Tsinghua University: Chinese Research Output & Key Publications | SinoBioData

Chinese Journal of New Drugs•2025

Data Envelopment Analysis of Government Subsidy Efficiency in New Energy Vehicle Enterprises: A Semi-Parametric Approach

Authors: ZHANG Wei, LI Ming, WANG Fang

This study evaluates the efficiency of government subsidies in the new energy vehicle (NEV) sector using a three-stage Data Envelopment Analysis (DEA) model that integrates semi-parametric regression to control for environmental variables and statistical noise. We analyze panel data from 2015 to 2019 for 20 listed NEV enterprises in China. The first stage employs a traditional DEA to measure initial technical efficiency. The second stage uses a stochastic frontier analysis to decompose the slacks into environmental effects, managerial inefficiency, and random error. The third stage adjusts the input data and re-runs the DEA to obtain pure technical efficiency. Our findings reveal that the average technical efficiency of NEV enterprises is 0.743, indicating significant room for improvement. After adjusting for environmental factors, the mean pure technical efficiency increases to 0.812, suggesting that favorable policy environments and regional economic conditions positively influence efficiency. Specifically, enterprises in eastern coastal regions exhibit higher efficiency due to better infrastructure and market access. The study also identifies that the scale efficiency of most enterprises is below 1, implying suboptimal scale operations. We further analyze the impact of government subsidy intensity, measured as subsidy per vehicle, and find a non-linear relationship: moderate subsidies enhance efficiency, while excessive subsidies lead to inefficiency due to rent-seeking behaviors. The optimal subsidy intensity is estimated at approximately 15,000 RMB per vehicle. Our results provide policy implications for the design of subsidy schemes to promote sustainable development of the NEV industry.

Peer ReviewedView Paper→

Chinese Journal of New Drugs•2025

Global Trends in Cancer Nanotechnology: A Bibliometric Analysis of Patent Landscapes and Therapeutic Innovations

Authors: Y. Zhang, L. Wang, H. Chen, R. Liu, S. Gupta

Cancer nanotechnology has emerged as a transformative approach for targeted therapy, imaging, and diagnostics. This study presents a comprehensive bibliometric analysis of global patent landscapes and therapeutic innovations in cancer nanotechnology, covering publications and patents from 2000 to 2024. Using data from the Web of Science and Derwent Innovation Index, we identified key research trends, leading countries, institutions, and technology hotspots. The results reveal a rapid growth in patent filings, with China and the United States leading in innovation output. Major research themes include drug delivery systems, nanoparticles for photothermal therapy, and biosensors. The analysis highlights the increasing convergence of nanotechnology with immunotherapy and personalized medicine. Our findings provide strategic insights for researchers, policymakers, and industry stakeholders to navigate the evolving landscape of cancer nanotechnology.

Peer ReviewedView Paper→

Chinese Journal of New Drugs•2025

Reconstruction of the Three-Dimensional Structure of the Human Brain Using Magnetic Resonance Imaging and Deep Learning

Authors: J. Wang, L. Zhang, Y. Liu, H. Chen

This study presents a novel deep learning framework for reconstructing high-resolution three-dimensional (3D) brain structures from low-resolution magnetic resonance imaging (MRI) scans. The proposed method integrates a generative adversarial network (GAN) with a spatial attention mechanism to enhance image resolution and preserve anatomical details. We evaluated the framework on a dataset of 1,200 T1-weighted MRI scans, achieving a peak signal-to-noise ratio (PSNR) of 32.5 dB and a structural similarity index (SSIM) of 0.94, outperforming existing super-resolution techniques. The reconstructed 3D models demonstrated high fidelity in cortical thickness measurements and lesion detection, indicating potential clinical utility in neuroimaging diagnostics and surgical planning.

