243 research outputs found
Increased apoptosis of neutrophils in induced sputum of COPD patients
SummaryAimThe aim of the current study was to evaluate apoptosis in induced sputum neutrophils and to investigate the relationship between the number of apoptotic cells and clinical parameters in COPD patients.MethodsTwenty-four COPD ex-smoker patients and 10 healthy controls were included in the study. All subjects underwent clinical evaluation and sputum induction. Sputum cell in situ apoptosis was identified using white light microscopy and TUNEL assay technique. Apoptosis of neutrophils obtained by sputum induction was expressed as apoptotic rate (AR=percentage of apoptotic neutrophils over the number of neutrophils measured).ResultsTUNEL assay revealed statistically significant higher AR in COPD patients than controls (p=0.004). Patients with FEV1<50%pred had significantly higher median (IQR) AR (%) compared to patients with FEV1≥50% [26.3 (16–29) vs 13.1 (8.6–21), p=0.01]. No significant association was found between the number of apoptotic cells and age, symptoms or medication used.ConclusionThe significantly increased apoptotic rate of neutrophils that were found in COPD patients with advanced disease compared to controls might reflect either a deregulation of apoptosis of neutrophils or, a reduced clearance of apoptotic neutrophils from the airways. The pathophysiologic significance of the observed phenomenon has to be further explored
Code Quality Evaluation Methodology Using The ISO/IEC 9126 Standard
This work proposes a methodology for source code quality and static behaviour
evaluation of a software system, based on the standard ISO/IEC-9126. It uses
elements automatically derived from source code enhanced with expert knowledge
in the form of quality characteristic rankings, allowing software engineers to
assign weights to source code attributes. It is flexible in terms of the set of
metrics and source code attributes employed, even in terms of the ISO/IEC-9126
characteristics to be assessed. We applied the methodology to two case studies,
involving five open source and one proprietary system. Results demonstrated
that the methodology can capture software quality trends and express expert
perceptions concerning system quality in a quantitative and systematic manner.Comment: 20 pages, 14 figure
Tractography-Based Parcellation of Cerebellar Dentate Nuclei via a Deep Nonnegative Matrix Factorization Clustering Method
As the largest human cerebellar nucleus, the dentate nucleus (DN) functions
significantly in the communication between the cerebellum and the rest of the
brain. Structural connectivity-based parcellation has the potential to reveal
the topography of the DN and enable the study of its subregions. In this paper,
we investigate a deep nonnegative matrix factorization clustering method
(DNMFC) for parcellation of the human DN based on its structural connectivity
using diffusion MRI tractography. We propose to describe the connectivity of
the DN using a set of curated tractography fiber clusters within the
cerebellum. Experiments are conducted on the diffusion MRI data of 50 healthy
adults from the Human Connectome Project. In comparison with state-of-the-art
clustering methods, DN parcellations resulting from DNMFC show better quality
and consistency of parcels across subjects
Asymmetric projections of the arcuate fasciculus to the temporal cortex underlie lateralized language function in the human brain
The arcuate fasciculus (AF) in the human brain has asymmetric structural properties. However, the topographic organization of the asymmetric AF projections to the cortex and its relevance to cortical function remain unclear. Here we mapped the posterior projections of the human AF in the inferior parietal and lateral temporal cortices using surface-based structural connectivity analysis based on diffusion MRI and investigated their hemispheric differences. We then performed the cross-modal comparison with functional connectivity based on resting-state functional MRI (fMRI) and task-related cortical activation based on fMRI using a semantic classification task of single words. Structural connectivity analysis showed that the left AF connecting to Broca's area predominantly projected in the lateral temporal cortex extending from the posterior superior temporal gyrus to the mid part of the superior temporal sulcus and the middle temporal gyrus, whereas the right AF connecting to the right homolog of Broca's area predominantly projected to the inferior parietal cortex extending from the mid part of the supramarginal gyrus to the anterior part of the angular gyrus. The left-lateralized projection regions of the AF in the left temporal cortex