Search

Aims The black box nature of artificial intelligence (AI) hinders the development of interpretable AI models that are applicable in clinical practice. We aimed to develop an AI model for classifying patients of reduced left ventricular ejection fraction (LVEF) from 12-lead electrocardiograms (ECG) with the decision-interpretability. Methods and results We acquired paired ECG and echocardiography datasets from the central and co-operative institutions. For the central institution dataset, a random forest model was trained to identify patients with reduced LVEF among 29 907 ECGs. Shapley additive explanations were applied to 7196 ECGs. To extract the model’s decision criteria, the calculated Shapley additive explanations values were clustered for 192 non-paced rhythm patients in which reduced LVEF was predicted. Although the extracted criteria were different for each cluster, these criteria generally comprised a combination of six ECG findings: negative T-wave inversion in I/V5–6 leads, low voltage in I/II/V4–6 leads, Q wave in V3–6 leads, ventricular activation time prolongation in I/V5–6 leads, S-wave prolongation in V2–3 leads, and corrected QT interval prolongation. Similarly, for the co-operative institution dataset, the extracted criteria comprised a combination of the same six ECG findings. Furthermore, the accuracy of seven cardiologists’ ECG readings improved significantly after watching a video explaining the interpretation of these criteria (before, 62.9% ± 3.9% vs. after, 73.9% ± 2.4%; P = 0.02). Conclusion We visually interpreted the model’s decision criteria to evaluate its validity, thereby developing a model that provided the decision-interpretability required for clinical application.

Left ventricular dilatation (LVD) and left ventricular hypertrophy (LVH) are risk factors for heart failure, and their detection improves heart failure screening. This study aimed to investigate the ability of deep learning to detect LVD and LVH from a 12-lead electrocardiogram (ECG). Using ECG and echocardiographic data, we developed deep learning and machine learning models to detect LVD and LVH. We also examined conventional ECG criteria for the diagnosis of LVH. We calculated the area under the receiver operating characteristic (AUROC) curve, sensitivity, specificity, and accuracy of each model and compared the performance of the models. We analyzed data for 18,954 patients (mean age (standard deviation): 64.2 (16.5) years, men: 56.7%). For the detection of LVD, the value (95% confidence interval) of the AUROC was 0.810 (0.801-0.819) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods (P0.001). The AUROCs for the logistic regression and random forest methods (machine learning models) were 0.770 (0.761-0.779) and 0.757 (0.747-0.767), respectively. For the detection of LVH, the AUROC was 0.784 (0.777-0.791) for the deep learning model, and this was significantly higher than that of the logistic regression and random forest methods and conventional ECG criteria (P0.001). The AUROCs for the logistic regression and random forest methods were 0.758 (0.751-0.765) and 0.716 (0.708-0.724), respectively. This study suggests that deep learning is a useful method to detect LVD and LVH from 12-lead ECGs.

BACKGROUND Aortic regurgitation (AR) is a common heart disease, with a relatively high prevalence of 4.9% in the Framingham Heart Study. Because the prevalence increases with advancing age, an upward shift in the age distribution may increase the burden of AR. To provide an effective screening method for AR, we developed a deep learning-based artificial intelligence algorithm for the diagnosis of significant AR using electrocardiography (ECG). METHODS Our dataset comprised 29,859 paired data of ECG and echocardiography, including 412 AR cases, from January 2015 to December 2019. This dataset was divided into training, validation, and test datasets. We developed a multi-input neural network model, which comprised a two-dimensional convolutional neural network (2D-CNN) using raw ECG data and a fully connected deep neural network (FC-DNN) using ECG features, and compared its performance with the performances of a 2D-CNN model and other machine learning models. In addition, we used gradient-weighted class activation mapping (Grad-CAM) to identify which parts of ECG waveforms had the most effect on algorithm decision making. RESULTS The area under the receiver operating characteristic curve of the multi-input model (0.802; 95% CI, 0.762-0.837) was significantly greater than that of the 2D-CNN model alone (0.734; 95% CI, 0.679-0.783; p

Background Previous pathology studies demonstrated that thin-cap fibroatheroma (TCFA) is localized in specific segments of the epicardial coronary arteries. A detailed description of in vivo coronary plaques of various phenotypes has not been reported. Objectives We performed a comprehensive analysis on the distribution of coronary plaques with different phenotypes from our 3-vessel optical coherence tomography (OCT) database. Methods OCT images of all 3 coronary arteries in 131 patients were analyzed every 1 mm to assess plaque phenotype and features of vulnerability. In addition, plaques were divided into tertiles according to percent area stenosis (%AS). Results Among 534 plaques identified in 393 coronary arteries, 27.0% were fibrous plaques, 13.3% fibrocalcific plaques, 40.8% thick-cap fibroatheromas, and 18.9% thin-cap fibroatheromas (TCFA). TCFAs showed clustering in the proximal segment, particularly in the left anterior descending (LAD) artery. On the other hand, fibrous plaques were relatively evenly distributed throughout the entire length of the coronary arteries. In patients with acute coronary syndromes (ACS), TCFAs showed stronger proximal clustering in the LAD, two clustering peaks in the right coronary artery, and one clustering peak in the circumflex artery. The pattern of TCFA distribution was less obvious in non-ACS patients. The prevalence of TCFA was higher in the highest %AS tertile, compared to the lowest %AS tertile (30% vs. 9%, p Conclusions The present 3-vessel OCT study demonstrated that TCFAs cluster at specific locations in the epicardial coronary arteries, especially in ACS patients. TCFA was more prevalent in segments with tight stenosis.

