Mode I Fatigue and Fracture Assessment of Polyimide-Epoxy and Silicon-Epoxy Interfaces in Chip-Package Components

Semiconductor advancements demand greater integrated circuit density, structural miniaturization, and complex material combinations, resulting in stress concentrations from property mismatches. This study investigates the failure in two types of interfaces found in chip packages: silicon-epoxy mold compound (EMC) and polyimide-EMC. These interfaces were subjected to quasi-static and fatigue loading conditions. Employing a compliance-based beam method, the tests determined interfacial critical fracture energy values, (GIC), of 0.051 N/mm and 0.037 N/mm for the silicon-EMC and polyimide-EMC interfaces, respectively. Fatigue testing on the polyimide-epoxy interface revealed a fatigue threshold strain energy, (Gth), of 0.042 N/mm. We also observed diverse failure modes and discuss potential mechanical failures in multi-layer chip packages. The findings of this study can contribute to the prediction and mitigation of failure modes in the analyzed chip packaging. The obtained threshold energy and crack growth rate provide insights for designing safe lives for bi-material interfaces in chip packaging under cyclic loads. These insights can guide future research directions, emphasizing the improvement of material properties and exploration of the influence of manufacturing parameters on delamination in multilayer semiconductors.

Pseudouridylation-mediated gene expression modulation

RNA-guided pseudouridylation, a widespread post-transcriptional RNA modification, has recently gained recognition for its role in cellular processes such as pre-mRNA splicing and the modulation of premature termination codon (PTC) readthrough. This review provides insights into its mechanisms, functions, and potential therapeutic applications. It examines the mechanisms governing RNA-guided pseudouridylation, emphasizing the roles of guide RNAs and pseudouridine synthases in catalyzing uridine-to-pseudouridine conversion. A key focus is the impact of RNA-guided pseudouridylation of U2 small nuclear RNA on pre-mRNA splicing, encompassing its influence on branch site recognition and spliceosome assembly. Additionally, the review discusses the emerging role of RNA-guided pseudouridylation in regulating PTC readthrough, impacting translation termination and genetic disorders. Finally, it explores the therapeutic potential of pseudouridine modifications, offering insights into potential treatments for genetic diseases and cancer and the development of mRNA vaccine.

Cohesive Properties of Bimaterial Interfaces in Semiconductors: Experimental Study and Numerical Simulation Using an Inverse Cohesive Contact Approach

Examining crack propagation at the interface of bimaterial components under various conditions is essential for improving the reliability of semiconductor designs. However, the fracture behavior of bimaterial interfaces has been relatively underexplored in the literature, particularly in terms of numerical predictions. Numerical simulations offer vital insights into the evolution of interfacial damage and stress distribution in wafers, showcasing their dependence on material properties. The lack of knowledge about specific interfaces poses a significant obstacle to the development of new products and necessitates active remediation for further progress. The objective of this paper is twofold: firstly, to experimentally investigate the behavior of bimaterial interfaces commonly found in semiconductors under quasi-static loading conditions, and secondly, to determine their respective interfacial cohesive properties using an inverse cohesive zone modeling approach. For this purpose, double cantilever beam specimens were manufactured that allow Mode I static fracture analysis of the interfaces. A compliance-based method was used to obtain the crack size during the tests and the Mode I energy release rate (GIc). Experimental results were utilized to simulate the behavior of different interfaces under specific test conditions in Abaqus. The simulation aimed to extract the interfacial cohesive contact properties of the studied bimaterial interfaces. These properties enable designers to predict the strength of the interfaces, particularly under Mode I loading conditions. To this extent, the cohesive zone modeling (CZM) assisted in defining the behavior of the damage propagation through the bimaterial interfaces. As a result, for the silicon-epoxy molding compound (EMC) interface, the results for maximum strength and GIc are, respectively, 26 MPa and 0.05 N/mm. The second interface tested consisted of polyimide and silicon oxide between the silicon and EMC layers, and the results obtained are 21.5 MPa for the maximum tensile strength and 0.02 N/mm for GIc. This study's findings aid in predicting and mitigating failure modes in the studied chip packaging. The insights offer directions for future research, focusing on enhancing material properties and exploring the impact of manufacturing parameters and temperature conditions on delamination in multilayer semiconductors.

CholecTriplet2022: Show me a tool and tell me the triplet - An endoscopic vision challenge for surgical action triplet detection

Formalizing surgical activities as triplets of the used instruments, actions performed, and target anatomies is becoming a gold standard approach for surgical activity modeling. The benefit is that this formalization helps to obtain a more detailed understanding of tool-tissue interaction which can be used to develop better Artificial Intelligence assistance for image-guided surgery. Earlier efforts and the CholecTriplet challenge introduced in 2021 have put together techniques aimed at recognizing these triplets from surgical footage. Estimating also the spatial locations of the triplets would offer a more precise intraoperative context-aware decision support for computer-assisted intervention. This paper presents the CholecTriplet2022 challenge, which extends surgical action triplet modeling from recognition to detection. It includes weakly-supervised bounding box localization of every visible surgical instrument (or tool), as the key actors, and the modeling of each tool-activity in the form of ‹instrument, verb, target› triplet. The paper describes a baseline method and 10 new deep learning algorithms presented at the challenge to solve the task. It also provides thorough methodological comparisons of the methods, an in-depth analysis of the obtained results across multiple metrics, visual and procedural challenges; their significance, and useful insights for future research directions and applications in surgery.

Infective endocarditis risk in patients with bicuspid aortic valve: Systematic review and meta-analysis

Background

Antibiotic prophylaxis in bicuspid aortic valve patients is currently a matter of debate. Although it is no longer recommended by international guidelines, some studies indicate a high risk of infective endocarditis. We aim to evaluate the risk of native valve infective endocarditis in bicuspid aortic valve patients and compare to individuals with tricuspid aortic valve.

Methods

Study search of longitudinal studies regarding infective endocarditis incidence in bicuspid aortic valve patients (compared with tricuspid aortic valve/overall population) was conducted through OVID in the following electronic databases: MEDLINE, CENTRAL, EMBASE; from inception until October 2020. The outcomes of interest were the incidence rate and relative risk of infective endocarditis. The relative risk and incidence rate (number of cases for each 10 000 persons-year) with their 95 % confidence intervals (95 %CI) were estimated using a random effects model meta-analysis. The study protocol was registered at PROSPERO CRD42020218639.

Results

Eight cohort studies were selected, with a total of 5351 bicuspid aortic valve patients. During follow up, 184 bicuspid aortic valve patients presented infective endocarditis, with an incidence rate of 48.13 per 10,000 patients-year (95 %CI 22.24-74.02), and a 12-fold (RR: 12.03, 95 %CI 5.45-26.54) increased risk compared with general population, after adjusted estimates.

Conclusions

This systematic review and meta-analysis suggests that bicuspid aortic valve patients have a significant high risk of native valve infective endocarditis. Large prospective high-quality studies are required to estimate more accurately the incidence of infective endocarditis, the relative risk and the potential benefit of antibiotic prophylaxis.

Deep Learning Networks for Breast Lesion Classification in Ultrasound Images: A Comparative Study

Accurate lesion classification as benign or malignant in breast ultrasound (BUS) images is a critical task that requires experienced radiologists and has many challenges, such as poor image quality, artifacts, and high lesion variability. Thus, automatic lesion classification may aid professionals in breast cancer diagnosis. In this scope, computer-aided diagnosis systems have been proposed to assist in medical image interpretation, outperforming the intra and inter-observer variability. Recently, such systems using convolutional neural networks have demonstrated impressive results in medical image classification tasks. However, the lack of public benchmarks and a standardized evaluation method hampers the performance comparison of networks. This work is a benchmark for lesion classification in BUS images comparing six state-of-the-art networks: GoogLeNet, InceptionV3, ResNet, DenseNet, MobileNetV2, and EfficientNet. For each network, five input data variations that include segmentation information were tested to compare their impact on the final performance. The methods were trained on a multi-center BUS dataset (BUSI and UDIAT) and evaluated using the following metrics: precision, sensitivity, F1-score, accuracy, and area under the curve (AUC). Overall, the lesion with a thin border of background provides the best performance. For this input data, EfficientNet obtained the best results: an accuracy of 97.65% and an AUC of 96.30%.Clinical Relevance- This study showed the potential of deep neural networks to be used in clinical practice for breast lesion classification, also suggesting the best model choices.

