Background Coronary artery calcification is a well-known marker of atherosclerotic plaque burden.High-resolution intravascular optical coherence tomography(OCT)imaging has shown the potential to characterize the detai...Background Coronary artery calcification is a well-known marker of atherosclerotic plaque burden.High-resolution intravascular optical coherence tomography(OCT)imaging has shown the potential to characterize the details of coronary calcification in vivo.In routine clinical practice,it is a time-consuming and laborious task for clinicians to review the over 250 images in a single pullback.Besides,the imbalance label distribution within the entire pullbacks is another problem,which could lead to the failure of the classifier model.Given the success of deep learning methods with other imaging modalities,a thorough understanding of calcified plaque detection using Convolutional Neural Networks(CNNs)within pullbacks for future clinical decision was required.Methods All 33 IVOCT clinical pullbacks of 33 patients were taken from Affiliated Drum Tower Hospital,Nanjing University between December 2017 and December 2018.For ground-truth annotation,three trained experts determined the type of plaque that was present in a B-Scan.The experts assigned the labels'no calcified plaque','calcified plaque'for each OCT image.All experts were provided the all images for labeling.The final label was determined based on consensus between the experts,different opinions on the plaque type were resolved by asking the experts for a repetition of their evaluation.Before the implement of algorithm,all OCT images was resized to a resolution of 300×300,which matched the range used with standard architectures in the natural image domain.In the study,we randomly selected 26 pullbacks for training,the remaining data were testing.While,imbalance label distribution within entire pullbacks was great challenge for various CNNs architecture.In order to resolve the problem,we designed the following experiment.First,we fine-tuned twenty different CNNs architecture,including customize CNN architectures and pretrained CNN architectures.Considering the nature of OCT images,customize CNN architectures were designed that the layers were fewer than 25 layers.Then,three with good performance were selected and further deep fine-tuned to train three different models.The difference of CNNs was mainly in the model architecture,such as depth-based residual networks,width-based inception networks.Finally,the three CNN models were used to majority voting,the predicted labels were from the most voting.Areas under the receiver operating characteristic curve(ROC AUC)were used as the evaluation metric for the imbalance label distribution.Results The imbalance label distribution within pullbacks affected both convergence during the training phase and generalization of a CNN model.Different labels of OCT images could be classified with excellent performance by fine tuning parameters of CNN architectures.Overall,we find that our final result performed best with an accuracy of 90%of'calcified plaque'class,which the numbers were less than'no calcified plaque'class in one pullback.Conclusions The obtained results showed that the method is fast and effective to classify calcific plaques with imbalance label distribution in each pullback.The results suggest that the proposed method could be facilitating our understanding of coronary artery calcification in the process of atherosclerosis andhelping guide complex interventional strategies in coronary arteries with superficial calcification.展开更多
Plaque erosion,together with plaque rupture,is a common cause for acute coronary syndrome(ACS).Plaque erosion alone is responsible for about one third of the patients with ACS.Eroded plaque is defined as thrombosed,en...Plaque erosion,together with plaque rupture,is a common cause for acute coronary syndrome(ACS).Plaque erosion alone is responsible for about one third of the patients with ACS.Eroded plaque is defined as thrombosed,endothelium-absent and non-ruptured but often-inflamed plaques based on histological findings.Even though there is efficient imaging technologies to detect the eroded plaque in vivo and tailored treatment strategy has also been developed for ACScaused by erosion in clinics,the pathogenesis mechanisms that cause plaque erosion are not fully understood.It is widely postulated that thrombus formation and endothelial apoptosis(the precursors of plaque erosion)have closed association with biomechanical conditions in the coronary vessel.Revealing of the mechanical conditions in the eroded plaque could advance our knowledge in understanding the formation of plaque erosion.To this end,patient-specific OCT-based fluid-structure interaction(FSI)models were developed to investigate the plaque biomechanical conditions and investigate the impact of erosioninduced inflammation on biomechanical conditions.In vivo OCTand Biplane X-ray angiographic data of eroded coronary plaque were acquired from one male patient(age:64). OCT images were segmented manually with external elastic membrane contour and the trailing edge of the lipid-rich necrotic core(lipid)assumed to have positive remodeling ratio 1.1.Locations with luminal surface having direct contact with intraluminal thrombus on OCT images were identified erosion sites.Fusion of OCT and biplane X-ray angiographic data were performed to obtain the 3D coronary geometry.OCT-based FSI models with pre-shrink-stretch process and anisotropic material properties were constructed following previously established procedures.To reflect tissue weakening caused by erosion-induced inflammation,the material stiffness of plaque intima at the erosion site was adjust to one tenth of un-eroded fibrous plaque tissue.Three FSI models were constructed to investigate the impacts of inflammation and lipid component on plaque biomechanics:M1,without erosion(this means plaque intima at the erosion sites were not softened)and without inclusion of lipid component;M2,with erosion but no lipid;M3,with erosion and inclusion of lipid.FSI models were solved by ADINA to obtain the biomechanical conditions at peak blood pressure including plaque wall