Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Projecting Wintertime Newly Formed Arctic Sea Ice through Weighting CMIP6 Model Performance and Independence

Precipitous Arctic sea-ice decline and the corresponding increase in Arctic open-water areas in summer months give more space for sea-ice growth in the subsequent cold seasons. Compared to the decline of the entire Arctic multiyear sea ice,changes in newly formed sea ice indicate more thermodynamic and dynamic information on Arctic atmosphere–ocean–ice interaction and northern mid–high latitude atmospheric teleconnections. Here, we use a large multimodel ensemble from phase 6 of the Coupled Model Intercomparison Project(CMIP6) to investigate future changes in wintertime newly formed Arctic sea ice. The commonly used model-democracy approach that gives equal weight to each model essentially assumes that all models are independent and equally plausible, which contradicts with the fact that there are large interdependencies in the ensemble and discrepancies in models' performances in reproducing observations. Therefore, instead of using the arithmetic mean of well-performing models or all available models for projections like in previous studies, we employ a newly developed model weighting scheme that weights all models in the ensemble with consideration of their performance and independence to provide more reliable projections. Model democracy leads to evident bias and large intermodel spread in CMIP6 projections of newly formed Arctic sea ice. However, we show that both the bias and the intermodel spread can be effectively reduced by the weighting scheme. Projections from the weighted models indicate that wintertime newly formed Arctic sea ice is likely to increase dramatically until the middle of this century regardless of the emissions scenario.Thereafter, it may decrease(or remain stable) if the Arctic warming crosses a threshold(or is extensively constrained).

Research on Electrical Remodeling After Short Term Pacing in Canine Model

Objectives To evaluate the changes in atrial effective refractory period （AERP） proprieties and in ionic currents in PVs myocytes from dogs subjected to rapid atrial pacing in PVs and right atrial appendage （RAA） and to relate these changes to the ability to induce AF. Methods Twelve mongrel dogs in normal sinus rhythm were paced from the superior left PVs or RAA at 500 bpm for 4 hours. Electrophysiologic studies conducted to determine changes in AERP, dispersion and rhythm. Ionic currents were studies with the patch clamp technique in single PVs myocytes in sham operated dogs and compared with those from PVs pacing and RAA pacing groups. Results The presence of rapid atrial pacing was associated with a marked shortening in AERP in both PVs and RAA pacing group with a marked increase of AERP dispersion in PVs pacing. Both L-type calcium current （Ica L ） and the transient outward current （ Ito ） were reduced in both groups with an increased significance in PVs pacing group. The density of ICa-L was decreased significantly from （ - 6. 03 ± 0. 63 ） pA./pF in the control group to （ -3.21 ±0. 34） pA/pF in PVs pacing group and （ - 4. 75 ± 0. 41 ） pA./pF in RAA pacing group （ n = 6, P 〈 0. 05 ） while the density of Ito was decreased significantly from （8.45 ± 0. 71 ） pA./pF in the control group to （ 5.21 ± 0. 763 ） pA./pF in PVs pacing group and （6. 84 ±0. 69 ） pA./pF in RAA pacing group （ n = 6, P 〈 0. 05 ）. Conclusions Our findings provide likely ionic mechanisms of shortened repolarization in induced atrial tachycardia with a decrease in Ica L and /tocurrent densities which is the likely mechanism for a decrease in Action potential duration （APD） rate adaptation in the canine rapid pacing model more pronounced in PVs pacing group underlying the crucial role of PVs in initiating AF.