Classification of Continuous ECG Segments - Performance Analysis of a Deep Learning Model

Classification of electrocardiogram (ECG) signals plays an important role in the diagnosis of heart diseases. It is a complex and non-linear signal, which is the first option to preliminary identify specific pathologies/conditions (e.g., arrhythmias). Currently, the scientific community has proposed a multitude of intelligent systems to automatically process the ECG signal, through deep learning techniques, as well as machine learning, where this present high performance, showing state-of-the-art results. However, most of these models are designed to analyze the ECG signal individually, i.e., segment by segment. The scientific community states that to diagnose a pathology in the ECG signal, it is not enough to analyze a signal segment corresponding to the cardiac cycle, but rather an analysis of successive segments of cardiac cycles, to identify a pathological pattern.In this paper, an intelligent method based on a Convolutional Neural Network 1D paired with a Multilayer Perceptron (CNN 1D+MLP) was evaluated to automatically diagnose a set of pathological conditions, from the analysis of the individual segment of the cardiac cycle. In particular, we intend to study the robustness of the referred method in the analysis of several simultaneous ECG signal segments. Two ECG signal databases were selected, namely: MIT-BIH Arrhythmia Database (D1) and European ST-T Database (D2). The data was processed to create datasets with two, three and five segments in a row, to train and test the performance of the method. The method was evaluated in terms of classification metrics, such as: precision, recall, f1-score, and accuracy, as well as through the calculation of confusion matrices.Overall, the method demonstrated high robustness in the analysis of successive ECG signal segments, which we can conclude that it has the potential to detect anomalous patterns in the ECG signal. In the future, we will use this method to analyze the ECG signal coming in real-time, acquired by a wearable device, through a cloud system.Clinical Relevance-This study evaluates the potential of a deep learning method to classify one or several segments of the cardiac cycle and diagnose pathologies in ECG signals.

Robust 3D breast reconstruction based on monocular images and artificial intelligence for robotic guided oncological interventions

Breast cancer is a global public health concern. For women with suspicious breast lesions, the current diagnosis requires a biopsy, which is usually guided by ultrasound (US). However, this process is challenging due to the low quality of the US image and the complexity of dealing with the US probe and the surgical needle simultaneously, making it largely reliant on the surgeon's expertise. Some previous works employing collaborative robots emerged to improve the precision of biopsy interventions, providing an easier, safer, and more ergonomic procedure. However, for these equipment to be able to navigate around the breast autonomously, 3D breast reconstruction needs to be available. The accuracy of these systems still needs to improve, with the 3D reconstruction of the breast being one of the biggest focuses of errors. The main objective of this work is to develop a method to obtain a robust 3D reconstruction of the patient's breast, based on RGB monocular images, which later can be used to compute the robot's trajectories for the biopsy. To this end, depth estimation techniques will be developed, based on a deep learning architecture constituted by a CNN, LSTM, and MLP, to generate depth maps capable of being converted into point clouds. After merging several from multiple points of view, it is possible to generate a real-time reconstruction of the breast as a mesh. The development and validation of our method was performed using a previously described synthetic dataset. Hence, this procedure takes RGB images and the cameras' position and outputs the breasts' meshes. It has a mean error of 3.9 mm and a standard deviation of 1.2 mm. The final results attest to the ability of this methodology to predict the breast's shape and size using monocular images.Clinical Relevance- This work proposes a method based on artificial intelligence and monocular RGB images to obtain the breast's volume during robotic guided breast biopsies, improving their execution and safety.

CycleGAN-Based Image to Image Translation for Realistic Surgical Training Phantoms

Training in surgery is essential for surgeons to develop skill and dexterity. Physical training phantoms provide excellent haptic feedback and tissue properties for stitching and operating with authentic instruments and are easily available. However, they lack realistic traits and fail to reflect the complex environment of a surgical scene. Generative Adversarial Networks can be used for image-to-image translation, addressing the lack of realism in physical phantoms, by mapping patterns from the intraoperative domain onto the video stream captured during training with these surgical simulators. This work aims to achieve a successful I2I translation, from intra-operatory mitral valve surgery images onto a surgical simulator, using the CycleGAN model. Different experiments are performed - comparing the Mean Square Error Loss with the Binary Cross Entropy Loss; validating the Fréchet Inception Distance as a training and image quality metric; and studying the impact of input variability on the model performance. Differences between MSE and BCE are modest, with MSE being marginally more robust. The FID score proves to be very useful in identifying the best training epochs for the CycleGAN I2I translation architecture. Carefully selecting the input images can have a great impact in the end results. Using less style variability and input images with good feature details and clearly defined characteristics enables the network to achieve better results.Clinical Relevance- This work further contributes for the domain of realistic surgical training, successfully generating fake intra operatory images from a surgical simulator of the cardiac mitral valve.

Comparative analysis of deep learning methods for lesion detection on full screening mammography

Breast cancer is the most prevalent type of cancer in women. Although mammography is used as the main imaging modality for the diagnosis, robust lesion detection in mammography images is a challenging task, due to the poor contrast of the lesion boundaries and the widely diverse sizes and shapes of the lesions. Deep Learning techniques have been explored to facilitate automatic diagnosis and have produced outstanding outcomes when used for different medical challenges. This study provides a benchmark for breast lesion detection in mammography images. Five state-of-art methods were evaluated on 1592 mammograms from a publicly available dataset (CBIS-DDSM) and compared considering the following seven metrics: i) mean Average Precision (mAP); ii) intersection over union; iii) precision; iv) recall; v) True Positive Rate (TPR); and vi) false positive per image. The CenterNet, YOLOv5, Faster-R-CNN, EfficientDet, and RetinaNet architectures were trained with a combination of the L1 localization loss and L2 localization loss. Despite all evaluated networks having mAP ratings greater than 60%, two managed to stand out among the evaluated networks. In general, the results demonstrate the efficiency of the model CenterNet with Hourglass-104 as its backbone and the model YOLOv5, achieving mAP scores of 70.71% and 69.36%, and TPR scores of 96.10% and 92.19%, respectively, outperforming the state-of-the-art models.Clinical Relevance - This study demonstrates the effectiveness of deep learning algorithms for breast lesion detection in mammography, potentially improving the accuracy and efficiency of breast cancer diagnosis.

A Comparative Study of Deep Learning Methods for Multi-Class Semantic Segmentation of 2D Kidney Ultrasound Images

Ultrasound (US) imaging is a widely used medical imaging modality for the diagnosis, monitoring, and surgical planning for kidney conditions. Thus, accurate segmentation of the kidney and internal structures in US images is essential for the assessment of kidney function and the detection of pathological conditions, such as cysts, tumors, and kidney stones. Therefore, there is a need for automated methods that can accurately segment the kidney and internal structures in US images. Over the years, automatic strategies were proposed for such purpose, with deep learning methods achieving the current state-of-the-art results. However, these strategies typically ignore the segmentation of the internal structures of the kidney. Moreover, they were evaluated in different private datasets, hampering the direct comparison of results, and making it difficult to determination the optimal strategy for this task. In this study, we perform a comparative analysis of 7 deep learning networks for the segmentation of the kidney and internal structures (Capsule, Central Echogenic Complex (CEC), Cortex and Medulla) in 2D US images in an open access multi-class kidney US dataset. The dataset includes 514 images, acquired in multiple clinical centers using different US machines and protocols. The dataset contains the annotation of two experts, but 321 images with complete segmentation of all 4 classes were used. Overall, the results demonstrate that the DeepLabV3+ network outperformed the inter-rater variability with a Dice score of 78.0% compared to 75.6% for inter-rater variability. Specifically, DeepLabV3Plus achieved mean Dice scores of 94.2% for the Capsule, 85.8% for the CEC, 62.4% for the Cortex, and 69.6% for the Medulla. These findings suggest the potential of deep learning-based methods in improving the accuracy of kidney segmentation in US images.Clinical Relevance- This study shows the potential of DL for improving accuracy of kidney segmentation in US, leading to increased diagnostic efficiency, and enabling new applications such as computer-aided diagnosis and treatment, ultimately resulting in improved patient outcomes and reduced healthcare costs.1.

Dual consistency loss for contour-aware segmentation in medical images

Medical image segmentation is a paramount task for several clinical applications, namely for the diagnosis of pathologies, for treatment planning, and for aiding image-guided surgeries. With the development of deep learning, Convolutional Neural Networks (CNN) have become the state-of-the-art for medical image segmentation. However, issues are still raised concerning the precise object boundary delineation, since traditional CNNs can produce non-smooth segmentations with boundary discontinuities. In this work, a U-shaped CNN architecture is proposed to generate both pixel-wise segmentation and probabilistic contour maps of the object to segment, in order to generate reliable segmentations at the object's boundaries. Moreover, since the segmentation and contour maps must be inherently related to each other, a dual consistency loss that relates the two outputs of the network is proposed. Thus, the network is enforced to consistently learn the segmentation and contour delineation tasks during the training. The proposed method was applied and validated on a public dataset of cardiac 3D ultrasound images of the left ventricle. The results obtained showed the good performance of the method and its applicability for the cardiac dataset, showing its potential to be used in clinical practice for medical image segmentation.Clinical Relevance- The proposed network with dual consistency loss scheme can improve the performance of state-of-the-art CNNs for medical image segmentation, proving its value to be applied for computer-aided diagnosis.

