Kidney transplant recipients have been supposed vulnerable to severe Covid-19 infection, due to their comorbidities and immunosuppressive therapies. Mild-term complications of Covid-19 are currently unknown, especially in this population. Herein, we report two cases of BKV replication after non-severe SARS-CoV-2 infection. The first case was a 59-year-old man, transplanted 3 months ago, with recent history of slight BKV viremia (3.3 log10 DNA copies/ml). Despite strong reduction of maintenance immunosuppression (interruption of mycophenolic acid and important decrease of calcineurin inhibitors), BKV replication largely increased after Covid-19 and viremia persisted at 4.5 log copy/ml few months later. The second case was a 53-year-old woman, transplanted 15 years ago. She had recent history of BKV cystitis, which resolved with decrease of MPA dosage. Few weeks after SARS-CoV-2 infection, she presented recurrence of lower urinary tract symptoms. Our reports highlight that SARS-CoV-2 infection, even without severity, could disrupt immune system and particularly lymphocytes, thus leading to viral replication. Monitoring of viral replications after Covid-19 in kidney transplant recipients could permit to confirm these preliminary observations.

We present evidence of cheating that took place in online examinations during COVID-19 lockdowns and propose two solutions with and without a camera for the cheating problem based on the experience accumulated by online chess communities over the past two decades The best implementable solution is a uniform online exam policy where a camera capturing each students computer screen and room is a requirement We recommend avoiding grading on a curve and giving students less time but simpler questions on tests

Background and Purpose: The impact of the coronavirus disease 2019 (COVID-19) pandemic on stroke systems has not been systematically evaluated Our study aims to investigate trends in telestroke consults during the pandemic

The coronavirus disease 2019 (COVID-19) has infected more than 50 million people in more than 100 countries, resulting in a major global impact. Many studies on the potential roles of environmental factors in the transmission of the novel COVID-19 have been published. However, the impact of environmental factors on COVID-19 remains controversial. Machine learning techniques have been used effectively in combating the COVID-19 epidemic. However, researches related to machine learning on weather conditions in spreading COVID-19 is generally lacking. Therefore, in this study, three machine learning models (Convolution Neural Network (CNN), ADtree Classifier and BayesNet) based on the confirmed cases and weather variables such as temperature, humidity, wind and precipitation are developed. This study aims to identify the best classification model to classify COVID-19 by using significant weather features chosen by Principle Component Analysis (PCA) feature selection method. The DS4C COVID-19 data set is used to train and validate each machine learning model. Several data preprocessing tasks such as data cleaning and feature selection have been conducted on the raw dataset to ensure the quality of the training data. The performance of these machine learning algorithms is further rectified based on the selected features set by PCA. Each classifier is then optimized using different tuning parameters to achieve optimum values before comparing the output of the three classifiers against each other. The observational results have shown that the optimized CNN classifier with seven weather variables selected by PCA achieved the highest performance among all the techniques. The experimental results obtained show that the weather variables are more relevant in predicting the confirmed cases as compared to the other variables. Thus, from this result, it is evident that temperature, humidity, wind and precipitation are important features for predicting COVID-19 confirmed cases.

The forcing online of higher education classes should have constituted a major reckoning of pedagogical practices in universities, particularly in the humanities. Such a reckoning seems to have been muted by a focus on logistical concerns and by what might be called a false sense of preparedness within university departments. This study attempts to counter that general trend by taking seriously the cognitive and emotional demands of the online transition through a philosophical lens. This essay presents a phenomenological study of the abrupt transition from physical to online classes during the COVID\19 lockdown of universities. Reworking two central theses of Marshall McLuhan's (1994) study of media, the author proposes two dispositions as necessary to encountering the role of new media in the teaching of the humanities: a state of wilful unpreparedness and a state of artistic creativity. These in turn pose educational dilemmas that require changes to the relationship between teachers, students and the learning environment, wherein these relationships become subjects of conscious study by the participants. The article draws on a range of classroom experiences in which the instructor and students attempted such a study. In encountering the shock of the new media, a series of concepts emerge that can help promote an artistic approach towards the medium itself.

Angiotensin-converting enzyme 2 (ACE2) has been recognized as the entry receptor of the novel severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2). Structural and sequence variants in ACE2 gene may affect its expression in different tissues and determine a differential response to SARS-Cov-2 infection and the COVID-19-related phenotype. The present study investigated the genetic variability of ACE2 in terms of single nucleotide variants (SNVs), copy number variations (CNVs), and expression quantitative loci (eQTLs) in a cohort of 268 individuals representative of the general Italian population. The analysis identified five SNVs (rs35803318, rs41303171, rs774469453, rs773676270, and rs2285666) in the Italian cohort. Of them, rs35803318 and rs2285666 displayed a significant different frequency distribution in the Italian population with respect to worldwide population. The eQTLs analysis located in and targeting ACE2 revealed a high distribution of eQTL variants in different brain tissues, suggesting a possible link between ACE2 genetic variability and the neurological complications in patients with COVID-19. Further research is needed to clarify the possible relationship between ACE2 expression and the susceptibility to neurological complications in patients with COVID-19. In fact, patients at higher risk of neurological involvement may need different monitoring and treatment strategies in order to prevent severe, permanent brain injury.

A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.

The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or 2019 novel coronavirus (2019-nCoV), took tens of thousands of lives and caused tremendous economic losses The main protease (M(pro)) of SARS-CoV-2 is a potential target for treatment of COVID-19 due to its critical role in maturation of viral proteins and subsequent viral replication Conceptually and technically, targeting therapy against M(pro) is similar to target therapy to treat cancer Previous studies show that GC376, a broad-spectrum dipeptidyl M(pro) inhibitor, efficiently blocks the proliferation of many animal and human coronaviruses including SARS-CoV, Middle East respiratory syndrome coronavirus (MERS-CoV), porcine epidemic diarrhea virus (PEDV), and feline infectious peritonitis virus (FIPV) Due to the conservation of structure and catalytic mechanism of coronavirus main protease, repurposition of GC376 against SARS-CoV-2 may be an effective way for the treatment of COVID-19 in humans To validate this conjecture, the binding affinity and IC(50) value of M(pro) with GC376 was determined by isothermal titration calorimetry (ITC) and fluorescence resonance energy transfer (FRET) assay, respectively The results showed that GC376 binds to SARS-CoV-2 M(pro) tightly (K(D) = 1 6 M) and efficiently inhibit its proteolytic activity (IC(50) = 0 89 M) We also elucidate the high-resolution structure of dimeric SARS-CoV-2 M(pro) in complex with GC376 The cocrystal structure showed that GC376 and the catalytic Cys145 of M(pro) covalently linked through forming a hemithioacetal group and releasing a sulfonic acid group Because GC376 is already known as a broad-spectrum antiviral medication and successfully used in animal, it will be a suitable candidate for anti-COVID-19 treatment

COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world The case and death numbers are increasing day by day Some tests have been used to determine the COVID-19 Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19 And new methods have been searching for determination of the COVID-19 In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out Chest X-ray images are used as input to the proposed approach As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered For deep feature extraction, pretrained, ResNet50 model is employed For classification of the deep features, the Support Vector Machines (SVM) classifier is used The ResNet50 model is also used in the fine-tuning The experimental works show that multiresolution approaches produced better performance than the deep learning approaches Especially, Shearlet transform outperformed at all 99 29% accuracy score is obtained by using Shearlet transform

In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age, days in hospital, Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19.

During the COVID-19 pandemic, many institutions have announced that their counterparties are struggling to fulfill contracts.Therefore, it is necessary to consider the counterparty default risk when pricing options. After the 2008 financial crisis, a variety of value adjustments have been emphasized in the financial industry. The total value adjustment (XVA) is the sum of multiple value adjustments, which is also investigated in many stochastic models such as Heston and Bates models. In this work, a widely used pure jump L\'evy process, the CGMY process has been considered for pricing a Bermudan option with various value adjustments. Under a pure jump L\'evy process, the value of derivatives satisfies a fractional partial differential equation(FPDE). Therefore, we construct a method which combines Monte Carlo with finite difference of FPDE (MC-FF) to find the numerical approximation of exposure, and compare it with the benchmark Monte Carlo-COS (MC-COS) method. We use the discrete energy estimate method, which is different with the existing works, to derive the convergence of the numerical scheme.Based on the numerical results, the XVA is computed by the financial

This study investigates the clinical and imaging characteristics of coronavirus disease 2019 (COVID-19) patients with false-negative nucleic acids. Mild-to-moderate COVID-19 patients, including 19 cases of nucleic acid false-negative patients and 31 cases of nucleic acid positive patients, were enrolled. Their epidemiological, clinical, and laboratory examination data and imaging characteristics were analyzed. Risk factors for false negatives were discussed. Compared with the nucleic acid positive group, the false-negative group had less epidemiological exposure (52.6% vs 83.9%; P = .025), less chest discomfort (5.3% vs 32.3%; P = .035), and faster recovery (10 [8, 13] vs 15 [11, 18.5] days; P = .005). The number of involved lung lobes was (2 [1, 2.5] vs 3 [2, 4] days; P = .004), and the lung damage severity score was (3 [2.5, 4.5] vs 5 [4, 9] days; P = .007), which was lighter in the nucleic acid false-negative group. Thus, the absence of epidemiological exposure may be a potential risk factor for false-negative nucleic acids. The false-negative cases of COVID-19 are worth noting because they have a risk of viral transmission without positive test results, lighter clinical manifestations, and less history of epidemiological exposure.

The COVID-19 pandemic has resulted in a worldwide health crisis. Rapid diagnosis, new therapeutics and effective vaccines will all be required to stop the spread of COVID-19. Quantitative evaluation of serum antibody levels against the SARS-CoV-2 virus provides a means of monitoring a patient's immune response to a natural viral infection or vaccination, as well as evidence of a prior infection. In this paper, a portable and low-cost electrochemical immunosensor is developed for the rapid and accurate quantification of SARS-CoV-2 serum antibodies. The immunosensor is capable of quantifying the concentrations of immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies against the SARS-CoV-2 spike protein in human serum. For IgG and IgM, it provides measurements in the range of 10.1 ng/mL--60 g/mL and 1.64 ng/mL -- 50 g/mL, respectively, and both antibodies can be assayed in 13 min. We also developed device stabilization and storage strategies to achieve stable performance of the immunosensor within 24-week storage at room temperature. We evaluated the performance of the immunosensor using COVID-19 patient serum samples collected at different time points after symptom onset. The rapid and sensitive detection of IgG and IgM provided by our immunosensor fulfills the need of rapid COVID-19 serology testing for both point-of-care diagnosis and population immunity screening.

PURPOSE To retrospectively evaluate the clinical utility of radiofrequency ablation (RFA) following transarterial injection of miriplatin-iodized oil suspension (MPT-RFA) for hepatocellular carcinoma treatment. MATERIALS AND METHODS We evaluated clinical outcomes of MPT-RFA for three or fewer hepatocellular carcinomas. Twenty-one patients with 30 tumors (maximum diameter: mean 1.4 0.4 cm, range 0.7-2.2 cm) received MPT-RFA. RESULTS Nineteen patients (90.5 %, 19/21) achieved complete ablation at the first RFA session. Two patients (9.5 %, 2/21) required a second RFA session but achieved complete ablation. Primary and secondary technical success rates were 90.5 and 100 %. There were no deaths related to the procedures performed. Grade 3 or 4 increases in the serum aspartate aminotransferase, alanine aminotransferase, and bilirubin levels were found in six patients (38.1 %, 8/21). There were no liver infarctions. During the median follow-up period of 24.1 months (mean SD 24.7 6.9 months, range 13.8-38.9 months), the local tumor progression rate and overall survival rate at 2 years was 5.0 % (95 % confidence interval 0.3-20.6 %) and 86.7 % (95 % confidence interval 56.3-96.5 %), respectively. The mean hospital stay was 8.4 3.1 days (range 5-18 days). CONCLUSION MPT-RFA is a safe therapeutic option that initially provides therapeutic results.

