Synthesis and Semi-Synthesis of Alpha-Synuclein: Insight into the Chemical Complexity of Synucleinopathies

\ua9 2024 The Author(s). ChemBioChem published by Wiley-VCH GmbH. The chemical rules governing protein folding have intrigued generations of researchers for decades. With the advent of artificial intelligence (AI), prediction of protein structure has improved tremendously. However, there is still a level of analysis that is only possible through wet laboratory experiments, especially in respect to the investigation of the pathological effect of mutations and posttranslational modifications (PTMs) on proteins of interest. This requires the availability of pure peptides and proteins in sufficient quantities for biophysical, biochemical, and functional studies. In this context, chemical protein synthesis and semi-synthesis are powerful tools in protein research, which help to enlighten the role of protein modification in the physiology and pathology of proteins. A protein of high interest in the field of biomedicine is alpha-synuclein (aSyn), a protein deeply associated with several devastating neurodegenerative disorders such as Parkinson\u27s disease (PD), dementia with Lewy bodies (DLB), or multiple systems atrophy (MSA). Here, we describe several methods and pathways to synthesize native or modified aSyn, and discuss how these approaches enable us to address pathological mechanisms that may open novel perspectives for therapeutic intervention

Safe Parallelism: Compiler Analysis Techniques for Ada and OpenMP

There is a growing need to support parallel computation in Ada to cope with the performance requirements of the most advanced functionalities of safety-critical systems. In that regard, the use of parallel programming models is paramount to exploit the benefits of parallelism. Recent works motivate the use of OpenMP for being a de facto standard in high-performance computing for programming shared memory architectures. These works address two important aspects towards the introduction of OpenMP in Ada: the compatibility of the OpenMP syntax with the Ada language, and the interoperability of the OpenMP and the Ada runtimes, demonstrating that OpenMP complements and supports the structured parallelism approach of the tasklet model. This paper addresses a third fundamental aspect: functional safety from a compiler perspective. Particularly, it focuses on race conditions and considers the fine-grain and unstructured capabilities of OpenMP. Hereof, this paper presents a new compiler analysis technique that: (1) identifies potential race conditions in parallel Ada programs based on OpenMP or Ada tasks or both, and (2) provides solutions for the detected races.This work was supported by the Spanish Ministry of Science and Innovation under contract TIN2015-65316-P, and by the FCT (Portuguese Foundation for Science and Technology) within the CISTER Research Unit (CEC/04234).Peer ReviewedPostprint (author's final draft

The challenge of acute-stroke management: does telemedicine offer a solution?

<p><b>Background:</b> Several studies have described successful experiences with the use of telemedicine in acute stroke. The objective of this study was to assess the feasibility, acceptability, and treatment delivery reliability, of telemedicine systems for the clinical and radiological assessment, and management of acute-stroke patients.</p> <p><b>Summary of Review:</b> A systematic review of the literature was carried out. Studies were included if they met the following criteria: (1) study population included participants with a diagnosis of suspected acute stroke, (2) intervention included the use of telemedicine systems to aid assessment, diagnosis, or treatment in acute stroke, and (3) outcomes measured related to feasibility in clinical practice, acceptability to patients, carers, and staff, reliability of telemedicine systems, and effectiveness in delivering treatment, especially tissue plasminogen activator (tPA). Overall, 17 relevant non-randomised studies reported that telemedicine systems were feasible and acceptable. Interrater reliability was excellent for global clinical assessments and decisions on radiological exclusion criteria although agreement for individual assessment items was more variable. Telemedicine systems were associated with increased use of tPA.</p> <p><b>Conclusion:</b> Although there is limited reliable evidence, observational studies have indicated that telemedicine systems can be feasible, acceptable, and reliable in acute-stroke management. In addition, telemedicine consultations were associated with improved delivery of tPA.</p&gt

Intrinsic ripples in graphene

The stability of two-dimensional (2D) layers and membranes is subject of a long standing theoretical debate. According to the so called Mermin-Wagner theorem, long wavelength fluctuations destroy the long-range order for 2D crystals. Similarly, 2D membranes embedded in a 3D space have a tendency to be crumpled. These dangerous fluctuations can, however, be suppressed by anharmonic coupling between bending and stretching modes making that a two-dimensional membrane can exist but should present strong height fluctuations. The discovery of graphene, the first truly 2D crystal and the recent experimental observation of ripples in freely hanging graphene makes these issues especially important. Beside the academic interest, understanding the mechanisms of stability of graphene is crucial for understanding electronic transport in this material that is attracting so much interest for its unusual Dirac spectrum and electronic properties. Here we address the nature of these height fluctuations by means of straightforward atomistic Monte Carlo simulations based on a very accurate many-body interatomic potential for carbon. We find that ripples spontaneously appear due to thermal fluctuations with a size distribution peaked around 70 \AA which is compatible with experimental findings (50-100 \AA) but not with the current understanding of stability of flexible membranes. This unexpected result seems to be due to the multiplicity of chemical bonding in carbon.Comment: 14 pages, 6 figure

STM Spectroscopy of ultra-flat graphene on hexagonal boron nitride

Graphene has demonstrated great promise for future electronics technology as well as fundamental physics applications because of its linear energy-momentum dispersion relations which cross at the Dirac point. However, accessing the physics of the low density region at the Dirac point has been difficult because of the presence of disorder which leaves the graphene with local microscopic electron and hole puddles, resulting in a finite density of carriers even at the charge neutrality point. Efforts have been made to reduce the disorder by suspending graphene, leading to fabrication challenges and delicate devices which make local spectroscopic measurements difficult. Recently, it has been shown that placing graphene on hexagonal boron nitride (hBN) yields improved device performance. In this letter, we use scanning tunneling microscopy to show that graphene conforms to hBN, as evidenced by the presence of Moire patterns in the topographic images. However, contrary to recent predictions, this conformation does not lead to a sizable band gap due to the misalignment of the lattices. Moreover, local spectroscopy measurements demonstrate that the electron-hole charge fluctuations are reduced by two orders of magnitude as compared to those on silicon oxide. This leads to charge fluctuations which are as small as in suspended graphene, opening up Dirac point physics to more diverse experiments than are possible on freestanding devices.Comment: Nature Materials advance online publication 13/02/201

A Weak Neutralizing Antibody Response to Hepatitis C Virus Envelope Glycoprotein Enhances Virus Infection

We have completed a phase 1 safety and immunogenicity trial with hepatitis C virus (HCV) envelope glycoproteins, E1 and E2, with MF59 adjuvant as a candidate vaccine. Neutralizing activity to HCV genotype 1a was detected in approximately 25% of the vaccinee sera. In this study, we evaluated vaccinee sera from poor responders as a potential source of antibody dependent enhancement (ADE) of HCV infection. Sera with poor neutralizing activity enhanced cell culture grown HCV genotype 1a or 2a, and surrogate VSV/HCV pseudotype infection titer, in a dilution dependent manner. Surrogate pseudotypes generated from individual HCV glycoproteins suggested that antibody to the E2 glycoprotein; but not the E1 glycoprotein, was the principle target for enhancing infection. Antibody specific to FcRII expressed on the hepatic cell surface or to the Fc portion of Ig blocked enhancement of HCV infection by vaccinee sera. Together, the results from in vitro studies suggested that enhancement of viral infectivity may occur in the absence of a strong antibody response to HCV envelope glycoproteins

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur