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Ma, Wei, Xinyao Cheng, Xiangyang Xu, Furong Wang, Ran Zhou, Aaron Fenster, and Mingyue Ding. "Multilevel Strip Pooling-Based Convolutional Neural Network for the Classification of Carotid Plaque Echogenicity." Computational and Mathematical Methods in Medicine 2021 (August 18, 2021): 1–13. http://dx.doi.org/10.1155/2021/3425893.
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Carotid plaque echogenicity in ultrasound images has been found to be closely correlated with the risk of stroke in atherosclerotic patients. The automatic and accurate classification of carotid plaque echogenicity is of great significance for clinically estimating the stability of carotid plaques and predicting cardiovascular events. Existing convolutional neural networks (CNNs) can provide an automatic carotid plaque echogenicity classification; however, they require a fixed-size input image, while the carotid plaques are of varying sizes. Although cropping and scaling the input carotid plaque images is promising, it will cause content loss or distortion and hence reduce the classification accuracy. In this study, we redesign the spatial pyramid pooling (SPP) and propose multilevel strip pooling (MSP) for the automatic and accurate classification of carotid plaque echogenicity in the longitudinal section. The proposed MSP module can accept arbitrarily sized carotid plaques as input and capture a long-range informative context to improve the accuracy of classification. In our experiments, we implement an MSP-based CNN by using the visual geometry group (VGG) network as the backbone. A total of 1463 carotid plaques (335 echo-rich plaques, 405 intermediate plaques, and 723 echolucent plaques) were collected from Zhongnan Hospital of Wuhan University. The 5-fold cross-validation results show that the proposed MSP-based VGGNet achieves a sensitivity of 92.1%, specificity of 95.6%, accuracy of 92.1%, and F1-score of 92.1%. These results demonstrate that our approach provides a way to enhance the applicability of CNN by enabling the acceptance of arbitrary input sizes and improving the classification accuracy of carotid plaque echogenicity, which has a great potential for an efficient and objective risk assessment of carotid plaques in the clinic.
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Li, Lincan, Tong Jia, Tianqi Meng, and Yizhe Liu. "Deep convolutional neural networks for cardiovascular vulnerable plaque detection." MATEC Web of Conferences 277 (2019): 02024. http://dx.doi.org/10.1051/matecconf/201927702024.
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In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.
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Kim, Jun-Min, Woo Ram Lee, Jun-Ho Kim, Jong-Mo Seo, and Changkyun Im. "Light-Induced Fluorescence-Based Device and Hybrid Mobile App for Oral Hygiene Management at Home: Development and Usability Study." JMIR mHealth and uHealth 8, no. 10 (October 16, 2020): e17881. http://dx.doi.org/10.2196/17881.
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Background Dental diseases can be prevented through the management of dental plaques. Dental plaque can be identified using the light-induced fluorescence (LIF) technique that emits light at 405 nm. The LIF technique is more convenient than the commercial technique using a disclosing agent, but the result may vary for each individual as it still requires visual identification. Objective The objective of this study is to introduce and validate a deep learning–based oral hygiene monitoring system that makes it easy to identify dental plaques at home. Methods We developed a LIF-based system consisting of a device that can visually identify dental plaques and a mobile app that displays the location and area of dental plaques on oral images. The mobile app is programmed to automatically determine the location and distribution of dental plaques using a deep learning–based algorithm and present the results to the user as time series data. The mobile app is also built with convergence of naive and web applications so that the algorithm is executed on a cloud server to efficiently distribute computing resources. Results The location and distribution of users’ dental plaques could be identified via the hand-held LIF device or mobile app. The color correction filter in the device was developed using a color mixing technique. The mobile app was built as a hybrid app combining the functionalities of a native application and a web application. Through the scrollable WebView on the mobile app, changes in the time series of dental plaque could be confirmed. The algorithm for dental plaque detection was implemented to run on Amazon Web Services for object detection by single shot multibox detector and instance segmentation by Mask region-based convolutional neural network. Conclusions This paper shows that the system can be used as a home oral care product for timely identification and management of dental plaques. In the future, it is expected that these products will significantly reduce the social costs associated with dental diseases.
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Streit, Wolfgang J., Jonas Rotter, Karsten Winter, Wolf Müller, Habibeh Khoshbouei, and Ingo Bechmann. "Droplet Degeneration of Hippocampal and Cortical Neurons Signifies the Beginning of Neuritic Plaque Formation." Journal of Alzheimer's Disease 85, no. 4 (February 15, 2022): 1701–20. http://dx.doi.org/10.3233/jad-215334.
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Background: Neuritic plaques contain neural and microglial elements, and amyloid-β protein (Aβ), but their pathogenesis remains unknown. Objective: Elucidate neuritic plaque pathogenesis. Methods: Histochemical visualization of hyperphosphorylated-tau positive (p-tau+) structures, microglia, Aβ, and iron. Results: Disintegration of large projection neurons in human hippocampus and neocortex presents as droplet degeneration: pretangle neurons break up into spheres of numerous p-tau+ droplets of various sizes, which marks the beginning of neuritic plaques. These droplet spheres develop in the absence of colocalized Aβ deposits but once formed become encased in diffuse Aβ with great specificity. In contrast, neurofibrillary tangles often do not colocalize with Aβ. Double-labelling for p-tau and microglia showed a lack of microglial activation or phagocytosis of p-tau+ degeneration droplets but revealed massive upregulation of ferritin in microglia suggesting presence of high levels of free iron. Perl’s Prussian blue produced positive staining of microglia, droplet spheres, and Aβ plaque cores supporting the suggestion that droplet degeneration of pretangle neurons in the hippocampus and cortex represents ferroptosis, which is accompanied by the release of neuronal iron extracellularly. Conclusion: Age-related iron accumulation and ferroptosis in the CNS likely trigger at least two endogenous mechanisms of neuroprotective iron sequestration and chelation, microglial ferritin expression and Aβ deposition, respectively, both contributing to the formation of neuritic plaques. Since neurofibrillary tangles and Aβ deposits colocalize infrequently, tangle formation likely does not involve release of neuronal iron extracellularly. In human brain, targeted deposition of Aβ occurs specifically in response to ongoing ferroptotic droplet degeneration thereby producing neuritic plaques.
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Li, Yanhan, Lian Zou, Li Xiong, Fen Yu, Hao Jiang, Cien Fan, Mofan Cheng, and Qi Li. "FRDD-Net: Automated Carotid Plaque Ultrasound Images Segmentation Using Feature Remapping and Dense Decoding." Sensors 22, no. 3 (January 24, 2022): 887. http://dx.doi.org/10.3390/s22030887.
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Automated segmentation and evaluation of carotid plaques ultrasound images is of great significance for the diagnosis and early intervention of high-risk groups of cardiovascular and cerebrovascular diseases. However, it remains challenging to develop such solutions due to the relatively low quality of ultrasound images and heterogenous characteristics of carotid plaques. To address those problems, in this paper, we propose a novel deep convolutional neural network, FRDD-Net, with an encoder–decoder architecture to automatically segment carotid plaques. We propose the feature remapping modules (FRMs) and incorporate them into the encoding and decoding blocks to ameliorate the reliability of acquired features. We also propose a new dense decoding mechanism as part of the decoder, thus promoting the utilization efficiency of encoded features. Additionally, we construct a compound loss function to train our network to further enhance its robustness in the face of numerous cases. We train and test our network in multiple carotid plaque ultrasound datasets and our method yields the best performance compared to other state-of-the-art methods. Further ablation studies consistently show the advancement of our proposed architecture.
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Zafar, Haroon, Junaid Zafar, and Faisal Sharif. "Automated Clinical Decision Support for Coronary Plaques Characterization from Optical Coherence Tomography Imaging with Fused Neural Networks." Optics 3, no. 1 (January 10, 2022): 8–18. http://dx.doi.org/10.3390/opt3010002.
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Deep Neural Networks (DNNs) are nurturing clinical decision support systems for the detection and accurate modeling of coronary arterial plaques. However, efficient plaque characterization in time-constrained settings is still an open problem. The purpose of this study is to develop a novel automated classification architecture viable for the real-time clinical detection and classification of coronary artery plaques, and secondly, to use the novel dataset of OCT images for data augmentation. Further, the purpose is to validate the efficacy of transfer learning for arterial plaques classification. In this perspective, a novel time-efficient classification architecture based on DNNs is proposed. A new data set consisting of in-vivo patient Optical Coherence Tomography (OCT) images labeled by three trained experts was created and dynamically programmed. Generative Adversarial Networks (GANs) were used for populating the coronary aerial plaques dataset. We removed the fully connected layers, including softmax and the cross-entropy in the GoogleNet framework, and replaced them with the Support Vector Machines (SVMs). Our proposed architecture limits weight up-gradation cycles to only modified layers and computes the global hyper-plane in a timely, competitive fashion. Transfer learning was used for high-level discriminative feature learning. Cross-entropy loss was minimized by using the Adam optimizer for model training. A train validation scheme was used to determine the classification accuracy. Automated plaques differentiation in addition to their detection was found to agree with the clinical findings. Our customized fused classification scheme outperforms the other leading reported works with an overall accuracy of 96.84%, and multiple folds reduced elapsed time demonstrating it as a viable choice for real-time clinical settings.
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Xia Wei, 夏巍, 韩婷婷 Han Tingting, 陶魁园 Tao Kuiyuan, 王为 Wang Wei, and 高静 Gao Jing. "基于卷积神经网络的IVOCT冠状动脉钙化斑块分割方法." Chinese Journal of Lasers 51, no. 18 (2024): 1801019. http://dx.doi.org/10.3788/cjl240833.
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Busche, Marc Aurel, and Arthur Konnerth. "Impairments of neural circuit function in Alzheimer's disease." Philosophical Transactions of the Royal Society B: Biological Sciences 371, no. 1700 (August 5, 2016): 20150429. http://dx.doi.org/10.1098/rstb.2015.0429.
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An essential feature of Alzheimer's disease (AD) is the accumulation of amyloid-β (Aβ) peptides in the brain, many years to decades before the onset of overt cognitive symptoms. We suggest that during this very extended early phase of the disease, soluble Aβ oligomers and amyloid plaques alter the function of local neuronal circuits and large-scale networks by disrupting the balance of synaptic excitation and inhibition ( E / I balance) in the brain. The analysis of mouse models of AD revealed that an Aβ-induced change of the E / I balance caused hyperactivity in cortical and hippocampal neurons, a breakdown of slow-wave oscillations, as well as network hypersynchrony. Remarkably, hyperactivity of hippocampal neurons precedes amyloid plaque formation, suggesting that hyperactivity is one of the earliest dysfunctions in the pathophysiological cascade initiated by abnormal Aβ accumulation. Therapeutics that correct the E / I balance in early AD may prevent neuronal dysfunction, widespread cell loss and cognitive impairments associated with later stages of the disease. This article is part of the themed issue ‘Evolution brings Ca 2+ and ATP together to control life and death’.
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Zhang, Yue, Haitao Gan, Furong Wang, Xinyao Cheng, Xiaoyan Wu, Jiaxuan Yan, Zhi Yang, and Ran Zhou. "A self-supervised fusion network for carotid plaque ultrasound image classification." Mathematical Biosciences and Engineering 21, no. 2 (2024): 3110–28. http://dx.doi.org/10.3934/mbe.2024138.
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<abstract><p>Carotid plaque classification from ultrasound images is crucial for predicting ischemic stroke risk. While deep learning has shown effectiveness, it heavily relies on substantial labeled datasets. Achieving high performance with limited labeled images is essential for clinical use. Self-supervised learning (SSL) offers a potential solution; however, the existing works mainly focus on constructing the SSL tasks, neglecting the use of multiple tasks for pretraining. To overcome these limitations, this study proposed a self-supervised fusion network (Fusion-SSL) for carotid plaque ultrasound image classification with limited labeled data. Fusion-SSL consists of two SSL tasks: classifying image block order (Ordering) and predicting image rotation angle (Rotating). A dual-branch residual neural network was developed to fuse feature presentations learned by the two tasks, which can extract richer visual boundary shape and contour information than a single task. In this experiment, 1270 carotid plaque ultrasound images were collected from 844 patients at Zhongnan Hospital (Wuhan, China). The results showed that Fusion-SSL outperforms single SSL methods across different percentages of labeled training data, ranging from 10 to 100%. Moreover, with only 40% labeled training data, Fusion-SSL achieved comparable results to a single SSL method (predicting image rotation angle) with 100% labeled data. These results indicate that Fusion-SSL could be beneficial for the classification of carotid plaques and the early warning of a stroke in clinical practice.</p></abstract>
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Guang, Yang, Wen He, Bin Ning, Hongxia Zhang, Chen Yin, Mingchang Zhao, Fang Nie, et al. "Deep learning-based carotid plaque vulnerability classification with multicentre contrast-enhanced ultrasound video: a comparative diagnostic study." BMJ Open 11, no. 8 (August 2021): e047528. http://dx.doi.org/10.1136/bmjopen-2020-047528.
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ObjectivesThe aim of this study was to evaluate the performance of deep learning-based detection and classification of carotid plaque (DL-DCCP) in carotid plaque contrast-enhanced ultrasound (CEUS).Methods and analysisA prospective multicentre study was conducted to assess vulnerability in patients with carotid plaque. Data from 547 potentially eligible patients were prospectively enrolled from 10 hospitals, and 205 patients with CEUS video were finally enrolled for analysis. The area under the receiver operating characteristic curve (AUC) was used to evaluate the effectiveness of DL-DCCP and two experienced radiologists who manually examined the CEUS video (RA-CEUS) in diagnosing and classifying carotid plaque vulnerability. To evaluate the influence of dynamic video input on the performance of the algorithm, a state-of-the-art deep convolutional neural network (CNN) model for static images (Xception) was compared with DL-DCCP for both training and holdout validation cohorts.ResultsThe AUCs of DL-DCCP were significantly better than those of the experienced radiologists for both the training and holdout validation cohorts (training, DL-DCCP vs RA-CEUS, AUC: 0.85 vs 0.69, p<0.01; holdout validation, DL-DCCP vs RA-CEUS, AUC: 0.87 vs 0.66, p<0.01), that is, also better than the best deep CNN model Xception we had performed, for both the training and holdout validation cohorts (training, DL-DCCP vs Xception, AUC:0.85 vs 0.82, p<0.01; holdout validation, DL-DCCP vs Xception, AUC: 0.87 vs 0.77, p<0.01).ConclusionDL-DCCP shows better overall performance in assessing the vulnerability of carotid atherosclerotic plaques than RA-CEUS. Moreover, with a more powerful network structure and better utilisation of video information, DL-DCCP provided greater diagnostic accuracy than a state-of-the-art static CNN model.Trial registration numberChiCTR1900021846,
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Dašić, Lazar, Nikola Radovanović, Tijana Šušteršič, Anđela Blagojević, Leo Benolić, and Nenad Filipović. "Patch-based Convolutional Neural Network for Atherosclerotic Carotid Plaque Semantic Segmentation." Ipsi Transactions on Internet research 18, no. 1 (January 1, 2022): 56–61. http://dx.doi.org/10.58245/ipsi.tir.22jr.10.
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Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and if not adequately treated, it may potentially have deteriorating consequences, such as a debilitating stroke, thus making early detection of the most importance. The manual plaque components annotation process is both time and resource consuming, therefore, an automatic and accurate segmentation tool is necessary. The main aim of this paper is to present the model for identification and segmentation of the atherosclerotic plaque components such as lipid core, fibrous and calcified tissue, by using Convolutional Neural Network on patch-based segments of ultrasound images. There was some research done on the topic of plaque components segmentation, but not in ultrasound imaging data. Due to the size of some plaque components being only a couple of millimeters, we argue that training a neural network on smaller image patches will perform better than a classifier based on the whole image. Besides the size of components, this decision is motivated by the observation that plaque components are not uniformly distributed throughout the whole carotid wall and that a locality-sensitive segmentation is likely to obtain better segmentation accuracy. Our model achieved good results in the segmentation of fibrous tissue but had difficulties in the segmentation of lipid and calcified tissue due to the quality of ultrasound images.
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Cairns, Dana M., Nicolas Rouleau, Rachael N. Parker, Katherine G. Walsh, Lee Gehrke, and David L. Kaplan. "A 3D human brain–like tissue model of herpes-induced Alzheimer’s disease." Science Advances 6, no. 19 (May 2020): eaay8828. http://dx.doi.org/10.1126/sciadv.aay8828.
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Alzheimer’s disease (AD) is a neurodegenerative disorder that causes cognitive decline, memory loss, and inability to perform everyday functions. Hallmark features of AD—including generation of amyloid plaques, neurofibrillary tangles, gliosis, and inflammation in the brain—are well defined; however, the cause of the disease remains elusive. Growing evidence implicates pathogens in AD development, with herpes simplex virus type I (HSV-1) gaining increasing attention as a potential causative agent. Here, we describe a multidisciplinary approach to produce physiologically relevant human tissues to study AD using human-induced neural stem cells (hiNSCs) and HSV-1 infection in a 3D bioengineered brain model. We report a herpes-induced tissue model of AD that mimics human disease with multicellular amyloid plaque–like formations, gliosis, neuroinflammation, and decreased functionality, completely in the absence of any exogenous mediators of AD. This model will allow for future studies to identify potential downstream drug targets for treating this devastating disease.
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Prat, Annik, Maik Behrendt, Edwige Marcinkiewicz, Sebastien Boridy, Ram M. Sairam, Nabil G. Seidah, and Dusica Maysinger. "A Novel Mouse Model of Alzheimer's Disease with Chronic Estrogen Deficiency Leads to Glial Cell Activation and Hypertrophy." Journal of Aging Research 2011 (2011): 1–12. http://dx.doi.org/10.4061/2011/251517.
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The role of estrogens in Alzheimer's disease (AD) involving β-amyloid (Aβ) generation and plaque formation was mostly tested in ovariectomized mice with or without APP mutations. The aim of the present study was to explore the abnormalities of neural cells in a novel mouse model of AD with chronic estrogen deficiency. These chimeric mice exhibit a total FSH-R knockout (FORKO) and carry two transgenes, one expressing the β-amyloid precursor protein (APPsw, Swedish mutation) and the other expressing presenilin-1 lacking exon 9 (PS1Δ9). The most prominent changes in the cerebral cortex and hippocampus of these hypoestrogenic mice were marked hypertrophy of both cortical neurons and astrocytes and an increased number of activated microglia. There were no significant differences in the number of Aβ plaques although they appeared less compacted and larger than those in APPsw/PS1Δ9 control mice. Similar glia abnormalities were obtained in wild-type primary cortical neural cultures treated with letrozole, an aromatase inhibitor. The concordance of results from APPsw/PS1Δ9 mice with or without FSH-R deletion and those with letrozole treatment in vitro (with and without Aβ treatment) of primary cortical/hippocampal cultures suggests the usefulness of these models to explore molecular mechanisms involved in microglia and astrocyte activation in hypoestrogenic states in the central nervous system.
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Alkatan, Hind Manaa, Mohammed D. Alotaibi, Azza Y. Maktabi, Deepak P. Edward, and Igor Kozak. "Immunohistochemical characterization of sub retinalmembranes (SRMs) in proliferative vitreoretinopathy." Advances in Ophthalmology & Visual System 8, no. 1 (February 21, 2018): 60–63. http://dx.doi.org/10.15406/aovs.2018.08.00270.
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Purpose: To provide immuno histo chemical characterization of sub retinal bands removed during retinal surgery in eyes with proliferative vitreo retinopathy (PVR). Methods: This study included all eyes with the clinical diagnosis of PVR that underwent pars plana vitrectomy surgery during which the subretinal tissue causing retinal detachment was obtained. The subretinal bands were removed “en bloc” through retinotomy using subretinal intraocular forceps. The excised tissue was sent for histopathologic analysis. Immunohistochemistry (IHC) was performed to confirm the cellular nature and components of these subretinal membranes. The IHC stains included, glial fibrillary acidic protein (GFAP), Pancytokeratin, CD3 CD20 CD68 and CD34. Results: Subretinal membranes (SRMs) from 7 eyes were included in the analysis. All cases had successful surgical outcome with reattachment six months after surgery. The microscopic examination of the excised tissue nicely demonstrated the constituents of the SRM as follows: retinal pigmented epithelial (RPE) cells that stained positively with cytokeratin (7/7), avascular plaques of RPE cells showing metaplasia in the form of spindle cells (7/7). Fragments of gliotic GFAP-positive neural retina was adherent to the fibrous plaque (6/7). Bruch’s membrane was identified in one specimen. CD68 positive macrophages were seen in (5/7) being silicon oil- laden macrophages in2/5. Rare CD3 positive cells were also noted in 1 specimen. Conclusion: Subretinal bands in PVR are mainly composed of reactive avascular plaques of RPE metaplasia and macrophage infiltration. The overlying gliotic retina or Bruch’s membrane are likely to be adherent to such plaques and might be inadvertently excised during removal of such membranes. Removal of SRMs is essential for successful reattachment of the retina.
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Sanagala, Skandha S., Andrew Nicolaides, Suneet K. Gupta, Vijaya K. Koppula, Luca Saba, Sushant Agarwal, Amer M. Johri, Manudeep S. Kalra, and Jasjit S. Suri. "Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification." Diagnostics 11, no. 11 (November 15, 2021): 2109. http://dx.doi.org/10.3390/diagnostics11112109.
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Background and Purpose: Only 1–2% of the internal carotid artery asymptomatic plaques are unstable as a result of >80% stenosis. Thus, unnecessary efforts can be saved if these plaques can be characterized and classified into symptomatic and asymptomatic using non-invasive B-mode ultrasound. Earlier plaque tissue characterization (PTC) methods were machine learning (ML)-based, which used hand-crafted features that yielded lower accuracy and unreliability. The proposed study shows the role of transfer learning (TL)-based deep learning models for PTC. Methods: As pertained weights were used in the supercomputer framework, we hypothesize that transfer learning (TL) provides improved performance compared with deep learning. We applied 11 kinds of artificial intelligence (AI) models, 10 of them were augmented and optimized using TL approaches—a class of Atheromatic™ 2.0 TL (AtheroPoint™, Roseville, CA, USA) that consisted of (i–ii) Visual Geometric Group-16, 19 (VGG16, 19); (iii) Inception V3 (IV3); (iv–v) DenseNet121, 169; (vi) XceptionNet; (vii) ResNet50; (viii) MobileNet; (ix) AlexNet; (x) SqueezeNet; and one DL-based (xi) SuriNet-derived from UNet. We benchmark 11 AI models against our earlier deep convolutional neural network (DCNN) model. Results: The best performing TL was MobileNet, with accuracy and area-under-the-curve (AUC) pairs of 96.10 ± 3% and 0.961 (p < 0.0001), respectively. In DL, DCNN was comparable to SuriNet, with an accuracy of 95.66% and 92.7 ± 5.66%, and an AUC of 0.956 (p < 0.0001) and 0.927 (p < 0.0001), respectively. We validated the performance of the AI architectures with established biomarkers such as greyscale median (GSM), fractal dimension (FD), higher-order spectra (HOS), and visual heatmaps. We benchmarked against previously developed Atheromatic™ 1.0 ML and showed an improvement of 12.9%. Conclusions: TL is a powerful AI tool for PTC into symptomatic and asymptomatic plaques.
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Xu, Juan, Lingling Wang, Hongxia Sun, and Shanshan Liu. "Evaluation of the Effect of Comprehensive Nursing Interventions on Plaque Control in Patients with Periodontal Disease in the Context of Artificial Intelligence." Journal of Healthcare Engineering 2022 (March 23, 2022): 1–11. http://dx.doi.org/10.1155/2022/6505672.
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Plaque is a bacterial biofilm that adheres to each other and exists on the tooth surface, and new plaque can continuously reform after removing it from the tooth surface. The pathogenesis of periodontal disease is related to the bacteria, the host and the environment, with the bacteria and bacterial products in plaque being the main initiators of periodontal disease. The effective control of plaque is an effective method for the treatment and prevention of periodontal disease and is often underappreciated in clinical practice. For the traditional diagnostic method through experience and visual observation, it may lead to misdiagnosis and underdiagnosis. In order to accurately diagnose plaque disease, this study designed a convolutional neural network-based oral dental disease diagnosis system for oral care interventions to improve oral health awareness. Thus motivate patients to implement proper oral health care measures, and continuously and lifelong insist on thorough daily plaque removal to improve patients’ physical health and quality of life in periodontal disease patients.
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Wang, Cheng, Haotian Qin, Guangyun Lai, Gang Zheng, Huazhong Xiang, Jun Wang, and Dawei Zhang. "Automated classification of dual channel dental imaging of auto-fluorescence and white lightby convolutional neural networks." Journal of Innovative Optical Health Sciences 13, no. 04 (May 7, 2020): 2050014. http://dx.doi.org/10.1142/s1793545820500145.
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Prevention is the most effective way to reduce dental caries. In order to provide a simple way to achieve oral healthcare direction in daily life, dual Channel, portable dental Imaging system that combine white light with autofluorescence techniques was established, and then, a group of volunteers were recruited, 7200 tooth pictures of different dental caries stage and dental plaque were taken and collected. In this work, a customized Convolutional Neural Networks (CNNs) have been designed to classify dental image with early stage caries and dental plaque. Eighty percentage ([Formula: see text]) of the pictures taken were used to supervised training of the CNNs based on the experienced dentists’ advice and the rest 20% ([Formula: see text]) were used to a test dataset to test the trained CNNs. The accuracy, sensitivity and specificity were calculated to evaluate performance of the CNNs. The accuracy for the early stage caries and dental plaque were 95.3% and 95.9%, respectively. These results shown that the designed image system combined the customized CNNs that could automatically and efficiently find early caries and dental plaque on occlusal, lingual and buccal surfaces. Therefore, this will provide a novel approach to dental caries prevention for everyone in daily life.
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Rezaei, Zahra, Ali Selamat, Arash Taki, Mohd Mohd Rahim, Mohammed Abdul Kadir, Marek Penhaker, Ondrej Krejcar, Kamil Kuca, Enrique Herrera-Viedma, and Hamido Fujita. "Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features." Applied Sciences 8, no. 9 (September 12, 2018): 1632. http://dx.doi.org/10.3390/app8091632.
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Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.
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Binti Roslan, Rosniza, Iman Najwa Mohd Razly, Nurbaity Sabri, and Zaidah Ibrahim. "Evaluation of psoriasis skin disease classification using convolutional neural network." IAES International Journal of Artificial Intelligence (IJ-AI) 9, no. 2 (June 1, 2020): 349. http://dx.doi.org/10.11591/ijai.v9.i2.pp349-355.
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<span lang="EN-GB">Skin disease has lower impact on mortality compared to others but instead it has greater effect on quality of life because it involves symptoms such as pain, stinging and itchiness. Psoriasis is one of the ordinary skin diseases which are relapsing, chronic and immune-mediated inflammatory disease. It is estimated about 125 million people worldwide being infected with various types of skin infection. </span><span lang="EN-GB">Challenges arise when patients only predict the skin type disease they had without being accurately and precisely examined. This is because as human being, they only observe and look at the diseases on the surface of the skin with their naked eye, where there are some limits, for example, human vision lacks of accuracy, reproducibility and quantification in the collection of image information. As Plaque and Guttate are the most common Psoriasis skin disease happened among people, this paper presents an evaluation of Psoriasis skin disease classification using Convolutional Neural Network. A total of 187 images which consist of 82 images for Plaque Psoriasis and 105 images for Guttate Psoriasis has been used which are retrieved from Psoriasis Image Library, International Psoriasis Council (IPC) and DermNet NZ. Convolutional Neural Network (CNN) is applied in extracting features and analysing the classification of Psoriasis skin disease. This paper showed the promising used of CNN with the accuracy rate of 82.9% and 72.4% for Plaque and Guttate Psoriasis skin disease, respectively.</span>
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Kang, Hongyan, Xinyu Li, Kewen Xiong, Zhiyun Song, Jiaxin Tian, Yuqiao Wen, Anqiang Sun, and Xiaoyan Deng. "The Entry and Egress of Monocytes in Atherosclerosis: A Biochemical and Biomechanical Driven Process." Cardiovascular Therapeutics 2021 (July 8, 2021): 1–17. http://dx.doi.org/10.1155/2021/6642927.
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In accordance with “the response to injury” theory, the entry of monocytes into the intima guided by inflammation signals, taking up cholesterol and transforming into foam cells, and egress from plaques determines the progression of atherosclerosis. Multiple cytokines and receptors have been reported to be involved in monocyte recruitment such as CCL2/CCR2, CCL5/CCR5, and CX3CL1/CX3CR1, and the egress of macrophages from the plaque like CCR7/CCL19/CCL21. Interestingly, some neural guidance molecules such as Netrin-1 and Semaphorin 3E have been demonstrated to show an inhibitory effect on monocyte migration. During the processes of monocytes recruitment and migration, factors affecting the biomechanical properties (e.g., the membrane fluidity, the deformability, and stiffness) of the monocytes, like cholesterol, amyloid-β peptide (Aβ), and lipopolysaccharides (LPS), as well as the biomechanical environment that the monocytes are exposed, like the extracellular matrix stiffness, mechanical stretch, blood flow, and hypertension, were discussed in the latter section. Till now, several small interfering RNAs (siRNAs), monoclonal antibodies, and antagonists for CCR2 have been designed and shown promising efficiency on atherosclerosis therapy. Seeking more possible biochemical factors that are chemotactic or can affect the biomechanical properties of monocytes, and uncovering the underlying mechanism, will be helpful in future studies.
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Ahyayauch, Hasna, Massimo E. Masserini, Alicia Alonso, and Félix M. Goñi. "Understanding Aβ Peptide Binding to Lipid Membranes: A Biophysical Perspective." International Journal of Molecular Sciences 25, no. 12 (June 10, 2024): 6401. http://dx.doi.org/10.3390/ijms25126401.
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Aβ peptides are known to bind neural plasma membranes in a process leading to the deposit of Aβ-enriched plaques. These extracellular structures are characteristic of Alzheimer’s disease, the major cause of late-age dementia. The mechanisms of Aβ plaque formation and deposition are far from being understood. A vast number of studies in the literature describe the efforts to analyze those mechanisms using a variety of tools. The present review focuses on biophysical studies mostly carried out with model membranes or with computational tools. This review starts by describing basic physical aspects of lipid phases and commonly used model membranes (monolayers and bilayers). This is followed by a discussion of the biophysical techniques applied to these systems, mainly but not exclusively Langmuir monolayers, isothermal calorimetry, density-gradient ultracentrifugation, and molecular dynamics. The Methodological Section is followed by the core of the review, which includes a summary of important results obtained with each technique. The last section is devoted to an overall reflection and an effort to understand Aβ-bilayer binding. Concepts such as Aβ peptide membrane binding, adsorption, and insertion are defined and differentiated. The roles of membrane lipid order, nanodomain formation, and electrostatic forces in Aβ–membrane interaction are separately identified and discussed.
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Zhao, Mengxue, Xiangjiu Che, Hualuo Liu, and Quanle Liu. "Medical Prior Knowledge Guided Automatic Detection of Coronary Arteries Calcified Plaque with Cardiac CT." Electronics 9, no. 12 (December 11, 2020): 2122. http://dx.doi.org/10.3390/electronics9122122.
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Calcified plaque in coronary arteries is one major cause and prediction of future coronary artery disease risk. Therefore, the detection of calcified plaque in coronary arteries is exceptionally significant in clinical for slowing coronary artery disease progression. At present, the Convolutional Neural Network (CNN) is exceedingly popular in natural images’ object detection field. Therefore, CNN in the object detection field of medical images also has a wide range of applications. However, many current calcified plaque detection methods in medical images are based on improving the CNN model algorithm, not on the characteristics of medical images. In response, we propose an automatic calcified plaque detection method in non-contrast-enhanced cardiac CT by adding medical prior knowledge. The training data merging with medical prior knowledge through data augmentation makes the object detection algorithm achieve a better detection result. In terms of algorithm, we employ a deep learning tool knows as Faster R-CNN in our method for locating calcified plaque in coronary arteries. To reduce the generation of redundant anchor boxes, Region Proposal Networks is replaced with guided anchoring. Experimental results show that the proposed method achieved a decent detection performance.
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Gessert, Nils, Matthias Lutz, Markus Heyder, Sarah Latus, David M. Leistner, Youssef S. Abdelwahed, and Alexander Schlaefer. "Automatic Plaque Detection in IVOCT Pullbacks Using Convolutional Neural Networks." IEEE Transactions on Medical Imaging 38, no. 2 (February 2019): 426–34. http://dx.doi.org/10.1109/tmi.2018.2865659.
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Dehghani, Reihaneh, Farzaneh Rahmani, and Nima Rezaei. "MicroRNA in Alzheimer’s disease revisited: implications for major neuropathological mechanisms." Reviews in the Neurosciences 29, no. 2 (February 23, 2018): 161–82. http://dx.doi.org/10.1515/revneuro-2017-0042.
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AbstractPathology of Alzheimer’s disease (AD) goes far beyond neurotoxicity resulting from extracellular deposition of amyloid β (Aβ) plaques. Aberrant cleavage of amyloid precursor protein and accumulation of Aβ in the form of the plaque or neurofibrillary tangles are the known primary culprits of AD pathogenesis and target for various regulatory mechanisms. Hyper-phosphorylation of tau, a major component of neurofibrillary tangles, precipitates its aggregation and prevents its clearance. Lipid particles, apolipoproteins and lipoprotein receptors can act in favor or against Aβ and tau accumulation by altering neural membrane characteristics or dynamics of transport across the blood-brain barrier. Lipids also alter the oxidative/anti-oxidative milieu of the central nervous system (CNS). Irregular cell cycle regulation, mitochondrial stress and apoptosis, which follow both, are also implicated in AD-related neuronal loss. Dysfunction in synaptic transmission and loss of neural plasticity contribute to AD. Neuroinflammation is a final trail for many of the pathologic mechanisms while playing an active role in initiation of AD pathology. Alterations in the expression of microRNAs (miRNAs) in AD and their relevance to AD pathology have long been a focus of interest. Herein we focused on the precise pathomechanisms of AD in which miRNAs were implicated. We performed literature search through PubMed and Scopus using the search term: (‘Alzheimer Disease’) OR (‘Alzheimer’s Disease’) AND (‘microRNAs’ OR ‘miRNA’ OR ‘MiR’) to reach for relevant articles. We show how a limited number of common dysregulated pathways and abnormal mechanisms are affected by various types of miRNAs in AD brain.
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Farook, I. Mohammed, S. Dhanalakshmi, V. Manikandan, and C. Venkatesh. "Optimal Feature Selection for Carotid Artery Image Segmentation Using Evolutionary Computation." Applied Mechanics and Materials 626 (August 2014): 79–86. http://dx.doi.org/10.4028/www.scientific.net/amm.626.79.
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Atherosclerosis is hardening of arteries due to high blood pressure and high cholesterol. It causes heart attacks, stroke and peripheral vascular disease and is the major cause of death. In this paper we have attempted a method to identify the presence of plaque in carotid artery from ultrasound images. The ultrasound image is segmented using improved spatial Fuzzy c means algorithm to identify the presence of plaque in carotid artery. Spatial wavelet, Hilbert Huang Transform (HHT), Moment of Gray Level Histogram (MGLH) and Gray Level Co-occurrence Matrix (GLCM) features are extracted from ultrasound images and the feature set is reduced using genetic search process. The intima media thickness is measured using the proposed method. The IMT values are measured from the segmented image and trained using MLBPNN neural network. The neural network classifies the images into normal and abnormal.
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Cheimariotis, Grigorios-Aris, Maria Riga, Kostas Haris, Konstantinos Toutouzas, Aggelos K. Katsaggelos, and Nicos Maglaveras. "Automatic Classification of A-Lines in Intravascular OCT Images Using Deep Learning and Estimation of Attenuation Coefficients." Applied Sciences 11, no. 16 (August 12, 2021): 7412. http://dx.doi.org/10.3390/app11167412.
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Intravascular Optical Coherence Tomography (IVOCT) images provide important insight into every aspect of atherosclerosis. Specifically, the extent of plaque and its type, which are indicative of the patient’s condition, are better assessed by OCT images in comparison to other in vivo modalities. A large amount of imaging data per patient require automatic methods for rapid results. An effective step towards automatic plaque detection and plaque characterization is axial lines (A-lines) based classification into normal and various plaque types. In this work, a novel automatic method for A-line classification is proposed. The method employed convolutional neural networks (CNNs) for classification in its core and comprised the following pre-processing steps: arterial wall segmentation and an OCT-specific (depth-resolved) transformation and a post-processing step based on the majority of classifications. The important step was the OCT-specific transformation, which was based on the estimation of the attenuation coefficient in every pixel of the OCT image. The dataset used for training and testing consisted of 183 images from 33 patients. In these images, four different plaque types were delineated. The method was evaluated by cross-validation. The mean values of accuracy, sensitivity and specificity were 74.73%, 87.78%, and 61.45%, respectively, when classifying into plaque and normal A-lines. When plaque A-lines were classified into fibrolipidic and fibrocalcific, the overall accuracy was 83.47% for A-lines of OCT-specific transformed images and 74.94% for A-lines of original images. This large improvement in accuracy indicates the advantage of using attenuation coefficients when characterizing plaque types. The proposed automatic deep-learning pipeline constitutes a positive contribution to the accurate classification of A-lines in intravascular OCT images.
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Cao, Yankun, Xiaoyan Xiao, Zhi Liu, Meijun Yang, Dianmin Sun, Wei Guo, Lizhen Cui, and Pengfei Zhang. "Detecting vulnerable plaque with vulnerability index based on convolutional neural networks." Computerized Medical Imaging and Graphics 81 (April 2020): 101711. http://dx.doi.org/10.1016/j.compmedimag.2020.101711.
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Hsia, A. Y., E. Masliah, L. McConlogue, G. Q. Yu, G. Tatsuno, K. Hu, D. Kholodenko, R. C. Malenka, R. A. Nicoll, and L. Mucke. "Plaque-independent disruption of neural circuits in Alzheimer's disease mouse models." Proceedings of the National Academy of Sciences 96, no. 6 (March 16, 1999): 3228–33. http://dx.doi.org/10.1073/pnas.96.6.3228.
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Rogers, Jack T., Ashley I. Bush, Hyan-Hee Cho, Deborah H. Smith, Andrew M. Thomson, Avi L. Friedlich, Debomoy K. Lahiri, Peter J. Leedman, Xudong Huang, and Catherine M. Cahill. "Iron and the translation of the amyloid precursor protein (APP) and ferritin mRNAs: riboregulation against neural oxidative damage in Alzheimer's disease." Biochemical Society Transactions 36, no. 6 (November 19, 2008): 1282–87. http://dx.doi.org/10.1042/bst0361282.
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The essential metals iron, zinc and copper deposit near the Aβ (amyloid β-peptide) plaques in the brain cortex of AD (Alzheimer's disease) patients. Plaque-associated iron and zinc are in neurotoxic excess at 1 mM concentrations. APP (amyloid precursor protein) is a single transmembrane metalloprotein cleaved to generate the 40–42-amino-acid Aβs, which exhibit metal-catalysed neurotoxicity. In health, ubiquitous APP is cleaved in a non-amyloidogenic pathway within its Aβ domain to release the neuroprotective APP ectodomain, APP(s). To adapt and counteract metal-catalysed oxidative stress, as during reperfusion from stroke, iron and cytokines induce the translation of both APP and ferritin (an iron storage protein) by similar mechanisms. We reported that APP was regulated at the translational level by active IL (interleukin)-1 (IL-1-responsive acute box) and IRE (iron-responsive element) RNA stem–loops in the 5′ untranslated region of APP mRNA. The APP IRE is homologous with the canonical IRE RNA stem–loop that binds the iron regulatory proteins (IRP1 and IRP2) to control intracellular iron homoeostasis by modulating ferritin mRNA translation and transferrin receptor mRNA stability. The APP IRE interacts with IRP1 (cytoplasmic cis-aconitase), whereas the canonical H-ferritin IRE RNA stem–loop binds to IRP2 in neural cell lines, and in human brain cortex tissue and in human blood lysates. The same constellation of RNA-binding proteins [IRP1/IRP2/poly(C) binding protein] control ferritin and APP translation with implications for the biology of metals in AD.
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Yin, Yifan, Chunliu He, and Biao Xu and Zhiyong Li. "Characterization of Coronary Atherosclerotic Plaque Composition Based on Convolutional Neural Network (CNN)." Molecular & Cellular Biomechanics 16, s1 (2019): 57. http://dx.doi.org/10.32604/mcb.2019.05732.
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YAHATA, Ayano, Yuichi KITAGAWA, Ayumu OKUBO, Kyohei OKUBO, Kohei SOGA, Tomoko OHSHIMA, and Hiroshi TAKEMURA. "Dental Plaque Detection on Near-infrared Hyperspectral Image using Artificial Neural Network." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2020 (2020): 2P1—F12. http://dx.doi.org/10.1299/jsmermd.2020.2p1-f12.
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Cui, Jiapeng, and Feng Tan. "Rice Plaque Detection and Identification Based on an Improved Convolutional Neural Network." Agriculture 13, no. 1 (January 9, 2023): 170. http://dx.doi.org/10.3390/agriculture13010170.
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Rice diseases are extremely harmful to rice growth, and achieving the identification and rapid classification of rice disease spots is an essential means to promote intelligent rice production. However, due to the large variety of rice diseases and the similar appearance of some rice diseases, the existing deep learning methods are less effective at classification and detection. Aiming at such problems, this paper took the spot images of five common rice diseases as the research object and constructed a rice disease data set containing 2500 images of rice bacterial blight, sheath blight, flax leaf spot, leaf streak and rice blast, including 500 images of each disease. An improved lightweight deep learning network model was proposed to realize the accurate identification of disease types and disease spots. A rice disease image classification network was designed based on the RlpNet (rice leaf plaque net) network model, Which is the underlying network, in addition to the YOLOv3 target detection network model in order to achieve the optimization of the feature extraction link, i.e., upsampling by transposed convolution and downsampling by dilated convolution. The improved YOLOv3 model was compared with traditional convolutional neural network models, including the AlexNet, GoogLeNet, VGG-16 and ResNet-34 models, for disease recognition, and the results showed that the average recall, average precision, average F1-score and overall accuracy of the network model for rice disease classification were 91.84%, 92.14%, 91.87% and 91.84%, respectively, which were all improved compared with the traditional algorithms. The improved YOLOv3 network model was compared with FSSD, Faster-RCNN, YOLOv3 and YOLOv4 for spot detection studies, and the results showed that it could achieve a mean average precision (mAP) of 86.72%, a detection rate (DR) of 93.92%, a frames per second (FPS) rate of 63.4 and a false alarm rate (FAR) of only 5.12%. In summary, the comprehensive performance of the proposed model was better than that of the traditional YOLOv3 algorithm, so this study provides a new method for rice disease identification and disease spot detection. It also had good performance in terms of the common detection and classification of multiple rice diseases, which provides some support for the common differentiation of multiple rice diseases and has some practical application value.
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Liu, Xiuling, Jiaxing Du, Jianli Yang, Peng Xiong, Jing Liu, and Feng Lin. "Coronary Artery Fibrous Plaque Detection Based on Multi-Scale Convolutional Neural Networks." Journal of Signal Processing Systems 92, no. 3 (January 8, 2020): 325–33. http://dx.doi.org/10.1007/s11265-019-01501-5.
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Fang, Xing, Fan Fan, and Richard J. Roman. "Cerebrovascular Dysfunction in Alzheimer’s Disease and Transgenic Rodent Models." Journal of Experimental Neurology 5, no. 2 (2024): 42–64. http://dx.doi.org/10.33696/neurol.5.087.
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Alzheimer’s Disease (AD) and Alzheimer’s Disease-Related Dementia (ADRD) are the primary causes of dementia that has a devastating effect on the quality of life and is a tremendous economic burden on the healthcare system. The accumulation of extracellular beta-amyloid (Aβ) plaques and intracellular hyperphosphorylated tau-containing neurofibrillary tangles (NFTs) in the brain are the hallmarks of AD. They are also thought to be the underlying cause of inflammation, neurodegeneration, brain atrophy, and cognitive impairments that accompany AD. The discovery of APP, PS1, and PS2 mutations that increase Aβ production in families with early onset familial AD led to the development of numerous transgenic rodent models of AD. These models have provided new insight into the role of Aβ in AD; however, they do not fully replicate AD pathology in patients. Familial AD patients with mutations that elevate the production of Aβ represent only a small fraction of dementia patients. In contrast, those with late-onset sporadic AD constitute the majority of cases. This observation, along with the failure of previous clinical trials targeting Aβ or Tau and the modest success of recent trials using Aβ monoclonal antibodies, has led to a reappraisal of the view that Aβ accumulation is the sole factor in the pathogenesis of AD. More recent studies have established that cerebral vascular dysfunction is one of the earliest changes seen in AD, and 67% of the candidate genes linked to AD are expressed in the cerebral vasculature. Thus, there is an increasing appreciation of the vascular contribution to AD, and the National Institute on Aging (NIA) and the Alzheimer’s Disease Foundation recently prioritized it as a focused research area. This review summarizes the strengths and limitations of the most commonly used transgenic AD animal models and current views about the contribution of Aβ accumulation versus cerebrovascular dysfunction in the pathogenesis of AD.
