Immunobiology of COVID-19: Mechanistic and therapeutic insights from animal models

The distribution of the immune system throughout the body complicates in vitro assessments of coronavirus disease 2019(COVID-19)immunobiology,often resulting in a lack of reproducibility when extrapolated to the whole organism.Consequently,developing animal models is imperative for a comprehensive understanding of the pathology and immunology of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection.This review summarizes current progress related to COVID-19 animal models,including non-human primates(NHPs),mice,and hamsters,with a focus on their roles in exploring the mechanisms of immunopathology,immune protection,and long-term effects of SARS-CoV-2 infection,as well as their application in immunoprevention and immunotherapy of SARS-CoV-2 infection.Differences among these animal models and their specific applications are also highlighted,as no single model can fully encapsulate all aspects of COVID-19.To effectively address the challenges posed by COVID-19,it is essential to select appropriate animal models that can accurately replicate both fatal and non-fatal infections with varying courses and severities.Optimizing animal model libraries and associated research tools is key to resolving the global COVID-19 pandemic,serving as a robust resource for future emerging infectious diseases.

Modeling human gastric cancers in immunocompetent mice

Gastric cancer(GC)is a major cause of cancer-related mortality worldwide.GC is determined by multiple(epi)genetic and environmental factors;can occur at distinct anatomic positions of the stomach;and displays high heterogeneity,with different cellular origins and diverse histological and molecular features.This heterogeneity has hindered efforts to fully understand the pathology of GC and develop efficient therapeutics.In the past decade,great progress has been made in the study of GC,particularly in molecular subtyping,investigation of the immune microenvironment,and defining the evolutionary path and dynamics.Preclinical mouse models,particularly immunocompetent models that mimic the cellular and molecular features of human GC,in combination with organoid culture and clinical studies,have provided powerful tools for elucidating the molecular and cellular mechanisms underlying GC pathology and immune evasion,and the development of novel therapeutic strategies.Herein,we first briefly introduce current progress and challenges in GC study and subsequently summarize immunocompetent GC mouse models,emphasizing the potential application of genetically engineered mouse models in antitumor immunity and immunotherapy studies.

Prognostic analysis of related factors of adverse reactions to immunotherapy in advanced gastric cancer and establishment of a nomogram model

BACKGROUND Immunotherapy for advanced gastric cancer has attracted widespread attention in recent years.However,the adverse reactions of immunotherapy and its relationship with patient prognosis still need further study.In order to determine the association between adverse reaction factors and prognosis,the aim of this study was to conduct a systematic prognostic analysis.By comprehensively evaluating the clinical data of patients with advanced gastric cancer treated by immunotherapy,a nomogram model will be established to predict the survival status of patients more accurately.AIM To explore the characteristics and predictors of immune-related adverse reactions(irAEs)in advanced gastric cancer patients receiving immunotherapy with programmed death protein-1(PD-1)inhibitors and to analyze the correlation between irAEs and patient prognosis.METHODS A total of 140 patients with advanced gastric cancer who were treated with PD-1 inhibitors in our hospital from June 2021 to October 2023 were selected.Patients were divided into the irAEs group and the non-irAEs group according to whether or not irAEs occurred.Clinical features,manifestations,and prognosis of irAEs in the two groups were collected and analyzed.A multivariate logistic regression model was used to analyze the related factors affecting the occurrence of irAEs,and the prediction model of irAEs was established.The receiver operating characteristic(ROC)curve was used to evaluate the ability of different indicators to predict irAEs.A Kaplan-Meier survival curve was used to analyze the correlation between irAEs and prognosis.The Cox proportional risk model was used to analyze the related factors affecting the prognosis of patients.RESULTS A total of 132 patients were followed up,of whom 63(47.7%)developed irAEs.We looked at the two groups’clinical features and found that the two groups were statistically different in age≥65 years,Ki-67 index,white blood cell count,neutrophil count,and regulatory T cell(Treg)count(all P<0.05).Multivariate logistic regression analysis showed that Treg count was a protective factor affecting irAEs occurrence(P=0.030).The ROC curve indicated that Treg+Ki-67+age(≥65 years)combined could predict irAEs well(area under the curve=0.753,95%confidence interval:0.623-0.848,P=0.001).Results of the Kaplan-Meier survival curve showed that progressionfree survival(PFS)was longer in the irAEs group than in the non-irAEs group(P=0.001).Cox proportional hazard regression analysis suggested that the occurrence of irAEs was an independent factor for PFS(P=0.006).CONCLUSION The number of Treg cells is a separate factor that affects irAEs in advanced gastric cancer patients receiving PD-1 inhibitor immunotherapy.irAEs can affect the patients’PFS and result in longer PFS.Treg+Ki-67+age(≥65 years old)combined can better predict the occurrence of adverse reactions.

