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Since the outbreak of COVID-19, it is becoming important to screen SARS-CoV-2 with high accuracy, high efficiency, and rapidness, for epidemic prevention and control. Conventional detection technologies can not satisfy the requirements of examining massive people in a very short time. Biosensor technology, with the advantages of high sensitivity, good selectivity, low cost, easy miniaturization, and short detection time, is being used to develop real-time detection equipment, thus as a potential alternative for real-time detection of SARS-CoV-2 in clinical diagnosis. In the present study, the authors summarized the construction methods and principles for optical biosensors, electrochemical biosensors, wearable biosensors, magnetic biosensors, gold nanoparticle biosensors, and aptamer biosensors, followed by the introduction of the current application of multiple biosensors in SARS-CoV-2 detection. Conclusively, the technical bottlenecks and future development trends of biosensors in SARS-CoV-2 detection are proposed.
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(Aim) To make a more accurate and precise COVID-19 diagnosis system, this study proposed a novel deep rank-based average pooling network (DRAPNet) model, i.e., deep rank-based average pooling network, for COVID-19 recognition. (Methods) 521 subjects yield 1164 slice images via the slice level selection method. All the 1164 slice images comprise four categories: COVID-19 positive;community-acquired pneumonia;second pulmonary tuberculosis;and healthy control. Our method firstly introduced an improved multiple-way data augmentation. Secondly, an n-cony rank-based average pooling module (NRAPM) was proposed in which rank-based pooling-particularly, rank-based average pooling (RAP)-was employed to avoid overfitting. Third, a novel DRAPNet was proposed based on NRAPM and inspired by the VGG network. Grad-CAM was used to generate heatmaps and gave our AI model an explainable analysis. (Results) Our DRAPNet achieved a micro-averaged F1 score of 95.49% by 10 runs over the test set. The sensitivities of the four classes were 95.44%, 96.07%, 94.41%, and 96.07%, respectively. The precisions of four classes were 96.45%, 95.22%, 95.05%, and 95.28%, respectively. The F1 scores of the four classes were 95.94%, 95.64%, 94.73%, and 95.67%, respectively. Besides, the confusion matrix was given. (Conclusions) The DRAPNet is effective in diagnosing COVID-19 and other chest infectious diseases. The RAP gives better results than four other methods: strided convolution, l(2)-norm pooling, average pooling, and max pooling.
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Prior research has undertheorized the profound impact of digital technologies by dwelling on the implications of individual technologies on distinct organizational dimensions to explain the phenomenon of digital transformation. This study adopts a view of digital transformation as organizational change that is facilitated by digital technologies and has the potential to reshape every aspect of an organization. The paper aims to explore the underpinning mechanisms of a Higher Education Institution in managing digital transformation and capitalizing upon the benefits of digital technologies amidst extreme uncertainty. We engage in a qualitative, in-depth case study of a UK-based Higher Education Institution to empirically explore the combination of digital technologies and transfiguration of its organizational dimensions during the Covid-19 pandemic. Our findings highlight three mechanisms: fostering technologies to stay afloat, scaling functionalities to create new value, and justifying value to design change. Our contribution is threefold. First, we capture the conjugation of digital technologies and transfiguration of business processes, strategy, and culture in a paradigmatic case of digital transformation. Second, we articulate the mechanisms through which our case organization managed such transformation. Third, we demonstrate that extreme uncertainty catalyzed rather than hindered digital transformation. © 2023
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Since the outbreak of COVID-19, it is becoming important to screen SARS-CoV-2 with high accuracy, high efficiency, and rapidness, for epidemic prevention and control. Conventional detection technologies can not satisfy the requirements of examining massive people in a very short time. Biosensor technology, with the advantages of high sensitivity, good selectivity, low cost, easy miniaturization, and short detection time, is being used to develop real-time detection equipment, thus as a potential alternative for real-time detection of SARS-CoV-2 in clinical diagnosis. In the present study, the authors summarized the construction methods and principles for optical biosensors, electrochemical biosensors, wearable biosensors, magnetic biosensors, gold nanoparticle biosensors, and aptamer biosensors, followed by the introduction of the current application of multiple biosensors in SARS-CoV-2 detection. Conclusively, the technical bottlenecks and future development trends of biosensors in SARS-CoV-2 detection are proposed. © 2023 Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences.
