Effects of endoscopic reprocessing on disinfection and its influential factors during coronavirus disease 2019 pandemic.

Objective To explore the effects of endoscopic reprocessing on disinfection and its influential factors under the coronavirus disease 2019 (COVID-19) pandemic. Methods A total of 450 endoscopes cleaned and disinfected according to Technical Specifications for Cleaning and Disinfection of Endoscopes from November 2019 to January 2020, and 450 endoscopes cleaned and disinfected according to The recommended procedure for cleaning and disinfection of gastrointestinal endoscopes during COVID-19 epidemic by Chinese Society of Digestive Endoscopology from February to April 2020 in the Second Affiliated Hospital of Chongqing Medical University were enrolled in the control group and observation group respectively by random number method. Both the control group and the observation group contained 200 gastroscopes, 200 enteroscopes and 50 ultrasound endoscopes. ATP fluorescence detection method and pour plate technique were used to evaluate the disinfection effect of endoscopes. Single factor analysis and multiple logistic regression were used to analyze the risk factors for unqualified sterilization after endoscopic reprocessing. Results The disinfection pass rates of gastroscopes, enteroscopes and ultrasound endoscopes in the observation group were not significantly different compared with those of the control group (P>0. 05). The sterilization pass rates and ATP test pass rates of gastroscopes, enteroscopes and ultrasound endoscopes in the observation group were significantly higher than those in the control group (all P<0. 05). Multivariate logistic regression analysis showed that non-strict implementation of endoscopic reprocessing (OR = 7. 96, 95%CI: 4. 55-22. 84, P<0. 001), non-standard operation (OR = 2. 26, 95%CI: 1. 24-5. 63, P<0. 001), insufficient concentration of disinfectant (OR = 5. 43, 95% CI: 2. 52-9. 02, P < 0. 001), insufficient concentration ratio of multi-enzyme solution (OR = 4. 38, 95% CI: 1. 95-8. 61, P < 0. 001), non-timely cleaning (OR= 2. 86, 95%CI: 1. 33-6. 42, P<0. 001), incomplete cleaning (OR = 3. 75, 95%CI: 1. 61-7. 49, P<0. 001) and improper endoscopic preservation (OR= 2. 12, 95%CI: 1. 36-4. 12, P<0. 001) were independent risk factors for unqualified sterilization after endoscopic reprocessing. Conclusion In COVID-19 pandemic, endoscope reprocessing can significantly improve the disinfection effect of endoscopes, worthy of further clinical promotion. The failure to strictly implement the reprocessing procedure is an important factor that may lead to unqualified sterilization.Copyright © 2021 The authors.

Comparison of Comorbidities between Adults and Children with Glycogen Storage Disease Type Ia (GSDIA)

Objectives: Glycogen Storage Disease Type Ia (GSDIa) is a rare inherited disorder resulting in acute hypoglycemia due to impaired release of glucose from glycogen. Despite dietary management practices to prevent hypoglycemia in patients with GSDIa, complications still occur in children and throughout adulthood. This retrospective cohort study compared the prevalence of complications in adults and children with GSDIa. Method(s): Using ICD-10 diagnosis codes, the IQVIA Pharmetrics Plus database was searched for patients with >=2 GSDI claims (E74.01) from January 2016 through February 2020, with >=12 months continuous enrollment beginning prior to March 2019 (for one year of follow-up before COVID-19), and no inflammatory bowel disease diagnoses (indicative of GSDIb). Complication prevalence in adults and children with GSDIa was summarized descriptively. Result(s): In total, 557 patients with GSDIa were identified (adults, 67%;male, 63%), including 372 adults (median age, 41 years) and 185 children (median age, 7 years). Complications occurring only in adults were atherosclerotic heart disease (8.6%), pulmonary hypertension (3.0%), primary liver cancer (1.9%), dialysis (0.8%), and focal segmental glomerulosclerosis (0.3%). Other complications with the greatest prevalence in adults/children included gout (11.8%/0.5%), insomnia (10.0%/1.1%), osteoarthritis (22.0%/2.7%), severe chronic kidney disease (4.3%/0.5%), malignant neoplasm (10.8%/1.6%), hypertension (49.7%/8.7%), acute kidney failure (15.3%/2.7%), pancreatitis (3.0%/0.5%), gallstones (7.8%/1.6%), benign neoplasm (37.4%/8.1%), hepatocellular adenoma (7.0%/1.6%), neoplasm (41.1%/9.7%), and hyperlipidemia (45.2%/10.8%). Complications with the greatest prevalence in children/adults included poor growth (22.2%/1.9%), gastrostomy (29.7%/3.2%), kidney hypertrophy (2.7%/0.8%), seizure (1.6%/0.5%), hypoglycemia (27.0%/11.3%), hepatomegaly (28.7%/15.9%), kidney transplant (1.6%/1.1%), diarrhea (26.5%/18.6%), nausea and/or vomiting (43.8%/35.8%), acidosis (20.0%/17.2%), and anemia due to enzyme disorders (43.8%/40.6%). Conclusion(s): GSDIa is associated with numerous, potentially serious complications. Compared with children, adults with GSDIa had a greater prevalence of chronic complications, potentially indicating the progressive nature of disease. Children with GSDIa had more acute complications related to suboptimal metabolic control.Copyright © 2023