Antiarrhythmic Efficacy of Neferine Assessed by Programmed Electrical Stimulation in a Canine Model of Electropharmacology

An ishemic ventricular tachyarrhythmias canine model was established in open-chest dogs subjected to programmed electrical stimulation (PES)for 5-8 days after acute myocardial infarction. The electrophysiologic effects of neferine (Nef) and procainamide (PA) were observed in this model. With routine methods of PES,ventricular tachycardia (VT)and ventricular fibrillation (VF) could be reproducibly initiated. Both drugs lengthened the QTc interval (P【0.01) and effective refractory period(ERP)of normal and ischemic ventricular myocardia (NERP and IERP) respectively (P【0.01), decreased the dispersion of ERP in ischemic myocardium and the dispersion of ERP in left ventricle (P【0.01), and increased the diastolic excitability threshold of normal and ischemic ventricular myocardia (P【0.01). The two compounds prevented the PES-induced VT or VF (Nef group P【0.01, PA group P【0.05) and ischemia-induced VF (P【0.05). The results indicated that neferine and procainamide may be effective in preventing the onset of reentrant ventricular tachyarrhythmias after myocardial ischemic damage in dogs.

Research status and application of artificial intelligence large models in the oil and gas industry

This article elucidates the concept of large model technology,summarizes the research status of large model technology both domestically and internationally,provides an overview of the application status of large models in vertical industries,outlines the challenges and issues confronted in applying large models in the oil and gas sector,and offers prospects for the application of large models in the oil and gas industry.The existing large models can be briefly divided into three categories:large language models,visual large models,and multimodal large models.The application of large models in the oil and gas industry is still in its infancy.Based on open-source large language models,some oil and gas enterprises have released large language model products using methods like fine-tuning and retrieval augmented generation.Scholars have attempted to develop scenario-specific models for oil and gas operations by using visual/multimodal foundation models.A few researchers have constructed pre-trained foundation models for seismic data processing and interpretation,as well as core analysis.The application of large models in the oil and gas industry faces challenges such as current data quantity and quality being difficult to support the training of large models,high research and development costs,and poor algorithm autonomy and control.The application of large models should be guided by the needs of oil and gas business,taking the application of large models as an opportunity to improve data lifecycle management,enhance data governance capabilities,promote the construction of computing power,strengthen the construction of“artificial intelligence+energy”composite teams,and boost the autonomy and control of large model technology.

D-galactosamine based canine acute liver failure model

Background: Appropriate preclinical evaluation of a bioartificial liver assist device (BAL) demands a large animal model, as presented here, that demon- strates many of the clinical features of acute liver failure and that is suitable for clinical qualitative and quantitative evaluation of the BAL. A lethal canine liver failure model of acute hepatic failure that re- moves many of the artifacts evidenced in prior canine models is presented. Methods: Six male hounds, 24-30 kg, under isoflu- rane anesthesia, were administered 1.5 g/kg D- galactosamine intravenously. Canine supportive care followed a well-defined management protocol that was guided by electrolyte and invasive monitoring consisting of arterial pressure, central venous pres- sure, extradural intracranial pressure (ICP), pul- monary artery pressure, and end-tidal CO_2. The animals were treated until death-equivalent, defined as inability to sustain systolic blood pressure>80 mmHg for 20 minutes despite maximal fluids and 20 μg·kg^(-1)·min^(-1) dopamine infusion. Results: The mean survival time was 43.7±4.6 hours (mean±SE). All animals showed evidence of progressive liver failure characterized by increasing liver enzymes (aspartate transaminase from 26 to 5977 IU/L; alanine transaminase from 32 to 9740 IU/L), bilirubin (0.25 to 1.30 mg/dl), ammonia (19. 8 to 85. 3 μmol/L), and coagulopathy (pro- thrombin time from 8.7 to 46 s). Increased lability and elevations in intracranial pressures were ob- served. All animals were refractory to maintenance of cerebral perfusion pressure even with only mode- rately elevated intracranial pressure. Severe neuro- logic obtundation, seen in 2 of 6 animals, was associ- ated with elevations of ICP above 50 mmHg. Post- mortem liver histology showed evidence of massive hepatic necrosis. Postmortem blood and ascites mi- crobial growth was consistent with possible transloca- tion of intestinal microbes. Conclusions: The improved lethal canine liver failure model presented here reproduces many of the clinical features of acute liver failure. The model may prove useful for qualitative and quantitative evaluation of BALs.