stress/strain(PWS/PWSn)and flow wall shear stress(WSS).The average values of three biomechanical conditions at the erosion sites and at the fibrous cap overlaying lipid component were calculated from three models for analysis.The results of M1 and M2 were compared to investigate the impact of erosion-induced inflammation on plaque biomechanics.Mean PWS value decreases from 49.98 kPa to 18.83 kPa(62.32%decrease)while Mean PWSn value increases from 0.123 1 to 0.138 4(12%increase)as the material stiffness becomes 10times soft.Comparing M2 and M3 at the cap sites,M3(with inclusion of lipid)will elevates mean PWS and PWSn values by48.59%and 16.09%,respectively.The impacts of erosion and lipid on flow shear stress were limited(<2%).To conclude,erosion-induced inflammation would lead to lower stress distribution but larger strain distribution,while lipid would elevate both stress and strain conditions.This shows the influence of erosion and lipid component has impacts on stress/strain cal-culations which are closely related to plaque assessment.展开更多
Background Current bottleneck of patient-specific coronary plaque model construction is the resolution of in vivo medical imaging.The threshold of cap thickness of vulnerable coronary plaques is 65 microns,while the r...Background Current bottleneck of patient-specific coronary plaque model construction is the resolution of in vivo medical imaging.The threshold of cap thickness of vulnerable coronary plaques is 65 microns,while the resolution of in vivo coronary intravascular ultrasound(IVUS)images is 150-200 microns,which is not enough to identify vulnerable plaques with thin caps and construct accurate biomechanical plaque models.Optical coherence tomography(OCT)with a 15-20μm resolution has the capacity to identify thin fibrous cap.IVUS and OCT images could complement each other and provide for more accurate plaque morphology,especially,fibrous cap thickness measurements.A modeling approach combining IVUS and OCT was introduced in our previous publication for cap thickness quantification and more accurate cap stress/strain calculations.In this paper,patient baseline and follow-up IVUS and OCT data were acquired and multimodality image-based Fluidstructure interaction(FSI)models combining 3D IVUS,OCT,angiography were constructed to better quantify human coronary atherosclerotic plaque morphology and plaque stress/strain conditions and investigate the relationship of plaque vulnerability and morphological and mechanical factors.Methods Baseline and 10-Month follow-up in vivo IVUS and OCT coronary plaque data were acquired from one patient with informed consent obtained.Co-registration and segmentation of baseline and follow-up IVUS and OCT images were performed for modeling use.Baseline and follow-up 3D FSI models based on IVUS and OCT were constructed to simulate the mechanical factors which integrating plaque morphology were employed to predict plaque vulnerability.These 3D models were solved by ADINA(ADINA R&D,Watertown,MA,USA).The quantitative indices of cap thickness,lipid percentage were classified according to histological literatures and denoted as Cap Index and Lipid Index.Cap Index,Lipid Index and Morphological Plaque Vulnerability Index(MPVI)were chosen to quantify plaque vulnerability,respectively.Random forest(RF)which was based 13 extracted features including morphological and mechanical factors was used for plaque vulnerability classification and prediction.Over sampling scheme and a 5-fold crossvalidation procedure was employed in all 45 slices for training and testing sets.Single and all different combinations of morphological and mechanical risk factors were used for plaque progression prediction.Results When Cap Index was used as the measurement,minimum cap thickness(MCT)was the best single predictor which area under curve(AUC)is 0.782 0;the combination of MCT,critical plaque wall strain(CPWSn),critical wall shear stress(CWSS)and cap wall shear stress(CapWSS)was the best predictor with ACU=0.868 6.When Lipid Index was used as the measurement,the lipid percentage(LP)was the best single predictor which AUC value is 0.857 8;the combination of Mean cap thickness(MeanCT),LP,CWSS and cap plaque wall stress(CapPWS)and was the best predictor with ACU=0.9821.When MPVI was used as the measurement,MCT was the best single predictor which AUC value is 0.782 9;the combination of MCT,LP,plaque area(PA),CPWSn and CapWSS was the best predictor with ACU=0.872 9.Conclusions Combinations of morphological and mechanical risk factors had higher prediction accuracy,compared to the prediction of single factors and other combination of morphological factors.展开更多
Introduction Stroke or heart attack,the leading cause of death and disability worldwide,is usually caused by rupture of atheromatous plaque.Therefore,the identification of vulnerable atheroma pre rupture has become ex...Introduction Stroke or heart attack,the leading cause of death and disability worldwide,is usually caused by rupture of atheromatous plaque.Therefore,the identification of vulnerable atheroma pre rupture has become extremely important for patient risk stratification.Previous studies have shown that the vulnerable plaque,i.e.one that is prone to rupture with thromboembolic complications,is often associated with a thin fibrous cap,a large lipid core and a high inflammatory burden.The mechanism of plaque rupture is not entirely clear but is thought to be a multi-factorial process involving thinning and weakening of the fibrous cap by enzymes secreted by activa-展开更多
