Honesty-Based Social Technique to Enhance Cooperation in Social Internet of Things

The Social Internet of Things (SIoT) can be seen as integrating the social networking concept into the Internet of Things (IoT). Such networks enable different devices to form social relationships among themselves depending on pre-programmed rules and the preferences of their owners. When SIoT devices encounter one another on the spur of the moment, they seek out each other's assistance. The connectivity of such smart objects reveals new horizons for innovative applications empowering objects with cognizance. This enables smart objects to socialize with each other based on mutual interests and social aspects. Trust building in social networks has provided a new perspective for providing services to providers based on relationships like human ones. However, the connected IoT nodes in the community may show a lack of interest in forwarding packets in the network communication to save their resources, such as battery, energy, bandwidth, and memory. This act of selfishness can highly degrade the performance of the network. To enhance the cooperation among nodes in the network a novel technique is needed to improve the performance of the network. In this article, we address the issue of the selfishness of the nodes through the formation of a credible community based on honesty. A social process is used to form communities and select heads in these communities. The selected community heads having social attributes prove effective in determining the social behavior of the nodes as honest or selfish. Unlike other schemes, the dishonest nodes are isolated in a separate domain, and they are given several chances to rejoin the community after increasing their honesty levels. The proposed social technique was simulated using MATLAB and compared with existing schemes to show its effectiveness. Our proposed technique outperforms the existing techniques in terms of throughput, overhead, packet delivery ratio (PDR), and packet-delivery latency.

A Novel Technology for Extraction and Enrichment of Hcv Antigen Using Temperature-Sensitive Smart Polymer: A Comparison with Pcr, a Pilot Study

Background: Achievement of WHO goals for HCV elimination needs scaling up of diagnosis and treatment capacities in low-income and middle-income countries, where HCV is highly endemic. However, in these regions, access to HCV diagnostic tools is severely limited. The current diagnostic algorithm is based on using two steps;rapid diagnostic test (RDTs) and viral load confirmation. So there is a need for simple point of care (POC) test that can be done in the field. Recently, we developed a novel technology for extraction and enrichment of HCV antigen using temperature-sensitive Smart Polymer, which used previously For enabling affinity enrichment of current coronavirus (SARS-CoV- 2) to improve its diagnostic sensitivity Our aim is to design a novel method for extraction and enrichment of HCV antigen using temperature-responsive smart polymer 'NIPAAm-co- HIPAAm- co- SAKIPAAm' (Patent: 2019/2002) for enabling affinity enrichment of HCV antigen to be used as a part of POC test to improve the cascade of care Methods: We used temperature-responsive smart polymer 'NIPAAm-co- HIPAAm- co- SAKIPAAm' (Patent: 2019/2002) for extraction and enrichment HCV antigen for 15 positive HCV serum samples and 5 negative HCV serum samples confirmed by PCR technique. After extraction and enrichment of HCV antigen by temperature-responsive smart polymer 'NIPAAm-co- HIPAAm- co- SAKIPAAm' samples undergo amplification and detection by thermal cycler as amplification and detection of Rt-PCR technique (SLAN-96P Real-Time PCR System ). Result(s): . The time needed for extraction and enrichment of HCV Antigen using temperature-sensitive smart polymer (10mins) which is shorter than extraction time in RT-PCR ( One hour). The results showed that HCV antigen extracted and enriched by temperature-responsive smart polymer gave the same results with positive and negative HCV samples detected by PCR, Sensitivity 100%, Specificity 100% Conclusion(s): The novel temperature-responsive smart polymer 'NIPAAm-co- HIPAAm- co- SAKIPAAm' (Patent: 2019/2002) is able to extract and enrich HCV antigen in the same sensitivity and specificity like the current PCR. The time needed for this simple technique may make it be suitable to be a part of POC test for rapid, affordable, easy-to- use test in comparison to PCR technique which needs dedicated facilities and highly qualified personnel.

Unravel: AI-Driven Educational Platform

2020 has been a challenging year for the educational field due to the COVID-19 virus. Changing to online learning was not easy for both the students and the educators. In an attempt to address the problems of online learning to help educators around the world, this paper proposes unravel application to facilitate the job of educators and give them a smooth experience while providing educational materials to their students. Unravel is a web-based solution that enables educators from all backgrounds to upload their lectures to an online cloud video hosting service. Using cutting-edge Speech to Text Technology and NLP tools, video files are transcribed, and a timeline is provided by the application which allows the user to manipulate the file using the generated text. It facilitates the video editing process for users with no video editing experience via an intuitive graphical user interface. The platform will also enable educators to monitor the students and get analytics from the system about the views and will enable an anonymous feedback system so educators can get reviews from their students. The objective of the proposed approach is to build a comprehensive platform that fills the gap between educators and students while unraveling the complex educational problems in online learning. © 2022 by the authors.

Profiling of phthalates, brominated, and organophosphate flame retardants in COVID-19 lockdown house dust; implication on the human health.

In this study, brominated flame retardants (BFRs), phthalates, and organophosphate flame retardants (PFRs) were analyzed in indoor household dust collected during the COVID-19 related strict lockdown (April-July 2020) period. Floor dust samples were collected from 40 households in Jeddah, Saudi Arabia. The levels of most of the analyzed chemicals were visibly high and for certain chemicals multifold high in analyzed samples compared to earlier studies on indoor dust from Jeddah. Bis (2-ethylhexyl) phthalate (DEHP) was the primary chemical in these dust samples, with a median concentration of 769,500 ng/g of dust. Tris (2-butoxy ethyl) phosphate (TBEP) and Decabromodiphenyl ether (BDE 209) contributed the highest among PFRs and BFRs with median levels of 5990 and 940 ng/g of dust, respectively. The estimated daily exposure in the worst case scenario (23,700 ng/kg bw/day) for Saudi children was above the reference dose (20,000 ng/kg bw/day) for DEHP, and the hazardous index (HI) was also >1. The long-term carcinogenic risk was above the 1 × 10-5, indicating a risk to the health of Saudi young children from getting exposed to DEHP from indoor dust. This study draws attention to the increased indoor pollution during the lockdown period when all of the daily activities by adults and children were performed indoors, which negatively impacted human health, as suggested by the calculated risk. However, the current study has limitations and warrants more monitoring studies from different parts of the world to understand the phenomenon. At the same time, this study also highlights another side of COVID-19 related to our lives.

Tele-clinics in palliative care during the COVID-19 outbreak: Tertiary care cancer center experience.

OBJECTIVES: To investigate the effect of tele-clinics on palliative care patients during the COVID-19 pandemic. METHODS: This is a retrospective cross-sectional study (chart review) carried out from March 17, 2020, to September 16, 2020, included all patients who were booked into the palliative care clinic. Patients were assessed by the palliative nurse specialist for COVID-19 symptoms using the acute respiratory illness screening form and Edmonton Symptoms Assessment System, also identifies the needs of the patient. Data were analyzed to investigate the effect of tele-clinics on the patients regarding ER visits and admission. RESULTS: A total of 167 individuals were analyzed and the results showed that 234 of 447 visits were virtual, supporting the increasing value of telemedicine. The number of virtual patients' visits dropped slightly at the beginning of the pandemic (46.4% in March to 39.8% in July). Subsequently, it increased steadily to 72.2% in September. The choice of virtual/non-virtual visits for individuals with cancer diagnosis significantly depends on other factors. Code status, palliative patients or follow-up service, and the frequency of oncology center visits, admissions, or ER visits were crucial in explaining the means of receiving treatment. CONCLUSION: Virtual visits in palliative care are efficient means of decreasing the threat of COVID-19 contagion. It is recommended to increase the palliative care patients' awareness of tele-clinics and their positive outcomes, particularly during the pandemic.

Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network.

It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model's training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.

Safety, tolerability, and immunogenicity of a SARS-CoV-2 recombinant spike RBD protein vaccine: A randomised, double-blind, placebo-controlled, phase 1-2 clinical trial (ABDALA Study).

Background: Multiple vaccine candidates against COVID-19 are currently being evaluated. We evaluate the safety and immunogenicity protein of a novel SARS-CoV-2 virus receptor-binding domain (RBD) vaccine. Methods: A phase 1-2, randomised, double-blind, placebo-controlled trial was carried out in "Saturnino Lora" Hospital, Santiago de Cuba, Cuba. Subjects (healthy or those with controlled chronic diseases) aged between 19 and 80 years, who gave written informed consent were eligible. Subjects were randomly assigned (1:1:1, in blocks) to three groups: placebo, 25 µg and 50 µg RBD vaccine (Abdala). The product was administered intramuscularly, 0·5 mL in the deltoid region. During the first phase, two immunization schedules were studied: 0-14-28 days (short) and 0-28-56 days (long). In phase 2, only the short schedule was evaluated. The organoleptic characteristics and presentations of vaccine and placebo were identical. All participants (subjects, clinical researchers, statisticians, laboratory technicians, and monitors) remained masked during the study period. The main endpoints were safety and the proportion of subjects with seroconversion of anti-RBD IgG antibodies, analysed by intention to treat and per protocol, respectively. The trial is registered with the Cuban Public Registry of Clinical Trials, RPCEC00000346. Findings: Between Dec 7, 2020, and Feb 9, 2021, 792 subjects were included; 132 (66 in each vaccination schedule, divided into 22 for each group) in phase 1, and 660 (220 in each group plus 66 from the short scheme of phase 1) in phase 2. The product was well tolerated. No severe adverse events were reported. During phase 1, the incidence of adverse events in the 25 µg, 50 µg, and placebo arms for the short schedule were 6/22 (27·3%), 6/22 (27·3%), 3/22 (13·6%), respectively, and for the long schedule were 8/22 (36·4%), 9/22 (40·9%), 4/22 (18·2%), respectively. In phase 2, adverse reactions were reported by 53/242 (21·9%), 75/242 (31·0%) and 41/242 (16·9%) participants in the 25 µg, 50 µg, and placebo group, respectively. Adverse reactions were minimal, mostly mild, and from the injection site, which resolved in the first 24-48 hours. In phase 1, seroconversion at day 56 was seen in 95·2% of the participants (20/21) in the 50 µg group, 81% (17/21) in the 25 µg group, and none in the placebo group (0/22). For the long schedule, seroconversion at day 70 was seen in 100% of the participants (21/21) in the 50 µg group, 94·7% (18/19) in the 25 µg group, and none in the placebo group (0/22). In phase 2, seroconversion of anti-RBD IgG antibodies at day 56 was seen in 89·2% of the participants in the 50 µg group (214/240; 95% CI 84·5-92·82), 77·7% in the 25 µg group (185/238; 72·0-82·9) and 4·6% in the placebo group (11/239; 2·3-8·1). Compared with the placebo arm, the differences in the proportion of participants with seroconversion were 73·1% (95% CI 66·8-79·5) and 84·6% (79·4-89·7) in the 25 µg and 50 µg groups, respectively. The seroconversion rate in the 50 µg group was significantly higher than in the 25 µg group (p=0·0012). Interpretation: The Abdala vaccine was safe, well tolerated, and induced humoral immune responses against SARS-CoV-2. These results, in the context of the emergency COVID-19 pandemic, support the 50 µg dose, applied in a 0-14-28 days schedule, for further clinical trials to confirm vaccine efficacy. Funding: Centre for Genetic Engineering and Biotechnology (CIGB), Havana, Cuba.

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification.

Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology scan is a significant tool for early management and diagnosis of COVID-19 since the virus targets the respiratory system. Chest X-ray (CXR) images are highly useful in the effective detection of COVID-19, thanks to its availability, cost-effective means, and rapid outcomes. In addition, Artificial Intelligence (AI) techniques such as deep learning (DL) models play a significant role in designing automated diagnostic processes using CXR images. With this motivation, the current study presents a new Quantum Seagull Optimization Algorithm with DL-based COVID-19 diagnosis model, named QSGOA-DL technique. The proposed QSGOA-DL technique intends to detect and classify COVID-19 with the help of CXR images. In this regard, the QSGOA-DL technique involves the design of EfficientNet-B4 as a feature extractor, whereas hyperparameter optimization is carried out with the help of QSGOA technique. Moreover, the classification process is performed by a multilayer extreme learning machine (MELM) model. The novelty of the study lies in the designing of QSGOA for hyperparameter optimization of the EfficientNet-B4 model. An extensive series of simulations was carried out on the benchmark test CXR dataset, and the results were assessed under different aspects. The simulation results demonstrate the promising performance of the proposed QSGOA-DL technique compared to recent approaches.

Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images.

Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches.

