JOSE EDUARDO KRIEGER
Projetos de Pesquisa
Unidades Organizacionais
Departamento de Cardio-Pneumologia, Faculdade de Medicina - Docente
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina
LIM/13 - Laboratório de Genética e Cardiologia Molecular, Hospital das Clínicas, Faculdade de Medicina - Líder
Instituto do Coração, Hospital das Clínicas, Faculdade de Medicina
LIM/13 - Laboratório de Genética e Cardiologia Molecular, Hospital das Clínicas, Faculdade de Medicina - Líder
5 resultados
Resultados de Busca
Agora exibindo 1 - 5 de 5
- Automated radiographic bone suppression with deep convolutional neural networks(2021) CARDENAS, Diego Armando Cardona; FERREIRA JUNIOR, Jose Raniery; MORENO, Ramon Alfredo; REBELO, Marina de Fatima de Sa; KRIEGER, Jose Eduardo; GUTIERREZ, Marco AntonioDual-energy subtraction (DES) is a technique that separates soft tissue from bones in a chest radiograph (CR). As DES requires specialized equipment, we propose an automatic method based on convolutional neural networks (CNNs) to generate virtual soft tissue images. A dataset comprising 35 pairs of CR and its soft-tissue version split in training (28 image pairs) and testing (7 image pairs) sets were used with data augmentation. We tested two types of images: the lung region's cropped image and the segmented lung image. The ribs suppression was treated as a local problem, so each image was divided into 784 patches. The U-Net architecture was used to perform bone suppression. We tested two types of loss functions: mean squared error (L-mse) and L-sm, which combines L-mse with the structural similarity index measure (SSIM). Due to the patches overlapping, it was necessary to interpolate the gray levels on the reconstructed image from the predicted patches. Evaluations were based on SSIM and root mean square error (RMSE) over the reconstructed lung area. The combination that presented the best results used the loss L-sm and the segmented lung image as input to the U-Net (SSIM of 0.858 and RMSE of 0.033). We observed that the U-Net has poor performance when trained with cropped images containing all information from the chest cavity and how the loss using local information can improve CR rib bone suppression. Our results suggest that it is possible removing the rib bones accurately in CR using CNN and a patch-based approach.y
- A general fully automated deep-learning method to detect cardiomegaly in chest x-rays(2021) FERREIRA-JUNIOR, Jose Raniery; CARDENAS, Diego Armando Cardona; MORENO, Ramon Alfredo; REBELO, Marina de Fdtima de Sa; KRIEGER, Jose Eduardo; GUTIERREZ, Marco AntonioCardiomegaly is a medical condition that leads to an increase in cardiac size. It can be manually assessed using the cardiothoracic ratio from chest x-rays (CXRs). However, as that task can be challenging in such limited examinations, we propose the fully automated cardiomegaly detection in CXR. For this, we first trained convolutional networks (ConvNets) to classify the CXR as positive or negative to cardiomegaly and then evaluated the generalization potential of the trained ConvNets on independent cohorts. This work used frontal CXR images from a public dataset for training/testing and another public and one private dataset to test the models' generalization externally. Training and testing were performed using images cropped with a previously developed U-Net model. Experiments were performed with five topologically different ConvNets, data augmentation techniques, and a 50-50 class-weighing strategy to improve performance and reduce the possibility of bias to the majority class. The receiver operating characteristic curve assessed the performance of the models. DenseNet yielded the highest area under the curve (AUC) on testing (0.818) and external validation (0.809) datasets. Moreover, DenseNet obtained the highest sensitivity overall, yielding up to 0.971 on the private dataset with patients from our hospital. Therefore, DenseNet had a statistically higher potential to identify cardiomegaly. The proposed models, especially those trained with DenseNet convolutional core, automatically detected cardiomegaly with high sensitivity. To the best of our knowledge, this was the first work to design a novel general model for classifying specific deep-learning patterns of cardiomegaly in CXRs.
