56 vs. 0.27). SBP estimated from central PWV undercuts the IEEE mean absolute deviation threshold of 5 mmHg, significantly lower than peripheral PWV or PAT (4.2 vs. 7.1 vs. 10.1 mmHg).
 
  Cardiac IVC onset signaled in carotid distension waveforms enables PAT segmentation to obtain unbiased vascular pulse transit time. Corresponding PWV estimates provide the basis for single-site assessment of central arterial stiffness, confirmed by significant correlations with Bramwell-Hill PWV and SBP.
 
  In a small-scale cohort, we present proof-of-concept for a novel method to estimate central PWV and BP, bearing potential to improve the practicality of cardiovascular risk assessment in clinical routines.
 In a small-scale cohort, we present proof-of-concept for a novel method to estimate central PWV and BP, bearing potential to improve the practicality of cardiovascular risk assessment in clinical routines.Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients with chronic treatment-resistant schizophrenia (TRS-SCZ), commonly estimated to be around one third of all cases. However, clinicians sometimes delay the initiation of this drug because of its adverse side-effects. Therefore, identification of predictive biological markers of clozapine response are extremely valuable to aid on-time initiation of treatment. In this study, we develop a machine learning (ML) algorithm based on pre-treatment electroencephalogram (EEG) data sets to predict response to clozapine treatment in 57 TRS-SCZs, where the treatment outcome, after at least one-year follow-up is determined using the positive and negative syndrome scale (PANSS). The ML algorithm has three steps 1) a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on the EEG signals to extract source waveforms from 30 specified brain regions. 2) An effective connectivity measure named symbolic transfer entropy (STE) is applied to the source waveforms. 3) A ML algorithm is applied to the STE matrix to determine whether a set of features can be found to discriminate most-responder (MR) SCZ patients from least-responder (LR) ones. The findings of this study reveal that STE features can achieve an accuracy of 95.83%. This finding implies that analysis of pre-treatment EEG could contribute to our ability to distinguish MR from LR SCZs, and that the source STE matrix may prove to be a promising tool for the prediction of the clinical response to clozapine.Insights into the conformational organization and dynamics of proteins complexes at membranes is essential for our mechanistic understanding of numerous key biological processes. Here, we introduce graphene-induced energy transfer (GIET) to probe axial orientation of arrested macromolecules at lipid monolayers. Based on a calibrated distance-dependent efficiency within a dynamic range of 25 nm, we analyzed the conformational organization of proteins and complexes involved in tethering and fusion at the lysosome-like yeast vacuole. We observed that the membrane-anchored Rab7-like GTPase Ypt7 shows conformational reorganization upon interactions with effector proteins. Ensemble and time-resolved single-molecule GIET experiments revealed that the HOPS tethering complex, when recruited via Ypt7 to membranes, is dynamically alternating between a 'closed' and an 'open' conformation, with the latter possibly interacting with incoming vesicles. Our work highlights GIET as a unique spectroscopic ruler to reveal the axial orientation and dynamics of macromolecular complexes at biological membranes with sub-nanometer resolution.Mutations in the fukutin-related protein (FKRP) cause Walker-Warburg syndrome (WWS), a severe form of congenital muscular dystrophy. Here, we established a WWS human induced pluripotent stem cell-derived myogenic model that recapitulates hallmarks of WWS pathology. We used this model to investigate the therapeutic effect of metabolites of the pentose phosphate pathway in human WWS. We show that functional recovery of WWS myotubes is promoted not only by ribitol but also by its precursor ribose. Moreover, we found that the combination of each of these metabolites with NAD+ results in a synergistic effect, as demonstrated by rescue of α-dystroglycan glycosylation and laminin binding capacity. Mechanistically, we found that FKRP residual enzymatic capacity, characteristic of many recessive FKRP mutations, is required for rescue as supported by functional and structural mutational analyses. These findings provide the rationale for testing ribose/ribitol in combination with NAD+ to treat WWS and other diseases associated with FKRP mutations.The risk of developing cancer is correlated with body size and lifespan within species. Between species, however, there is no correlation between cancer and either body size or lifespan, indicating that large, long-lived species have evolved enhanced cancer protection mechanisms. Elephants and their relatives (Proboscideans) are a particularly interesting lineage for the exploration of mechanisms underlying the evolution of augmented cancer resistance because they evolved large bodies recently within a clade of smaller-bodied species (Afrotherians).  https://www.selleckchem.com/products/go-6983.html  Here, we explore the contribution of gene duplication to body size and cancer risk in Afrotherians. Unexpectedly, we found that tumor suppressor duplication was pervasive in Afrotherian genomes, rather than restricted to Proboscideans. Proboscideans, however, have duplicates in unique pathways that may underlie some aspects of their remarkable anti-cancer cell biology. These data suggest that duplication of tumor suppressor genes facilitated the evolution of increased body size by compensating for decreasing intrinsic cancer risk.Although atrial fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk factors, targeting an individualized treatment of AF demands a large amount of patient data to identify specific patterns. Artificial intelligence (AI) algorithms are particularly well suited for treating high-dimensional data, predicting outcomes, and eventually, optimizing strategies for patient management. The analysis of large patient samples combining different sources of information such as blood biomarkers, electrical signals, and medical images opens a new paradigm for improving diagnostic algorithms. In this review, we summarize suitable AI techniques for this purpose. In particular, we describe potential applications for understanding the structural and functional bases of the disease, as well as for improving early noninvasive diagnosis, developing more efficient therapies, and predicting long-term clinical outcomes of patients with AF.