Relationship of Atherosclerotic Plaque Structure with the History of COVID-19 in Patients Undergoing Coronary Computed Tomographic Angiography.

Although there is no sign of reinfection, individuals who have a history of coronavirus disease 2019 (COVID-19) may experience prolonged chest discomfort and shortness of breath on exertion. This study aimed to examine the relationship between atherosclerotic coronary plaque structure and COVID-19. This retrospective cohort comprised 1269 consecutive patients who had coronary computed tomographic angiography (CCTA) for suspected coronary artery disease (CAD) between July 2020 and April 2021. The type of atherosclerotic plaque was the primary outcome. Secondary outcomes included the severity of coronary stenosis as determined via the Coronary Artery Disease-Reporting and Data System (CAD-RADS) classification and the coronary artery calcium (CAC) score. To reveal the relationship between the history of COVID-19 and the extent and severity of CAD, propensity score analysis and further multivariate logistic regression analysis were performed. The median age of the study population was 52 years, with 53.5% being male. COVID-19 was present in 337 individuals. The median duration from COVID-19 diagnosis to CCTA extraction was 245 days. The presence of atherosclerotic soft plaque (OR: 2.05, 95% confidence interval [CI]: 1.32-3.11, P = 0.001), mixed plaque (OR: 2.48, 95% CI: 1.39-4.43, P = 0.001), and high-risk plaque (OR: 2.75, 95% CI: 1.98-3.84, P < 0.001) was shown to be linked with the history of COVID-19 on the conditional multivariate regression analysis of the propensity-matched population. However, no statistically significant association was found between the history of COVID-19 and the severity of coronary stenosis based on CAD-RADS and CAC score. We found that the history of COVID-19 might be associated with coronary atherosclerosis assessed via CCTA.

Computer-aided diagnosis systems: a comparative study of classical machine learning versus deep learning-based approaches.

The diagnostic phase of the treatment process is essential for patient guidance and follow-up. The accuracy and effectiveness of this phase can determine the life or death of a patient. For the same symptoms, different doctors may come up with different diagnoses whose treatments may, instead of curing a patient, be fatal. Machine learning (ML) brings new solutions to healthcare professionals to save time and optimize the appropriate diagnosis. ML is a data analysis method that automates the creation of analytical models and promotes predictive data. There are several ML models and algorithms that rely on features extracted from, for example, a patient's medical images to indicate whether a tumor is benign or malignant. The models differ in the way they operate and the method used to extract the discriminative features of the tumor. In this article, we review different ML models for tumor classification and COVID-19 infection to evaluate the different works. The computer-aided diagnosis (CAD) systems, which we referred to as classical, are based on accurate feature identification, usually performed manually or with other ML techniques that are not involved in classification. The deep learning-based CAD systems automatically perform the identification and extraction of discriminative features. The results show that the two types of DAC have quite close performances but the use of one or the other type depends on the datasets. Indeed, manual feature extraction is necessary when the size of the dataset is small; otherwise, deep learning is used.

A DEEP LEARNING APPROACH LUNG SEGMENTATION AND PNEUMONIA DETECTION FROM X-RAYS

Lung segmentation is a process of detection and identification of lung cancer and pneumonia with the help of image processing techniques. Deep learning algorithms can be incorporated to build the computer-aided diagnosis (CAD) system for detecting or recognizing broad objects like acute respiratory distress syndrome (ARDS), Tuberculosis, Pneumonia, Lung cancer, Covid, and several other respiratory diseases. This paper presents pneumonia detection from lung segmentation using deep learning methods on chest radiography. Chest X-ray is the most useful technique among other existing techniques, due to its lesser cost. The main drawback of a chest x-ray is that it cannot detect all problems in the chest. Thus, implementing convolutional neural networks (CNN) to perform lung segmentation and to obtain correct results. The 'lost' regions of the lungs are reconstructed by an automatic segmentation method from raw images of chest X-ray. © 2022 IEEE.

COVID-19 and coronary artery disease; A systematic review and meta-analysis.

Background and aim: Patients with underlying cardiovascular disorders such as coronary artery disease (CAD) are more prone to severe forms and multiple complications of COVID-19. The present systematic review and meta-analysis aimed to investigate the impact of CAD on patients with COVID-19. Methods: Main electronic databases, including Medline (via PubMed), EMBASE, and Web of Science, were carefully searched and reviewed for original research articles published between 2019 and 2021. One hundred nine studies that address CAD in patients with COVID-19 were selected and analyzed. Results: Following search and screening processes, 109 relevant publications were selected for analysis. The meta-analysis of prevalence studies indicated that the frequency of CAD among patients with COVID-19 was reported in 10 countries with an overall frequency of 12.4% [(95% CI) 11.1-13.8] among 20079 COVID-19 patients. According to case reports/case series studies, 50.9% of COVID-19 patients suffered from CAD. Fever was the most common symptom in these patients (47%); 36.5% also had hypertension. Conclusion: The results obtained during the present study show that the simultaneous presence of COVID-19 and CAD, especially in men and elderly patients, can increase the risks and complications of both diseases. Therefore, careful examination of the condition of this group of patients for timely diagnosis and treatment is strongly recommended.

Enhancing the student experience in pre‐ and mid‐COVID‐19 pandemic period by upgrading the power electronics laboratory practice curriculum

Laboratory practices, which represent a vital part of electrical engineering education, have especially in the last few years been subjected to numerous challenges. The paper presents a concept of upgrading the laboratory practice curriculum in power electronics by introducing computer simulations. Due to the recognized shortcomings of the previous approach, the curriculum was closely reviewed, compared to the concepts from existing literature, and intensively upgraded by the introduction of the Ansys Simplorer computer program. The intensity of the process upgrade was enhanced by the COVID‐19 pandemic and related lockdowns. The introduced curriculum changes enabled the students to approach individual topics more gradually, reducing the gaps between the behavior of ideal and real power electronics circuits. The results of student feedback, obtained by a web‐based survey and a pre‐exam quiz, demonstrate that students recognize the new approach as being more gradual and beneficial, enabling them to improve their understanding of specific phenomena and to master the topics of power electronics with ease and satisfaction.

Ranolazine Toxicity Secondary to Paxlovid.

An Emergency Use Authorization (EUA) was issued by the FDA on December 22, 2021 for the investigational antiviral drug nirmatrelvir copackaged with the HIV-1 protease inhibitor ritonavir (Paxlovid - Pfizer) for outpatient treatment of mild to moderate COVID-19 in children 12 years and old that are high risk of severe disease. Due to the effects, Paxlovid has on liver metabolism it has a copious amount of drug-to-drug interactions. Here we present a rare case of a patient that was given Paxlovid and continued to take her Ranolazine at home. She presented to the emergency department obtunded and after an initial workup, it was determined to be secondary to ranolazine toxicity. She eventually recovered over 54 hours and returned to her baseline.

AAPM task group report 273: Recommendations on best practices for AI and machine learning for computer-aided diagnosis in medical imaging.

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both "traditional" machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support.

Fashion CAD education during the COVID-19 pandemic in South Korea: comparison of online and offline learning achievements

The 2019 Coronavirus Infectious Disease-19 (COVID-19) pandemic has maximized interest in the need for and the effectiveness of e-learning classes as an alternative to face-to-face classes in schools. This study aimed to identify the factors that determine the successful implementation of e-learning classes. In this study, 99 fashion majors who attended the computer-aided design (CAD) programming classes held in the spring semesters of 2019 and 2020 participated. This study analyzed and evaluated the students' achievement process to see how self-motivated learning and interactive learning affected the process in face-to-face classes and real-time online Zoom classes. The results demonstrated the potential of creating an efficient e-learning environment for fashion CAD education where students could learn concepts and achieve academic competence even in the absence of face-to-face introduction.

Challenges, opportunities, and advances related to COVID-19 classification based on deep learning

The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities: - computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches is highlighted a future research possibility.

GSAL: Geometric structure adversarial learning for robust medical image segmentation

• A new learning mechanism for medical image segmentation. We introduce a novel Geometric Structure Learning Mechanism (GSLM) that enhances model learning "focus, path, and difficulty". It enables geometric structure attention learning to bridge image features with large differences, thus capturing the contextual dependencies of images. The image features maintain consistency and continuity along the internal and external geometry structure, which improves the integrity and boundary accuracy of the segmentation results. To the best of our knowledge, we are the first attempt to explicitly establish the target's geometric structure, which has been successfully applied to medical image segmentation. • A novel geometric structure adversarial learning for robust medical image segmentation. We present the geometric structure adversarial learning model (GSAL) that consists of a geometric structure generator, skeleton-like and boundary discriminators, and a geometric structure fusion sub-network. The generator yields the geometric structure that preserves interior characteristics consistency and external boundary structure continuity. The dual discriminators are trained simultaneously to enhance and correct the characterization of interior structure and boundary structure, respectively. The fusion sub-network aims to fuse the geometric structure that optimized by adversarial learning to refine the final segmentation results with higher credibility. • State-of-art results on widely-used benchmarks. Our GSAL achieves SOTA performance on a variety of benchmarks, including Kvasir&CVC-612 dataset, COVID-19 dataset, and LIDC-IDRI dataset. It confirms the robustness and generalizability of our framework. In addition, our method has great advantages in terms of the integrity and boundary accuracy of the segmentation target compared to other competitive methods. GSAL can also achieve a considerable trade-off in terms of accuracy, inference speed, and model complexity, which helps deploy in clinical practice systems. Automatic medical image segmentation plays a crucial role in clinical diagnosis and treatment. However, it is still a challenging task due to the complex interior characteristics (e.g. , inconsistent intensity, low contrast, texture heterogeneity) and ambiguous external boundary structures. In this paper, we introduce a novel geometric structure learning mechanism (GSLM) to overcome the limitations of existing segmentation models that lack learning "focus, path, and difficulty." The geometric structure in this mechanism is jointly characterized by the skeleton-like structure extracted by the mask distance transform (MDT) and the boundary structure extracted by the mask distance inverse transform (MDIT). Among them, the skeleton-like and boundary pay attention to the trend of interior characteristics consistency and external structure continuity, respectively. With this idea, we design GSAL, a novel end-to-end geometric structure adversarial learning for robust medical image segmentation. GSAL has four components: a geometric structure generator, which yields the geometric structure to learn the most discriminative features that preserve interior characteristics consistency and external boundary structure continuity, skeleton-like and boundary structure discriminators, which enhance and correct the characterization of internal and external geometry to mutually promote the capture of global contextual dependencies, and a geometric structure fusion sub-network, which fuses the two complementary and refined skeleton-like and boundary structures to generate the high-quality segmentation results. The proposed approach has been successfully applied to three different challenging medical image segmentation tasks, including polyp segmentation, COVID-19 lung infection segmentation, and lung nodule segmentation. Extensive experimental results demonstrate that the proposed GSAL achieves favorably against most state-of-the-art methods under different evaluation metrics. The code is available at: https://github.com/DLWK/GSAL. [ BSTRACT FROM AUTHOR] Copyright of Pattern Recognition is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Report on the 28th Asia and South Pacific Design Automation Conference

The 28th Asia and South Pacific Design Automation Conference (ASP-DAC 2023) was held at Miraikan, National Museum of Emerging Science and Innovation, Tokyo, Japan, 16 char6319 January 2023. ASP-DAC, started in 1995, is a high-quality and premium conference on Electronic Design Automation (EDA) like other sister conferences such as Design Automation Conference (DAC);Design, Automation Test in Europe (DATE);International Conference on Computer Aided Design (ICCAD);and Embedded Systems Week (ESWEEK). ASP-DAC 2023 adopted an in-person conference style with online features which is the first time in ASP-DAC. Even though the last two ASP-DAC conferences were held as virtual conferences due to the COVID-19 pandemic, ASPDAC 2023 provided opportunities for face-to-face communication not only at sessions, but also at coffee breaks, banquet, and so on for in-person attendees. Online access mainly for participants who were difficult to physically attend was also provided as much as possible.