Deep learning networks in the segmentation of the left atrial appendage in 2D ultrasound: A comparative analysis

Left atrial appendage (LAA) is the major source of thromboembolism in patients with non-valvular atrial fibrillation. Currently, LAA occlusion can be offered as a treatment for these patients, obstructing the LAA through a percutaneously delivered device. Nevertheless, correct device sizing is a complex task, requiring manual analysis of medical images. This approach is sub-optimal, time-demanding, and highly variable between experts. Different solutions were proposed to improve intervention planning, but, no efficient solution is available to 2D ultrasound, which is the most used imaging modality for intervention planning and guidance. In this work, we studied the performance of recently proposed deep learning methods when applied for the LAA segmentation in 2D ultrasound. For that, it was created a 2D ultrasound database. Then, the performance of different deep learning methods, namely Unet, UnetR, AttUnet, TransAttUnet was assessed. All networks were compared using seven metrics: i) Dice coefficient; ii) Accuracy iii) Recall; iv) Specificity; v) Precision; vi) Hausdorff distance and vii) Average distance error. Overall, the results demonstrate the efficiency of AttUnet and TransAttUnet with dice scores of 88.62% and 89.28%, and accuracy of 88.25% and 86.30%, respectively. The current results demonstrate the feasibility of deep learning methods for LAA segmentation in 2D ultrasound.Clinical relevance- Our results proved the clinical potential of deep neural networks for the LAA anatomical analysis.

The benthic food web connects the estuarine habitat mosaic to adjacent ecosystems

Energy flows from land to sea and between pelagic and benthic environments have the potential to increase the connectivity between estuaries and adjacent ecosystems as well as among estuarine habitats. To identify such energy flows and the main trophic pathways of energy transfer in the Minho River estuary, we investigated the spatial and temporal fluctuations of carbon and nitrogen stable isotope ratios in benthic (and their potential food sources) and epibenthic consumers. Sampling was conducted along the estuarine salinity gradient from winter to summer of 2011. We found that the carbon (δ13C = 13C/12C) and nitrogen (δ15N = 15N/14N) stable isotope ratios of the most abundant benthic and epibenthic consumers varied along the salinity gradient. The δ13C values increased seaward, whereas the opposite pattern was found for the δ15N, especially during the summer. The stable isotope ratios revealed two trophic pathways in the Minho estuary food web. The first pathway is supported by phytoplankton and represented by filter feeders such as zooplankton and some deposit feeders, particularly amphipods and polychaetes. The second pathway is supported by detritus and composed essentially of deposit feeders, which by being consumed, allow detritus to be incorporated into higher trophic levels. Spatial and temporal feeding variations in the estuarine benthic food web are driven by hydrology and proximity to adjacent ecosystems (terrestrial, marine). During high river discharge periods, the δ13CPOC (ca. -28‰) and C: NPOM (>10) values suggested an increase of terrestrial-derived OM to the particulate OM pool, which was then used by suspension feeders. During low river discharge periods, marine intrusion increased upriver, which was reflected in benthic consumers' 13C-enriched stable isotope values. No relationship was found between food quality (phytoplankton vs. detritus) and food chain length because the lowest and highest values were associated with freshwater and saltmarsh areas, respectively, both dominated by the detrital pathway. This study demonstrates that benthic consumers enhance the connectivity between estuaries and its adjacent ecosystems by utilizing subsidies of terrestrial and marine origin and that benthic-pelagic coupling is an important energy transfer mechanism to the benthic food web.

Remote Monitoring System of Dynamic Compression Bracing to Correct Pectus Carinatum

Pectus carinatum (PC) is a chest deformity caused by disproportionate growth of the costal cartilages compared with the bony thoracic skeleton, pulling the sternum forwards and leading to its protrusion. Currently, the most common non-invasive treatment is external compressive bracing, by means of an orthosis. While this treatment is widely adopted, the correct magnitude of applied compressive forces remains unknown, leading to suboptimal results. Moreover, the current orthoses are not suitable to monitor the treatment. The purpose of this study is to design a force measuring system that could be directly embedded into an existing PC orthosis without relevant modifications in its construction. For that, inspired by the currently commercially available products where a solid silicone pad is used, three concepts for silicone-based sensors, two capacitive and one magnetic type, are presented and compared. Additionally, a concept of a full pipeline to capture and store the sensor data was researched. Compression tests were conducted on a calibration machine, with forces ranging from 0 N to 300 N. Local evaluation of sensors' response in different regions was also performed. The three sensors were tested and then compared with the results of a solid silicon pad. One of the capacitive sensors presented an identical response to the solid silicon while the other two either presented poor repeatability or were too stiff, raising concerns for patient comfort. Overall, the proposed system demonstrated its potential to measure and monitor orthosis's applied forces, corroborating its potential for clinical practice.

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹instrument, verb, target› combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery.

Targeted pseudouridylation: An approach for suppressing nonsense mutations in disease genes

Nonsense mutations create premature termination codons (PTCs), activating the nonsense-mediated mRNA decay (NMD) pathway to degrade most PTC-containing mRNAs. The undegraded mRNA is translated, but translation terminates at the PTC, leading to no production of the full-length protein. This work presents targeted PTC pseudouridylation, an approach for nonsense suppression in human cells. Specifically, an artificial box H/ACA guide RNA designed to target the mRNA PTC can suppress both NMD and premature translation termination in various sequence contexts. Targeted pseudouridylation exhibits a level of suppression comparable with that of aminoglycoside antibiotic treatments. When targeted pseudouridylation is combined with antibiotic treatment, a much higher level of suppression is observed. Transfection of a disease model cell line (carrying a chromosomal PTC) with a designer guide RNA gene targeting the PTC also leads to nonsense suppression. Thus, targeted pseudouridylation is an RNA-directed gene-specific approach that suppresses NMD and concurrently promotes PTC readthrough.

Augmented Reality-Assisted Ultrasound Breast Biopsy

Breast cancer is the most prevalent cancer in the world and the fifth-leading cause of cancer-related death. Treatment is effective in the early stages. Thus, a need to screen considerable portions of the population is crucial. When the screening procedure uncovers a suspect lesion, a biopsy is performed to assess its potential for malignancy. This procedure is usually performed using real-time Ultrasound (US) imaging. This work proposes a visualization system for US breast biopsy. It consists of an application running on AR glasses that interact with a computer application. The AR glasses track the position of QR codes mounted on an US probe and a biopsy needle. US images are shown in the user's field of view with enhanced lesion visualization and needle trajectory. To validate the system, latency of the transmission of US images was evaluated. Usability assessment compared our proposed prototype with a traditional approach with different users. It showed that needle alignment was more precise, with 92.67 ± 2.32° in our prototype versus 89.99 ± 37.49° in a traditional system. The users also reached the lesion more accurately. Overall, the proposed solution presents promising results, and the use of AR glasses as a tracking and visualization device exhibited good performance.

A multi-task convolutional neural network for classification and segmentation of chronic venous disorders

Chronic Venous Disorders (CVD) of the lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. Due to the exponential growth of the aging population and the worsening of CVD with age, it is expected that the healthcare costs and the resources needed for the treatment of CVD will increase in the coming years. The early diagnosis of CVD is fundamental in treatment planning, while the monitoring of its treatment is fundamental to assess a patient's condition and quantify the evolution of CVD. However, correct diagnosis relies on a qualitative approach through visual recognition of the various venous disorders, being time-consuming and highly dependent on the physician's expertise. In this paper, we propose a novel automatic strategy for the joint segmentation and classification of CVDs. The strategy relies on a multi-task deep learning network, denominated VENet, that simultaneously solves segmentation and classification tasks, exploiting the information of both tasks to increase learning efficiency, ultimately improving their performance. The proposed method was compared against state-of-the-art strategies in a dataset of 1376 CVD images. Experiments showed that the VENet achieved a classification performance of 96.4%, 96.4%, and 97.2% for accuracy, precision, and recall, respectively, and a segmentation performance of 75.4%, 76.7.0%, 76.7% for the Dice coefficient, precision, and recall, respectively. The joint formulation increased the robustness of both tasks when compared to the conventional classification or segmentation strategies, proving its added value, mainly for the segmentation of small lesions.

In Vitro Reconstitution of Pseudouridylation Catalyzed by Human Box H/ACA Ribonucleoprotein Particles

Pseudouridine (Ψ) is the most common chemical modification in RNA. In eukaryotes and archaea, pseudouridine synthases, mainly guided by box H/ACA snoRNAs, convert uridine to Ψ. Ψ stabilizes RNA structure and alters RNA-RNA and RNA-protein interactions, conferring important roles in gene expression. Notably, several Ψ-linked human diseases have been identified over the years. In addition, Ψ has also been extensively used in developing mRNA vaccines. Furthermore, it has been shown that pseudouridylation can be site-specifically directed to modify specific nonsense codons, leading to nonsense suppression. All of these, together with a need to better understand the specific functions of Ψs, have motivated the development of in vitro pseudouridylation assays using purified and reconstituted box H/ACA RNPs. Here, we describe an in vitro system for box H/ACA RNA-guided RNA pseudouridylation using human cell extracts. We show that a half guide RNA (only one hairpin) is just as functionally competent as the full-length guide RNA (two hairpins) in guiding site-specific pseudouridylation in the human cell extracts. This discovery offers the opportunity for direct delivery of a short guide RNA to human cells to promote site-specific nonsense suppression and therefore has potential clinical applications.

Characterization of the Workspace and Limits of Operation of Laser Treatments for Vascular Lesions of the Lower Limbs

The increase of the aging population brings numerous challenges to health and aesthetic segments. Here, the use of laser therapy for dermatology is expected to increase since it allows for non-invasive and infection-free treatments. However, existing laser devices require doctors' manually handling and visually inspecting the skin. As such, the treatment outcome is dependent on the user's expertise, which frequently results in ineffective treatments and side effects. This study aims to determine the workspace and limits of operation of laser treatments for vascular lesions of the lower limbs. The results of this study can be used to develop a robotic-guided technology to help address the aforementioned problems. Specifically, workspace and limits of operation were studied in eight vascular laser treatments. For it, an electromagnetic tracking system was used to collect the real-time positioning of the laser during the treatments. The computed average workspace length, height, and width were 0.84 ± 0.15, 0.41 ± 0.06, and 0.78 ± 0.16 m, respectively. This corresponds to an average volume of treatment of 0.277 ± 0.093 m³. The average treatment time was 23.2 ± 10.2 min, with an average laser orientation of 40.6 ± 5.6 degrees. Additionally, the average velocities of 0.124 ± 0.103 m/s and 31.5 + 25.4 deg/s were measured. This knowledge characterizes the vascular laser treatment workspace and limits of operation, which may ease the understanding for future robotic system development.

Ultrasound training simulator using augmented reality glasses: an accuracy and precision assessment study

Ultrasound (US) imaging despite being safe, cost-effective, and radiation-free, presents poor quality and artifacts, requiring specific medical training in US probe handling and image evaluation. The use of simulators to train physicians has proven its effectiveness, but most of them require specific facilities and hardware. In the last few years, augmented reality has gained relevance to simulate real scenarios which can avoid large setups and broaden medical training to more physicians. This work proposes a new framework for the training of US images acquisition. It consists of a custom-made application that runs on AR glasses (Microsoft HoloLens 2) and interacts with a US simulator application. The AR glasses track the orientation of a QR code mounted on a US probe, communicating its orientation with the US simulator application. This allows the physician to interact with a US probe seeing in real-time the US image in the physician's field of view. The QR code tracking assessment of the AR glasses was conducted by measuring the orientation accuracy and precision when compared with the measures of an electromagnetic tracking device (i.e., NDI Aurora). The proposed solution presented a good performance, rendering the US image in AR glasses with real-time feedback. Moreover, it can track the QR code on the US probe with an accuracy of 0.755°, and a precision of 0.018°. Overall, the proposed framework presents promising results and the use of AR glasses as a tracking device reached a good performance. Clinical Relevance- Simulation is a relevant tool to train physicians, especially in US imaging. AR glasses can broaden the training to less trained physicians by reducing the need for complex setups. This technology allows the implementation of a more natural user interface, which can be relevant in scenarios where good coordination between the eyes and hands of the physician is necessary (i.e., Biopsies).

Comparative Analysis of Current Deep Learning Networks for Breast Lesion Segmentation in Ultrasound Images

Automatic lesion segmentation in breast ultrasound (BUS) images aids in the diagnosis of breast cancer, the most common type of cancer in women. Accurate lesion segmentation in ultrasound images is a challenging task due to speckle noise, artifacts, shadows, and lesion variability in size and shape. Recently, convolutional neural networks have demonstrated impressive results in medical image segmentation tasks. However, the lack of public benchmarks and a standardized evaluation method hampers the networks' performance comparison. This work presents a benchmark of seven state-of-the-art methods for the automatic breast lesion segmentation task. The methods were evaluated on a multi-center BUS dataset composed of three public datasets. Specifically, the U-Net, Dynamic U-Net, Semantic Segmentation Deep Residual Network with Variational Autoencoder (SegResNetVAE), U-Net Transformers, Residual Feedback Network, Multiscale Dual Attention-Based Network, and Global Guidance Network (GG-Net) architectures were evaluated. The training was performed with a combination of the cross-entropy and Dice loss functions and the overall performance of the networks was assessed using the Dice coefficient, Jaccard index, accuracy, recall, specificity, and precision. Despite all networks having obtained Dice scores superior to 75%, the GG-Net and SegResNetVAE architectures outperform the remaining methods, achieving 82.56% and 81.90%, respectively. Clinical Relevance- The results of this study allowed to prove the potential of deep neural networks to be used in clinical practice for breast lesion segmentation also suggesting the best model choices.

Analysis of Current Deep Learning Networks for Semantic Segmentation of Anatomical Structures in Laparoscopic Surgery

Semantic segmentation of anatomical structures in laparoscopic videos is a crucial task to enable the development of new computer-assisted systems that can assist surgeons during surgery. However, this is a difficult task due to artifacts and similar visual characteristics of anatomical structures on the laparoscopic videos. Recently, deep learning algorithms have been showed promising results on the segmentation of laparoscopic instruments. However, due to the lack of large public datasets for semantic segmentation of anatomical structures, there are only a few studies on this task. In this work, we evaluate the performance of five networks, namely U-Net, U-Net++, DynUNet, UNETR and DeepLabV3+, for segmentation of laparoscopic cholecystectomy images from the recently released CholecSeg8k dataset. To the best of our knowledge, this is the first benchmark performed on this dataset. Training was performed with dice loss. The networks were evaluated on segmentation of 8 anatomical structures and instruments, performance was quantified through the dice coefficient, intersection over union, recall, and precision. Apart from the U-Net, all networks obtained scores similar to each other, with the U-Net++ being the network with the best overall score with a mean Dice value of 0.62. Overall, the results show that there is still room for improvement in the segmentation of anatomical structures from laparoscopic videos. Clinical Relevance- The results of this study show the potential of deep neural networks for the segmentation of anatomical structures in laparoscopic images which can later be incorporated into computer-aided systems for surgeons.

Deep learning methods for lesion detection on mammography images: a comparative analysis

Automatic lesion segmentation in mammography images assists in the diagnosis of breast cancer, which is the most common type of cancer especially among women. The robust segmentation of mammography images has been considered a backbreaking task due to: i) the low contrast of the lesion boundaries; ii) the extremely variable lesions' sizes and shapes; and iii) some extremely small lesions on the mammogram image. To overcome these drawbacks, Deep Learning methods have been implemented and have shown impressive results when applied to medical image segmentation. This work presents a benchmark for breast lesion segmentation in mammography images, where six state-of-the-art methods were evaluated on 1692 mammograms from a public dataset (CBIS-DDSM), and compared considering the following six metrics: i) Dice coefficient; ii) Jaccard index; iii) accuracy; iv) recall; v) specificity; and vi) precision. The base U-Net, UNETR, DynUNet, SegResNetVAE, RF-Net, MDA-Net architectures were trained with a combination of the cross-entropy and Dice loss functions. Although the networks presented Dice scores superior to 86%, two of them managed to distinguish themselves. In general, the results demonstrate the efficiency of the MDA-Net and DynUnet with Dice scores of 90.25% and 89.67%, and accuracy of 93.48% and 93.03%, respectively. Clinical Relevance--- The presented comparative study allowed to identify the current performance of deep learning strategies on the segmentation of breast lesions.

Classification of Chronic Venous Disorders using an Ensemble Optimization of Convolutional Neural Networks

Chronic Venous Disorders (CVD) of lower limbs are one of the most prevalent medical conditions, affecting 35% of adults in Europe and North America. The early diagnosis of CVD is critical, however, the diagnosis relies on a visual recognition of the various venous disorders which is time- consuming and dependent on the physician's expertise. Thus, automatic strategies for the classification of the CVD severity are claimed. This paper proposed an automatic ensemble-based strategy of Deep Convolutional Neural Networks (DCNN) for the classification of CVDs severity from medical images. First, a clinical dataset containing 1376 images of patients' legs with CVD of 5 different levels of severity was constructed. Then, the constructed dataset was randomly split into training, testing, and validation datasets. Subsequently, a set of DCNN were individually applied to the images for classification. Finally, instead of a traditional voting ensemble strategy, extracted feature vectors from each DCNN were concatenated and fed into a new ensemble optimization network. Experiments showed that the proposed strategy achieved a classification with 93.8%, 93.4%, 92.4% of accuracy, precision, and recall, respectively. Moreover, compared to the traditional ensemble strategy, improvement in the accuracy of ~2% was registered. The proposed strategy showed to be accurate and robust for the diagnosis of CVD severity from medical images. Nevertheless, further research using an extensive clinical database is required to validate the potential of this strategy. Clinical Relevance- An automatic classification of CVD to reduce the probability of underdiagnoses and promote the treatment of CVD in the early stages.

A deep learning method for kidney segmentation in 2D ultrasound images

Ultrasound (US) is a medical imaging modality widely used for diagnosis, monitoring, and guidance of surgical procedures. However, the accurate interpretation of US images is a challenging task. Recently, portable 2D US devices enhanced with Artificial intelligence (AI) methods to identify, in real-time, specific organs are widely spreading worldwide. Nevertheless, the number of available methods that effectively work in such devices is still limited. In this work, we evaluate the performance of the U-NET architecture to segment the kidney in 2D US images. To accomplish this task, we studied the possibility of using multiple sliced images extracted from 3D US volumes to achieve a large, variable, and multi-view dataset of 2D images. The proposed methodology was tested with a dataset of 66 3D US volumes, divided in 51 for training, 5 for validation, and 10 for testing. From the volumes, 3792 2D sliced images were extracted. Two experiments were conducted, namely: (i) using the entire database (WWKD); and (ii) using images where the kidney area is > 500 mm2 (500KD). As a proof-of-concept, the potential of our strategy was tested in real 2D images (acquired with 2D probes). An average error of 2.88 ± 2.63 mm in the testing dataset was registered. Moreover, satisfactory results were obtained in our initial proof-of-concept using pure 2D images. In short, the proposed method proved, in this preliminary study, its potential interest for clinical practice. Further studies are required to evaluate the real performance of the proposed methodology. Clinical Relevance- In this work a deep learning methodology to segment the kidney in 2D US images is presented. It may be a relevant feature to be included in the recent portable US ecosystems easing the interpretation of image and consequently the clinical analysis.

3D Facial Landmark Localization for cephalometric analysis

Cephalometric analysis is an important and routine task in the medical field to assess craniofacial development and to diagnose cranial deformities and midline facial abnormalities. The advance of 3D digital techniques potentiated the development of 3D cephalometry, which includes the localization of cephalometric landmarks in the 3D models. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra/inter-observer variability. In this paper, a framework to automatically locate cephalometric landmarks in 3D facial models is presented. The landmark detector is divided into two stages: (i) creation of 2D maps representative of the 3D model; and (ii) landmarks' detection through a regression convolutional neural network (CNN). In the first step, the 3D facial model is transformed to 2D maps retrieved from 3D shape descriptors. In the second stage, a CNN is used to estimate a probability map for each landmark using the 2D representations as input. The detection method was evaluated in three different datasets of 3D facial models, namely the Texas 3DFR, the BU3DFE, and the Bosphorus databases. An average distance error of 2.3, 3.0, and 3.2 mm were obtained for the landmarks evaluated on each dataset. The obtained results demonstrated the accuracy of the method in different 3D facial datasets with a performance competitive to the state-of-the-art methods, allowing to prove its versability to different 3D models. Clinical Relevance- Overall, the performance of the landmark detector demonstrated its potential to be used for 3D cephalometric analysis.

A sensorized needle guide for ultrasound assisted breast biopsy

One in every eight women will get breast cancer during their lifetime. Therefore, the early diagnosis of the lesions is fundamental to improve the chances of recovery. To find breast cancers, breast screening using techniques such as mammography and ultrasound (US) imaging scans are often used. When a lesion is found, a breast biopsy is performed to extract a tissue sample for analysis. The breast biopsy is usually assisted by an US to help find the lesion and guide the needle to its location. However, the identification of the needle tip in US image is challenging, possibly resulting in puncture failures. In this paper, we intend to study the potential of a sensorized needle guide system that provides information about the needle angle and displacement in respect to the US probe. Laboratory tests were initially conducted to evaluate the accuracy of each sensor in controlled conditions. After, a practical experiment with the US probe, working as a proof of concept, was performed. The angle sensor showed a root mean square error (RMSE) of 0.48 degrees and the displacement sensor showed a RMSE of 0.26mm after being calibrated. For the US probe tests, the displacement sensor shows high errors in the range of 1.19mm to 2.05mm due to mechanical reasons. Overall, the proposed system showed its potential to be used to accurately estimate needle tip localization throughout breast biopsies guided by US, corroborating its potential clinical application. Clinical relevance - Potential for clinical application where precise needle localization in ultrasound image is required.

Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of cranial deformities

Evaluation of the head shape of newborns is needed to detect cranial deformities, disturbances in head growth, and consequently, to predict short- and long-term neurodevelopment. Currently, there is a lack of automatic tools to provide a detailed evaluation of the head shape. Artificial intelligence (AI) methods, namely deep learning (DL), can be explored to develop fast and automatic approaches for shape evaluation. However, due to the clinical variability of patients' head anatomy, generalization of AI networks to the clinical needs is paramount and extremely challenging. In this work, a new framework is proposed to augment the 3D data used for training DL networks for shape evaluation. The proposed augmentation strategy deforms head surfaces towards different deformities. For that, a point-based 3D morphable model (p3DMM) is developed to generate a statistical model representative of head shapes of different cranial deformities. Afterward, a constrained transformation approach (3DHT) is applied to warp a head surface towards a target deformity by estimating a dense motion field from a sparse one resulted from the p3DMM. Qualitative evaluation showed that the proposed method generates realistic head shapes indistinguishable from the real ones. Moreover, quantitative experiments demonstrated that DL networks training with the proposed augmented surfaces improves their performance in terms of head shape analysis. Overall, the introduced augmentation allows to effectively transform a given head surface towards different deformity shapes, potentiating the development of DL approaches for head shape analysis.

A review of image processing methods for fetal head and brain analysis in ultrasound images

BACKGROUND AND OBJECTIVE

Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods.

METHODS

In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement.

RESULTS

For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications.

CONCLUSIONS

A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection.

Long term left ventricular impairment after SARS-COV2 infection

Funding Acknowledgements

Type of funding sources: None.

Introduction

The impact of acute infection by SARS-COV2 on the cardiovascular system has been previously reported in the literature, with a higher propensity in patients with more serious pattern of disease and pro-inflammatory status. Nevertheless, the long-term burden and sequels of COVID-19 on the cardiovascular system is still unknown.

Purpose

To evaluate the long-term impact of COVID-19 on left ventricular function in patients with severe clinical presentation requiring intensive care hospitalization.

Methods

 

This was a single-center observational, prospective study which included patients requiring admission to the Intensive Care Unit (ICU) due to COVID-19 infection from January to November 2020. All discharged patients were contacted to perform a clinical, electrocardiographic and echocardiographic evaluation and those who accepted were included on the protocol. Baseline and clinical characteristics were collected from clinical reports. For the global longitudinal strain (GLS) analysis all patients with significant wall motion abnormalities and valvular cardiopathy were excluded. Statistical analysis was performed with Mann-Whitney and a safety cut-off was established with ROC curve analysis.

