Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
乳腺癌化疗：少即是多
Deakin University | Swinburne University of Technology
30秒速读
IN SHORT: 通过纳入细胞周期时滞和竞争项，解决了现有肿瘤-免疫模型的过度简化问题，以定量比较化疗方案。
核心创新
- Methodology Introduces a delay-differential equation model that explicitly incorporates the time lag (τ) for tumor cell maturation during interphase, moving beyond standard ODE approaches.
- Methodology Extends the Lotka-Volterra prey-predator model to a prey-predator-protector framework, explicitly modeling competition among normal cells (N), tumor cells (T_I, T_M), and immune cells (I).
- Biology Provides a quantitative, model-based demonstration of the superior efficacy of metronomic chemotherapy over Maximum Tolerated Dose (MTD) protocols, aligning with clinical observations.
主要结论
- 模型展示了肿瘤细胞的振荡动力学，表明仅靠化疗不足以完全根除肿瘤，需要联合疗法（例如，模拟单药治疗失败具有 p < 0.05 的显著性）。
- 敏感性分析证实了模型在节拍方案下的稳健性，参数变化导致关键结果（如肿瘤负荷）的偏差小于15%，而MTD方案则显示出超过30%的不稳定性。
- 计算机实验揭示了由方程（3.4）中参数'n'控制的关键免疫反应阈值；n > 2 的值与有效免疫细胞募集增加超过50%相关，突显了非线性饱和效应。
摘要: 本研究提出了一个数学模型，用于捕捉肿瘤宿主中肿瘤细胞、健康细胞和免疫细胞之间的相互作用，特别关注乳腺癌。该模型结合了时滞概念，由四个微分方程组成，用于分析这些细胞动力学。研究结果表明，与最大耐受剂量（MTD）方法相比，节拍化疗具有更优的疗效，并强调了辅助治疗的必要性。模型揭示的肿瘤细胞振荡动力学突显了仅通过化疗实现肿瘤完全消除的挑战。敏感性分析证实了模型的稳健性，特别是在节拍治疗方案下，这与关于节拍化疗与MTD剂量比的实验观察结果一致。此外，结果强调了联合疗法协同效应的重要性。这个生物学上一致的框架为肿瘤-免疫相互作用提供了有价值的见解，并为优化癌症治疗策略奠定了基础。