Patch-wise brain age longitudinal reliability (2020)

We recently introduced a patch‐wise technique to estimate brain age from anatomical T1‐weighted magnetic resonance imaging (T1w MRI) data. Here, we sought to assess its longitudinal reliability by leveraging a unique dataset of 99 longitudinal MRI scans from a single, cognitively healthy volunteer acquired over a period of 17 years (aged 29–46 years) at multiple sites. We built a robust patch‐wise brain age estimation framework on the basis of 100 cognitively healthy individuals from the MindBoggle dataset (aged 19–61 years) using the Desikan‐Killiany‐Tourville atlas, then applied the model to the volunteer dataset. The results show a high prediction accuracy on the independent test set (R2 = .94, mean absolute error of 0.63 years) and no statistically significant difference between manufacturers, suggesting that the patch‐wise technique has high reliability and can be used for longitudinal multi‐centric studies

Evaluation of the Nutritional Status in Children Admitted to the Neurology Ward of Mofid Children’s Hospital

ObjectiveMalnutrition is commonly considered as an important risk factor that can produce a negative influence on the prognosis of patients with chronic neurological diseases. We aimed to evaluate the nutritional status of patients admitted to the neurology ward of Mofid children's hospital via subjective and objective methods.Materials & Methods61 children (2-6 years of age) who were consecutively hospitalized at the neurology ward between January and March 2008 underwent objective (weight, height, mid upper arm circumference- MUAC- and triceps skinfold thickness- TSF) and subjective nutritional assessment.ResultsThe result showed that 42.6%, 37.7% and 25.9% of patients were consecutively wasted, underweight and stunted. The z- Scores for TSF and MUAC were below -1 in 32.7% and 41.8% of the patients,  consecutively. According to subjective Assessments (SGNA), 52.7% were malnourished. The prevalence of malnutrition was not significantly different between sex or diagnosis groups.Conclusionmalnutrition is of high prevalence in patients with neurologic diseases. Regular assessment and timely nutritional support may improve the situation.

Clinical Application of Machine Learning Models for Brain Imaging in Epilepsy: A Review

Epilepsy is a common neurological disorder characterized by recurrent and disabling seizures. An increasing number of clinical and experimental applications of machine learning (ML) methods for epilepsy and other neurological and psychiatric disorders are available. ML methods have the potential to provide a reliable and optimal performance for clinical diagnoses, prediction, and personalized medicine by using mathematical algorithms and computational approaches. There are now several applications of ML for epilepsy, including neuroimaging analyses. For precise and reliable clinical applications in epilepsy and neuroimaging, the diverse ML methodologies should be examined and validated. We review the clinical applications of ML models for brain imaging in epilepsy obtained from a PubMed database search in February 2021. We first present an overview of typical neuroimaging modalities and ML models used in the epilepsy studies and then focus on the existing applications of ML models for brain imaging in epilepsy based on the following clinical aspects: (i) distinguishing individuals with epilepsy from healthy controls, (ii) lateralization of the temporal lobe epilepsy focus, (iii) the identification of epileptogenic foci, (iv) the prediction of clinical outcomes, and (v) brain-age prediction. We address the practical problems and challenges described in the literature and suggest some future research directions

Braak neurofibrillary tangle staging prediction from in vivo MRI metrics

INTRODUCTION: Alzheimer’s disease (AD) diagnosis requires postmortem visualization of amyloid and tau deposits. As brain atrophy can provide assessment of consequent neurodegeneration, our objective was to predict postmortem neurofibrillary tangles (NFT) from in vivo MRI measurements. METHODS: All participants with neuroimaging and neuropathological data from the Alzheimer’s Disease Neuroimaging Initiative, the National Alzheimer’s Coordinating Center and the Rush Memory and Aging Project were selected (n=186). 232 variables were extracted from last MRI before death using FreeSurfer. Nonparametric correlation analysis and multivariable support vector machine classification were performed to provide a predictive model of Braak NFT staging. RESULTS: We demonstrated that 59 of our MRI variables, mostly temporal lobe structures, were significantly associated with Braak NFT stages (p<.005). We obtained a 62.4% correct classification rate for discrimination between transentorhinal, limbic and isocortical groups. DISCUSSION: Structural neuroimaging may therefore be considered as a potential biomarker for early detection of AD-associated neurofibrillary degeneration

