Robotic Assistance in Radiology: A Covid-19 Scenario

During the COVID-19 Pandemic, the need for rapid and reliable alternative COVID-19 screening methods have motivated the development of learning networks to screen COVID-19 patients based on chest radiography obtained from Chest X-ray (CXR) and Computed Tomography (CT) imaging. Although the effectiveness of developed models have been documented, their adoption in assisting radiologists suffers mainly due to the failure to implement or present any applicable framework. Therefore in this paper, a robotic framework is proposed to aid radiologists in COVID-19 patient screening. Specifically, Transfer learning is employed to first develop two well-known learning networks (GoogleNet and SqueezeNet) to classify positive and negative COVID-19 patients based on chest radiography obtained from Chest X-Ray (CXR) and CT imaging collected from three publicly available repositories. A test accuracy of 90.90%, sensitivity and specificity of 94.70% and 87.20% were obtained respectively for SqueezeNet and a test accuracy of 96.40%, sensitivity and specificity of 95.50% and 97.40% were obtained respectively for GoogleNet. Consequently, to demonstrate the clinical usability of the model, it is deployed on the Softbank NAO-V6 humanoid robot which is a social robot to serve as an assistive platform for radiologists. The strategy is an end-to-end explainable sorting of X-ray images, particularly for COVID-19 patients. Laboratory-based implementation of the overall framework demonstrates the effectiveness of the proposed platform in aiding radiologists in COVID-19 screening. Author

eCOVID-19: Development of ontology-based clinical decision support system for COVID-19

Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman. Material and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested. Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances. Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics. © 2022, Published by Frontiers in Health Informatics.

Structural and Computational Studies of Cobalt(II) and Copper(II) Complexes with Aromatic Heterocyclic Ligand

Two coordination complexes, a cobalt(II) complex tris(1,10-phenanthroline)-cobalt perchlorate hydrate, [Co(phen)3]·(ClO4)2·H2O(1), and a copper(II) complex tris(1,10-phenanthroline)-copper perchlorate 4-bromo-2-{[(naphthalene-1-yl)imino]methyl}phenol hydrate, [Cu(phen)3]·(ClO4)2·HL·[O] (2), [where, phen = 1,10-phenathroline as aromatic heterocyclic ligand, HL = 4-bromo-2-((Z)-(naphthalene-4-ylimino) methyl) phenol] have been synthesized and structurally characterized. Single crystal X-ray analysis of both complexes has revealed the presence of a distorted octahedral geometry around cobalt(II) and copper(II) ions. density functional theory (DFT)-based quantum chemical calculations were performed on the cationic complex [Co(phen)3]2+ and copper(II) complex [Cu(phen)3]2+ to get the structure property relationship. Hirshfeld surface and 2-D fingerprint plots have been explored in the crystal structure of both the metal complexes. To find potential SARS-CoV-2 drug candidates, both the complexes were subjected to molecular docking calculations with SARS-CoV-2 virus (PDB ID: 7BQY and 7C2Q). We have found stable docked structures where docked metal chelates could readily bound to the SARS-CoV-2 Mpro. The molecular docking calculations of the complex (1) into the 7C2Q-main protease of SARS-CoV-2 virus revealed the binding energy of −9.4 kcal/mol with a good inhibition constant of 1.834 µM, while complex (2) exhibited the binding energy of −9.0 kcal/mol, and the inhibition constant of 1.365 µM at the inhibition binding site of receptor protein. Overall, our in silico studies explored the potential role of cobalt(II) complex (1), and copper(II) complex (2) complex as the viable and alternative therapeutic solution for SARS-CoV-2.

SD-GAN: A Style Distribution Transfer Generative Adversarial Network for Covid-19 Detection through X-ray Images

The Covid-19 pandemic is a prevalent health concern around the world in recent times. Therefore, it is essential to screen the infected patients at the primary stage to prevent secondary infections from person to person. The reverse transcription polymerase chain reaction (RT-PCR) test is commonly performed for Covid-19 diagnosis, while it requires significant effort from health professionals. Automated Covid-19 diagnosis using chest X-ray images is one of the promising directions to screen infected patients quickly and effectively. Automatic diagnostic approaches are used with the assumption that data originating from different sources have the same feature distributions. However, the X-ray images generated in different laboratories using different devices experience style variations e.g., intensity and contrast which contradict the above assumption. The prediction performance of deep models trained on such heterogeneous images of different distributions with different noises is affected. To address this issue, we have designed an automatic end-to-end adaptive normalization-based model called style distribution transfer generative adversarial network (SD-GAN). The designed model is equipped with the generative adversarial network (GAN) and task-specific classifier to transform the style distribution of images between different datasets belonging to different race people and carried out Covid-19 detection effectively. Evaluated results on four different X-ray datasets show the superiority of the proposed model to state-of-the-art methods in terms of the visual quality of style transferred images and the accuracy of Covid-19 infected patient detection. SD-GAN is publicly available at: https://github.com/tasleem-hello/SD-GAN/tree/SD-GAN. Author

Automated Pneumonia Diagnosis using a 2D Deep Convolutional Neural Network with Chest X-Ray Images

Tiny air sacs in one or both lungs become inflamed as a result of the lung infection known as pneumonia. In order to provide the best possible treatment plan, pneumonia must be accurately and quickly diagnosed at initial stages. Nowadays, a chest X-ray is regarded as the most effective imaging technique for detecting pneumonia. However, performing chest X-ray analysis may be quite difficult and laborious. For this purpose, in this study we propose deep convolutional neural network (CNN) with 24 hidden layers to identify pneumonia using chest X-ray images. In order to get high accuracy of the proposed deep CNN we applied an image processing method as well as rescaling and data augmentation methods as shear_range, rotation, zooming, CLAHE, and vertical_flip. The proposed approach has been evaluated using different evaluation criteria and has demonstrat-ed 97.2%, 97.1%, 97.43%, 96%, 98.8% performance in terms of accuracy, precision, recall, F-score, and AUC-ROC curve. Thus, the applied deep CNN obtain a high level of performance in pneumonia detection. In general, the provided approach is intended to aid radiologists in making an accurate pneumonia diagnosis. Additionally, our suggested models could be helpful in the early detection of other chest-related illnesses such as COVID-19 © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

CLINICAL AND MORPHOLOGICAL ANALYSIS OF PULMONARY ASPERGILLOSIS COINFECTION IN COVID-19.

The article provided the information about the results of clinical-morphological analysis of the practical observation with pulmonary aspergillosis associated with COVID-19 and undiagnosed when the patient was alive. The pulmonary aspergillosis associated with COVID-19 is one of the widespread complications. However, in numerous cases, the pulmonary aspergillosis associated with COVID-19 is not diagnosed due to its unclear signs and lack of information about it. An infiltrate with signs of destruction was noted during X-ray examination of the lower part of the right lung of the observed patient. It was evaluated as destructive pneumonia associated with bacterial infection. However, despite the patient had type II diabetes, no additional examination methods were applied to exclude aspergillosis. Disruption of the protective properties of the bronchial epithelium and the effect of oseltamivir type drugs may also contribute to the entry of aspergillus fungi into the lung tissue. According to the authors, during the development of diagnosis, treatment and prevention strategy of COVID-19in the patients with pulmonary aspergillosis it is important to study the interaction of these diseases.Copyright © 2022 Authors. All rights reserved.

Structure, Morphology, and Surface Chemistry of Surgical Masks and Their Evolution up to 10 Washing Cycles

The Covid-19 crisis has led to a massive surge in the use of surgical masks worldwide, causing risks of shortages and high pollution. Various decontamination techniques are currently being studied to reduce these risks by allowing the reuse of masks. In this study, surgical masks were washed up to 10 times, each cycle under the same conditions. The consequences of the washing cycles on the structure, fiber morphology, and surface chemistry have been studied through several characterization techniques: scanning electron microscopy, wetting angle measurements, infrared spectroscopy, X-ray diffraction, and X-ray photoelectrons spectroscopy. The washing process did not induce large changes in the hydrophobicity of the surface, the contact angle remaining constant throughout the cycles. The composition observed in the IR spectrum also remained unchanged for washed masks up to 10 cycles. Some slight variations were observed during X-ray analysis: the crystallinity of the fibers as well as the size of the crystals increases with the number of wash cycles. The XPS analysis shows that after 10 cycles, the surface of the masks underwent a slight oxidation. In the SEM images, changes were observed in the arrangement of the fibers, which are more visible the more times the mask has been washed: they align themselves in bundles, form areas with holes in the mask layer, and are crushed in some areas. © 2023 American Chemical Society

X-Ray Analysis of Donor Lungs within an Ex Vivo Lung Perfusion System (EVLP): A Pilot Study for the Assessment of the Brixia Score to Predict Transplantability

