scholarly journals Accelerated brain aging and cerebral blood flow reduction in persons with HIV

2021 ◽  
Author(s):  
Kalen J Petersen ◽  
Nicholas Metcalf ◽  
Sarah Cooley ◽  
Dimitre Tomov ◽  
Florin Vaida ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Brain Aging ◽  
Learning Algorithm ◽  
Psychomotor Speed ◽  
Deep Learning Algorithm ◽  
Blood Flow Reduction ◽  
Brain Structure And Function ◽  
N 93 ◽  
And Function

Abstract Background Persons with HIV (PWH) are characterized by altered brain structure and function. As they attain normal lifespans, it has become crucial to understand potential interactions between HIV and aging. However, it remains unclear how brain aging varies with viral load (VL). Methods In this study, we compare MRI biomarkers amongst PWH with undetectable VL (UVL; ≤50 genomic copies/ml; n=230), PWH with detectable VL (DVL; >50 copies/ml; n=93), and HIV uninfected (HIV-) controls (n=206). To quantify gray matter cerebral blood flow (CBF), we utilized arterial spin labeling. To measure structural aging, we used a publicly available deep learning algorithm to estimate brain age from T1-weighted MRI. Cognitive performance was measured using a neuropsychological battery covering five domains. Results Associations between age and CBF varied with VL. Older PWH with DVL had reduced CBF vs. PWH with UVL (p=0.02). Structurally predicted brain aging was accelerated in PWH vs. HIV- controls regardless of VL (p<0.001). Overall, PWH had impaired learning, executive function, psychomotor speed, and language compared to HIV- controls. Structural brain aging was associated with reduced psychomotor speed (p<0.001). Conclusions Brain aging in HIV is multifaceted. CBF depends on age and current VL, and is improved by medication adherence. By contrast, structural aging is an indicator of cognitive function and reflects serostatus rather than current VL.

Patterns of cerebral blood flow reduction in patients with ischemic leukoaraiosis

Neurology ◽  
2002 ◽  
Vol 59 (3) ◽  
pp. 321-326 ◽  
Author(s):  
M. O'Sullivan ◽  
D. J. Lythgoe ◽  
A. C. Pereira ◽  
P. E. Summers ◽  
J. M. Jarosz ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Flow Reduction ◽  
Blood Flow Reduction

scholarly journals Increased TRPM4 Activity in Cerebral Artery Myocytes Contributes to Cerebral Blood Flow Reduction After Subarachnoid Hemorrhage in Rats

2019 ◽  
Vol 16 (3) ◽  
pp. 901-911
Author(s):  
Yi Gong ◽  
Ming-yue Du ◽  
Hua-lin Yu ◽  
Zhi-yong Yang ◽  
Yu-jin Li ◽  
...  
Keyword(s):  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Subarachnoid Hemorrhage ◽  
Cerebral Artery ◽  
Flow Reduction ◽  
Blood Flow Reduction

scholarly journals Noninvasive Optical Monitoring of Cerebral Blood Flow and EEG Spectral Responses after Severe Traumatic Brain Injury: A Case Report

2021 ◽  
Vol 11 (8) ◽  
pp. 1093
Author(s):  
Chien-Sing Poon ◽  
Benjamin Rinehart ◽  
Dharminder S. Langri ◽  
Timothy M. Rambo ◽  
Aaron J. Miller ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Brain Injury ◽  
Brain Activity ◽  
Correlation Spectroscopy ◽  
Secondary Brain Injury ◽  
Diffuse Correlation Spectroscopy ◽  
Flow Monitoring ◽  
And Function

Survivors of severe brain injury may require care in a neurointensive care unit (neuro-ICU), where the brain is vulnerable to secondary brain injury. Thus, there is a need for noninvasive, bedside, continuous cerebral blood flow monitoring approaches in the neuro-ICU. Our goal is to address this need through combined measurements of EEG and functional optical spectroscopy (EEG-Optical) instrumentation and analysis to provide a complementary fusion of data about brain activity and function. We utilized the diffuse correlation spectroscopy method for assessing cerebral blood flow at the neuro-ICU in a patient with traumatic brain injury. The present case demonstrates the feasibility of continuous recording of noninvasive cerebral blood flow transients that correlated well with the gold-standard invasive measurements and with the frequency content changes in the EEG data.

