COVID-19 and Eye Banking: Ongoing Impacts of the Pandemic

Purpose:The purpose of this study was to assess the impact of ongoing waves of the COVID-19 pandemic and resulting guidelines on the corneal donor pool with resumption of clinical operations.Methods:A retrospective analysis of donors deemed eligible for corneal transplantation at an eye bank from July 1, 2020, through December 31, 2021. Donors ineligible due to meeting Eye Bank Association of America (EBAA) COVID-19 guidelines or a positive postmortem COVID-19 testing were examined. The correlation between COVID-19 rule outs and state COVID positivity was calculated. The number of scheduled surgeries, suitable corneas, imports, and international exports was compared with a pre-COVID period. Postmortem testing was reduced for the final 5 months of the study, and numbers were compared before and after the policy change.Results:2.85% of referrals to the eye bank were ruled out because of EBAA guidelines. 3.2% of postmortem tests were positive or indeterminate resulting in an ineligible tissue donor (0.42% of referrals). Over the 18-month period, there was a 4.30% shortage of suitable corneas compared with transplantation procedures. There was a significant correlation between postmortem testing and state COVID-19 positivity (r = 0.37, P <0.01), but not with EBAA guidelines (r = 0.19, P = 0.07). When postmortem testing was reduced, significantly more corneas were exported internationally.Conclusions:Although corneal transplant procedures were back to normal levels, there was a shortage of suitable corneal tissue. The discontinuation of postmortem testing was associated with a significant increase in international exports of corneal donor tissue. © 2023 Lippincott Williams and Wilkins. All rights reserved.

Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning

We study the co-movement between innovative financial assets (i.e., FinTech-related stocks, green bonds and cryptocurrencies) and traditional assets. We construct a co-movement mode transmission network and discuss the network topology during the pre-COVID-19 and COVID-19 periods. We extract network topology information to predict the co-movement mode by machine learning algorithms. We further propose dynamic trading strategies based on the co-movement mode prediction. The empirical results show that (i) the evolution of co-movement is dominated by some key modes, and the mode transmission relies on intermediate modes and shows certain periodicity;(ii) the co-movement relationships are influenced by the ongoing COVID-19 outbreak;and (iii) the novel approach, which combines complex network and machine learning, is superior in co-movement mode prediction and can effectively bring diversification benefits. Our work provides valuable insights for market participants. © 2022 Elsevier B.V.

Personal GHG emissions accounting and the driving forces decomposition in the past 10 years

Personal greenhouse gas (PGHG) emissions were crucial for achieving carbon peak and neutrality targets. The accounting methodology and driving forces identification of PGHG emissions were helpful for the quantification and the reduction of the PGHG emissions. In this study, the methodology of PGHG emissions was developed from resource obtaining to waste disposal, and the variations of Shanghainese PGHG emissions from 2010 to 2020 were evaluated, with the driving forces analysis based on Logarithmic Mean Divisia Index (LMDI) model. It showed that the emissions decreased from 3796.05 (2010) to 3046.87 kg carbon dioxides (CO2) (2014) and then increased to 3411.35 kg CO2 (2018). The emissions from consumptions accounted for around 62.1% of the total emissions, and that from waste disposal were around 3.1%, which were neglected in most previous studies. The PGHG emissions decreased by around 0.53 kg CO2 (2019) and 405.86 kg CO2 (2020) compared to 2018 and 2019, respectively, which were mainly affected by the waste forced source separation policy and the COVID-19 pandemic. The income level and consumption GHG intensity were two key factors influencing the contractively of GHG emissions from consumption, with the contributing rate of 169.3% and − 188.1%, respectively. Energy consumption was the main factor contributing to the growth of the direct GHG emissions (296.4%), and the energy GHG emission factor was the main factor in suppressing it (− 92.2%). Green consumption, low carbon lifestyles, green levy programs, and energy structure optimization were suggested to reduce the PGHG emissions. © 2023, The Author(s).

Sourcing decisions with uncertain time-dependent supply from an unreliable supplier

Recently, an increasing number of companies have encountered random production disruptions due to the COVID-19 pandemic. In this study, we investigate a two-stage supply chain in which a retailer can order products from a low-price ("cheap”) unreliable supplier (who may be subject to an uncertain production disruption and partially deliver the order) and an "expensive” reliable supplier at Stage 1 and a more "expensive” backup supplier at Stage 2. If the disruption happens, only the products that were produced before the disruption time can be obtained from the unreliable supplier. It is found that in the case with imperfect demand information updating, the unreliable supplier is always used while the reliable supplier can be abandoned. The time-dependent supply property of the unreliable supplier reduces the retailer's willingness of adopting the dual sourcing strategy at Stage 1, compared with the scenario with all-or-nothing supply. Different from the case with imperfect demand information updating, either the reliable or unreliable supplier can be abandoned in the case with perfect demand information updating. We derive the optimal ordering decisions and the conditions where single sourcing or dual sourcing is adopted at Stage 1. We conduct numerical experiments motivated by the sourcing problem of 3M Company in the US during the COVID-19 and observe that the unreliable supplier is more preferable when the demand uncertainty before or after the emergency order is higher. Interestingly, the retailer tends to order more from the unreliable supplier when the production disruption probability is larger in some cases. © 2022 The Author(s)

Vaccination with Span, an antigen guided by SARS-CoV-2 S protein evolution, protects against challenge with viral variants in mice

SARS-CoV-2 continues to accumulate mutations to evade immunity, leading to breakthrough infections after vaccination. How researchers can anticipate the evolutionary trajectory of the virus in advance in the design of next-generation vaccines requires investigation. Here, we performed a comprehensive study of 11,650,487 SARS-CoV-2 sequences, which revealed that the SARS-CoV-2 spike (S) protein evolved not randomly but into directional paths of either high infectivity plus low immune resistance or low infectivity plus high immune resistance. The viral infectivity and immune resistance of variants are generally incompatible, except for limited variants such as Beta and Kappa. The Omicron variant has the highest immune resistance but showed high infectivity in only one of the tested cell lines. To provide cross-clade immunity against variants that undergo diverse evolutionary pathways, we designed a new pan-vaccine antigen (Span). Span was designed by analyzing the homology of 2675 SARS-CoV-2 S protein sequences from the NCBI database before the Delta variant emerged. The refined Span protein harbors high-frequency residues at given positions that reflect cross-clade generality in sequence evolution. Compared with a prototype wild-type (Swt) vaccine, which, when administered to mice, induced serum with decreased neutralization activity against emerging variants, Span vaccination of mice elicited broad immunity to a wide range of variants, including those that emerged after our design. Moreover, vaccinating mice with a heterologous Span booster conferred complete protection against lethal infection with the Omicron variant. Our results highlight the importance and feasibility of a universal vaccine to fight against SARS-CoV-2 antigenic drift.

Pathogenic infection characteristics and risk factors for bovine respiratory disease complex based on the detection of lung pathogens in dead cattle in northeast China.

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.

Generation of microbial protein feed (MPF) from waste and its application in aquaculture in China

Aquaculture is regarded as one of the fastest methods for preparing food and may be relied upon more and more in the future. Production can be seeded from fish caught in the wild and can be maintained with imported fish food however, aquaculture output and quality is limited by cost and resources, and there is an incentive to make it more environmentally sustainable. If these goals can be achieved, we will produce better quality fish and in higher volumes. Microbial protein feed (MPF) offers a sustainable feedstuff solution for the aquaculture industry in China, with the net benefits of taking less time to prepare, using less water and land, being recyclable and also reducing carbon emissions. MPF provides stable and high quality proteins and is produced through the fermentation of microorganisms by utilizing agricultural and industrial waste as substrates and been extensively used in fish and shrimp production in China. This review describes the microorganisms, raw materials, fermentation processes and nutritional components used in MPF production in aquaculture. We shall discuss also MPF large-scale production processes in detail and then finally, what opportunities and challenges are faced by MPF in Chinese aquaculture in the context of "double carbon"targets and Covid-19. High-efficiency biosynthesis technology using mono-carbon gases to produce protein will become an important field in the future, as it shall facilitate sustainable and healthy feedstocks for the aquaculture industry, and allow China to achieve the goal of lower carbon emissions. © 2023 Elsevier Ltd.

Investigation report on China's health emergency popularisation of science about the COVID-19 pandemic

Background: The COVID-19 pandemic is a huge challenge to world health systems. The harm of public panic is more serious than that of the virus infection. Public panic will create a lot of rumours;Rumours will not only hinder the government's handling of public health emergencies, but also disrupt the public's awareness and behaviour of preventing viruses and cause social unrest. Purpose(s): In order to investigate the demand of the ordinary personnel and health professionals for emergency popularisation of science, discover the current problems in popular science work during public health emergencies, and provided suggestions for future health popular science work. Method(s): This study designed two versions of the health emergency science questionnaire, which are divided into ordinary personnel version and health professional version. From 21st February to 10th March 2020, the authors received questionnaires from 25,935 ordinary personnel and 30,143 professionals from all provinces of China. Result(s): The public has a high demand for health emergency popularisation of science about COVID-19, and the professional demand is higher than the ordinary personnel. Ordinary personnel's evaluation of the role of health emergency popular science in COVID-19 pandemic is 8.58+/-1.80 points (out of ten points), and the professional's evaluation is 8.93+/-1.44 points. Conclusion(s): Ordinary personnel and professionals have highly evaluated the role of health emergency popular science during the COVID-19 pandemic. Mobile Internet is currently the main channel for the public to obtain emergency popular science information, but due to rumours, the public's trust in mobile Internet is low.

DGCddG: Deep Graph Convolution for Predicting Protein-Protein Binding Affinity Changes Upon Mutations

Effectively and accurately predicting the effects of interactions between proteins after amino acid mutations is a key issue for understanding the mechanism of protein function and drug design. In this study, we present a deep graph convolution (DGC) network-based framework, DGCddG, to predict the changes of protein-protein binding affinity after mutation. DGCddG incorporates multi-layer graph convolution to extract a deep, contextualized representation for each residue of the protein complex structure. The mined channels of the mutation sites by DGC is then fitted to the binding affinity with a multi-layer perceptron. Experiments with results on multiple datasets show that our model can achieve relatively good performance for both single and multi-point mutations. For blind tests on datasets related to angiotensin-converting enzyme 2 binding with the SARS-CoV-2 virus, our predicted results are consistent with some biological insights to explain the higher infectiousness of Sars-CoV-2. Code and data availability: https://github.com/lennylv/DGCddG. IEEE

Towards Privacy-Preserving Computation on Gene: Construct Covid-19 Phylogenetic Tree using Homomorphic Encryption

Constructing a phylogenetic tree is an essential method of analyzing the evolution of the covid-19 virus. In the case of multiple entities holding different coronavirus genetic data, it is simple to aggregate all data into one entity and then calculate the phylogenetic tree. However, such a method is challenging to carry out. Genetic data is susceptible and has high economic value, and it is usually impossible to copy between different entities directly. Also, the direct sharing of genetic data can lead to data leaks or even legal problems. In this paper, we propose a homomorphic-encryption-based solution to tackle this problem, where two participants, A and B, both hold a part of covid-19 genetic data and compute the gene distance matrix calculation of the overall dataset without revealing the genetic data held by both parties. After the computation, participant A can decrypt the final distance matrix from the encrypted result and then use the plain-text result to construct the covid-19 phylogenetic tree. Experiment results show that the proposed method can process the genetic data accurately in a short time, and the phylogenetic tree generated by the proposed solution has no loss of accuracy compared to plain-text calculation. In terms of engineering optimization, we propose an optimized encryption method, which can further shorten the encryption time of the entire dataset without reducing the security level. © 2022 IEEE.

An Ensemble Learning Approach for COVID-19 Fact Verification

The rapid development of social media platforms has resulted in a fast-paced spread of misinformation, which is especially common in the COVID-19 pandemic. In the global pandemic, the amount of COVID-19 related fake news generated online becomes enormous, which negatively results in public tension. Moreover, rumours are spread across platforms from different countries in such a global pandemic. Thus, automated fact-checking, which refers to automatically verifying the correctness of a claim, is of great importance. In this paper, we propose and examine ensemble learning approaches that exploit the power of multiple large-scale pre-trained language models. We conduct extensive experiments on traditional approaches, learning-based approaches, and our proposed ensemble methods. We successfully advance state-of-the-art performance by a significant margin. Further, we show that our ensemble method is especially suited to tasks with scarce training data, making it more suitable for many real-world applications. © 2022 IEEE.

Open data: an innovative learning resource for postgraduates

Purpose: With the continuous development of the global COVID-19 epidemic, mobile learning has become one of the most significant learning approaches. The mobile learning resource is the basis of mobile learning;it may directly affect the effectiveness of mobile learning. However, the current learning resources cannot meet users' needs. This study aims to analyze the influencing factors of accepting open data as learning resources among users. Design/methodology/approach: Based on the technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT), this study proposed a comprehensive theoretical research model. Data were obtained from 398 postgraduates from several universities in central China. Confirmatory factor analysis was used to determine the reliability and validity of the measurement model. Data has been analyzed using SPSS and AMOS software. Findings: The results suggested that perceived usefulness, performance expectancy, social influence and facilitating conditions have a positive influence on accepting open data as learning resources. Perceived ease of use was not found significant. Moreover, it was further shown in the study that behavioural intention significantly influenced the acceptance of open data as learning resources. Originality/value: There is a lack of research on open data as learning resources in developing countries, especially in China. This study addresses the gap and helps us understand the acceptance of open data as learning resources in higher education. This study also pays attention to postgraduates' choice of learning resources, which has been little noticed before. Additionally, this study offers opportunities for further studies on the continuous usage of open data in higher education. © 2022, Emerald Publishing Limited.

Real-World Experience of Cefiderocol in Treating Bacterial Infection in US Hospitals (Jan 2020-June 2021)

Background. Cefiderocol (CFDC) is a Gram-negative antibiotic (GNA) with a unique mode of cell entry against carbapenem resistance. This study described the initial use of CFDC in US hospitals since its approval in November 2019. Methods. This was a retrospective study of patients treated with CFDC consecutively for >=3 days in US hospitals, as captured in Premier Healthcare Data from January 2020 to June 2021. This study described the clinical characteristics, CFDC usage, and Post-CFDC initiation 14-day and 28-day in-hospital all-cause mortality (IH-ACM). For patients with microbiology results, the pathogen, susceptibility and culture site associated with CFDC use were described. Index culture was the culture(s) taken on the day closest to CFDC initiation. Results. Among 313 of 360 in-patients who received >=3 days CFDC, the median age was 58 years (range: 17 - 89 years), and 91% were hospitalized via emergency room, trauma, or urgent admission. The most common conditions were severe sepsis with septic shock, palliative care, and multi-drug resistant infection. Also 34% had a 'do not resuscitate order'. About 64% of patients received mechanical ventilation and 79% had ICU stay. Median length of hospital stay was 27 days (range: 3-310 days). Median days on CFDC was 8 days (range: 3 - 66 days). Over 58% received >=2 other GNAs within 14-days of initiatingCFDC.Among 187 patients withmicrobiology results, 75% had index cultures with one pathogen, and 73% had confirmed carbapenem resistant pathogens. The most common pathogens were Pseudomonas aeruginosa, Stenotrophomonas maltophilia, Klebsiella pneumoniae and Acinetobacter baumannii. The most common index culture site was respiratory. The 14-day and 28-day crude IH-ACM from CFDC initiation was 16.3% (95%CI: 12.2%-20.4%) and 23.6% (95%CI: 18.9 - 28.4%), respectively. Among those with microbiology results, 14-day and 28-day IH-ACMwas 17.1% (95%CI: 11.7% - 22.5%) and 23.5%(95%CI: 17.4-29.6%), respectively. Among patients who died, 83% had severe sepsis with septic shock, 76% were in palliative care, 71% had a 'do not resuscitate order', and 44% had COVID-19. Conclusion. CFDC was used most frequently in critically ill patients. IH-ACM was comparable with other studies.

Cefiderocol in Treating Patients Confirmed with Gram-negative Infections in US Hospital During Jan 2020 - Jun 2021

Background. Cefiderocol (CFDC) has a broad activity against Gram-negative (GN) pathogens. This study describes the usage of CFDC in US hospitals in patients with microbiology data during the initial phase of commercialization. Methods. This retrospective study included patients with laboratory-confirmed GN infections in US hospitals treated with CFDC consecutively for >=3 days between March 2020 to June 2021, as captured by Premier Healthcare data. This study describes the clinical characteristics, microbiology profile, CFDC usage, and post-CFDC initiation 14-day and 28-day in-hospital all-cause mortality (IH-ACM). Index culture was defined as the last day that culture sample(s) was taken before CFDC initiation or the first day the culture sample(s) was taken after CFDC initiation if no microbiology data before CFDC use was available. Index pathogens were all pathogens identified from the index culture(s). The index culture site was where the index culture was taken. Results. A total 187 in-patients received >=3 days CFDC and had >=1 microbiological result(s). The clinical characteristics of the patients and index culture results are provided in Table 1 and Table 2. About 60% of patients had at least one positive respiratory culture. The most frequent pathogens from the index culture were Pseudomonas aeruginosa, Stenotrophomonas maltophilia, Klebsiella pneumoniae, and Acinetobacter baumannii. Nearly 75% of patients had one index pathogen, and 91% with one culture site. Almost 30% of patients had either one pathogen identified in multiple culture sites, or multiple pathogens from >=1 culture site. Crude 28-day IH-ACM for patients with any A. baumannii was 8.3% (95%CI: 0% -19.4%), any P. aeruginosa was 17.3% (95%CI: 9.9-24.8%), any S. maltophilia was 18.4%, (95%CI: 6.1%-44.0%) and any K. pneumoniae was 26.1% (95%CI: 8.1%-44.0%). Crude 28-day IH-ACM for patients with positive respiratory culture was five times higher in COVID patients than non-COVID patients. Conclusion. During the initial phase of CFDC availability, the most frequent pathogens treated using CFDC were non-fermenters, and the most frequent culture site was respiratory. IH-ACM appears to be affected by infection characteristics, especially COVID-19 status.

A novel nonparametric mixture model for the detection pattern of COVID-19 on Diamond Princess cruise

The outbreak of COVID-19 on the Diamond Princess cruise ship has attracted much attention. Motivated by the PCR testing data on the Diamond Princess, we propose a novel cure mixture nonparametric model to investigate the detection pattern. It combines a logistic regression for the probability of susceptible subjects with a nonparametric distribution for the detection of infected individuals. Maximum likelihood estimators are proposed. The resulting estimators are shown to be consistent and asymptotically normal. Simulation studies demonstrate that the proposed approach is appropriate for practical use. Finally, we apply the proposed method to PCR testing data on the Diamond Princess to show its practical utility.

Impact of quarantine policies on port network performance and robustness during pandemics: a simulation-based analysis

To contain the spread of the virus at ports, many countries have implemented quarantine policies for vessels from abroad during COVID-19. In response, vessels chose to skip the port to save time or undergo a 14-day quarantine to ensure critical supplies, both of which significantly affected the performance of the port network. However, due to the combined effect of many factors, data analysis techniques can hardly identify the impact of quarantine policies on the outcomes. Therefore, to enable both networkwide performance assessment and detailed evaluation for individual vessels and ports under such an unprecedented policy, a microscopic simulation model for the global port network (GPN) is desired. The proposed simulation method is based on real-world vessel movement data from Automatic Identification Systems (AIS) combined with a port database. It is found that the effect of the quarantine policy on a particular port consists of two parts, i.e. the direct impact caused by vessels' port skipping and the indirect impact caused by network interaction, which is further determined by the location of, and the policy implemented by the port. Furthermore, the ability of the global port network to maintain its performance under different levels of pandemic situations and different rates for vessels to skip the ports requiring quarantine is investigated. Interestingly, in most cases, a moderate port skipping rate (mostly between 20% and 50%) could help improve network performance. The results and presented simulation method can assist policymakers in coping with COVID-19 and potential global catastrophes.

Modernizing Pressure Injury Risk Assessment in the ICU in the COVID Era: Ensemble Super-Learning and Explainable AI

Objectives: Hospital-acquired pressure injuries (HAPrI) are areas of injury to the skin and/or underlying tissues. Risk stratification is essential for guiding prevention in the ICU, but current risk assessment tools require labor-intensive input. This motivates a tactical, parsimonious, and automatic risk profiling algorithm, that can be based on readily available clinical measures (e.g., COVID status, race, Medicare/Medicaid status). Additionally, International Pressure Injury Prevention guidelines call for the development of machine learning-based risk assessment algorithms that are clinician-interpretable and context-informed. Method(s): Adult patients admitted to one of two ICUs between April 2020, and April 2021 were eligible for inclusion. Discrete and ensemble super-learning models, adjusting for class imbalance, were created from a rich library of candidate base learners. For explainability, SHAP (SHapley Additive exPlanations) global and local values were derived to help explain variable average marginal contributions (across all permutations) to the model. An iteration of clinical expert review was performed with the SHAP values, and simulations of patient profiles and results were used to reformat and re-weight predictor variables. All analysis was run in open Python (version 3.7), and code/results will be made available via a GitHub page. Result(s): The final sample consisted of 1,911 patients (removing 9 with missing pressure injury status). Hospital-acquired pressure injuries (defined as stage 2, or worse) occurred in 18.5% of the sample (n=354). We achieved the best overall performance on the testing data with a stacked ensemble using three base models: random forest (rf), gradient boosted machine (gbm), and neural network (NN) (Performance on 20% holdout: Accuracy: 81%;AUC: 0.77;AUCPR: 0.53). Conclusion(s): Prediction engineering should be done in collaboration with clinical experts to optimize tactical implementation to both optimize performance, with minimal interruption to workflow. XAI enhanced adoption of the experts' advice based on the selected model features. Copyright © 2022

The novel hyaluronic acid granular hydrogel attenuates osteoarthritis progression by inhibiting the TLR-2/NF-kappaB signaling pathway through suppressing cellular senescence

In patients with mild osteoarthritis (OA), two to four monthly injections are required for 6 months due to the degradation of hyaluronic acid (HA) by peroxidative cleavage and hyaluronidase. However, frequent injections may lead to local infection and also cause inconvenience to patients during the COVID-19 pandemic. Herein, we developed a novel HA granular hydrogel (n-HA) with improved degradation resistance. The chemical structure, injectable capability, morphology, rheological properties, biodegradability, and cytocompatibility of the n-HA were investigated. In addition, the effects of the n-HA on the senescence-associated inflammatory responses were studied via flow cytometry, cytochemical staining, Real time quantitative polymerase chain reaction (RT-qPCR), and western blot analysis. Importantly, the treatment outcome of the n-HA with one single injection relative to the commercial HA product with four consecutive injections within one treatment course in an OA mouse model underwent anterior cruciate ligament transection (ACLT) was systematically evaluated. Our developed n-HA exhibited a perfect unification of high crosslink density, good injectability, excellent resistance to enzymatic hydrolysis, satisfactory biocompatibility, and anti-inflammatory responses through a series of in vitro studies. Compared to the commercial HA product with four consecutive injections, a single injection of n-HA contributed to equivalent treatment outcomes in an OA mouse model in terms of histological analysis, radiographic, immunohistological, and molecular analysis results. Furthermore, the amelioration effect of the n-HA on OA development was partially ascribed to the attenuation of chondrocyte senescence, thereby leading to inhibition of TLR-2 expression and then blockade of NF-kappaB activation. Collectively, the n-HA may be a promising therapeutic alternative to current commercial HA products for OA treatment. Copyright © 2022 The Authors. Bioengineering & Translational Medicine published by Wiley Periodicals LLC on behalf of The American Institute of Chemical Engineers.

Analysis On the Challenges for Smart Elderly Care to Face in the Post-pandemic Era of Covid-19

Based on the investigation of the current situation of smart elderly care services in Wuhan, Hubei Province, combined with the results of field visits, this paper combs and analyzes the common problems faced by smart elderly care and the new problems and opportunities brought by the epidemic. Put forward countermeasures and suggestions to solve the problems faced by smart elderly care, so as to enhance the suitability and availability of smart elderly care services, and finally achieve the purpose of improving the well-being of the elderly and sharing the fruits of social development with the elderly. © 2022 ACM.

OODA Model Study of Port Epidemic Transmission Process and Emergency Prevention and Control

COVID-19 is highly efficient for port operation, and it is crucial to improve the port epidemic's emergency prevention and control capability. Therefore, according to the port operation process, the process of epidemic transmission in the port is analyzed, and the relationship map of port epidemic transmission is clarified;then, with the help of OODA theory, the OODA model of port epidemic emergency prevention and control is constructed. Through the analysis of the model operation cycle process, this paper summarizes the advantages of the model in port epidemic prevention and control. OODA port emergency prevention and control model can better improve the ability and efficiency of port epidemic emergency prevention and control. © 2022 SPIE.