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Microfluidic biosensors integrating fluid control, target recognition, as well as signal transduction and output, have been widely used in the field of disease diagnosis, drug screening, food safety and environmental monitoring in the past two decades. As the central part and technical characteristics of microfluidic biosensors, the fluid control is not only associated with accuracy and convenience of the sensors, but also affects the material selection and working mode of the sensors. This review summarizes the fluid driving forces for microfluidic biosensors, including gravity, capillary force, centrifugal force, pressure, light, sound, electrical, and magnetic forces. Then, the recent advances in microfluidic biosensors for the detection of viruses, cells, nucleic acids, proteins and small molecules are discussed. Finally, we propose the current challenges and future perspectives of microfluidic biosensors. We hope this review can provide readers with a new perspective to understand the technical characteristics and application potential of microfluidic biosensors.Copyright © 2022 Elsevier B.V.
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The pandemic outbreak of COVID-19 created panic all over the world. As therapeutics that can effectively wipe out the virus and terminate transmission are not available, supportive therapeutics are the main clinical treatments for COVID-19. Repurposing available therapeutics from other viral infections is the primary surrogate in ameliorating and treating COVID-19. The therapeutics should be tailored individually by analyzing the severity of COVID-19, age, gender, comorbidities, and so on. We aim to investigate the effects of COVID-19 therapeutics and to search for laboratory parameters indicative of severity of illness. Multi-center collaboration and large cohort of patients will be required to evaluate therapeutics combinations in the future. This study is a single-center retrospective observational study of COVID-19 clinical data in China. Information on patients' treatment modalities, previous medical records, individual disease history, and clinical outcomes were considered to evaluate treatment efficacy. After screening, 2,844 patients are selected for the study. The result shows that treatment with TCM (Hazard Ratio (HR) 0.191 [95% Confidence Interval (CI), 0.14–0.25];p < 0.0001), antiviral therapy (HR 0.331 [95% CI 0.19–0.58];p = 0.000128), or Arbidol (HR 0.454 [95% CI 0.34–0.60];p < 0.0001) is associated with good prognostic of patients. Multivariate Cox regression analysis showed TCM treatment decreased the mortality hazard ratio by 69.4% (p < 0.0001). © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
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The Corona Virus Disease 2019 (COVID-19) pandemic is still imposing a devastating impact on public health, the economy, and society. Predicting the development of epidemics and exploring the effects of various mitigation strategies have been a research focus in recent years. However, the spread simulation of COVID-19 in the dynamic social system is relatively unexplored. To address this issue, considering the outbreak of COVID-19 at Nanjing Lukou Airport in 2021, we constructed an artificial society of Nanjing Lukou Airport based on the Artificial societies, Computational experiments, and Parallel execution (ACP) approach. Specifically, the artificial society includes an environmental model, population model, contact networks model, disease spread model, and intervention strategy model. To reveal the dynamic variation of individuals in the airport, we first modeled the movement of passengers and designed an algorithm to generate the moving traces. Then, the mobile contact networks were constructed and aggregated with the static networks of staff and passengers. Finally, the complex dynamical network of contacts between individuals was generated. Based on the artificial society, we conducted large-scale computational experiments to study the spread characteristics of COVID-19 in an airport and to investigate the effects of different intervention strategies. Learned from the reproduction of the outbreak, it is found that the increase in cumulative incidence exhibits a linear growth mode, different from that (an exponential growth mode) in a static network. In terms of mitigation measures, promoting unmanned security checks and boarding in an airport is recommended, as to reduce contact behaviors between individuals and staff. © 2021 TUP.
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This paper investigates the mood changes of youth groups during the social closure control of the COVID-19 pan-demic and the primary causes of those changes, taking Chinese online video platforms as an example. We also compare the main concerns of various periods to provide feasible references and suggestions on psychological interventions for young people during the social closure control period. In this study, we identified mood changes during the COVID-19 pandemic with 31,213 comments on the news videos of the Bilibili video platform through four stages: data collection, data processing, LDA topic modeling, and mood identification. Through a comparative analysis, we investigated the topical features of young people's mood changes in three COVID-19 periods: pre-, mid-, and late-epidemic. As a result, we found that social isolation measures such as closure and homeschooling with long-term Internet use during the epidemic were more likely to cause depression in young people. © 2023 IEEE.