Investigating structural features of dimeric SARS-CoV-2 Mpro catalytic site with bound covalent ligands at physiological temperature

The SARS-CoV-2 main protease (Mpro) plays an important role in the viral transcription and replication of the SARS-CoV-2 virus that is causing the Covid-19 pandemic worldwide. Therefore, it represents a very attractive target for drug development for treatment of this disease. It is a cysteine protease because it has in the active site the catalytic dyad composed of cysteine (C145) and histidine (H41). The catalytic site represents the binding site for inhibitors, many of them bind to Mpro with a covalent bond. In this research, structural and physiochemical characteristics of the Mpro binding site are investigated when the ligand 11a is covalently and non-covalently bound. All-atom molecular dynamics (MD) simulations were run for 500 ns at physiological temperature (310 K). It is found that conformations of both the Mpro protein and the ligand are stable during the simulation with covalently bound complex showing stronger stability. When the ligand is covalently bound (its final state), residues that stably interact with the ligand are H41, C145, H163, H164 and E166. The optimal conformation of these residues is stabilized also via the Hbond interactions with the catalytic water present in the Mpro binding site. In the case of the non-covalently bound ligand (state before the covalent bond is formed), the binding site residues retain their conformations similar to the covalent binding site, and they still form Hbonds with the catalytic water, except H41. This residue, instead, adopts a different conformation and looses the Hbond with the catalytic water, leaving more freedom to move to the ligand. We hypothesize that H41 could play a role in guiding the ligand to the optimal position for final covalent bonding. Further analyses are in process to check this hypothesis. These results represent an important basis for studying drug candidates against SARS-CoV-2 by means of computer aided drug design.

Did the COVID-19 pandemic affect auditors' client acceptance and continuance decision-making process?

Purpose: The aim of the present study is to explore the impact of the COVID-19 pandemic on the first stage of external audit, namely, on the auditors' client acceptance and continuance decisions (CACDs). Design/methodology/approach: Survey data was collected on the basis of a structured questionnaire, which was answered by 21.02% of the Greek certified auditors/accountants. Parametric hypothesis testing and regression analysis were used in data analysis. Findings: The results of the survey showed that the COVID-19 pandemic had a different impact on the client acceptance decision-making (CAD) process and the client continuance decision-making (CCD) process. The CAD process appears to have been affected in a mostly negative way, and to a greater extent than is the case with the CCD process. The impact of the COVID-19 pandemic on the CACD process appears to be mainly related to the difficulty arising in auditor–client communication. Additionally, as far as the CAD process is concerned, the COVID-19 pandemic appears to have had a negative impact on the audit fees, while, when it comes to the CCD process, the pandemic has had a positive impact with regard to clientele expansion. Finally, survey findings showed that the COVID-19 pandemic affected in a different way Big6 and non-Big6 auditors. Originality/value: The present study aspires to fill significant gaps identified in relevant literature with regard to auditors' work in correlation with the COVID-19 pandemic. More specifically, to the best of the author's knowledge, it is the first study exploring the impact of the COVID-19 pandemic on the first stage of external audit. Moreover, the study is based on primary data collected in real time, under the actual conditions of emergency related to the health crisis. Last but not least, the findings of the present study could be of value to professionals and regulative authorities in case of similar future emergencies or potential crisis situations. © 2023, Emerald Publishing Limited.

Emergency remote CAD teaching using licensed software in apparel during the COVID-19 pandemic: a collaborative learning approach

Computer-Aided Design (CAD) training has become essential in apparel education as it is widely applied in design and development activities in the industry. This study presents how physical CAD teaching converted to remote delivery during the emergency COVID-19 pandemic using online technologies. This study evaluated five distinct methods adopted in this period: online Zoom sessions, pre-recorded practical demonstrations, guided hand-outs, online collaborative learning methods and forum discussions using Moodle. TeamViewer application was utilised for real-time remote access and support during teaching. This study instrumented two online questionnaires intended to assess the effectiveness of online hands-on sessions and collaborative learning in a remote online environment. This study was conducted with 58 participants at a recognised Sri Lankan state university. More importantly, the results confirmed the feasibility of collaborative engagement within the online learning environment. This study discovered students' pref-erences for synchronous teaching and learning approaches. Also, it revealed the limitations of remote CAD teaching using online technologies. Finally, this study underlined the success of the collaborative learning approach and students' perspectives on flipped classroom model for apparel CAD training. © 2023 R.K.J. De Silva and A. Peramunugamage. Research in Learning Technology is the journal of the Association for Learning Technology (ALT), a UK-based professional and scholarly society and membership organisation.