EVALUATING ATTITUDES AND INTENTION TO USE OF PERSONAL PROTECTIVE EQUIPMENT (PPE) DURING THE COVID- 19 PANDEMIC

Current study aimed at evaluating of attitudes, beliefs, satisfaction, actual practice and intention to use of Personal Protective Equipment (PPE) during the Covid-19 pandemic. Current study's key messages are divided into two main parts;first one is an approach for policy makers to identify approaches that would help individuals to commit to health rules and regulations when it comes to socializing during the pandemic and the second one would be helpful for society individuals to be more able to identify their attitudes and satisfaction towards PPE and start figuring out their options and preferences. Through depending on quantitative approach, 421 individuals were exposed to an online self-administered questionnaire which aimed at collecting primary data for analysis. Depending on SPSS and AMOS, results of study indicated that customers' attitude positively affect beliefs and satisfaction but has no effect on actual practice and intention to use, beliefs were found to be influential in a positive way on actual practice but has no positive effect on intention to use, in addition to that satisfaction was found to be positively influential on actual practice and intention to use, and there was a positive relationship between actual practice and intention to use. Study recommended socially monitoring individuals' attitudes and satisfaction regarding PPE that are available in the market, and control vague parties that may appear in the marketing promoting for new types and material of personal protective equipment.

Compliance with safety measures and risk of COVID-19 transmission among healthcare workers.

AIM: This study aimed to determine the compliance of healthcare workers (HCWs) with the hospital safety measures and the prevalence of hospital-acquired COVID-19 infection among them. METHODOLOGY: HCWs at King Abdullah University Hospital (KAUH) assigned for COVID-19 patients between 18 March and 10 June 2020 were tested for past infection using total anti-SARS-CoV-2 immunoglobulin assay, demographic data and compliance with safety measures were assessed using a questionnaire. RESULTS: A total of 340 HCWs participated in the study, 260 were close direct care. Three HCWs tested positive for total anti-SARS-CoV-2 immunoglobulin. Close direct care were more compliant with personal protective guidelines than those providing direct care. CONCLUSION: HCWs compliance with personal protective guidelines might explain the low prevalence of COVID-19 infection in hospital settings.

Attitudes toward the SARS-CoV-2 Vaccine: Results from the Saudi Residents' Intention to Get Vaccinated against COVID-19 (SRIGVAC) Study.

Vaccine uptake could influence vaccination efforts to control the widespread COVID-19 pandemic; however, little is known about vaccine acceptance in Saudi Arabia. The present study aimed to assess the Saudi public's intent to get vaccinated against COVID-19 and explore the associated demographic determinants of their intentions as well as the reasons for vaccine hesitancy. A cross-sectional, web-based survey was distributed to public individuals in Saudi Arabia between 25 December 2020 and 15 February 2021. Participants were asked if they were willing to get vaccinated, and the responses, along with demographic data were entered into a multinomial logistic regression model to assess the relative risk ratio (RRR) for responding "no" or "unsure" versus "yes". Among 3048 participants (60.1% female, 89.5% Saudi), 52.9% intend to get vaccinated, 26.8% were unsure, and 20.3% refused vaccination. Vaccine hesitancy was significantly higher among females (RRR = 2.70, p < 0.0001) and those who had not been recently vaccinated for influenza (RRR = 2.63, p < 0.0001). The likelihood was lower among Saudis (RRR = 0.49, p < 0.0001), those with less than a secondary education (RRR = 0.16, p < 0.0001), perceived risks of COVID-19, and residents of the southern region (RRR = 0.46, p < 0.0001). The most often cited reasons for hesitancy were short clinical testing periods and concerns about adverse events or effectiveness. Vaccine hesitancy is mediated by many demographic factors and personal beliefs. To address vaccine-related concerns and amend deeply rooted health beliefs, communication should provide transparent information.

Molecular Docking Studies of HIV TAT and sitagliptin nano-formula as potential therapeutic targeting SARS-CoV2 protease

The outbreak of COVID-19 pandemic regarded as a major health/economic hazard. The importance of coming up with mechanisms for preventing or treating SARS-CoV-2infection has been felt across the world. This work aimed at examining the efficiency of Sitagliptin (SIT) and human immunodeficiency virus type 1 (HIV-1) trans-activator transcription peptide (TAT) against SARS-CoV-2 virus. 3CL-protease inhibition activity and docking studies were examined. According to the results, the prepared complex’s formula was as follows 1: 1 SIT: TAT molar ratio, whereas zeta potential and particle size values were at 34.17 mV and 97.19 nm, respectively. This combination did exhibit its antiviral potentiality against SARS-CoV-2 via IC50 values of 9.083 5.415, and 16.14 μM for TAT, SIT-TAT, and SIT, respectively. In addition, the complex SIT-TAT showed a significant (P < 0.001) viral-3CL-protease inhibitory effect. This was further confirmed via in silico study. Molecular docking investigation has shown promising binding affinity of the formula components towards SARS-CoV-2 main protease (3-CL).

