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Complex microenvironmental stimuli influence neural cell properties. To study this, we developed a three-dimensional (3-D) neural culture system, composed of different populations including neurons, astrocytes, and neural stem cells (NSCs). In particular, these last-mentioned cells represent a source potentially exploitable to test drugs, to study neurodevelopment and cell-therapies for neuroregenerations. On seeding on matrigel in a medium supplemented with serum and mitogens, cells obtained from human fetal brain tissue formed 3-D self-organizing neural architectures. Immunocytochemical analysis demonstrated the presence of undifferentiated nestin+ and CD133+ cells, surrounded by β-tub-III+ and GFAP+ cells, suggesting the formation of niches containing potential human NSCs (hNSCs). The presence of hNSCs was confirmed by both neurosphere assay and RT-PCR, and their multipotentiality was demonstrated by both immunofluorescent staining and RT-PCR. Flow cytometry analysis revealed that neurosphere forming cells originating from at least two different subsets expressing, respectively, CD133 and CD146 markers were endowed with different proliferative and differentiation potential. Our data implicate that the complexity of environment within niches and aggregates of heterogeneous neural cell subsets may represent an innovative platform for neurobiological and neurodevelopmental investigations and a reservoir for a rapid expansion of hNSCs.
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http://dx.doi.org/10.1089/ten.TEC.2010.0622 | DOI Listing |
PLoS One
September 2025
Institut für Theoretische Physik, Universität Innsbruck, Innsbruck, Austria.
In the last decade, the free energy principle (FEP) and active inference (AIF) have achieved many successes connecting conceptual models of learning and cognition to mathematical models of perception and action. This effort is driven by a multidisciplinary interest in understanding aspects of self-organizing complex adaptive systems, including elements of agency. Various reinforcement learning (RL) models performing active inference have been proposed and trained on standard RL tasks using deep neural networks.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
September 2025
Effective gait monitoring and rehabilitation are essential for improving the quality of life in individuals with disabilities. Inertial sensors have the potential to enable long-term gait monitoring and assessment beyond the clinical setting. However, developing minimally intrusive systems that accommodate a wide range of gait deviations remains challenging.
View Article and Find Full Text PDFActa Trop
August 2025
EIACP Resource Partner on Climate Change and Public Health, Applied Biology Division, CSIR-Indian Institute of Chemical Technology (CSIR-IICT), Tarnaka, Hyderabad-500007, Telangana, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India. Electronic address: msrinivas@i
Malaria continues to pose a significant public health challenge in India, particularly in the North East Region (NER), which presents a multifaceted epidemiological landscape. Malaria control and prevention programs demonstrate greater efficiency and cost-effectiveness when they target hotspot regions. This study is aimed to explore spatiotemporal clusters of malaria incidence at the district level across NER of India.
View Article and Find Full Text PDFHuan Jing Ke Xue
August 2025
School of Water Resources and Environment, China University of Geosciences (Beijing), Beijing 100083, China.
To explore the pollution characteristics, sources, and possible human health risks of soil heavy metals in a coal mine concentration area near western Jiangxi. The Nemerow index method was used to evaluate the characteristics of soil heavy metal pollution in the study area, the correlation of soil heavy metal pollution characteristics was described based on a self-organizing neural network (SOM), the PMF model was used to trace the soil heavy metal pollution, and the exposure risk model established by EPA was used for health risk assessment. The results showed that the average content of heavy metals in the six soils ranged from 0.
View Article and Find Full Text PDFFront Syst Neurosci
July 2025
Departments of Mathematics and Statistics, Psychological and Brain Sciences, and Biomedical Engineering, Boston University, Boston, MA, United States.
This article describes a biological neural network model that explains how humans learn to understand large language models and their meanings. This kind of learning typically occurs when a student learns from a teacher about events that they experience together. Multiple types of self-organizing brain processes are involved, including content-addressable memory; conscious visual perception; joint attention; object learning, categorization, and cognition; conscious recognition; cognitive working memory; cognitive planning; neural-symbolic computing; emotion; cognitive-emotional interactions and reinforcement learning; volition; and goal-oriented actions.
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