- High-volume endurance exercise training stimulates hematopoiesis by increasing ACE NH2-terminal activity(2021) MAGALHAES, Flavio de Castro; FERNANDES, Tiago; BASSANEZE, Vinicius; MATTOS, Katt Coelho; SCHETTERT, Isolmar; MARQUES, Fabio Luiz Navarro; KRIEGER, Jose Eduardo; NAVA, Roberto; BARAUNA, Valerio Garrone; OLIVEIRA, Edilamar Menezes deOne of the health benefits of endurance exercise training (ET) is the stimulation of hematopoiesis. However, the mechanisms underlying ET-induced hematopoietic adaptations are understudied. N-Acetyl-Seryl-Aspartyl-Lysyl-Proline (Ac-SDKP) inhibits proliferation of early hematopoietic progenitor cells. The angiotensin I-converting enzyme (ACE) NH2-terminal promotes hematopoiesis by inhibiting the anti-hematopoietic effect of Ac-SDKP. Here we demonstrate for the first time the role of ACE NH2-terminal in ET-induced hematopoietic adaptations. Wistar rats were subjected to 10 weeks of moderate-(T1) and high-(T2) volume swimming-training. Although both protocols induced classical ET-associated adaptations, only T2 increased plasma ACE NH2-domain activity (by 40%, P=0.0003) and reduced Ac-SDKP levels (by 50%, P<0.0001). T2 increased the number of hematopoietic stem cells (HSCs; similar to 200%, P=0.0008), early erythroid progenitor colonies (similar to 300%, P<0.0001) and reticulocytes (similar to 500%, P=0.0007), and reduced erythrocyte lifespan (similar to 50%, P=0.022). Following, Wistar rats were subjected to T2 or T2 combined with ACE NH2-terminal inhibition (captopril (Cap) treatment: 10 mg.kg(-1).day(-1)). T2 combined with ACE NH2-terminal inhibition prevented Ac-SDKP decrease and attenuated ET-induced hematopoietic adaptations. Altogether, our findings show that ET-induced hematopoiesis was at least partially associated with increased ACE NH2-terminal activity and reduction in the hematopoietic inhibitor Ac-SDKP.
- Dynamic Crosstalk between Vascular Smooth Muscle Cells and the Aged Extracellular Matrix(2021) RIBEIRO-SILVA, Joao Carlos; NOLASCO, Patricia; KRIEGER, Jose Eduardo; MIYAKAWA, Ayumi AureaVascular aging is accompanied by the fragmentation of elastic fibers and collagen deposition, leading to reduced distensibility and increased vascular stiffness. A rigid artery facilitates elastin to degradation by MMPs, exposing vascular cells to greater mechanical stress and triggering signaling mechanisms that only exacerbate aging, creating a self-sustaining inflammatory environment that also promotes vascular calcification. In this review, we highlight the role of crosstalk between smooth muscle cells and the vascular extracellular matrix (ECM) and how aging promotes smooth muscle cell phenotypes that ultimately lead to mechanical impairment of aging arteries. Understanding the underlying mechanisms and the role of associated changes in ECM during aging may contribute to new approaches to prevent or delay arterial aging and the onset of cardiovascular diseases.
- Novel Chest Radiographic Biomarkers for COVID-19 Using Radiomic Features Associated with Diagnostics and Outcomes(2021) FERREIRA JUNIOR, Jose Raniery; CARDENAS, Diego Armando Cardona; MORENO, Ramon Alfredo; REBELO, Marina de Fatima de Sa; KRIEGER, Jose Eduardo; GUTIERREZ, Marco AntonioCOVID-19 is a highly contagious disease that can cause severe pneumonia. Patients with pneumonia undergo chest X-rays (XR) to assess infiltrates that identify the infection. However, the radiographic characteristics of COVID-19 are similar to the other acute respiratory syndromes, hindering the imaging diagnosis. In this work, we proposed identifying quantitative/radiomic biomarkers for COVID-19 to support XR assessment of acute respiratory diseases. This retrospective study used different cohorts of 227 patients diagnosed with pneumonia; 49 of them had COVID-19. Automatically segmented images were characterized by 558 quantitative features, including gray-level histogram and matrices of co-occurrence, run-length, size zone, dependence, and neighboring gray-tone difference. Higher-order features were also calculated after applying square and wavelet transforms. Mann-Whitney U test assessed the diagnostic performance of the features, and the log-rank test assessed the prognostic value to predict Kaplan-Meier curves of overall and deterioration-free survival. Statistical analysis identified 51 independently validated radiomic features associated with COVID-19. Most of them were wavelet-transformed features; the highest performance was the small dependence matrix feature of ""low gray-level emphasis"" (area under the curve of 0.87, sensitivity of 0.85, p<0.001). Six features presented short-term prognostic value to predict overall and deterioration-free survival. The features of histogram ""mean absolute deviation"" and size zone matrix ""non-uniformity"" yielded the highest differences on Kaplan-Meier curves with a hazard ratio of 3.20 (p<0.05). The radiomic markers showed potential as quantitative measures correlated with the etiologic agent of acute infectious diseases and to stratify short-term risk of COVID-19 patients.