Projecting the Dynamic Trends of Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome:Modeling the Epidemic in Sichuan Province, China

Objective Our study aimed to provide a comprehensive overview of the current status and dynamic trends of the human immunodeficiency virus(HIV)prevalence in Sichuan,the second most heavily affected province in China,and to explore future interventions.Methods The epidemiological,behavioral,and population census data from multiple sources were analyzed to extract inputs for an acquired immunodeficiency syndrome(AIDS)epidemic model(AEM).Baseline curves,derived from historical trends in HIV prevalence,were used,and the AEM was employed to examine future intervention scenarios.Results In 2015,the modeled data suggested an adult HIV prevalence of 0.191%in Sichuan,with an estimated 128,766 people living with HIV/AIDS and 16,983 individuals with newly diagnosed infections.Considering current high-risk behaviors,the model predicts an increase in the adult prevalence to 0.306%by 2025,projecting an estimated 212,168 people living with HIV/AIDS and 16,555 individuals with newly diagnosed infections.Conclusion Heterosexual transmission will likely emerge as the primary mode of AIDS transmission in Sichuan.Furthermore,we anticipate a stabilization in the incidence of AIDS with a concurrent increase in prevalence.Implementing comprehensive intervention measures aimed at high-risk groups could effectively alleviate the spread of AIDS in Sichuan.

Establishing prognostic models for intrahepatic cholangiocarcinoma based on immune cells

BACKGROUND Intrahepatic cholangiocarcinoma(ICC)is a malignant liver tumor that is challenging to treat and manage and current prognostic models for the disease are inefficient or ineffective.Tumor-associated immune cells are critical for tumor development and progression.The main goal of this study was to establish models based on tumor-associated immune cells for predicting the overall survival of patients undergoing surgery for ICC.AIM To establish 1-year and 3-year prognostic models for ICC after surgical resection.METHODS Immunohistochemical staining was performed for CD4,CD8,CD20,pan-cytokeratin(CK),and CD68 in tumors and paired adjacent tissues from 141 patients with ICC who underwent curative surgery.Selection of variables was based on regression diagnostic procedures and goodness-of-fit tests(PH assumption).Clinical parameters and pathological diagnoses,combined with the distribution of immune cells in tumors and paired adjacent tissues,were utilized to establish 1-and 3-year prognostic models.RESULTS This is an important application of immune cells in the tumor microenvironment.CD4,CD8,CD20,and CK were included in the establishment of our prognostic model by stepwise selection,whereas CD68 was not significantly associated with the prognosis of ICC.By integrating clinical data associated with ICC,distinct prognostic models were derived for 1-and 3-year survival outcomes using variable selection.The 1-year prediction model yielded a C-index of 0.7695%confidence interval(95%CI):0.65-0.87 and the 3-year prediction model produced a C-index of 0.69(95%CI:0.65-0.73).Internal validation yielded a C-index of 0.761(95%CI:0.669-0.853)for the 1-year model and 0.693(95%CI:0.642-0.744)for the 3-year model.CONCLUSION We developed Cox regression models for 1-year and 3-year survival predictions of patients with ICC who underwent resection,which has positive implications for establishing a more comprehensive prognostic model for ICC based on tumor immune microenvironment and immune cell changes in the future.