China Cardiovascular Disease Report 2017(Summary)pointed out that at present,cardiovascular diseases(CVD)account for the highest number of deaths among urban and rural residents.In the middle or later stages of athero...China Cardiovascular Disease Report 2017(Summary)pointed out that at present,cardiovascular diseases(CVD)account for the highest number of deaths among urban and rural residents.In the middle or later stages of atherosclerosis,the plaques become increasingly unstable with high chance to rupture,which may lead acute death from coronary heart diseases.Medical imaging and image-based computational modeling have been used in recent years to quantify ather-osclerotic plaque morphological and biomechanical characteristics and predict the coronary plaque growth and rupture processes.Analyzing the vulnerability of plaques effectively could lead to better patient screening strategies and enable physicians to adopt timely and necessary intervention or conservative treatment.Earlier investigations of vulnerable plaques were mostly based on histopathological data.With the accumulation of experience in pathology and the gradual enrichment of autopsy materials,the criteria for the diagnosis of vulnerable plaques appeared in 2001,mainly manifested as the necrotic lipid nuclei,fibrous caps that are infiltrated by a large number of macrophages,and fibrous cap thickness less than 65μm.Because of the obvious importance of the thin fibrous cap in the study of plaque vulnerability,it has been a focus of attention by many investigations.Watson,M.G.et al.are concerned about the formation of early fibrous caps in recent years.The presentation of local maximum stress on plaque further confirmed the importance of thin fibrous cap.The development of medical images has greatly promoted the study of coronary atherosclerosis.Compared with autopsy ex vivo,medical image could provide plaque data under in vivo conditions and greatly promote the study of coronary atherosclerosis.Huang XY et al.used ex vivo magnetic resonance imaging(MRI)to study the relationship between plaque wall stress(PWS)and death caused by coronary artery disease.Due to technical limitations and the accessibility of the coronary artery in the body,MRI is not widely used for in vivo coronary studies.Interventional intravascular ultrasound(IVUS),with an image resolution of 150-200μm,has been used in research and clinical practice to identify plaques,quantify plaque morphology,and characterize plaque components.More recently,optical coherence tomography(OCT),with its resolution of 5-10μm,has emerged as an imaging modality which can be used to detect thin fibrous caps and improve diagnostic accuracy.It is commonly believed that mechanical forces play an important role in plaque progression and rupture.Image-based biomechanical plaque models have been developed and used to quantify plaque mechanical conditions and seek their linkage to plaque progression and vulnerability development activities.Based on recent advances in imaging and modeling,this paper attempts to provide a brief review on plaque research,including histological classification,image preparation,biomechanical modeling and analysis methods including medical imaging techniques represented by intravascular ultrasound(IVUS)and optical coherence tomography(OCT),computational modeling and their applications in plaque progression and vulnerability analyses and predictions.The clinical application and future development direction are also briefly described.We focus more on human coronary plaque modeling and mainly included results from our group for illustration purpose.We apologize in advance for our limitations.展开更多
Iron-modified biochar(FeOS)is known to be effective at immobilization of arsenic(As)in soils.A pot experiment was conducted to investigate the effects of FeOS on As availability and ttransportation in the soil-rice sy...Iron-modified biochar(FeOS)is known to be effective at immobilization of arsenic(As)in soils.A pot experiment was conducted to investigate the effects of FeOS on As availability and ttransportation in the soil-rice system at different growth stages of rice with different pollution levels.The results showed that Fe concentration decreased and As concentration increased in paddy soils with the FeOS addition,especially in 120 mg/kg As treatment,the As concentration decreased by 16.46%and 30.56%at the maturity stage with 0.5%and 1%FeOS additions,respectively.Compared with the control,the application of FeOS reduced the arsenic content in rice tissues and increased the biomass,with the root biomass increased by 12.68%and the shoot biomass was increased by 8.94%with the addition of 1%FeOS.This may be related to the promotion of iron plaque formation and the transformation of microbial community structure in FeOS treatments,in accordance with the result of gene abundance and Fe/As contents of iron plaque in the study.This study is expected to provide further support and theoretical basis for the application of FeOS in the remediation of As contaminated paddy soil.展开更多
Direct gene transfer into somatic tissue in vivo is adeveloping technology with potential application forcancer gene therapy. Retrovirus vector, which was aneffective vehicle, still has some disadvantages ingenerating...Direct gene transfer into somatic tissue in vivo is adeveloping technology with potential application forcancer gene therapy. Retrovirus vector, which was aneffective vehicle, still has some disadvantages ingenerating high titer recombinant vectors andmanipulating to mediate in viro gene transfer. In thispaper, recombinant vaccinia virus vector encoding展开更多
Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and rupture.Traditional medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(...Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and rupture.Traditional medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(IVUS)were unable to identify vulnerable plaques due to their limited resolution.Fortunately,optical coherence tomography(OCT)is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque.In particular,it has the ability to identify plaques with fibrous cap thickness<65μm,an accepted threshold value for vulnerable plaques.However,segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective.To overcome time consumption,several methodologies have been proposed for automatic segmentation of OCT images but most of these methods were still limited by intricate image preprocessing and expensive computation.In this research,two automatic segmentation methods for intracoronary OCT image based on support vector machine(SVM)and convolutional neural network(CNN)were performed to identify the plaque region and characterize plaque components.Methods In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained.OCT were obtained from ILUMIEN OPTIS System(St.Jude,Minnesota,MN).The OCT catheter was traversed to the segment of interest and the catheter pullback was limited at a rate of 20 mm/sec.Following the OCT image acquisition,the IVUS catheter was traversed distally though the artery to the same coronary segment(Volcano Therapeutics,Rancho Cordova)and the catheter pullback speed was at a standard rate of 0.5 mm/sec.Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for our segmentation study.Manual OCT segmentation was performed by experts and used as gold standard in the automatic segmentations.VH-IVUS was used as references and guide by the experts in the manual segmentation process.Three plaque component tissue classes were identified from OCT images in this work:lipid tissue(LT),fibrous tissue(FT)and background(BG).Procedures using two machine learning methods(CNN and SVM)were developed to segment OCT images,respectively.For CNN method,the U-Net architecture was selected due to its good performance in very different biomedical segmentation and very few annotated images.For SVM method,local binary patterns(LBPs),gray level co-occurrence matrices(GLCMs)which contains contrast,correlation,energy and homogeneity,entropy and mean value were calculated as features and assembled to feed SVM classifier.The accuracies of two segmentation methods were evaluated and compared using the OCT dataset.Segmentation accuracy is defined as the ratio of the number of pixels correctly classified over the total number of pixels.Results The overall classification accuracy based CNN method reached 95.8%,and the accuracies for LT,FT and BG were 86.8%,83.4%,and 98.2%,respectively.The overall classification accuracy based SVM was 71.9%,and per-class accuracy for LT,FT and BG was 75.4%,78.3%,and67.0%,respectively.Conclusions The two methods proposed can automatically identify plaque region and characterize plaque compositions for OCT images and potentially reduce the time spent by doctors in segmenting and evaluating coronary plaque OCT images.CNN provided better segmentation accuracies compared to those achieved by SVM.展开更多
The most common age-related neurodegenerative disease is Alzheimer disease(AD),the imbalance between Aβ generation and clearance accumulated in extracellular plaques(ECS). And aggregated hyperphosphorylated tau prote...The most common age-related neurodegenerative disease is Alzheimer disease(AD),the imbalance between Aβ generation and clearance accumulated in extracellular plaques(ECS). And aggregated hyperphosphorylated tau protein in intraneuronal neurofibrillary tangles, together with loss of cholinergic neurons, synaptic alterations, and chronic inflammation within the brain. These lead to progressive impairment of cognitive function. The sporadic form of AD is characterized by an overall impairment in Aβ clearance.(1) Immunotherapy for AD: Immunotherapy targeting Aβ clearance is believed to be a promising approach and is under active clinical investigation. Autophagy is a conserved pathway for degrading abnormal protein aggregates and is crucial for Aβ clearance. We found that oral vaccination with a recombinant AAV/Aβ vaccine increased the clearance of Aβ from the brain and improved cognitive ability in AD animal models, while the underlying mechanisms were not well understood. In this study, we first demonstrated that oral vaccination with r AAV/Aβ decreased the p62 level and up-regulated the LC3 B-Ⅱ/LC3 B-Ⅰ ratio in APP/PS1 mouse brain, suggesting enhanced autophagy. Further, inhibition of the Akt/m TOR pathway may account for autophagy enhancement. We also found increased anti-Aβ antibodies in the sera of APP/PS1 mice with oral vaccination, accompanied by elevation of complement factors C1 q and C3 levels in the brain. Our results indicate that autophagy is closely involved in oral vaccination-induced Aβ clearance and modulating the autophagy pathway may be an important strategy for pre-clinical stage of AD(PCAD) prevention and intervention.(2)ECS in AD: Strategies that promote local growth of lymphatic vessels have the potential to improve clearance Aβ by meningeal lymphatics. whether enhancing clearance at the blood–brain barrier can improve lymphatic drainage function, remains to be addressed. Understanding substance transportation in brain ECS, especially in deep brain is essential for the complete answer to the question of how brain functions in the absence of a lymphatic drainage pathway. The brain interstitial fluid(ISF) drainage in deep brain was recently studied using a tracer-based MRI method, and a non-uniform ISF drainage was demonstrated with various distribution territories and movement speeds in different regions. In addition, Aβ deposition in the ECS and rescued memory in an APP/PS1 transgenic mouse of AD model,our findings are expected to have a potentially significant influence on brain-inspired and artificial intelligence, the future of which is very promising. Immunotherapy and local brain drug delivery via the brain ECS could circumvent the BBB and reduce systemic toxicity. Updated knowledge of the whole-brain ISF drainage system(ISS) provides a beneficial reference for improving the immunotherapeutic efficacy via ISS. New insights into how behave our and genetics modify ECS-ISS function should lead to the development of new preventive tools for PCAD and novel immunotherapy targeting Aβ clearance therapeutic targets.展开更多
基金supported in part by the National Natural Science Foundation of China ( NSFC ) ( 11772093)ARC ( FT140101152)