Results

 

A total of 43 patients were included (mean age 64 ± 12, 67.4% males). During SARS-COV2 infection 49% presented with severe ARDS and 51% with moderate, 35% required invasive mechanical ventilation, 14% noninvasive mechanical ventilation and 52% with high nasal flow cannula. On the follow-up analysis, fatigue was the most reported in symptom (52% patients) and the majority did not present other signs or symptoms suggestive of heart failure, with the mean NT-proBNP of 49 ± 389 pg/dL. The standard ECG and echocardiogram did not show significant changes with a mean LVEF of 58 ± 7.8 and mean TAPSE of 21 ± 4. The strain analysis showed low value of GLS (mean GLS of -17.14 ± 2.36) for a reference cut-off of -18%, suggesting subclinical left ventricular dysfunction in this subset of patients with preserved ejection fraction. Maximum CPR values during ICU did not correlate either with the extent of disease evolvement in CT (p= NS) or ARDS severity (p= NS). Nevertheless, maximum CPR correlated significantly with GLS reduction (R = 0.44, p = 0.019). A CPR value higher than iger30mg/dL had 100% specificity for GLS reduction and a cut-off of 14gm/dL reported a sensitivity of 65% and specificity pf 75% for reduction in GLS.

Conclusion

In our study, we reported subclinical impairment in left ventricular function detected with global longitudinal strain after serious infection with SARS-COV2. The detected myocardial dysfunction was related with higher inflammatory as expressed by CPR values. Long-term monitoring of these patients should be undertaken in order to timely detect late complications. Abstract Figure.

Assessment of LAA strain and thrombus mobility and its impact on thrombus resolution - value of a novel echocardiographic thrombus tracking method

Funding Acknowledgements

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): FCT – Fundação para a Ciência e Tecnologia Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER)

Background

The left atrial appendage (LAA) is the major nidus for thrombus in patients with non-valvular atrial fibrillation. LAA thrombus mobility and changes hereof under anticoagulation may serve as a marker of both risk of embolism and efficacy of treatment. In this study, we hypothesized that LAA dynamics and thrombus mobility could serve as a baseline marker of thrombus dissolvability.

Methods

Transesophageal echocardiographic (TEE) images of in whom LAA thrombi were previously diagnosed were evaluated. Each image was tracked using a state-of-the-art tracking toolbox and functional information from the LAA and thrombi extracted. Global LAA motion was quantified through the longitudinal strain, while thrombus mobility was measured through a novel tracking scheme by directly capturing and measuring the thrombus motion isolated from the global cardiac motion. Baseline characteristics and echocardiographic parameters were compared between responders (thrombus resolution, i.e. no thrombus found at follow-up TEE) and non-responders (thrombus persistence or growth, i.e. thrombus found at follow up TEE) groups.

Results

35 patients (54.3% male and 45.7% female) with a mean age of 72.9 ± 14.1 years were included. Atrial fibrillation was present in all patients, showing a high risk for thromboembolism (CHA2DS2-VASc-Score 4.1 ± 1.5). Moderately reduced LVEF (41.7 ± 14.4%) and signs of diastolic dysfunction (E/E’ = 19.7 ± 8.5) was found in the cohort. While anticoagulation was initiated in all patients, resolution was achieved in 51.4% of patients. Significantly higher thrombus mobility (0.33 ± 0.13mm vs. 0.18 ± 0.08mm, p < 0.01 – Figure 1A) and LAA peak strain (-3.0 ± 1.3 vs -1.6 ± 1.5%, p < 0.01 – Figure 1B) were observed in responders against the non-responders group.

Conclusions

The quantification of the thrombus mobility through a tracking scheme is feasible. In our study population, higher thrombus mobility appeared to be associated with thrombus resolution. Further studies are required to evaluate the additional prognostic of the proposed technique.

Figure 1 – Quantification of the thrombus mobility (A) and peak LAA longitudinal strain (B) in both responder (blue) and non-responder group (green). *p < 0.05, unpaired t-test between non-responder and responder groups. Abstract Figure 1

Assessment of LAA Strain and Thrombus Mobility and Its Impact on Thrombus Resolution-Added-Value of a Novel Echocardiographic Thrombus Tracking Method

PURPOSE

The mobility of left atrial appendage (LAA) thrombi and changes hereof under anticoagulation may serve as a marker of both risk of embolism and efficacy of treatment. In this study, we sought to evaluate thrombus mobility and hypothesized that LAA dynamics and thrombus mobility could serve as a baseline marker of thrombus dissolvability.

METHODS

Patients with two-dimensional transesophageal echocardiographic images of the LAA, and with evidence of LAA thrombus were included in this study. Using a speckle tracking algorithm, functional information from the LAA and thrombi of different patients was computed. While the LAA motion was quantified through the longitudinal strain, thrombus mobility was evaluated using a novel method by directly tracking the thrombus, isolated from the global cardiac motion. Baseline characteristics and echocardiographic parameters were compared between responders (thrombus resolution) and non-responders (thrombus persistence) to anticoagulation.

RESULTS

We included 35 patients with atrial fibrillation with evidence of LAA thrombi. Patients had a mean age of 72.9 ± 14.1 years, exhibited a high risk for thromboembolism (CHA2DS2-VASc-Score 4.1 ± 1.5) and had moderately reduced LVEF (41.7 ± 14.4%) and signs of diastolic dysfunction (E/E' = 19.7 ± 8.5). While anticoagulation was initiated in all patients, resolution was achieved in 51.4% of patients. Significantly higher LAA peak strain (- 3.0 ± 1.3 vs. - 1.6 ± 1.5%, p < 0.01) and thrombus mobility (0.33 ± 0.13 mm vs. 0.18 ± 0.08 mm, p < 0.01) were observed in patients in whom thrombi resolved (i.e. responders against non-responders). Receiver operating characteristic (ROC) analysis revealed a high discriminatory ability for thrombus mobility with regards to thrombus resolution (AUC 0.89).

CONCLUSION

Isolated tracking of thrombus mobility from echocardiographic images is feasible. In patients with LAA thrombus, higher thrombus mobility appeared to be associated with thrombus resolution. Future studies should be conducted to evaluate the role of the described technique to predict LAA thrombus resolution or persistence.

SYNTHESIS OF GLYCEROL-FLUORINATED TRIAZOLE DERIVATIVES AND EVALUATION OF THEIR FUNGICIDAL ACTIVITY

The control of fungal species in agriculture is mainly conducted with the use of contact or systemic fungicides. However, environmental and human health concerns and increased resistance of fungal species to existing fungicides have increased the pressure on researchers to find new active ingredients for fungal control which present low toxicity to non-target organisms, are environmentally safe, and can be applied at very low concentrations. It is herein described the synthesis of eleven glycerol triazole containing compounds (ten of them fluorinated derivatives) and evaluation of their fungicidal activity. Eight out of eleven synthesized compounds are novel and all of the glycerol derivatives were characterized using infrared (IR), nuclear magnetic ressonance (NMR), and mass spectrometry (MS) techniques. Theoretical calculations were also carried out and the results are discussed. Starting from glycerol, the triazole derivatives were prepared in four steps. Evaluation of them against Colletrotricum gloesporioides showed that compound 1-((2,2-dimethyl-1,3-dioxolan-4-yl)methyl)-4-(2-fluorophenyl)-1H-1,2,3-triazole (4d) (ED50 = 59.14 µg mL-1) was slightly more active than commercial fungicide tebuconazole (61.35 µg mL-1). Compound 4d presented attractive physicochemical features for agrochemical purposes as revealed by the calculated physicochemical parameters. It is believed that glycerol-fluorinated triazole derivatives can be explored towards the development of new chemicals for the control of fungal species.

The Critical Contribution of Pseudouridine to mRNA COVID-19 Vaccines

The current COVID-19 pandemic is a massive source of global disruption, having led so far to two hundred and fifty million COVID-19 cases and almost five million deaths worldwide. It was recognized in the beginning that only an effective vaccine could lead to a way out of the pandemic, and therefore the race for the COVID-19 vaccine started immediately, boosted by the availability of the viral sequence data. Two novel vaccine platforms, based on mRNA technology, were developed in 2020 by Pfizer-BioNTech and Moderna Therapeutics (comirnaty® and spikevax®, respectively), and were the first ones presenting efficacies higher than 90%. Both consisted of N1-methyl-pseudouridine-modified mRNA encoding the SARS-COVID-19 Spike protein and were delivered with a lipid nanoparticle (LNP) formulation. Because the delivery problem of ribonucleic acids had been known for decades, the success of LNPs was quickly hailed by many as the unsung hero of COVID-19 mRNA vaccines. However, the clinical trial efficacy results of the Curevac mRNA vaccine (CVnCoV) suggested that the delivery system was not the only key to the success. CVnCoV consisted of an unmodified mRNA (encoding the same spike protein as Moderna and Pfizer-BioNTech's mRNA vaccines) and was formulated with the same LNP as Pfizer-BioNTech's vaccine (Acuitas ALC-0315). However, its efficacy was only 48%. This striking difference in efficacy could be attributed to the presence of a critical RNA modification (N1-methyl-pseudouridine) in the Pfizer-BioNTech and Moderna's mRNA vaccines (but not in CVnCoV). Here we highlight the features of N1-methyl-pseudouridine and its contributions to mRNA vaccines.

