The Experiences of Clinical Engineering when Responding to the COVID-19 Pandemic.

The scope of this article is limited to the actions and experiences of the Landstuhl Regional Medical Center (LRMC) Clinical Engineering Branch (CEB) when planning and executing the COVID-19 response at the only US military Role 4 medical treatment facility (MTF) in Europe between 1 February and 1 May 2020. Aspects of the COVID-19 response extended throughout the Regional Health Command; therefore, the full breadth and scope of the total response is far too great to expound within this account alone. The entire medical staff, along with an innumerable number of partners, were immensely engaged in the response and performed remarkably well given the rapidly developing pandemic. It is a testament to the agility of Army Medicine and the robustness of the American and European health systems to develop such a complicated medical response in such a short amount of time.

Toward a More Ethical and Sustainable Biomedical Engineering Education.

Important Lessons Can be learned from the COVID-19 pandemic, which after more than two years of worldwide suffering is still among us. First, we now better understand that global health concerns cannot be tackled and solved individually and verify that the dream for universal health care is far from being fulfilled. Besides, biomedical technologies and medical devices, despite their transformative potential, cannot always reach those urgently needing them, due to centralized production, supply chain issues, intellectual property restrictions, and lack of raw materials and resources close to the point of care, which calls for a renovation of the biomedical industry aimed at sustainability and equity. To make matters worse, unethical behaviors of governments, companies, and citizens, from which the ongoing pandemic has provided plenty of examples, also endanger the already challenging progress toward the Sustainable Development Goals (SDGs), including "Goal 3" on "Good Health and Well Being."

Identifying the molecular targets and mechanisms of xuebijing injection for the treatment of COVID-19 via network parmacology and molecular docking.

Xuebijing Injection have been found to improve the clinical symptoms of COVID-19 and alleviate disease severity, but the mechanisms are currently unclear. This study aimed to investigate the potential molecular targets and mechanisms of the Xuebijing injection in treating COVID-19 via network pharmacology and molecular docking analysis. The main active ingredients and therapeutic targets of the Xuebijing injection, and the pathogenic targets of COVID-19 were screened using the TCMSP, UniProt, and GeneCard databases. According to the 'Drug-Ingredients-Targets-Disease' network built by STRING and Cytoscape, AKT1 was identified as the core target, and baicalein, luteolin, and quercetin were identified as the active ingredients of the Xuebijing injection in connection with AKT1. R language was used for enrichment analysis that predict the mechanisms by which the Xuebijing injection may inhibit lipopolysaccharide-mediated inflammatory response, modulate NOS activity, and regulate the TNF signal pathway by affecting the role of AKT1. Based on the results of network pharmacology, a molecular docking was performed with AKT1 and the three active ingredients, the results indicated that all three active ingredients could stably bind with AKT1. These findings identify potential molecular mechanisms by which Xuebijing Injection inhibit COVID-19 by acting on AKT1.

Assessment of biomedical engineering knowledge using true-false questions.

The COVID-19 pandemic has caused a shift from on-campus to remote online examinations, which are usually difficult to invigilate. Meanwhile, closed-ended question formats, such as true-false (TF), are particularly suited to these examination conditions, as they allow automatic marking by computer software. While previous studies have reported the score characteristics in TF questions in conventional supervised examinations, this study investigates the efficacy of using TF questions in online, unsupervised examinations at the undergraduate level of Biomedical Engineering. We examine the TF and other question-type scores of 57 students across three examinations held in 2020 under online, unsupervised conditions. Our analysis shows significantly larger coefficient of variance (CV) in scores in TF questions (42.7%) than other question types (22.3%). The high CV in TF questions may be explained by different answering strategies among students, with 13.3 ± 17.2% of TF questions left unanswered (zero marks) and 16.4 ± 11.5% of TF questions guessed incorrectly (negative marks awarded). In unsupervised, open-book examination where sharing of answers among students is a potential risk; questions that induce a larger variation in responses may be desirable to differentiate among students. We also observed a significant relationship (r = 0.64, p < 0.05) between TF scores and the overall subject scores, indicating that TF questions are an effective predictor of overall student performance. Our results from this initial analysis suggests that TF questions are useful for assessing biomedical-theme content in online, unsupervised examinations, and are encouraging for their ongoing use in future assessments.

Automatic system for high-throughput and high-sensitivity diagnosis of SARS-CoV-2.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had severe consequences for health and the global economy. To control the transmission, there is an urgent demand for early diagnosis and treatment in the general population. In the present study, an automatic system for SARS-CoV-2 diagnosis is designed and built to deliver high specification, high sensitivity, and high throughput with minimal workforce involvement. The system, set up with cross-priming amplification (CPA) rather than conventional reverse transcription-polymerase chain reaction (RT-PCR), was evaluated using more than 1000 real-world samples for direct comparison. This fully automated robotic system performed SARS-CoV-2 nucleic acid-based diagnosis with 192 samples in under 180 min at 100 copies per reaction in a "specimen in data out" manner. This throughput translates to a daily screening capacity of 800-1000 in an assembly-line manner with limited workforce involvement. The sensitivity of this device could be further improved using a CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based assay, which opens the door to mixed samples, potentially include SARS-CoV-2 variants screening in extensively scaled testing for fighting COVID-19.

Paper-based microfluidic chip for rapid detection of SARS-CoV-2 N protein.

This research has developed a method for rapid detection of SARS-CoV-2 N protein on a paper-based microfluidic chip. The chitosan-glutaraldehyde cross-linking method is used to fix the coated antibody, and the sandwich enzyme-linked immunosorbent method is used to achieve the specific detection of the target antigen. The system studied the influence of coating antibody concentration and enzyme-labeled antibody concentration on target antigen detection. According to the average gray value measured under different N protein concentrations, the standard curve of the method was established and the sensitivity was tested, and its linear regression was obtained. The equation is y = 9.8286x+137.6, R2 = 0.9772 > 0.90, which shows a high degree of fit. When the concentration of coating antibody and enzyme-labeled antibody were 1 µg/mL and 2 µg/mL, P > 0.05, the difference was not statistically significant, so the lower concentration of 1 µg/mL was chosen as the coating antibody concentration. The results show that the minimum concentration of N protein that can be detected by this method is 8 µg/mL, and the minimum concentration of coating antibody and enzyme-labeled antibody is 1 µg/mL, which has the characteristics of high sensitivity and good repeatability.

Design and Implementation of "SHU-AP" system for Biomedical Engineering Course "Anatomy and physiology" in COVID-19.

Online courses played important roles in biomedical engineering (BME) teaching during the COVID-19 epidemic. However, existing virtual experiment systems are not suitable for BME course "anatomy and Physiology". Therefore we developed a virtual experiment system "SHU-AP". The system is composed of six virtual experiments. To judge the feasibility and teaching effectiveness of the system, we used it for real teaching in Year 2020 and compared it with offline teaching. We divided BME students into two classes: Class A (offline experiment) and Class B (virtual experiment). Both classes were taught by the same teacher. At the end of the semester, we issued questionnaires for the two classes. The results showed that there was no significant difference in students' performance and teaching satisfaction under the two teaching methods. As a conclusion, virtual experiments could achieve the same teaching effectiveness same as offline experiments.

Recent findings and applications of biomedical engineering for COVID-19 diagnosis: a critical review.

COVID-19 is one of the most severe global health crises that humanity has ever faced. Researchers have restlessly focused on developing solutions for monitoring and tracing the viral culprit, SARS-CoV-2, as vital steps to break the chain of infection. Even though biomedical engineering (BME) is considered a rising field of medical sciences, it has demonstrated its pivotal role in nurturing the maturation of COVID-19 diagnostic technologies. Within a very short period of time, BME research applied to COVID-19 diagnosis has advanced with ever-increasing knowledge and inventions, especially in adapting available virus detection technologies into clinical practice and exploiting the power of interdisciplinary research to design novel diagnostic tools or improve the detection efficiency. To assist the development of BME in COVID-19 diagnosis, this review highlights the most recent diagnostic approaches and evaluates the potential of each research direction in the context of the pandemic.

Biomedical engineering and ethics: reflections on medical devices and PPE during the first wave of COVID-19.

In March 2019, the World Health Organization (WHO) declared that humanity was entering a global pandemic phase. This unforeseen situation caught everyone unprepared and had a major impact on several professional categories that found themselves facing important ethical dilemmas. The article revolves around the category of biomedical and clinical engineers, which were among those most involved in dealing with and finding solutions to the pandemic. In hindsight, the major issues brought to the attention of biomedical engineers have raised important ethical implications, such as the allocation of resources, the responsibilities of science and the inadequacy and non-universality of the norms and regulations on biomedical devices and personal protective equipment. These issues, analyzed one year after the first wave of the pandemic, come together in the appeal for responsibility for thought, action and, sometimes, even silence. This highlights the importance of interdisciplinarity and the definitive collapse of the Cartesian fragmentation of knowledge, calling for the creation of more fora, where this kind of discussions can be promoted.

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images.

We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients who were confirmed as positive by nucleic acid test of RT-PCR and 90 other pneumonias patients) were collected, and the two groups of data were manually drawn to outline the region of interest (ROI) of pneumonias. The radiomics method was used to extract textural features and histogram features of the ROI and obtain a radiomics features vector from each sample. Then, we divided the data into two independent radiomic cohorts for training (70 COVID-19 patients and 70 other pneumonias patients), and validation (20 COVID-19 patients and 20 other pneumonias patients) by using support vector machine (SVM). This model used 20 rounds of tenfold cross-validation for training. Finally, single-shot testing of the final model was performed on the independent validation cohort. In the COVID-19 patients, correlation analysis (multiple comparison correction-Bonferroni correction, P < 0.05/7) was also conducted to determine whether the textural and histogram features were correlated with the laboratory test index of blood, i.e., blood oxygen, white blood cell, lymphocytes, neutrophils, C-reactive protein, hypersensitive C-reactive protein, and erythrocyte sedimentation rate. The final model showed good discrimination on the independent validation cohort, with an accuracy of 89.83%, sensitivity of 94.22%, specificity of 85.44%, and AUC of 0.940. This proved that the radiomics features were highly distinguishable, and this SVM model can effectively identify and diagnose patients with COVID-19 and other pneumonias. The correlation analysis results showed that some textural features were positively correlated with WBC, and NE, and also negatively related to SPO2H and NE. Our results showed that radiomic features can classify COVID-19 patients and other pneumonias patients. The SVM model can achieve an excellent diagnosis of COVID-19.

Crafting the Future-Opportunities for Students by the Reinvigorated IEEE EMBS Student Activities Committee.

"As we look ahead into the next century, leaders will be those who empower others."-Bill Gates.

Engineering of the current nucleoside-modified mRNA-LNP vaccines against SARS-CoV-2.

Currently, there are over 230 different COVID-19 vaccines under development around the world. At least three decades of scientific development in RNA biology, immunology, structural biology, genetic engineering, chemical modification, and nanoparticle technologies allowed the accelerated development of fully synthetic messenger RNA (mRNA)-based vaccines within less than a year since the first report of a SARS-CoV-2 infection. mRNA-based vaccines have been shown to elicit broadly protective immune responses, with the added advantage of being amenable to rapid and flexible manufacturing processes. This review recapitulates current advances in engineering the first two SARS-CoV-2-spike-encoding nucleoside-modified mRNA vaccines, highlighting the strategies followed to potentiate their effectiveness and safety, thus facilitating an agile response to the current COVID-19 pandemic.

The RADxSM Tech Process: Accelerating Innovation for COVID-19 Testing.

In the wake of the COVID-19 pandemic, the need for rapid and accurate diagnostic testing across populations quickly became evident. In response, the National Institutes of Health (NIH) was determined not only to invest heavily in this area but to change the process by which grant proposals were reviewed and funded in order to spur faster development of viable technologies. The Rapid Acceleration of Diagnostics (RADx) initiative was designed to speed innovation, commercialization, and implementation of potential COVID-19 diagnostic technology. As part of this effort, the RADx Tech initiative focuses on the development, validation, and commercialization of innovative point-of-care, home-based, and clinical lab-based tests that can detect SARS-CoV-2. This effort was enabled through the NIH's National Institute of Biomedical Imaging and Bioengineering (NIBIB) Point-of-Care Technology Research Network (POCTRN).

COVID-19 Efforts at the Institute of Image Guided Surgery (IHU-Strasbourg): 2020.

We submit a summary of some of the activities of the IHU-Strasbourg during the initial period of the COVID-19 pandemic. These were presented as part of the coronnavation effort coordinated by Dr Adrian Park. Three initiatives are presented as follows: Protect-Est App, healthcare worker stress, and converted diving mask for ventilation. Two of the 3 projects are still ongoing, and one (Predoict-Est) has been adopted nationally.

