We have utilized cultured neuronal networks grown on microelectrode arrays to demonstrate rapid, reliable detection of a toxic compound, trimethylolpropane phosphate (TMPP). Initial experiments, which were performed blind, demonstrated rapid classification of the compound as a convulsant, a finding consistent with previous whole animal neurobehavioral studies. TMPP (2-200 microM) reorganized network spike activity into synchronous, quasi-periodic burst episodes. Integrated burst amplitudes invariably increased, reflecting higher spike frequencies within each burst. The variability of network burst parameters, quantified as coefficients of variation (CVs), was decreased. Mean CVs for burst duration, interburst interval, and burst rate were lowered by 42+/-13, 58+/-5.5, and 62+/-1.8%, respectively (mean+/-SEM, n=8 cultures, 197 channels). These changes in network activity paralleled the effects induced by bicuculline, a known disinhibitory and seizure-inducing drug, and confirmed classification of TMPP as a potential epileptogenic compound. Simple pharmacological tests permit exploration of mechanisms underlying observed activity shifts. The EC(50) for GABA inhibition of network activity was increased from 2.8 to 7.0 microM by 20 microM TMPP and to 20.5 microM by 200 microM TMPP. Parallel dose-response curves suggest that TMPP acts by a competitive antagonism of GABA inhibition, and are consistent with reported patch-clamp analysis of TMPP-induced reduction of inhibitory postsynaptic current amplitudes. The potency of TMPP in generating epileptiform activity in vitro was comparable to concentrations reported for in vivo studies. TMPP and bicuculline produced both increases and decreases in burst rate depending on native spontaneous bursting levels. These results demonstrate a need for multivariate analysis of network activity changes to yield accurate predictions of compound effects.
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Ecotoxicol Environ Saf
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College of Bioscience and Biotechnology, Hunan Agricultural University, Changsha 410128, China; Yuelushan Laboratory, Changsha 410125, China. Electronic address:
Soil heavy metal pollution presents substantial risks to food security and human health. This study focused on the efficiency of plant growth-promoting fungus-Beauveria bassiana FE14 and Miscanthus floridulus on the synergistic remediation of soil Cd contamination. Results revealed that B.
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College of Resource and Environment, Henan Polytechnic University, Jiaozuo 454003, China.
Identifying and quantifying the dominant factors influencing heavy metal (HM) pollution sources are essential for maintaining soil ecological health and implementing effective pollution control measures. This study analyzed soil HM samples from 53 different land use types in Jiaozuo City, Henan Province, China. Pollution sources were identified using Absolute Principal Component Score (APCS), with 8 anthropogenic factors, 9 natural factors, and 4 soil physicochemical properties mapped using Geographic Information System (GIS) kernel density estimation.
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January 2025
College of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing 100029, China. Electronic address:
This study aimed to investigate the potential protective properties of a traditional Chinese medicine (TCM) herbal product, Siraitia grosvenorii granules (SGG) against PM2.5-induced lung injury, as well as their active constituents and underlying mechanisms. The chemical composition of SGG, such as wogonin (MOL000173), luteolin (MOL000006), nobiletin (MOL005828), naringenin (MOL004328), acacetin (MOL001689), were identified via ultra-high-performance liquid chromatography-Q Exactive (UHPLC-QE) Orbitrap/MS.
View Article and Find Full Text PDFJ Neural Eng
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Department of Neuroscience, Northwestern University, 303 East Chicago Ave, Chicago, Illinois, 60611, UNITED STATES.
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School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, China.
With the popularity of circular economy around the world, transactions in the second-hand sailboat market are extremely active. Determining pricing strategies and exploring their regional effects is a blank area of existing research and has important practical and statistical significance. Therefore, this article uses the random forest model and XGBoost algorithm to identify core price indicators, and uses an innovative rolling NAR dynamic neural network model to simulate and predict second-hand sailboat price data.
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