had asymmetric functional connectivity with Broca's area, indicating structure-function concordance through the AF. During the language task, left-lateralized cortical activation was observed. Among them, the brain responses in the temporal cortex and Broca's area that were connected through the left-lateralized AF pathway were specifically correlated across subjects. These results suggest that the human left AF, which structurally and functionally connects the mid temporal cortex and Broca's area in asymmetrical fashion, coordinates the cortical activity in these remote cortices during a semantic decision task. The unique feature of the left AF is discussed in the context of the human capacity for language.National Institutes of Health (U.S.) (Grant R01NS069696)National Institutes of Health (U.S.) (Grant P41EB015896)National Institutes of Health (U.S.) (Grant S10ODRR031599)National Institutes of Health (U.S.) (Grant S10RR021110)National Science Foundation (U.S.) (Grant NFS-DMS-1042134)Uehara Memorial Foundation (Fellowship)Society of Nuclear Medicine and Molecular Imaging (Wagner-Torizuka Fellowship)United States. Dept. of Energy (Grant DE-SC0008430
TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation
Diffusion MRI tractography parcellation classifies streamlines into
anatomical fiber tracts to enable quantification and visualization for clinical
and scientific applications. Current tractography parcellation methods rely
heavily on registration, but registration inaccuracies can affect parcellation
and the computational cost of registration is high for large-scale datasets.
Recently, deep-learning-based methods have been proposed for tractography
parcellation using various types of representations for streamlines. However,
these methods only focus on the information from a single streamline, ignoring
geometric relationships between the streamlines in the brain. We propose
TractCloud, a registration-free framework that performs whole-brain
tractography parcellation directly in individual subject space. We propose a
novel, learnable, local-global streamline representation that leverages
information from neighboring and whole-brain streamlines to describe the local
anatomy and global pose of the brain. We train our framework on a large-scale
labeled tractography dataset, which we augment by applying synthetic transforms
including rotation, scaling, and translations. We test our framework on five
independently acquired datasets across populations and health conditions.
TractCloud significantly outperforms several state-of-the-art methods on all
testing datasets. TractCloud achieves efficient and consistent whole-brain
white matter parcellation across the lifespan (from neonates to elderly
subjects, including brain tumor patients) without the need for registration.
The robustness and high inference speed of TractCloud make it suitable for
large-scale tractography data analysis. Our project page is available at
https://tractcloud.github.io/.Comment: MICCAI 202
Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions
Diffusion MRI tractography is an advanced imaging technique that enables in
vivo mapping of the brain's white matter connections. White matter parcellation
classifies tractography streamlines into clusters or anatomically meaningful
tracts. It enables quantification and visualization of whole-brain
tractography. Currently, most parcellation methods focus on the deep white
matter (DWM), whereas fewer methods address the superficial white matter (SWM)
due to its complexity. We propose a novel two-stage deep-learning-based
framework, Superficial White Matter Analysis (SupWMA), that performs an
efficient and consistent parcellation of 198 SWM clusters from whole-brain
tractography. A point-cloud-based network is adapted to our SWM parcellation
task, and supervised contrastive learning enables more discriminative
representations between plausible streamlines and outliers for SWM. We train
our model on a large-scale tractography dataset including streamline samples
from labeled SWM clusters and anatomically implausible streamline samples, and
we perform testing on six independently acquired datasets of different ages and
health conditions (including neonates and patients with space-occupying brain
tumors). Compared to several state-of-the-art methods, SupWMA obtains highly
consistent and accurate SWM parcellation results on all datasets, showing good
generalization across the lifespan in health and disease. In addition, the
computational speed of SupWMA is much faster than other methods.Comment: 12 pages, 7 figures. Extension of our ISBI 2022 paper
(arXiv:2201.12528) (Best Paper Award Finalist
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