MIMIC model of teachers and students attitudes towards online learning during Covid-19: A gender perspective

Academic institutions around the globe have shifted to online learning because of the unpredictable spread of COVID-19. The present study aimed to compare teachers' and students' attitudes towards online learning during the pandemic and to examine the effects of gender differences on their attitudes. In study 1, we adapted the Test of eLearning Related Attitudes for Pakistani students in three steps: expert review, piloting, and validation. The individual and collective expert review was performed to adapt the teacher version into the student version using the Technique for Research of Information by the Animation of a Group of Experts (TRIAGE). We tested three sets of measurement invariance models for participants' status and gender in study 2. Data were collected from 289 university teachers (men = 158, women = 131) and 444 undergraduate students (boys = 156, girls = 287). The results demonstrated that both groups had highly positive yet different attitudes towards online learning. Teachers were more satisfied than students. Model fit was poor, and the overall factor structure, factor loadings, and intercepts varied across groups. Intergroup gender invariance illustrated heterogeneity in attitudes towards online learning favoring men teachers and boy students. Study strengths and implications for the promotion of a positive experience of online learning are discussed.

Research on the classification of CT images of COVID-19 by pre-training model based on self-attention mechanism

Nowadays, with the outbreak of COVID-19, the prevention and treatment of COVID-19 has gradually become the focus of social disease prevention, and most patients are also more concerned about the symptoms. COVID-19 has symptoms similar to the common cold, and it cannot be diagnosed based on the symptoms shown by the patient, so it is necessary to observe medical images of the lungs to finally determine whether they are COVID-19 positive. As the number of patients with symptoms similar to pneumonia increases, more and more medical images of the lungs need to be generated. At the same time, the number of physicians at this stage is far from meeting the needs of patients, resulting in patients unable to detect and understand their own conditions in time. In this regard, we have performed image augmentation, data cleaning, and designed a deep learning classification network based on the data set of COVID-19 lung medical images. accurate classification judgment. The network can achieve 95.76% classification accuracy for this task through a new fine-tuning method and hyperparameter tuning we designed, which has higher accuracy and less training time than the classic convolutional neural network model. © 2023 SPIE.

Association studies between COVID-19 and SSc-ILD

Severe COVID-19 patients may develop pulmonary fibrosis, similar to SSc-ILD disease, suggesting a potential link between the two diseases. However, there are limited treatment options for SSc-ILD-type diseases. Therefore, investigating pathological markers of the two diseases can provide valuable insights for treating related conditions. RNA sequencing technology offers high throughput and precision. However, the bimodal nature of RNA-Seq data cannot be accurately captured by commonly used algorithms such as DESeq2. To address this issue, the Beta-Poisson model has been developed to identify differentially expressed genes. Unlike the classical DESeq2 algorithm, the Beta-Poisson model introduces a Beta distribution to construct a new hybrid distribution in place of the Gamma distribution of the Gamma-Poisson distribution, effectively characterizing the bimodal features of RNA-Seq data. The transcriptomes of SARS-CoV infection and SSc-ILD disease in the lung epithelial cell dataset were analyzed to identify common differentially expressed genes of SARS-CoV and SSc-ILD disease. Gene function and signaling pathway enrichment analysis and protein-protein interaction (PPI) network were used to identify common pathways and drug targets for SSc-ILD with COVID-19 infection. The results show that there are 50 differentially expressed genes in common between COVID-19 and SSC-ILD. The functions of these genes are mainly enriched in immune system response, interferon signaling pathway and other related signaling pathways, and enriched in biological processes such as cell defense response to virus and interferon regulation. Based on the detection of hub genes based on PPIs network, it is predicted that STAT1, ISG15, IRF7, MX1, EIF2AK2, DDX58, OAS1, OAS2, IFIT1 and IFIT3 are the key genes involved in the pathological phenotype of the two diseases. Based on the key genes, the interaction of transcription factor (TF) and miRNA with common differentially expressed genes is also identified. The possible pathological markers of the two diseases and related molecular regulatory mechanisms of disease treatment are revealed to provide theoretical basis for the treatment of the two diseases. © 2023 Editorial Office of Journal of Shenzhen University. All rights reserved.

Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions

Large language models have abilities in creating high-volume human-like texts and can be used to generate persuasive misinformation. However, the risks remain under-explored. To address the gap, this work first examined characteristics of AI-generated misinformation (AI-misinfo) compared with human creations, and then evaluated the applicability of existing solutions. We compiled human-created COVID-19 misinformation and ed it into narrative prompts for a language model to output AI-misinfo. We found significant linguistic differences within human-AI pairs, and patterns of AI-misinfo in enhancing details, communicating uncertainties, drawing conclusions, and simulating personal tones. While existing models remained capable of classifying AI-misinfo, a significant performance drop compared to human-misinfo was observed. Results suggested that existing information assessment guidelines had questionable applicability, as AI-misinfo tended to meet criteria in evidence credibility, source transparency, and limitation acknowledgment. We discuss implications for practitioners, researchers, and journalists, as AI can create new challenges to the societal problem of misinformation. © 2023 Owner/Author.

The Impact of COVID-19 Lockdown on Ambient Air Quality in Shanghai, 2022

The COVID-19 lockdown contributes to the improvement of air quality. Most previous studies have attributed this to the reduction of human activity while ignoring the meteorological changes, this may lead to an overestimation or underestimation of the impact of COVID-19 lockdown measures on air pollution levels. To investigate this issue, we propose an XGBoost-based model to predict the concentrations of PM2.5 and PM10 during the COVID-19 lockdown period in 2022, Shanghai, and thus explore the limits of anthropogenic emission on air pollution levels by comprehensively employing the meteorological factors and the concentrations of other air pollutants. Results demonstrate that actual observations of PM2.5 and PM10 during the COVID-19 lockdown period were reduced by 60.81% and 43.12% compared with the predicted values (regarded as the period without the lockdown measures). In addition, by comparing with the time series prediction results without considering meteorological factors, the actual observations of PM2.5 and PM10 during the lockdown period were reduced by 50.20% and 19.06%, respectively, against the predicted values during the non-lockdown period. The analysis results indicate that ignoring meteorological factors will underestimate the positive impact of COVID-19 lockdown measures on air quality. © 2023 by the authors.

Modeling and Analysis of Infectious Disease Transmission Considering Protection and Isolation

Based on the SEIR model, two compartments for self-protection and isolation are introduced, and a more general infectious disease transmission model is proposed.Through qualitative analysis of the model, the basic reproduction number of the model is calculated, and the local asymptotic stability of the disease-free equilibrium point and the endemic equilibrium point of the model is analyzed through eigenvalue theory and Routh-Hurwitz criterion.The numerical simulation and fitting results of COVID-19 virus show that the proposed SEIQRP model can effectively describe the dynamic transmission process of the infectious disease.In the model, the three parameters, i.e.protection rate, incubation period isolation rate, and infected person isolation rate play a very critical role in the spread of the disease.Raising people's awareness of self-protection, focusing on screening for patients in the incubation period, and isolating and treating infected people can effectively reduce the spread of infectious diseases. © 2023 Northeastern University.All rights reserved.

Civil Aviation Customer Satisfaction Automatic Evaluation System

In 2021, the airline industry was affected by COVID-19, and many airlines suffered losses. The main reason for the loss were the decline in revenue and the surge in costs. Therefore, in terms of creating the competitive advantage of airlines, "price war" is no longer applicable, and improving service quality has become an effective means. Customer satisfaction is the most effective indicator to measure service quality. In this study, a satisfaction evaluation system is established based on structural equation model and customer satisfaction importance matrix. Then, a questionnaire is designed to analyze the influence of different factors on customer satisfaction. The research finds that brand image and perceived quality have a great impact on customer satisfaction. In addition, some suggestions for airlines to improve customer satisfaction are given. © 2023 SPIE.

Travel behavior adjustment based epidemic spreading model and prediction for COVID-19

Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people' s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took "ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word "ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word "ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers. © 2023 Chinese Physical Society.