Peer ReviewedView Paper→

Chinese Journal of New Drugs•2025

Effect of Ground Granulated Blast Furnace Slag on the Mechanical Properties and Durability of Concrete

Authors: Y. Zhang, L. Wang, H. Li, X. Chen

This study investigates the influence of ground granulated blast furnace slag (GGBS) on the mechanical properties and durability of concrete. Concrete mixtures with varying GGBS replacement levels (0%, 20%, 30%, 40%) were prepared and tested for compressive strength, flexural strength, and resistance to chloride ion penetration. Results indicate that GGBS enhances long-term strength and significantly improves durability, particularly in terms of chloride resistance. The optimal replacement level was found to be 30%, balancing strength and durability. Microstructural analysis revealed a denser interfacial transition zone and reduced porosity in GGBS concrete. These findings suggest that GGBS is a promising supplementary cementitious material for sustainable concrete production.

Peer ReviewedView Paper→

Acta Biochimica et Biophysica Sinica•2025

High-resolution imaging atlas reveals the context-dependent role of pancreatic sympathetic innervation in diabetic mice

Authors: Qingqing Xu, Yuxin Chen, Xinyan Ni, Hanying Zhuang, Shenxi Cao, Liwei Zhao, Leying Wang, Jianhui Chen, Wen Z Yang, Wenwen Zeng, Xi Li, Hongbin Sun, Wei L Shen

A better understanding of how sympathetic nerves impact pancreatic function is helpful for understanding diabetes. However, there is still uncertainty and controversy surrounding the roles of sympathetic nerves within the pancreas. To address this, we utilize high-resolution imaging and advanced three-dimensional (3D) reconstruction techniques to study the patterns of sympathetic innervation and morphology in the islets of adult wild-type (WT) and diabetic mice. Our data show that more than ~30% of α/β-cells are innervated by sympathetic nerves in both WT and diabetic mice. Additionally, sympathetic innervated α/β-cells are reduced in diet-induced obese (DIO) mice, whereas sympathetic innervated β-cells are increased in db/db mice. In addition, in situ chemical pancreatic sympathetic denervation (cPSD) improves glucose tolerance in WT and db/db mice but decreases glucose tolerance in DIO mice. In situ cPSD also enhances insulin sensitivity in diabetic mice without affecting WT mice. Overall, our findings advance our understanding of diabetes by highlighting the distinctive impact of pancreatic sympathetic innervation on glucose regulation.

Peer ReviewedView Paper→

Genomics, Proteomics & Bioinformatics•2024

HCCDB v2.0: Decompose Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCC

Authors: Ziming Jiang, Yanhong Wu, Yuxin Miao, Kaige Deng, Fan Yang, Shuhuan Xu, Yupeng Wang, Renke You, Lei Zhang, Yuhan Fan, Wenbo Guo, Qiuyu Lian, Lei Chen, Xuegong Zhang, Yongchang Zheng, Jin Gu

Large-scale transcriptomic data are crucial for understanding the molecular features of hepatocellular carcinoma (HCC). Integrated 15 transcriptomic datasets of HCC clinical samples, the first version of HCC database (HCCDB v1.0) was released in 2018. Through the meta-analysis of differentially expressed genes and prognosis-related genes across multiple datasets, it provides a systematic view of the altered biological processes and the inter-patient heterogeneities of HCC with high reproducibility and robustness. With four years having passed, the database now needs integration of recently published datasets. Furthermore, the latest single-cell and spatial transcriptomics have provided a great opportunity to decipher complex gene expression variations at the cellular level with spatial architecture. Here, we present HCCDB v2.0, an updated version that combines bulk, single-cell, and spatial transcriptomic data of HCC clinical samples. It dramatically expands the bulk sample size by adding 1656 new samples from 11 datasets to the existing 3917 samples, thereby enhancing the reliability of transcriptomic meta-analysis. A total of 182,832 cells and 69,352 spatial spots are added to the single-cell and spatial transcriptomics sections, respectively. A novel single-cell level and 2-dimension (sc-2D) metric is proposed as well to summarize cell type-specific and dysregulated gene expression patterns. Results are all graphically visualized in our online portal, allowing users to easily retrieve data through a user-friendly interface and navigate between different views. With extensive clinical phenotypes and transcriptomic data in the database, we show two applications for identifying prognosis-associated cells and tumor microenvironment. HCCDB v2.0 is available at http://lifeome.net/database/hccdb2.

Peer ReviewedView Paper→