FibroScan-aspartate transaminase:A superior non-invasive model for diagnosing high-risk metabolic dysfunction-associated steatohepatitis

BACKGROUND Non-alcoholic fatty liver disease(NAFLD)with hepatic histological NAFLD activity score≥4 and fibrosis stage F≥2 is regarded as“at risk”non-alcoholic steatohepatitis(NASH).Based on an international consensus,NAFLD and NASH were renamed as metabolic dysfunction-associated steatotic liver disease(MASLD)and metabolic dysfunction-associated steatohepatitis(MASH),respectively;hence,we introduced the term“high-risk MASH”.Diagnostic values of seven non-invasive models,including FibroScan-aspartate transaminase(FAST),fibrosis-4(FIB-4),aspartate transaminase to platelet ratio index(APRI),etc.for high-risk MASH have rarely been studied and compared in MASLD.AIM To assess the clinical value of seven non-invasive models as alternatives to liver biopsy for diagnosing high-risk MASH.METHODS A retrospective analysis was conducted on 309 patients diagnosed with NAFLD via liver biopsy at Beijing Ditan Hospital,between January 2012 and December 2020.After screening for MASLD and the exclusion criteria,279 patients wereincluded and categorized into high-risk and non-high-risk MASH groups.Utilizing threshold values of each model,sensitivity,specificity,positive predictive value(PPV),and negative predictive values(NPV),were calculated.Receiver operating characteristic curves were constructed to evaluate their diagnostic efficacy based on the area under the curve(AUROC).RESULTS MASLD diagnostic criteria were met by 99.4%patients with NAFLD.The MASLD population was analyzed in two cohorts:Overall population(279 patients)and the subgroup(117 patients)who underwent liver transient elastography(FibroScan).In the overall population,FIB-4 showed better diagnostic efficacy and higher PPV,with sensitivity,specificity,PPV,NPV,and AUROC of 26.9%,95.2%,73.5%,72.2%,and 0.75.APRI,Forns index,and aspartate transaminase to alanine transaminase ratio(ARR)showed moderate diagnostic efficacy,whereas S index and gamma-glutamyl transpeptidase to platelet ratio(GPR)were relatively weaker.In the subgroup,FAST had the highest diagnostic efficacy,its sensitivity,specificity,PPV,NPV,and AUROC were 44.2%,92.3%,82.1%,67.4%,and 0.82.The FIB-4 AUROC was 0.76.S index and GPR exhibited almost no diagnostic value for high-risk MASH.CONCLUSION FAST and FIB-4 could replace liver biopsy as more effectively diagnostic methods for high-risk MASH compared to APRI,Forns index,ARR,S index,and GPR;FAST is superior to FIB-4.

Rifaximin on epigenetics and autophagy in animal model of hepatocellular carcinoma secondary to metabolic-dysfunction associated steatotic liver disease

BACKGROUND Prevalence of hepatocellular carcinoma(HCC)is increasing,especially in patients with metabolic dysfunctionassociated steatotic liver disease(MASLD).AIM To investigate rifaximin(RIF)effects on epigenetic/autophagy markers in animals.METHODS Adult Sprague-Dawley rats were randomly assigned(n=8,each)and treated from 5-16 wk:Control[standard diet,water plus gavage with vehicle(Veh)],HCC[high-fat choline deficient diet(HFCD),diethylnitrosamine(DEN)in drinking water and Veh gavage],and RIF[HFCD,DEN and RIF(50 mg/kg/d)gavage].Gene expression of epigenetic/autophagy markers and circulating miRNAs were obtained.RESULTS All HCC and RIF animals developed metabolic-dysfunction associated steatohepatitis fibrosis,and cirrhosis,but three RIF-group did not develop HCC.Comparing animals who developed HCC with those who did not,miR-122,miR-34a,tubulin alpha-1c(Tuba-1c),metalloproteinases-2(Mmp2),and metalloproteinases-9(Mmp9)were significantly higher in the HCC-group.The opposite occurred with Becn1,coactivator associated arginine methyltransferase-1(Carm1),enhancer of zeste homolog-2(Ezh2),autophagy-related factor LC3A/B(Map1 Lc3b),and p62/sequestosome-1(p62/SQSTM1)-protein.Comparing with controls,Map1 Lc3b,Becn1 and Ezh2 were lower in HCC and RIF-groups(P<0.05).Carm1 was lower in HCC compared to RIF(P<0.05).Hepatic expression of Mmp9 was higher in HCC in relation to the control;the opposite was observed for p62/Sqstm1(P<0.05).Expression of p62/SQSTM1 protein was lower in the RIF-group compared to the control(P=0.024).There was no difference among groups for Tuba-1c,Aldolase-B,alpha-fetoprotein,and Mmp2(P>0.05).miR-122 was higher in HCC,and miR-34a in RIF compared to controls(P<0.05).miR-26b was lower in HCC compared to RIF,and the inverse was observed for miR-224(P<0.05).There was no difference among groups regarding miR-33a,miR-143,miR-155,miR-375 and miR-21(P>0.05).CONCLUSION RIF might have a possible beneficial effect on preventing/delaying liver carcinogenesis through epigenetic modulation in a rat model of MASLD-HCC.

Ornithine aspartate effects on bacterial composition and metabolic pathways in a rat model of steatotic liver disease

BACKGROUND Metabolic-dysfunction associated steatotic liver disease(MASLD)is a hepatic manifestation of metabolic syndrome.Studies suggest ornithine aspartate(LOLA)as drug therapy.AIM To analyze the influence of LOLA intake on gut microbiota using a nutritional model of MASLD.METHODS Adult male Sprague Dawley rats were randomized into three groups:Control(10 rats fed with a standard diet),MASLD(10 rats fed with a high-fat and choline-deficient diet),and LOLA(10 rats receiving 200 mg/kg/d LOLA,after the 16th week receiving high-fat and choline-deficient diet).After 28 wk of the experiment,animals were euthanized,and feces present in the intestine were collected.Following fecal DNA extraction,the V4 region of the 16S rRNA gene was amplified followed by sequencing in an Ion S5™system.RESULTS Alpha and beta diversity metrics were comparable between MASLD and LOLA.3 OTUs were differentially abundant between MASLD and LOLA,which belong to the species Helicobacter rodentium,Parabacteroides goldsteinii,and Parabacteroides distasonis.The functional prediction provided two different metabolic profiles between MASLD and LOLA.The 9 pathways differentially abundant in MASLD are related to a change in energy source,adenosine/purine nucleotides degradation as well as guanosine and adenosine deoxyribonucleotides biosynthesis.The 14 pathways differentially abundant in LOLA are associated with four major metabolic functions primarily influenced by L-aspartate,including tricarboxylic acid cycle pathways,purine/guanosine nucleotides biosynthesis,pyrimidine ribonucleotides biosynthesis and salvage as well as lipid IVA biosynthesis.CONCLUSION Although LOLA had no influence on alpha and beta diversity in this nutritional model of MASLD,it was associated with changes in specific gut microbes and their related metabolic pathways.

Prognostic model and treatment plan analysis of hepatocellular carcinoma based on genes related to glutamine metabolism

Objective:To identify the prognosis of hepatocellular carcinoma(HCC)and the effect of anti-cancer drug therapy by screening glutamine metabolism-related signature genes because glutamine metabolism plays an important role in tumor development.Methods:We obtained gene expression samples of normal liver tissue and hepatocellular carcinoma from the TCGA database and GEO database,screened for differentially expressed glutamine metabolismrelated genes(GMRGs),constructed a prognostic model by lasso regression and step cox analysis,and assessed the differences in drug sensitivity between high-and low-risk groups.Results:We screened 23 differentially expressed GMRGs by differential analysis,and correlation loop plots and PPI protein interaction networks indicated that these differential genes were strongly correlated.The four most characterized genes(CAD,PPAT,PYCR3,and SLC7A11)were obtained by lasso regression and step cox,and a risk model was constructed and confirmed to have reliable predictive power in the TCGA dataset and GEO dataset.Finally,immunotherapy is better in the high-risk group than in the low-risk group,and chemotherapy and targeted drug therapy are better in the low-risk group than in the high-risk group.Conclusion:In conclusion,we have developed a reliable prognostic risk model characterized by glutamine metabolism-related genes,which may provide a viable basis for the prognosis and Treatment options of HCC patients.

基于ADAMS的六轴搬运机器人动力学仿真分析

以折弯生产线中上下料工序中所使用的六轴搬运机器人为例开展研究,首先确定了机器人的结构形式,设计了各关键零部件并建立了六轴搬运机器人的三维模型;其次基于ADAMS软件建立了搬运机器人的动力学仿真模型,对机器人的零部件材料、约束、驱动,以及载荷进行定义;最后通过对机器人进行动力学仿真模拟得到了机器人的基本运动数据。研究结果表明:所设计的搬运机器人6个轴的位移、速度、加速度、关节受力以及扭矩曲线基本上连续、无异变。六轴搬运机器人在运动过程中运动平稳、振荡较小、设计合理,为改进其结构提供了理论支撑。

Characteristics of glucose and lipid metabolism and the interaction between gut microbiota and colonic mucosal immunity in pigs during cold exposure

Background Cold regions have long autumn and winter seasons and low ambient temperatures.When pigs are unable to adjust to the cold,oxidative damage and inflammation may develop.However,the differences between cold and non-cold adaptation regarding glucose and lipid metabolism,gut microbiota and colonic mucosal immunological features in pigs are unknown.This study revealed the glucose and lipid metabolic responses and the dual role of gut microbiota in pigs during cold and non-cold adaptation.Moreover,the regulatory effects of dietary glucose supplements on glucose and lipid metabolism and the colonic mucosal barrier were evaluated in cold-exposed pigs.Results Cold and non-cold-adapted models were established by Min and Yorkshire pigs.Our results exhibited that cold exposure induced glucose overconsumption in non-cold-adapted pig models(Yorkshire pigs),decreasing plasma glucose concentrations.In this case,cold exposure enhanced the ATGL and CPT-1αexpression to promote liver lipolysis and fatty acid oxidation.Meanwhile,the two probiotics(Collinsella and Bifidobacterium)depletion and the enrichment of two pathogens(Sutterella and Escherichia-Shigella)in colonic microbiota are not conducive to colonic mucosal immunity.However,glucagon-mediated hepatic glycogenolysis in cold-adapted pig models(Min pigs)maintained the stability of glucose homeostasis during cold exposure.It contributed to the gut microbiota(including the enrichment of the Rikenellaceae RC9 gut group,[Eubacterium]coprostanoligenes group and WCHB1-41)that favored cold-adapted metabolism.Conclusions The results of both models indicate that the gut microbiota during cold adaptation contributes to the protection of the colonic mucosa.During non-cold adaptation,cold-induced glucose overconsumption promotes thermogenesis through lipolysis,but interferes with the gut microbiome and colonic mucosal immunity.Furthermore,glucagon-mediated hepatic glycogenolysis contributes to glucose homeostasis during cold exposure.

Rapid metabolic fingerprinting with the aid of chemometric models to identify authenticity of natural medicines: Turmeric, Ocimum, and Withania somnifera study

Herbal medicines are popular natural medicines that have been used for decades.The use of alternative medicines continues to expand rapidly across the world.The World Health Organization suggests that quality assessment of natural medicines is essential for any therapeutic or health care applications,as their therapeutic potential varies between different geographic origins,plant species,and varieties.Classification of herbal medicines based on a limited number of secondary metabolites is not an ideal approach.Their quality should be considered based on a complete metabolic profile,as their pharmacological activity is not due to a few specific secondary metabolites but rather a larger group of bioactive compounds.A holistic and integrative approach using rapid and nondestructive analytical strategies for the screening of herbal medicines is required for robust characterization.In this study,a rapid and effective quality assessment system for geographical traceability,species,and variety-specific authenticity of the widely used natural medicines turmeric,Ocimum,and Withania somnifera was investigated using Fourier transform near-infrared(FT-NIR)spectroscopy-based metabolic fingerprinting.Four different geographical origins of turmeric,five different Ocimum species,and three different varieties of roots and leaves of Withania somnifera were studied with the aid of machine learning approaches.Extremely good discrimination(R^(2)>0.98,Q^(2)>0.97,and accuracy=1.0)with sensitivity and specificity of 100%was achieved using this metabolic fingerprinting strategy.Our study demonstrated that FT-NIR-based rapid metabolic fingerprinting can be used as a robust analytical method to authenticate several important medicinal herbs.

