Comparison of the Development Model of DTP Pharmacy between China and the United States and Its Enlightenment

Objective To systematically analyze the development status of DTP pharmacy in the United States and China,and to find out the problems in China’s DTP pharmacy and put forward some countermeasures and suggestions.Methods Relevant literature was searched and reviewed to study the history,market scale and development trend of DTP pharmacy in the United States and China.Then,the problems in China’s DTP pharmacy were found out.Results and Conclusion DTP pharmacy model in the U.S.has taken shape and is maturing.The high degree of industry concentration is an important feature of the U.S.pharmacy.In contrast,China’s DTP pharmacy started late,and the industry pattern has not yet been finalized.There are still some problems in the DTP pharmacy certification and quality management.It is recommended to promote the rapid development of DTP pharmacies in China by encouraging the outflow of prescription drugs,establishing a third-party pharmacy accreditation organization and strengthening the team of licensed pharmacists and other personnel.

A Study of a Moral Dilemma Stories Teaching Model Focusing on the Development of Higher-Order Thinking --"Synthetic polymers" as an example

Cultivating students'higher-order thinking is one of the important goals of modern education,and innovative teaching model is an effective way to achieve this goal.Aiming at the inadequacy of the existing moral dilemma stories approach in the transformation of knowledge and behavior,this research constructs a new Project Based Learning-Ethical Dilemma Stories(PBL-EDS)Teaching Model applicable to China's secondary education stage based on the innovative features of the moral dilemma stories approach on the core competencies,taking the chemistry subject as an example to carry out practice,and puts forward suggestions for the implementation of the teaching model.Chemistry as an example to carry out the practice,and suggestions are made for the implementation of the teaching model.

The Impact of The Three-Dimensional Cultivation Model on the Development of the Suzhou Tea Industry:Estimating Value of Output Per Acre and Diversified Sales Models

This article explores the impact of the three-dimensional cultivation mode on the development of the Suzhou tea industry,focusing on the diversified estimation of the value of output per acre and sales mode.It introduces the history and traditional cultivation practices of tea in Suzhou,as well as the current challenges and problems faced by the industry.An in-depth analysis was conducted on the overview and improvement plans of the three-dimensional cultivation mode,covering relevant technical methods.Based on this analysis,the impact of the three-dimensional cultivation on the value of output per acre was studied and predicted.Its potential and advantages were explored and compared with the effectiveness of traditional cultivation models.Additionally,the impact of the three-dimensional cultivation mode on sales was analyzed,examining its market adaptability and competitiveness,as well as its advantages in expanding sales channels and market coverage.The study also focused on the promoting effect of diversified sales models on the Suzhou tea industry,including direct consumption market development,tea processing product development and promotion,and the integration of tea culture and the tourism industry.To ensure sustainable development,the article evaluates the environmental impact,economic feasibility,social benefits,and farmer benefits of the three-dimensional cultivation model.Finally,the prospects for the development of the Suzhou tea industry were discussed,and the positioning and response strategies of the threedimensional cultivation model were proposed.

Optimized Allocation Model for Regional Agricultural Fertilizer Resources in Southern Xinjiang from the Perspective of Sustainable Development

[Objective] This study was to establish an optimized model for the allocation of agricultural fertilizer resources in Southern Xinjiang from the perspective of sustainable development.[Method] An optimized model for the allocation of agricultural fertilizer resources was established based on their allocation structure.Combined with the actual agricultural production in Aksu areas of Southern Xinjiang,by establishing a rational evaluation index system,under the premise of considering the planting area constraints,the total water resources constraints and the security constraints of food production,we established the empirical optimal allocation model of the regional agricultural fertilizer resources in Aksu area of Southern Xinjiang.Moreover,we solved the model by using the search algorithm of computer and lingo programming.[Result] The increased economic benefit was near to 1.8 billion Yuan by adopting the optimal allocation methods,with a relative increment of about 34.4%.[Conclusion] Our results provided theoretical basis for achieving the sustainable development of agricultural economy in Southern Xinjiang.

