Von Hippel–Lindau Disease (VHL): Characteristic Lesions with Classic Imaging Findings

Von Hippel–Lindau disease (VHL) is a multisystem cancer syndrome caused by the inactivation of the VHL tumor suppressor gene and involves various organ systems including the central nervous system (CNS), endocrine system, and the kidneys. Tumors seen in patients with VHL disease can be benign or malignant and are usually multifocal, bilateral, and hypervascular in nature. As most lesions associated with VHL are asymptomatic initially, early diagnosis and the institution of an evidence-based surveillance protocol are of paramount importance. Screening, surveillance, and genetic counseling are key aspects in the management of patients diagnosed with VHL disease and often require a multidis-ciplinary approach and referral to specialized centers. This article will discuss the characteristic lesions seen with VHL disease, their diagnosis, screening protocols and management strategies, as well as an illustrative case to demonstrate the natural progression of the disease with classic imaging findings

PLAS-5k: Dataset of Protein-Ligand Affinities from Molecular Dynamics for Machine Learning Applications

Computational methods and recently modern machine learning methods have played a key role in structure-based drug design. Though several benchmarking datasets are available for machine learning applications in virtual screening, accurate prediction of binding affinity for a protein-ligand complex remains a major challenge. New datasets that allow for the development of models for predicting binding affinities better than the state-of-the-art scoring functions are important. For the first time, we have developed a dataset, PLAS-5k comprised of 5000 protein-ligand complexes chosen from PDB database. The dataset consists of binding affinities along with energy components like electrostatic, van der Waals, polar and non-polar solvation energy calculated from molecular dynamics simulations using MMPBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) method. The calculated binding affinities outperformed docking scores and showed a good correlation with the available experimental values. The availability of energy components may enable optimization of desired components during machine learning-based drug design. Further, OnionNet model has been retrained on PLAS-5k dataset and is provided as a baseline for the prediction of binding affinities

MUSIC COLLAB: An IoT and ML Based Solution for Remote Music Collaboration (Student Abstract)

Communication using mediums like video and audio is essential for a lot of professions. In this paper, interaction with real-time audio transmission is looked upon using the tools in the domains of IoT and machine learning. Two transport layer protocols - TCP and UDP are examined for audio transmission quality. Further, different RNN models are examined for their efficiency in predicting music and being used as a substitute in case of loss of packets during transmission

Convergence of Kirkwood–Buff Integrals of Ideal and Nonideal Aqueous Solutions Using Molecular Dynamics Simulations

The computation of Kirkwood–Buff integrals (KBIs) using molecular simulations of closed systems is challenging due to finite system-size effects. One of the problems involves the incorrect asymptotic behavior of the radial distribution function. Corrections to rectify such effects have been proposed in the literature. This study reports a systematic comparison of the proposed corrections (as given by Ganguly et al. <i>J. Chem. Theory Comput.</i> <b>2013</b>, <i>9</i>, 1347–1355 and Krüger et al. <i>J. Phys. Chem. Lett.</i> <b>2013</b>, <i>4</i>, 4–7) to assess the asymptotic behavior of the RDFs, the KBIs, as well as the estimation of thermodynamic quantities for ideal urea–water and nonideal modified-urea–water mixtures using molecular dynamics simulations. The results show that applying the KBI correction suggested by Krüger et al. on the RDF corrected with the Ganguly et al. correction (denoted as B-KBI) yields improved KBI convergence for the ideal and nonideal aqueous mixtures. Different averaging regions in the running KBIs (correlated or long-range) are assessed, and averaging over the correlated region for large system sizes is found to be robust toward the change in the degree of solvent nonideality and concentration, providing good estimates of thermodynamic quantities. The study provides new insights into improving the KBI convergence, the suitability of different averaging regions in KBIs to estimate thermodynamic properties, as well as the applicability of correction methods to achieve KBI convergence for nonideal aqueous binary mixtures

Current advances in bio-fabricated quantum dots emphasising the study of mechanisms to diversify their catalytic and biomedical applications

Quantum dots (QDs), owing to their single atom-like electronic structure due to quantum confinement, are often referred to as artificial atoms. This unique physical property results in the diverse functions exhibited by QDs. A wide array of applications have been achieved by the surface functionalization of QDs, resulting in exceptional optical, antimicrobial, catalytic, cytotoxic and enzyme inhibition properties. Ordinarily, traditionally prepared QDs are subjected to post synthesis functionalizationviaa variety of methods, such as ligand exchange or covalent and non-covalent conjugation. Nevertheless, solvent toxicity, combined with the high temperature and pressure conditions during the preparation of QDs and the low product yield due to multiple steps in the functionalization, limit their overall use. This has driven scientists to investigate the development of greener, environmental friendly and cost-effective methods that can circumvent the complexity and strenuousness associated with traditional processes of bio-functionalization. In this review, a detailed analysis of the methods to bio-prepare pre-functionalized QDs, with elucidated mechanisms, and their application in the areas of catalysis and biomedical applications has been conducted. The environmental and health and safety aspects of the bio-derived QDs have been briefly discussed to unveil the future of nano-commercialization.This publication was supported by the Qatar University internal grant no. IRCC-2020-013. The findings achieved herein are solely the responsibility of the authors. P.K. and C.S.T. acknowledge AOARD (Asian Office of Aerospace Research and Development) grant no. FA2386-19-1-4039. C.S.T. acknowledges Ramanujan fellowship and core research grant of SERB, India. CST acknowledges the funding received from the STARS project by MHRD, India.Scopu

Intrinsic Conformational Preferences and Interactions in α‑Synuclein Fibrils: Insights from Molecular Dynamics Simulations

Amyloid formation by the intrinsically disordered α-synuclein protein is the hallmark of Parkinson’s disease. We present atomistic Molecular Dynamics simulations of the core of α-synuclein using enhanced sampling techniques to describe the conformational and binding free energy landscapes of fragments implicated in fibril stabilization. The theoretical framework is derived to combine the free energy profiles of the fragments into the reaction free energy of a protein binding to a fibril. Our study shows that individual fragments in solution have a propensity toward attaining non-β conformations, indicating that in a fibril β-strands are stabilized by interactions with other strands. We show that most dimers of hydrogen-bonded fragments are unstable in solution, while hydrogen bonding stabilizes the collective binding of five fragments to the end of a fibril. Hydrophobic effects make further contributions to the stability of fibrils. This study is the first of its kind where structural and binding preferences of the five major fragments of the hydrophobic core of α-synuclein have been investigated. This approach improves sampling of intrinsically disordered proteins, provides information on the binding mechanism between the core sequences of α-synuclein, and enables the parametrization of coarse grained models