The Framingham Risk equation uses sex, age, smoking, total cholesterol, high-density lipoprotein (HDL) cholesterol and systolic blood pressure to predict 10-year risk of coronary heart disease (FR-10). The American Heart Association's Ideal Cardiovascular Health (IDEAL) score uses smoking, total cholesterol, fasting glucose, blood pressure, body mass index (BMI), diet, and physical activity to encourage a healthy cardiovascular phenotype. This study aimed to compare 6-month changes in the FR-10 vs. IDEAL score among young adults with BMI 25 to <40kg/m(2) enrolled in a behavioral weight loss intervention at the University of Pittsburgh (2010-12). Medians [25th, 75th percentiles] are reported. Weight decreased by 8kg [-12, -4] among 335 participants. Of 7 possible points, IDEAL score was 4 [3, 4] at baseline, improved (i.e., increased) by 1 [0, 2] over 6months, and improved in 64.2% and worsened in 6.6% of participants (p<0.001). IDEAL classification of BMI, physical activity, total cholesterol, blood pressure and glucose improved (all p<0.001), but not of smoking or diet (both p0.05). FR-10 was <1% at baseline for 88.1% of participants and changed in few participants (improved, i.e. decreased, in 7.5%, worsened in 1.8%, p<0.001). Among young adults with overweight or obesity enrolled in a weight loss intervention, IDEAL detected positive changes in a majority of participants while the FR-10 did not. These findings suggest that IDEAL score may be more sensitive to positive cardiovascular health changes resulting from a behavioral intervention in this population.

OBJECTIVE: We aimed to examine the associations of obesity-related traits (body mass index [BMI], central obesity) and their genetic predisposition with the risk of developing severe COVID-19 in a population-based data. Research Design and Methods. We analyzed data from 489,769 adults enrolled in the UK Biobanka population-based cohort study. The exposures of interest are BMI categories and central obesity (e.g., larger waist circumference). Using genome-wide genotyping data, we also computed polygenic risk scores (PRSs) that represent an individual's overall genetic risk for each obesity trait. The outcome was severe COVID-19, defined by hospitalization for laboratory-confirmed COVID-19. RESULTS: Of 489,769 individuals, 33% were normal weight (BMI, 18.5C24.9 kg/m(2)), 43% overweight (25.0C29.9 kg/m(2)), and 24% obese (30.0 kg/m(2)). The UK Biobank identified 641 patients with severe COVID-19. Compared to adults with normal weight, those with a higher BMI had a dose-response increases in the risk of severe COVID-19, with the following adjusted ORs: for 25.0C29.9 kg/m(2), 1.40 (95%CI 1.14C1.73; P = 0.002); for 30.0C34.9 kg/m(2), 1.73 (95%CI 1.36C2.20; P < 0.001); for 35.0C39.9 kg/m(2), 2.82 (95%CI 2.08C3.83; P < 0.001); and for 40.0 kg/m(2), 3.30 (95%CI 2.17C5.03; P < 0.001). Likewise, central obesity was associated with significantly higher risk of severe COVID-19 (P < 0.001). Furthermore, larger PRS for BMI was associated with higher risk of outcome (adjusted OR per BMI PRS Z-score 1.14, 95%CI 1.05C1.24; P = 0.004). CONCLUSIONS: In this large population-based cohort, individuals with more-severe obesity, central obesity, or genetic predisposition for obesity are at higher risk of developing severe-COVID-19.

