3D Magnetic Particle Imaging of Human Stem Cell-Derived Islet Organoid Transplantation Using a Machine Learning Algorithm.

Stem cell-derived islet organoids constitute a promising treatment of type 1 diabetes. A major hurdle in the field is the lack of appropriate method to determine graft outcome. Here, we investigate the feasibility of tracking of transplanted stem cell-derived islet organoids using magnetic particle imaging (MPI) in a mouse model.

A Concise Review: The Role of Stem Cells in Cancer Progression and Therapy.

The properties of cancer stem cells (CSCs) have recently gained attention as an avenue of intervention for cancer therapy. In this review, we highlight some of the key roles of CSCs in altering the cellular microenvironment in favor of cancer progression. We also report on various studies in this field which focus on transformative properties of CSCs and their influence on surrounding cells or targets through the release of cellular cargo in the form of extracellular vesicles.

Artificial Intelligence Analysis of Magnetic Particle Imaging for Islet Transplantation in a Mouse Model.

Purpose: Current approaches to quantification of magnetic particle imaging (MPI) for cell-based therapy are thwarted by the lack of reliable, standardized methods of segmenting the signal from background in images. This calls for the development of artificial intelligence (AI) systems for MPI analysis.

Procedures: We utilize a canonical algorithm in the domain of unsupervised machine learning, known as K-means++, to segment the regions of interest (ROI) of images and perform iron quantification analysis using a standard curve model.

Nucleic acid-based theranostics in type 1 diabetes.

Application of RNAi interference for type 1 diabetes (T1D) therapy bears tremendous potential. This review will discuss vehicles for oligonucleotide delivery, imaging modalities used for delivery monitoring, therapeutic targets, and different theranostic strategies that can be applied for T1D treatment.