Novel magnetic compression technique for establishment of a canine model of tracheoesophageal fistula

BACKGROUND Clinically,tracheoesophageal fistula(TEF)is lack of effective surgical strategies.One reason is due to the lack of appropriate animal models of acquired TEF,which is usually complex and difficult.Recently,the magnetic compression technique has been applied for digestive tract anastomosis or vascular anastomosis in animals.In this study,an animal model of TEF in dogs was developed by using the magnetic compression technique,hoping to provide a new method for mimicking TEF.AIM To establish a TEF model in dogs by using the magnetic compression technique.METHODS Six male beagles were used as models with two Nd-Fe-B permanent magnets for TEF.The parent magnet and the daughter magnet were placed in the cervical esophagus and trachea,respectively.The anterior wall of the esophagus and the posterior wall of the trachea were compressed when the two magnets coupled.After 4-6 d,the necrotic tissue between the two magnets fell off and the parent and daughter magnets disengaged from the target location,leaving a fistula.Gastroscopy/bronchoscopy,upper gastrointestinal contrast study,and histological analysis were performed.RESULTS The establishment of the TEF model in all six beagles was successful.The average time of magnet placement was 4.33±1.11 min(range,3-7 min).Mean time for the magnets to disengage from the target location was 4.67±0.75 d(range,4-6 d).TEFs were observed by gastroscopy/bronchoscopy and esophageal angiography.The gross anatomical structure of the esophagus and the trachea was in good condition.There was no esophageal mucosa or pseudostratified ciliated columnar epithelium at the site of the fistula according to histological analysis.CONCLUSION It is simple,feasible,and minimally invasive to use the magnetic compression technique for the establishment of the TEF model in dogs.

Changes in the crown angulation and dental arch widths after nonextraction orthodontic treatment: Model analysis of mild crowding with high canines

This study was undertaken to examine which factors contributed to the correction of crowding in two patients who underwent nonextraction orthodontic treatment. A study model analysis was conducted to determine the effects of the orthodontic treatment for crowding with high canines on crown angulation and dental arch width in two patients. The results showed that the crown angulation was significantly increased, indicating distal tipping in the maxillary dental arch. This tendency was most commonly observed in the premolars among the lateral teeth. With respect to the dental arch width, the largest change was evident in the first molar and first premolar regions in cases 1 and 2, respectively. On the basis of these results, up-righting of mesially tipped lateral teeth and expansion of narrow dental arches could prove to be the keys to the success of space regaining or correction of high canines and mild crowding.

Establishment of a sensitized canine model for kidney transplantation

Objective:To establish a sensitized canine model for kidney transplantation.Methods:12 male dogs were averagely grouped as donors and recipients. A small number of donor canine lymphocytes was infused into different anatomic locations of a paired canine recipient for each time and which was repeated weekly. Specific immune sensitization was monitored by means of Complement Dependent Cytotoxicity (CDC) and Mixed Lymphocyte Culture (MLC) test. When CDC test conversed to be positive and MLC test showed a significant proliferation of reactive lymphocytes of canine recipients, the right kidneys of the paired dogs were excised and transplanted to each other concurrently. Injury of renal allograft function was scheduled determined by ECT dynamic kidney photography and pathologic investigation. Results:CDC test usually conversed to be positive and reactive lymphocytes of canine recipients were also observed to be proliferated significantly in MLC test after 3 to 4 times of canine donor lymphocyte infusions. Renal allograft function deterioration occurred 4 d post-operatively in 4 of 6 canine recipients, in contrast to none in control dogs. Pathologic changes suggested antibody-mediated rejection (delayed) or acute rejection in 3 excised renal allograft of sensitized dogs. Seven days after operation, all sensitized dogs had lost graft function, pathologic changes of which showed that the renal allografts were seriously rejected. 2 of 3 dogs in control group were also acutely rejected.Conclusion:A convenient method by means of repeated stimulation of canine lymphocyte may induce specific immune sensitization in canine recipients. Renal allografts in sensitized dogs will be earlier rejected and result in a more deteriorated graft function.