Updated efficacy and safety of taletrectinib in patients (pts) with ROS1+ non-small cell lung cancer (NSCLC)

Background Taletrectinib is a potent, next-generation, CNS-active, ROS1 tyrosine kinase inhibitor (TKI) with selectivity over TRKB. In previous reports from TRUST-I, taletrectinib showed meaningful clinical efficacy and was well tolerated in pts with ROS1+ NSCLC (n = 109) regardless of crizotinib (CRZ) pretreatment status. We report updated efficacy and safety data with ~1.5 yr follow-up. Methods TRUST-I is a multicenter, open-label, single-arm study with two cohorts: ROS1 TKI-naive and CRZ-pretreated. Pts in both cohorts received taletrectinib 600 mg QD. Key study endpoints included IRC-confirmed ORR (cORR), DoR, disease control rate (DCR), PFS, and safety. A pooled analysis of ORR, PFS, and safety including pts from additional clinical trials was also conducted. Results In the 109 pts from TRUST-I (enrolled prior to Feb 2022) the median follow-up was 18.0 mo in TKI-naive (n = 67) and 16.9 mo in CRZ-pretreated pts (n = 42). cORR was 92.5% in TKI-naive and 52.6% in CRZ-pretreated pts (table). Median DoR (mDoR) and mPFS were not reached. Intracranial-ORR was 91.6%;ORR in pts with G2032R was 80.0%. In a pooled analysis with phase I studies, ORR was 89.5% and 50.0% for TKI-naive and CRZ-pretreated pts, respectively;mPFS was 33.2 mo and 9.8 mo. In 178 pts treated at 600 mg QD, treatment-emergent adverse events (TEAEs) were 92.7%;most (64.0%) were grade 1-2. The most common TEAEs were increased AST (60.7%), increased ALT (55.6%), and diarrhea (55.6%). Neurological TEAEs (dizziness, 18.5%;dysgeusia, 12.4%) and discontinuations due to TEAEs (3.4%) were low. Further updated results will be presented. [Formula presented] Conclusions With additional follow-up, taletrectinib continued to demonstrate meaningful efficacy outcomes including high response rates, prolonged PFS, robust intracranial activity, activity against G2032R, and tolerable safety with low incidence of neurological AEs. Clinical trial identification NCT04395677. Editorial acknowledgement Medical writing and editorial assistance were provided by Arpita Kulshrestha of Peloton Advantage, LLC, an OPEN Health company, and funded by AnHeart Therapeutics, Inc Legal entity responsible for the study AnHeart Therapeutics, Inc. Funding AnHeart Therapeutics, Inc. Disclosure S. He: Financial Interests, Personal, Other, Employment: AnHeart Therapeutics. T. Seto: Financial Interests, Institutional, Research Grant: AbbVie, Chugai Pharmaceutical, Daiichi Sankyo, Eli Lilly Japan, Kissei Pharmaceutical, MSD, Novartis Pharma, Pfizer Japan, Takeda Pharmaceutical;Financial Interests, Personal, Other, Employment: Precision Medicine Asia;Financial Interests, Personal, Speaker's Bureau, Honoraria for lectures: AstraZeneca, Bristol-Myers Squibb, Chugai Pharmaceutical, Covidien Japan, Daiichi Sankyo, Eli Lilly Japan, Kyowa Hakko Kirin, MSD, Mochida Pharmaceutical, Nippon Boehringer Ingelheim, Novartis Pharma, Ono Pharmaceutical, Pfizer Japan, Taiho Pharmaceutical, Takeda Pharmaceutical, Towa Pharmaceutical. C. Zhou: Financial Interests, Personal, Other, Consulting fees: Innovent Biologics Qilu, Hengrui, TopAlliance Biosciences Inc;Financial Interests, Personal, Speaker's Bureau, Payment or honoraria: Eli Lilly China, Sanofi, BI, Roche, MSD, Qilu, Hengrui, Innovent Biologics, C-Stone LUYE Pharma, TopAlliance Biosciences Inc, Amoy Diagnositics, AnHeart. All other authors have declared no conflicts of interest.Copyright © 2023 International Association for the Study of Lung Cancer. Published by Elsevier Inc.