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Ghai, Roma, Kandasamy Nagarajan, Meenakshi Arora, Parul Grover, Nazakat Ali, and Garima Kapoor. "Current Strategies and Novel Drug Approaches for Alzheimer Disease." CNS & Neurological Disorders - Drug Targets 19, no. 9 (December 31, 2020): 676–90. http://dx.doi.org/10.2174/1871527319666200717091513.
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Alzheimer’s Disease (AD) is a chronic, devastating dysfunction of neurons in the brain leading to dementia. It mainly arises due to neuronal injury in the cerebral cortex and hippocampus area of the brain and is clinically manifested as a progressive mental failure, disordered cognitive functions, personality changes, reduced verbal fluency and impairment of speech. The pathology behind AD is the formation of intraneuronal fibrillary tangles, deposition of amyloid plaque and decline in choline acetyltransferase and loss of cholinergic neurons. Tragically, the disease cannot be cured, but its progression can be halted. Various cholinesterase inhibitors available in the market like Tacrine, Donepezil, Galantamine, Rivastigmine, etc. are being used to manage the symptoms of Alzheimer’s disease. The paper’s objective is to throw light not only on the cellular/genetic basis of the disease, but also on the current trends and various strategies of treatment including the use of phytopharmaceuticals and nutraceuticals. Enormous literature survey was conducted and published articles of PubMed, Scifinder, Google Scholar, Clinical Trials.org and Alzheimer Association reports were studied intensively to consolidate the information on the strategies available to combat Alzheimer’s disease. Currently, several strategies are being investigated for the treatment of Alzheimer’s disease. Immunotherapies targeting amyloid-beta plaques, tau protein and neural pathways are undergoing clinical trials. Moreover, antisense oligonucleotide methodologies are being approached as therapies for its management. Phytopharmaceuticals and nutraceuticals are also gaining attention in overcoming the symptoms related to AD. The present review article concludes that novel and traditional therapies simultaneously promise future hope for AD treatment.
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Ye, Jian-Ya, Qingmao Hao, Yijun Zong, Yongqing Shen, Zhiqin Zhang, and Changsheng Ma. "Sophocarpine Attenuates Cognitive Impairment and Promotes Neurogenesis in a Mouse Model of Alzheimer’s Disease." Neuroimmunomodulation 28, no. 3 (2021): 166–77. http://dx.doi.org/10.1159/000508655.
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<b><i>Introduction:</i></b> Alzheimer’s disease (AD), which is characterized by abnormal deposition of amyloid-β (Aβ) plaques and impaired neurogenesis and cognition, still lacks an optimally effective therapeutic agent for its management, and mounting evidence has shown that inflammatory processes are implicated in AD. Sophocarpine has been reported to exert inflammation-regulating effects in various diseases. However, whether sophocarpine can exert anti-neuroinflammatory and neuroprotective effects in AD remains unclear. This study investigated whether sophocarpine could ameliorate the pathological features and potential mechanisms in a mouse AD model. <b><i>Methods:</i></b> APP/PS1 mice were treated with sophocarpine for 8 weeks. We quantified the effects of sophocarpine treatment on cognitive performance using a behavioral test. Brain Aβ deposits and neurogenesis were evaluated using immunofluorescence staining. We also assessed the morphology and inflammatory changes induced by sophocarpine administration and its expression in the hippocampus. <b><i>Results:</i></b> Administration of sophocarpine significantly alleviated cognitive impairment and reduced neural loss. APP/PS1 mice treated with sophocarpine showed reduced Aβ plaque deposits and enhanced neurogenesis. Sophocarpine markedly decreased the expression of inflammation markers and inhibited microglial activation. <b><i>Conclusions:</i></b> Sophocarpine could potentially alleviate cognitive impairment and brain damage in APP/PS1 mice with its neuroprotective effects via modulation of the inflammatory pathway.
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Caligiore, Daniele, Massimo Silvetti, Marcello D’Amelio, Stefano Puglisi-Allegra, and Gianluca Baldassarre. "Computational Modeling of Catecholamines Dysfunction in Alzheimer’s Disease at Pre-Plaque Stage." Journal of Alzheimer's Disease 77, no. 1 (September 1, 2020): 275–90. http://dx.doi.org/10.3233/jad-200276.
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Background: Alzheimer’s disease (AD) etiopathogenesis remains partially unexplained. The main conceptual framework used to study AD is the Amyloid Cascade Hypothesis, although the failure of recent clinical experimentation seems to reduce its potential in AD research. Objective: A possible explanation for the failure of clinical trials is that they are set too late in AD progression. Recent studies suggest that the ventral tegmental area (VTA) degeneration could be one of the first events occurring in AD progression (pre-plaque stage). Methods: Here we investigate this hypothesis through a computational model and computer simulations validated with behavioral and neural data from patients. Results: We show that VTA degeneration might lead to system-level adjustments of catecholamine release, triggering a sequence of events leading to relevant clinical and pathological signs of AD. These changes consist first in a midfrontal-driven compensatory hyperactivation of both VTA and locus coeruleus (norepinephrine) followed, with the progression of the VTA impairment, by a downregulation of catecholamine release. These processes could then trigger the neural degeneration at the cortical and hippocampal levels, due to the chronic loss of the neuroprotective role of norepinephrine. Conclusion: Our novel hypothesis might contribute to the formulation of a wider system-level view of AD which might help to devise early diagnostic and therapeutic interventions.
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Zhang, Huaqi, Guanglei Wang, Yan Li, Feng Lin, Yechen Han, and Hongrui Wang. "Automatic Plaque Segmentation in Coronary Optical Coherence Tomography Images." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 14 (May 9, 2019): 1954035. http://dx.doi.org/10.1142/s0218001419540351.
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Coronary optical coherence tomography (OCT) is a new high-resolution intravascular imaging technology that clearly depicts coronary artery stenosis and plaque information. Study of coronary OCT images is of significance in the diagnosis of coronary atherosclerotic heart disease (CAD). We introduce a new method based on the convolutional neural network (CNN) and an improved random walk (RW) algorithm for the recognition and segmentation of calcified, lipid and fibrotic plaque in coronary OCT images. First, we design CNN with three different depths (2, 4 or 6 convolutional layers) to perform the automatic recognition and select the optimal CNN model. Then, we device an improved RW algorithm. According to the gray-level distribution characteristics of coronary OCT images, the weights of intensity and texture term in the weight function of RW algorithm are adjusted by an adaptive weight. Finally, we apply mathematical morphology in combination with two RWs to accurately segment the plaque area. Compared with the ground truth of clinical segmentation results, the Jaccard similarity coefficient (JSC) of calcified and lipid plaque segmentation results is 0.864, the average symmetric contour distance (ASCD) is 0.375[Formula: see text]mm, the JSC and ASCD reliabilities are 88.33% and 92.50% respectively. The JSC of fibrotic plaque is 0.876, the ASCD is 0.349[Formula: see text]mm, the JSC and ASCD reliabilities are 90.83% and 95.83% respectively. In addition, the average segmentation time (AST) does not exceed 5 s. Reliable and significantly improved results have been achieved in this study. Compared with the CNN, traditional RW algorithm and other methods. The proposed method has the advantages of fast segmentation, high accuracy and reliability, and holds promise as an aid to doctors in the diagnosis of CAD.
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Lekadir, Karim, Alfiia Galimzianova, Angels Betriu, Maria del Mar Vila, Laura Igual, Daniel L. Rubin, Elvira Fernandez, Petia Radeva, and Sandy Napel. "A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound." IEEE Journal of Biomedical and Health Informatics 21, no. 1 (January 2017): 48–55. http://dx.doi.org/10.1109/jbhi.2016.2631401.
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Kim, Gene H., Pedram Gerami, and Amy S. Paller. "Congenital hypertrichotic melanoneurocytoma: A congenital hypertrichotic plaque with overlapping neural and nevoid features." Journal of the American Academy of Dermatology 67, no. 4 (October 2012): 799–801. http://dx.doi.org/10.1016/j.jaad.2010.03.032.
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Knowles, R. B., C. Wyart, S. V. Buldyrev, L. Cruz, B. Urbanc, M. E. Hasselmo, H. E. Stanley, and B. T. Hyman. "Plaque-induced neurite abnormalities: Implications for disruption of neural networks in Alzheimer's disease." Proceedings of the National Academy of Sciences 96, no. 9 (April 27, 1999): 5274–79. http://dx.doi.org/10.1073/pnas.96.9.5274.
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Annita, Annita, Gusti Revilla, Hirowati Ali, and Almurdi Almurdi. "Adipose-Derived Mesenchymal Stem Cell (AD-MSC)-Like Cells Restore Nestin Expression and Reduce Amyloid Plaques in Aluminum Chloride (AlCl3)-Driven Alzheimer's Rat Models." Molecular and Cellular Biomedical Sciences 8, no. 3 (November 1, 2024): 159. http://dx.doi.org/10.21705/mcbs.v8i3.387.
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Background: Alzheimer's disease (AD) is a neurodegenerative disorder with a significant burden on public health, and current treatments offer limited efficacy. This study investigated the effectiveness of adipose-derived mesenchymal stem cells (AD-MSCs) on the expression of the nestin gene and amyloid plaque in an aluminum chloride (AlCl3)-driven Alzheimer's rat model.Materials and methods: AD-MSCs were characterized using flow cytometry. Adult male Wistar rats were treated with/without AlCl3 and injected with/without AD-MSCs. After 5 days of AlCl3 ingestion and 4 weeks of subsequent AD-MSCs intraperitoneal injection, behavioral and molecular assessments were conducted. The Y-maze alternation test was used to test spatial learning of rats. Nestin gene expression was evaluated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). The presence of amyloid plaque in the cortex and the hippocampus was evaluated through Congo red staining.Results: AD-MSC-like cells expressed the MSC markers CD90, CD73 and CD105. The Y-maze alternation result for rats treated with AlCl3 and AD-MSC-like cells was significantly higher compared with rats treated with AlCl3 only. Nestin gene expression was significantly higher in rats treated with AlCl3 and AD-MSC-like cells compared to those treated with AlCl3 only. After AD-MSC-like cells treatment, the Congo red staining results of rat’s cortex and hippocampus were significantly decreased.Conclusion: The findings suggest that AD-MSC-like cells possess therapeutic potential in restoring neural plasticity, amyloid plaque clearance and warrant further investigation for AD treatment. This study contributes to the emerging field of stem cell therapy for neurodegenerative diseases by highlighting the promise of AD-MSCs.Keywords: Alzheimer's disease, adipose-derived mesenchymal stem cells, neural plasticity, congo red staining, stem cell therapy
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He, Lan, Zekun Yang, Yudong Wang, Weidao Chen, Le Diao, Yitong Wang, Wei Yuan, et al. "A deep learning algorithm to identify carotid plaques and assess their stability." Frontiers in Artificial Intelligence 7 (June 17, 2024). http://dx.doi.org/10.3389/frai.2024.1321884.
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BackgroundCarotid plaques are major risk factors for stroke. Carotid ultrasound can help to assess the risk and incidence rate of stroke. However, large-scale carotid artery screening is time-consuming and laborious, the diagnostic results inevitably involve the subjectivity of the diagnostician to a certain extent. Deep learning demonstrates the ability to solve the aforementioned challenges. Thus, we attempted to develop an automated algorithm to provide a more consistent and objective diagnostic method and to identify the presence and stability of carotid plaques using deep learning.MethodsA total of 3,860 ultrasound images from 1,339 participants who underwent carotid plaque assessment between January 2021 and March 2023 at the Shanghai Eighth People’s Hospital were divided into a 4:1 ratio for training and internal testing. The external test included 1,564 ultrasound images from 674 participants who underwent carotid plaque assessment between January 2022 and May 2023 at Xinhua Hospital affiliated with Dalian University. Deep learning algorithms, based on the fusion of a bilinear convolutional neural network with a residual neural network (BCNN-ResNet), were used for modeling to detect carotid plaques and assess plaque stability. We chose AUC as the main evaluation index, along with accuracy, sensitivity, and specificity as auxiliary evaluation indices.ResultsModeling for detecting carotid plaques involved training and internal testing on 1,291 ultrasound images, with 617 images showing plaques and 674 without plaques. The external test comprised 470 ultrasound images, including 321 images with plaques and 149 without. Modeling for assessing plaque stability involved training and internal testing on 764 ultrasound images, consisting of 494 images with unstable plaques and 270 with stable plaques. The external test was composed of 279 ultrasound images, including 197 images with unstable plaques and 82 with stable plaques. For the task of identifying the presence of carotid plaques, our model achieved an AUC of 0.989 (95% CI: 0.840, 0.998) with a sensitivity of 93.2% and a specificity of 99.21% on the internal test. On the external test, the AUC was 0.951 (95% CI: 0.962, 0.939) with a sensitivity of 95.3% and a specificity of 82.24%. For the task of identifying the stability of carotid plaques, our model achieved an AUC of 0.896 (95% CI: 0.865, 0.922) on the internal test with a sensitivity of 81.63% and a specificity of 87.27%. On the external test, the AUC was 0.854 (95% CI: 0.889, 0.830) with a sensitivity of 68.52% and a specificity of 89.49%.ConclusionDeep learning using BCNN-ResNet algorithms based on routine ultrasound images could be useful for detecting carotid plaques and assessing plaque instability.
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Bhatt, Nitish, Rashmi Nedadur, Blair Warren, Sebastian Mafeld, Sneha Raju, Jason E. Fish, Bo Wang, and Kathryn L. Howe. "Abstract 347: Using Deep Convolutional Neural Networks To Automate Classification Of Carotid Plaques From Ultrasound Imaging." Arteriosclerosis, Thrombosis, and Vascular Biology 42, Suppl_1 (May 2022). http://dx.doi.org/10.1161/atvb.42.suppl_1.347.
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Background: Stroke is a devastating consequence of plaque rupture from the carotid arteries. Current management of carotid plaques involves waiting for symptoms (e.g., stroke or mini-stroke), as intervention itself has risk of stroke and not all plaques are vulnerable to rupture. There is a need to better risk-stratify plaque that causes stroke. Carotid ultrasound (US) is a non-invasive and inexpensive visualization of plaques but is limited by human interpretation. We hypothesize that convolutional neural networks (CNNs) will identify unique features of carotid plaques for automated risk stratification. Methods: Our workflow is illustrated in Fig. a. A total of 141 B-mode US images of carotid arteries were included; 64 high-risk with symptomatic carotid plaques and 75 low-risk with no significant plaque. Data was cropped and divided into training (70%) and holdout test (30%) subsets. During model training, an ensemble of ResNet-18 CNNs learned classification of low-risk and high-risk cases using five-fold stratified cross validation and was used to predict on the holdout test set. The model was evaluated using ROC-AUC and sensitivity. Saliency maps were used for model interpretability to highlight relevant pixels for model decisions. Results: The cross-validation AUC was 0.995 ± 0.010. The testing AUC was 0.909 and class-wise sensitivities were 0.88 (low-risk) and 0.79 (high-risk). The density plot (Fig. b) shows that the classifier correctly identifies both classes with confidence. Model interpretability using saliency maps (Fig. c) shows pixels corresponding to carotid artery vessel edges and in high-risk cases, carotid plaques. Conclusions: Using this proof-of-concept model, carotid US long axis images are sufficient to identify high-risk plaques in symptomatic patients - we now need to determine whether we can identify high-risk plaques before symptoms to prevent devastating stroke caused by carotid disease.
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Zhang, Han, Yixian Wang, Mingyu Liu, Yao Qi, Shikai Shen, Qingwei Gang, Han Jiang, Yu Lun, and Jian Zhang. "Deep Learning and Single‐Cell Sequencing Analyses Unveiling Key Molecular Features in the Progression of Carotid Atherosclerotic Plaque." Journal of Cellular and Molecular Medicine 28, no. 22 (November 2024). http://dx.doi.org/10.1111/jcmm.70220.
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ABSTRACTRupture of advanced carotid atherosclerotic plaques increases the risk of ischaemic stroke, which has significant global morbidity and mortality rates. However, the specific characteristics of immune cells with dysregulated function and proven biomarkers for the diagnosis of atherosclerotic plaque progression remain poorly characterised. Our study elucidated the role of immune cells and explored diagnostic biomarkers in advanced plaque progression using single‐cell RNA sequencing and high‐dimensional weighted gene co‐expression network analysis. We identified a subcluster of monocytes with significantly increased infiltration in the advanced plaques. Based on the monocyte signature and machine‐learning approaches, we accurately distinguished advanced plaques from early plaques, with an area under the curve (AUC) of 0.899 in independent external testing. Using microenvironment cell populations (MCP) counter and non‐negative matrix factorisation, we determined the association between monocyte signatures and immune cell infiltration as well as the heterogeneity of the patient. Finally, we constructed a convolutional neural network deep learning model based on gene‐immune correlation, which achieved an AUC of 0.933, a sensitivity of 92.3%, and a specificity of 87.5% in independent external testing for diagnosing advanced plaques. Our findings on unique subpopulations of monocytes that contribute to carotid plaque progression are crucial for the development of diagnostic tools for clinical diseases.
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Volleberg, R. H. J. A., G. Rodgriguez Esteban, J. H. Q. Mol, S. Quax, I. Isgum, C. I. Sanchez, B. Van Ginneken, J. Thannhauser, and N. Van Royen. "Deep-learning based analysis of intracoronary optical coherence tomography images: detection and characterization of lipid plaques." European Heart Journal 44, Supplement_2 (November 2023). http://dx.doi.org/10.1093/eurheartj/ehad655.2120.
