AIMC Topic: Mice
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Accelerating ionizable lipid discovery for mRNA delivery using machine learning and combinatorial chemistry.
Nature materials
May 13, 2024
To unlock the full promise of messenger (mRNA) therapies, expanding the toolkit of lipid nanoparticles is paramount. However, a pivotal component of lipid nanoparticle development that remains a bottleneck is identifying new ionizable lipids. Here we...
Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.
The Analyst
May 13, 2024
Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmo...
N-GlycoPred: A hybrid deep learning model for accurate identification of N-glycosylation sites.
Methods (San Diego, Calif.)
May 10, 2024
Studies have shown that protein glycosylation in cells reflects the real-time dynamics of biological processes, and the occurrence and development of many diseases are closely related to protein glycosylation. Abnormal protein glycosylation can be us...
Finding and following: a deep learning-based pipeline for tracking platelets during thrombus formation and .
Platelets
May 9, 2024
The last decade has seen increasing use of advanced imaging techniques in platelet research. However, there has been a lag in the development of image analysis methods, leaving much of the information trapped in images. Herein, we present a robust an...
A Siamese Convolutional Neural Network for Identifying Mild Traumatic Brain Injury and Predicting Recovery.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
May 9, 2024
Timely diagnosis of mild traumatic brain injury (mTBI) remains challenging due to the rapid recovery of acute symptoms and the absence of evidence of injury in static neuroimaging scans. Furthermore, while longitudinal tracking of mTBI is essential i...
A reliable deep-learning-based method for alveolar bone quantification using a murine model of periodontitis and micro-computed tomography imaging.
Journal of dentistry
May 8, 2024
OBJECTIVES: This study focuses on artificial intelligence (AI)-assisted analysis of alveolar bone for periodontitis in a mouse model with the aim to create an automatic deep-learning segmentation model that enables researchers to easily examine alveo...
Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.
Medical physics
May 6, 2024
BACKGROUND: Preclinical low-count positron emission tomography (LC-PET) imaging offers numerous advantages such as facilitating imaging logistics, enabling longitudinal studies of long- and short-lived isotopes as well as increasing scanner throughpu...
Consistent and effective method to define the mouse estrous cycle stage by a deep learning-based model.
The Journal of endocrinology
May 6, 2024
The mouse estrous cycle is divided into four stages: proestrus (P), estrus (E), metestrus (M), and diestrus (D). The estrous cycle affects reproductive hormone levels in a wide variety of tissues. Therefore, to obtain reliable results from female mic...
Deep learning models for atypical serotonergic cells recognition.
Journal of neuroscience methods
May 3, 2024
BACKGROUND: The serotonergic system modulates brain processes via functionally distinct subpopulations of neurons with heterogeneous properties, including their electrophysiological activity. In extracellular recordings, serotonergic neurons to be in...
Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.
PLoS computational biology
May 3, 2024
Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Ye...
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