Ethnic differences in the pathobiology of acute coronary syndromes (ACS) have not been systematically studied. We compared the underlying mechanisms of ACS between Asians and Whites. ACS patients with the culprit lesion imaged by optical coherence tomography were included. Patients were stratified into ST-elevation myocardial infarction (STEMI) and non-ST-elevation-ACS (NSTE-ACS), and baseline characteristics, underlying mechanisms of ACS, and culprit plaque characteristics were compared between Asians and Whites. Of 1,225 patients, 1,019 were Asian (567 STEMI and 452 NSTE-ACS) and 206 were White (71 STEMI and 135 NSTE-ACS). Asians had more diabetes and hypertension among STEMI patients; among NSTE-ACS patients, Asians had higher prevalence of diabetes and renal insufficiency, and lower prevalence of hyperlipidemia. There were no differences in the incidence of plaque rupture, plaque erosion and calcified plaque between Asians and Whites with STEMI (61.2%, 28.6%, 10.2% vs 46.5%, 38.0%, 15.5%, respectively, p = 0.055). Among NSTE-ACS patients, there was a significant difference between Asians and Whites (40.5%, 47.6%, 11.9% vs 27.4%, 48.9%, 23.7%, respectively, p = 0.001). After adjustment for clinical confounders, the risk of plaque rupture (p = 0.713), plaque erosion (p = 0.636), and calcified plaque (p = 0.986) was similar between the groups with STEMI. In NSTE-ACS patients, the only difference was an increased risk of calcified plaque in Whites (odds ratio: 2.125, 95% confidence interval: 1.213 to 3.723, p = 0.008). In conclusion, after adjustment for clinical confounders, Asian and White patients presenting with STEMI and NSTE-ACS showed similar underlying mechanisms of ACS, except for a higher risk of calcified plaque in Whites with NSTE-ACS.

Recent studies have shown that healed plaque at the culprit lesion detected by optical coherence tomography (OCT) is a sign of pan-vascular vulnerability and advanced atherosclerosis. However, the clinical significance of healed plaque is unknown. A total of 265 patients who had OCT imaging of a culprit vessel and 2-year clinical follow-up data were included. Patients were stratified based on the presence or absence of a layered plaque phenotype, defined as layers of different optical density by OCT at either culprit or non-culprit lesions. The association between layered plaque and major adverse cardiac events (MACE), defined as cardiac death, acute coronary syndromes (ACS), or revascularization, was studied. Among 265 patients, 96 (36.2%) had the layered plaque phenotype. Layered plaque was more frequently observed in stable angina pectoris patients than in ACS patients (57.8%vs. 25.1%, p

Many patients with atrial septal defects (ASD) are asymptomatic and undiagnosed during the first few decades of life, but have overt heart failure, arrhythmias, cerebral infarction, and increased mortality in adults with advancing age. To provide a non-invasive, easy-to-use, and effective method for detecting ASD, we aimed to develop and validate a deep learning-based algorithm to diagnose ASD on chest radiographs. The ASD dataset was created from 173 chest radiographs of 74 patients with ASD and 170 chest radiographs of 100 patients without ASD. Convolutional neural network models (VGG16, ResNet50, DenseNet121, and Xception) for diagnosing ASD were pretrained using two different datasets, the large-scale real-world ImageNet dataset and the ChestX-ray14 dataset released by National Institutes of Health, followed by a round of training using the training set of the ASD dataset. Model performance was evaluated by five-fold stratified cross-validation. The best performance in ImageNet pretraining was achieved by ResNet50 model, and the cross-validation area under the curve (AUC) was 0.95, with sensitivity of 0.86, specificity of 0.87, and overall accuracy of 0.87. The best performance in ChestX-ray pretraining was achieved by Xception, and the cross-validation AUC was 0.93, with sensitivity of 0.85, specificity of 0.85, and overall accuracy of 0.85. The diagnostic performances of these models were comparable to those of cardiologists. Gradient-weighted Class Activation Mapping showed that the ImageNet-pretrained model focused on bilateral hilar regions, while the ChestX-ray14-pretrained model focused on areas around cardiac silhouette and lower lung fields. Our deep learning-based algorithms made a diagnosis of ASD on the input chest radiographs with high accuracy, and had potential to help clinicians make accurate diagnosis of ASD from routine chest radiography, leading to improvement of prognosis and quality of life in patients with ASD.