Background: Ex vivo lung perfusion (EVLP) is a safe and effective technique for lung evaluation and reconditioning of marginal donor lungs (DLs). The assessment of the DLs during EVLP is crucial for the transplantability decision making. There are a limited number of studies regarding the radiographic analysis of EVLP lungs. Furthermore, there are only few Xray grading scores available. The Brixia score is a proven radiological score for the severity grading of lung abnormalities with confirmed predictive power of the clinical outcome that was successfully used in pneumonia patients during the COVID-19 pandemic. It was the aim of our study to evaluate the X-ray findings of DLs within EVLP and investigate the prognostic potential of this score regarding transplantability and clinical outcome. Method(s): This is a retrospective observational pilot study. Between 2016 and 2022, a total of 277 double-lung transplantations (DLTx) were performed in our department. X-Rays of the last ten consecutive EVLP-DLs were blindly evaluated regarding the severity of interstitial and alveolar infiltrates and the Brixia score was calculated. Furthermore, the results (transplantability, severe primary graft dysfunction PGD, survival, hospital stay) and EVLP parameters (delta pO2) of these EVLP-DLs cases were analyzed and compared with the Brixia score for each case. Result(s): A range of Brixia score values from min 4 to max 18 was determined. Seven DLs were transplanted (mean delta pO 391 mm Hg, mean Brixia score 6.7) while three were rejected (mean delta pO 211 mm Hg, mean Brixia score 6). The two EVLP-DLs cases with the higher Brixia score (mean 15) were transplanted after EVLP. Postoperative PGD Grade 3 at 48 hours was recorded in one case without correlation to the Brixia score (Brixia score 4). All patients survived hospital discharge with a mean ICU and hospital stay of 9 and 30 days, respectively. Conclusion(s): In our pilot study, the Brixia score did not predict transplantability or postoperative function during EVLP. Additional studies are needed to further evaluate the use and clinical prognostic power of radiologic assessment with this or other scores in the EVLP lung assessment.

Protein Crystallography: Achievements and Challenges

Proteins are the most important biological macromolecules, and are involved in almost all aspects of life. Therefore, the study of the structure of proteins is of great practical and fundamental importance. On the one hand, knowledge of the spatial structure is necessary to study the basic principles of protein functioning;for example, the mechanisms of enzymatic reactions. On the other hand, knowledge of the spatial structure of proteins is used, for example, in biotechnology, for the design of enzymes with desired properties, as well as in drug design. Today, the main method for determining the spatial structure of a protein is X-ray structural analysis of protein crystals. The main difficulty in applying this method is in obtaining a perfect protein-crystal. This review is devoted to the successes and challenges of modern protein crystallography.

Thorax Disease Classification Based on Pyramidal Convolution Shuffle Attention Neural Network

Chest X-ray is one of the most common radiological examinations for screening thoracic diseases. Despite the existing methods based on convolution neural network that have achieved remarkable progress in thoracic disease classification from chest X-ray images, the scale variation of the pathological abnormalities in different thoracic diseases is still challenging in chest X-ray image classification. Based on the above problems, this paper proposes a residual network model based on a pyramidal convolution module and shuffle attention module (PCSANet). Specifically, the pyramid convolution is used to extract more discriminative features of pathological abnormality compared with the standard $3\times 3$ convolution;the shuffle attention enables the PCSANet model to focus on more pathological abnormality features. The extensive experiment on the ChestX-ray14 and COVIDx datasets demonstrate that the PCSANet model achieves superior performance compared with the other state-of-the-art methods. The ablation study further proves that pyramidal convolution and shuffle attention can effectively improve thoracic disease classification performance. © 2022 IEEE.

In Silico and Experimental Investigation of the Biological Potential of Some Recently Developed Carprofen Derivatives

The efficient regioselective bromination and iodination of the nonsteroidal anti-inflammatory drug (NSAID) carprofen were achieved by using bromine and iodine monochloride in glacial acetic acid. The novel halogenated carprofen derivatives were functionalized at the carboxylic group by esterification. The regioselectivity of the halogenation reaction was evidenced by NMR spectroscopy and confirmed by X-ray analysis. The compounds were screened for their in vitro antibacterial activity against planktonic cells and also for their anti-biofilm effect, using Gram-positive bacteria (Staphylococcus aureus ATCC 29213, Enterococcus faecalis ATCC 29212) and Gram-negative bacteria (Escherichia coli ATCC 25922 and Pseudomonas aeruginosa ATCC 27853). The cytotoxic activity of the novel compounds was tested against HeLa cells. The pharmacokinetic and pharmacodynamic profiles of carprofen derivatives, as well as their toxicity, were established by in silico analyses.