Scaffold Hopping of α-Rubromycin Enables Direct Access to FDA-Approved Cromoglicic Acid as a SARS-CoV-2 MPro Inhibitor.

The COVID-19 pandemic is still active around the globe despite the newly introduced vaccines. Hence, finding effective medications or repurposing available ones could offer great help during this serious situation. During our anti-COVID-19 investigation of microbial natural products (MNPs), we came across α-rubromycin, an antibiotic derived from Streptomyces collinus ATCC19743, which was able to suppress the catalytic activity (IC50 = 5.4 µM and Ki = 3.22 µM) of one of the viral key enzymes (i.e., MPro). However, it showed high cytotoxicity toward normal human fibroblasts (CC50 = 16.7 µM). To reduce the cytotoxicity of this microbial metabolite, we utilized a number of in silico tools (ensemble docking, molecular dynamics simulation, binding free energy calculation) to propose a novel scaffold having the main pharmacophoric features to inhibit MPro with better drug-like properties and reduced/minimal toxicity. Nevertheless, reaching this novel scaffold synthetically is a time-consuming process, particularly at this critical time. Instead, this scaffold was used as a template to explore similar molecules among the FDA-approved medications that share its main pharmacophoric features with the aid of pharmacophore-based virtual screening software. As a result, cromoglicic acid (aka cromolyn) was found to be the best hit, which, upon in vitro MPro testing, was 4.5 times more potent (IC50 = 1.1 µM and Ki = 0.68 µM) than α-rubromycin, with minimal cytotoxicity toward normal human fibroblasts (CC50 > 100 µM). This report highlights the potential of MNPs in providing unprecedented scaffolds with a wide range of therapeutic efficacy. It also revealed the importance of cheminformatics tools in speeding up the drug discovery process, which is extremely important in such a critical situation.

Knowledge of health care workers regarding COVID-19 pandemic response plan; A study from a university affiliated hospital in Jordan.

BACKGROUND: The education and training of healthcare workers (HCWs) about pandemic response plans, infection control protocols (ICP), and precautions are critical for breaking the virus transmission chain. This study aimed to assess the knowledge of HCWs regarding the COVID-19 pandemic response plan and the ICP. Concomitantly, evaluating the effectiveness and impact of institutional measures, policies, and training programs. METHODS: A cross-sectional study using an electronic questionnaire was used to collect data from 267 HCWs at a University-affiliated-hospital during April 2020. RESULTS: In this study, 90.7% of participants were nurses and 66% were females. 60.3% of participants received specific training about caring for COVID-19 patients while at least 94.3% reviewed the pandemic response plan and ICP for COVID-19. 47.5% and 29.2% of participants provided direct care for suspected or confirmed COVID-19 patients, respectively. Between 85.7% and 100% of the participants answered all items correctly regarding the COVID-19 pandemic response plan, measures, and precautions. Furthermore, the correct answers were above 95% for each item. CONCLUSION: HCWs have a high level of knowledge about the COVID-19 pandemic response plan and ICP. Training and disseminating updated plans and protocols in different methods such as an intranet and onsite training in addition to regular methods were very effective.

Better clinical outcomes in hospitalized COVID-19 minority patients with accompanying gastrointestinal symptoms.

BACKGROUND AND AIMS: The novel coronavirus (SARS-CoV-2) is highly contagious pathogen that primarily causes respiratory illnesses. Howerver, multiple gastrointestinal (GI) symptoms have been reported in Coronavirus Disease of 2019 (COVID-19). We conducted a retrospective cohort study of inpatients with COVID-19 at the George Washington University Hospital (GWUH) to assess the prevalence of GI symptoms and their association with clinical outcomes. METHODS: We reviewed the charts of 401 adults admitted to GWUH with positive SARS-CoV-2 tests from February 24 to May 21, 2020, ultimately including 382 inpatients. RESULTS: 87% of our cohort was African American or Latinx. 59% of patients reported at least one GI symptom, with diarrhea being the most common (29%). Patients with GI symptoms were slightly younger (58 +/- 15.8 vs. 65 +/- 16.9, p = 0.0005), have higher body mass index (31.5 +/- Standard Deviation of 8.7 vs. 28 +/- 8.2, p = 0.0001), and more likely to be Latinx (34 vs. 27, p = 0.01). Patients who presented with abdominal pain, nausea, vomiting, or diarrhea had significantly lower rates of death during hospitalization compared to those who did not present those symptoms (Odds Ratio 0.48, 95% Confidence Interval 0.28-0.8, p = 0.004). CONCLUSIONS: Our study suggests that GI symptoms portend a less-severe clinical course of COVID-19 which may reflect a different disease phenotype and lower overall immune response. Additional research should focus on more robust symptom reporting and longer follow-up.

Adversarial Examples-Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices.

Medical IoT devices are rapidly becoming part of management ecosystems for pandemics such as COVID-19. Existing research shows that deep learning (DL) algorithms have been successfully used by researchers to identify COVID-19 phenomena from raw data obtained from medical IoT devices. Some examples of IoT technology are radiological media, such as CT scanning and X-ray images, body temperature measurement using thermal cameras, safe social distancing identification using live face detection, and face mask detection from camera images. However, researchers have identified several security vulnerabilities in DL algorithms to adversarial perturbations. In this article, we have tested a number of COVID-19 diagnostic methods that rely on DL algorithms with relevant adversarial examples (AEs). Our test results show that DL models that do not consider defensive models against adversarial perturbations remain vulnerable to adversarial attacks. Finally, we present in detail the AE generation process, implementation of the attack model, and the perturbations of the existing DL-based COVID-19 diagnostic applications. We hope that this work will raise awareness of adversarial attacks and encourages others to safeguard DL models from attacks on healthcare systems.

Better outcome of COVID-19 positive kidney transplant recipients during the unremitting stage with optimized anticoagulation and immunosuppression.

INTRODUCTION: COVID-19 is an ongoing pandemic with high morbidity and mortality and with a reported high risk of severe disease in kidney transplant recipients (KTR). AIM: We aimed to report the largest number of COVID-19-positive cases in KTR in a single center and to discuss their demographics, management, and evolution. METHODS: We enrolled all the two thousand KTR followed up in our center in Kuwait and collected the data of all COVID-19-positive KTR (104) from the start of the outbreak till the end of July 2020 and have reported the clinical features, management details, and both patient and graft outcomes. RESULTS: Out of the one hundred and four cases reported, most of them were males aged 49.3 ± 14.7 years. Eighty-two of them needed hospitalization, of which thirty-one were managed in the intensive care unit (ICU). Main comorbidities among these patients were hypertension in 64.4%, diabetes in 51%, and ischemic heart disease in 20.2%. Management strategies included anticoagulation in 56.7%, withdrawal of antimetabolites in 54.8%, calcineurin inhibitor (CNI) withdrawal in 33.7%, the addition of antibiotics in 57.7%, Tocilizumab in 8.7%, and antivirals in 16.3%. During a follow-up of 30 days, the reported number of acute kidney injury (AKI) was 28.7%, respiratory failure requiring oxygen therapy 46.2%, and overall mortality rate was 10.6% with hospital mortality of 13.4% including an ICU mortality rate of 35.5%. CONCLUSION: Better outcome of COVID-19-positive KTR in our cohort during this unremitting stage could be due to the younger age of patients and early optimized management of anticoagulation, modification of immunosuppression, and prompt treatment of secondary bacterial infections. Mild cases can successfully be managed at home without any change in immunosuppression.

Repurposing of Sitagliptin- Melittin Optimized Nanoformula against SARS-CoV-2: Antiviral Screening and Molecular Docking Studies.

The outbreak of the COVID-19 pandemic in China has become an urgent health and economic challenge. The objective of the current work was to evaluate the efficacy of the combined complex of Sitagliptin (SIT) with melittin (MEL) against SARS-CoV-2 virus. SIT-MEL nano-conjugates were optimized by a full three-factor bi-level (23) factorial design. In addition, SIT concentration (mM, X1), MEL concentration (mM, X2), and pH (X3) were selected as the critical factors. Particle size (nm, Y1) and zeta potential (mV, Y2) were assessed as responses. Characterization of the optimized formula for Fourier-transformed infrared (FTIR) was carried out. The optimized formula showed particle size and zeta potential values of 77.42 nm and 27.67 mV, respectively. When compared with SIT and MEL, the combination of SIT-MEL complex has shown anti-viral potential against isolate of SARS-CoV-2 with IC50 values of 8.439 µM with significant improvement (p < 0.001). In addition, the complex showed IC50 in vitro 3CL-protease inhibition with IC50 7.216 µM. Molecular docking has revealed that formula components have good predicted pocket accommodation of the SARS-CoV-2 3-CL protease. An optimized formulation of SIT-MEL could guarantee both enhanced delivery to the target cells and the enhanced cellular uptake with promising activities against SARS-CoV-2.