文摘Background Coronary artery calcification is a well-known marker of atherosclerotic plaque burden.High-resolution intravascular optical coherence tomography(OCT)imaging has shown the potential to characterize the details of coronary calcification in vivo.In routine clinical practice,it is a time-consuming and laborious task for clinicians to review the over 250 images in a single pullback.Besides,the imbalance label distribution within the entire pullbacks is another problem,which could lead to the failure of the classifier model.Given the success of deep learning methods with other imaging modalities,a thorough understanding of calcified plaque detection using Convolutional Neural Networks(CNNs)within pullbacks for future clinical decision was required.Methods All 33 IVOCT clinical pullbacks of 33 patients were taken from Affiliated Drum Tower Hospital,Nanjing University between December 2017 and December 2018.For ground-truth annotation,three trained experts determined the type of plaque that was present in a B-Scan.The experts assigned the labels'no calcified plaque','calcified plaque'for each OCT image.All experts were provided the all images for labeling.The final label was determined based on consensus between the experts,different opinions on the plaque type were resolved by asking the experts for a repetition of their evaluation.Before the implement of algorithm,all OCT images was resized to a resolution of 300×300,which matched the range used with standard architectures in the natural image domain.In the study,we randomly selected 26 pullbacks for training,the remaining data were testing.While,imbalance label distribution within entire pullbacks was great challenge for various CNNs architecture.In order to resolve the problem,we designed the following experiment.First,we fine-tuned twenty different CNNs architecture,including customize CNN architectures and pretrained CNN architectures.Considering the nature of OCT images,customize CNN architectures were designed that the layers were fewer than 25 layers.Then,three with good performance were selected and further deep fine-tuned to train three different models.The difference of CNNs was mainly in the model architecture,such as depth-based residual networks,width-based inception networks.Finally,the three CNN models were used to majority voting,the predicted labels were from the most voting.Areas under the receiver operating characteristic curve(ROC AUC)were used as the evaluation metric for the imbalance label distribution.Results The imbalance label distribution within pullbacks affected both convergence during the training phase and generalization of a CNN model.Different labels of OCT images could be classified with excellent performance by fine tuning parameters of CNN architectures.Overall,we find that our final result performed best with an accuracy of 90%of'calcified plaque'class,which the numbers were less than'no calcified plaque'class in one pullback.Conclusions The obtained results showed that the method is fast and effective to classify calcific plaques with imbalance label distribution in each pullback.The results suggest that the proposed method could be facilitating our understanding of coronary artery calcification in the process of atherosclerosis andhelping guide complex interventional strategies in coronary arteries with superficial calcification.
基金supported in part by NSFC ( 11672001,11802060)Jiangsu NSF ( BK20180352)Jiangsu Province Science and Technology Agency ( BE2016785)
文摘Plaque erosion,together with plaque rupture,is a common cause for acute coronary syndrome(ACS).Plaque erosion alone is responsible for about one third of the patients with ACS.Eroded plaque is defined as thrombosed,endothelium-absent and non-ruptured but often-inflamed plaques based on histological findings.Even though there is efficient imaging technologies to detect the eroded plaque in vivo and tailored treatment strategy has also been developed for ACScaused by erosion in clinics,the pathogenesis mechanisms that cause plaque erosion are not fully understood.It is widely postulated that thrombus formation and endothelial apoptosis(the precursors of plaque erosion)have closed association with biomechanical conditions in the coronary vessel.Revealing of the mechanical conditions in the eroded plaque could advance our knowledge in understanding the formation of plaque erosion.To this end,patient-specific OCT-based fluid-structure interaction(FSI)models were developed to investigate the plaque biomechanical conditions and investigate the impact of erosioninduced inflammation on biomechanical conditions.In vivo OCTand Biplane X-ray angiographic data of eroded coronary plaque were acquired from one male patient(age:64). OCT images were segmented manually with external elastic membrane contour and the trailing edge of the lipid-rich necrotic core(lipid)assumed to have positive remodeling ratio 1.1.Locations with luminal surface having direct contact with intraluminal thrombus on OCT images were identified erosion sites.Fusion of OCT and biplane X-ray angiographic data were performed to obtain the 3D coronary geometry.OCT-based FSI models with pre-shrink-stretch process and anisotropic material properties were constructed following previously established procedures.To reflect tissue weakening caused by erosion-induced inflammation,the material stiffness of plaque intima at the erosion site was adjust to one tenth of un-eroded fibrous plaque tissue.Three FSI models were constructed to investigate the impacts of inflammation and lipid component on plaque biomechanics:M1,without erosion(this means plaque intima at the erosion sites were not softened)and without inclusion of lipid component;M2,with erosion but no lipid;M3,with erosion and inclusion of lipid.FSI models were solved by ADINA to obtain the biomechanical conditions at peak blood pressure including plaque wall stress/strain(PWS/PWSn)and flow wall shear stress(WSS).The average values of three biomechanical conditions at the erosion sites and at the fibrous cap overlaying lipid component were calculated from three models for analysis.The results of M1 and M2 were compared to investigate the impact of erosion-induced inflammation on plaque biomechanics.Mean PWS value decreases from 49.98 kPa to 18.83 kPa(62.32%decrease)while Mean PWSn value increases from 0.123 1 to 0.138 4(12%increase)as the material stiffness becomes 10times soft.Comparing M2 and M3 at the cap sites,M3(with inclusion of lipid)will elevates mean PWS and PWSn values by48.59%and 16.09%,respectively.The impacts of erosion and lipid on flow shear stress were limited(<2%).To conclude,erosion-induced inflammation would lead to lower stress distribution but larger strain distribution,while lipid would elevate both stress and strain conditions.This shows the influence of erosion and lipid component has impacts on stress/strain cal-culations which are closely related to plaque assessment.
