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Journal of Zhejiang University SCIENCE B

ISSN 1673-1581(Print), 1862-1783(Online), Monthly

Secondary donor-derived CD19 CAR-T therapy is safe and efficacious in acute lymphoblastic leukemia with extramedullary relapse after first autologous CAR-T therapy

Abstract: Despite the advancement of treatments, adults with relapsed/refractory (R/R) B-lineage acute lymphoblastic leukemia (B-ALL) have poor prognosis, with an expected five-year overall survival (OS) rate of 10%‒20% (Nguyen et al., 2008; Oriol et al., 2010). Extramedullary relapse of B-ALL is regarded as a high-risk factor generally associated with poor survival, occurring in about 15% to 20% of all relapsed patients (Ding et al., 2017; Sun et al., 2018). The central nervous system (CNS) and the testes are the most common sites of extramedullary relapse of B-ALL. In addition, extramedullary leukemia can appear in the skin, eyes, breasts, bones, muscles, and abdominal organs. The prognosis of relapsed extramedullary B-ALL after allogeneic hematopoietic stem cell transplantation (allo-HSCT) is extremely poor (Spyridonidis et al., 2012; Dahlberg et al., 2019). Conventional chemotherapy or radiation is often ineffective in such patients. At present, there are no optimal treatment strategies for treating extramedullary leukemia after allo-HSCT.

Key words: Extramedullary relapse; Allogeneic anti-CD19 chimeric antigen receptor T cells; Haploidentical hematopoietic stem cell transplantation; Acute lymphoblastic leukemia

Chinese Summary  <38> 基于混合强化学习的自动驾驶汽车行人避撞方法

李惠乾1,黄晋1,曹重1,杨殿阁1,钟志华2
1清华大学车辆与运载学院,中国北京市,100084
2中国工程院,中国北京市,100088
摘要:确保行人的安全对自动驾驶汽车而言至关重要,同时也具有一定挑战。经典的行人避撞策略无法应对不确定性,而基于学习的方法缺乏明确的性能保障。本文提出一种基于混合强化学习的行人避撞方法,以使自动驾驶车辆能够与具有行为不确定性的行人安全交互。该方法集成了规则策略和强化学习策略,并设计了一个激活函数选择具有更高置信度的作为最终策略,通过这种方式保证最终策略的表现不亚于规则策略。为说明所提方法的有效性,本文使用一种加速测试方法生成了行为随机的行人进行仿真验证。结果表明,该方法在测试场景中的成功率,相比基准方法的94.4%,提升至98.8%。

关键词组:行人;混合强化学习;自动驾驶汽车;决策


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DOI:

10.1631/jzus.B2200128

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On-line Access:

2022-10-12

Received:

2022-03-14

Revision Accepted:

2022-05-17

Crosschecked:

2022-10-13

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