ProteinCprotein interactions within protein networks shape the human interactome, which often is promoted by specialized protein interaction modules, such as the postsynaptic density\95 (PSD\95), discs\large, zona occludens 1 (ZO\1) (PDZ) domains. PDZ domains play a role in several cellular functions, from cellCcell communication and polarization, to regulation of protein transport and protein metabolism. PDZ domain proteins are also crucial in the formation and stability of protein complexes, establishing an important bridge between extracellular stimuli detected by transmembrane receptors and intracellular responses. PDZ domains have been suggested as promising drug targets in several diseases, ranging from neurological and oncological disorders to viral infections. In this review, the authors describe structural and genetic aspects of PDZ\containing proteins and discuss the current status of the development of small\molecule and peptide modulators of PDZ domains. An overview of potential new therapeutic interventions in PDZ\mediated protein networks is also provided.

Context: Multisystem inflammatory syndrome in children (MIS-C) is an emerging condition after the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, termed COVID-19. This study aimed to describe the cardiac manifestations of children diagnosed with MIS-C. Evidence Acquisition: This narrative review was conducted by searching the PubMed, Scopus, and Google Scholar databases to review MIS-C cardiac manifestations up to September 30, 2020. The demographic features, past medical history, clinical signs and symptoms, cardiac involvement, and the type of COVID-19 diagnosis confirmation were extracted. Results: In many children, MIS-C seems to be a post-infectious complication of the COVID-19 infection. This syndrome affects mul-tiple organs and has various clinical manifestations mimicking Kawasaki disease. Patients frequently present with persistent fever, kidney injury, gastrointestinal (GI) problems, neurologic symptoms, mucosal changes, conjunctivitis, and cardiac involvement. Children with MIS are more likely to present with hypotension, shock, and cardiac dysfunction, rather than coronary artery abnormalities and arrhythmia. Children with MIS need close observation;some need to be hospitalized, and a few may need a Pediatric Intensive Care Unit (PICU) admission. Treatment currently includes anticoagulants, IV immunoglobulin, and anti-inflammatory drugs. Conclusions: As a novel syndrome associated with SARS-CoV-2 infection, MIS-C is potentially lethal. Cardiac manifestations, including coronary and myocardial involvement, are common and should be carefully identified. With prompt diagnosis and proper treatment, most children will survive, but the outcomes of the disease are unknown, so long-term follow-ups are required.

Antimicrobial peptides stand as promising therapeutics to mitigate the global rise of antibiotic resistance. They generally act by perturbing the bacterial cell membrane, and are thus less likely to induce resistance. Since they are membrane-active molecules, it is critical to verify and understand their potential action towards eukaryotic cells to help design effective and safe drugs. In this work, we studied the interaction of two antimicrobial peptides, aurein 1.2 and caerin 1.1, with red blood cell (RBC) membranes using in situ31P and 2H solid-state nuclear magnetic resonance (SS-NMR). To do so, we established a protocol to integrate up to 25% of deuterated fatty acids (FAs) in the membranes of ghosts, which are obtained when hemoglobin is removed from RBCs. The FA incorporation and the integrity of the lipid bilayer were confirmed by SS-NMR and fluorescence confocal microscopy. Leakage assays were performed to assess the lytic power of the AMPs. The in situ perturbation of the ghost membranes by aurein 1.2 and caerin 1.1 revealed by 31P and 2H SS-NMR is consistent with a membrane perturbation through a carpet mechanism for aurein 1.2, while caerin 1.1 would act on RBCs via pore formation. These results are compatible with fluorescence microscopy images of the ghosts. The peptides interact with eukaryotic membranes following similar mechanisms that take place in bacteria, thus highlighting the importance of hydrophobicity in determining such interactions. Our work bridges model membranes and in vitro studies and provides an analytical toolbox to assess drug toxicity towards eukaryotic cells.

We share the experience of a clinical relationship that arose between a medical student and a patient hospitalized due to a SARS-CoV-2 pneumonia. The analysis of this experience and the discussion of medical students' possible role in patient care suggest that they should be included as members of the health care team during their clinical practice. This would mean a positive contribution for both the patients' care and the students' learning experience.