基于ADAMS的植保机操纵稳定性分析与研究

为改善自走式植保机的操纵稳定性,基于动力学软件ADAMS/Car模块建立植保机多体仿真模型,对植保机在不同速度行驶工况下的转向盘角阶跃输入性能及横摆角速度响应等评价指标进行了仿真。对仿真结果进行分析和实车试验验证,结果表明:利用ADAMS/Car模块能恰当地分析和评价自走式植保机的操纵稳定性能,当车速为10m/s时,自走式植保机的操纵稳定性较好,可为动力分配控制系统优化奠定基础。

Construction and validation of a severity prediction model for metabolic associated fatty liver disease

Objective:To analyze the independent risk factors for the occurrence of moderate-to-severe metabolic-associated fatty liver disease(MAFLD),to construct a prediction model for moderate-to-severe MAFLD,and to verify the validity of the model.Methods:In the first part,278 medical examiners who were diagnosed with MAFLD in Medical Examination Center at the Second Affiliated Hospital of Hainan University from January to May 2022 were taken as the study subjects(training set),and they were divided into mild MAFLD group(200)and moderate-severe MAFLD group(78)based on ultrasound results.Demographic data and laboratory indexes were collected,and risk factors were screened by univariate and multifactor analysis.In the second part,a dichotomous logistic regression equation was used to construct a prediction model for moderate-to-severe MAFLD,and the model was visualized in a line graph.In the third part,the MAFLD population(200 people in the external validation set)from our physical examination center from November to December 2022 was collected as the moderate-to-severe MAFLD prediction model,and the risk factors in both groups were compared.The receiver operating characteristic(ROC)curves,calibration curves,and clinical applicability of the model were plotted to represent model discrimination for internal and external validation.Results:The risk factors of moderate-to-severe MAFLD were fasting glucose(FPG),blood uric acid(UA),triglycerides(TG),triglyceride glucose index(TyG),total cholesterol(CHOL),and high-density lipoprotein(HDL-C).UA[OR=1.021,95%CI(1.015,1.027),P<0.001]and FPG[OR=1.575,95%CI(1.158,2.143),P=0.004]were independent risk factors for people with moderate to severe MAFLD.The visualized line graph model showed that UA was the factor contributing more to the risk of moderate to severe MAFLD in this model.The ROC curves showed AUC values of 0.8701,0.8686 and 0.7991 for the training set,internal validation set and external validation set,respectively.The curves almost coincided with the reference line after calibration of the model calibration degree with P>0.05 in Hosmer-Lemeshow test.The decision curve analysis(DCA)plotted by the clinical applicability of the model was higher than the two extreme curves,predicting that patients with moderate to severe MAFLD would benefit from the prediction model.Conclusion:The prediction model constructed by combining FPG with UA has higher accuracy and better clinical applicability,and can be used for clinical diagnosis.