Deep learning models can be applied to electrocardiograms (ECGs) to detect left ventricular (LV) dysfunction. We hypothesized that applying a deep learning model may improve the diagnostic accuracy of cardiologists in predicting LV dysfunction from ECGs. We acquired 37,103 paired ECG and echocardiography data records of patients who underwent echocardiography between January 2015 and December 2019. We trained a convolutional neural network to identify the data records of patients with LV dysfunction (ejection fraction < 40%) using a dataset of 23,801 ECGs. When tested on an independent set of 7,196 ECGs, we found the area under the receiver operating characteristic curve was 0.945 (95% confidence interval: 0.936-0.954). When 7 cardiologists interpreted 50 randomly selected ECGs from the test dataset of 7,196 ECGs, their accuracy for predicting LV dysfunction was 78.0% ± 6.0%. By referring to the model's output, the cardiologist accuracy improved to 88.0% ± 3.7%, which indicates that model support significantly improved the cardiologist diagnostic accuracy (P = 0.02). A sensitivity map demonstrated that the model focused on the QRS complex when detecting LV dysfunction on ECGs. We developed a deep learning model that can detect LV dysfunction on ECGs with high accuracy. Furthermore, we demonstrated that support from a deep learning model can help cardiologists to identify LV dysfunction on ECGs.

In recent years, there has been considerable research on the use of artificial intelligence to estimate age and disease status from medical images. However, age estimation from chest X-ray (CXR) images has not been well studied and the clinical significance of estimated age has not been fully determined.To address this, we trained a deep neural network (DNN) model using more than 100,000 CXRs to estimate the patients' age solely from CXRs. We applied our DNN to CXRs of 1562 consecutive hospitalized heart failure patients, and 3586 patients admitted to the intensive care unit with cardiovascular disease.The DNN's estimated age (X-ray age) showed a strong significant correlation with chronological age on the hold-out test data and independent test data. Elevated X-ray age is associated with worse clinical outcomes (heart failure readmission and all-cause death) for heart failure. Additionally, elevated X-ray age was associated with a worse prognosis in 3586 patients admitted to the intensive care unit with cardiovascular disease.Our results suggest that X-ray age can serve as a useful indicator of cardiovascular abnormalities, which will help clinicians to predict, prevent and manage cardiovascular diseases.Chest X-ray is one of the most widely used medical imaging tests worldwide to diagnose and manage heart and lung diseases. In this study, we developed a computer-based tool to predict patients’ age from chest X-rays. The tool precisely estimated patients’ age from chest X-rays. Furthermore, in patients with heart failure and those admitted to the intensive care unit for cardiovascular disease, elevated X-ray age estimated by our tool was associated with poor clinical outcomes, including readmission for heart failure or death from any cause. With further testing, our tool may help clinicians to predict outcomes in patients with heart disease based on a simple chest X-ray.

Chest X-ray (CXR) is one of the most commonly performed medical imaging tests. Although aging, sex and disease status have been known to cause changes in CXR findings, the extent of these effects has not been fully characterized. Here, we present a deep neural network (DNN) model trained using more than 100,000 CXRs to estimate the patient’s age and sex solely from CXRs. Our DNN exhibited high performance in terms of estimating age and sex, with Pearson’s correlation coefficient between the actual and estimated age of above 0.9 and an area under the ROC curve of 0.98 for sex estimation. The difference between the actual and estimated age is large in CXRs with abnormal findings, suggesting that the estimated age (“CXR age”) can be a biomarker for disease status. Furthermore, by applying our DNN to CXRs of consecutive 1,562 hospitalized heart failure patients, we demonstrated that an elevated CXR age is not only associated with aging-related diseases, such as hypertension and atrial fibrillation, but also a worse outcome of heart failure. Given these results, our new concept “CXR age” serves as a novel biomarker for cardiovascular aging and can help clinicians to predict, prevent, and manage cardiovascular diseases.

Percutaneous coronary intervention (PCI) is sometimes considered as an alternative therapeutic strategy to surgical revascularization in patients with coronary artery disease (CAD) and reduced left ventricular ejection fraction (LVEF). However, the types or conditions of patients that receive the clinical benefit of left ventricular reverse remodelling (LVRR) remain unknown. The purpose of this study was to investigate the determinants of LVRR following PCI in CAD patients with reduced LVEF. From 4394 consecutive patients who underwent PCI, a total of 286 patients with reduced LV systolic function (LVEF

Intravascular ultrasound (IVUS) is a diagnostic modality used during percutaneous coronary intervention. However, specialist skills are required to interpret IVUS images. To address this issue, we developed a new artificial intelligence (AI) program that categorizes vessel components, including calcification and stents, seen in IVUS images of complex lesions. When developing our AI using U-Net, IVUS images were taken from patients with angina pectoris and were manually segmented into the following categories: lumen area, medial plus plaque area, calcification, and stent. To evaluate our AI’s performance, we calculated the classification accuracy of vessel components in IVUS images of vessels with clinically significantly narrowed lumina (< 4 mm2) and those with severe calcification. Additionally, we assessed the correlation between lumen areas in manually-labeled ground truth images and those in AI-predicted images, the mean intersection over union (IoU) of a test set, and the recall score for detecting stent struts in each IVUS image in which a stent was present in the test set. Among 3738 labeled images, 323 were randomly selected for use as a test set. The remaining 3415 images were used for training. The classification accuracies for vessels with significantly narrowed lumina and those with severe calcification were 0.97 and 0.98, respectively. Additionally, there was a significant correlation in the lumen area between the ground truth images and the predicted images (ρ = 0.97, R2 = 0.97, p < 0.001). However, the mean IoU of the test set was 0.66 and the recall score for detecting stent struts was 0.64. Our AI program accurately classified vessels requiring treatment and vessel components, except for stents in IVUS images of complex lesions. AI may be a powerful tool for assisting in the interpretation of IVUS imaging and could promote the popularization of IVUS-guided percutaneous coronary intervention in a clinical setting.

