National Brain Tumour Registry of China (NBTRC) Statistical Report of Primary Brain Tumours Diagnosed in China in years 2019 – 2020

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翻译：Bioorthogonal catalysis mediated by transition metals has inspired a new subfield of artificial chemistry complementary to enzymatic reactions, enabling the selective labelling of biomolecules or in situ synthesis of bioactive agents via non-natural processes. However, the effective deployment of bioorthogonal catalysis in vivo remains challenging, mired by the safety concerns of metal toxicity or complicated procedures to administer catalysts. Here, we describe a bioorthogonal catalytic device comprising a microneedle array patch integrated with Pd nanoparticles deposited on TiO2 nanosheets. This device is robust and removable, and can mediate the local conversion of caged substrates into their active states in high-level living systems. In particular, we show that such a patch can promote the activation of a prodrug at subcutaneous tumour sites, restoring its parent drug’s therapeutic anticancer properties. This in situ applied device potentiates local treatment efficacy and eliminates off-target prodrug activation and dose-dependent side effects in healthy organs or distant tissues

过渡金属介导的生物正交催化已经启发了一个新的人工化学子领域，补充了酶反应，使得生物分子或非天然过程中的生物活性剂的选择性标记或合成成为可能。然而，在体内有效部署生物正交催化仍然具有挑战性，因为金属毒性或管理催化剂的复杂程序带来的安全问题。在这里，我们描述了一种生物正交催化装置，包括一个微针阵列贴片，集成了Pd纳米颗粒沉积在TiO2纳米片上。这种装置是坚固且可拆卸的，可以在高水平的生物体系中局部转化封闭的底物为其活性状态。特别地，我们表明，这种贴片可以促进前体药物在皮下肿瘤病灶的激活，恢复其父药物的抗癌治疗性质。这种原位应用的装置增强了局部治疗疗效，消除了健康器官或远离组织的剂量依赖性副作用。

The RADLER framework is demonstrated with a DL ar- chitecture for CT/PET; in detail, a 3D multimodal CNN is adapted from a 2D solution originally aimed at classifying lung nodules from CT imaging [39]. Secondly, we adopted an internal transfer learning approach, starting from the diag- nostic classification of tumour stage. This domain adaptation approach proved useful in dealing with class unbalance and a relatively low number of samples, while achieving good predictive performance, as shown on the HN dataset, with high class unbalance and low number of samples. 解释

这段话大概是在说明RADLER框架采用了一个针对CT/PET的DL架构；具体而言，是从最初旨在从CT成像中对肺结节进行分类的2D解决方案中改编的3D多模态CNN。其次，我们采用了一种内部迁移学习方法，从肿瘤分期的诊断分类开始。这种领域自适应方法在处理类别不平衡和相对较少的样本时证明是有用的，同时在HN数据集上取得了良好的预测性能，该数据集具有高度类别不平衡和少量样本。