Comparison of immune responses and intestinal flora in epicutaneously sensitized BALB/c or C57BL/6 mouse models of food allergy

Cutaneous exposure to food allergens through a disrupted skin barrier is recognized as an important cause of food allergy,and the cutaneous sensitized mouse model has been established to investigate relevant allergic disorders.However,the role of different genetic backgrounds of mice on immune responses to food allergens upon epicutaneous sensitization is largely unknown.In this study,two strains of mice,i.e.,the BALB/c and C57BL/6 mice,were epicutaneously sensitized with ovalbumin on atopic dermatitis(AD)-like skin lesions,followed by intragastric challenge to induce IgE-mediated food allergy.Allergic outcomes were measured as clinical signs,specific antibodies and cytokines,and immune cell subpopulations,as well as changes in intestinal barrier function and gut microbiota.Results showed that both strains of mice exhibited typical food-allergic symptoms with a Th2-skewed response.The C57BL/6 mice,rather than the BALB/c mice,were fitter for establishing an epicutaneously sensitized model of food allergy since a stronger Th2-biased response and severer disruptions in the intestinal barrier and gut homeostasis were observed.This study provides knowledge for selecting an appropriate mouse model to study food-allergic responses associated with AD-like skin lesions and highlights the role of genetic variations in the immune mechanism underlying pathogenesis of food allergy.

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Analytical and NumericalMethods to Study the MFPT and SR of a Stochastic Tumor-Immune Model

The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model withnoise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian whitenoise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. Asfollows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPTis obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrenceof the tumor from the extinction state to the tumor-present state is more concerned in this paper. A moreefficient algorithmof Back-Propagation Neural Network (BPNN) is utilized in order to testify the correction of thetheoretical SPDandMFPT.With the existence of aweak signal, the functional relationship between Signal-to-NoiseRatio (SNR), noise intensities and correlation time is also studied. Numerical results show that both multiplicativeGaussian colored noise and additive Gaussian white noise can promote the extinction of the tumors, and themultiplicative Gaussian colored noise can lead to the resonance-like peak on MFPT curves, while the increasingintensity of the additiveGaussian white noise results in theminimum of MFPT. In addition, the correlation timesare negatively correlated with MFPT. As for the SNR, we find the intensities of both the Gaussian white noise andthe Gaussian colored noise, as well as their correlation intensity can induce SR. Especially, SNR is monotonouslyincreased in the case ofGaussian white noisewith the change of the correlation time.At last, the optimal parametersin BPNN structure are analyzed for MFPT from three aspects: the penalty factors, the number of neural networklayers and the number of nodes in each layer.

Stochastic Bifurcation of an SIS Epidemic Model with Treatment and Immigration

In this paper, we investigate an SIS model with treatment and immigration. Firstly, the two-dimensional model is simplified by using the stochastic averaging method. Then, we derive the local stability of the stochastic system by computing the Lyapunov exponent of the linearized system. Further, the global stability of the stochastic model is analyzed based on the singular boundary theory. Moreover, we prove that the model undergoes a Hopf bifurcation and a pitchfork bifurcation. Finally, several numerical examples are provided to illustrate the theoretical results. .

一种基于模型概率单调性变化的自适应IMM-UKF改进算法

针对现有交互式多模型(IMM)算法模型间切换迟滞和转换速率慢的缺点,提出一种基于模型概率单调性变化的自适应交互式多模型无迹卡尔曼滤波改进算法(mIMM-UKF)。该算法利用后验信息模型概率的单调性,对马尔可夫转移概率矩阵及模型估计概率进行二次修正,加快了匹配模型的切换速度及转换速率。仿真结果表明,与现有算法相比,该算法通过快速切换匹配模型,有效提高了水下目标跟踪精度。

基于改进自适应IMM算法的高速列车组合定位

针对列车高精度定位问题,该文提出基于改进自适应交互多模型(IMM)的高速列车高精度组合定位方法。首先,根据列车定位需求和各传感器特点,设计了卫星接收器、轮轴测速传感器、测速雷达以及单轴陀螺仪4种传感器的组合定位方案。然后,针对IMM融合滤波算法因先验信息不准导致固定参数设置不当的问题,引入Sage-Husa自适应滤波和转移概率矩阵(TPM)自适应更新集成为自适应IMM算法。针对多模型切换的滞后问题,利用子模型似然函数值能快速反映模型变化趋势的特点,将似然函数值设为判定标志,并引入判定窗对TPM矩阵元素进行修正,有效提升了模型的切换速度。最后,基于改进自适应IMM算法对4种传感器定位信息进行融合滤波,实现高速列车的高精度组合定位。仿真结果表明:改进后的算法相比其他自适应IMM算法提升定位精度1.6%~14.7%,并且能通过提高模型间切换速度来有效降低位置误差峰值,同时具备较好的抗噪性能。

