Spatial and Temporal Mapping of Breast Cancer Lung Metastases Identify TREM2 Macrophages as Regulators of the Metastatic Boundary.

Unlabelled: Cancer mortality primarily stems from metastatic recurrence, emphasizing the urgent need for developing effective metastasis-targeted immunotherapies. To better understand the cellular and molecular events shaping metastatic niches, we used a spontaneous breast cancer lung metastasis model to create a single-cell atlas spanning different metastatic stages and regions. We found that premetastatic lungs are infiltrated by inflammatory neutrophils and monocytes, followed by the accumulation of suppressive macrophages with the emergence of metastases.

Artificial immune system features added to breast cancer clinical data for machine learning (ML) applications.

We here propose a new method of combining a mathematical model that describes a chemotherapy treatment for breast cancer with a machine-learning (ML) algorithm to increase performance in predicting tumor size using a five-step procedure. The first step involves modeling the chemotherapy treatment protocol using an analytical function. In the second step, the ML algorithm is trained to predict the tumor size based on clinico-pathological data and data obtained from magnetic resonance imaging results at different time points of treatment.

Analysis of a breast cancer mathematical model by a new method to find an optimal protocol for HER2-positive cancer.

Treatment of breast cancer (positive for HER2, i.e., ERBB2) is described by a mathematical model involving non-linear ordinary differential equations with a hidden hierarchy.

Machine learning vs. classic statistics for the prediction of IVF outcomes.

Purpose: To assess whether machine learning methods provide advantage over classic statistical modeling for the prediction of IVF outcomes.

Methods: The study population consisted of 136 women undergoing a fresh IVF cycle from January 2014 to August 2016 at a tertiary, university-affiliated medical center. We tested the ability of two machine learning algorithms, support vector machine (SVM) and artificial neural network (NN), vs.

NeuroConstruct-based implementation of structured-light stimulated retinal circuitry.

Background: Retinal circuitry provides a fundamental window to neural networks, featuring widely investigated visual phenomena ranging from direction selectivity to fast detection of approaching motion. As the divide between experimental and theoretical visual neuroscience is fading, neuronal modeling has proven to be important for retinal research. In neuronal modeling a delicate balance is maintained between bio-plausibility and model tractability, giving rise to myriad modeling frameworks.

BCG and IL - 2 model for bladder cancer treatment with fast and slow dynamics based on SPVF method-stability analysis.

In this study, we apply the method of singularly perturbed vector field (SPVF) and its application to the problem of bladder cancer treatment that takes into account the combination of Bacillus CalmetteGurin vaccine (BCG) and interleukin (IL)-2 immunotherapy (IL - 2). The model is presented with a hidden hierarchy of time scale of the dynamical variables of the system. By applying the SPVF, we transform the model to SPS (Singular Perturbed System) form with explicit hierarchy, i.

Combination of singularly perturbed vector field method and method of directly defining the inverse mapping applied to complex ODE system prostate cancer model.

We propose a new method to solve a system of complex ordinary differential equations (ODEs) with hidden hierarchy. Given a complex system of the ODE, the hierarchy of the system is generally hidden. Once we reveal the hierarchy of the system, the system can be reduced into subsystems called slow and fast subsystems.