodystrophy-associated FLD (LAFLD)), genetics-associated FLD (GAFLD), FLD of multiple/unknown causes (XAFLD), and combined causes of FLD (CAFLD) as well as advanced stage fibrotic FLD (FAFLD) and end-stage FLD (ESFLD) subcategories. These and other related advances, as a whole, are expected to enable not only improved patient care, quality of life, and long-term disease outcomes, but also a considerable reduction in healthcare system costs associated with FLD, along with more options for better-targeted, more effective treatments in the near future.
Project description:The generally unsupervised nature of autoencoder models implies that the main training metric is formulated as the error between input images and their corresponding reconstructions. Different reconstruction loss variations and latent space regularizations have been shown to improve model performances depending on the tasks to solve and to induce new desirable properties such as disentanglement. Nevertheless, measuring the success in, or enforcing properties by, the input pixel space is a challenging endeavour. In this work, we want to make use of the available data more efficiently and provide design choices to be considered in the recording or generation of future datasets to implicitly induce desirable properties during training. To this end, we propose a new sampling technique which matches semantically important parts of the image while randomizing the other parts, leading to salient feature extraction and a neglection of unimportant details. The proposed method can be combined with any existing reconstruction loss and the performance gain is superior to the triplet loss. We analyse the resulting properties on various datasets and show improvements on several computer vision tasks: illumination and unwanted features can be normalized or smoothed out and shadows are removed such that classification or other tasks work more reliably; a better invariances with respect to unwanted features is induced; the generalization capacities from synthetic to real images is improved, such that more of the semantics are preserved; uncertainty estimation is superior to Monte Carlo Dropout and an ensemble of models, particularly for datasets of higher visual complexity. Finally, classification accuracy by means of simple linear classifiers in the latent space is improved compared to the triplet loss. For each task, the improvements are highlighted on several datasets commonly used by the research community, as well as in automotive applications.