Evaluation of hemodynamic parameters, such as fractional flow reserve (FFR), is recommended before percutaneous coronary intervention (PCI) for patients with angina pectoris (AP). However, the advantage of FFR-guided PCI has not been fully established. This study was performed to confirm whether FFR-guided PCI improves the prognosis compared with other treatments. Multiple databases were searched for studies published from 2000 to 2018, and a network meta-analysis (NMA) was performed to compare outcomes of FFR-guided PCI, non-FFR-guided PCI, coronary artery bypass grafting (CABG), and medical treatment (MT) for AP based on estimated odds ratios (ORs). The study included 18,093 patients from 15 randomized controlled trials (RCTs). No evidence of inconsistency was observed among the studies. The NMA showed that the all-cause mortality of FFR-guided PCI was not significantly different from that of the other treatment groups (CABG: OR, 1.1; 95% confidence interval [CI], 0.67-1.7; non-FFR-guided PCI: OR, 0.85; 95% CI, 0.53-1.4; and MT: OR, 0.83; 95% CI, 0.52-1.3). The NMA for the composite of all-cause mortality and myocardial infarction, which included 15,454 patients from 12 RCTs, showed that FFR-guided PCI significantly reduced the composite outcome compared with non-FFR-guided PCI and MT (non-FFR-guided PCI: OR, 0.66; 95% CI, 0.46-0.95 and MT: OR, 0.66; 95% CI, 0.46-0.95). Although FFR-guided PCI for AP did not show significant prognostic improvement compared with non-FFR-guided PCI, CABG, and MT, FFR-guided PCI may significantly reduce the composite of all-cause mortality and myocardial infarction compared with non-FFR-guided PCI and MT.

The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules and cardiomegaly in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this study, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists respectively verified and relabeled these images, for a total of 260 “normal” and 378 “heart failure” images, and the remainder were discarded because they had been incorrectly labeled. In this study “heart failure” was defined as “cardiomegaly or congestion”, in a chest X-ray with cardiothoracic ratio (CTR) over 50% or radiographic presence of pulmonary edema. To enable the machine to extract a sufficient number of features from the images, we used the general machine learning approach called data augmentation and transfer learning. Owing mostly to this technique and the adequate relabeling process, we established a model to detect heart failure in chest X-ray by applying deep learning, and obtained an accuracy of 82%. Sensitivity and specificity to heart failure were 75% and 94.4%, respectively. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. The figure shows randomly selected examples of the prediction probabilities and heatmaps of the chest X-rays from the dataset. The original image is on the left and its heatmap is on the right, with its prediction probability written below. The red areas on the heatmaps show important regions, according to which the machine determined the classification. While some images with ambiguous radiolucency such as (e) and (f) were prone to be misdiagnosed by this model, most of the images like (a)–(d) were diagnosed correctly. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images. Heatmaps and probabilities of prediction Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): JSPS KAKENHI

Background Evaluation of hemodynamic parameters, such as fractional flow reserve (FFR), is recommended before percutaneous coronary intervention (PCI) for patients with angina pectoris (AP). However, the advantage of FFR-guided PCI has not been fully established. A network meta-analysis (NMA) synthesizes the results of studies on multiple competing interventions. Moreover, no NMA has evaluated randomized controlled trials for AP to compare FFR-guided PCI and other treatments. To clarify whether FFR-guided PCI improves the prognosis in patients with AP, we performed this study. Methods Multiple databases were searched for studies published from 2000 to 2018. The search terms were based on Medical Subject Headings and keywords including “angioplasty”, “coronary artery bypass”, “percutaneous coronary intervention”, “coronary disease”, and “randomized controlled trial”. And an NMA was performed to compare outcomes of FFR-guided PCI, non-FFR-guided PCI, coronary artery bypass grafting (CABG), and medical treatment (MT) for AP based on estimated odds ratios (ORs). The primary endpoint was all-cause mortality. The secondary endpoints were the occurrence of MI. Treatments were ranked by the surface under the cumulative ranking curve. Results The study included 18,093 patients from 15 randomized controlled trials. No evidence of inconsistency was observed among the studies. The NMA showed that the all-cause mortality of FFR-guided PCI was not significantly different from that of the other treatment groups (CABG: OR, 1.1; 95% confidence interval [CI], 0.67–1.7; non-FFR-guided PCI: OR, 0.85; 95% CI, 0.53–1.4; and MT: OR, 0.83; 95% CI, 0.52–1.3). The NMA for myocardial infarction, which included 13,548 patients from 11 randomized controlled trials, showed that FFR-guided PCI tended to reduce the occurrence of myocardial infarction compared with MT (OR, 0.60; 95% CI, 0.36–1.0). According to the surface under the cumulative ranking curve, CABG was the best treatment, followed by FFR-guided PCI, non-FFR-guided PCI, and MT. Conclusions FFR-guided PCI for AP showed no significant prognostic improvement compared with non-FFR-guided PCI, CABG, and MT. CABG was the best treatment for AP, followed by FFR-guided PCI, non-FFR-guided PCI, and MT. Network plot and interval plot Funding Acknowledgement Type of funding source: None

An error appeared in the article entitled "Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning" by Takuya Matsumoto, Satoshi Kodera, Hiroki Shinohara, Hirotaka Ieki, Toshihiro Yamaguchi, Yasutomi Higashikuni, Arihiro Kiyosue, Kaoru Ito, Jiro Ando, Eiki Takimoto, Hiroshi Akazawa, Hiroyuki Morita, Issei Komuro (Vol. 61, No. 4, 781-786, 2020). The Figure 5on page 784 should be replaced by the following figure.

The development of deep learning technology has enabled machines to achieve high-level accuracy in interpreting medical images. While many previous studies have examined the detection of pulmonary nodules in chest X-rays using deep learning, the application of this technology to heart failure remains rare. In this paper, we investigated the performance of a deep learning algorithm in terms of diagnosing heart failure using images obtained from chest X-rays. We used 952 chest X-ray images from a labeled database published by the National Institutes of Health. Two cardiologists verified and relabeled a total of 260 "normal" and 378 "heart failure" images, with the remainder being discarded because they had been incorrectly labeled. Data augmentation and transfer learning were used to obtain an accuracy of 82% in diagnosing heart failure using the chest X-ray images. Furthermore, heatmap imaging allowed us to visualize decisions made by the machine. Deep learning can thus help support the diagnosis of heart failure using chest X-ray images.

Healed coronary plaques, morphologically characterized by a layered pattern, are signatures of previous plaque disruption and healing. Recent optical coherence tomography (OCT) studies showed that layered plaque is associated with vascular vulnerability. However, factors associated with layered plaques have not been studied. The aim of this study was to investigate predictors for layered plaque at the culprit plaques and at non-culprit plaques. Patients with coronary artery disease who underwent pre-intervention OCT imaging of the culprit lesion were included. Layered plaques were defined as plaques with one or more layers of different optical density and a clear demarcation from underlying components. Among 313 patients, layered plaque at the culprit lesion was observed in 18.8% of ST-segment elevation myocardial infarction patients, 36.3% of non-ST-segment elevation acute coronary syndrome patients, and 53.4% of stable angina pectoris (SAP) patients (p 0.001). In the multivariable model, SAP, multivessel disease, type B2/C lesion, and diameter stenosis 70% were independent predictors for layered plaque at the culprit lesion. In addition, 394 non-culprit plaques in 190 patients were assessed to explore predictors for layered plaques at non-culprit lesions. SAP, and thin-cap fibroatheroma and layered plaque at the culprit lesion were independent predictors for layered plaques at non-culprit lesions. In conclusion, clinical presentation of SAP was a strong predictor for layered plaque at both culprit plaques and non-culprit plaques. Development and biologic significance of layered plaques may be related to a balance between pan-vascular vulnerability and endogenous anti-thrombotic protective mechanism.

Objective: Healed plaques, signs of previous plaque destabilization, are frequently found in the coronary arteries. Healed plaques can now be diagnosed in living patients. We investigated the prevalence, angiographic, and optical coherence tomography features of healed plaques in patients with stable angina pectoris. Approach and Results: Patients with stable angina pectoris who had undergone optical coherence tomography imaging were included. Healed plaques were defined as plaques with one or more signal-rich layers of different optical density. Patients were divided into 2 groups based on layered or nonlayered phenotype at the culprit lesion. Among 163 patients, 87 (53.4%) had layered culprit plaque. Patients with layered culprit plaque had more multivessel disease (62.1% versus 44.7%, P =0.027) and more angiographically complex culprit lesions (64.4% versus 35.5%, P P =0.004), macrophage infiltration (58.6% versus 35.5%, P =0.003), calcifications (78.2% versus 63.2%, P =0.035), and thrombus (28.7% versus 14.5%, P =0.029). Lipid index ( P =0.001) and percent area stenosis ( P =0.015) were greater in the layered group. The number of nonculprit plaques, evaluated using coronary angiograms, tended to be greater in patients with layered culprit plaque (4.2±2.5 versus 3.5±2.1, P =0.053). Nonculprit plaques in patients with layered culprit lesion had higher prevalence of layered pattern ( P =0.002) and lipid phenotype ( P =0.005). Lipid index ( P =0.013) and percent area stenosis ( P =0.002) were also greater in this group. Conclusions: In patients with stable angina pectoris, healed culprit plaques are common and have more features of vulnerability and advanced atherosclerosis both at culprit and nonculprit lesions.

Objectives This study aimed to investigate the vascular response of lesions with a layered phenotype. Background Recent studies have shown that layered plaques at culprit lesions detected by optical coherence tomography (OCT) have greater plaque burden and more inflammatory features than non-layered plaques. Methods This is a retrospective observational study. A total of 193 target lesions from 193 patients [100 patients with acute coronary syndromes (ACS) and 93 with stable angina pectoris (SAP)] who had undergone OCT imaging of the culprit lesion both before and after stenting were included. Layered plaques were identified by OCT as plaques with layers of different optical density. Patients were divided into two groups based on the presence or absence of a layered phenotype at the culprit lesion, and pre- and post-procedure OCT findings were compared. Results Among 193 patients, 36 (36.0%) lesions in ACS patients and 56 (60.2%) lesions in SAP patients were found to have a layered phenotype at the culprit lesion. At baseline, percent area stenosis was greater in layered plaque than in non-layered plaque (p = .019). Following stent implantation, the stent expansion ratio and mean stent eccentricity index were significantly lower in layered plaques than in non-layered plaques (p = .041, p = .017, respectively), mainly derived from ACS patients. Conclusion Following stent implantation, plaques with a layered phenotype had less stent expansion and more eccentric lumens. Aggressive balloon dilation may be required to obtain optimal stent outcomes in patients with a layered plaque phenotype at the culprit lesion.

Aims Layered plaques represent signs of previous plaque destabilization. A recent study showed that acute coronary syndrome (ACS) patients with layered culprit plaque have more vulnerability at the culprit lesion and systemic inflammation. We aimed to compare the characteristics of non-culprit plaques between patients with or without layered plaque at the culprit lesion. We also evaluated the characteristics of layered non-culprit plaques, irrespective of culprit plaque phenotype. Methods and results We studied ACS patients who had undergone pre-intervention optical coherence tomography (OCT) imaging. The number of non-culprit lesions was evaluated on coronary angiogram and morphological characteristics of plaques were studied by OCT. In 349 patients, 99 (28.4%) had layered culprit plaque. The number of non-culprit plaques in patients with or without layered culprit plaque was similar (3.2 ± 0.8 and 2.8 ± 0.8, P = 0.23). Among 465 non-culprit plaques, 145 from patients with layered culprit plaque showed a higher prevalence of macrophage infiltration (71.0% vs. 60.9%, P = 0.050). When analysed irrespective of culprit plaque phenotype, layered non-culprit plaques showed higher prevalence of lipid (93.3% vs. 86.0%, P = 0.028), thin cap fibroatheroma (29.7% vs. 13.7%, P Conclusion In ACS patients, those with layered phenotype at the culprit lesion demonstrated greater macrophage infiltration at the non-culprit sites. Layered plaque at the non-culprit lesions was associated with more features of plaque vulnerability, particularly when the culprit lesion also had a layered pattern.

The authors performed a comprehensive analysis on the distribution of coronary plaques with different phenotypes from our 3-vessel optical coherence tomography (OCT) database.Previous pathology studies demonstrated that thin-cap fibroatheroma (TCFA) is localized in specific segments of the epicardial coronary arteries. A detailed description of in vivo coronary plaques of various phenotypes has not been reported.OCT images of all 3 coronary arteries in 131 patients were analyzed every 1 mm to assess plaque phenotype and features of vulnerability. In addition, plaques were divided into tertiles according to percent area stenosis (%AS).Among 534 plaques identified in 393 coronary arteries, 27.0% were fibrous plaques, 13.3% were fibrocalcific plaques, 40.8% were thick-cap fibroatheromas, and 18.9% were TCFAs. TCFAs showed clustering in the proximal segment, particularly in the left anterior descending artery. On the other hand, fibrous plaques were relatively evenly distributed throughout the entire length of the coronary arteries. In patients with acute coronary syndromes (ACS), TCFAs showed stronger proximal clustering in the left anterior descending, 2 clustering peaks in the right coronary artery, and 1 clustering peak in the circumflex artery. The pattern of TCFA distribution was less obvious in patients without ACS. The prevalence of TCFA was higher in the highest %AS tertile, compared with the lowest %AS tertile (30% vs. 9%; p 0.001).The present 3-vessel OCT study showed that TCFAs cluster at specific locations in the epicardial coronary arteries, especially in patients with ACS. TCFA was more prevalent in segments with tight stenosis. (The Massachusetts General Hospital Optical Coherence Tomography Registry; NCT01110538).

Objective In this study, we aimed to clarify the precise mechanism underlying lipopolysaccharide (LPS)-induced osteoclastogenesis in periodontal disease with a special reference to double-stranded RNA-dependent protein kinase (PKR). Material and methods We dissected the role of PKR in LPS-induced osteoclast differentiation and function using primary mouse bone marrow cells and RAW264.7 pre-osteoclastic cell line. We used a rat experimental periodontitis (PD) model induced by ligature placement with a Porphyromonas gingivalis LPS injection (PD rat) and analyzed the therapeutic effects of C16, a PKR inhibitor, on bone loss in PD rats. Results Protein kinase is strongly upregulated and phosphorylated by LPS in the osteoclasts. The inhibition of PKR suppressed LPS-stimulated osteoclast formation and activation. PKR inhibition also suppressed the LPS-mediated activation of NF-κB and MAPK, which are critical pathways for osteoclastogenesis. High expressions of PKR were detected in osteoclasts of PD rats, and the treatment with C16 effectively prevented alveolar bone destruction in PD rats. Conclusions PKR plays a pivotal role in LPS-induced bone loss in PD and, thus, has potential as a therapeutic target for PD.

