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Chongjian Zhang

As seen in: Nature, Wiley Online Library, Frontiers, ACS Publications
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Articles by Chongjian Zhang

Neoadjuvant anti-HER2 antibody-drug conjugate SHR-A1811 in Hormone Receptor-positive, HER2-low Breast Cancer: A Phase 2 Clinical Trial (HELEN-015)

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By Dechuang Jiao, Jiujun Zhu, Xianfu Sun, Jianghua Qiao, Xiuchun Chen, Chengzheng Wang, Min Yan, Chongjian Zhang, Lianfang Li, Jianfei Wang, Min Jin, Yijia Wu, Zhenzhen Liu, Zhenduo Lu
| Nature Verified
Abstract As an anti-HER2 antibody-drug conjugate, SHR-A1811 has exhibited significant antitumor activity in patients with HER2-low advanced breast cancer. This trial aims to explore the efficacy and safety profile of SHR-A1811 as a neoadjuvant therapy for hormone receptor-positive, HER2-low (HR + /HER2-low) breast cancer.
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Identification of prostate cancer bone metastasis related genes and potential therapy targets by bioinformatics and in vitro experiments

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By Haiyang Jiang, Mingcheng Liu, Chongjian Zhang, Yingfei Deng
| Wiley Online Library Verified
1 INTRODUCTION Prostate cancer (PCa) has emerged as the most prevalent malignancy in males and the second leading cause of cancer-related deaths among men.1 Although localized PCa is often curable, the number of patients initially diagnosed at a metastatic stage has increased significantly in the last decade.2 Metastasis, particularly in the bones, represents a major cause of mortality in patients with PCa,3 affecting approximately 70%–90% of those with advanced disease.
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Perovskite Quantum Dots as Multifunctional Interlayers in Perovskite Solar Cells with Dopant-Free Organic Hole Transporting Layers

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By Fangwen Cheng, Ruiqin He, Siqing Nie, Chongjian Zhang
| ACS Publications
Perovskite solar cells (PSCs) with organic hole transporting layers (o-HTLs) have been widely studied due to their convenient solution processing, but it remains a big challenge to improve the hole mobilities of commercially available organic hole transporting materials without ion doping while maintaining the stability of PSCs. In this work, we demonstrated that the introduction of perovskite quantum dots (QDs) as interlayers between perovskite layers and dopant-free o-HTLs (P3HT, PTAA,...
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Machine learning approaches for predicting progression in hormone-sensitive prostate cancer patients

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By Bingyu Zhu, Haiyang Jiang, Chongjian Zhang, Qilin Wang, Libo Yang, Ruiqian Li, Jun Li, Yufeng Zheng, Yu Wang, Yu Zuo Bai, Xusong Pang, Lingtao Yan
| Frontiers
Introduction Prostate cancer ranks as the second most common malignancy in men worldwide, after lung cancer (1). Prognostic indicators such as Gleason score, prostate−specific antigen (PSA), and other serum markers have been shown to play important roles in assessing outcomes for prostate cancer patients (2). For instance, Ali Amin et al. (3) reported that a Gleason score of 4 + 3 = 7 is associated with an elevated risk of biochemical progression after radical prostatectomy.
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