SAFER framework for moving forward on the medical device right to repair

Across various industries, the right to repair (RTR) movement has gained momentum as more than 20 states have proposed RTR laws to expand access to repair of consumer products. Medical device equipment shortages during the COVID-19 pandemic demonstrated that stronger repair mechanisms are necessary for the US health system to become more efficient, affordable and sustainable. We propose a 5-point SAFER framework including safety and security, adaptability, fiscal, environmental and regulatory factors for consideration in implementing medical device RTR. The healthcare community can help advance RTR legislation in a manner that serves our patients and healthcare system best.Copyright © 2023 BMJ Publishing Group. All rights reserved.

MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs

The medical conversational system can relieve doctors' burden and improve healthcare effi-ciency, especially during the COVID-19 pan-demic. However, the existing medical dialogue systems have die problems of weak scalability, insufficient knowledge, and poor controlla-bility. Thus, we propose a medical conversa-tional question-answering (CQA) system based on the knowledge graph, namely MedConQA, which is designed as a pipeline framework to maintain high flexibility. Our system utilizes automated medical procedures, including medi-cal triage, consultation, image-text drug recom-mendation, and record. Each module has been open-sourced as a tool, which can be used alone or in combination, with robust scalability. Besides, to conduct knowledge-grounded dia-logues with users, we first construct a Chinese Medical Knowledge Graph (CMKG) and col-lect a large-scale Chinese Medical CQA (CM-CQA) dataset, and we design a series of meth-ods for reasoning more intellectually. Finally, we use several state-of-the-art (SOTA) tech-niques to keep the final generated response more controllable, which is further assured by hospital and professional evaluations. We have open-sourced related code, datasets, web pages, and tools, hoping to advance future research. © 2022 Association for Computational Linguistics.

Unraveling the dynamic Airbnb-gentrification interrelation before and after the COVID-19 Pandemic: Evidence from Beijing, China

Arising as an efficient and flexible model of the rental business amidst the rising asset economy, short-term-rental (STR) platforms such as Airbnb are prevalent globally and have induced neighborhood changes in many aspects. Debates on Airbnb-induced gentrification concern scholars and policymakers worldwide. Nonetheless, most existing studies consider it a unidirectional process, and the dynamic interactions and mutual influence between Airbnb and gentrification remain unexamined. To address this salient lacuna, this study unravels the changing dynamic of Airbnb-gentrification interactions in central Beijing during the COVID-19 pandemic. Through matching housing transaction records in the secondary market and Airbnb's data, we develop two indexes and employ a series of regression models, as well as difference-in-difference estimation to unravel the variegated Airbnb-gentrification patterns, their interrelation, and the impacts brought by the pandemic. Results reveal a general pattern of intensifying gentrification caused by clustering Airbnb. Meanwhile, in neighborhoods experiencing different stages of gentrification, heterogeneous outcomes of Airbnb development are unveiled concerning impacts on rentals and housing prices during the pandemic. Our findings provide a more nuanced understanding of the dynamic Airbnb-gentrification interrelation and add to the ongoing debates on "fifth-wave gentrification”. © 2023 Elsevier Ltd

Distinct Laboratory Test Result Profiles Between SARS-CoV-2 and Seasonable Influenza Infected Patients.

