AIMC Topic: Mice, Nude

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Machine learning-aided discovery of T790M-mutant EGFR inhibitor CDDO-Me effectively suppresses non-small cell lung cancer growth.

Cell communication and signaling : CCS
BACKGROUND: Epidermal growth factor receptor (EGFR) T790M mutation often occurs during long durational erlotinib treatment of non-small cell lung cancer (NSCLC) patients, leading to drug resistance and disease progression. Identification of new selec...

Resolution-dependent MRI-to-CT translation for orthotopic breast cancer models using deep learning.

Physics in medicine and biology
This study aims to investigate the feasibility of utilizing generative adversarial networks (GANs) to synthesize high-fidelity computed tomography (CT) images from lower-resolution MR images. The goal is to reduce patient exposure to ionizing radiati...

Salidroside sensitizes Triple-negative breast cancer to ferroptosis by SCD1-mediated lipogenesis and NCOA4-mediated ferritinophagy.

Journal of advanced research
INTRODUCTION: Triple-negative breast cancer (TNBC) is the primary cause of breast cancer-induced death in women. Literature has confirmed the benefits of Salidroside (Sal) in treating TNBC. However, the study about potential therapeutic targets and m...

Experimental Research on the Antitumor Effect of Human Gastric Cancer Cells Transplanted in Nude Mice Based on Deep Learning Combined with Spleen-Invigorating Chinese Medicine.

Computational and mathematical methods in medicine
Gastric cancer is still the fifth most common malignant tumor in the world and has the fourth highest mortality rate in the world. Gastric cancer is difficult to treat because of its unobvious onset, low resection rate, and rapid deterioration. There...

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo.

Laboratory investigation; a journal of technical methods and pathology
In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancemen...

Molecular imaging and deep learning analysis of uMUC1 expression in response to chemotherapy in an orthotopic model of ovarian cancer.

Scientific reports
Artificial Intelligence (AI) algorithms including deep learning have recently demonstrated remarkable progress in image-recognition tasks. Here, we utilized AI for monitoring the expression of underglycosylated mucin 1 (uMUC1) tumor antigen, a biomar...

Deep Learning of Cancer Stem Cell Morphology Using Conditional Generative Adversarial Networks.

Biomolecules
Deep-learning workflows of microscopic image analysis are sufficient for handling the contextual variations because they employ biological samples and have numerous tasks. The use of well-defined annotated images is important for the workflow. Cancer...

An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI.

Scientific reports
Saturation transfer MRI can be useful in the characterization of different tumour types. It is sensitive to tumour metabolism, microstructure, and microenvironment. This study aimed to use saturation transfer to differentiate between intratumoural re...

Deep Learning of Spatiotemporal Filtering for Fast Super-Resolution Ultrasound Imaging.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Super-resolution ultrasound (SR-US) imaging is a new technique that breaks the diffraction limit and allows visualization of microvascular structures down to tens of micrometers. The image processing methods for the spatiotemporal filtering needed in...

Fast fit-free analysis of fluorescence lifetime imaging via deep learning.

Proceedings of the National Academy of Sciences of the United States of America
Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI...