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Peripheral straightness leads to shape diversification during formations of entire leaves 外围平直度导致整个叶片形成过程中的形状多样化。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-15 DOI: 10.1016/j.jtbi.2024.111990
Akiko M. Nakamasu
The ways to read out positional information are essential to determine final shapes in developmental processes. Relative shaping to different sizes of positional information enables robust morphogenesis; however, the same difference sometimes causes diversity. Different responses to a positional information will enable such switching of identical/diverse shapes, though detail mechanisms remain unknown.
In this paper, we describe growing forms by constructing the contour of a two-dimensional object using propagating points and segments connecting them. In plant morphogenesis that lacks almost cell movements, tissue growth accompanied by cell divisions is central. We focused on peripheral cell composition in leaf formation as a frame. The growth with or without cell division on the periphery was analyzed with simple algorithms. We calculated the shapes of entire leaves with different ovality using combined growth algorithms as a model. Responces of the respective algorithms to simple positional information were explored to seek the origin of the shape diversification.
The algorithm for “growth with cell divisions” maintained identical shapes; however, diverse shapes were generated by the algorithm “growth without cell division” with gradients. The simplified model allowed us to interpret the oval shape diversity due to slants on edges. We concluded that peripheral straightness can generate shape diversity, at least in leaf morphogenesis.
在发育过程中,读出位置信息的方法对于确定最终形状至关重要。对不同大小的位置信息进行相对塑形,可实现稳健的形态发生;然而,相同的差异有时也会导致多样性。对位置信息的不同反应将使相同/不同形状的切换成为可能,但具体机制仍不清楚。在本文中,我们通过使用传播点和连接点的线段构建二维物体的轮廓来描述生长形态。在几乎没有细胞运动的植物形态发生中,伴随细胞分裂的组织生长是核心。我们以叶片形成过程中的外围细胞组成为框架进行研究。我们用简单的算法分析了外围细胞分裂与否的生长情况。我们以组合生长算法为模型,计算了不同椭圆度的整个叶片的形状。我们探讨了各算法对简单位置信息的响应,以寻找形状多样化的起源。有细胞分裂的生长 "算法保持了相同的形状,而 "无细胞分裂的生长 "算法则产生了不同的梯度形状。通过简化模型,我们可以解释椭圆形的形状多样性是由于边缘的斜度造成的。我们的结论是,至少在叶片形态发生过程中,边缘的平直度可以产生形状多样性。
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引用次数: 0
A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis 结核病预测模型综合研究:将机器学习融入流行病学分析。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-13 DOI: 10.1016/j.jtbi.2024.111988
Hamna Mariyam K.B. , Sayooj Aby Jose , Anuwat Jirawattanapanit , Karuna Mathew
Tuberculosis (TB), the second leading infectious killer globally, claimed the lives of 1.3 million individuals in 2022, after COVID-19, surpassing the toll of HIV and AIDS. With an estimated 10.6 million new TB cases worldwide in 2022, the gravity of the disease persists, necessitating urgent attention. Tuberculosis remains a critical public health crisis, and efforts to combat this infectious disease demand intensified global commitment and resources. This study utilizes predictive modeling techniques to forecast the incidence of Tuberculosis (TB), employing a range of machine learning models. Additionally, the research incorporates impactful visualizations for comprehensive data exploration, analysis and comparison. Various machine learning models are developed to anticipate TB incidence, with the optimal performing model to customize a user-defined function. This research provides valuable insights into the potential determinants influencing TB incidence, contributing to the identification of strategies for preventing the spread of Tuberculosis.
结核病(TB)是全球第二大传染病杀手,2022 年将夺走 130 万人的生命,仅次于 COVID-19,超过艾滋病毒和艾滋病的致死人数。据估计,2022 年全球将新增 1060 万肺结核病例,该疾病的严重性依然存在,亟需引起重视。结核病仍然是一个严重的公共卫生危机,全球必须加大力度、投入更多资源来抗击这一传染病。本研究采用一系列机器学习模型,利用预测建模技术预测结核病(TB)的发病率。此外,该研究还采用了极具影响力的可视化方法来进行全面的数据探索、分析和比较。研究人员开发了各种机器学习模型来预测结核病的发病率,并根据用户定义的函数定制了性能最佳的模型。这项研究为了解影响结核病发病率的潜在决定因素提供了宝贵的见解,有助于确定预防结核病传播的策略。
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引用次数: 0
On the misuse of evolutionary theory to bolster the ‘scientific’ case for intelligent design: A cautionary note 关于滥用进化论为智能设计 "科学 "辩护:警言。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-09 DOI: 10.1016/j.jtbi.2024.111985
Arne Traulsen , Mikkel Nif Rasmussen , Joachim Krug , Andreas Beyer
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引用次数: 0
Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission 基于随机差分模型的空气污染及相关疾病传播的统计推理和神经网络训练。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-08 DOI: 10.1016/j.jtbi.2024.111987
Sha He, Mengqi He, Sanyi Tang
A polluted air environment can potentially provoke infections of diverse respiratory diseases. The development of mathematical models can study the mechanism of air pollution and its effect on the spread of diseases. The key is to characterize the intrinsic correlation between the disease infection and the change in air pollutant concentration. In this paper, we establish a coupled discrete susceptible–exposed–infectious–susceptible (SEIS) model with demography to characterize the transmission of disease, and the change in the concentration of air pollutants is described in the form of the Beverton–Holt (BH) model with a time-varying inflow rate of air pollutants. Considering the periodic variation characteristics of data, time-varying parameters are defined as specific functional forms. We estimate the change point at which the parameters switch and the parameter values within the switching interval based on Bayesian statistical theory. The data fitting of the model can reflect the seasonal peaks and annual growth trends of values of air quality index (AQI) and the number of influenza-like illnesses (ILI) cases. However, the bias in data fitting indicates a more complex correlation pattern between disease and pollutant concentration changes. To explore unknown mechanisms, we propose the extended transmission-dynamics-informed neural network (TDINN) algorithm by combining deep learning with difference equations and obtain the curves of the transmission rate and inflow rate functions over time. The results show that neural network models can help us determine time-varying parameters in the model, thereby better reflecting the trend of data changes.
污染的空气环境有可能引发各种呼吸道疾病的感染。建立数学模型可以研究空气污染的机理及其对疾病传播的影响。关键是要确定疾病感染与空气污染物浓度变化之间的内在相关性。本文建立了一个具有人口统计学特征的离散易感-暴露-传染-易感(SEIS)耦合模型来表征疾病的传播,并以空气污染物流入率时变的贝弗顿-霍尔特(BH)模型的形式来描述空气污染物浓度的变化。考虑到数据的周期性变化特征,时变参数被定义为特定的函数形式。我们根据贝叶斯统计理论估算了参数切换的变化点以及切换区间内的参数值。模型的数据拟合能够反映空气质量指数(AQI)值和流感样病例数(ILI)的季节性峰值和年度增长趋势。然而,数据拟合的偏差表明疾病与污染物浓度变化之间存在更复杂的相关模式。为了探索未知的机制,我们通过将深度学习与差分方程相结合,提出了扩展的传播动力学信息神经网络(TDINN)算法,并得到了传播率和流入率函数随时间变化的曲线。结果表明,神经网络模型可以帮助我们确定模型中的时变参数,从而更好地反映数据的变化趋势。
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引用次数: 0
Optimal seasonal schedule for producing biogenic volatile organic compounds for tree defense 生产用于树木防御的生物挥发性有机化合物的最佳季节安排。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-08 DOI: 10.1016/j.jtbi.2024.111986
Yoh Iwasa, Rena Hayashi, Akiko Satake
The leaves of many trees emit biogenic volatile organic compounds (BVOCs) that protect them from various threats, including herbivory, pathogens, and heat stress. In a previous study, we analyzed the optimal seasonal schedule for producing isoprene, a highly volatile BVOC, in leaves to mitigate heat damage and maximize net carbon gain. In this paper, we investigate the seasonal production schedule of BVOCs stored in leaves, such as monoterpenes and sesquiterpenes, which decay slowly. When the leaves are bitten, these chemicals are emitted and help to prevent further herbivory. The optimal seasonal schedule, analyzed using Pontryagin’s maximum principle, includes a period of singular control. Producing BVOCs for defense is advantageous if their decay rate is slow and the photosynthetic rate is fast. The amount of BVOCs produced increases with slower decay rate and faster photosynthetic rate. But it does not increase monotonically with the magnitude of the threat. BVOCs are produced earlier than the peak period of the threat for which the chemicals are intended. Based on the results of the model, we discuss the reported variations in BVOC production among different chemical species and tree species, as well as the seasonal patterns of gene expression in different pathways for BVOC production.
许多树木的叶子会释放生物挥发性有机化合物(BVOCs),保护它们免受各种威胁,包括草食性动物、病原体和热应力。在之前的一项研究中,我们分析了叶片产生异戊二烯(一种高挥发性生物挥发性有机化合物)的最佳季节安排,以减轻热损伤并最大限度地增加净碳增量。在本文中,我们研究了储存在叶片中的 BVOC(如单萜烯和倍半萜烯)的季节生产计划,这些 BVOC 腐烂速度较慢。当树叶被咬时,这些化学物质就会释放出来,有助于防止进一步的食草动物侵害。根据庞特里亚金的最大原则分析,最佳季节安排包括一个单一控制期。如果 BVOCs 的衰减速度慢而光合作用速度快,则产生 BVOCs 进行防御是有利的。产生的 BVOC 量会随着衰变速度的减慢和光合作用速度的加快而增加。但它并不随威胁程度的增加而单调增加。BVOC 的产生早于化学品所针对的威胁的高峰期。根据该模型的结果,我们讨论了不同化学品种类和树种之间 BVOC 生成量的变化,以及 BVOC 生成不同途径中基因表达的季节性模式。
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引用次数: 0
Implementation of actin polymerization and depolymerization in a two-dimensional cell migration model and its implications on mammalian cell morphology and velocity 二维细胞迁移模型中肌动蛋白聚合和解聚的实现及其对哺乳动物细胞形态和速度的影响。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-11-05 DOI: 10.1016/j.jtbi.2024.111977
Lingxing Yao , Yizeng Li
Cell migration, a pivotal process in wound healing, immune response, and even cancer metastasis, manifests through intricate interplay between morphology, speed, and cytoskeletal dynamics. Mathematical modeling emerges as a powerful tool to dissect these complex interactions. This work presents a two-dimensional immersed boundary model for mammalian cell migration, incorporating both filamentous actin (F-actin) and monomeric actin (G-actin) to explicitly capture polymerization and depolymerization. This model builds upon our previous one-dimensional efforts, now enabling us to explore the impact of G-actin on not just cell velocity but also morphology. We compare predictions from both models, revealing that while the one-dimensional model captures core dynamics along the cell’s axis, the two-dimensional model excels in portraying cell shape evolution and transverse variations in actin concentration and velocity. Our findings highlight the crucial role of including G-actin in shaping cell morphology. Actin velocity aligned with migration direction elongates the cell, while velocity normal to the membrane promotes spreading. Importantly, the model establishes a link between these microscopic aspects and macroscopic observables like cell shape, offering a deeper understanding of cell migration dynamics. This work not only provides a more comprehensive picture of cell migration but also paves the way for future studies exploring the interplay of actin dynamics, cell morphology, and biophysical parameters in diverse biological contexts.