Discussion on the Current Situation and Development Model of Sightseeing Agriculture in Zhenjiang

Zhenjiang has distinct advantages to develop sightseeing agriculture as it possesses outstanding agricultural features, superior geographical locations and abundant cultural resources. At present, the situation of sightseeing agriculture in Zhenjiang is complicated. Though the development is going fast, it is quite blindfold for there is no reasonable overall plan; the production is at low level and requires more cultural connotation; the investment source is thin while the cooperation management hasn't been accomplished; a rudiment mode of varied sightseeing agriculture is forming rapidly. Based on this situation, five development modes are put forward for Zhenjiang's sightseeing agriculture, including: "enterprises as the main body of management" mode, "village committees as the main body of management" mode, "villagers as the main body of management" mode, government operating mode and joint development mode.

Development and validation of a prediction model for early screening of people at high risk for colorectal cancer

BACKGROUND Colorectal cancer(CRC)is a serious threat worldwide.Although early screening is suggested to be the most effective method to prevent and control CRC,the current situation of early screening for CRC is still not optimistic.In China,the incidence of CRC in the Yangtze River Delta region is increasing dramatically,but few studies have been conducted.Therefore,it is necessary to develop a simple and efficient early screening model for CRC.AIM To develop and validate an early-screening nomogram model to identify individuals at high risk of CRC.METHODS Data of 64448 participants obtained from Ningbo Hospital,China between 2014 and 2017 were retrospectively analyzed.The cohort comprised 64448 individuals,of which,530 were excluded due to missing or incorrect data.Of 63918,7607(11.9%)individuals were considered to be high risk for CRC,and 56311(88.1%)were not.The participants were randomly allocated to a training set(44743)or validation set(19175).The discriminatory ability,predictive accuracy,and clinical utility of the model were evaluated by constructing and analyzing receiver operating characteristic(ROC)curves and calibration curves and by decision curve analysis.Finally,the model was validated internally using a bootstrap resampling technique.RESULTS Seven variables,including demographic,lifestyle,and family history information,were examined.Multifactorial logistic regression analysis revealed that age[odds ratio(OR):1.03,95%confidence interval(CI):1.02-1.03,P<0.001],body mass index(BMI)(OR:1.07,95%CI:1.06-1.08,P<0.001),waist circumference(WC)(OR:1.03,95%CI:1.02-1.03 P<0.001),lifestyle(OR:0.45,95%CI:0.42-0.48,P<0.001),and family history(OR:4.28,95%CI:4.04-4.54,P<0.001)were the most significant predictors of high-risk CRC.Healthy lifestyle was a protective factor,whereas family history was the most significant risk factor.The area under the curve was 0.734(95%CI:0.723-0.745)for the final validation set ROC curve and 0.735(95%CI:0.728-0.742)for the training set ROC curve.The calibration curve demonstrated a high correlation between the CRC high-risk population predicted by the nomogram model and the actual CRC high-risk population.CONCLUSION The early-screening nomogram model for CRC prediction in high-risk populations developed in this study based on age,BMI,WC,lifestyle,and family history exhibited high accuracy.

Aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders:progress of experimental models based on disease pathogenesis

Neuromyelitis optica spectrum disorders are neuroinflammatory demyelinating disorders that lead to permanent visual loss and motor dysfunction.To date,no effective treatment exists as the exact causative mechanism remains unknown.Therefore,experimental models of neuromyelitis optica spectrum disorders are essential for exploring its pathogenesis and in screening for therapeutic targets.Since most patients with neuromyelitis optica spectrum disorders are seropositive for IgG autoantibodies against aquaporin-4,which is highly expressed on the membrane of astrocyte endfeet,most current experimental models are based on aquaporin-4-IgG that initially targets astrocytes.These experimental models have successfully simulated many pathological features of neuromyelitis optica spectrum disorders,such as aquaporin-4 loss,astrocytopathy,granulocyte and macrophage infiltration,complement activation,demyelination,and neuronal loss;however,they do not fully capture the pathological process of human neuromyelitis optica spectrum disorders.In this review,we summarize the currently known pathogenic mechanisms and the development of associated experimental models in vitro,ex vivo,and in vivo for neuromyelitis optica spectrum disorders,suggest potential pathogenic mechanisms for further investigation,and provide guidance on experimental model choices.In addition,this review summarizes the latest information on pathologies and therapies for neuromyelitis optica spectrum disorders based on experimental models of aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorders,offering further therapeutic targets and a theoretical basis for clinical trials.