Pulmonary Image Analysis with Computer Vision Using Binary Classification Method for COVID-19 Detection

The advent of COVID-19 raised a terrorizing situation across the globe. The virus is spreading at an exponential rate since its beginning which was first identified in the Wuhan province, China, in the year 2019 in the month of December. The virus belongs to a family similar to that of the severe acute respiratory syndrome (SARS) which was identified in the year 2002. It has a complex structure which makes it difficult for scientists to get the exact action and cure for the disease. The spread of the virus takes place only by body fluids (saliva, mucosa, etc.). Various research organizations, pharmaceuticals, and institutes are working on the production of testing kits and vaccinations, though some of them are already being produced in the market. The testing kits produced are less in numbers due to the lack of resources and knowledge gathered to detect and fight the virus properly because of which not everyone is getting the chance to get themselves checked. Computer tomography and X-rays of the pulmonary region along with various acquaintance and methods of AI deep learning give an effective alternative that can be employed. This stratagem can be applied using a dataset consisting of images of various X-rays and CT-scans which are of patients who are COVID-19 positive and also of healthy people. This diagnosing tool uses the binomial classification method. The accuracy and the working of the tool primarily depend on accessible information and data for better processing. Post testing of the tool shows us that it is flexible and accurate to use. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Diagnosis of COVID-19 Pneumonia from Chest X-ray Scans using Modified MobileNet Architecture

COVID-19 disease has been laid out across the world recently as a global pandemic. Generally, rapid antigen tests have been performed to detect this dangerous disease at an early stage. Due to the increased number of false classification rate caused by rapid antigen tests, real time reverse transcription polymerase chain reaction (rRT-PCR) tests have been used as a conventional pathogenic testing tool. However, the efficacy of rRT-PCR tests have been affected by the several mutations in SARS-CoV-2 virus. Therefore, in this paper, a modified MobileNet-based intelligent methodology using chest X-ray (CXR) scans has been put forward to diagnose the COVID-19 disease precisely and early. The propounded method has been applied on benchmark chest X-ray dataset exploratory results establish the usefulness of the propounded approach. © 2021 IEEE.

70th Annual Denver X-ray Conference Report: A Virtual Event

Materials Identification Quantitative Phase Analysis Advanced PDF Modelling EDS Detectors Sample Preparation of XRF Basic XRF Trace Analysis Layered Structures Intermediate to Advanced XRD Imaging Micro XRF Non-ambient XRD Quantitative Analysis of XRF Handheld XRF III. New Developments in XRD and XRF Instrumentation Quantitative Phase Analysis Advanced Total Scattering Methods for Complex Material Studies Quantitative Analysis of XRF Trace Analysis including TXRF Machine Learning Techniques in X-ray Analysis General XRD Imaging Stress Analysis Functional Materials General XRF Non-ambient Analysis X-ray Absorption Spectroscopy Industrial Applications of XRD and XRF Cultural Heritage V. POSTER SESSIONS Both XRD and XRF Posters were available on the first day of the conference. Residual Stress and Microstructural Analysis of Laser Powder Bed Fusion Processed Ti-5Al-5V-5Mo-3Cr Alloy Using Synchrotron X-ray Diffraction C. XRF best poster award M. Sunder*, M.A. Zaitz, IBM, USA, for their work: Andrés Servando Aguirre Sánchez, Instituto Tecnológico y de Estudios Superiores de Monterrey, Mexico Adam Corrao, Stony Brook University, USA Hannah Cross, Keele University, United Kingdom Samantha Davies, Cranfield University, United Kingdom Benedikt Eger, Bonn-Rhein-Sieg University of Applied Sciences, Germany Gesa Goetzke, TU Berlin, Germany Sarah Gosling, Cranfield University, United Kingdom Sven Hampel, Clausthal University of Technology, Germany Benjamin Hulbert, University of Illinois at Urbana-Champaign, USA Rahul Lalge, University of Minnesota, USA Marcelo Augusto Malagutti, Universidade Federal de Santa Catarina, Brazil Otavio Jovino Marques, Illinois Institute of Technology, USA Ana Cecilia Murrieta Muñoz, Tecnológico de Monterrey, Mexico Steffen Staeck, TU Berlin, Germany VIII.

Advancing insights on ß-cyclodextrin inclusion complexes with SSRIs through lens of X-ray diffraction and DFT calculation.