基金supported in part by a Jiangsu Province Science and Technology Agency grant ( BE2016785)
文摘Background Current bottleneck of patient-specific coronary plaque model construction is the resolution of in vivo medical imaging.The threshold of cap thickness of vulnerable coronary plaques is 65 microns,while the resolution of in vivo coronary intravascular ultrasound(IVUS)images is 150-200 microns,which is not enough to identify vulnerable plaques with thin caps and construct accurate biomechanical plaque models.Optical coherence tomography(OCT)with a 15-20μm resolution has the capacity to identify thin fibrous cap.IVUS and OCT images could complement each other and provide for more accurate plaque morphology,especially,fibrous cap thickness measurements.A modeling approach combining IVUS and OCT was introduced in our previous publication for cap thickness quantification and more accurate cap stress/strain calculations.In this paper,patient baseline and follow-up IVUS and OCT data were acquired and multimodality image-based Fluidstructure interaction(FSI)models combining 3D IVUS,OCT,angiography were constructed to better quantify human coronary atherosclerotic plaque morphology and plaque stress/strain conditions and investigate the relationship of plaque vulnerability and morphological and mechanical factors.Methods Baseline and 10-Month follow-up in vivo IVUS and OCT coronary plaque data were acquired from one patient with informed consent obtained.Co-registration and segmentation of baseline and follow-up IVUS and OCT images were performed for modeling use.Baseline and follow-up 3D FSI models based on IVUS and OCT were constructed to simulate the mechanical factors which integrating plaque morphology were employed to predict plaque vulnerability.These 3D models were solved by ADINA(ADINA R&D,Watertown,MA,USA).The quantitative indices of cap thickness,lipid percentage were classified according to histological literatures and denoted as Cap Index and Lipid Index.Cap Index,Lipid Index and Morphological Plaque Vulnerability Index(MPVI)were chosen to quantify plaque vulnerability,respectively.Random forest(RF)which was based 13 extracted features including morphological and mechanical factors was used for plaque vulnerability classification and prediction.Over sampling scheme and a 5-fold crossvalidation procedure was employed in all 45 slices for training and testing sets.Single and all different combinations of morphological and mechanical risk factors were used for plaque progression prediction.Results When Cap Index was used as the measurement,minimum cap thickness(MCT)was the best single predictor which area under curve(AUC)is 0.782 0;the combination of MCT,critical plaque wall strain(CPWSn),critical wall shear stress(CWSS)and cap wall shear stress(CapWSS)was the best predictor with ACU=0.868 6.When Lipid Index was used as the measurement,the lipid percentage(LP)was the best single predictor which AUC value is 0.857 8;the combination of Mean cap thickness(MeanCT),LP,CWSS and cap plaque wall stress(CapPWS)and was the best predictor with ACU=0.9821.When MPVI was used as the measurement,MCT was the best single predictor which AUC value is 0.782 9;the combination of MCT,LP,plaque area(PA),CPWSn and CapWSS was the best predictor with ACU=0.872 9.Conclusions Combinations of morphological and mechanical risk factors had higher prediction accuracy,compared to the prediction of single factors and other combination of morphological factors.