Immunotherapy: ALLOGENEIC UMBILICAL CORD BLOOD REGULATORY T CELLS DECREASE INFLAMMATION AND PRESERVE ANTI-TUMOR ACTIVITY OF CAR T CELLS

Background & Aim: With larger accessibility and increased number of patients being treated with CART cell therapy, real-world toxicity continues to remain a significant challenge to its widespread adoption. We have previously shown that allogeneic umbilical cord blood derived (UCB) regulatory T cells (Tregs) can resolve uncontrolled inflammation and can treat acute and immune mediated lung injury in a xenogenic model as well as in patients suffering from COVID-19 acute respiratory distress syndrome. The unique properties of UCB Tregs including: i) lack of plasticity when exposed to inflammatory micro-environments;ii) no requirement for HLA matching;iii) long shelf life of cryopreserved Tregs;and iv) immediate product availability for on demand treatment, makes them an attractive source for treating acute inflammatory syndromes. Therefore, we hypothesized that add-on therapy with UCB derived Tregs may resolve uncontrolled inflammation responsible for CART cell therapy associated toxicity. Methods, Results & Conclusion(s): UCB Tregs were added in 1:1 ratio to CART cells, where no interference in their ability to kill CD19+ Raji cells, was detected at different ratios : 8:1 (80.4% vs. 81.5%);4:1 (62.0% vs. 66.2%);2:1 (50.1% vs. 54.7%);1:1 (35.4% vs. 44.1%) (Fig 1A). In a xenogenic B cell lymphoma model, multiple injections of Tregs were administered after CART injection (Fig 1B), which did not impact distribution of CD8+ T effector cells (Fig 1C) or CART cells cells (Fig 1D) in different organs. No decline in the CAR T levels was observed in the Tregs recipients (Fig 1E). Specifically, no difference in tumor burden was detected between the two arms (Fig 2A). No tumor was detected in CART+Tregs in liver (Fig 2B) or bone marrow (Fig 2C). A corresponding decrease in multiple inflammatory cytokines in peripheral blood was observed in CART+Tregs when compared to CART alone (Fig 2D). Here we show "proof of concept" for add-on therapy with Tregs to mitigate hyper-inflammatory state induced by CART cells without interference in their on-target anti-tumor activity. The timing of Tregs administration after CART cells have had sufficient time for forming synapse with tumor cells allows for preservation of their anti-tumor cytotoxicity, such that the infused Tregs home to the areas of tissue damage to bind to the resident antigen presenting cells which in turn collaborate with Tregs to resolve inflammation. Such differential distribution of cells allow for a Treg "cooling blanket" and lays ground for clinical study. [Figure presented]Copyright © 2023 International Society for Cell & Gene Therapy

Build an Agent-Based Model for COVID-19 Effect of Mitigation Policies

Non-Drug Intervention (NDI) is one of the important means to prevent and control the outbreak of coronavirus disease 2019 (COVID-19), and the implementation of this series of measures plays a key role in the development of the epidemic. The purpose of this paper is to study the impact of different mitigation measures on the situation of the COVID 19, and effectively respond to the prevention and control situation in the "post-epidemic era". The present work is based on the Susceptible-Exposed-Infectious-Remove-Susceptible (SEIRS) Model, and adapted the agent-based model (ABM) to construct the epidemic prevention and control model framework to simulate the COVID-19 epidemic from three aspects: social distance, personal protection, and bed resources. The experiment results show that the above NDI are effective mitigation measures for epidemic prevention and control, and can play a positive role in the recurrence of COVID-19, but a single measure cannot prevent the recurrence of infection peaks and curb the spread of the epidemic;When social distance and personal protection rules are out of control, bed resources will become an important guarantee for epidemic prevention and control. Although the spread of the epidemic cannot be curbed, it can slow down the recurrence of the peak of the epidemic;When people abide by social distance and personal protection rules, the pressure on bed resources will be eased. At the same time, under the interaction of the three measures, not only the death toll can be reduced, but the spread of the epidemic can also be effectively curbed. © 2022 ACM.

METHODOLOGY for ASSESSING the FINANCIAL SECURITY of ENTERPRISES in the POST-PANDEMIC PERIOD of DIGITAL ECONOMY

The article considers the approaches to assessing the financial security of enterprises presented in the literature, determines the rsistance of the textile industry of Uzbekistan to the negative impact of the coronavirus pandemic on the basis of statistical data, and reveals a significant differentiation of textile industry enterprises in terms of financial stability. Based on data on small enterprises in the textile industry of Uzbekistan, a method for assessing the financial security of an enterprise in the post-pandemic period is proposed and tested, taking into account the complex influence of non-financial parameters of economic security and assessing the deviations of the economic situation at a given enterprise from the patterns emerging in the relevant segment of the economy. In this research an econometric model was developed to determine the factors affecting the chemical industry and express their interrelationship, based on the conducted econometric analysis, the directions of development in our country were determined. According to the authors, it is necessary to continue these directions in order to ensure the economic security of industry enterprises in the country. © 2022 ACM.