Evolution and Prospects of Foundation Models: From Large Language Models to Large Multimodal Models

Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language intelligence.Language modeling,crucial for AI development,has evolved from statistical to neural models over the last two decades.Recently,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training corpora.Increasing the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models lack.The advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal interest.This survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language understanding.Then,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI systems.Finally,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.

Progressive fragmentation of granular assemblies within rockslides: Insights from discrete-continuous numerical modeling

Rock fragmentation plays a critical role in rock avalanches,yet conventional approaches such as classical granular flow models or the bonded particle model have limitations in accurately characterizing the progressive disintegration and kinematics of multi-deformable rock blocks during rockslides.The present study proposes a discrete-continuous numerical model,based on a cohesive zone model,to explicitly incorporate the progressive fragmentation and intricate interparticle interactions inherent in rockslides.Breakable rock granular assemblies are released along an inclined plane and flow onto a horizontal plane.The numerical scenarios are established to incorporate variations in slope angle,initial height,friction coefficient,and particle number.The evolutions of fragmentation,kinematic,runout and depositional characteristics are quantitatively analyzed and compared with experimental and field data.A positive linear relationship between the equivalent friction coefficient and the apparent friction coefficient is identified.In general,the granular mass predominantly exhibits characteristics of a dense granular flow,with the Savage number exhibiting a decreasing trend as the volume of mass increases.The process of particle breakage gradually occurs in a bottom-up manner,leading to a significant increase in the angular velocities of the rock blocks with increasing depth.The simulation results reproduce the field observations of inverse grading and source stratigraphy preservation in the deposit.We propose a disintegration index that incorporates factors such as drop height,rock mass volume,and rock strength.Our findings demonstrate a consistent linear relationship between this index and the fragmentation degree in all tested scenarios.

Theoretical investigation on axial cyclic performance of monopile in sands using interface constitutive models

Cyclic loads generated by environmental factors,such as winds,waves,and trains,will likely lead to performance degradation in pile foundations,resulting in issues like permanent displacement accumulation and bearing capacity attenuation.This paper presents a semi-analytical solution for predicting the axial cyclic behavior of piles in sands.The solution relies on two enhanced nonlinear load-transfer models considering stress-strain hysteresis and cyclic degradation in the pile-soil interaction.Model parameters are calibrated through cyclic shear tests of the sand-steel interface and laboratory geotechnical testing of sands.A novel aspect involves the meticulous formulation of the shaft loadtransfer function using an interface constitutive model,which inherently inherits the interface model’s advantages,such as capturing hysteresis,hardening,degradation,and particle breakage.The semi-analytical solution is computed numerically using the matrix displacement method,and the calculated values are validated through model tests performed on non-displacement and displacement piles in sands.The results demonstrate that the predicted values show excellent agreement with the measured values for both the static and cyclic responses of piles in sands.The displacement pile response,including factors such as bearing capacity,mobilized shaft resistance,and convergence rate of permanent settlement,exhibit improvements compared to non-displacement piles attributed to the soil squeezing effect.This methodology presents an innovative analytical framework,allowing for integrating cyclic interface models into the theoretical investigation of pile responses.

PAL-BERT:An Improved Question Answering Model

In the field of natural language processing(NLP),there have been various pre-training language models in recent years,with question answering systems gaining significant attention.However,as algorithms,data,and computing power advance,the issue of increasingly larger models and a growing number of parameters has surfaced.Consequently,model training has become more costly and less efficient.To enhance the efficiency and accuracy of the training process while reducing themodel volume,this paper proposes a first-order pruningmodel PAL-BERT based on the ALBERT model according to the characteristics of question-answering(QA)system and language model.Firstly,a first-order network pruning method based on the ALBERT model is designed,and the PAL-BERT model is formed.Then,the parameter optimization strategy of the PAL-BERT model is formulated,and the Mish function was used as an activation function instead of ReLU to improve the performance.Finally,after comparison experiments with traditional deep learning models TextCNN and BiLSTM,it is confirmed that PALBERT is a pruning model compression method that can significantly reduce training time and optimize training efficiency.Compared with traditional models,PAL-BERT significantly improves the NLP task’s performance.