OBJECTIVE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza viruses are contagious respiratory pathogens with similar symptoms but require different treatment and management strategies. This study investigated the differences in laboratory test result profiles between SARS-CoV-2 and influenza infected patients upon presentation to emergency department (ED). METHODS: Laboratory test results and demographic information from 723 influenza positive (2018/1/1 to 2020/3/15) and 1,281 SARS-CoV-2 positive (2020/3/11 to 2020/6/30) ED patients were retrospectively analyzed. The dataset was randomly divided into a training/validation set (2/3) and a test set (1/3) with the same SARS-CoV-2/influenza ratio. Four machine learning models in differentiating the laboratory profiles of RT-PCR confirmed SARS-CoV-2 and influenza positive patients were evaluated. The Shapley Additive Explanations technique was employed to visualize the impact of laboratory tests on the overall differentiation. Furthermore, the model performance was also evaluated in a new test dataset including 519 SARS-CoV-2 ED patients (2020/12/1 to 2021/2/28) and the previous influenza positive patients (2018/1/1 to 2020/3/15). RESULTS: A laboratory test result profile consisting of 15 blood tests, together with patient age, gender, and race can discriminate the two types of viral infections using a random forest (RF) model. The RF model achieved an area under the receiver operating characteristic curve (AUC) of 0.90 in the test set. Among the profile of 15 laboratory tests, the serum total calcium level exhibited the greatest contribution to the overall differentiation. Furthermore, the model achieved an AUC of 0.81 in a new test set. CONCLUSION: We developed a laboratory tests-based RF model differentiating SARS-CoV-2 from influenza, which may be useful for the preparedness of overlapping COVID-19 resurgence and future seasonal influenza.

Leveraging Human Mobility Data for Efficient Parameter Estimation in Epidemic Models of COVID-19

Effectively predicting the evolution of COVID-19 is of great significance to contain the pandemic. Extensive previous studies proposed a great number of SIR variants, which are efficient to capture the transmission characteristics of COVID-19. However, the parameter estimation methods in previous studies are based on data from epidemiological investigations, which inevitably have caused a large delay. The popularity of digital trajectory data world-wide makes it possible to understand epidemic spreading from human mobility perspective. The major advantage of digital trajectory data lies in that the co-location level of a population is reflected at every moment, making it possible to forecast the evolution in advance. We showed that the mobility data contributed by mobile phone users could be exploited to estimate the contact probability between individuals, thus revealing the dynamic transmission of COVID-19. Specifically, we developed an estimation method to obtain human co-location levels and quantified the variations of human mobility during the epidemic. Then, we extended the infection rate with a real-time co-location level to further forecast the transmission of an epidemic, predicting the epidemic size much more accurately than conventional methods. Finally, the proposed method was applied to evaluate the quantitative effect of different non-pharmacological interventions by predicting the epidemic situations with various mobility characteristics. The empirical results and simulations corroborated our theoretical analysis, providing effective guidance to contain the pandemic. IEEE

Secure and Efficient Agreement Signing Atop Blockchain and Decentralized Identity

In recent years, we have witnessed an exorbitant growth of online interactions, especially during the pandemic of COVID-19, which requires diversified digital agreements in different application scenarios. In essence, multi-party agreement signing (MPAS) can be reckoned as a special case of the multi-party fair exchange (MPFE) for signatures over a digital agreement. However, such general MPAS protocols have not seen wide adoptions in practice, possibly due to the lack of incentives to maintain the signing platform. Practically, monetary-incentivized enterprises exist to act as a trusted third party (TTP) and provide signing services. However, such an agreement signing flow still suffers from certain limitations in terms of insecure and inefficient operations. To this end, we propose a secure and efficient framework for multi-party agreement signing based on decentralized identity, blockchain and decentralized storage network (DSN). The framework consists of two subsystems where the identity subsystem contains an extensible three-tier user identity model atop decentralized identity, and the agreement signing subsystem, empowered by the identity subsystem, DSN and several novel designs, achieves security and efficiency design goals. For the convenience of explanation, we suppose that a centralized signing service provider (SSP) is properly involved, i.e., sensitive information is still protected against the SSP, and acts as a TTP to ensure the crucial properties such as fairness and coordinate the signing activities. Our design is also compatible with other methods, e.g., ensuring fairness via blockchain, which removes the single point of failure of SSP. We prototype the framework and the performance evaluation shows effectiveness and efficiency. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Analysis of Influencing Factors of Perceived Stress among Frontline Nurses under the Background of COVID-1 9

To explore the influencing factors of perceived stress among frontline nurses during COVID-1 9, and the role of positive psychological capital in anxiety sensitivity and stress perception, so as to provide evidence for reducing the perceived stress of frontline nurses. From December 2021 to January 2022, 475 frontline nurses from 8 hospitals in X city were investigated by general data questionnaire, positive psychological capital, anxiety sensitivity index and perceived stress scale. The results showed that there were significant differences in nurses’ perceived stress in age, working years, professional title and salary. Perceived stress was positively correlated with anxiety sensitivity, negatively correlated with positive psychological capital, and anxiety sensitivity was negatively correlated with positive psychological capital. Anxiety sensitivity can not only directly predict perceived stress, but also negatively affect perceived stress through positive psychological capital. Therefore, during the epidemic prevention and control in COVID-1 9, nurses can obtain effective stress coping strategies by strengthening their positive psychological capital, thus reducing their anxiety and perceived stress, and better providing high-quality nursing services for patients. © 2022, Editorial department of Chinese Medical Ethics. All rights reserved.

Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile

Background New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.

Work In Progress: Reversi: A Platform for Teaching Programming Languages

This paper introduces a new set of infrastructures and online interactive tools that can be employed to motivate students to learn programming languages. The tools were used to experiment in one of the introductory first-year engineering courses. The final project of the course requires implementing an AI program for a game called “Reversi''. Reversi is a medium to hard level programming project that has been used in the course for several years requiring an immediate restructuring. Furthermore, due to COVID-19 and the restriction of in-person teaching, it has been a challenge for educators to excite, support, and encourage students. The new infrastructure provided an interactive platform for the students to familiarize themselves with the Reversi game project. It also provided a leaderboard, an interactive scoreboard, allowing students to compete with their classmates. The tools can instantaneously synchronize to students' code submission to help students check their latest ranking among their classmates in real-time. This increased students' level of engagement and learning. In addition, it allowed students to collaborate with their fellow classmates and discuss their algorithms. The tools and platform developed can also be employed in other courses as well other programming games. The result from students' surveys and the active trend of the class online discussion forum indicates that the new online interactive system created a positive atmosphere and increased students' motivation in learning programming languages. © American Society for Engineering Education, 2022.

[Progress in the study of antiviral activity of cepharanthine against SARS-CoV-2].

As a member of the dibenzyl isoquinoline alkaloid family, cepharathine is an alkaloid from the traditional Chinese medicine cepharathine, which is mainly used for treatment of leukopenia and other diseases. Recent studies of the inhibitory effect of cepharathine against SARS-CoV-2 have attracted widespread attention and aroused heated discussion. As the original discoverer of the anti-SARS-CoV-2 activity of cepharanthine, here we briefly summarize the discovery of cepharanthine and review important progress in relevant studies concerning the discovery and validation of anti-SARS-CoV-2 activity of cepharathine, its antiviral mechanisms and clinical trials of its applications in COVID-19 therapy.

Integrative metabolomic and proteomic signatures define clinical outcomes in severe COVID-19.

The coronavirus disease-19 (COVID-19) pandemic has ravaged global healthcare with previously unseen levels of morbidity and mortality. In this study, we performed large-scale integrative multi-omics analyses of serum obtained from COVID-19 patients with the goal of uncovering novel pathogenic complexities of this disease and identifying molecular signatures that predict clinical outcomes. We assembled a network of protein-metabolite interactions through targeted metabolomic and proteomic profiling in 330 COVID-19 patients compared to 97 non-COVID, hospitalized controls. Our network identified distinct protein-metabolite cross talk related to immune modulation, energy and nucleotide metabolism, vascular homeostasis, and collagen catabolism. Additionally, our data linked multiple proteins and metabolites to clinical indices associated with long-term mortality and morbidity. Finally, we developed a novel composite outcome measure for COVID-19 disease severity based on metabolomics data. The model predicts severe disease with a concordance index of around 0.69, and shows high predictive power of 0.83-0.93 in two independent datasets.

AIR QUALITY ASSESSMENT OF IMMIGRANT RURAL COMMUNITIES BASED ON THE THEORY OF HEALTHY AND SUSTAINABLE DEVELOPMENT - A CASE STUDY OF IMMIGRANT COMMUNITIES IN HUBEI, CHINA

In the context of the global outbreak of COVID-19, health issues have attracted worldwide attention. Building a healthy ecological environment is particularly important for human beings, and among the ecological environmental factors, air quality is particularly prominent. The study takes the air quality of newly-built immigrant relocation communities in Western China as the research object, and adopts a number of technical methods, such as professional laboratory test report, instrument test, calculation test and so on. Obtain the data of regional ambient air quality and building indoor air quality, and comprehensively judge the regional environment and building ventilation efficiency of the experimental point. So as to comprehensively grade the air quality of the experimental point. A number of technologies and methods are studied and integrated to form a comprehensive three-dimensional air quality detection technology integration. From the perspective of air quality inspection, provide technical support for the healthy and sustainable development of relocated new rural communities. It is of great practical significance to supervise and urge the construction of a healthy and sustainable new township village.