Responding to a Pandemic: COVID-19 Projects in the Malone Center.

As the scope and scale of the COVID-19 pandemic became clear in early March of 2020, the faculty of the Malone Center engaged in several projects aimed at addressing both immediate and long-term implications of COVID-19. In this article, we briefly outline the processes that we engaged in to identify areas of need, the projects that emerged, and the results of those projects. As we write, some of these projects have reached a natural termination point, whereas others continue. We identify some of the factors that led to projects that moved to implementation, as well as factors that led projects to fail to progress or to be abandoned.

Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning.

Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, including those with COVID-19. In light of the challenges faced with diagnostic and prognostic indicators in SARS-CoV-2 infection, our center has developed an international clinical protocol to collect standardized thoracic point of care ultrasound data in these patients for later AI/ML modeling. We surmise that in the future AI/ML may assist in the management of SARS-CoV-2 patients potentially leading to improved outcomes, and to that end, a corpus of curated ultrasound images and linked patient clinical metadata is an invaluable research resource.

Solving Unmet Needs With Innovative Pediatric Medical Devices.

In the last decade, only 24% of class III life-saving devices approved by the U.S. Food and Drug Administration (FDA) were for pediatric use-and most of those were for children over 12. Of these, less than 4% were labeled for pediatric patients ages 0-2 years old and the number of approved devices is even lower for neonatal patients. For these young patients, adult medical devices are often manipulated by pediatric specialists in order to provide stop-gap solutions. However, these repurposed devices are not always able to fulfill the unique needs of children's biology and growth patterns.

Innovation at a Children's Hospital: Personal Protective Equipment Efforts During the Pandemic.

The COVID-19 pandemic has affected life for everyone, and hospitals, in particular have been hard hit. In this study, we describe our efforts to develop personal protective equipment at a children's hospital early in the pandemic. We convened an innovation working group to organize our efforts and respond to the rapidly changing situation. We describe our work in four areas: (1) plexiglass shields for the emergency department, (2) face shields for clinical providers, (3) breath shields for ophthalmology, and (4) flip-up safety glasses for nurses. The hospital's supply chain is now caught up with addressing many pandemic-related shortages. Nevertheless, through our multidisciplinary approach to reacting to the pandemic's urgent needs, we demonstrated agility to bring stakeholders together to maximize the use of scarce resources and build resiliency. We believe this method can be rapidly replicated as future needs arise.

A deep learning-based medication behavior monitoring system.

The internet of things (IoT) and deep learning are emerging technologies in diverse research fields, including the provision of IT services in medical domains. In the COVID-19 era, intelligent medication behavior monitoring systems for stable patient monitoring are further required, because many patients cannot easily visit hospitals. Several previous studies made use of wearable devices to detect medication behaviors of patients. However, the wearable devices cause inconvenience while equipping the devices. In addition, they suffer from inconsistency problems due to errors of measured values. We devise a medication behavior monitoring system that uses the IoT and deep learning to avoid sensing errors and improve user experiences by effectively detecting various activities of patients. Based on the real-time operation of our proposed IoT device, the proposed solution processes captured images of patents via OpenPose to check medication situations. The proposed system identifies medication status on time by using a human activity recognition scheme and provides various notifications to patients' mobile devices. To support reliable communication between our system and doctors, we employ MQTT protocol with periodic data transmissions. Thus, the measured information of patient's medication status is transmitted to the doctors so that they can periodically perform remote treatments. Experimental results show that all medication behaviors are accurately detected and notified to the doctor efficiently, improving the accuracy of monitoring the patient's medication behavior.

"Coronavation: Keep Calm, and Carry On".

In this essay, I summarize a few ideas inspired by my involvement in the "Coronavation" working group, which spanned 2020's COVID-19 crisis. Health-care practitioners, computer scientists, and engineers alike, we strive to meet the challenges associated with practice under threat of pandemic with the same ideals driving the rapid, positive developments in health care today: innovation, collaboration and technology convergence, and acquisition of valuable data that leads to better approaches and new ideas. The ideas sketched here, forged by the need for practical pandemic responses, are rooted in those ideals.