基金partially supported by the National 973 Basic Research Program of China (No.2013CB733803)the National Natural Science Foundation of China(NSFC)(No.11272091)
文摘Introduction Stroke or heart attack,the leading cause of death and disability worldwide,is usually caused by rupture of atheromatous plaque.Therefore,the identification of vulnerable atheroma pre rupture has become extremely important for patient risk stratification.Previous studies have shown that the vulnerable plaque,i.e.one that is prone to rupture with thromboembolic complications,is often associated with a thin fibrous cap,a large lipid core and a high inflammatory burden.The mechanism of plaque rupture is not entirely clear but is thought to be a multi-factorial process involving thinning and weakening of the fibrous cap by enzymes secreted by activa-
基金supported in part by NIH grant ( R01 EB004759)Jiangsu Province Science and Technology Agency grant ( BE2016785)
文摘China Cardiovascular Disease Report 2017(Summary)pointed out that at present,cardiovascular diseases(CVD)account for the highest number of deaths among urban and rural residents.In the middle or later stages of atherosclerosis,the plaques become increasingly unstable with high chance to rupture,which may lead acute death from coronary heart diseases.Medical imaging and image-based computational modeling have been used in recent years to quantify ather-osclerotic plaque morphological and biomechanical characteristics and predict the coronary plaque growth and rupture processes.Analyzing the vulnerability of plaques effectively could lead to better patient screening strategies and enable physicians to adopt timely and necessary intervention or conservative treatment.Earlier investigations of vulnerable plaques were mostly based on histopathological data.With the accumulation of experience in pathology and the gradual enrichment of autopsy materials,the criteria for the diagnosis of vulnerable plaques appeared in 2001,mainly manifested as the necrotic lipid nuclei,fibrous caps that are infiltrated by a large number of macrophages,and fibrous cap thickness less than 65μm.Because of the obvious importance of the thin fibrous cap in the study of plaque vulnerability,it has been a focus of attention by many investigations.Watson,M.G.et al.are concerned about the formation of early fibrous caps in recent years.The presentation of local maximum stress on plaque further confirmed the importance of thin fibrous cap.The development of medical images has greatly promoted the study of coronary atherosclerosis.Compared with autopsy ex vivo,medical image could provide plaque data under in vivo conditions and greatly promote the study of coronary atherosclerosis.Huang XY et al.used ex vivo magnetic resonance imaging(MRI)to study the relationship between plaque wall stress(PWS)and death caused by coronary artery disease.Due to technical limitations and the accessibility of the coronary artery in the body,MRI is not widely used for in vivo coronary studies.Interventional intravascular ultrasound(IVUS),with an image resolution of 150-200μm,has been used in research and clinical practice to identify plaques,quantify plaque morphology,and characterize plaque components.More recently,optical coherence tomography(OCT),with its resolution of 5-10μm,has emerged as an imaging modality which can be used to detect thin fibrous caps and improve diagnostic accuracy.It is commonly believed that mechanical forces play an important role in plaque progression and rupture.Image-based biomechanical plaque models have been developed and used to quantify plaque mechanical conditions and seek their linkage to plaque progression and vulnerability development activities.Based on recent advances in imaging and modeling,this paper attempts to provide a brief review on plaque research,including histological classification,image preparation,biomechanical modeling and analysis methods including medical imaging techniques represented by intravascular ultrasound(IVUS)and optical coherence tomography(OCT),computational modeling and their applications in plaque progression and vulnerability analyses and predictions.The clinical application and future development direction are also briefly described.We focus more on human coronary plaque modeling and mainly included results from our group for illustration purpose.We apologize in advance for our limitations.
基金Project(2019YFC1803601)supported by the National Key Research and Development Program of ChinaProject(41771512)supported by the National Natural Science Foundation of ChinaProject(2018RS3004)supported by Hunan Science&Technology Innovation Program,China。
文摘Iron-modified biochar(FeOS)is known to be effective at immobilization of arsenic(As)in soils.A pot experiment was conducted to investigate the effects of FeOS on As availability and ttransportation in the soil-rice system at different growth stages of rice with different pollution levels.The results showed that Fe concentration decreased and As concentration increased in paddy soils with the FeOS addition,especially in 120 mg/kg As treatment,the As concentration decreased by 16.46%and 30.56%at the maturity stage with 0.5%and 1%FeOS additions,respectively.Compared with the control,the application of FeOS reduced the arsenic content in rice tissues and increased the biomass,with the root biomass increased by 12.68%and the shoot biomass was increased by 8.94%with the addition of 1%FeOS.This may be related to the promotion of iron plaque formation and the transformation of microbial community structure in FeOS treatments,in accordance with the result of gene abundance and Fe/As contents of iron plaque in the study.This study is expected to provide further support and theoretical basis for the application of FeOS in the remediation of As contaminated paddy soil.