The relationship between compartment models and their stochastic counterparts:A comparative study with examples of the COVID-19 epidemic modeling

Deterministic compartment models(CMs)and stochastic models,including stochastic CMs and agent-based models,are widely utilized in epidemic modeling.However,the relationship between CMs and their corresponding stochastic models is not well understood.The present study aimed to address this gap by conducting a comparative study using the susceptible,exposed,infectious,and recovered(SEIR)model and its extended CMs from the coronavirus disease 2019 modeling literature.We demonstrated the equivalence of the numerical solution of CMs using the Euler scheme and their stochastic counterparts through theoretical analysis and simulations.Based on this equivalence,we proposed an efficient model calibration method that could replicate the exact solution of CMs in the corresponding stochastic models through parameter adjustment.The advancement in calibration techniques enhanced the accuracy of stochastic modeling in capturing the dynamics of epidemics.However,it should be noted that discrete-time stochastic models cannot perfectly reproduce the exact solution of continuous-time CMs.Additionally,we proposed a new stochastic compartment and agent mixed model as an alternative to agent-based models for large-scale population simulations with a limited number of agents.This model offered a balance between computational efficiency and accuracy.The results of this research contributed to the comparison and unification of deterministic CMs and stochastic models in epidemic modeling.Furthermore,the results had implications for the development of hybrid models that integrated the strengths of both frameworks.Overall,the present study has provided valuable epidemic modeling techniques and their practical applications for understanding and controlling the spread of infectious diseases.

An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

Bayesian model averaging(BMA)for nuclear data evaluation

To ensure agreement between theoretical calculations and experimental data,parameters to selected nuclear physics models are perturbed and fine-tuned in nuclear data evaluations.This approach assumes that the chosen set of models accurately represents the‘true’distribution of considered observables.Furthermore,the models are chosen globally,indicating their applicability across the entire energy range of interest.However,this approach overlooks uncertainties inherent in the models themselves.In this work,we propose that instead of selecting globally a winning model set and proceeding with it as if it was the‘true’model set,we,instead,take a weighted average over multiple models within a Bayesian model averaging(BMA)framework,each weighted by its posterior probability.The method involves executing a set of TALYS calculations by randomly varying multiple nuclear physics models and their parameters to yield a vector of calculated observables.Next,computed likelihood function values at each incident energy point were then combined with the prior distributions to obtain updated posterior distributions for selected cross sections and the elastic angular distributions.As the cross sections and elastic angular distributions were updated locally on a per-energy-point basis,the approach typically results in discontinuities or“kinks”in the cross section curves,and these were addressed using spline interpolation.The proposed BMA method was applied to the evaluation of proton-induced reactions on ^(58)Ni between 1 and 100 MeV.The results demonstrated a favorable comparison with experimental data as well as with the TENDL-2023 evaluation.

