Pulmonary artery embolism: comprehensive transcriptomic analysis in understanding the pathogenic mechanisms of the disease.

BACKGROUND: Pulmonary embolism (PE) is a severe disease that usually originates from deep vein thrombosis (DVT) of the lower extremities. This study set out to investigate the changes in the transcriptome of the pulmonary artery (PA) in the course of the PE in the porcine model. METHODS: The study was performed on 11 male pigs: a thrombus was formed in each right femoral vein in six animals, and then was released to induce PE, the remaining five animals served as a control group. In the experimental animals total RNA was isolated from the PA where the blood clot lodged, and in the control group, from the corresponding PA segments. High-throughput RNA sequencing was used to analyse the global changes in the transcriptome of PA with induced PE (PA-E). RESULTS: Applied multistep bioinformatics revealed 473 differentially expressed genes (DEGs): 198 upregulated and 275 downregulated. Functional Gene Ontology annotated 347 DEGs into 27 biological processes, 324 to the 11 cellular components and 346 to the 2 molecular functions categories. In the signaling pathway analysis, KEGG 'protein processing in endoplasmic reticulum' was identified for the mRNAs modulated during PE. The same KEGG pathway was also exposed by 8 differentially alternative splicing genes. Within single nucleotide variants, the 61 allele-specific expression variants were localised in the vicinity of the genes that belong to the cellular components of the 'endoplasmic reticulum'. The discovered allele-specific genes were also classified as signatures of the cardiovascular system. CONCLUSIONS: The findings of this research provide the first thorough investigation of the changes in the gene expression profile of PA affected by an embolus. Evidence from this study suggests that the disturbed homeostasis in the biosynthesis of proteins in the endoplasmic reticulum plays a major role in the pathogenesis of PE.

Design and Implementation of a Cloud PACS Architecture.

The limitations of the classic PACS (picture archiving and communication system), such as the backward-compatible DICOM network architecture and poor security and maintenance, are well-known. They are challenged by various existing solutions employing cloud-related patterns and services. However, a full-scale cloud-native PACS has not yet been demonstrated. The paper introduces a vendor-neutral cloud PACS architecture. It is divided into two main components: a cloud platform and an access device. The cloud platform is responsible for nearline (long-term) image archive, data flow, and backend management. It operates in multi-tenant mode. The access device is responsible for the local DICOM (Digital Imaging and Communications in Medicine) interface and serves as a gateway to cloud services. The cloud PACS was first implemented in an Amazon Web Services environment. It employs a number of general-purpose services designed or adapted for a cloud environment, including Kafka, OpenSearch, and Memcached. Custom services, such as a central PACS node, queue manager, or flow worker, also developed as cloud microservices, bring DICOM support, external integration, and a management layer. The PACS was verified using image traffic from, among others, computed tomography (CT), magnetic resonance (MR), and computed radiography (CR) modalities. During the test, the system was reliably storing and accessing image data. In following tests, scaling behavior differences between the monolithic Dcm4chee server and the proposed solution are shown. The growing number of parallel connections did not influence the monolithic server's overall throughput, whereas the performance of cloud PACS noticeably increased. In the final test, different retrieval patterns were evaluated to assess performance under different scenarios. The current production environment stores over 450 TB of image data and handles over 4000 DICOM nodes.

Transcriptomic Profiling of Femoral Veins in Deep Vein Thrombosis in a Porcine Model.

Deep vein thrombosis (DVT) is a severe disease affecting the human venous system, accompanied by high morbidity and mortality rates caused by early and late complications. The study aimed at analyzing the changes in the transcriptome of the femoral vein caused by DVT in the porcine model based on the formation of the thrombus in vivo. The study was performed on 11 castrated male pigs: A thrombus was formed in each left femoral vein in six animals; the remaining five served as a control group. Total RNA was isolated from the left femoral veins of the experimental and control animals. High-throughput RNA sequencing was used to analyze the global changes in the transcriptome of veins with induced DVT. Applied multistep bioinformatics revealed 1474 differentially expressed genes (DEGs): 1019 upregulated and 455 downregulated. Functional Gene Ontology annotated 1220 of DEGs into 225 biological processes, 30 molecular functions and 40 cellular components categories. KEGG analysis disclosed TNF, NF-κB and apoptosis pathways' overexpression in DVT samples. A thorough analysis of the detected DEGs indicated that a dysregulated inflammatory response and disturbed balance between clotting and anti-clotting factors play a crucial role in the process of DVT.

A New Experimental Porcine Model of Venous Thromboembolism.

Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a severe disease affecting the human venous system, accompanied by high morbidity and mortality rates. The aim of the study was to establish a new porcine VTE model based on the formation of the thrombus in vivo. The study was performed on 10 castrated male pigs: thrombus was formed in each closed femoral vein and then successfully released from the right femoral vein into the circulation of animals. In six pigs PE was confirmed via both computed tomography pulmonary angiography and an autopsy. Our research presents a novel experimental porcine model of VTE that involves inducing DVT and PE in the same animal in vivo, making it suitable for advanced clinical research and testing of future therapies.

Automated size-specific dose estimates using deep learning image processing.

An automated vendor-independent system for dose monitoring in computed tomography (CT) medical examinations involving ionizing radiation is presented in this paper. The system provides precise size-specific dose estimates (SSDE) following the American Association of Physicists in Medicine regulations. Our dose management can operate on incomplete DICOM header metadata by retrieving necessary information from the dose report image by using optical character recognition. For the determination of the patient's effective diameter and water equivalent diameter, a convolutional neural network is employed for the semantic segmentation of the body area in axial CT slices. Validation experiments for the assessment of the SSDE determination and subsequent stages of our methodology involved a total of 335 CT series (60 352 images) from both public databases and our clinical data. We obtained the mean body area segmentation accuracy of 0.9955 and Jaccard index of 0.9752, yielding a slice-wise mean absolute error of effective diameter below 2 mm and water equivalent diameter at 1 mm, both below 1%. Three modes of the SSDE determination approach were investigated and compared to the results provided by the commercial system GE DoseWatch in three different body region categories: head, chest, and abdomen. Statistical analysis was employed to point out some significant remarks, especially in the head category.