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Public facilities are important transmission places for respiratory infectious diseases (e.g., COVID-19), due to the frequent crowd interactions inside. Usually, changes of obstacle factors can affect the movements of human crowds and result in different epidemic transmissions among individuals. However, most related studies only focus on the specific scenarios, but the common rules are usually ignored for the impacts of obstacles' spatial elements on epidemic transmission. To tackle these problems, this study aims to evaluate the impacts of three spatial factors of obstacles (i.e., size, quantity, and placement) on infection spreading trends in two-dimension, which can provide scientific and concise spatial design guidelines for indoor public places. Firstly, we used the obstacle area proportion as the indicator of the size factor, gave the mathematical expression of the quantity factor, and proposed the walkable-space distribution indicator to represent the placement factor by introducing the Space Syntax. Secondly, two spreading epidemic indicators (i.e., daily new cases and people's average exposure risk) were estimated based on the fundamental model named exposure risk with the virion-laden particles, which accurately forecasted the disease spreading between individuals. Thirdly, 120 indoor scenarios were built and simulated, based on which the value of independent and dependent variables can be measured. Besides, structural equation modeling was employed to examine the effects of obstacle factors on epidemic transmissions. Finally, three obstacle-related guidelines were provided for policymakers to mitigate the disease spreading: minimizing the size of obstacles, dividing the obstacle into more sub-ones, and placing obstacles evenly distributed in space. © 2022 Elsevier Ltd
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In recent years, chest X-ray (CXR) imaging has become one of the significant tools to assist in the diagnosis and treatment of novel coronavirus pneumonia. However, CXR images have complex-shaped and changing lesion areas, which makes it difficult to identify novel coronavirus pneumonia from the images. To address this problem, a new deep learning network model (BoT-ViTNet) for automatic classification is designed in this study, which is constructed on the basis of ResNet50. First, we introduce multi-headed self-attention (MSA) to the last Bottleneck block of the first three stages in the ResNet50 to enhance the ability to model global information. Then, to further enhance the feature expression performance and the correlation between features, the TRT-ViT blocks, consisting of Transformer and Bottleneck, are used in the final stage of ResNet50, which improves the recognition of complex lesion regions in CXR images. Finally, the extracted features are delivered to the global average pooling layer for global spatial information integration in a concatenated way and used for classification. Experiments conducted on the COVID-19 Radiography database show that the classification accuracy, precision, sensitivity, specificity, and F1-score of the BoT-ViTNet model is 98.91%, 97.80%, 98.76%, 99.13%, and 98.27%, respectively, which outperforms other classification models. The experimental results show that our model can classify CXR images better. © 2022 by the authors.
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Bovine respiratory disease complex (BRDC) involves multiple pathogens, shows diverse lung lesions, and is a major concern in calves. Pathogens from 160 lung samples of dead cattle from 81 cattle farms in northeast China from 2016 to 2021 were collected to characterize the molecular epidemiology and risk factors of BRDC and to assess the major pathogens involved in bovine suppurative or caseous necrotizing pneumonia. The BRDC was diagnosed by autopsy, pathogen isolation, PCR, or reverse transcription-PCR detection, and gene sequencing. More than 18 species of pathogens, including 491 strains of respiratory pathogens, were detected. The positivity rate of bacteria