Depression-the global crisis hastened by the coronavirus outbreak, can be efficaciously treated by the selective serotonin reuptake inhibitors (SSRIs). Cyclodextrin (CD) inclusion complexation is a method of choice for reducing side effects and improving bioavailability of drugs. Here, we investigate in-depth the ß-CD encapsulation of sertraline (STL) HCl (1) and fluoxetine (FXT) HCl (2) by single-crystal X-ray diffraction and DFT complete-geometry optimization, in comparison to the reported complex of paroxetine (PXT) base. X-ray analysis unveiled the 2:2 ß-CD-STL/FXT complexes with two drug molecules inserting their halogen-containing aromatic ring in the ß-CD dimeric cavity, which are stabilized by the interplay of intermolecular O2-H⋯N1-H⋯O3 H-bonds, C3/C5-H⋯π and halogen⋯halogen interactions. Similarly, the 1:1 ß-CD-tricyclic-antidepressant (TCA) complexes have an exclusive inclusion mode of the aromatic ring, which is maintained by C3/C5-H⋯π interactions. By contrast, the 2:1 ß-CD-PXT complex has a total inclusion that is stabilized by host-guest O6-H⋯N1-H⋯O5 H-bonds and C3-H⋯π interactions. The inherent stabilization energies of 1 and 2 evaluated using DFT calculation suggested that the improved thermodynamic stabilities via CD encapsulation facilitates the reduction of drug side effects. Moreover, the SSRI conformational flexibilities are thoroughly discussed for understanding of their pharmacoactivity.

Distinctive Supramolecular Features of ß-Cyclodextrin Inclusion Complexes with Antidepressants Protriptyline and Maprotiline: A Comprehensive Structural Investigation.

Depression, a global mental illness, is worsened due to the coronavirus disease 2019 (COVID-2019) pandemic. Tricyclic antidepressants (TCAs) are efficacious for the treatment of depression, even though they have more side effects. Cyclodextrins (CDs) are powerful encapsulating agents for improving molecular stability, water solubility, and lessening the undesired effects of drugs. Because the atomic-level understanding of the ß-CD-TCA inclusion complexes remains elusive, we carried out a comprehensive structural study via single-crystal X-ray diffraction and density functional theory (DFT) full-geometry optimization. Here, we focus on two complexes lining on the opposite side of the ß-CD-TCA stability spectrum based on binding constants (Kas) in solution, ß-CD-protriptyline (PRT) 1-most stable and ß-CD-maprotiline (MPL) 2-least stable. X-ray crystallography unveiled that in the ß-CD cavity, the PRT B-ring and MPL A-ring are aligned at a nearly perfect right angle against the O4 plane and primarily maintained in position by intermolecular C-H···π interactions. The increased rigidity of the tricyclic cores is arising from the PRT -CH=CH- bridge widens, and the MPL -CH2-CH2- flexure narrows the butterfly angles, facilitating the deepest and shallower insertions of PRT B-ring (1) and MPL A-ring (2) in the distorted round ß-CD cavity for better complexation. This is indicated by the DFT-derived complex stabilization energies (ΔEstbs), although the complex stability orders based on Kas and ΔEstbs are different. The dispersion and the basis set superposition error (BSSE) corrections were considered to improve the DFT results. Plus, the distinctive 3D arrangements of 1 and 2 are discussed. This work provides the first crystallographic evidence of PRT and MPL stabilized in the ß-CD cavity, suggesting the potential application of CDs for efficient drug delivery.

COVID-19 pulmonary consolidations detection in chest X-ray using progressive resizing and transfer learning techniques.

A viral outbreak with a lower respiratory tract febrile illness causes pulmonary syndrome named COVID-19. Pulmonary consolidations developed in the lungs of the patients are imperative factors during prognosis and diagnosis. Existing Deep Learning techniques demonstrate promising results in analyzing X-ray images when employed with Transfer Learning. However, Transfer Learning has its inherent limitations, which can be prevaricated by employing the Progressive Resizing technique. The Progressive Resizing technique reuses old computations while learning new ones in Convolution Neural Networks (CNN), enabling it to incorporate prior knowledge of the feature hierarchy. The proposed classification model can classify pulmonary consolidation into normal, pneumonia, and SARS-CoV-2 classes by analyzing X-rays images. The method exhibits substantial enhancement in classification results when the Transfer Learning technique is applied in consultation with the Progressive Resizing technique on EfficientNet CNN. The customized VGG-19 model attained benchmark scores in all evaluation criteria over the baseline VGG-19 model. GradCam based feature interpretation, coupled with X-ray visual analysis, facilitates improved assimilation of the scores. The model highlights its strength to assist medical experts in the COVID-19 identification during the prognosis and subsequently for diagnosis. Clinical implications exist in peripheral and remotely located health centers with the paucity of trained human resources to interpret radiological investigations' findings.