scholarly journals Automatic and unbiased segmentation and quantification of myofibers in skeletal muscle

2021 ◽  
Author(s):  
Ariel Waisman ◽  
Alessandra Norris ◽  
Martín Elías Costa ◽  
Daniel Kopinke
Keyword(s):  
Skeletal Muscle ◽  
Primary Cilia ◽  
Learning Algorithm ◽  
Fixed Tissue ◽  
Deep Learning Algorithm ◽  
Ability To Regenerate ◽  
Murine Skeletal Muscle ◽  
And Function ◽  
Injury Models ◽  
Imagej Plugin

ABSTRACTSkeletal muscle has the remarkable ability to regenerate. However, with age and disease muscle strength and function decline. Myofiber size, which is affected by injury and disease, is a critical measurement to assess muscle health. Here, we test and apply Cellpose, a recently developed deep learning algorithm, to automatically segment myofibers within murine skeletal muscle. We first show that tissue fixation is necessary to preserve cellular structures such as primary cilia, small cellular antennae, and adipocyte lipid droplets. However, fixation generates heterogeneous myofiber labeling, which impedes intensity-based segmentation. We demonstrate that Cellpose efficiently delineates thousands of individual myofibers outlined by a variety of markers, even within fixed tissue with highly uneven myofiber staining. We created a novel ImageJ plugin (LabelsToRois) that allows processing multiple Cellpose segmentation images in batch. The plugin also contains a semi-automatic erosion function to correct for the area bias introduced by the different stainings, identifying myofibers as accurately as human experts. We successfully applied our segmentation pipeline to uncover myofiber size differences between two different muscle injury models, cardiotoxin and glycerol. Thus, Cellpose combined with LabelsToRois allows for fast, unbiased, and reproducible myofiber quantification for a variety of staining and fixation conditions.

PVsiRNAPred: Prediction of plant exclusive virus-derived small interfering RNAs by deep convolutional neural network

2019 ◽  
Vol 17 (06) ◽  
pp. 1950039
Author(s):  
Bifang He ◽  
Jian Huang ◽  
Heng Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Permutation Test ◽  
Deep Convolutional Neural Network ◽  
Small Interfering Rnas ◽  
Sequence Composition ◽  
Deep Learning Algorithm ◽  
Testing Dataset ◽  
And Function

Plant exclusive virus-derived small interfering RNAs (vsiRNAs) regulate various biological processes, especially important in antiviral immunity. The identification of plant vsiRNAs is important for understanding the biogenesis and function mechanisms of vsiRNAs and further developing anti-viral plants. In this study, we extracted plant vsiRNA sequences from the PVsiRNAdb database. We then utilized deep convolutional neural network (CNN) to develop a deep learning algorithm for predicting plant vsiRNAs based on vsiRNA sequence composition, known as PVsiRNAPred. The key part of PVsiRNAPred is the CNN module, which automatically learns hierarchical representations of vsiRNA sequences related to vsiRNA profiles in plants. When evaluated using an independent testing dataset, the accuracy of the model was 65.70%, which was higher than those of five conventional machine learning method-based classifiers. In addition, PVsiRNAPred obtained a sensitivity of 67.11%, specificity of 64.26% and Matthews correlation coefficient (MCC) of 0.31, and the area under the receiver operating characteristic (ROC) curve (AUC) of PVsiRNAPred was 0.71 in the independent test. The permutation test with 1000 shuffles resulted in a [Formula: see text] value [Formula: see text]. The above results reveal that PVsiRNAPred has favorable generalization capabilities. We hope PVsiRNAPred, the first bioinformatics algorithm for predicting plant vsiRNAs, will allow efficient discovery of new vsiRNAs.

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