in the 160 lung samples was 31.77%, including Trueperella pyogenes (9.37%), Pasteurella multocida (8.35%), Histophilus somni (4.48%), Mannheimia haemolytica (2.44%), and other bacteria (7.13%). The positivity rate of Mycoplasma spp. was 38.9%, including M. bovis (7.74%), M. dispar (11.61%), M. bovirhinis (7.94%), M. alkalescens (6.11%), M. arginini (0.81%), and undetermined species (4.68%). Six species of viruses were detected with a positivity rate of 29.33%, including bovine herpesvirus-1 (BoHV-1; 13.25%), bovine respiratory syncytial virus (BRSV; 5.50%), bovine viral diarrhea virus (BVDV; 4.89%), bovine parainfluenza virus type-3 (BPIV-3; 4.28%), bovine parainfluenza virus type-5 (1.22%), and bovine coronavirus (2.24%). Mixed infections among bacteria (73.75%), viruses (50%), and M. bovis (23.75%) were the major features of BRDC in these cattle herds. The risk analysis for multi-pathogen co-infection indicated that BoHV-1 and H. somni; BVDV and M. bovis, P. multocida, T. pyogenes, or Mann. haemolytica; BPIV-3 and M. bovis; BRSV and M. bovis, P. multocida, or T. pyogenes; P. multocida and T. pyogenes; and M. bovis and T. pyogenes or H. somni showed co-infection trends. A survey on molecular epidemiology indicated that the occurrence rate of currently prevalent pathogens in BRDC was 46.15% (6/13) for BoHV-1.2b and 53.85% (7/13) for BoHV-1.2c, 53.3% (8/15) for BVDV-1b and 46.7% (7/15) for BVDV-1d, 29.41% (5/17) for BPIV-3a and 70.59% (12/17) for BPIV-3c, 100% (2/2) for BRSV gene subgroup IX, 91.67% (33/36) for P. multocida serotype A, and 8.33% (3/36) for P. multocida serotype D. Our research discovered new subgenotypes for BoHV-1.2c, BRSV gene subgroup IX, and P. multocida serotype D in China's cattle herds. In the BRDC cases, bovine suppurative or caseous necrotizing pneumonia was highly related to BVDV [odds ratio (OR) = 4.18; 95% confidence interval (95% CI): 1.6-10.7], M. bovis (OR = 2.35; 95% CI: 1.1-4.9), H. somni (OR = 8.2; 95% CI: 2.6-25.5) and T. pyogenes (OR = 13.92; 95% CI: 5.8-33.3). The risk factor analysis found that dairy calves <3 mo and beef calves >3 mo (OR = 5.39; 95% CI: 2.7-10.7) were more susceptible to BRDC. Beef cattle were more susceptible to bovine suppurative or caseous necrotizing pneumonia than dairy cattle (OR = 2.32; 95% CI: 1.2-4.4). These epidemiological data and the new pathogen subgenotypes will be helpful in formulating strategies of control and prevention, developing new vaccines, improving clinical differential diagnosis by necropsy, predicting the most likely pathogen, and justifying antimicrobial use.
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OBJECTIVE: Transplant recipients have a higher risk of SARS-CoV-2 infection owing to the use of immunosuppressive drugs like tacrolimus (FK506). FK506 and nirmatrelvir (NMV) (an anti-SARS-CoV-2 drug) are metabolized by cytochrome P450 3A4 and may have potential drug-drug interactions. It is important to determine the effect of NMV on FK506 concentrations. PATIENTS AND METHODS: Following protein precipitation from blood, FK506 and its internal standard (FK506-13C,2d4) were detected by ultra-high performance liquid chromatography/tandem mass spectrometry (UHPLC-MS/MS). Total 22 blood samples (valley concentrations) from two coronavirus disease 2019 (COVID-19) patients were collected and analyzed for FK506 concentrations. RESULTS: Blood levels of FK506 (0.5-100 ng/mL) showed good linearity. The UHPLC-MS/MS method was validated with intra- and inter-batch accuracies of 104.55-107.85%, and 99.52-108.01%, respectively, and precisions of < 15%. Mean blood FK506 concentration was 12.01 ng/mL (range, 3.15-33.1 ng/mL). Five-day co-administration with NMV increased the FK506 concentrations from 3.15 ng/mL to 33.1 ng/mL, returning to 3.36 ng/mL after a 9-day-washout. CONCLUSIONS: We developed a simple quantification method for therapeutic drug monitoring of FK506 in patients with COVID-19 using UHPLC-MS/MS with protein precipitation. We found that NMV increased FK506 blood concentration 10-fold. Therefore, it is necessary to re-consider co-administration of FK506 with NMV.
