147 research outputs found
Epigenetic Instability Induced by DNA Base Lesion via DNA Base Excision Repair
DNA damage can cause genome instability, which may lead to human cancer. The most common form of DNA damage is DNA base damage, which is efficiently repaired by DNA base excision repair (BER). Thus BER is the major DNA repair pathway that maintains the stability of the genome. On the other hand, BER mediates DNA demethylation that can occur on the promoter region of important tumor suppressor genes such as Breast Cancer 1 (BRCA1) gene that is also involved in prevention and development of cancer. In this study, employing cell-based and in vitro biochemical approaches along with bisulfite DNA sequencing, we initially discovered that an oxidized nucleotide, 5’,2-cyclo-2-deoxyadenosine in DNA duplex can either cause misinsertion by DNA polymerase β (pol β) during pol β-mediated BER or inhibit lesion bypass of pol β resulting in DNA strand breaks. We then explored how a T/G mismatch resulting from active DNA demethylation can affect genome integrity during BER and found that pol β can extend the mismatched T to cause mutation. We found that AP endonuclease 1 (APE1) can use its 3\u27-5\u27 exonuclease to remove the mismatched T before pol β can extend the nucleotide preventing a C to T mutation. The results demonstrate that the 3\u27-5\u27 exonuclease activity of APE1 can serve as a proofreader for pol β to prevent mutation. We further explored the effects of exposure of environmental toxicants, bromate and chromate on the DNA methylation pattern on the promoter region of BRCA1 gene with bisulfite DNA sequencing. We found that bromate and chromate induced demethylation of 5-methylcytosines (5mC) at the CpG sites as well as created additional methylation at several unmethylated CpG sites at BRCA1 gene in human embryonic kidney (HEK) 293 cells. We further demonstrated that the demethylation was mediated by pol β nucleotide misinsertion and an interaction between pol β and DNA methyltransferase 1 (DNMT1) suggesting a cross-talk between BER and DNA methyltransferases. We suggest that DNA base damage and BER govern the interactions among the environment, the genome and epigenome, modulating the stability of the genome and epigenome and disease development
Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network
Automatically extracting useful information from electronic medical records
along with conducting disease diagnoses is a promising task for both clinical
decision support(CDS) and neural language processing(NLP). Most of the existing
systems are based on artificially constructed knowledge bases, and then
auxiliary diagnosis is done by rule matching. In this study, we present a
clinical intelligent decision approach based on Convolutional Neural
Networks(CNN), which can automatically extract high-level semantic information
of electronic medical records and then perform automatic diagnosis without
artificial construction of rules or knowledge bases. We use collected 18,590
copies of the real-world clinical electronic medical records to train and test
the proposed model. Experimental results show that the proposed model can
achieve 98.67\% accuracy and 96.02\% recall, which strongly supports that using
convolutional neural network to automatically learn high-level semantic
features of electronic medical records and then conduct assist diagnosis is
feasible and effective.Comment: 9 pages, 4 figures, Accepted by Scientific Report
Robotic Ultrasound Imaging: State-of-the-Art and Future Perspectives
Ultrasound (US) is one of the most widely used modalities for clinical
intervention and diagnosis due to the merits of providing non-invasive,
radiation-free, and real-time images. However, free-hand US examinations are
highly operator-dependent. Robotic US System (RUSS) aims at overcoming this
shortcoming by offering reproducibility, while also aiming at improving
dexterity, and intelligent anatomy and disease-aware imaging. In addition to
enhancing diagnostic outcomes, RUSS also holds the potential to provide medical
interventions for populations suffering from the shortage of experienced
sonographers. In this paper, we categorize RUSS as teleoperated or autonomous.