Development of a new cerebral ischemia reperfusion model of Mongolian gerbils and standardized evaluation system

Background:The Mongolian gerbil is an excellent laboratory animal for preparing the cerebral ischemia model due to its inherent deficiency in the circle of Willis.However,the low incidence and unpredictability of symptoms are caused by numerous complex variant types of the circle.Additionally,the lack of an evaluation system for the cer-ebral ischemia/reperfusion(I/R)model of gerbils has shackled the application of this model.Methods:We created a symptom-oriented principle and detailed neurobehavioral scoring criteria.At different time points of reperfusion,we analyzed the alteration in locomotion by rotarod test and grip force score,infarct volume by triphenyltetrazo-lium chloride(TTC)staining,neuron loss using Nissl staining,and histological charac-teristics using hematoxylin-eosin(H&E)straining.Results:With a successful model rate of 56%,32 of the 57 gerbils operated by our method harbored typical features of cerebral I/R injury,and the mortality rate in the male gerbils was significantly higher than that in the female gerbils.The suc-cessfully prepared I/R gerbils demonstrated a significant reduction in motility and grip strength at 1 day after reperfusion;formed obvious infarction;exhibited typi-cal pathological features,such as tissue edema,neuronal atrophy and death,and vacuolated structures;and were partially recovered with the extension of reperfu-sion time.Conclusion:This study developed a new method for the unilateral common carotid artery ligation I/R model of gerbil and established a standardized evaluation system for this model,which could provide a new cerebral I/R model of gerbils with more practical applications.

Assessing the Performance of CMIP6 Models in Simulating Droughts across Global Drylands

Both the attribution of historical change and future projections of droughts rely heavily on climate modeling. However,reasonable drought simulations have remained a challenge, and the related performances of the current state-of-the-art Coupled Model Intercomparison Project phase 6(CMIP6) models remain unknown. Here, both the strengths and weaknesses of CMIP6 models in simulating droughts and corresponding hydrothermal conditions in drylands are assessed.While the general patterns of simulated meteorological elements in drylands resemble the observations, the annual precipitation is overestimated by ~33%(with a model spread of 2.3%–77.2%), along with an underestimation of potential evapotranspiration(PET) by ~32%(17.5%–47.2%). The water deficit condition, measured by the difference between precipitation and PET, is 50%(29.1%–71.7%) weaker than observations. The CMIP6 models show weaknesses in capturing the climate mean drought characteristics in drylands, particularly with the occurrence and duration largely underestimated in the hyperarid Afro-Asian areas. Nonetheless, the drought-associated meteorological anomalies, including reduced precipitation, warmer temperatures, higher evaporative demand, and increased water deficit conditions, are reasonably reproduced. The simulated magnitude of precipitation(water deficit) associated with dryland droughts is overestimated by 28%(24%) compared to observations. The observed increasing trends in drought fractional area,occurrence, and corresponding meteorological anomalies during 1980–2014 are reasonably reproduced. Still, the increase in drought characteristics, associated precipitation and water deficit are obviously underestimated after the late 1990s,especially for mild and moderate droughts, indicative of a weaker response of dryland drought changes to global warming in CMIP6 models. Our results suggest that it is imperative to employ bias correction approaches in drought-related studies over drylands by using CMIP6 outputs.

Investigating the elliptic anisotropy of identified particles in p-Pb collisions with a multi-phase transport model

The elliptic azimuthal anisotropy coefficient(v_(2))of the identified particles at midrapidity(|η|<0.8)was investigated in p-Pb collisions at√s_(NN)=5.02 TeV using a multi-phase transport model(AMPT).The calculations of differential v_(2)based on the advanced flow extraction method of light flavor hadrons(pions,kaons,protons,andΛ)in small collision systems were extended to a wider transverse momentum(p_(T))range of up to 8 GeV/c for the first time.The string-melting version of the AMPT model provides a good description of the measured p_(T)-differential v_(2)of the mesons but exhibits a slight deviation from the baryon v_(2).In addition,we observed the features of mass ordering at low p_(T)and the approximate number-of-constituentquark(NCQ)scaling at intermediate p_(T).Moreover,we demonstrate that hadronic rescattering does not have a significant impact on v_(2)in p-Pb collisions for different centrality selections,whereas partonic scattering dominates in generating the elliptic anisotropy of the final particles.This study provides further insight into the origin of collective-like behavior in small collision systems and has referential value for future measurements of azimuthal anisotropy.

