Perception of competence achievement and students' satisfaction using virtual laboratories in Medical Biochemistry course: Lessons from the COVID-19 pandemic.

After the COVID-19 pandemic, there was an increasing demand for remote learning and an expansion in the substitution of traditional practical sessions with lab-based virtual tools. This study aimed to assess the effectiveness of virtual labs in practicing biochemical experiments and to examine the student's feedback regarding this tool. Virtual and traditional labs training were compared in teaching qualitative analysis of proteins and carbohydrates experiments for first-year medical students. Students' achievements were assessed, and their satisfaction regarding virtual labs was estimated using a questionnaire. A total of 633 students were enrolled in the study. There was a significant increase in the average scores of students performing the virtual lab of protein analysis compared with those trained in a real lab and those who watched videos explaining the experiment (p < 0.001). The opposite was noticed in the qualitative analysis of carbohydrates with significantly high grades of students trained conventionally compared with those who practiced with virtual labs (p < 0.001). Students' feedback rates on the virtual labs were high (>70% satisfaction rate). Most students believed virtual labs were supported with a clear explanation, yet they thought it did not give a realistic experience. Students accepted virtual labs, but they still prefer using them as preparatory to classic labs. In conclusion, virtual labs can offer good laboratory practice in the Medical Biochemistry course. Their impact on students' learning might be increased if selected cautiously and implemented properly in the curriculum.

COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization.

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.

Nanotechnology-Enhanced Face Masks: Future Scopes and Perspectives

The coronavirus disease 2019 pandemic has shown that a disposable surgical face mask is a good protective wall against infection due to its ability to prevent virus transmission from sick to healthy people. Nevertheless, these surgical masks are disposable, not ecofriendly, and are single-use items. The use and disposal of traditional masks lead to high secondary risks such as environmental pollution, pathogen transmission, overload demands, and user discomfort. Nanotechnology is one of the most investigated strategies to safely and economically reuse masks in the 21st century. These strategies are based on four key elements as follows: (1) super mechanical properties that give masks flexibility, durability, and good lifetime storage;(2) high thermal properties that give masks heat self-sterilization;(3) an electric charge controller that gives masks triboelectric (TE) filtration;and (4) response to the antimicrobial effect that stays in the mask before, during, and after safe use. These properties give new-generation masks the ability to remove the drawbacks of traditional surgical masks, such as microbial growth and low filtration efficiency. The graphene family has introduced the self-sterilization and TE effects of surgical masks. Silver nanoparticles have supported antimicrobial effects. Nanofiber membranes are fabricated to have a high surface area that improves the fiber diameter and porosity ratio. A traditional mask could only block a maximum of 50% of the exhaled viruses, but a nanofiber-based mask has been tested to intercept 90% to 99% of particle viruses while breathing during use. Complex nanocomposite materials have succeeded in collecting all these advantages.

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization.

Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.

Remdesivir analog as SARS-CoV-2 polymerase inhibitor: virtual screening of a database generated by scaffold replacement.

By the end of 2019, a novel strain of the corona viral family named SARS-CoV-2 emerged in Wuhan, China and started to spread worldwide causing one of the most dangerous lethal pandemics. Researchers utilized various reported inhibitors and drug databases for virtual screening analysis against this novel strain. Later on, they succeeded to fish and repurpose remdesivir, an antiviral nucleotide analogue that inhibits RNA polymerase of the Ebola virus, as a promising candidate against SARS-CoV-2. In this study, we used the interactions of the co-crystallized metabolite of remdesivir with SARS-CoV-2 RdRp isozyme (PDB 7BV2) to design an analog with potential extra activity. This design was based on a scaffold replacement of a pyrrolotriazine moiety. This design was guided by a generated structure-based pharmacophore. The database generated from scaffold replacement was subjected to molecular docking and molecular dynamics simulations within the active site of SARS-CoV-2 RdRp (PDB 7BV2) to suggest HA-130383 and HA-130384 as potential lead compounds.

Effect of the COVID-19 Pandemic on Rates of Ninety-Day Peri-Prosthetic Joint and Surgical Site Infections after Primary Total Joint Arthroplasty: A Multicenter, Retrospective Study.

Background: The impact of the coronavirus 2019 (COVID-19) pandemic on the rate of primary total joint arthroplasty (TJA) peri-prosthetic joint infection (PJI) and superficial surgical site infections (SSI) is currently unknown. The purpose of this multicenter study was to evaluate any changes in the rates of 90-day PJI or 30-day SSI, including trends in microbiology of the infections, during the COVID-19 pandemic compared to the three years prior. Patients and Methods: An Institutional Review Board-approved, multicenter, retrospective study was conducted with five participating academic institutions across two healthcare systems in the northeastern United States. Primary TJA patients from the years 2017-2019 were grouped as a pre-COVID-19 pandemic cohort and patients from the year 2020 were grouped as a COVID-19 pandemic cohort. Differences in patient demographics, PJI, SSI, and microbiology between the two cohorts were assessed. Results: A total of 14,844 TJAs in the pre-COVID-19 pandemic cohort and 5,453 TJAs in the COVID-19 pandemic cohort were evaluated. There were no substantial differences of the combined 90-day PJI and 30-day superficial SSI rates between the pre-COVID-19 pandemic cohort (0.35%) compared with the COVID-19 pandemic cohort (0.26%; p = 0.303). Conclusions: This study did not find any change in the rates of 90-day PJI or 30-day superficial SSI in patients undergoing primary TJA between a pre-COVID-19 pandemic and COVID-19 pandemic cohort. Larger national database studies may identify small but substantial differences in 90-day PJI and 30-day superficial SSI rates between these two time periods. Our data may support continued efforts to maintain high compliance with hand hygiene, use of personal protective equipment, and limited hospital visitation whenever possible.