Regarding teleoperated RUSS, we summarize their technical developments, and
clinical evaluations, respectively. This survey then focuses on the review of
recent work on autonomous robotic US imaging. We demonstrate that machine
learning and artificial intelligence present the key techniques, which enable
intelligent patient and process-specific, motion and deformation-aware robotic
image acquisition. We also show that the research on artificial intelligence
for autonomous RUSS has directed the research community toward understanding
and modeling expert sonographers' semantic reasoning and action. Here, we call
this process, the recovery of the "language of sonography". This side result of
research on autonomous robotic US acquisitions could be considered as valuable
and essential as the progress made in the robotic US examination itself. This
article will provide both engineers and clinicians with a comprehensive
understanding of RUSS by surveying underlying techniques.Comment: Accepted by Medical Image Analysi
DefCor-Net: Physics-Aware Ultrasound Deformation Correction
The recovery of morphologically accurate anatomical images from deformed ones
is challenging in ultrasound (US) image acquisition, but crucial to accurate
and consistent diagnosis, particularly in the emerging field of
computer-assisted diagnosis. This article presents a novel anatomy-aware
deformation correction approach based on a coarse-to-fine, multi-scale deep
neural network (DefCor-Net). To achieve pixel-wise performance, DefCor-Net
incorporates biomedical knowledge by estimating pixel-wise stiffness online
using a U-shaped feature extractor. The deformation field is then computed
using polynomial regression by integrating the measured force applied by the US
probe. Based on real-time estimation of pixel-by-pixel tissue properties, the
learning-based approach enables the potential for anatomy-aware deformation
correction. To demonstrate the effectiveness of the proposed DefCor-Net, images
recorded at multiple locations on forearms and upper arms of six volunteers are
used to train and validate DefCor-Net. The results demonstrate that DefCor-Net
can significantly improve the accuracy of deformation correction to recover the
original geometry (Dice Coefficient: from to when
the force is ).Comment: Accepted by MedIA. code is availabl
Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph
Autonomous ultrasound (US) imaging has gained increased interest recently,
and it has been seen as a potential solution to overcome the limitations of
free-hand US examinations, such as inter-operator variations. However, it is
still challenging to accurately map planned paths from a generic atlas to
individual patients, particularly for thoracic applications with high
acoustic-impedance bone structures under the skin. To address this challenge, a
graph-based non-rigid registration is proposed to enable transferring planned
paths from the atlas to the current setup by explicitly considering
subcutaneous bone surface features instead of the skin surface. To this end,
the sternum and cartilage branches are segmented using a template matching to
assist coarse alignment of US and CT point clouds. Afterward, a directed graph
is generated based on the CT template. Then, the self-organizing map using
geographical distance is successively performed twice to extract the optimal
graph representations for CT and US point clouds, individually. To evaluate the
proposed approach, five cartilage point clouds from distinct patients are
employed. The results demonstrate that the proposed graph-based registration
can effectively map trajectories from CT to the current setup for displaying US
views through limited intercostal space. The non-rigid registration results in
terms of Hausdorff distance (MeanSD) is 9.480.27 mm and the path
transferring error in terms of Euclidean distance is 2.211.11 mm.Comment: Accepted by IROS2
Motion Magnification in Robotic Sonography: Enabling Pulsation-Aware Artery Segmentation
Ultrasound (US) imaging is widely used for diagnosing and monitoring arterial
diseases, mainly due to the advantages of being non-invasive, radiation-free,
and real-time. In order to provide additional information to assist clinicians
in diagnosis, the tubular structures are often segmented from US images. To
improve the artery segmentation accuracy and stability during scans, this work
presents a novel pulsation-assisted segmentation neural network (PAS-NN) by
explicitly taking advantage of the cardiac-induced motions. Motion
magnification techniques are employed to amplify the subtle motion within the
frequency band of interest to extract the pulsation signals from sequential US
images. The extracted real-time pulsation information can help to locate the
arteries on cross-section US images; therefore, we explicitly integrated the
pulsation into the proposed PAS-NN as attention guidance. Notably, a robotic
arm is necessary to provide stable movement during US imaging since magnifying
the target motions from the US images captured along a scan path is not
manually feasible due to the hand tremor. To validate the proposed robotic US
system for imaging arteries, experiments are carried out on volunteers' carotid
and radial arteries. The results demonstrated that the PAS-NN could achieve
comparable results as state-of-the-art on carotid and can effectively improve
the segmentation performance for small vessels (radial artery).Comment: Accepted Paper IROS 202
MI-SegNet: Mutual Information-Based US Segmentation for Unseen Domain Generalization
Generalization capabilities of learning-based medical image segmentation
across domains are currently limited by the performance degradation caused by
the domain shift, particularly for ultrasound (US) imaging. The quality of US
images heavily relies on carefully tuned acoustic parameters, which vary across
sonographers, machines, and settings. To improve the generalizability on US
images across domains, we propose MI-SegNet, a novel mutual information (MI)
based framework to explicitly disentangle the anatomical and domain feature
representations; therefore, robust domain-independent segmentation can be
expected. Two encoders are employed to extract the relevant features for the
disentanglement. The segmentation only uses the anatomical feature map for its
prediction. In order to force the encoders to learn meaningful feature
representations a cross-reconstruction method is used during training.
Transformations, specific to either domain or anatomy are applied to guide the
encoders in their respective feature extraction task. Additionally, any MI
present in both feature maps is punished to further promote separate feature
spaces. We validate the generalizability of the proposed domain-independent
segmentation approach on several datasets with varying parameters and machines.
Furthermore, we demonstrate the effectiveness of the proposed MI-SegNet serving
as a pre-trained model by comparing it with state-of-the-art networks.Comment: Accepted by MICCAI 202
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