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.

Development and validation of a new prognostic model for patients with acute-on-chronic liver failure in intensive care unit

BACKGROUND Cirrhotic patients with acute-on-chronic liver failure(ACLF)in the intensive care unit(ICU)have a poor but variable prognoses.Accurate prognosis evaluation can guide the rational management of patients with ACLF.However,existing prognostic scores for ACLF in the ICU environment lack sufficient accuracy.AIM To develop a new prognostic model for patients with ACLF in ICU.METHODS Data from 938 ACLF patients in the Medical Information Mart for Intensive Care(MIMIC)database were used to develop a new prognostic model(MIMIC ACLF)for ACLF.Discrimination,calibration and clinical utility of MIMIC ACLF were assessed by area under receiver operating characteristic curve(AUROC),calibration curve and decision curve analysis(DCA),respectively.MIMIC ACLF was then externally validated in a multiple-center cohort,the Electronic Intensive Care Collaborative Research Database and a single-center cohort from the Second Hospital of Hebei Medical University in China.RESULTS The MIMIC ACLF score was determined using nine variables:ln(age)×2.2+ln(white blood cell count)×0.22-ln(mean arterial pressure)×2.7+respiratory failure×0.6+renal failure×0.51+cerebral failure×0.31+ln(total bilirubin)×0.44+ln(internationalized normal ratio)×0.59+ln(serum potassium)×0.59.In MIMIC cohort,the AUROC(0.81/0.79)for MIMIC ACLF for 28/90-day ACLF mortality were significantly greater than those of Chronic Liver Failure Consortium ACLF(0.76/0.74),Model for End-stage Liver Disease(MELD;0.73/0.71)and MELD-Na(0.72/0.70)(all P<0.001).The consistency between actual and predicted 28/90-day survival rates of patients according to MIMIC ACLF score was excellent and superior to that of existing scores.The net benefit of MIMIC ACLF was greater than that achieved using existing scores within the 50%threshold probability.The superior predictive accuracy and clinical utility of MIMIC ACLF were validated in the external cohorts.CONCLUSION We developed and validated a new prognostic model with satisfactory accuracy for cirrhotic patients with ACLF hospitalized in the ICU.The model-based risk stratification and online calculator might facilitate the rational management of patients with ACLF.

Construction and validation of a pancreatic cancer prognostic model based on genes related to the hypoxic tumor microenvironment

BACKGROUND Pancreatic cancer is one of the most lethal malignancies,characterized by poor prognosis and low survival rates.Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy,often failing to capture the complexity of the disease.The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment.This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer.AIM To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules.METHODS This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2,PLAU,and CCNA2.The results were validated in an independent dataset.This study also examined the correlations between the model risk score and various clinical features,components of the immune microenvironment,chemotherapeutic drug sensitivity,and metabolism-related pathways.Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines.RESULTS The prognostic model demonstrated significant predictive value,with the risk score showing a strong correlation with clinical features:It was significantly associated with tumor grade(G)(bP<0.01),moderately associated with tumor stage(T)(aP<0.05),and significantly correlated with residual tumor(R)status(bP<0.01).There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs.Furthermore,the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer.CONCLUSION The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.

Delineation of urban growth boundary based on FLUS model under the perspective of land use evaluation in hilly mountainous areas

