Objective:Glioblastoma(GBM)is the most prevalent and aggressive adult primary cancer in the central nervous system.Therapeutic approaches for GBM treatment are under intense investigation,including the use of emerging...Objective:Glioblastoma(GBM)is the most prevalent and aggressive adult primary cancer in the central nervous system.Therapeutic approaches for GBM treatment are under intense investigation,including the use of emerging immunotherapies.Here,we propose an alternative approach to treat GBM through reprogramming proliferative GBM cells into non-proliferative neurons.Methods:Retroviruses were used to target highly proliferative human GBM cells through overexpression of neural transcription factors.Immunostaining,electrophysiological recording,and bulk RNA-seq were performed to investigate the mechanisms underlying the neuronal conversion of human GBM cells.An in vivo intracranial xenograft mouse model was used to examine the neuronal conversion of human GBM cells.Results:We report efficient neuronal conversion from human GBM cells by overexpressing single neural transcription factor Neurogenic differentiation 1(Neuro D1),Neurogenin-2(Neurog2),or Achaete-scute homolog 1(Ascl1).Subtype characterization showed that the majority of Neurog2-and Neuro D1-converted neurons were glutamatergic,while Ascl1 favored GABAergic neuron generation.The GBM cell-converted neurons not only showed pan-neuronal markers but also exhibited neuron-specific electrophysiological activities.Transcriptome analyses revealed that neuronal genes were activated in glioma cells after overexpression of neural transcription factors,and different signaling pathways were activated by different neural transcription factors.Importantly,the neuronal conversion of GBM cells was accompanied by significant inhibition of GBM cell proliferation in both in vitro and in vivo models.Conclusions:These results suggest that GBM cells can be reprogrammed into different subtypes of neurons,leading to a potential alternative approach to treat brain tumors using in vivo cell conversion technology.展开更多
Cadmium and its compounds are currently known as Class I carcinogens,and excessive intake can cause severe health damage to humans.Rice has a strong absorption effect on cadmium,and rice products with excessive cadmiu...Cadmium and its compounds are currently known as Class I carcinogens,and excessive intake can cause severe health damage to humans.Rice has a strong absorption effect on cadmium,and rice products with excessive cadmium content have caused several significant public health contamination incidents.It is essential to predict the development trend of cadmium hazards in the rice supply chain so that countermeasures can be formulated to reduce the hazards.This paper proposes a deep prediction model for cadmium hazards in the rice supply chain based on the regularization method.Firstly,a long and short-term memory network is used to build the depth prediction model by using the regularization method,and the noise penalty term is added to reduce the model fitting to the noise and prevent the over-fitting caused by the noise.Finally,the optimization of the model hyperparameters was carried out using a Bayesian optimization approach to develop the prediction performance.Then,early warning system for prediction of cadmium hazards in the rice supply chain is built based on the deep prediction model proposed in this paper with SOA architecture,including data resource,business logic,and application service layers.The proposed model performs well on an actual data set of cadmium hazards in the rice supply chain and fits the data well.展开更多
基金supported by the Charles H.“Skip”Smith Endowment Fund and the Verne M.Willaman Endowment Fund from the Pennsylvania State University to G.C。
文摘Objective:Glioblastoma(GBM)is the most prevalent and aggressive adult primary cancer in the central nervous system.Therapeutic approaches for GBM treatment are under intense investigation,including the use of emerging immunotherapies.Here,we propose an alternative approach to treat GBM through reprogramming proliferative GBM cells into non-proliferative neurons.Methods:Retroviruses were used to target highly proliferative human GBM cells through overexpression of neural transcription factors.Immunostaining,electrophysiological recording,and bulk RNA-seq were performed to investigate the mechanisms underlying the neuronal conversion of human GBM cells.An in vivo intracranial xenograft mouse model was used to examine the neuronal conversion of human GBM cells.Results:We report efficient neuronal conversion from human GBM cells by overexpressing single neural transcription factor Neurogenic differentiation 1(Neuro D1),Neurogenin-2(Neurog2),or Achaete-scute homolog 1(Ascl1).Subtype characterization showed that the majority of Neurog2-and Neuro D1-converted neurons were glutamatergic,while Ascl1 favored GABAergic neuron generation.The GBM cell-converted neurons not only showed pan-neuronal markers but also exhibited neuron-specific electrophysiological activities.Transcriptome analyses revealed that neuronal genes were activated in glioma cells after overexpression of neural transcription factors,and different signaling pathways were activated by different neural transcription factors.Importantly,the neuronal conversion of GBM cells was accompanied by significant inhibition of GBM cell proliferation in both in vitro and in vivo models.Conclusions:These results suggest that GBM cells can be reprogrammed into different subtypes of neurons,leading to a potential alternative approach to treat brain tumors using in vivo cell conversion technology.
基金supported in part by the National Key Research and Development Program of China(2017YFC1600605,2020YFC1606801).
文摘Cadmium and its compounds are currently known as Class I carcinogens,and excessive intake can cause severe health damage to humans.Rice has a strong absorption effect on cadmium,and rice products with excessive cadmium content have caused several significant public health contamination incidents.It is essential to predict the development trend of cadmium hazards in the rice supply chain so that countermeasures can be formulated to reduce the hazards.This paper proposes a deep prediction model for cadmium hazards in the rice supply chain based on the regularization method.Firstly,a long and short-term memory network is used to build the depth prediction model by using the regularization method,and the noise penalty term is added to reduce the model fitting to the noise and prevent the over-fitting caused by the noise.Finally,the optimization of the model hyperparameters was carried out using a Bayesian optimization approach to develop the prediction performance.Then,early warning system for prediction of cadmium hazards in the rice supply chain is built based on the deep prediction model proposed in this paper with SOA architecture,including data resource,business logic,and application service layers.The proposed model performs well on an actual data set of cadmium hazards in the rice supply chain and fits the data well.