Autosomal dominant polycystic kidney disease (ADPKD) is one of the most common hereditary disorders, characterized by the formation of multiple cysts in the kidneys and other organs, as well as noncystic manifestations such as cerebral aneurysm. The most common cardiovascular disorders associated with ADPKD include valvular abnormalities and aortic aneurysm. An association between ADPKD and impaired left ventricular function has occasionally been reported. We describe a 74-year-old woman with ADPKD and exertional dyspnea. Impaired left ventricular function resulting from noncompaction of the ventricular myocardium (NVM) and secondary left ventricular aneurysm were diagnosed. Cardiac sarcoidosis and ischemic heart disease were ruled out. Myocardial ischemia resulting from NVM was the presumptive cause of the ventricular aneurysm. To our knowledge, this is the first report of concurrent isolated NVM and left ventricular aneurysm in a patient with ADPKD. ADPKD and various cardiomyopathies, including NVM, are all reported to involve mutations of sarcomere genes, suggesting a possible link between the conditions.

Double-stranded RNA-dependent protein kinase (PKR) is involved in cell cycle progression, cell proliferation, cell differentiation, tumorgenesis, and apoptosis. We previously reported that PKR is required for differentiation and calcification in osteoblasts. TNF-α plays a key role in osteoclast differentiation. However, it is unknown about the roles of PKR in the TNF-α-induced osteoclast differentiation. The expression of PKR in osteoclast precursor RAW264.7 cells increased during TNF-α-induced osteoclastogenesis. The TNF-α-induced osteoclast differentiation in bone marrow-derived macrophages and RAW264.7 cells was markedly suppressed by the pre-treatment of PKR inhibitor, 2-aminopurine (2AP), as well as gene silencing of PKR. The expression of gene markers in the differentiated osteoclasts including TRAP, Calcitonin receptor, cathepsin K and ATP6V0d2 was also suppressed by the 2AP treatment. Bone resorption activity of TNF-α-induced osteoclasts was also supressed by 2AP treatment. Inhibition of PKR supressed the TNF-α-induced activation of NF-κB and MAPK in RAW264.7 cells. 2AP inhibited both the nuclear translocation of NF-κB and its transcriptional activity in RAW264.7 cells. 2AP inhibited the TNF-α-induced expression of NFATc1 and c-fos, master transcription factors in osteoclastogenesis. TNF-α-induced nuclear translocation of NFATc1 in mature osteoclasts was clearly inhibited by the 2AP treatment. The PKR inhibitor C16 decreased the TNF-α-induced osteoclast formation and bone resorption in mouse calvaria. The present study indicates that PKR is necessary for the TNF-α-induced osteoclast differentiation in vitro and in vivo.

Thrombotic microangiopathy (TMA) is a rare but lethal multisystem disease characterized by peripheral thrombocytopenia, microangiopathic hemolytic anemia, fever, and various stages of renal and neurological dysfunctions.(1,2)) The causes of TMA are mainly thrombotic thrombocytopenic purpura (TTP) or hemolytic-uremic syndrome (HUS), and cases of TMA related to myelodysplastic syndrome (MDS) are quite rare. Herein, we report a case of acute myocardial infarction (AMI) caused by TMA which is strongly suspected to have a relationship to MDS, and discuss the treatment of our patient who needed antiplatelet or anticoagulant therapy after AMI, while on the other hand, had pancytopenia and a bleeding event due to MDS.

Electrochemical sensing of ovalbumin (OVA) was performed using magnetic beads with OVA recognition (RNRCKGTDVQAW)/electron-transfer (YYYYC) peptides. The focus of this study was to construct a highly sensitive and regenerative tool for OVA detection based on the interaction between a protein and peptide-1(RNRCKGTDVQAWYYYYC). The peptide-1 was introduced to the bead through four types of cross-linking reagents. Magnetic beads of different sizes with N -(6-maleimidocaproyloxy)sulfosuccinimide (Sulfo-EMCS) were also prepared. An oxidation peak due to tyrosine residues at 0.65 V depended on the distance of the electron-transfer peptide from the bead surface and on the surface area of the magnetic beads that contacted the electrode surface. The response of the electro-transfer peptide moiety was decreased because the protein was accumulated via the recognition peptide on the beads. When using Sulfo-EMCS and beads that were 6.0–6.9 μm in diameter, the calibration curve of OVA was linear and ranged from 8.0 × 10 −13 to 2.0 × 10 −11 M. To regenerate the magnetic beads, the measurements were achieved after removal of the OVA using a denaturing reagent. When OVA was added to fetal bovine serum containing a complex matrix, OVA was recovered at a rate of 98–100%. Consequently, these magnetic beads could be a powerful tool for the sensing of OVA in real samples.

To electrochemically sense lymphoma cells (U937), we fabricated a multifunctional peptide probe that consists of cell-penetrating/apoptosis-inducing/electron-transfer peptides. Electron-transfer peptides derive from cysteine residue combined with the C-terminals of four tyrosine residues (Y4). A peptide whereby Y4C is bound to the C-terminals of protegrin 1 (RGGRLCYCRRRFCVCVGR-NH2) is known to be an apoptosis-inducing agent against U937 cells, and is referred to as a peptide-1 probe. An oxidation response of the peptide-1 probe has been observed due to a phenolic hydroxyl group, and this response is decreased by the uptake of the peptide probe into the cells. To improve the cell membrane permeability against U937 cells, the RGGR at the N-terminals of the peptide-1 probe was replaced by RRRR (peptide-2 probe). In contrast, RNRCKGTDVQAWY4C (peptide-3 probe), which recognizes ovalbumin, was constructed as a control. Compared with the other probes, the change in the peak current of the peptide-2 probe was the greatest at low concentrations and occurred in a short amount of time. Therefore, the cell membrane permeability of the peptide-2 probe was increased based on the arginine residues and the apoptosis-inducing peptides. The peak current was linear and ranged from 100 to 1000 cells/ml. The relative standard deviation of 600 cells/ml was 5.0% (n = 5). Furthermore, the membrane permeability of the peptide probes was confirmed using fluorescent dye.

