Retinitis pigmentosa and stem cell therapy

Retinitis pigmentosa(RP)is a group of genetic disorders characterized by progressive degeneration of photoreceptors and retinal pigment epithelium(RPE)cells.Its main clinical manifestations include night blindness and progressive loss of peripheral vision,making it a prevalent debilitating eye disease that significantly impacts patients’quality of life.RP exhibits significant phenotypic and genetic heterogeneity.For instance,numerous abnormal genes are implicated in RP,resulting in varying clinical presentations,disease progression rates,and pathological characteristics among different patients.Consequently,gene therapy for RP poses challenges due to these complexities.However,stem cells have garnered considerable attention in the field of RPE therapy since both RPE cells and photoreceptors can be derived from stem cells.In recent years,a large number of animal experiments and clinical trials based on stem cell transplantation attempts,especially cord blood mesenchymal stem cell(MSC)transplantation and bone marrow-derived MSC transplantation,have confirmed that stem cell therapy can effectively and safely improve the outer retinal function of the RP-affected eye.However,stem cell therapy also has certain limitations,such as the fact that RP patients may involve multiple types of retinal cytopathia,which brings great challenges to stem cell transplantation therapy,and further research is needed to solve various problems faced by this approach in the clinic.Through comprehensive analysis of the etiology and histopathological changes associated with RP,this study substantiates the efficacy and safety of stem cell therapy based on rigorous animal experimentation and clinical trials,while also highlighting the existing limitations that warrant further investigation.

基于个体化机器学习的原发性免疫性血小板减少症危重出血预测模型:一项全国前瞻性队列研究

原发性免疫性血小板减少症(ITP)中少见但至关重要的危重出血事件,给患者的预后、生活质量和治疗决策带来严重影响。尽管有一些研究探讨了ITP中与危重出血相关的风险因素,但目前尚缺乏大样本数据、大规模多中心研究结果以及针对ITP患者致命出血事件的预测模型。本研究首次采用国际血栓与止血学会新提出的ITP致命出血标准,利用大样本数据开发了首个基于机器学习的在线应用,用于预测ITP患者的致命出血.研究中,我们使用中国各地大型多中心数据进行开发,并对全国39家医疗中心进行为期一年的外部测试,得到了较好的训练、验证和测试数据集预测能力该基于新算法的便捷网络工具能够快速识别ITP患者的出血风险,辅助临床决策,有望未来降低不良事件的发生。