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Abstract Background Intracoronary optical coherence tomography (OCT) enables in vivo detection and characterization of atherosclerotic disease. However, manual assessment of OCT images is time-consuming and subject to intra- and interobserver variability. Therefore, automated and trustworthy methods for plaque assessment are warranted. Purpose To develop and validate an artificial intelligence based algorithm for detection and characterization of lipid plaques on OCT images. Methods From the prospective, observational PECTUS-obs study, we studied a representative subset of OCT pullbacks performed in fractional flow reserve-negative non-culprit lesions in patients presenting with myocardial infarction. Manual segmentation of cross-sectional frames (e.g. lumen, tunica intima including the fibrous cap, tunica media and lipid pools) was performed by trained experts. Pullbacks were randomly divided into a training and test set. For automated segmentation and analysis, a two-dimensional no-new U-shaped neural network (nnUNet) was constructed with 5-fold cross validation. To test the diagnostic performance of the nnUnet for detection of lipid plaques, sensitivity, specificity and Cohen’s kappa were calculated. As for plaque characterization, the lipid arc and minimal fibrous cap thickness were measured manually in the test set on each frame containing a lipid plaque. Values of lipid arc and minimal fibrous cap thickness obtained following automated assessment were compared to manual analysis using the intraclass correlation coefficient (ICC) for absolute agreement. Results In total, 1215 frames were used for training and 162 frames for testing of the nnUNet. Lipid plaques were present in 77 frames (47.5%) in the test set. Substantial agreement (κ=0.79) for detection of a lipid plaque was achieved with a sensitivity and specificity of 96.1% and 83.5%, respectively. As for plaque characterization, we found good reliability for lipid arc assessment (ICC 0.88, mean difference 6±32º) and moderate reliability for assessment of the minimal fibrous cap thickness (ICC 0.54, mean difference 23±140µm). Conclusion We developed a deep learning algorithm for automated analysis of intracoronary OCT images, that was able to accurately detect lipid plaques. Moreover, the algorithm showed promising results in terms of plaque characterization. With further refinements, the developed algorithm has the potential to reduce the time required for OCT interpretation, ultimately enabling more efficient, real-time use of this imaging modality in clinical practice.
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Reiter, Russel J., Ramaswamy Sharma, Alejandro Romero, Fedor Simko, Alberto Dominguez-Rodriguez, and Daniel P. Cardinali. "Melatonin stabilizes atherosclerotic plaques: an association that should be clinically exploited." Frontiers in Medicine 11 (December 11, 2024). https://doi.org/10.3389/fmed.2024.1487971.
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Atherosclerosis is the underlying factor in the premature death of millions of humans annually. The cause of death is often a result of the rupture of an atherosclerotic plaque followed by the discharge of the associated molecular debris into the vessel lumen which occludes the artery leading to ischemia of downstream tissue and to morbidity or mortality of the individual. This is most serious when it occurs in the heart (heart attack) or brain (stroke). Atherosclerotic plaques are classified as either soft, rupture-prone, or hard, rupture resistant. Melatonin, the production of which diminishes with age, has major actions in converting soft to hard plaques. Experimentally, melatonin reduces the ingrowth of capillaries from the tunica media into the plaque relieving pressure on the plaque, reducing intraplaque hemorrhage and limiting the size of the necrotic core. Moreover, melatonin promotes the formation of collagen by invading vascular smooth muscle cells which strengthen the plaque crown making it resistant to rupture. Melatonin is also a powerful antioxidant and anti-inflammatory agent such that is reduces oxidative damage to tissues associated with the plaque and limits inflammation both of which contribute to plaque cap weakness. Additional benefits of melatonin relative to atherosclerosis is inhibition of adhesion molecules on the endothelial cell surface, limiting the invasion of monocytes into the arterial intima, and reducing the conversion of anti-inflammatory M2 macrophages to pro-inflammatory M1 macrophages. Given the high physiological and financial cost of cardiac and neural ischemic events, this information should be given high priority in the clinical setting.
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Yin, Yifan, Chunliu He, Biao Xu, and Zhiyong Li. "Coronary Plaque Characterization From Optical Coherence Tomography Imaging With a Two-Pathway Cascade Convolutional Neural Network Architecture." Frontiers in Cardiovascular Medicine 8 (June 16, 2021). http://dx.doi.org/10.3389/fcvm.2021.670502.
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Background: The morphological structure and tissue composition of a coronary atherosclerotic plaque determine its stability, which can be assessed by intravascular optical coherence tomography (OCT) imaging. However, plaque characterization relies on the interpretation of large datasets by well-trained observers. This study aims to develop a convolutional neural network (CNN) method to automatically extract tissue features from OCT images to characterize the main components of a coronary atherosclerotic plaque (fibrous, lipid, and calcification). The method is based on a novel CNN architecture called TwopathCNN, which is utilized in a cascaded structure. According to the evaluation, this proposed method is effective and robust in the characterization of coronary plaque composition from in vivo OCT imaging. On average, the method achieves 0.86 in F1-score and 0.88 in accuracy. The TwopathCNN architecture and cascaded structure show significant improvement in performance (p < 0.05). CNN with cascaded structure can greatly improve the performance of characterization compared to the conventional CNN methods and machine learning methods. This method has a higher efficiency, which may be proven to be a promising diagnostic tool in the detection of coronary plaques.
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Zhu, Xi, Wei Xia, Zhuqing Bao, Yaohui Zhong, Yu Fang, Fei Yang, Xiaohua Gu, Jing Ye, and Wennuo Huang. "Artificial Intelligence Segmented Dynamic Video Images for Continuity Analysis in the Detection of Severe Cardiovascular Disease." Frontiers in Neuroscience 14 (February 10, 2021). http://dx.doi.org/10.3389/fnins.2020.618481.
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In this paper, an artificial intelligence segmented dynamic video image based on the process of intensive cardiovascular and cerebrovascular disease monitoring is deeply investigated, and a sparse automatic coding deep neural network with a four layers stack structure is designed to automatically extract the deep features of the segmented dynamic video image shot, and six categories of normal, atrial premature, ventricular premature, right bundle branch block, left bundle branch block, and pacing are achieved through hierarchical training and optimization. Accurate recognition of heartbeats with an average accuracy of 99.5%. It provides technical assistance for the intelligent prediction of high-risk cardiovascular diseases like ventricular fibrillation. An intelligent prediction algorithm for sudden cardiac death based on the echolocation network was proposed. By designing an echolocation network with a multilayer serial structure, an intelligent distinction between sudden cardiac death signal and non-sudden death signal was realized, and the signal was predicted 5 min before sudden death occurred, with an average prediction accuracy of 94.32%. Using the self-learning capability of stack sparse auto-coding network, a large amount of label-free data is designed to train the stack sparse auto-coding deep neural network to automatically extract deep representations of plaque features. A small amount of labeled data then introduced to micro-train the entire network. Through the automatic analysis of the fiber cap thickness in the plaques, the automatic identification of thin fiber cap-like vulnerable plaques was achieved, and the average overlap of vulnerable regions reached 87%. The overall time for the automatic plaque and vulnerable plaque recognition algorithm was 0.54 s. It provides theoretical support for accurate diagnosis and endogenous analysis of high-risk cardiovascular diseases.
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Bot, Ilze, Saskia C. de Jager, Martine Bot, Sandra H. van Heiningen, Theo J. van Berkel, Jan H. von der Thüsen, and Erik A. Biessen. "Abstract 5066: Substance P Mediated Adventitial Mast Cell Activation Induces Intraplaque Hemorrhage in Advanced Atherosclerosis." Circulation 120, suppl_18 (November 3, 2009). http://dx.doi.org/10.1161/circ.120.suppl_18.s1043.
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Previously, we and others have shown that mast cells, present in intima and adventitia of advanced atherosclerotic lesions, play an important role in plaque progression and destabilization. However, the nature of the endogenous trigger for activation of these (peri)vascular cells during atherosclerosis is still unresolved. In this study, we show that perivascular mast cell content as demonstrated by CD117 staining correlates with the number of neurofilament + nerve fibers in the adventitia of human coronary atherosclerotic plaque specimens (P<0.05, r=0.42), suggestive of neural regulation of mast cell activation. Our attention turned to the neuropeptide substance P (SP) as mediator of mast cell activation via the neurokinin-1 receptor. Local perivascular administration of SP (0.1 nmol in 25% (w/v) F-127 pluronic gel) to advanced carotid artery plaques in apoE −/− mice did not affect plaque size at 3 days after challenge (SP: 67±14*10 3 μ m 2 versus controls: 51±10*10 3 μ m 2 , P=NS), however the number of adventitial mast cells was significantly enhanced in SP treated mice compared to PBS controls (4.9±0.8 versus 2.2±0.5 mast cells/mm 2 adventitial tissue, P<0.01). Also, mast cell activation status was increased in the SP challenged group (56±9% compared to 29±9% in controls, P<0.05). This was accompanied by a significant increase in the incidence of intraplaque hemorrhages (IPHs) in SP treated mice (5/12 compared to 0/15 in controls, P=0.01). SP mediated mast cell recruitment was inhibited by co-administration of the neurokinin-1 receptor antagonist Spantide-I (2.6±0.4 mast cells/mm 2 adventitial tissue, P<0.01), while in these mice hardly any IPHs occurred (1/16, P=0.06). Furthermore, SP was not effective in inducing IPHs in advanced carotid artery plaques of mast cell deficient apoE −/− Kit(W −sh /W −sh ) mice (1/18, P<0.05), establishing the critical involvement of mast cells in SP elicited plaque destabilization. In conclusion, our data suggest that neurotransmitters such as SP are capable of promoting mast cell dependent plaque destabilization and our study thus provides a new, direct link between neural factors and vascular inflammation involving mast cells, which may be particularly relevant in acute cardiovascular syndromes.