Immune-related adverse events induced by programmed death protein-1 inhibitors from the perspective of lymphoma immunotherapy

Lymphoma,which is highly malignant,stems from lymph nodes and lymphoid tissue.Lymphoma cells express programmed death-ligand 1/2(PD-L1/PD-L2),which binds with programmed cell death 1 protein(PD-1)to establish inhibitory signaling that impedes the normal function of T cells and allows tumor cells to escape immune system surveillance.Recently,immune checkpoint inhibitor immunotherapies such as PD-1 inhibitors(nivolumab and pembrolizumab)have been introduced into the lymphoma treatment algorithm and have shown remarkable clinical efficacy and greatly improve prognosis in lymphoma patients.Accordingly,the number of lymphoma patients who are seeking treatment with PD-1 inhibitors is growing annually,which results in an increasing number of patients developing immune-related adverse events(irAEs).The occurrence of irAEs inevitably affects the benefits provided by immunotherapy,particularly when PD-1 inhibitors are applied.However,the mechanisms and characteristics of irAEs induced by PD-1 inhibitors in lymphoma need further investigation.This review article summarizes the latest research advances in irAEs during treatment of lymphoma with PD-1 inhibitors.A comprehensive understanding of irAEs incurred in immunotherapy can help to achieve better efficacy with PD-1 inhibitors in lymphoma.

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。