Exploiting fly models to investigate rare human neurological disorders

Rare neurological diseases,while individually are rare,collectively impact millions globally,leading to diverse and often severe neurological symptoms.Often attributed to genetic mutations that disrupt protein function or structure,understanding their genetic basis is crucial for accurate diagnosis and targeted therapies.To investigate the underlying pathogenesis of these conditions,researchers often use non-mammalian model organisms,such as Drosophila(fruit flies),which is valued for their genetic manipulability,cost-efficiency,and preservation of genes and biological functions across evolutionary time.Genetic tools available in Drosophila,including CRISPR-Cas9,offer a means to manipulate gene expression,allowing for a deep exploration of the genetic underpinnings of rare neurological diseases.Drosophila boasts a versatile genetic toolkit,rapid generation turnover,and ease of large-scale experimentation,making it an invaluable resource for identifying potential drug candidates.Researchers can expose flies carrying disease-associated mutations to various compounds,rapidly pinpointing promising therapeutic agents for further investigation in mammalian models and,ultimately,clinical trials.In this comprehensive review,we explore rare neurological diseases where fly research has significantly contributed to our understanding of their genetic basis,pathophysiology,and potential therapeutic implications.We discuss rare diseases associated with both neuron-expressed and glial-expressed genes.Specific cases include mutations in CDK19 resulting in epilepsy and developmental delay,mutations in TIAM1 leading to a neurodevelopmental disorder with seizures and language delay,and mutations in IRF2BPL causing seizures,a neurodevelopmental disorder with regression,loss of speech,and abnormal movements.And we explore mutations in EMC1 related to cerebellar atrophy,visual impairment,psychomotor retardation,and gain-of-function mutations in ACOX1 causing Mitchell syndrome.Loss-of-function mutations in ACOX1 result in ACOX1 deficiency,characterized by very-long-chain fatty acid accumulation and glial degeneration.Notably,this review highlights how modeling these diseases in Drosophila has provided valuable insights into their pathophysiology,offering a platform for the rapid identification of potential therapeutic interventions.Rare neurological diseases involve a wide range of expression systems,and sometimes common phenotypes can be found among different genes that cause abnormalities in neurons or glia.Furthermore,mutations within the same gene may result in varying functional outcomes,such as complete loss of function,partial loss of function,or gain-of-function mutations.The phenotypes observed in patients can differ significantly,underscoring the complexity of these conditions.In conclusion,Drosophila represents an indispensable and cost-effective tool for investigating rare neurological diseases.By facilitating the modeling of these conditions,Drosophila contributes to a deeper understanding of their genetic basis,pathophysiology,and potential therapies.This approach accelerates the discovery of promising drug candidates,ultimately benefiting patients affected by these complex and understudied diseases.

The bio-active components of the Mongolian medicine Horcha-6 and therapeutic mechanism in the rat migraine model

Background:The active components of Horcha-6 were identified using liquid chromatography with tandem mass spectrometry.Also,we investigated the potential mechanisms that explain why Horcha-6 may be effective in treating migraines through the use of network pharmacology and a rat migraine model.Methods:After identifying the active components of Horcha-6,the corresponding genes of the active components’target were obtained from the Universal Protein database,and a“compound-target-disease”network was constructed using Cytoscape 3.9.0 software.For the in vivo experiments,nitroglycerin was injected intraperitoneally into rats to create a migraine model.Pre-treatment with Horcha-6 was administered orally for 14 days,and rats were subjected to migraine-related behavior tests.RNA sequencing was performed to identify the gene expression regulated by Horcha-6 in the trigeminal nerve.Results:A total of 903 chemical components of Horcha-6 have been collected in the liquid chromatography with tandem mass spectrometry.We discovered 55 of the Horcha-6 bio-active components that were evaluated based on their Percent Human Oral Absorption(≥30%)and DL values(≥0.185)on the traditional Chinese medicine systems pharmacology database.The“compound-target-disease”network contained 163 intersection targets with the migraine state.Gene Ontology analysis indicated that these components significantly regulated the immune response,vascular function,oxidative stress,etc.When Kyoto Encyclopedia of Genes and Genomes enrichment analysis was performed,we observed that most of the target genes were significantly enriched in the inflammation and neuro-related signaling pathway,toll-like receptor signaling pathway,neuroactive ligand-receptor interaction,etc.These predictions were further demonstrated via in vivo animal model experiments.The RNA sequencing results showed that 41 genes were down-regulated(P<0.05)and 86 genes were up-regulated(P<0.05)in the Horcha-6 treated group compared with the untreated group.Those genes were mainly involved in neuromodulation,vascular function,and hormone metabolism.Conclusion:The 55 bio-active components in Horcha-6 regulate inflammation,hormone metabolism,and neurotransmitters and have potential as a therapy to treat migraines.