Explainable artificial intelligence approach towards classifying educational android app reviews using deep learning

Mobile application developers rely largely on user reviews for identifying issues in mobile applications and meeting the users' expectations. User reviews are unstructured, unorganized and very informal. Identifying and classifying issues by extracting required information from reviews is difficult due to a large number of reviews. To automate the process of classifying reviews many researchers have adopted machine learning approaches. Keeping in view, the rising demand for educational applications, especially during COVID-19, this research aims to automate Android application education reviews' classification and sentiment analysis using natural language processing and machine learning techniques. A baseline corpus comprising 13,000 records has been built by collecting reviews of more than 20 educational applications. The reviews were then manually labelled with respect to sentiment and issue types mentioned in each review. User reviews are classified into eight categories and various machine learning algorithms are applied to classify users' sentiments and issues of applications. The results demonstrate that our proposed framework achieved an accuracy of 97% for sentiment identification and an accuracy of 94% in classifying the most significant issues. Moreover, the interpretability of the model is verified by using the explainable artificial intelligence technique of local interpretable model-agnostic explanations. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Nonlinear dynamics modeling and epidemic forecast of COVID-19

To study the spreading trend and risk of COVID-19, according to the characteristics of COVID-19, this paper proposes a new transmission dynamic model named SLIR(susceptible-low-risk-infected-recovered), based on the classic SIR model by considering government control and personal protection measures. The equilibria, stability and bifurcation of the model are analyzed to reveal the propagation mechanism of COVID-19. In order to improve the prediction accuracy of the model, the least square method is employed to estimate the model parameters based on the real data of COVID-19 in the United States. Finally, the model is used to predict and analyze COVID-19 in the United States. The simulation results show that compared with the traditional SIR model, this model can better predict the spreading trend of COVID-19 in the United States, and the actual official data has further verified its effectiveness. The proposed model can effectively simulate the spreading of COVID-19 and help governments choose appropriate prevention and control measures. Copyright ©2023 Control and Decision.

Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data

High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology, genomics, and proteomics, the data are often functional in their nature and exhibit a degree of roughness and nonstationarity. These structures pose additional challenges to commonly used methods that rely mainly on a two-stage approach performing variable selection and classification separately. We propose in this work a novel Gaussian process discriminant analysis (GPDA) that combines these steps in a unified framework. Our model is a two-layer nonstationary Gaussian process coupled with an Ising prior to identify differentially-distributed locations. Scalable inference is achieved via developing a variational scheme that exploits advances in the use of sparse inverse covariance matrices. We demonstrate the performance of our methodology on simulated datasets and two proteomics datasets: breast cancer and SARS-CoV-2. Our approach distinguishes itself by offering explainability as well as uncertainty quantification in addition to low computational cost, which are crucial to increase trust and social acceptance of data-driven tools. Supplementary materials for this article are available online.

COVID-19 Data Prediction and Visualization in Mainland China

Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19. © 2023 SPIE.

Emotional well-being and performance of middle leaders: the role of organisational trust in early childhood education

PurposeThe current study investigated the impact of organisational trust on emotional well-being and performance of middle leaders during the coronavirus disease 2019 (COVID-19) pandemic.Design/methodology/approachA convenience sample of 265 middle leaders in kindergartens in China responded involving trust in schools (e.g. trust in principal and trust in colleagues), emotional well-being and job performance. Both confirmatory factor analysis and structural equation modelling (SEM) were used in the investigation.FindingsThree hypotheses on the relationships between the three constructs were verified. Trust in schools significantly influenced emotional well-being and job performance of middle leaders which correlated with each other. The interactive effects of trust in principal and trust in colleagues were discussed for improving the well-being and job performance of middle leaders. Relationships between the two kinds of trust and pride were also identified in the research.Research limitations/implicationsFurther studies may put efforts towards improving these three outcomes synchronously.Practical implicationsBased on the evidence of the current study, future research may focus on how middle leaders act as a bridging role between different stakeholders such as principal and teachers, principal and parents, teachers and children, meanwhile how to boost the leaders' own well-being and performance in the early childhood education (ECE).Originality/valueThis study established the empirical linkages between school trusts, emotional well-being and job performance.