Segmental evaluation of right ventricular systolic function in atrial septal defect (ASD) type II patients

Introduction

A left to right (LR) shunt in atrial septal defect (ASD) may cause right heart and pulmonary overfilling, at the expense of the systemic circulation.

Purpose

The study objective was to evaluate the impact of LR shunt on left (LV) and right ventricular (RV) filling, function, and myocardial strain by using cardiovascular magnetic resonance imaging (CMR).

Methods

Thirty-five ASD type secundum patients (42±18 y.o.) were compared to a control group (n=40). Cine imaging was used to calculate ventricular volumes and ejection fraction (EF), global longitudinal (GLS) and circumferential (GCS), free wall (FW) and interventricular septal (IVS) longitudinal strain. Phase-contrast imaging was used to calculate pulmonary flow to systemic flow ratio (Qp/Qs).

Results

Qp/Qs was 2.2±0.60 (range 1.3–3.6), which resulted in higher RV end-diastolic volume/BSA (EDVi, 152±42 vs. 82±11 ml/m2), lower LV EDVi (72±17 vs. 83±10 ml/m2), and higher RV/LV EDVi ratio (2.1±0.5 vs. 1±0.1) compared to controls (all p&lt;0.001) [Figure 1]. Patients also presented with higher RV, but lower LV indexed stroke volumes (both p&lt;0.001), and a strong trend toward lower RVEF (p=0.08). They demonstrated significantly lower RV GLS (p=0.03) and longitudinal IVS strain (p&lt;0.001) [Figure 2]. RV FW strain or RV GCS did not differ among study groups. Shunt severity correlated with RV size and stroke volume, right atrial size and pulmonary trunk diameter (all p&lt;0.001). In contrast, no correlation was identified with functional nor strain parameters.

Conclusion

Cardiac remodeling in ASD patients with long-standing LR shunt negatively affects RV systolic performance, which is likely related to longitudinal septal dysfunction.

Funding Acknowledgement

Type of funding sources: None. Figure 1Figure 2

Anthropometric Landmark Detection in 3D Head Surfaces Using a Deep Learning Approach

Landmark labeling in 3D head surfaces is an important and routine task in clinical practice to evaluate head shape, namely to analyze cranial deformities or growth evolution. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra-/inter-observer variability, and can mislead the diagnose. Thus, automatic methods for anthropometric landmark detection in 3D models have a high interest in clinical practice. In this paper, a novel framework is proposed to accurately detect landmarks in 3D infant's head surfaces. The proposed method is divided into two stages: (i) 2D representation of the 3D head surface; and (ii) landmark detection through a deep learning strategy. Moreover, a 3D data augmentation method to create shape models based on the expected head variability is proposed. The proposed framework was evaluated in synthetic and real datasets, achieving accurate detection results. Furthermore, the data augmentation strategy proved its added value, increasing the method's performance. Overall, the obtained results demonstrated the robustness of the proposed method and its potential to be used in clinical practice for head shape analysis.

Wells and Geneva decision rules to predict pulmonary embolism: can we use them in Covid-19 patients?

Funding Acknowledgements

Type of funding sources: None.

Introduction

 Pulmonary embolism (PE) is a recognized complication of SARS-COV2 infection due to hypercoagulability. Before the COVID era, the need for computed tomography pulmonary angiography (CTPA) to rule out PE was determined by clinical probability, based on Wells and Geneva scores, in association with D-dimer measurements. However, patients with SARS-COV2 infection have a pro-thrombotic and pro-inflammatory state which may compromise the usefulness of these algorithms to select patients for CTPA. 

Purpose

 To evaluate the accuracy of the Wells and Geneva scores to predict PE in patients with SARS-COV2 infection.

Methods

 Retrospective study of consecutive outpatients with SARS-COV2 infection proved by positive PCR who underwent CTPA due to suspected PE. The Wells and Geneva scores were calculated and the area under the curve (AUC) of the receiver operating characteristic curve was measured.

Results

 We enrolled 235 patients (61% males, mean age 69.10 ± 16.69 years) and the incidence of pulmonary embolism was 15% (35 patients). In patients with PE, emboli were located mainly in segmental arteries (60%) and bilaterally (46%). Patients with PE were older (mean age 75.06 ± 2.23 vs. 68.06 ± 1.21 years, p = 0.022), and did not differ in sex or risk factors for thromboembolic diseases from the non-PE group. Patients with PE had higher D-dimer levels (median 15.41 mg/dl, IQR 1.17 – 20.00) compared to patients without PE (median 5.99 mg/dl, IQR 0.47 – 2.82, p &lt; 0.001).

 There was no statically significant difference between the average Wells score in patients with PE and without PE (1.04 and 0.89 respectively, p = 0.733) and the AUC demonstrated that the Wells score had no discriminatory power (AUC = 0.52). Within patients with PE, 19 patients had a Wells score of zero. Regarding the Geneva score, there was also no difference between the average score in patients with and without PE (4.20 vs 3.93 respectively, p = 0.420). AUC for Geneva score was 0.54.

Clinical probability combined with D-dimer measurement had a 100% sensitivity for both Wells and Geneva scores, but a specificity of 10% and 11%, respectively. 

Conclusion

 PE diagnosis may be challenging in patients with SARS-COV2 infection since both conditions may have similar signs and symptoms and may be associated with increased D-dimers. According to our results, traditional clinical prediction scores have little discriminatory power in these patients and a higher D-dimer cut-off should be considered to better select patients for CTPA to minimize radiation exposure and contrast-related complications in COVID-19 patients.

Hypertensive response to exercise - to treat or not to treat?

Funding Acknowledgements

Type of funding sources: None.

Introduction

 Hypertensive response to exercise (HRE) is often documented in individuals without known cardiovascular disease. However, its impact on patient prognosis and the necessity of treatment are still not clear.

Objective

 We aimed to evaluate the impact of a hypertensive response (HRE) on exercise test (ET) on clinical prognosis and outcome.

Methods

 This was a single-center retrospective study of patients with HRE on stress exercise testing (STE) performed between January 2012 and December 2015. In our center, we define HRE as systolic blood pressure (SBP) &gt; 210mmHg in men and &gt;190mmHg in women, diastolic blood pressure (DBP) &gt; 90mmHg or an increase in baseline systolic BP at least 60 mmHg in men or 50 mmHg in women, during exercise. Demographic, clinical, echocardiographic, electrocardiographic data were collected, and results were obtained using Chi-square and Student-t tests; logistic regression.

Results

 We evaluated 500 patients who underwent STE, 457 of which had hypertensive response vs 43 patients without HRE (mean age 57 ± 11 vs 61 ± 8 years, p = 0,01). Among the two groups there were no differences between gender (76.5% men vs 69.7%) and race nor between the cardiovascular risk factors, namely hypertension, diabetes and dyslipidaemia. We evaluated their responses in STE and their outcomes, with a mean follow-up of 60 ± 22 months.

In the univariate and multivariate analysis, presence of Sokolow-Lion criteria of left ventricular hypertrophy in the ECG was associated with HRE during the exam (OR 5.26; CI95% 2.4-11.6; p &lt; 0.001). In patients who had previously known hypertension, therapy with calcium channel blockers seemed to protect against hypertensive response prior to ET (OR 0.48, CI95% 0.24-0.97, p = 0.004) compared to other antihypertensive drugs.

Regarding the clinical outcomes, patients with HRE were associated with an increased risk of developing heart failure (p = 0.027) (versus patients without HRE) during follow up but failed to predict adverse outcomes such as acute coronary syndrome, atrial

fibrillation or stroke.

Within the patients with HRE in ET, 78 patients did not have an established diagnosis of HTA (mean age 49 ± 12.16 years, 75.6% men). In these patients we observed initiation on antihypertensive therapy after ET on 27.6% patients, but on univariate and multivariate analysis, starting therapy with anti-hypertensives did not have a significant impact on incidence of stroke, AF, HF, hospitalization for cardiovascular events or death.

Conclusions

 We did not observe any significant differences among the studied groups regarding prognosis, except for the highest incidence of heart failure in patients with HRE. Initiation of antihypertensive therapy in patients with HRE failed to modify outcomes,

however our sample was underpowered, so, further studies are required in order to clarify the value of treatment in patients with HRE.