Studies on Models,Patterns and Requirements of Digestible Amino Acids for Layers by Nitrogen Metabolism

The nitrogen (N) metabolic experiments were made to estimate separately amino acid requirements of 4348 weeks old layers for maintenance, for protein accretion to estabolish models to estimate digestible amino acid requirements. The regression relationship of nitrogen retention vs amino acid intake was estimated for each amino acid by giving, at rate of N intake of 091, 052, 015 and 0007gkg-1 body-weight (W075) per d, the semi-synthetic diets was made specially deficient in one amino acid. From the regression coefficients, it was calculated that, for the accretion of 1 g protein, the dietary digestible amino acid requirements were (mg) Thr 631, Val 1004, Met 399, Ile 886, Leu 1143, Phe 632, Lys 870, His 205, Arg 879, Trp 214, Met+Cys 776, and Phe+Tyr 1143. Daily amino acid requirements for N equilibrium were estimated to be (mgkg-1W075 per day) Thr 506, Val 747, Met 303, ILe 667 Leu 814, Phe 448, Lys 605 His 147, Arg 739 ,Trp 173, Met+Cys 586, and Phe+Tyr 839 The dietary degestible amino acid patterns for protein accretion and N equilibrium were also proposed. The models of estimating digestible amino acid requirements for the different productions were developed.

Urban Metabolism Based on Emergy and Slack Based Model: A Case Study of Beijing, China

The key to studying urban sustainable development depends on quantifying stores, efficiencies of urban metabolisms and capturing urban metabolisms′ mechanisms. This paper builds up the metabolic emergy account and quantifies some important concepts of emergy stores. Emphasis is placed on the urban metabolic model based on the slack based model(SBM) method to measure urban metabolic efficiencies. Urban metabolic mechanisms are discussed by using the regression method. By integrating these models, this paper analyzes the urban metabolic development in Beijing from 2001 to 2010. We conclude that the metabolic emergy stores of Beijing increased significantly from 2001 to 2010, with the emergy imported accounting for most of the increase. The metabolic efficiencies in Beijing have improved since the 2008 Olympic Games. The population, economic growth, industrial structures, and environmental governance positively affect the overall urban metabolism, while the land expansion, urbanization and environmentally technical levels hinder the improving of urban metabolic efficiencies. The SBM metabolic method and the regression model based on the emergy analysis provide insights into the urban metabolic efficiencies and the mechanism. They can promote to integrate such concepts into their sustainability analyses and policy decisions.

A Mathematical Model for the Effect of Enzymes on Metabolism of Pharmacologically Active Substances

Addiction is a societal issue with many negative effects. Substances that cause addictive reactions are easily ingested and interact with some part of the neural pathway. This paper describes a mathematical model for the systemic level of a substance subject to degradation (via metabolism) and reversible binding to psychoactive sites. The model allows the determination of bound substance levels during the processing of a dose, and how the maximum level depends on system parameters. The model also allows the study of a particular periodic repetitive dosing described by a rapid ingestion if a dose is at constant intervals.

A Strategy for Development of Realistic Mathematical Models of Whole-Body Metabolism

When realistic mathematical models of whole body metabolism eventually become available, they are likely to add entirely new dimensions to the understanding of the integrated physiological function of the organism, in particular the mechanisms governing the regulation of transitions between different physiological states, like fed-fasted, exercise-rest and normal-diseased. So far the strategy for whole body modelling has primarily been a bottom-up approach where the central problem is an apparently insurmountable barrier of complexity involved in defining and optimising the huge number of parameters. Here we follow a top-down strategy and present a complete mathematical framework for realistic whole body model development. The approach proposed is modular and hierarchical and whole body metabolism is taken as the top level. Next are the organs, where the sum of the contributions from the individual organs must equal the top level metabolism. This hierarchy can be extended to lower levels of organisation, i.e. clusters of cells, individual cells, organelle and individual pathways. Exploiting this hierarchy, metabolism at each level forms an absolute constraint on the contributions from lower level. Importantly, these constraints can in many ways be defined experimentally through mass balance and flux data. Furthermore, the constrained approach allows the lower level models to be developed independently and subsequently adapted to the whole body model. The paper describes the process of whole body modelling in practical terms, centred on a mathematical framework, devised to allow whole-body models of any complexity to be developed. Furthermore, an example of sub-model incorporation in the whole-body framework is illustrated by adapting an existing erythrocyte model to the whole body constraints. Finally, we illustrate the operation of the system by including two sets of whole-body data from humans, reflecting two different physiological states.