Robo-Nurse Healthcare Complete System Using Artificial Intelligence

Significant with COVID-19 pandemic breakout, and the high risk of acquiring this infection that is facing the Healthcare Workers (HCWs), a safe alternative was needed. As a result, robotics, artificial intelligence (AI) and internet of things (IoT) usage rose significantly to assist HCWs in their missions. This paper aims to represent a humanoid robot capable of performing HCWs’ repetitive scheduled tasks such as monitoring vital signs, transferring medicine and food, or even connecting the doctor and patient remotely, is an ideal option for reducing direct contact between patients and HCWs, lowering the risk of infection for both parties. Humanoid robots can be employed in a variety of settings in hospitals, including cardiology, post-anesthesia care, and infection control. The creation of a humanoid robot that supports medical personnel by detecting the patient's body temperature and cardiac vital signs automatically and often and autonomously informs the HCWs of any irregularities is described in this study. It accomplishes this objective thanks to its integrated mobile vital signs unit, cloud database, image processing, and Artificial Intelligence (AI) capabilities, which enable it to recognize the patient and his situation, analyze the measured values, and alert the user to any potentially worrisome signals. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The effect of the COVID-19 pandemic on revision total hip and knee arthroplasty at a large academic hospital network.

We sought to quantify the impact of COVID-19 on canceled revision total joint arthroplasty (TJA) in a large academic hospital network. We performed a retrospective analysis of revision TKA and THA in a healthcare system containing 5 hospitals in a time period of 8 months prior to and 8 months after the cessation of elective surgery. We found a 30.1% decrease in revision TKA and a 6.80% decrease in revision THA. Revision TJA volume decreased in our healthcare system during COVID-19 compared to prior to the pandemic, which will likely have lasting financial and clinical ramifications for the healthcare system.

Importance of glutamine 189 flexibility in SARS-CoV-2 main protease: Lesson learned from in silico virtual screening of ChEMBL database and molecular dynamics.

The current global pandemic outbreak of COVID-19, caused by the SARS-CoV-2, strikes an invincible damage to both daily life and the global economy. WHO guidelines for COVID-19 clinical management includes infection control and prevention, social distancing and supportive care using supplemental oxygen and mechanical ventilator support. Currently, evolving researches and clinical reports regarding infected patients with SARS-CoV-2 suggest a potential list of repurposed drugs that may produce appropriate pharmacological therapeutic efficacies in treating COVID-19 infected patients. In this study, we performed virtual screening and evaluated the obtained results of US-FDA approved small molecular database library (302 drug molecule) against two important different protein targets in COVID-19. Best compounds in molecular docking were used as a training set for generation of two different pharmacophores. The obtained pharmacophores were employed for virtual screening of ChEMBL database. The filtered compounds were clustered using Finger print model to obtain two compounds that will be subjected to molecular docking simulations against the two targets. Compounds complexes with SARS-CoV-2 main protease and S-protein were studied using molecular dynamics (MD) simulation. MD simulation studies suggest the potential inhibitory activity of ChEMBL398869 against SARS-CoV-2 main protease and restress the importance of Gln189 flexibility in inhibitors recognition through increasing S2 subsite plasticity.

Egyptian dermatologists attitude toward telemedicine amidst the COVID19 pandemic: a cross-sectional study.

BACKGROUND: Telemedicine involves distant exchange of medical information between health providers and patients via a telecommunication device with/without the aid of an audiovisual interactive assistance. The current COVID 19 pandemic impact on health services mandated an utmost readiness to implement telemedicine which in part is dependent on health care providers willingness to adopt such platforms. AIM: The aim of this cross sectional study was to assess knowledge and attitude toward telemedicine Egyptian dermatologists amidst the COVID 19 pandemic. PATIENTS AND METHODS: A cross sectional study was designed and data were collected using structured self-administered online questionnaires. RESULTS: Dermatologists had a good knowledge about telemedicine (mean 4.17 ± 1.63; p < .05). Of those completing the questionnaire, 193 (68.9%) were familiar with the term 'telemedicine' and 164 (58.6%) were familiar with tools like teleconferencing. The majority of responding dermatologists 227 (81.1%) were confident that the COVID 19 pandemic is a good opportunity to start applying telemedicine protocols however the majority 234 (83.6%) preferred using it on trial basis at first before full implementation. CONCLUSION: In conclusion an overall good attitude toward telemedicine was reported with a mean of 3.39 (p < .05). Further large scale studies are required to verify such findings.