DNA Bound Avicel Network: The Beginnings of a Self-Healing Material

Self-healing materials could potentially provide many improvements to engineering projects, including reduced maintenance and cost, and increased lifespan. It is desired to create a self-healing material proof of concept, which can then be altered for eventual application to the surfaces of small satellites with the goal of increasing material lifetimes. The intrinsic properties and abilities of DNA base pairing will be studied as a first test of proof of concept. The exploratory research reported in this short communication utilizes oxidation of small (50µm) particles of Avicel using TEMPO, followed by activation of Avicel particles via an EDC (1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride) reaction. The cellulose prepared in this manner will next be reacted with short sequences of single stranded DNA (oligonucleotides) with the cellulose, although this has not yet been achieved. Complementary strands will be bound to a second aliquot of particles. The particles will be combined to test if they hybridize (bind in a directed manner), resulting in a network of Avicel particles glued together by DNA. A Malvern wet particle size instrument was used to determine zeta potential of the cellulose particles, and in the future will be used to compare the size of particles before and after chemical alterations. Colored nanoparticles will be used to dye the individual aliquots of the derivatized celluloses so that a change in color may be observed when cellulose derivatized with complimentary strands of DNA are brought in contact with each other. After washing to remove unbound particles, a change in color would be expected to occur, thus indicating binding. While this is a work in progress, key developments at this point are the experimental design, development of research hypotheses, and successful oxidation of cellulose. These experiments are part of a longer term project that is studying whether intrinsic self-healing materials are possible. Alterations in the particle and in binding sequences to be placed on the particles have potential for automobile, airline, satellites and spacecraft, military, and healthcare applications, where self-healing principles at a nano-scale would enable micro-damage to be identified and healing processes to occur

Deep Learning for Brain Age Estimation: A Systematic Review

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning model

System-wide transcriptome damage and tissue identity loss in COVID-19 patients

The molecular mechanisms underlying the clinical manifestations of coronavirus disease 2019 (COVID-19), and what distinguishes them from common seasonal influenza virus and other lung injury states such as acute respiratory distress syndrome, remain poorly understood. To address these challenges, we combine transcriptional profiling of 646 clinical nasopharyngeal swabs and 39 patient autopsy tissues to define body-wide transcriptome changes in response to COVID-19. We then match these data with spatial protein and expression profiling across 357 tissue sections from 16 representative patient lung samples and identify tissue-compartment-specific damage wrought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, evident as a function of varying viral loads during the clinical course of infection and tissue-type-specific expression states. Overall, our findings reveal a systemic disruption of canonical cellular and transcriptional pathways across all tissues, which can inform subsequent studies to combat the mortality of COVID-19 and to better understand the molecular dynamics of lethal SARS-CoV-2 and other respiratory infections., • Across all organs, fibroblast, and immune cell populations increase in COVID-19 patients • Organ-specific cell types and functional markers are lost in all COVID-19 tissue types • Lung compartment identity loss correlates with SARS-CoV-2 viral loads • COVID-19 uniquely disrupts co-occurrence cell type clusters (different from IAV/ARDS) , Park et al. report system-wide transcriptome damage and tissue identity loss wrought by SARS-CoV-2, influenza, and bacterial infection across multiple organs (heart, liver, lung, kidney, and lymph nodes) and provide a spatiotemporal landscape of COVID-19 in the lung

Ozone as Oxidant for Biomass Pretreatment and Nanocellulose Production

Ozone gas is a robust and easy to generate oxidant with proven efficacy in increasing enzymatic digestibility of lignocellulosic material. The motivation behind this study was a) elucidate the manner in which a packed column simulating a bale of biomass is delignified by ozone treatment b) develop a method to modify cellulose surfaces using ozone for production of value added byproducts from pulp. This information will be of value in determining ozone\u27s potential as a versatile and portable oxidant for cellulosic ethanol and value added by-product production. Pretreatment of compacted switchgrass with ozone was carried out in a packed bed reactor. The material density and particle size was similar to that in a bale of biomass. Pretreatment was conducted using low ozone concentrations feasible on a commercial scale. Kinetic analysis demonstrated that lower ozone concentrations (\u3c10 mg/L) combined with higher volumetric flow rates (\u3e3 L/min) are necessary to achieve a consistent increase in digestibility. A 58% increase in enzyme digestibility of the cellulose in switchgrass was achieved after treatment. Ozone transport in the reactor was modeled using combined reaction, diffusion and convection. The low effective reaction rate of 6.5e-4 s-1 and convective flow in the reactor were found to be the limiting factors. While ozone gas is an efficient oxidizing agent, ozone alone is relatively ineffective in oxidizing cellulose surfaces. The second study is motivated by the knowledge that radicals, such as hydroxyls, formed as a result of ozone reaction with the moisture in the biomass, are the main oxidants reacting with lignin. We demonstrate that lignin monomers formed as byproducts of pulping or bioprocessing of lignocellulosic biomass are an effective enhancer of ozone for oxidizing cellulose surfaces. This is demonstrated in the production of cellulose nanofibers (CNF) since the CNF films made by this method have carboxylate content similar to conventional, commercially carboxylated CNF prepared by TEMPO-mediated oxidation