Kidney Segmentation in 3-D Ultrasound Images Using a Fast Phase-Based Approach

Renal ultrasound (US) imaging is the primary imaging modality for the assessment of the kidney's condition and is essential for diagnosis, treatment and surgical intervention planning, and follow-up. In this regard, kidney delineation in 3-D US images represents a relevant and challenging task in clinical practice. In this article, a novel framework is proposed to accurately segment the kidney in 3-D US images. The proposed framework can be divided into two stages: 1) initialization of the segmentation method and 2) kidney segmentation. Within the initialization stage, a phase-based feature detection method is used to detect edge points at kidney boundaries, from which the segmentation is automatically initialized. In the segmentation stage, the B-spline explicit active surface framework is adapted to obtain the final kidney contour. Here, a novel hybrid energy functional that combines localized region- and edge-based terms is used during segmentation. For the edge term, a fast-signed phase-based detection approach is applied. The proposed framework was validated in two distinct data sets: 1) 15 3-D challenging poor-quality US images used for experimental development, parameters assessment, and evaluation and 2) 42 3-D US images (both healthy and pathologic kidneys) used to unbiasedly assess its accuracy. Overall, the proposed method achieved a Dice overlap around 81% and an average point-to-surface error of ~2.8 mm. These results demonstrate the potential of the proposed method for clinical usage.

Electrophysiological effects of mindfulness meditation in a concentration test

In this paper, we evaluate the effects of mindfulness meditation training in electrophysiological signals, recorded during a concentration task. Longitudinal experiments have been limited to the analysis of psychological scores through depression, anxiety, and stress state (DASS) surveys. Here, we present a longitudinal study, confronting DASS survey data with electrocardiography (ECG), electroencephalography (EEG), and electrodermal activity (EDA) signals. Twenty-five university student volunteers (mean age = 26, SD = 7, 9 male) attended a 25-h mindfulness-based stress reduction (MBSR) course, over a period of 8 weeks. There were four evaluation periods: pre/peri/post-course and a fourth follow-up, after 2 months. All three recorded biosignals presented congruent results, in line with the expected benefits of regular meditation practice. In average, EDA activity decreased throughout the course, -64.5%, whereas the mean heart rate displayed a small reduction, -5.8%, possibly as a result of an increase in parasympathetic nervous system activity. Prefrontal (AF3) cortical alpha activity, often associated with calm conditions, saw a very significant increase, 148.1%. Also, the number of stressed and anxious subjects showed a significant decrease, -92.9% and -85.7%, respectively. Easy to practice and within everyone's reach, this mindfulness meditation can be used proactively to prevent or enhance better quality of life. 25 volunteers attended a Mindfulness-Based Stress Reduction (MBSR) course in 4 evaluation periods: Pre/Peri/Post-course and a fourth follow-up after two months. A Depression, Anxiety and Stress State (DASS) survey is completed in each period. Electrodermal Activity (EDA), Electrocardiography (ECG) and Electroencephalography (EEG) are also recorded and processed. By integrating self-reported surveys and electrophysiological recordings there is strong evidence of evolution in wellbeing. Mindfulness meditation can be used proactively to prevent or enhance better quality of life.

Spliceosomal snRNA Epitranscriptomics

Small nuclear RNAs (snRNAs) are critical components of the spliceosome that catalyze the splicing of pre-mRNA. snRNAs are each complexed with many proteins to form RNA-protein complexes, termed as small nuclear ribonucleoproteins (snRNPs), in the cell nucleus. snRNPs participate in pre-mRNA splicing by recognizing the critical sequence elements present in the introns, thereby forming active spliceosomes. The recognition is achieved primarily by base-pairing interactions (or nucleotide-nucleotide contact) between snRNAs and pre-mRNA. Notably, snRNAs are extensively modified with different RNA modifications, which confer unique properties to the RNAs. Here, we review the current knowledge of the mechanisms and functions of snRNA modifications and their biological relevance in the splicing process.

Mitral valve prolapse: American versus European guidelines - which one is better

Funding Acknowledgements

Type of funding sources: None.

Introduction

According to the most recent recommendations of AHA, mitral valve prolapse (MVP) is defined as systolic displacement of the mitral leaflet into the left atrium (LA) of at least 2 mm from the mitral annular plane. The ESC recommendations define MVP, flail and billowing, according to the location of the leaflet tips in relation to the coaptation plan. Differences in outcomes considering these classifications are not established.

Purpose

To evaluate the differences in clinical presentation and outcomes of MVP considering AHA and ESC classifications.

Methods

Single-center retrospective study of consecutive patients with MVP (defined according to the AHA classification) documented in transthoracic echocardiogram between January 2014 and October 2019. Demographic, clinical, echocardiographic and electrocardiographic data were collected. The results were obtained using Chi-square and ANOVA tests.

Results

We included 247 patients (mean age 62.9 ± 18 years, 61% males) according to AHA classification; considering the ESC classification: 147 (59%) had prolapse, 30 (12%) flail and 67 (39%) billowing.

In comparison to patients with flail and billowing, patients with MVP had less cordae rupture (p = 0.02). Prolapse was associated with better survival (p = 0.037) and was an independent predictor of survival (OR = 0.372, CI95% [0.148-0.935], p = 0.035) Patients with flail were older in comparison to the ones with prolapse and billowing (71 ± 14 vs 63 ± 17 vs 60 ± 21 years, respectively, p = 0.022). Patients with flail were mostly men (80%, p = 0.028), with more significant mitral regurgitation (p = 0.003) and higher NYHA class (p = 0.018). They also had higher systolic pulmonary artery pressure (SPAP) (48 ± 23 vs 38 ± 18 vs 36 ± 12mmHg, p = 0.015) and higher values of LV mass and posterior wall thickness (144 ±32 vs 125 ± 44 vs 114 ± 37g/m2, p = 0.005 and 11 ± 1,5 vs 10 ± 1,7 vs 9 ± 1.9 mm, p = 0.009, respectively). Women had more billowing (p = 0.04) than prolapse and flail.

Conclusion

The ESC classification adds information to the AHA classification in what concerns to clinical presentation and prognosis of mitral valve prolapse, so both classifications should be used in daily practice.

Mitral annulus disjunction: is it a marker of ominous prognosis?

Funding Acknowledgements

Type of funding sources: None.

Introduction

Mitral annulus disjunction (MAD) has been proposed as a contributing factor for arrythmias and mitral regurgitation in patients with mitral valve prolapse (MVP), however its clinical relevance is still under investigation.

Objective

To evaluate the frequency of MAD in MVP patients, to characterize clinically patients with MAD and assess potential markers for events.

Methods

Single-center retrospective study of consecutive patients with MVP documented in transthoracic echocardiogram between January 2014 and October 2019. MVP was defined according to the 2017 AHA recommendations; MAD was defined as a separation between mitral valve annulus and the left ventricle free wall. Demographic, clinical, echocardiographic, electrocardiographic data were collected. The results were obtained using Chi-square and Mann-Whitney tests; logistic regression was used to find predictors of events.

Results

247 patients were included (mean age 62.9 ± 18 years, 61% males), of these 23 (9.3%) had MAD (mean age 56 ± 20 years, 56.5% males). The maximum diameter of MAD was 10 ± 3mm (range 5-18). 21 patients (92.3%) had mitral regurgitation, and it was at least of moderate severity in 65.2% of patients. Most of the patients (91.3, n = 21) were in sinus rhythm (SR).

During follow-up (FUP) of 29 ± 19 months, 39% (n = 9) of the patients developed symptoms, 22% (n = 5) had atrial fibrillation (AF), 4.3% (n = 1) had acute aortic syndrome (AAS), 4.3% (n = 1) needed ICD, 22% (n = 5) were submitted to mitral valve intervention, 8.7% (n = 2) were admitted to hospital and 8.7% (n = 2) died. None of the patients presented sustained ventricular arrhythmias (SVA) as assessed in regular Holter monitoring.

These patients had more AAS and needed more ICD in FUP compared to patients without MAD (p = 0.007 and p = 0.006, respectively)

Mitral cord rupture (p = 0.04), age (p = 0.044), maximum velocity of tricuspid regurgitation (p = 0.04) and IVS thickness (p = 0.017) were associated with AF in MAD patients. in univariate analysis, interventricular septum thickness was a predictor of AF in this subgroup (OR 4.0, 95%CI 1.1-14.3, p = 0-032) The presence of SR was associated with survival (p = 0.03). There were no predictors of hospital admission or mitral intervention.

Conclusion

Patients with MAD had a relatively benign prognosis with few events during follow-up, although with more AAS and ICD in FUP. In our sample, AF was more common than SVA. Left ventricle hypertrophy was a predictor of AF and sinus rhythm was associated with survival. Larger studies with more patients and other methods of imaging are needed to confirm our results.