Futuristic Rehabilitation Technique Of Acquired Maxillectomy Defect In Post Covid19 Mucormycosis Using Polyether Ether Ketone Obturator Prosthesis- A Case Report

Background: Oroantral communication can occur due to maxillectomy defects, jeopardizing the integrity and function of oral cavity. It is an interdisciplinary challenge to restore these by surgery and prosthetics since many facets need to be addressed, such as speech, deglutition, mastication, aesthetics and psychological distress. Rationale: Surgical repair of maxillectomy defects is not always achievable due to various reasons such as poor systemic health, advanced age etc. Thus prosthetic rehabilitation becomes the most suitable treatment option. Relevance for Patients: Post COVID-19 mucormycosis has seen a surge in the past two years. It is an opportunistic fungal infection in humans infecting intracranial structures by direct invasion in the blood stream. Fundamental goal of prosthetic rehabilitation is the closure of oronasal communication and restoring it functionally thereby improving quality of life for the patient. CAD/CAM (computer aided design/computer aided milling) technology was employed to fabricate a milled framework for maxillary obturator in the most innovative way using PEEK (Polyether ether ketone). Result(s): PEEK material due to its excellent biocompatibility ensured a light weight prosthesis for the large maxillectomy defect and closure of the patency was achieved by the obturator framework.Copyright © 2022 Authors. All rights reserved.

Design-as-a-Service Framework for Enabling Innovations in Small- and Medium-Sized Enterprises

Modern manufacturing enterprises must be agile to cope with sudden demand changes arising from increased global competition, geopolitical factors, and unforeseen circumstances such as the Covid-19 pandemic. Small- and Medium-Sized Enterprises (SMEs) in the manufacturing sector lack agility due to lower penetration of Information Technology (IT) and Operational Technology (OT), the inability to employ highly skilled human capital, and the absence of a formal innovation ecosystem for new products or solutions. In recent years, Cloud-based Design and Manufacturing (CBDM) has emerged as an enabler for product realization by integrating various service-based models. However, the existing framework does not thoroughly support the innovation ecosystem from concept to product realization by formally addressing economic challenges and human skillset requirements. The present work considers the augmentation of the Design-as-a-Service (DaaS) model into the existing CBDM framework for enabling systematic product innovations. The DaaS model proposes to connect skilled human resources with enterprises interested in transforming an idea into a product or solution through the CBDM framework. The model presents an approach for integrating human resources with various CBDM elements and end-users through a service-based model. The challenges associated with successfully implementing the proposed model are also discussed. It is established that the DaaS has the potential for rapid and economical product discovery and can be readily accessible to SMEs or independent individuals. [ FROM AUTHOR] Copyright of Journal of Mechanical Design is the property of American Society of Mechanical Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Development of HPLC-CAD method for simultaneous quantification of nine related substances in ursodeoxycholic acid and identification of two unknown impurities by HPLC-Q-TOF-MS.