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COVID-19 , Tacrolimus , Humans , Chromatography, High Pressure Liquid/methods , Tandem Mass Spectrometry/methods , SARS-CoV-2 , Lactams , Leucine , Reproducibility of Results , Drug MonitoringABSTRACT
Aim/Introduction: This study is designed to assess the therapeutic response and safety of the approximately 2.0 GBq177Lu-EB-PSMA-617 radioligand therapy (RLT) in patients with metastatic castrationresistant prostate cancer (mCRPC). Material(s) and Method(s): With institutional review board approval and informed consent, 45 patients with mCRPC underwent screening68Ga-PSMA and 18F-FDG PET/CT to confirm high PSMA expression. 30 patients were eligible for treatment and they received up to 3 cycles of intravenous177Lu- EB-PSMA-617 RLT with a mean dosage of 2.0 GBq (range: 1.8-2.2 GBq)/cycle, at 8-10 weekly intervals. The primary endpoint was PSA response according to Prostate Cancer Clinical Trial Working Group criteria and toxicity according to CTCAE. Result(s): After the 1st cycle of therapy, decline in the PSA value from baseline was observed in 21 (70.0%) patients. Of them, 10 (33.3%) patients reached PSA decline of 50% or more. Then, 22 patients accepted 2nd cycle of therapy, 15 (68.2%) patients showed a PSA value decline from baseline and 12 (54.5%) patients revealed PSA decline of 50% or more. Due to disease progression and the COVID-19 pandemic, however, only 11 patients accepted 3rd cycles of177Lu-EB-PSMA-617 RLT as schedule. Of them, 8 (72.7%) demonstrated PSA decline, and 5 (45.5%) patients achieved PSA decline of 50% or more. Among the 30 patients with median 2-cycle treatments, 13 (43.3%) patients achieved PR, 8 (26.7%) patients showed SD and 9 (30.0%) patients exhibited PD, and PSA progression occurred in 30 patients with median PSA progression-free survival of 3.2 months (95% CI 1.8-4.9). The most common toxic effects related to177Lu-EB-PSMA-617 were grade 1 dry mouth recorded in 12 (40.0%) patients, grade 1 and 2 transient fatigue in 11 (36.7%) patients. Grade 3 anemia, leucopenia or thrombocytopenia occurred in 9 (30.0%) patients and there were no G4 myelosuppression events. Conclusion(s): Our findings show that RLT with approximately 2.0 GBq177Lu-EB-PSMA-617 has good responses and acceptable toxic effects in men with mCRPC.
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Internet content providers often deliver content through bandwidth bottlenecks that are out of their control. Thus, despite often having massively over-provisioned upstream servers, the content providers still cannot control the end-to-end user experience. This paper explores remote traffic shaping, allowing the content provider to allocate its share of a remote bottleneck link across its users using a metric other than TCP fairness, while remaining TCP-friendly to cross traffic on the bottleneck link. To evaluate this approach, we designed FlowTele, the first system that shapes outbound traffic on an Internet-scale network to optimize provider-selected metrics, using source control with neither in-network support nor special client support. Our extensive evaluations over the Internet show that by strategically reallocating bandwidth among provider-owned co-bottlenecked flows, FlowTele improves the provider's total revenue by roughly 20% - 30% in various network settings, compared with both (i) status quo TCP fairshare and (ii) recent practice by content providers that proactively throttles video quality during the COVID-19 pandemic, while being TCP-friendly to cross-traffic. Besides revenue, we also study other metrics, such as QoE fairness, that a content provider may wish to optimize using FlowTele. © 2022 Owner/Author.
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At the end of 2019,a new coronavirus suddenly broke out all over the world.To date, there is still no targeted medicine available for the treatment of this disease. Vaccineis essential for controlling the epidemicofSARS-CoV-2. But the effective ofvaccine was reduced because of the SARS-CoV-2constant mutation. It is gratifying that scientistuncover theinfection mechanisms of the SARS-CoV-2. The main protease of SARS-CoV-2 is highly conserved and plays an important role of the life cycle of virus. Therefore, we executed virtual screening on the FDA-approved database and hoped to find a potential candidate against the main protease. As a result, we obtained eight available active compounds derived from the database through molecular dynamics simulations. As antiviral treatment candidates, the drugs can also be used to clinical emergencies. © 2022 SPIE. All rights reserved.
