Human cerebral organoids (hCOs) offer the possibility of deepening the knowledge of human brain development, as well as the pathologies that affect it. The method developed here describes the efficient generation of hCOs by going directly from two-dimensional (2D) pluripotent stem cell (PSC) cultures to three-dimensional (3D) neuroepithelial tissue, avoiding dissociation and aggregation steps. This has been achieved by subjecting 2D cultures, from the beginning of the neural induction step, to dual-SMAD inhibition in combination with CHIR99021. This is a simple and reproducible protocol in which the hCOs generated develop properly presenting proliferative ventricular zones (VZs) formed by neural precursor and radial glia (RG) that differentiate to give rise to mature neurons and glial cells. The hCOs present additional cell types such as oligodendrocyte precursors, astrocytes, microglia-like cells, and endothelial-like cells. This new approach could help to overcome some of the existing limitations in the field of organoid biotechnology, facilitating its execution in any laboratory setting.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10858658 | PMC |
| http://dx.doi.org/10.1177/20417314231226027 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Epidemiologic threats and outcome of evolving COVID-19-associated mucormycosis from a referral hospital in Egypt.
J Infect Dev Ctries
December 2024
Chest Dpt., Ahmed Maher Teaching Hospital, GOTHI, Cairo, Egypt.
Introduction: The present study aimed to explore the epidemiologic threats and factors associated with the coronavirus disease 2019 (COVID-19)-associated mucormycosis (CAM) epidemic that emerged in Egypt during the second COVID-19 wave. The study also aimed to explore the diagnostic features and the role of surgical interventions of CAM on the outcome of the disease in a central referral hospital.
Methodology: The study included 64 CAM patients from a referral hospital for CAM and a similar number of matched controls from COVID-19 patients who did not develop CAM.
HDN-DDI: a novel framework for predicting drug-drug interactions using hierarchical molecular graphs and enhanced dual-view representation learning.
BMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFA machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness.
Sci Rep
January 2025
College of Physical Education and Health Sciences, Zhejiang Normal University, Jinhua, 321004, China.
Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them.
View Article and Find Full Text PDFControlling the energies of the single-rotor large wind turbine system using a new controller.
Sci Rep
January 2025
Department of Electrical Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha, 61421, KSA, Saudi Arabia.
In wind energy generation systems, ensuring high energy quality is critical but is often compromised due to the limited performance and durability of conventional regulators. To address this, this work presents a novel controller for managing the machine-side inverter of a single-rotor large wind turbine system using an induction machine-type generator. The proposed controller is designed using proportional, integral, and derivative error-based mechanisms, which fundamentally differ from traditional proportional-integral (PI) regulators.
View Article and Find Full Text PDFImprovement of flipped classroom teaching in colleges and universities based on virtual reality assisted by deep learning.
Sci Rep
January 2025
School of Electronic and Information Engineering, Changsha Institute of Technology, Changsha, 410200, China.
In order to solve the limitations of flipped classroom in personalized teaching and interactive effect improvement, this paper designs a new model of flipped classroom in colleges and universities based on Virtual Reality (VR) by combining the algorithm of Contrastive Language-Image Pre-Training (CLIP). Through cross-modal data fusion, the model deeply combines students' operation behavior with teaching content, and improves teaching effect through intelligent feedback mechanism. The test data shows that the similarity between video and image modes reaches 0.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!