Ursodeoxycholic acid has gained increasing attention due to its recent discovery of the preventive effect on SARS-CoV-2 infection. Ursodeoxycholic acid has been included in various pharmacopoeias as an old drug, and the latest European Pharmacopoeia lists nine potential related substances (impurities A∼I). However, existing methods in pharmacopoeias and literature can only quantify up to five of these impurities simultaneously, and the sensitivity is inadequate, as the impurities are isomers or cholic acid analogues lacking chromophores. Herein, a novel gradient RP-HPLC method coupled to charged aerosol detection (CAD) was developed and validated for the simultaneous separation and quantification of the nine impurities in ursodeoxycholic acid. The method proved sensitive and allowed the quantification of the impurities as low as 0.02 %. Relative correction factors of the nine impurities were all within the range of 0.8-1.2 in the gradient mode by optimizing chromatographic conditions and CAD parameters. In addition, this RP-HPLC method is fully compatible with LC-MS due to the volatile additives and high percentage of the organic phase, which can be directly used for the identification of impurities. The newly developed HPLC-CAD method was successfully applied to commercial bulk drug samples, and two unknown impurities were identified by HPLC-Q-TOF-MS. The effect of CAD parameters on the linearity and correction factors was also discussed in this study. Overall, the established HPLC-CAD method can improve the methods in current pharmacopoeias and literature and contributes to understanding the impurity profile for process improvement.

Impact of COVID-19 on percutaneous coronary intervention utilization and mortality in New York.

BACKGROUND: COVID-19 has disrupted the care of all patients, and little is known about its impact on the utilization and short-term mortality of percutaneous coronary intervention (PCI) patients, particularly nonemergency patients. METHODS: New York State's PCI registry was used to study the utilization of PCI and the presence of COVID-19 in four patient subgroups ranging in severity from ST-elevation myocardial infarction (STEMI) to elective patients before (December 01, 2018-February 29, 2020) and during the COVID-19 era (March 01, 2020-May 31, 2021), as well as to examine the impact of different COVID severity levels on the mortality of different types of PCI patients. RESULTS: Decreases in the mean quarterly PCI volume from the prepandemic period to the first quarter of the pandemic ranged from 20% for STEMI patients to 61% for elective patients, with the other two subgroups having decreases in between these values. PCI quarterly volume rebounds from the prepandemic period to the second quarter of 2021 were in excess of 90% for all patient subgroups, and 99.7% for elective patients. Existing COVID-19 was rare among PCI patients, ranging from 1.74% for STEMI patients to 3.66% for elective patients. PCI patients with COVID-19 and acute respiratory distress syndrome (ARDS) who were not intubated, and PCI patients with COVID-19 and ARDS who were either intubated or were not intubated because of Do Not Resuscitate//Do Not Intubate status had higher risk-adjusted mortality ([adjusted ORs = 10.81 [4.39, 26.63] and 24.53 [12.06, 49.88], respectively]) than patients who never had COVID-19. CONCLUSIONS: There were large decreases in the utilization of PCI during COVID-19, with the percentage of decrease being highly sensitive to patient acuity. By the second quarter of 2021, prepandemic volumes were nearly restored for all patient subgroups. Very few PCI patients had current COVID-19 throughout the pandemic period, but the number of PCI patients with a COVID-19 history increased steadily during the pandemic. PCI patients with COVID-19 accompanied by ARDS were at much higher risk of short-term mortality than patients who never had COVID-19. COVID-19 without ARDS and history of COVID-19 were not associated with higher mortality for PCI patients as of the second quarter of 2021.

Developing Novel Learning Spaces Through Social Media Channels for Sustainable CAD Engineering Education

This study investigates learners' viewing behaviors and engagement patterns through educational video analytics. Based on the performance of 42 instructional videos aiming to provide asynchronous help in both online and traditional Engineering laboratories in Higher Education, a comparative analysis has been performed. Data from YouTube channel's reports have been collected and processed in three time periods: the first semester of the academic year 2019–2020 and 2020–2021 in strictly remote teaching environments, and the second semester of 2021–2022 in traditional and hybrid learning modes. Even though instructor-generated educational videos have been a common tool for asynchronous support in online learning spaces, an evaluation by the available social media channels analytics has not been performed yet in an adequate level for leading to results. The most important outcome of this research is that YouTube analytics of the educational videos have shown that the social media channel can perform under different learning environments, with the same efficiency, proving the long-term viability of this construct of the learning strategy. Instructors and stake holders may profit from this study for future course planning in Engineering online and hybrid learning environments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen-antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach. The review also provide comparison between these two emerging technologies and open research issues for the development of smart-IoMT-enabled platforms for the detection of COVID-19.