During lung cancer metastasis,tumor cells undergo epithelial-to-mesenchymal transition(EMT),enabling them to intravasate through the vascular barrier and enter the circulation before colonizing secondary sites.Here,a ...During lung cancer metastasis,tumor cells undergo epithelial-to-mesenchymal transition(EMT),enabling them to intravasate through the vascular barrier and enter the circulation before colonizing secondary sites.Here,a human in vitro microphysiological model of EMT-driven lung cancer intravasation-on-a-chip was developed and coupled with machine learning(ML)-assisted automatic identification and quantification of intravasation events.A robust EMT-inducing cocktail(EMT-IC)was formulated by augmenting macrophage-conditioned medium with transforming growth factor-β1.When introduced into microvascular networks(MVNs)in microfluidic devices,EMT-IC did not affect MVN stability and physiologically relevant barrier functions.To model lung cancer intravasation on-a-chip,EMT-IC was supplemented into co-cultures of lung tumor micromasses and MVNs.Wihin 24 h of exposure,EMT-IC facilitated the insertion of membrane protrusions of migratory A549 cells into microvascular structures,followed by successful intravasation.EMT-IC reduced key basement membrane and vascular junction proteins-laminin and VE-Cadherin-rendering vessel walls more permissive to intravasating cells.ML-assisted vessel segmentation combined with co-localization analysis to detect intravasation events confirmed that EMT induction significantly increased the number of intravasation events.Introducing metastatic(NCI-H1975)and non-metastatic(BEAS-2B)cell lines demonstrated that both,baseline intravasation potential and responsiveness to EMT-IC,are reflected in the metastatic predisposition of lung cancer cell lines,highlighting the model’s universal applicability and cell-specific sensitivity.The reproducible detection of intravasation events in the established model provides a physiologically relevant platform to study processes of cancer metastasis with high spatio-temporal resolution and short timeframe.This approach holds promise for improved drug development and informed personalized patient treatment plans.展开更多
The development of sustainable electrode materials for energy storage systems has become very important and porous carbons derived from biomass have become an important candidate because of their tunable pore structur...The development of sustainable electrode materials for energy storage systems has become very important and porous carbons derived from biomass have become an important candidate because of their tunable pore structure,environmental friendliness,and cost-effectiveness.Recent advances in controlling the pore structure of these carbons and its relationship between to is energy storage performance are discussed,emphasizing the critical role of a balanced distribution of micropores,mesopores and macropores in determining electrochemical behavior.Particular attention is given to how the intrinsic components of biomass precursors(lignin,cellulose,and hemicellulose)influence pore formation during carbonization.Carbonization and activation strategies to precisely control the pore structure are introduced.Finally,key challenges in the industrial production of these carbons are outlined,and future research directions are proposed.These include the establishment of a database of biomass intrinsic structures and machine learning-assisted pore structure engineering,aimed at providing guidance for the design of high-performance carbon materials for next-generation energy storage devices.展开更多
基金supported by the research fund to the Center for Neuromusculoskeletal Restorative Medicine from Health@InnoHK program launched by Innovation and Technology Commission,the Government of the Hong Kong Special Administrative Region of the People’s Republic of China(AB),by the Health and Medical Research Fund(08191066,AB)a direct grant(4054732,AB)from the Faculty of Medicine,CUHK+1 种基金supported by the Lee Quo Wei and Lee Yick Hoi Lun Professorship in Tissue Engineering and Regenerative Medicine.A.J.receives a Walter Benjamin postdoctoral fellowship from the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation,Germany521343357).
文摘During lung cancer metastasis,tumor cells undergo epithelial-to-mesenchymal transition(EMT),enabling them to intravasate through the vascular barrier and enter the circulation before colonizing secondary sites.Here,a human in vitro microphysiological model of EMT-driven lung cancer intravasation-on-a-chip was developed and coupled with machine learning(ML)-assisted automatic identification and quantification of intravasation events.A robust EMT-inducing cocktail(EMT-IC)was formulated by augmenting macrophage-conditioned medium with transforming growth factor-β1.When introduced into microvascular networks(MVNs)in microfluidic devices,EMT-IC did not affect MVN stability and physiologically relevant barrier functions.To model lung cancer intravasation on-a-chip,EMT-IC was supplemented into co-cultures of lung tumor micromasses and MVNs.Wihin 24 h of exposure,EMT-IC facilitated the insertion of membrane protrusions of migratory A549 cells into microvascular structures,followed by successful intravasation.EMT-IC reduced key basement membrane and vascular junction proteins-laminin and VE-Cadherin-rendering vessel walls more permissive to intravasating cells.ML-assisted vessel segmentation combined with co-localization analysis to detect intravasation events confirmed that EMT induction significantly increased the number of intravasation events.Introducing metastatic(NCI-H1975)and non-metastatic(BEAS-2B)cell lines demonstrated that both,baseline intravasation potential and responsiveness to EMT-IC,are reflected in the metastatic predisposition of lung cancer cell lines,highlighting the model’s universal applicability and cell-specific sensitivity.The reproducible detection of intravasation events in the established model provides a physiologically relevant platform to study processes of cancer metastasis with high spatio-temporal resolution and short timeframe.This approach holds promise for improved drug development and informed personalized patient treatment plans.
文摘The development of sustainable electrode materials for energy storage systems has become very important and porous carbons derived from biomass have become an important candidate because of their tunable pore structure,environmental friendliness,and cost-effectiveness.Recent advances in controlling the pore structure of these carbons and its relationship between to is energy storage performance are discussed,emphasizing the critical role of a balanced distribution of micropores,mesopores and macropores in determining electrochemical behavior.Particular attention is given to how the intrinsic components of biomass precursors(lignin,cellulose,and hemicellulose)influence pore formation during carbonization.Carbonization and activation strategies to precisely control the pore structure are introduced.Finally,key challenges in the industrial production of these carbons are outlined,and future research directions are proposed.These include the establishment of a database of biomass intrinsic structures and machine learning-assisted pore structure engineering,aimed at providing guidance for the design of high-performance carbon materials for next-generation energy storage devices.
