AIMC Topic: Cell Differentiation

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Deep Learning Neural Networks Highly Predict Very Early Onset of Pluripotent Stem Cell Differentiation.

Stem cell reports
Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recognition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train a CNN with...

Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers.

Cancer letters
This study aimed to identify the optimal radiomic machine-learning classifier for differentiating glioblastoma (GBM) from solitary brain metastases (MET) preoperatively. Four hundred and twelve patients with solitary brain tumors (242 GBM and 170 sol...

Whey Protein Complexes with Green Tea Polyphenols: Antimicrobial, Osteoblast-Stimulatory, and Antioxidant Activities.

Cells, tissues, organs
Polyphenols are known for their antimicrobial activity, whilst both polyphenols and the globular protein β-lactoglobulin (bLG) are suggested to have antioxidant properties and promote cell proliferation. These are potentially useful properties for a ...

DeepNEU: cellular reprogramming comes of age - a machine learning platform with application to rare diseases research.

Orphanet journal of rare diseases
BACKGROUND: Conversion of human somatic cells into induced pluripotent stem cells (iPSCs) is often an inefficient, time consuming and expensive process. Also, the tendency of iPSCs to revert to their original somatic cell type over time continues to ...

Integrated machine learning pipeline for aberrant biomarker enrichment (i-mAB): characterizing clusters of differentiation within a compendium of systemic lupus erythematosus patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Clusters of differentiation () are cell surface biomarkers that denote key biological differences between cell types and disease state. CD-targeting therapeutic monoclonal antibodies () afford rich trans-disease repositioning opportunities. Within a ...

Acquisition and reconstruction of 4D surfaces of axolotl embryos with the flipping stage robotic microscope.

Bio Systems
We have designed and constructed a Flipping Stage for a light microscope that can view the whole exterior surface of a 2 mm diameter developing axolotl salamander embryo. It works by rapidly inverting the bottom-heavy embryo, imaging it as it rights ...

Regulation of C2C12 Differentiation and Control of the Beating Dynamics of Contractile Cells for a Muscle-Driven Biosyncretic Crawler by Electrical Stimulation.

Soft robotics
Biosyncretic robots have potential advantages associated with both living organisms and electromechanical systems. Skeletal muscle tissue is a candidate as bioactuators for biosyncretic robots because of its excellent contraction force and controllab...

Automated Deep Learning-Based System to Identify Endothelial Cells Derived from Induced Pluripotent Stem Cells.

Stem cell reports
Deep learning technology is rapidly advancing and is now used to solve complex problems. Here, we used deep learning in convolutional neural networks to establish an automated method to identify endothelial cells derived from induced pluripotent stem...

Role of α-fetoprotein in differentiation of regulatory T lymphocytes.

Doklady biological sciences : proceedings of the Academy of Sciences of the USSR, Biological sciences sections
The effect of native α-fetoprotein (AFP) on the expression of T-regulatory lymphocyte (Treg) markers by activated CD4 lymphocytes with different proliferative status was studied. α-Fetoprotein did not affect the ratio of proliferating and non-prolife...