采用STAMP-24Model的多组织事故分析

安全生产事故往往由多组织交互、多因素耦合造成,事故原因涉及多个组织。为预防和遏制多组织生产安全事故的发生,基于系统理论事故建模与过程模型(Systems-Theory Accident Modeling and Process,STAMP)、24Model,构建一种用于多组织事故分析的方法,并以青岛石油爆炸事故为例进行事故原因分析。结果显示:STAMP-24Model可以分组织,分层次且有效、全面、详细地分析涉及多个组织的事故原因,探究多组织之间的交互关系;对事故进行动态演化分析,可得到各组织不安全动作耦合关系与形成的事故失效链及管控失效路径,进而为预防多组织事故提供思路和参考。

基于改进24Model-ISM-SNA建筑工人不安全行为关联路径研究

建筑施工现场环境复杂,为有效控制不安全行为发生,基于行为安全“2-4”模型对360份具有代表性的建筑安全事故调查报告进行分析,提取出22个不安全行为的主要影响因素。利用灰色关联分析方法(GRA)改进的集成ISM-SNA模型,将不安全行为风险因素划分为表层、过渡层与深层,然后对风险因素进行可视化分析、中心度分析及凝聚子群分析,揭示了各致因因素间的关联关系和传导路径。结果表明,建筑工人不安全行为影响因素可划分成7级3阶的多级递阶结构,安全意识、现场监管、外部环境是建筑工人不安全行为的关键影响因素,同时现场监管和隐患排查到位能有效降低不安全行为的发生。

基于IMM-JPDA-ISTUKF的车载毫米波雷达多目标跟踪算法

为提高车载毫米波雷达多目标跟踪精度指标,提升道路车辆行驶安全性,文中在交互多模型无迹卡尔曼滤波(IMM-UKF)和联合概率数据关联(JPDA)融合的算法基础上,针对车辆运动状态突变处UKF鲁棒性差、滤波精度低的问题,提出了一种基于改进强跟踪UKF(ISTUKF)的IMM-JPDA-ISTUKF算法。通过模拟道路场景搭建的仿真环境对算法性能进行了验证,且为证明该算法在实际道路工况下跟踪精度的提升,还进行了雷达道路测试,通过雷达在道路上获取的车辆数据进一步验证了该算法的有效性。结果表明,该算法在目标车辆运动状态发生变化时的距离跟踪精度和速度跟踪精度方面均得到了提高。

基于24Model-D-ISM的地铁站火灾疏散影响因素研究

为预防地铁站火灾事故,深入了解地铁站火灾人员疏散影响因素间的内在联系与层次结构,基于第6版“2-4”模型(24Model)分析63起地铁站火灾疏散事故,充分考虑各个因素之间的交互作用,提取19个影响地铁站人员疏散的关键因素,建立地铁站火灾人员疏散影响因素指标体系;采用算子客观赋权法(C-OWA)改进决策试验与评价实验法(DEMATEL),确定地铁站火灾人员疏散的重要影响因素;在此基础上,采用解释结构模型(ISM)分析各个因素间的层次结构及相互作用路径,构建地铁站火灾人员疏散影响因素的多级递阶结构模型。研究结果表明:疏散引导、恐慌从众行为、人员拥挤为地铁站火灾人员疏散的关键影响因素;地铁站火灾人员疏散受表层因素、中间层因素、深层因素共同作用的影响,其中,疏散教育与培训、设施维护与检查、疏散预案等因素是根源影响因素,重视根源影响因素的改善有利于从本质上预防和控制事故的发生。

基于改进ATPM-IMM算法的外辐射源雷达机动目标跟踪

针对外辐射源雷达进行机动目标跟踪时,现有的自适应交互式多模型(AIMM)算法难以达到高精确度跟踪的问题,提出一种基于改进的自适应转移概率交互式多模型(ATPM-IMM)的机动目标跟踪算法。该算法在ATPM-IMM算法的基础上增加了自适应控制窗,对转移概率矩阵进行再次修正,从而可根据目标的机动情况自适应切换机动模型,提高真实模型的匹配概率。仿真和实测数据结果表明,所提算法可有效提高外辐射源雷达进行机动目标跟踪的精确度。