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Medical professionals have started favoring the use of non-contact intravenous injection robots owing to their importance during the COVID-19 outbreak. However, there are currently few studies considering the robot's needle insertion angle, and most of the needle insertion operations are performed at a steep angle. This increases the rate of puncture failure, and sometimes causes significant pain in patients depending on their individual differences. Therefore, the intravenous injection of the dorsal hand is performed in this study to investigate the determination of the robot's needle insertion angle. with a focus on the optimization of the measurement data to ensure accuracy in the calculation of the needle insertion angle. First, the space point cloud of the needle insertion area on the dorsal hand is obtained by combining a monocular camera with the linear structured light scanning method , and the dorsal hand plane is obtained via fitting dorsal hand point clouds using the least squares method. During the calibration process for the linear structured light system , the measurement error is eliminated by formulating an error function and using the optimization method to iteratively solve it. Subsequently. the needle insertion angle is determined based on the obtained needle insertion area plane. Finally, experiments are conducted for the accuracy verification of the proposed method. Based on the experimental results, the average error in the optimized structured light plane position is approximately 0. 1 mm, and this serves as a foundation for subsequent automatic injection studies.
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Background: We aimed to evaluate the mortality of eight glucose-lowering therapies for COVID-19 patients with diabetes prior to diagnosis of COVID-19. Methods: We searched PubMed, Embase, Cochrane Central Register, Web of Science, and ClinicalTrials.gov through June 2021. COVID-19 patients with diabetes while receiving glucose-lowering therapies for at least 14 days prior to COVID-19 confirmed were included. The Newcastle Ottawa scale (NOS) was used to assess the risk of bias in nonrandomized studies. Bayesian network meta-analyses were performed. Results: Eleven distinct observational studies (3,631,682 COVID-19 patients with diabetes mellitus) were included. Compared with insulin, DPP4i, secretagogues, glucosidase inhibitors, and thiazolidinediones, the incidence of adverse outcomes in diabetics who took SGLT2i was relatively lower: OR 0.30 (95% CrI 0.17-0.55);0.42 (0.24-0.83);0.43 (0.24-0.83);0.32 (0.16-0.70);0.47 (0.23-0.95). The SUCRA value of SGLT2i was the lowest (1.8%), followed by GLPIRA (22.1%) and biguanides (33.3%). Conclusion: SGLT2I may be an optimal choice for diabetics before COVID-19 infection. GLP1RA and guanidine can also be a good choice for the protection of diabetics during COVID-19 pandemic times.Presentation: No date and time listed
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In an uncertain and escalating risk period resulting from the prolonged pandemic crisis, this study aimed to identify the dimensional nature of online travel agencies' (OTAs) website credibility, and empirically investigate the effects of its components on attitude and behavioral intentions. This study was conducted by collecting 559 questionnaires from mainland Chinese OTA users in the middle of the COVID-19 pandemic. The data analyses showed that OTA website credibility comprised six components. Other proposed paths, with the exception of four, were significant at the .05 or .001 level. Interestingly, the paths between content credibility and attitude toward the OTA and between content credibility and loyalty to the OTA were not significant. However, overall, it was confirmed that OTA website credibility determined attitude toward the OTA and loyalty to the OTA, which led to behavioral intention.
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The lockdown policy, resulted from the fight against the spread of Covid-19, offers a unique chance to test the effect of urban human activities on air quality. The study employs the event study method to analyze data on air quality (particulate matter 2.5 (PM2.5) and Air Quality Index (AQI)) levels and economic activity (vehicle sales growth, construction area, industrial added value, crude oil production) from 31 major cities across China. It aims to examine whether the lockdown event affects economic activities, which in turn has an effect on air quality. The findings suggest that the lockdown has a significant positive effect on air quality nationwide. The air quality has been greatly improved during the lockdown period. The placebo test verifies that when there is no lockdown, the changes in air quality over the same period are smaller. The study results will help understand the factors of economic activity that have a significant impact on air quality during the lockdown period and provide a reference for the government to formulate corresponding air pollution control measures. © 2022 The authors and IOS Press.
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We consider a novel partially linear additive functional regression model in which both a functional predictor and some scalar predictors appear. The functional part has a semiparametric continuously additive form, while the scalar predictors appear in the linear part. The functional part has the optimal convergence rate, and the asymptotic normality of the nonfunctional part is also shown. Simulations and an empirical analysis of a Covid-19 data set demonstrate the performance of the proposed estimator.