Head Pose Estimation in Complex Environment Based on Four-branch Feature Selective Extraction and Regional Information Exchange Fusion Network

Under the severe situation of the COVID-19 pandemic, masks cover most of the effective facial features of users, and their head pose changes significantly in a complex environment, which makes the accuracy of head pose estimation in some systems such as safe driving systems and attention detection systems impossible to guarantee. To this end, we propose a powerful four-branch feature selective extraction network (FSEN) structure, in which three branches are used to extract three independent discriminative features of pose angles, and one branch is used to extract composite features corresponding to multiple pose angles. By reducing the dimension of high-dimensional features, our method significantly reduces the amount of computation while improving the estimation accuracy. Our convolution method is an improved spatial channel dynamic convolution (SCDC) that initially enhances the extracted features. Additionally, we embed a regional information exchange network (RIEN) after each convolutional layer in each branch to fully mine the potential semantic correlation between regions from multiple perspectives and learn and fuse this correlation to further enhance feature expression. Finally, we fuse the independent discriminative features of each pose angle and composite features from the three directions of channel, space, and pixel to obtain perfect feature expression for each pose angle, and then obtain the head pose angle. We conducted extensive experiments on the controlled environment datasets and a self-built real complex environment dataset (RCE) and the results showed that our method outperforms state-of-the-art single-modality methods and performs on par with multimodality-based methods. This shows that our network meets the requirements of accurate head-pose estimation in real complex environments such as complex illumination and partial occlusion. Author

Measurements of SARS-CoV-2 antibody dissociation rate constant by chaotrope-free biolayer interferometry in serum of COVID-19 convalescent patients.

Kinetics measurements of antigen-antibody binding interactions are critical to understanding the functional efficiency of SARS-CoV-2 antibodies. Previously reported chaotrope-based avidity assays that rely on artificial disruption of binding do not reflect the natural binding kinetics. This study developed a chaotrope- and label-free biolayer interferometry (BLI) assay for the real-time monitoring of receptor binding domain (RBD) binding kinetics with SARS-CoV-2 spike protein in convalescent COVID-19 patients. An improved conjugation biosensor probe coated with streptavidin-polysaccharide (SA-PS) led to a six-fold increase of signal intensities and two-fold reduction of non-specific binding (NSB) compared to streptavidin only probe. Furthermore, by utilizing a separate reference probe and biotin-human serum albumin (B-HSA) blocking process to subtracted NSB signal in serum, this BLI biosensor can measure a wide range of the dissociation rate constant (koff), which can be measured without knowledge of the specific antibody concentrations. The clinical utility of this improved BLI kinetics assay was demonstrated by analyzing the koff values in sera of 24 pediatric (≤18 years old) and 63 adult (>18 years old) COVID-19 convalescent patients. Lower koff values for SARS-CoV-2 serum antibodies binding to RBD were measured in samples from children. This rapid, easy to operate and chaotrope-free BLI assay is suitable for clinical use and can be readily adapted to characterize SARS-CoV-2 antibodies developed by COVID-19 patients and vaccines.

Exploring Public Sentiment During COVID-19: A Cross Country Analysis

COVID-19 has spread all over the world, accounting for countless death and enormous economic loss. Since the World Health Organization (WHO) declared COVID-19 as a pandemic, governments from different countries have made various policies to prevent the pandemic from becoming worse. However, civilian reactions to the pandemic vary when they face similar situations. This behavioral variation creates a challenge when it comes to policy-making. Such differences are generally implicit, hidden in ones' social lives. As a result, it is challenging to analyze such differences when the governments make policies. In this work, we investigate social media posts on Twitter and Weibo in order to effectively explore the difference in reactions across various countries, with the aim to understand national differences. To this end, we employ natural language processing (NLP) methods and Linguistic Inquiry and Word Count (LIWC) tools to process six languages in different countries, including the USA, Germany, France, Italy, the U.K., and China. We provide a comprehensive analysis of public reaction differences from the emotional perspective. Our findings verify that the reactions vary noticeably among various countries for some policies. Therefore, sentiment analysis can significantly influence policy-making. Our work sheds light on the mechanism of detecting the reaction differences in various countries, which can be utilized to conduct effective communication and make appropriate policy decisions.