With rapid economic development,the size of urban land in China is expanding dramatically.The Urban Growth Boundary(UGB)is an expandable spatial boundary for urban construction in a certain period in order to control the urban sprawl.Reasonable delineation of UGB can inhibit the disorderly spread of urban space and guide the normal development of the city.It is of practical significance for the construction of green urban space.The study utilizes GIS technology to establish a land construction suitability evaluation system for Nankang city,which is experiencing rapid urban expansion,and outlines the preliminary UGB under the future land use simulation(FLUS)model.At the same time,considering the coupled coordination of"Production-Living-Ecological Space",and based on the suitability evaluation,we revised the preliminary UGB by combining the advantages of the patch-generating land use simulation(PLUS)model and the convex hull model to delineate the final UGB.The results show that:1)the comprehensive score of the evaluation of the suitability of the construction of land from high to low shows the distribution of the center of the city to the surrounding circle type spread,the center of the city has the highest suitability score.The results of convex hull model show that the urban expansion type of Nankang is epitaxial.In the future,the urban expansion will mainly occur in the northern part of the city.The PLUS model predicts an increase of 3359.97 hm^(2)of construction land in Nankang by 2035,of which 2022.97 hm^(2)is urban construction land.2)The FLUS model has a prediction accuracy of 86.3%and delineates a preliminary UGB area of 9215.07 hm^(2).3)We used the results of the construction suitability evaluation,PLUS model simulation results,and convex hull model predictions to revise the originally delineated UGB.The final delineated UGB area is 8895.67 hm^(2)and it is capable of meeting the future development of the study area.The results of the delineation can promote sustainable urban development,and the delineation methodology can provide a reference basis for the preparation of territorial spatial planning.

Genome-edited rabbits:Unleashing the potential of a promising experimental animal model across diverse diseases

Animal models are extensively used in all aspects of biomedical research,with substantial contributions to our understanding of diseases,the development of pharmaceuticals,and the exploration of gene functions.The field of genome modification in rabbits has progressed slowly.However,recent advancements,particularly in CRISPR/Cas9-related technologies,have catalyzed the successful development of various genome-edited rabbit models to mimic diverse diseases,including cardiovascular disorders,immunodeficiencies,agingrelated ailments,neurological diseases,and ophthalmic pathologies.These models hold great promise in advancing biomedical research due to their closer physiological and biochemical resemblance to humans compared to mice.This review aims to summarize the novel gene-editing approaches currently available for rabbits and present the applications and prospects of such models in biomedicine,underscoring their impact and future potential in translational medicine.

Use of machine learning models for the prognostication of liver transplantation: A systematic review

BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.

Predicting the probability distribution of Martian rocks mechanical property based on microscale rock mechanical experiments and accurate grain-based modeling

The exploration of Mars would heavily rely on Martian rocks mechanics and engineering technology.As the mechanical property of Martian rocks is uncertain,it is of utmost importance to predict the probability distribution of Martian rocks mechanical property for the success of Mars exploration.In this paper,a fast and accurate probability distribution method for predicting the macroscale elastic modulus of Martian rocks was proposed by integrating the microscale rock mechanical experiments(micro-RME),accurate grain-based modeling(AGBM)and upscaling methods based on reliability principles.Firstly,the microstructure of NWA12564 Martian sample and elastic modulus of each mineral were obtained by micro-RME with TESCAN integrated mineral analyzer(TIMA)and nanoindentation.The best probability distribution function of the minerals was determined by Kolmogorov-Smirnov(K-S)test.Secondly,based on best distribution function of each mineral,the Monte Carlo simulations(MCS)and upscaling methods were implemented to obtain the probability distribution of upscaled elastic modulus.Thirdly,the correlation between the upscaled elastic modulus and macroscale elastic modulus obtained by AGBM was established.The accurate probability distribution of the macroscale elastic modulus was obtained by this correlation relationship.The proposed method can predict the probability distribution of Martian rocks mechanical property with any size and shape samples.

Research and development of the automatic modeling system for Monte Carlo particle transport simulation

在广泛调研和分析现有几何建模方法特点的基础上研发了具有可视化用户界面的自动建模程序系统MCAM。它可以实现多种商用软件CAD模型与McNP模型之间的相互转换，且提供了模型建立、预处理、属性分析等基本功能和计算结果可视化及基于医学映像建模接口等扩展功能。全面系统地介绍了MCAM的设计思想与原理、总体结构、主要功能和国际合作协议框架下的应用测试等情况。实践表明，它是一个实用的MCNP计算辅助工具和核设计与核分析质量保证工具。

Establishment and validation of a predictive model for peripherally inserted central catheter-related thrombosis in patients with liver cancer