24Model与LCM原因因素定义对比研究

为探究损失致因模型(LCM)原因因素定义与事故致因“2-4”模型(24Model)存在的异同和优缺点,梳理2个模型各层面原因和结果的定义,对比定义内容及其对事故原因分析等安全实务的指导作用,并以一起瓦斯爆炸事故为例加以实证分析,获得二者分析结果之间的差异。研究结果表明:LCM是首个将管理因素纳入事故致因分析的一维事件序列模型,可明确各层面原因因素的定义和因素间的逻辑关系,但部分定义存在交叉重复的问题,并没有揭示安全工作指导思想等深层次事故致因因素;24Model作为系统性事故致因模型,对各类因素的定义均以组织为主体,描述事件、事故、安全的概念内涵,划分个体安全动作、安全能力和组织安全管理体系的类别并给出含义解析,探究组织安全文化层面的问题并以32个元素体现;2个模型的事故原因分析方法均建立在对各层级原因因素定义的基础上,并适用于模型理论体系本身。

Advances in viral encephalitis:Viral transmission,host immunity,and experimental animal models

Viral infections have led to many public health crises and pandemics in the last few centuries.Neurotropic virus infection-induced viral encephalitis(VE),especially the symptomatic inflammation of the meninges and brain parenchyma,has attracted growing attention due to its high mortality and disability rates.Understanding the infectious routes of neurotropic viruses and the mechanism underlying the host immune response is critical to reduce viral spread and improve antiviral therapy outcomes.In this review,we summarize the common categories of neurotropic viruses,viral transmission routes in the body,host immune responses,and experimental animal models used for VE study to gain a deeper understanding of recent progress in the pathogenic and immunological mechanisms under neurotropic viral infection.This review should provide valuable resources and perspectives on how to cope with pandemic infections.

基于IMM-KF算法改进的欺骗式干扰检测算法

针对基于接收机基线长度的欺骗干扰检测方法在短基线和低定位精度下检测性能差的问题,提出了一种交互多模型卡尔曼滤波(Interactive Multi-model Kalman Filtering,IMM-KF)算法改进的欺骗式干扰检测算法。该方法通过IMM-KF算法对两个接收机天线的位置信息进行最优估计,提高基线解算精度,从而提升基于基线长度的欺骗干扰检测方法的检测性能。首先,详细分析了基于基线长度的欺骗干扰检测方法的数学原理;其次,建立欺骗检测算法模型,并对其性能进行仿真分析;然后,根据算法应用场景引入IMM-KF算法优化基线长度估计量;最后,针对不同的基线长度和定位精度进行仿真实验,对算法进行性能评估。仿真结果表明,该算法可以在接收机伪距测量精度为0.1 m、基线长度为0.5 m的情况下达到86%的检测成功率,而传统算法在此情况下检测成功率仅为10%。

A novel pulmonary fibrosis murine model with immune-related liver injury

Idiopathic pulmonary fibrosis(IPF),characterized by aggravated alveolar destruc-tion and fibrotic matrix deposition,tendentiously experiences the stage called acute exacerbation IPF(AE-IPF)and progresses to multiple organ damage,especially liver injury.Recent studies have found a variety of immune microenvironment disorders associated with elevated IPF risk and secondary organ injury,whereas current animal models induced with bleomycin(BLM)could not completely reflect the pathologi-cal manifestations of AE-IPF patients in clinic,and the exact underlying mechanisms are not yet fully explored.In the current study,we established an AE-IPF model by tracheal administration of a single dose of BLM and then repeated administrations of lipopolysaccharide in mice.This mouse model successfully recapitulated the clinical features of AE-IPF,including excessive intrapulmonary inflammation and fibrosis and extrapulmonary manifestations,as indicated by significant upregulation of Il6,Tnfa,Il1b,Tgfb,fibronectin,and Col1a1 in both lungs and liver and elevated serum aspartate transaminase and alanine transaminase levels.These effects might be attributed to the regulation of Th17 cells.By sharing this novel murine model,we expect to pro-vide an appropriate experimental platform to investigate the pathogenesis of AE-IPF coupled with liver injury and contribute to the discovery and development of targeted interventions.