AIM: To assess cultured limbal epithelial stem cell transplantation in patients with limbal stem cell deficiency by analyzing and quantifying corneal neovascularization.METHODS: This retrospective, interventional case...AIM: To assess cultured limbal epithelial stem cell transplantation in patients with limbal stem cell deficiency by analyzing and quantifying corneal neovascularization.METHODS: This retrospective, interventional case series included eight eyes with total limbal stem cell deficiency. Ex vivo limbal epithelial stem cells were cultured on human amniotic membrane using an animalfree culture method. The clinical parameters of limbal stem cell deficiency, impression cytology, and quantification of corneal neovascularization were evaluated before and after cultured limbal stem cell transplantation. The area of corneal neovascularization,vessel caliber(VC), and invasive area(IA) were analyzed before and after stem cell transplantation by image analysis software. Best-corrected visual acuity(BCVA),epithelial transparency, and impression cytology were also measured.RESULTS: One year after surgery, successful cases showed a reduction(improvement) of all three parameters of corneal neovascularization [neovascular area(NA), VC, IA], while failed cases did not. NA decreased a mean of 32.31%(P =0.035), invasion area29.37%(P =0.018) and VC 14.29%(P =0.072). BCVA improved in all eyes(mean follow-up, 76 ±21mo).Epithelial transparency improved significantly from 2.00 ±0.93 to 0.88±1.25(P =0.014). Impression cytology showed that three cases failed after limbal epithelial stem cell therapy before 1y of follow-up.CONCLUSION: This method of analyzing andmonitoring surface vessels is useful for evaluating the epithelial status during follow-up, as successful cases showed a bigger reduction in corneal neovascularization parameters than failed cases. Using this method,successful cases could be differentiated from failed cases.展开更多
Background:Single-cell multi-omics technologies allow a profound system-level biology understanding of cells and tissues.However,an integrative and possibly systems-based analysis capturing the different modalities is...Background:Single-cell multi-omics technologies allow a profound system-level biology understanding of cells and tissues.However,an integrative and possibly systems-based analysis capturing the different modalities is challenging.In response,bioinformatics and machine learning methodologies are being developed for multi-omics single-cell analysis.It is unclear whether current tools can address the dual aspect of modality integration and prediction across modalities without requiring extensive parameter fine-tuning.Methods:We designed LIBRA,a neural network based framework,to learn translation between paired multi-omics profiles so that a shared latent space is constructed.Additionally,we implemented a variation,aLIBRA,that allows automatic fine-tuning by identifying parameter combinations that optimize both the integrative and predictive tasks.All model parameters and evaluation metrics are made available to users with minimal user iteration.Furthermore,aLIBRA allows experienced users to implement custom configurations.The LIBRA toolbox is freely available as R and Python libraries at GitHub(TranslationalBioinformaticsUnit/LIBRA).Results:LIBRA was evaluated in eight multi-omic single-cell data-sets,including three combinations of omics.We observed that LIBRA is a state-of-the-art tool when evaluating the ability to increase cell-type(clustering)resolution in the integrated latent space.Furthermore,when assessing the predictive power across data modalities,such as predictive chromatin accessibility from gene expression,LIBRA outperforms existing tools.As expected,adaptive parameter optimization(aLIBRA)significantly boosted the performance of learning predictive models from paired data-sets.Conclusion:LIBRA is a versatile tool that performs competitively in both“integration”and“prediction”tasks based on single-cell multi-omics data.LIBRA is a data-driven robust platform that includes an adaptive learning scheme.展开更多
文摘AIM: To assess cultured limbal epithelial stem cell transplantation in patients with limbal stem cell deficiency by analyzing and quantifying corneal neovascularization.METHODS: This retrospective, interventional case series included eight eyes with total limbal stem cell deficiency. Ex vivo limbal epithelial stem cells were cultured on human amniotic membrane using an animalfree culture method. The clinical parameters of limbal stem cell deficiency, impression cytology, and quantification of corneal neovascularization were evaluated before and after cultured limbal stem cell transplantation. The area of corneal neovascularization,vessel caliber(VC), and invasive area(IA) were analyzed before and after stem cell transplantation by image analysis software. Best-corrected visual acuity(BCVA),epithelial transparency, and impression cytology were also measured.RESULTS: One year after surgery, successful cases showed a reduction(improvement) of all three parameters of corneal neovascularization [neovascular area(NA), VC, IA], while failed cases did not. NA decreased a mean of 32.31%(P =0.035), invasion area29.37%(P =0.018) and VC 14.29%(P =0.072). BCVA improved in all eyes(mean follow-up, 76 ±21mo).Epithelial transparency improved significantly from 2.00 ±0.93 to 0.88±1.25(P =0.014). Impression cytology showed that three cases failed after limbal epithelial stem cell therapy before 1y of follow-up.CONCLUSION: This method of analyzing andmonitoring surface vessels is useful for evaluating the epithelial status during follow-up, as successful cases showed a bigger reduction in corneal neovascularization parameters than failed cases. Using this method,successful cases could be differentiated from failed cases.
基金supported by grants from the European Union under the Horizon 2020 programme(MultipleMS grant agreement 733161)to NKfrom the Spanish Government,through project PID2019-111192GA-I00(MICINN)to DGC.
文摘Background:Single-cell multi-omics technologies allow a profound system-level biology understanding of cells and tissues.However,an integrative and possibly systems-based analysis capturing the different modalities is challenging.In response,bioinformatics and machine learning methodologies are being developed for multi-omics single-cell analysis.It is unclear whether current tools can address the dual aspect of modality integration and prediction across modalities without requiring extensive parameter fine-tuning.Methods:We designed LIBRA,a neural network based framework,to learn translation between paired multi-omics profiles so that a shared latent space is constructed.Additionally,we implemented a variation,aLIBRA,that allows automatic fine-tuning by identifying parameter combinations that optimize both the integrative and predictive tasks.All model parameters and evaluation metrics are made available to users with minimal user iteration.Furthermore,aLIBRA allows experienced users to implement custom configurations.The LIBRA toolbox is freely available as R and Python libraries at GitHub(TranslationalBioinformaticsUnit/LIBRA).Results:LIBRA was evaluated in eight multi-omic single-cell data-sets,including three combinations of omics.We observed that LIBRA is a state-of-the-art tool when evaluating the ability to increase cell-type(clustering)resolution in the integrated latent space.Furthermore,when assessing the predictive power across data modalities,such as predictive chromatin accessibility from gene expression,LIBRA outperforms existing tools.As expected,adaptive parameter optimization(aLIBRA)significantly boosted the performance of learning predictive models from paired data-sets.Conclusion:LIBRA is a versatile tool that performs competitively in both“integration”and“prediction”tasks based on single-cell multi-omics data.LIBRA is a data-driven robust platform that includes an adaptive learning scheme.