BACKGROUND Peripherally inserted central catheters(PICCs)are commonly used in hospitalized patients with liver cancer for the administration of chemotherapy,nutrition,and other medications.However,PICC-related thrombosis is a serious complication that can lead to morbidity and mortality in this patient population.Several risk factors have been identified for the development of PICC-related thrombosis,including cancer type,stage,comorbidities,and catheter characteristics.Understanding these risk factors and developing a predictive model can help healthcare providers identify high-risk patients and implement preventive measures to reduce the incidence of thrombosis.AIM To analyze the influencing factors of PICC-related thrombosis in hospitalized patients with liver cancer,construct a predictive model,and validate it.METHODS Clinical data of hospitalized patients with liver cancer admitted from January 2020 to December 2023 were collected.Thirty-five cases of PICC-related thrombosis in hospitalized patients with liver cancer were collected,and 220 patients who underwent PICC placement during the same period but did not develop PICC-related thrombosis were randomly selected as controls.A total of 255 samples were collected and used as the training set,and 77 cases were collected as the validation set in a 7:3 ratio.General patient information,case data,catheterization data,coagulation indicators,and Autar Thrombosis Risk Assessment Scale scores were analyzed.Univariate and multivariate unconditional logistic regression analyses were performed on relevant factors,and the value of combined indicators in predicting PICC-related thrombosis in hospitalized patients with liver cancer was evaluated using receiver operating characteristic(ROC)curve analysis.RESULTS Univariate analysis showed statistically significant differences(P<0.05)in age,sex,Karnofsky performance status score(KPS),bedridden time,activities of daily living impairment,parenteral nutrition,catheter duration,distant metastasis,and bone marrow suppression between the thrombosis group and the non-thrombosis group.Other aspects had no statistically significant differences(P>0.05).Multivariate regression analysis showed that age≥60 years,KPS score≤50 points,parenteral nutrition,stage III to IV,distant metastasis,bone marrow suppression,and activities of daily living impairment were independent risk factors for PICC-related thrombosis in hospitalized patients with liver cancer(P<0.05).Catheter duration of 1-6 months and catheter duration>6 months were protective factors for PICC-related thrombosis(P<0.05).The predictive model for PICC-related thrombosis was obtained as follows:P predictive probability=[exp(Logit P)]/[1+exp(Logit P)],where Logit P=age×1.907+KPS score×2.045+parenteral nutrition×9.467+catheter duration×0.506+tumor-node-metastasis(TNM)staging×2.844+distant metastasis×2.065+bone marrow suppression×2.082+activities of daily living impairment×13.926.ROC curve analysis showed an area under the curve(AUC)of 0.827(95%CI:0.724-0.929,P<0.001),with a corresponding optimal cut-off value of 0.612,sensitivity of 0.755,and specificity of 0.857.Calibration curve analysis showed good consistency between the predicted occurrence of PICC-related thrombosis and actual occurrence(P>0.05).ROC analysis showed AUCs of 0.888 and 0.729 for the training and validation sets,respectively.CONCLUSION Age,KPS score,parenteral nutrition,TNM staging,distant metastasis,bone marrow suppression,and activities of daily living impairment are independent risk factors for PICC-related thrombosis in hospitalized patients with liver cancer,while catheter duration is a protective factor for the disease.The predictive model has an AUC of 0.827,indicating high predictive accuracy and clinical value.

Nonlinear constitutive models of rock structural plane and their applications

Structural planes play an important role in controlling the stability of rock engineering,and the influence of structural planes should be considered in the design and construction process of rock engineering.In this paper,mechanical properties,constitutive theory,and numerical application of structural plane are studied by a combination method of laboratory tests,theoretical derivation,and program development.The test results reveal the change laws of various mechanical parameters under different roughness and normal stress.At the pre-peak stage,a non-stationary model of shear stiffness is established,and threedimensional empirical prediction models for initial shear stiffness and residual stage roughness are proposed.The nonlinear constitutive models are established based on elasto-plastic mechanics,and the algorithms of the models are developed based on the return mapping algorithm.According to a large number of statistical analysis results,empirical prediction models are proposed for model parameters expressed by structural plane characteristic parameters.Finally,the discrete element method(DEM)is chosen to embed the constitutive models for practical application.The running programs of the constitutive models have been compiled into the discrete element model library.The comparison results between the proposed model and the Mohr-Coulomb slip model show that the proposed model can better describe nonlinear changes at different stages,and the predicted shear strength,peak strain and shear stiffness are closer to the test results.The research results of the paper are conducive to the accurate evaluation of structural plane in rock engineering.