文摘Direct gene transfer into somatic tissue in vivo is adeveloping technology with potential application forcancer gene therapy. Retrovirus vector, which was aneffective vehicle, still has some disadvantages ingenerating high titer recombinant vectors andmanipulating to mediate in viro gene transfer. In thispaper, recombinant vaccinia virus vector encoding
基金supported in part by National Sciences Foundation of China grant ( 11672001)Jiangsu Province Science and Technology Agency grant ( BE2016785)
文摘Background Cardiovascular diseases are closely associated with atherosclerotic plaque development and rupture.Traditional medical imaging techniques such as magnetic resonance imaging(MRI)and intravascular ultrasound(IVUS)were unable to identify vulnerable plaques due to their limited resolution.Fortunately,optical coherence tomography(OCT)is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque.In particular,it has the ability to identify plaques with fibrous cap thickness<65μm,an accepted threshold value for vulnerable plaques.However,segmentation of OCT images in clinic is still mainly performed manually by physicians which is time consuming and subjective.To overcome time consumption,several methodologies have been proposed for automatic segmentation of OCT images but most of these methods were still limited by intricate image preprocessing and expensive computation.In this research,two automatic segmentation methods for intracoronary OCT image based on support vector machine(SVM)and convolutional neural network(CNN)were performed to identify the plaque region and characterize plaque components.Methods In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient’s consent obtained.OCT were obtained from ILUMIEN OPTIS System(St.Jude,Minnesota,MN).The OCT catheter was traversed to the segment of interest and the catheter pullback was limited at a rate of 20 mm/sec.Following the OCT image acquisition,the IVUS catheter was traversed distally though the artery to the same coronary segment(Volcano Therapeutics,Rancho Cordova)and the catheter pullback speed was at a standard rate of 0.5 mm/sec.Seventy-seven matched IVUS and OCT slices with good image quality and lipid cores were selected for our segmentation study.Manual OCT segmentation was performed by experts and used as gold standard in the automatic segmentations.VH-IVUS was used as references and guide by the experts in the manual segmentation process.Three plaque component tissue classes were identified from OCT images in this work:lipid tissue(LT),fibrous tissue(FT)and background(BG).Procedures using two machine learning methods(CNN and SVM)were developed to segment OCT images,respectively.For CNN method,the U-Net architecture was selected due to its good performance in very different biomedical segmentation and very few annotated images.For SVM method,local binary patterns(LBPs),gray level co-occurrence matrices(GLCMs)which contains contrast,correlation,energy and homogeneity,entropy and mean value were calculated as features and assembled to feed SVM classifier.The accuracies of two segmentation methods were evaluated and compared using the OCT dataset.Segmentation accuracy is defined as the ratio of the number of pixels correctly classified over the total number of pixels.Results The overall classification accuracy based CNN method reached 95.8%,and the accuracies for LT,FT and BG were 86.8%,83.4%,and 98.2%,respectively.The overall classification accuracy based SVM was 71.9%,and per-class accuracy for LT,FT and BG was 75.4%,78.3%,and67.0%,respectively.Conclusions The two methods proposed can automatically identify plaque region and characterize plaque compositions for OCT images and potentially reduce the time spent by doctors in segmenting and evaluating coronary plaque OCT images.CNN provided better segmentation accuracies compared to those achieved by SVM.
基金NBRD Program of China(2016YFC1306302 2016YFC1305903)+3 种基金National Natural Science Foundation of China(81571044 81471633 61450004 and 81171015)
文摘The most common age-related neurodegenerative disease is Alzheimer disease(AD),the imbalance between Aβ generation and clearance accumulated in extracellular plaques(ECS). And aggregated hyperphosphorylated tau protein in intraneuronal neurofibrillary tangles, together with loss of cholinergic neurons, synaptic alterations, and chronic inflammation within the brain. These lead to progressive impairment of cognitive function. The sporadic form of AD is characterized by an overall impairment in Aβ clearance.(1) Immunotherapy for AD: Immunotherapy targeting Aβ clearance is believed to be a promising approach and is under active clinical investigation. Autophagy is a conserved pathway for degrading abnormal protein aggregates and is crucial for Aβ clearance. We found that oral vaccination with a recombinant AAV/Aβ vaccine increased the clearance of Aβ from the brain and improved cognitive ability in AD animal models, while the underlying mechanisms were not well understood. In this study, we first demonstrated that oral vaccination with r AAV/Aβ decreased the p62 level and up-regulated the LC3 B-Ⅱ/LC3 B-Ⅰ ratio in APP/PS1 mouse brain, suggesting enhanced autophagy. Further, inhibition of the Akt/m TOR pathway may account for autophagy enhancement. We also found increased anti-Aβ antibodies in the sera of APP/PS1 mice with oral vaccination, accompanied by elevation of complement factors C1 q and C3 levels in the brain. Our results indicate that autophagy is closely involved in oral vaccination-induced Aβ clearance and modulating the autophagy pathway may be an important strategy for pre-clinical stage of AD(PCAD) prevention and intervention.(2)ECS in AD: Strategies that promote local growth of lymphatic vessels have the potential to improve clearance Aβ by meningeal lymphatics. whether enhancing clearance at the blood–brain barrier can improve lymphatic drainage function, remains to be addressed. Understanding substance transportation in brain ECS, especially in deep brain is essential for the complete answer to the question of how brain functions in the absence of a lymphatic drainage pathway. The brain interstitial fluid(ISF) drainage in deep brain was recently studied using a tracer-based MRI method, and a non-uniform ISF drainage was demonstrated with various distribution territories and movement speeds in different regions. In addition, Aβ deposition in the ECS and rescued memory in an APP/PS1 transgenic mouse of AD model,our findings are expected to have a potentially significant influence on brain-inspired and artificial intelligence, the future of which is very promising. Immunotherapy and local brain drug delivery via the brain ECS could circumvent the BBB and reduce systemic toxicity. Updated knowledge of the whole-brain ISF drainage system(ISS) provides a beneficial reference for improving the immunotherapeutic efficacy via ISS. New insights into how behave our and genetics modify ECS-ISS function should lead to the development of new preventive tools for PCAD and novel immunotherapy targeting Aβ clearance therapeutic targets.