We designed a new electroactive peptide probe that has a molecular recognition function for the sensing of a protein. Ovalbumin (OVA) was the model protein, and when RNRCKGTDVQAW interacted with OVA, it conjugated with a tyrosine-rich peptide (Y4C). This peptide is electroactive, has a high degree of biocompatibility, and offers the possibility of gene expression. To measure the effect of a number of the tyrosine residues, voltammetric measurements were conducted using a series of tyrosine-rich peptides (YnC, n = 3-7) with sensitivities that ranged from 10(-9) to 10(-8) M. The electrode response of Y5C was the maximum value in the series. However, the peak current did not increase when the number of tyrosine residues was increased in a linear fashion. This may have been due to the micelles that are formed by a tyrosine-rich surfactant peptide. Thus, Y4C was suitable as an electroactive label for the construction of the peptide probe. The electrode response of Y4CRNRCKGTDVQAW obtained by a glassy carbon electrode was 100-fold that of tyrosine alone. The measurement of OVA via the peptide probe resulted in a detection on the order of 10(-12) M. In contrast, the sensitivity of OVA using RCKGTDVQAWY4C probe was at the 10(-11) M level, because the hydrophobic moiety gave it a molecular recognition function. The recoveries of the OVA using Y4CRNRCKGTDVQAW in a solution containing fetal bovine serum ranged between 98 and 101%. Consequently, the combination of a specific peptide and an electroactive element could be a powerful probe for the sensing of proteins.

Double-stranded RNA-dependent protein kinase (PKR) is involved in cell cycle progression, cell proliferation, cell differentiation, tumorgenesis, and apoptosis. We previously reported that PKR is required for differentiation and calcification in osteoblasts. TNF-α plays a key role in osteoclast differentiation. However, it is unknown about the roles of PKR in the TNF-α-induced osteoclast differentiation. The expression of PKR in osteoclast precursor RAW264.7 cells increased during TNF-α-induced osteoclastogenesis. The TNF-α-induced osteoclast differentiation in bone marrow-derived macrophages and RAW264.7 cells was markedly suppressed by the pretreatment of PKR inhibitor, 2-aminopurine (2AP), as well as gene silencing of PKR. The expression of gene markers in the differentiated osteoclasts including TRAP, Calcitonin receptor, cathepsin K, and ATP6V0d2 was also suppressed by the 2AP treatment. Bone resorption activity of TNF-α-induced osteoclasts was also supressed by 2AP treatment. Inhibition of PKR supressed the TNF-α-induced activation of NF-κB and MAPK in RAW264.7 cells. 2AP inhibited both the nuclear translocation of NF-κB and its transcriptional activity in RAW264.7 cells. 2AP inhibited the TNF-α-induced expression of NFATc1 and c-fos, master transcription factors in osteoclastogenesis. TNF-α-induced nuclear translocation of NFATc1 in mature osteoclasts was clearly inhibited by the 2AP treatment. The PKR inhibitor C16 decreased the TNF-α-induced osteoclast formation and bone resorption in mouse calvaria. The present study indicates that PKR is necessary for the TNF-α-induced osteoclast differentiation in vitro and in vivo.

In these days, organization of nutrition support team (NST) is gradually spreading out in Japan. But, its action is frequently limited within acute hospitals. In order to realize an ideal nutritional care of chronic phase (convalescent hospital, nursing home and home care), we are required to set up a regional combined NST. We organized a society named EIYOU NETWORK SHOUNAN (EN-S) as a study group of NST, treatment for swallowing disorder and care after percutaneous endoscopic gastrostomy (PEG). EN-S takes a regional active part of these study groups at Kanagawa prefecture. We held a seminar about the nutritional care after PEG and a conference about the regional combined NST. According to the questionnaire at the conference, most participants were expected to making a regional combined NST to come true. The convalescent hospital is located in between acute hospital and home care. And that position has a profitable opportunity to achieve a regional partnership, if actively make the effort to organize.

Artificial vessels have made great progress recently and the material has become almost perfect. Nevertheless, the incidence of complications in not negligible. Recently, we encountered a nonanastomotic aneurysm that is very rare among the complications. The patient was a 54-year-old male. He was treated surgically becouse of traumatic right iliac arterial occlusion 6 years ago. In the operation, an arterial Dacron graft was applied. The patient was readmitted owing to a pulsating tumor with pain in his right groin and surgery was performed. The operative finding was a nonanastomotic aneurysm of the graft. It was resected and re-revascularization was made. In confirmation of the resected graft, the aneurysmal enlargement was seen on the guide line of the distal portion which was located just under the inguinal band. Therefore, it was considered that the friction between the graft and the inguinal band caused chronic injury and that than was one of the causes.