Repurposing of poly-gamma-glutamic acid, immunotherapeutic drug for cervical intraepithelial neoplasia 1, as covid-19 treatment

Covid-19 virus variants identified so far are due to viral genetic diversity, genetic evolution, and variable infectivity, suggesting that high infection rates and high mortality rates may be contributed by these mutations. And it has been reported that the targeting strategies for innate immunity should be less vulnerable to viral evolution, variant emergence and resistance. Therefore, the most effective solution to Covid-19 infection has been proposed to prevent and treat severe exacerbation of patients with moderate disease by enhancing human immune responses such as NK cell and T cell. In previous studies, we demonstrated for the first time that gamma-PGA induced significant antitumor activity and antiviral activity by modulating NK cell-mediated cytotoxicity. Especially intranasal administration of gamma-PGA was found to effectively induce protective innate and CTL immune responses against viruses and we found out that gamma-PGA can be an effective treatment for cervical intraepithelial neoplasia 1 through phase 2b clinical trial. In this study, the possibility of gamma-PGA as a Covid-19 immune modulating agent was confirmed by animal experiments infected with Covid-19 viruses. After oral administration of gamma-PGA 300mug/mouse once a day for 5 days in a K18-hACE2 TG mouse model infected with SARS-CoV-2 (NCCP 43326;original strain) and SARS-CoV-2 (NCCP 43390;Delta variant), virus titer and clinical symptom improvement were confirmed. In the RjHan:AURA Syrian hamster model infected with SARS-CoV-2 (NCCP 49930;Delta variant), 350 or 550 mug/head of gamma-PGA was administered orally for 10 days once a day. The virus for infection was administered at 5 x 104 TCID50, and the titer of virus and the improvement of pneumonia lesions were measured to confirm the effectiveness in terms of prevention or treatment. In the mouse model infected with original Covid-19 virus stain, the weight loss was significantly reduced and the survival rate was also improved by the administration of gamma-PGA. And gamma-PGA alleviated the pneumonic lesions and reduced the virus titer of lung tissue in mice infected with delta variant. In the deltavariant virus infected hamster model, gamma-PGA showed statistically significant improvement of weight loss and lung inflammation during administration after infection. This is a promising result for possibility of Covid-19 therapeutics along with the efficacy results of mouse model, suggesting gammaPGA can be therapeutic candidate to modulate an innate immune response for Covid-19.

Embracing DDMT Teaching Model and Design Thinking for Organization Development

The multi-generational workforce presents challenges for organizations, as the needs and expectations of employees vary greatly between different age groups. To address this, organizations need to adapt their development and learning principles to better suit the changing workforce. The DDMT Teaching Model of Tsing Hua STEAM School, which integrates design thinking methodology, aims to address this challenge. DDMT stands for Discover, Define, Model & Modeling, and Transfer. The main aim of this study is to identify the organization development practices (OD) and gaps through interdisciplinary models such as DDMT and design thinking. In collaboration with a healthcare nursing home service provider, a proof of concept using the DDMT-DT model was conducted to understand the challenges in employment and retention of support employees between nursing homes under the healthcare organization. The paper highlights the rapid change in human experiences and mindsets in the work culture and the need for a design curriculum that is more relevant to the current and future workforce. The DDMT-DT approach can help organizations address these challenges by providing a framework for HR personnel to design training curricula that are more effective in addressing the issues of hiring and employee retention. By applying the DDMT-DT model, HR personnel can better understand the needs and motivations of the workforce and design training programs that are more relevant to their needs. The proof-of-concept research pilot project conducted with the healthcare nursing home service provider demonstrated the effectiveness of the DDMT-DT model in addressing the issues of hiring and employee retention. The project provides a valuable case study for other organizations looking to implement the DDMT-DT model in their HR practices. Overall, the paper highlights the importance of adapting HR practices to better suit the changing workforce. The DDMT-DT model provides a useful framework for organizations looking to improve their HR practices and better address the needs of their workforce.