<i>In Silico</i>Modeling of C1 Metabolism

An integrative computational, in silico, model of C1 metabolism is developed from molecular pathway systems identified from a recent, comprehensive systematic bioinformatics review of C1 metabolism. C1 metabolism is essential for all organisms to provide one-carbon units for methylation and other types of modifications, as well as for nucleic acid, amino acid, and other biomolecule syntheses. C1 metabolism consists of three important molecular pathway systems: 1) methionine biosynthesis, 2) methylation cycle, and 3) formaldehyde detoxification. Each of the three molecular pathway systems is individually modeled using the CytoSolve?? Collaboratory?, a proven and scalable computational systems biology platform for in silico modeling of complex molecular pathway systems. The individual models predict the temporal behavior of formaldehyde, formate, sarcosine, glutathione (GSH), and many other key biomolecules involved in C1 metabolism, which may be hard to measure experimentally. The individual models are then coupled and integrated dynamically using CytoSolve to produce, to the authors’ knowledge, the first comprehensive computational model of C1 metabolism. In silico modeling of the individual and integrated C1 metabolism models enables the identification of the most sensitive parameters involved in the detoxification of formaldehyde. This integrative model of C1 metabolism, giving its systems-based nature, can likely serve as a platform for: 1) generalized research and study of C1 metabolism, 2) hypothesis generation that motivates focused and specific in vitro and in vivo testing in perhaps a more efficient manner, 3) expanding a systems biology understanding of plant bio-molecular systems by integrating other known molecular pathway systems associated with C1 metabolism, and 4) exploring and testing the potential effects of exogenous inputs on the C1 metabolism system.

基于Adams宏命令的链传动弹箱动力学模型快速建模与分析

链传动弹箱因内部零部件数量多、接触关系复杂,一直是Adams动力学建模难点。通过研究Adams宏命令的语法结构并结合变量与循环对Adams进行二次开发,形成一套快速建模方法,在链条内、外链板上建立旋转铰的Marker点,建立某链传动弹箱的动力学模型;结果显示该快速建模方法建模效率高,能够反映弹箱实际运动规律,具备推广应用价值。

Evaluation value of three-dimensional finite element model analysis for bone mineral density and bone metabolism activity in patients with osteoporosis

Objective: To study the evaluation value of three-dimensional finite element model analysis for bone mineral density (BMD) and bone metabolism activity in patients with osteoporosis. Methods: A total of 218 patients who were diagnosed with osteoporosis in the hospital between February 2014 and January 2017 were collected as observation group, and 100 healthy volunteers who received physical examination in the hospital during the same period were selected as normal control group. The femoral head of the two groups was analyzed by three-dimensional finite element model, and the femoral head BMD levels and serum bone metabolism index contents were measured. Pearson test was used to evaluate the evaluation value of femoral head three-dimensional finite element model for osteoporosis. Results: The cancellous bone and cortical bone Von Mises stress value of observation group were lower than those of normal control group, and femoral neck BMD value of observation group was lower than that of normal control group;serum bone metabolism index BGP content was lower than that of normal control group while NBAP, TRACP-5b and CTX-1 contents were higher than those of normal control group. Pearson test showed that the cancellous bone and cortical bone Von Mises stress value of patients with osteoporosis were directly correlated with BMD value and bone metabolism index contents. Conclusion: The three-dimensional finite element model analysis resultsof patients with osteoporosis can objectively reflect the femoral headBMD value and bone metabolism activity, and is a reliable way to evaluate the risk of long-term fractures.

ADAMS虚拟仿真技术在理论力学运动学教学中的应用探讨

针对理论力学运动学部分内容抽象、求解过程繁琐的特点,在课堂教学中引入可视化虚拟仿真(Automatic Dynamic Analysis of Mechanical System,ADAMS)技术,增加教学直观性和丰富课堂教学内容。通过创建三维模型、约束模型和对运动环境建模,模拟真实的系统运动学约束和运动情况,对运动学问题进行快速、直观的仿真后处理分析,使理论力学运动学部分的理论知识教学更加直观生动。并通过与理论知识进行相互验证,可以加深学生对运动学知识的理解,激发主动学习和探究的主动性,提高教学效果。