杀莠素A对兔软骨细胞增殖和产生NO的影响

研究杀莠素Ａ对ＩＬ－１抑制软骨细胞增殖和诱导ＮＯ产生的作用。方法：ＩＬ－１单独或与杀莠素Ａ共育于软骨细胞,用结晶紫染色法测定软骨细胞增殖,Ｇｒｉｅｓｓ法测定ＮＯ的产生。结果：ＩＬ－１抑制软骨细胞增殖,杀莠素Ａ剂量依赖地逆转ＩＬ－１的作用。杀莠素Ａ还能剂量依赖地抑制ＩＬ－１诱导软骨细胞产生ＮＯ。结论：杀莠素Ａ逆转ＩＬ－１换制软骨细胞增殖的作用可能是通过ＮＯ介导的。     

新型重组人肿瘤坏死因子对大鼠肝细胞及小鼠肝组织中钙稳度的影响

目的;研究新型重组人肿瘤坏死因子（ｒｈＴＮＦ－ＮＣ）对培养大鼠肝细胞内游离Ｃａ＾２＋浓度（「Ｃａ＾２＋」ｉ）及小鼠肝组织中Ｃａ６２＋含量的影响。方法：以Ｆｕｒａ－２／ＡＭ为Ｃａ＾２＋荧光检测,荧光分光光度法测定培养大鼠肝细胞内「Ｃａ＾２＋」ｉ;原子吸收分光光度法测定小鼠肝组织中Ｃａ＾２＋含量。结果：在加入ｒｈＴＮＦ－ＮＣ ３ｈ后,可剂量依赖性地引起培养大鼠肝细胞内「Ｃａ＾２＋」ｉ显著升高。     

Hepatic epithelioid hemangioendothelioma: Update on diagnosis and therapy

With an estimated incidence of only 1-2 cases in every 1 million people, hepatic epithelioid hemangioendothelioma (HEHE) is a rare vascular endothelial cell tumor occurring in the liver and consisting of epithelioid and histiocyte-like vascular endothelial cells in mucus or a fibrotic matrix. HEHE is characterized as a low-to-moderate grade malignant tumor and is classified into three types: solitary, multiple, and diffuse. Both the etiology and characteristic clinical manifestations of HEHE are unclear. However, HEHE has a characteristic appearance on imaging including ultrasound, magnetic resonance imaging, and positron emission tomography/computerized tomography. Still, its diagnosis depends mainly on pathological findings, with immunohistochemical detection of endothelial markers cluster of differentiation 31 (CD31), CD34, CD10, vimentin, and factor VIII antigen as the basis of diagnosis. Hepatectomy and/or liver transplantation are the first choice for treatment, but various chemotherapeutic drugs are reportedly effective, providing a promising treatment option. In this review, we summarize the literature related to the diagnosis and treatment of HEHE, which provides future perspectives for the clinical management of HEHE.     

Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes

Parameter estimation for nonlinear dynamic system models, represented by ordinary differential equations (ODEs), using noisy and sparse data, is a vital task in many fields. We propose a fast and accurate method, manifold-constrained Gaussian process inference (MAGI), for this task. MAGI uses a Gaussian process model over time series data, explicitly conditioned on the manifold constraint that derivatives of the Gaussian process must satisfy the ODE system. By doing so, we completely bypass the need for numerical integration and achieve substantial savings in computational time. MAGI is also suitable for inference with unobserved system components, which often occur in real experiments. MAGI is distinct from existing approaches as we provide a principled statistical construction under a Bayesian framework, which incorporates the ODE system through the manifold constraint. We demonstrate the accuracy and speed of MAGI using realistic examples based on physical experiments.

Dynamic systems, represented as a set of ordinary differential equations (ODEs), are commonly used to model behaviors in scientific domains, such as gene regulation (1), biological rhythms (2), spread of disease (3), ecology (4), etc. We focus on models specified by a set of ODEsx˙(t)=dx(t)dt=f(x(t),θ,t), t∈[0,T],[1]where the vector x(t) contains the system outputs that evolve over time t and θ is the vector of model parameters to be estimated from experimental/observational data. When f is nonlinear, solving x(t) given initial conditions x(0) and θ generally requires a numerical integration method, such as Runge–Kutta.Historically, ODEs have mainly been used for conceptual or theoretical understanding rather than data fitting as experimental data were limited. Advances in experimental and data collection techniques have increased the capacity to follow dynamic systems closer to real time. Such data will generally be recorded at discrete times and subject to measurement error. Thus, we assume that we observe y(τ)=x(τ)+ϵ(τ) at a set of observation time points τ with error ϵ governed by noise level σ. Our focus here is inference of θ given y(τ), with emphasis on nonlinear f where specialized methods that exploit a linear structure (e.g., refs. 5 and 6), are not generally applicable. We shall present a coherent, statistically principled framework for dynamic system inference with the help of Gaussian processes (GPs). The key to our method is to restrict the GPs on a manifold that satisfies the ODE system: Thus, we name our method MAGI (manifold-constrained Gaussian process inference). Placing a GP on x(t) facilitates inference of θ without numerical integration, and our explicit manifold constraint is the key idea that addresses the conceptual incompatibility between the GP and the specification of the ODE model, as we shall discuss shortly when overviewing our method. We show that the resulting parameter inference is computationally efficient, statistically principled, and effective in a variety of practical scenarios. MAGI particularly works in the cases when some system component(s) is/are unobserved. To the best of our knowledge, none of the current available software packages that do not use numerical integration can analyze systems with unobserved component(s).     

Whether cognitive behavioral therapy is effective for Alzheimer's disease: A protocol for systematic review and network meta-analysis

Background:Alzheimer disease (AD) is a progressive neurodegenerative disease characterized by impaired memory and cognitive judgment. It is the leading cause of dementia in the elderly, and its high morbidity and mortality have also brought a significant social burden. So far, there is no method can completely cure Alzheimer''s dementia, but there are many non-drug treatments that have been praised by people, especially the cognitive behavioral therapy proposed in recent years. The main purpose of this article is to evaluate the effect of cognitive behavioral therapy on the cognitive function improvement of patients with Alzheimer''s dementia.Methods:We did a network meta-analysis to identify both direct and indirect evidence in relevant studies. A systematic literature search will be performed in the Cochrane Library, PubMed, and EMBASE from inception to October 2020. We extracted the relevant information from these trials with a predefined data extraction sheet and assessed the risk of bias with the Cochrane risk of bias tool.The outcomes investigated were Mini–Mental State Examination and AD Assessment Scale-Cognitive section scores. We did a pair-wise meta-analysis using the fixed-effects model and then did a random-effects network meta-analysis within a Bayesian framework. The = the Assessment of Multiple Systematic Reviews-2 scale, Preferred Reporting Items for Systematic Reviews and Meta-Analyses scale and Grading of Recommendations Assessment, Development and Evaluation were used to assess the quality and evidence grade of the literature. General characteristics of the eligible randomized controlled trials will be summarized and described. Meanwhile, The ADDIS software will be used to perform the network meta-analysis, and the result figures will be generated by STATA 15.0 software.Results:Using the draft search strategy of databases and after screening,7 randomized controlled trials met the a priori criteria and were included. This network mate-analysis will be published in a peer-reviewed journal.Conclusion:Our study will provide evidence for cognitive behavioral intervention in AD patients. And provide recommendations and guidelines for the clinic.Protocol Registration:INPLASY2020110052.