Lead (Pb) exposure during early life has been associated with an increased risk of neurodevelopmental disorders, including learning and memory deficits. The intestinal flora, the microbiome-gut-brain axis, could play a significant role in the nervous system. However, the effects of probiotics on ameliorating Pb-induced learning and memory deficits are still unclear. In this study, we showed that adolescent Pb exposure (150 ppm) for 2 months impaired spatial learning and memory ability, accompanied by the decreasing diversity of gut microbiota, and the decreasing abundance of s at the genus level. Surprisingly, administration of the GR-1 (10 organisms/rat/day), not LGG or RC-14, reversed learning and memory deficits induced by Pb exposure. Meanwhile, administration of the GR-1 increased the diversity of the gut microbiota composition and partially normalized the genus level of , , , and in Pb-exposed rats. Notably, supplementation of GR-1 decreased the gut permeability of Pb-exposed rats, reduced proinflammatory cytokines [interleukin-1β (IL-1β) and IL-6] expression, and promoted anti-inflammatory cytokines [granulocyte colony-stimulating factor (G-CSF)] expression. Interestingly, neural cell treatment with G-CSF rescued Pb-induced neurotoxicity. In general, GR-1 supplementation recovered the Pb-induced loss of intestinal bacteria (), which may have reversed the damage to learning and memory ability. Collectively, our findings demonstrate an unexpectedly pivotal role of GR-1 in Pb-induced cognitive deficits and identify a potential probiotic therapy for cognitive dysfunction during early life.
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http://dx.doi.org/10.3389/fnut.2022.934118 | DOI Listing |
Psychol Rev
January 2025
Department of Experimental Psychology, University of Groningen.
Time is a central dimension against which perception, action, and cognition play out. From anticipating when future events will happen to recalling how long ago previous events occurred, humans and animals are exquisitely sensitive to temporal structure. Empirical evidence seems to suggest that estimating time prospectively (i.
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December 2024
Institute of Theoretical Physics, Jagiellonian University, Kraków, Poland.
Understanding brain function relies on identifying spatiotemporal patterns in brain activity. In recent years, machine learning methods have been widely used to detect connections between regions of interest (ROIs) involved in cognitive functions, as measured by the fMRI technique. However, it's essential to match the type of learning method to the problem type, and extracting the information about the most important ROI connections might be challenging.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Industrial Engineering, King Khalid University, Abha, 61421, Saudi Arabia.
The integration of renewable energy sources has resulted in an increasing intricacy in the functioning and organization of power systems. Accurate load forecasting, particularly taking into account dynamic factors like as climatic and socioeconomic impacts, is essential for effective management. Conventional statistical analysis and machine learning methods struggle with accurately capturing the intricate temporal relationships present in load data.
View Article and Find Full Text PDFHeliyon
December 2024
Department of Computer Science & Engineering, K L E F Deemed To Be University, Green Fields, Vaddeswaram, Guntur (dt), Andhra Pradesh, 521230, India.
Real-time monitoring and anomaly detection are essential in healthcare to ensure safe conditions for patients and maintain the integrity of medical data samples. The majority of existing systems, despite improvements in healthcare technologies, cannot capture the spatial and temporal patterns of multimodal data simultaneously, process high Volume data in real-time, and ensure the privacy of patients' identity effectively. In this work, we handle these limitations by proposing a complete approach that uses state-of-the-art deep learning and data processing architectures to realize resilient anomaly detection in healthcare systems.
View Article and Find Full Text PDFFront Plant Sci
December 2024
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, China.
Introduction: In the context of climate variability, rapid and accurate estimation of winter wheat yield is essential for agricultural policymaking and food security. With advancements in remote sensing technology and deep learning, methods utilizing remotely sensed data are increasingly being employed for large-scale crop growth monitoring and yield estimation.
Methods: Solar-induced chlorophyll fluorescence (SIF) is a new remote sensing metric that is closely linked to crop photosynthesis and has been applied to crop growth and drought monitoring.
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