细胞迁移是伤口愈合、免疫反应甚至癌症转移的关键过程,它通过形态、速度和细胞骨架动力学之间错综复杂的相互作用表现出来。数学建模是剖析这些复杂相互作用的有力工具。本研究提出了哺乳动物细胞迁移的二维沉浸边界模型,其中包含丝状肌动蛋白(F-actin)和单体肌动蛋白(G-actin),以明确捕捉聚合和解聚过程。该模型建立在我们之前的一维模型基础之上,使我们现在能够探索 G-actin 不仅对细胞速度而且对形态的影响。我们比较了两种模型的预测结果,发现一维模型能捕捉到沿细胞轴向的核心动态,而二维模型则能出色地描绘细胞形态演变以及肌动蛋白浓度和速度的横向变化。我们的发现凸显了 G-肌动蛋白在塑造细胞形态中的关键作用。与迁移方向一致的肌动蛋白速度会拉长细胞,而与膜正常方向一致的速度则会促进细胞扩散。重要的是,该模型在这些微观方面与细胞形状等宏观观测指标之间建立了联系,从而加深了对细胞迁移动力学的理解。这项工作不仅为细胞迁移提供了一个更全面的图景,还为未来探索肌动蛋白动力学、细胞形态和生物物理参数在不同生物环境中的相互作用的研究铺平了道路。
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引用次数: 0
Event-based biological pest control: An LMI approach 基于事件的生物害虫控制:LMI 方法
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-10-28 DOI: 10.1016/j.jtbi.2024.111975
M. Sathishkumar , Maya Joby , Srimanta Santra , Yong-Ki Ma , S. Marshal Anthoni
This study focuses on the event-triggered control approach for the mathematical model describing the interaction between the sugarcane borer (Diatraea saccharalis) and its egg parasitoid Trichogramma galloi, as well as the combined interaction of Trichogramma galloi and Cotesia flavipes. By employing digital control design, an effective strategy can be devised to minimize the population of natural enemies. Therefore, proposing an event-triggered control mechanism for the sugarcane borer is essential. The primary objective of this study is to develop an event-triggered reliable state feedback controller, ensuring that the states of the sugarcane borer system converge to the desired steady-state equilibrium points. Additionally, this control design significantly reduces control updates and maintains the introduction of natural enemies into the environment. Ultimately, simulations are carried out using sugarcane borer systems to demonstrate the benefits and effectiveness of the proposed event-triggered design technique.
本研究的重点是对描述甘蔗螟(Diatraea saccharalis)与其卵寄生虫Trichogramma galloi以及Trichogramma galloi和Cotesia flavipes之间相互作用的数学模型进行事件触发控制。通过采用数字控制设计,可以设计出有效的策略,最大限度地减少天敌数量。因此,提出一种针对甘蔗螟的事件触发控制机制至关重要。本研究的主要目标是开发一种事件触发的可靠状态反馈控制器,确保甘蔗螟系统的状态收敛到所需的稳态平衡点。此外,这种控制设计还能大大减少控制更新,并保持将天敌引入环境。最后,我们利用甘蔗螟虫系统进行了模拟,以证明所提出的事件触发设计技术的好处和有效性。
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引用次数: 0
A continuous approach of modeling tumorigenesis and axons regulation for the pancreatic cancer 针对胰腺癌的肿瘤发生和轴突调控的连续建模方法。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-10-23 DOI: 10.1016/j.jtbi.2024.111967
Marie-Jose Chaaya , Sophie Chauvet , Florence Hubert , Fanny Mann , Mathieu Mezache , Pierre Pudlo
The pancreatic innervation undergoes dynamic remodeling during the development of pancreatic ductal adenocarcinoma (PDAC). Denervation experiments have shown that different types of axons can exert either pro- or anti-tumor effects, but conflicting results exist in the literature, leaving the overall influence of the nervous system on PDAC incompletely understood. To address this gap, we propose a continuous mathematical model of nerve-tumor interactions that allows in silico simulation of denervation at different phases of tumor development. This model takes into account the pro- or anti-tumor properties of different types of axons (sympathetic or sensory) and their distinct remodeling dynamics during PDAC development. We observe a “shift effect” where an initial pro-tumor effect of sympathetic axon denervation is later outweighed by the anti-tumor effect of sensory axon denervation, leading to a transition from an overall protective to a deleterious role of the nervous system on PDAC tumorigenesis. Our model also highlights the importance of the impact of sympathetic axon remodeling dynamics on tumor progression. These findings may guide strategies targeting the nervous system to improve PDAC treatment.
胰腺神经支配在胰腺导管腺癌(PDAC)的发展过程中经历了动态重塑。去神经支配实验表明,不同类型的轴突可发挥促癌或抗癌作用,但文献中存在相互矛盾的结果,导致人们对神经系统对 PDAC 的整体影响认识不足。为了填补这一空白,我们提出了一种神经-肿瘤相互作用的连续数学模型,可以在肿瘤发展的不同阶段对神经支配进行硅模拟。该模型考虑了不同类型轴突(交感神经或感觉神经)的促瘤或抗瘤特性,以及它们在 PDAC 发展过程中不同的重塑动态。我们观察到了一种 "转变效应",即交感神经轴突去神经化的初始促瘤效应后来被感觉轴突去神经化的抗瘤效应所抵消,从而导致神经系统对PDAC肿瘤发生的作用从整体保护性转变为有害性。我们的模型还强调了交感神经轴突重塑动态对肿瘤进展影响的重要性。这些发现可能会指导针对神经系统的策略,以改善 PDAC 的治疗。
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引用次数: 0
Pulmonary epithelial wound healing and the immune system. Mathematical modeling and bifurcation analysis of a bistable system 肺上皮伤口愈合与免疫系统。双稳态系统的数学建模和分岔分析。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-10-23 DOI: 10.1016/j.jtbi.2024.111968
Clara R. Lotter, Jonathan A. Sherratt
Respiratory diseases such as asthma, acute respiratory distress syndrome (ARDS), influenza or COVID-19 often directly target the epithelium. Elevated immune levels and a ‘cytokine storm’ are directly associated with defective healing dynamics of lung diseases such as COVID-19 or ARDS. The infected cells leave wounded regions in the epithelium which must be healed for the lung to return to a healthy state and carry out its main function of gas-exchange. Due to the complexity of the various interactions between cells of the lung epithelium and surrounding tissue, it is necessary to develop models that can complement experiments to fully understand the healing dynamics. In this mathematical study we model the mechanism of epithelial regeneration. We assume that healing is exclusively driven by progenitor cell proliferation, induced by a chemical activator such as epithelial growth factor (EGF) and cytokines such as interleukin-22 (IL22). Contrary to previous studies of wound healing, we consider the immune system, specifically the T effector cells TH1, TH17, TH22 and Treg to strongly contribute to the healing process, by producing IL22 or regulating the immune response. We therefore obtain a coupled system of two ordinary differential equations for the epithelial and immune cell densities and two functions for the levels of chemicals that either induce epithelial proliferation or recruit immune cells. These functions link the two cell equations. We find that to allow the epithelium to regenerate to a healthy state, the immune system must not exceed a threshold value at the onset of the healing phase. This immune threshold is supported experimentally but was not explicitly built into our equations. Our assumptions are therefore sufficient to reproduce experimental results concerning the ratio TH17/Treg cells as a threshold to predict higher mortality rates in patients. This immune threshold can be controlled by parameters of the model, specifically the base-level growth factor concentration. This conclusion is based on a mathematical bifurcation analysis and linearization of the model equations. Our results suggest treatment of severe cases of lung injury by reducing or suppressing the immune response, in an individual patient, assessed by their disease parameters such as course of lung injury and immune response levels.
哮喘、急性呼吸窘迫综合征(ARDS)、流感或 COVID-19 等呼吸系统疾病通常直接针对上皮细胞。免疫水平升高和 "细胞因子风暴 "与 COVID-19 或 ARDS 等肺部疾病的愈合动力学缺陷直接相关。受感染的细胞会在上皮细胞中留下损伤区域,这些区域必须愈合,肺部才能恢复健康状态,并发挥气体交换的主要功能。由于肺上皮细胞和周围组织之间的各种相互作用非常复杂,因此有必要建立模型来补充实验,以充分了解愈合动态。在这项数学研究中,我们建立了上皮再生机制模型。我们假设愈合完全由祖细胞增殖驱动,并由上皮细胞生长因子(EGF)等化学激活剂和白细胞介素-22(IL22)等细胞因子诱导。与以往的伤口愈合研究相反,我们认为免疫系统,特别是 T 效应细胞 TH1、TH17、TH22 和 Treg,通过产生 IL22 或调节免疫反应,对愈合过程做出了巨大贡献。因此,我们得到了上皮细胞和免疫细胞密度的两个常微分方程耦合系统,以及诱导上皮细胞增殖或招募免疫细胞的化学物质水平的两个函数。这些函数将两个细胞方程联系起来。我们发现,为了让上皮再生到健康状态,免疫系统在愈合阶段开始时不得超过一个阈值。这一免疫阈值得到了实验的支持,但并没有明确地建立在我们的方程中。因此,我们的假设足以重现有关 TH17/Treg 细胞比例的实验结果,作为预测患者较高死亡率的阈值。这一免疫阈值可由模型参数控制,特别是基础生长因子浓度。这一结论基于数学分岔分析和模型方程的线性化。我们的研究结果表明,在治疗严重肺损伤病例时,可以根据肺损伤病程和免疫反应水平等疾病参数,减少或抑制个体患者的免疫反应。
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引用次数: 0
PULSAR Effect: Revealing potential synergies in combined radiation therapy and immunotherapy via differential equations PULSAR效应:通过微分方程揭示联合放射治疗和免疫疗法的潜在协同作用。
IF 1.9 4区 数学 Q2 BIOLOGY Pub Date : 2024-10-22 DOI: 10.1016/j.jtbi.2024.111974
Samiha Rouf , Casey Moore , Debabrata Saha , Dan Nguyen , MaryLena Bleile , Robert Timmerman , Hao Peng , Steve Jiang
PULSAR (personalized ultrafractionated stereotactic adaptive radiotherapy) is a form of radiotherapy method where a patient is given a large dose or “pulse” of radiation a couple of weeks apart rather than daily small doses. The tumor response is then monitored to determine when the subsequent pulse should be given. Pre-clinical trials have shown better tumor response in mice that received immunotherapy along with pulses spaced 10 days apart. However, this was not the case when the pulses were 1 or 4 days apart. Therefore, a synergistic effect between immunotherapy and PULSAR is observed when the pulses are spaced out by a certain number of days. In our study, we aimed to develop a mathematical model that can capture the synergistic effect by considering a time-dependent weight function that takes into account the spacing between pulses. We determined feasible parameters by fitting murine tumor volume data of six treatment groups via simulated annealing algorithm. Applying these parameters to the model we simulated 4000 trials with varying sequencing of pulses. These simulations indicated that if pulses were spaced apart by at least 9 days the tumor volume was about 200 mm3 to 250 mm3 smaller when treated with PULSAR combined with immunotherapy. We successfully demonstrate that our model is simple to implement and can generate tumor volume data that is consistent with the pre-clinical trial data. Our model has the potential to aid in the development of clinical trials of PULSAR therapy.
PULSAR(个性化超分次立体定向自适应放射治疗)是一种放射治疗方法,患者将在几周内接受一次大剂量或 "脉冲 "放射治疗,而不是每天接受小剂量放射治疗。然后对肿瘤反应进行监测,以确定下一次脉冲放疗的时间。临床前试验表明,小鼠在接受免疫疗法的同时接受间隔 10 天的脉冲治疗,肿瘤反应会更好。然而,当脉冲间隔为 1 天或 4 天时,情况却并非如此。因此,当脉冲间隔一定天数时,就能观察到免疫疗法与 PULSAR 之间的协同效应。在我们的研究中,我们的目标是建立一个数学模型,通过考虑到脉冲间距的时间相关权重函数来捕捉协同效应。我们通过模拟退火算法拟合了六个治疗组的小鼠肿瘤体积数据,从而确定了可行的参数。将这些参数应用到模型中,我们模拟了 4000 次不同脉冲顺序的试验。模拟结果表明,如果脉冲间隔至少为 9 天,那么在使用 PULSAR 联合免疫疗法治疗时,肿瘤体积会缩小约 200 至 250 立方毫米。我们成功地证明了我们的模型易于实施,并能生成与临床前试验数据一致的肿瘤体积数据。我们的模型有望帮助开发 PULSAR 疗法的临床试验。
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引用次数: 0
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