Analysis of the inflection points of height-diameter models

The inflection point is an important feature of sigmoidal height-diameter(H-D)models.It is often cited as one of the properties favoring sigmoidal model forms.However,there are very few studies analyzing the inflection points of H-D models.The goals of this study were to theoretically and empirically examine the behaviors of inflection points of six common H-D models with a regional dataset.The six models were the Wykoff(WYK),Schumacher(SCH),Curtis(CUR),HossfeldⅣ(HOS),von Bertalanffy-Richards(VBR),and Gompertz(GPZ)models.The models were first fitted in their base forms with tree species as random effects and were then expanded to include functional traits and spatial distribution.The distributions of the estimated inflection points were similar between the two-parameter models WYK,SCH,and CUR,but were different between the threeparameter models HOS,VBR,and GPZ.GPZ produced some of the largest inflection points.HOS and VBR produced concave H-D curves without inflection points for 12.7%and 39.7%of the tree species.Evergreen species or decreasing shade tolerance resulted in larger inflection points.The trends in the estimated inflection points of HOS and VBR were entirely opposite across the landscape.Furthermore,HOS could produce concave H-D curves for portions of the landscape.Based on the studied behaviors,the choice between two-parameter models may not matter.We recommend comparing seve ral three-parameter model forms for consistency in estimated inflection points before deciding on one.Believing sigmoidal models to have inflection points does not necessarily mean that they will produce fitted curves with one.Our study highlights the need to integrate analysis of inflection points into modeling H-D relationships.

Parameter calibration of the tensile-shear interactive damage constitutive model for sandstone failure

The tensile-shear interactive damage(TSID)model is a novel and powerful constitutive model for rock-like materials.This study proposes a methodology to calibrate the TSID model parameters to simulate sandstone.The basic parameters of sandstone are determined through a series of static and dynamic tests,including uniaxial compression,Brazilian disc,triaxial compression under varying confining pressures,hydrostatic compression,and dynamic compression and tensile tests with a split Hopkinson pressure bar.Based on the sandstone test results from this study and previous research,a step-by-step procedure for parameter calibration is outlined,which accounts for the categories of the strength surface,equation of state(EOS),strain rate effect,and damage.The calibrated parameters are verified through numerical tests that correspond to the experimental loading conditions.Consistency between numerical results and experimental data indicates the precision and reliability of the calibrated parameters.The methodology presented in this study is scientifically sound,straightforward,and essential for improving the TSID model.Furthermore,it has the potential to contribute to other rock constitutive models,particularly new user-defined models.

Smaller & Smarter: Score-Driven Network Chaining of Smaller Language Models

With the continuous evolution and expanding applications of Large Language Models (LLMs), there has been a noticeable surge in the size of the emerging models. It is not solely the growth in model size, primarily measured by the number of parameters, but also the subsequent escalation in computational demands, hardware and software prerequisites for training, all culminating in a substantial financial investment as well. In this paper, we present novel techniques like supervision, parallelization, and scoring functions to get better results out of chains of smaller language models, rather than relying solely on scaling up model size. Firstly, we propose an approach to quantify the performance of a Smaller Language Models (SLM) by introducing a corresponding supervisor model that incrementally corrects the encountered errors. Secondly, we propose an approach to utilize two smaller language models (in a network) performing the same task and retrieving the best relevant output from the two, ensuring peak performance for a specific task. Experimental evaluations establish the quantitative accuracy improvements on financial reasoning and arithmetic calculation tasks from utilizing techniques like supervisor models (in a network of model scenario), threshold scoring and parallel processing over a baseline study.

A canine model of aortic arch aneurysm created with autologous pericardium

Background:To establish a canine model of aortic arch aneurysm that is suitable for research on new devices and techniques applied to the aortic arch.Materials and methods:Fifteen mongrel dogs underwent surgery.The autologous pericardial patch was sewn on the aortotomy site in the anterior wall of the aortic arch.The animals were followed up for 3 months postoperatively.Computed tomography angiography was used to visualize and measure the aneurysm model.Hematoxylin and eosin staining was used to observe the histological characteristics of the aneurysm model.Changes in aneurysm diameter over time were analyzed using analysis of variance.Results:One dog died of hemorrhage during surgery.Fourteen dogs survived the surgical procedure.Two of them died on the first postoperative day because of ruptures at the suturing margin.The diameter of the aneurysm model was twice as large as that of the aortic arch.There was no significant change in the maximum diameter of the aneurysm model during the follow-up period.Conclusions:We established a controllable and stable aortic arch aneurysm model created with an autologous pericardium patch.The aneurysm model can be used to research endoleaks after thoracic endovascular aortic repair and new endovascular techniques can be applied to the aortic arch.