AIMC Topic: Organoids

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Machine Learning-Enhanced Optimization for High-Throughput Precision in Cellular Droplet Bioprinting.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Organoids produce through traditional manual pipetting methods face challenges such as labor-intensive procedures and batch-to-batch variability in quality. To ensure consistent organoid production, 3D bioprinting platforms offer a more efficient alt...

Artificial Intelligence-Enabled Quantitative Assessment and Intervention for Heart Inflammation Model Organoids.

Angewandte Chemie (International ed. in English)
Inflammation plays a crucial role in progression of cardiovascular diseases (CVDs); thus, the discovery of rapid and precise analytical tools to assess inflammation related to CVDs is highly desirable for their diagnosis and therapeutic discovery. Ho...

Innovative construction and application of bile duct organoids: Unraveling the complexity of bile duct diseases and potential therapeutic strategies.

Cancer letters
The biliary system is crucial for liver function, regulating bile production, secretion, and transport. Dysfunctions within this system can lead to various diseases, such as cholangiopathies and biliary fibrosis, which may progress from benign to mal...

Label-Free Detection of Biochemical Changes during Cortical Organoid Maturation via Raman Spectroscopy and Machine Learning.

Analytical chemistry
Human cerebral organoids have become valuable tools in neurodevelopment research, holding promise for investigating neurological diseases and reducing drug development costs. However, clinical translation and large-scale production of brain organoids...

Deliod a lightweight detection model for intestinal organoids based on deep learning.

Scientific reports
Intestinal organoids are indispensable tools for exploring intestinal disorders. Deep learning methodologies are often employed in morphological analysis to evaluate the condition of these organoids. Nonetheless, prevailing analytical techniques face...

Protocol for functional screening of CFTR-targeted genetic therapies in patient-derived organoids using DETECTOR deep-learning-based analysis.

STAR protocols
Here, we present a protocol for the rapid functional screening of gene editing and addition strategies in patient-derived organoids using the deep-learning-based tool DETECTOR (detection of targeted editing of cystic fibrosis transmembrane conductanc...

Single-cell RNA sequencing and machine learning provide candidate drugs against drug-tolerant persister cells in colorectal cancer.

Biochimica et biophysica acta. Molecular basis of disease
Drug resistance often stems from drug-tolerant persister (DTP) cells in cancer. These cells arise from various lineages and exhibit complex dynamics. However, effectively targeting DTP cells remains challenging. We used single-cell RNA sequencing (sc...

Next-Gen Therapeutics: Pioneering Drug Discovery with iPSCs, Genomics, AI, and Clinical Trials in a Dish.

Annual review of pharmacology and toxicology
In the high-stakes arena of drug discovery, the journey from bench to bedside is hindered by a daunting 92% failure rate, primarily due to unpredicted toxicities and inadequate therapeutic efficacy in clinical trials. The FDA Modernization Act 2.0 he...

A deep learning approach to predict differentiation outcomes in hypothalamic-pituitary organoids.

Communications biology
We use three-dimensional culture systems of human pluripotent stem cells for differentiation into pituitary organoids. Three-dimensional culture is inherently characterized by its ability to induce heterogeneous cell populations, making it difficult ...

ReBiA-Robotic Enabled Biological Automation: 3D Epithelial Tissue Production.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The Food and Drug Administration's recent decision to eliminate mandatory animal testing for drug approval marks a significant shift to alternative methods. Similarly, the European Parliament is advocating for a faster transition, reflecting public p...