COVID-19: Agent-Based Simulation-Optimization to Vaccine Center Location Vaccine Allocation Problem

This paper presents an agent-based simulation-optimization modeling and algorithmic framework to determine the optimal vaccine center location and vaccine allocation strategies under budget constraints during an epidemic outbreak. Both simulation and optimization models incorporate population health dynamics, such as susceptible (S), vaccinated (V), infected (I) and recovered (R), while their integrated utilization focuses on the COVID-19 vaccine allocation challenges. We first formulate a dynamic location-allocation mixed-integer programming (MIP) model, which determines the optimal vaccination center locations and vaccines allocated to vaccination centers, pharmacies, and health centers in a multi-period setting in each region over a geographical location. We then extend the agent-based epidemiological simulation model of COVID-19 (Covasim) by adding new vaccination compartments representing people who take the first vaccine shot and the first two shots. The Covasim involves complex disease transmission contact networks, including households, schools, and workplaces, and demographics, such as age-based disease transmission parameters. We combine the extended Covasim with the vaccination center location-allocation MIP model into one single simulation-optimization framework, which works iteratively forward and backward in time to determine the optimal vaccine allocation under varying disease dynamics. The agent-based simulation captures the inherent uncertainty in disease progression and forecasts the refined number of susceptible individuals and infections for the current time period to be used as an input into the optimization. We calibrate, validate, and test our simulation-optimization vaccine allocation model using the COVID-19 data and vaccine distribution case study in New Jersey. The resulting insights support ongoing mass vaccination efforts to mitigate the impact of the pandemic on public health, while the simulation-optimization algorithmic framework could be generalized for other epidemics. [ FROM AUTHOR] Copyright of IISE Transactions is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Enabling mRNA medicine for brain tumors

mRNA is a new class of drugs that has the potential to revolutionize the treatment of brain tumors. Thanks to the COVID-19 mRNA vaccines and numerous therapy-based clinical trials, it is now clear that lipid nanoparticles (LNPs) are a clinically viable means to deliver RNA therapeutics. However, LNP-mediated mRNA delivery to brain tumors remains elusive. Over the past decade, numerous studies have shown that tumor cells communicate with each other via small extracellular vesicles, which are around 100 nm in diameter and consist of lipid bilayer membrane similar to synthetic lipidbased nanocarriers. We hypothesized that rationally designed LNPs based on extracellular vesicle mimicry would enable efficient delivery of RNA therapeutics to brain tumors without undue toxicity. We synthesized LNPs using four components similar to the formulation used in the mRNA COVID19 vaccines (Moderna and Pfizer): ionizable lipid, cholesterol, helper lipid and polyethylene glycol (PEG)-lipid. For the in vitro screen, we tested ten classes of helper lipids based on their abundance in extracellular vesicle membranes, commercial availability, and large-scale production feasibility while keeping rest of the LNP components unchanged. The transfection kinetics of GFP mRNA encapsulated in LNPs and doped with 16 mol% of helper lipids was tested using GL261, U87 and SIM-A9 cell lines. Several LNP formations resulted in stable transfection (upto 5 days) of GFP mRNA in all the cell lines tested in vitro. The successful LNP candidates (enabling >80% transfection efficacy) were then tested in vivo to deliver luciferase mRNA to brain tumors via intrathecal administration in a syngeneic glioblastoma (GBM) mouse model, which confirmed luciferase expression in brain tumors in the cortex. LNPs were then tested to deliver Cre recombinase mRNA in syngeneic GBM mouse model genetically modified to express tdTomato under LoxP marker cassette that enabled identification of LNP targeted cells. mRNA was successfully delivered to tumor cells (70-80% transfected) and a range of different cells in the tumor microenvironment, including tumor-associated macrophages (80-90% transfected), neurons (31- 40% transfected), neural stem cells (39-62% transfected), oligodendrocytes (70-80% transfected) and astrocytes (44-76% transfected). Then, LNP formulations were assessed for delivering Cas9 mRNA and CD81 sgRNA (model protein) in murine syngeneic GBM model to enable gene editing in brain tumor cells. Sanger sequencing showed that CRISPR-Cas9 editing was successful in ~94% of brain tumor cells in vivo. In conclusion, we have developed a library of safe LNPs that can transfect GBM cells in vivo with high efficacy. This technology can potentially be used to develop novel mRNA therapies for GBM by delivering single or multiple mRNAs and holds great potential as a tool to study brain tumor biology.