Dr. Ronghua Jin | Drug Delivery System | Best Researcher Award

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Dr. Ronghua Jin | Drug Delivery System | Best Researcher Award

Dr. Ronghua Jin, Guangxi Medical University, China

Dr. Ronghua Jin is a distinguished researcher in nanomedicine, drug delivery systems, and cancer therapy. He earned his Ph.D. in Chemical Engineering from Xi’an Jiaotong University, focusing on tumor microenvironment-triggered therapeutic platforms. With an M.D. in Physical Chemistry from South China University of Technology and a Bachelor’s in Applied Chemistry from Jiujiang University, Dr. Jin’s expertise spans theranostics and polymer-based drug delivery. As a Principal Investigator, he has led multiple nationally funded projects, developing targeted nanomedicine strategies for liver cancer treatment. His pioneering work in self-delivery nanomedicine and combined chemotherapy approaches has earned him recognition in the scientific community, making significant contributions to personalized cancer treatment and molecular-targeted therapies.

🌍 Professional Profile:

Orcid

Scopus

🏆 Suitability for the Best Researcher Award

Dr. Ronghua Jin’s groundbreaking research in nanomedicine and cancer therapy makes him an outstanding candidate for the Best Researcher Award. His expertise in drug delivery systems and theranostics has led to innovative advancements, including esterase-responsive polymer prodrug delivery and self-delivery nanomedicine. As a Principal Investigator of multiple national and institutional research grants, he has secured significant funding to develop targeted therapies for liver cancer. His work bridges chemistry, nanotechnology, and medicine, enhancing the effectiveness of chemotherapeutic and sonodynamic treatments. With a strong publication record and notable contributions to molecular-targeted cancer therapies, Dr. Jin exemplifies excellence in research, making him highly deserving of this prestigious award.

🎓 Education 

Dr. Ronghua Jin holds a Ph.D. in Chemical Engineering from Xi’an Jiaotong University (2016–2019), where he developed tumor microenvironment-triggered multimodal therapeutic platforms under the mentorship of Prof. Xin Chen. He earned his M.D. in Physical Chemistry from South China University of Technology (2008–2011), focusing on ionic liquid-assisted extraction of active pharmaceutical compounds, advised by Prof. Xiaoning An. His academic journey began with a Bachelor’s in Applied Chemistry from Jiujiang University (2004–2008), where he built a strong foundation in chemical applications for medicinal research. His interdisciplinary education has enabled him to integrate chemistry, nanotechnology, and biomedical sciences to develop novel drug delivery and cancer therapy systems.

🏢 Research Experience 

Dr. Ronghua Jin has extensive research experience in nanomedicine, polymer drug delivery, and cancer therapy. As a Principal Investigator, he has led multiple research projects, including a National Natural Science Foundation of China (NSFC) project (CNY 320,000) on esterase-responsive polymer prodrug delivery for liver cancer therapy. He is also leading a High-Level Talent Project at Guangxi Medical University (CNY 200,000) and a Traditional Chinese Medicine molecular biology research project (CNY 30,000). His work focuses on self-delivery nanomedicine, combining chemotherapy and sonodynamic therapy for targeted liver cancer treatment. His expertise in nanotechnology-driven drug delivery platforms has significantly contributed to advancing personalized medicine and molecular-targeted cancer therapies.

🏅 Awards and Honors

Dr. Ronghua Jin has received several prestigious research grants and honors for his innovative contributions to nanomedicine and drug delivery systems. He is a Principal Investigator on multiple national and institutional research projects, securing funding from NSFC, Guangxi Medical University, and Traditional Chinese Medicine Treatment Research Programs. His work on esterase-responsive polymer prodrug delivery and self-delivery nanomedicine has been recognized for its breakthrough potential in liver cancer treatment. Dr. Jin has been invited to present his findings at leading international conferences, and his research has been published in top-tier journals. His contributions to theranostics, cancer therapy, and nanomedicine make him a highly respected figure in the field.

🔬 Research Focus 

Dr. Ronghua Jin’s research specializes in nanomedicine, drug delivery systems, cancer therapy, and theranostics. His work integrates chemistry, biomedical engineering, and nanotechnology to develop targeted therapies for liver cancer. A key focus is on esterase-responsive polymer prodrug delivery, enhancing chemotherapy and sonodynamic treatments. He is also pioneering self-delivery nanomedicine, enabling drugs to actively transport and target cancer cells without external carriers. His research aims to improve the efficiency and precision of drug delivery, reducing side effects and enhancing treatment outcomes. Through his work, he contributes to the advancement of personalized medicine, molecular-targeted therapies, and multi-modal cancer treatment strategies.

📊 Publication Top Notes:

  1. Antibacterial, Nontoxic, Antifreezing, and Self-Adhesive Conductive Eutectogel for Strain Sensor

    • Year: 2025

  1. Amelioration of Osteoarthritis through Salicylic Acid Nano-Formulated Self-Therapeutic Prodrug

    • Year: 2024

  2. T Cell-Depleting Nanoparticles Ameliorate Bone Loss by Reducing Activated T Cells and Regulating the Treg/Th17 Balance

    • Year: 2021

  3. Multifunctional Hierarchical Nanohybrids Perform Triple Antitumor Theranostics in a Cascaded Manner for Effective Tumor Treatment

    • Year: 2021

  1. 2D PtS Nanorectangles/g-C₃N₄ Nanosheets with a Metal Sulfide–Support Interaction Effect for High-Efficiency Photocatalytic H₂ Evolution

    • Year: 2021

 

 

Dr. Zhenwei Shi | Deep learning in Medicine | Best Researcher Award

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Dr. Zhenwei Shi | Deep learning in Medicine | Best Researcher Award

Dr. Zhenwei Shi, Guangdong Provincial People’s Hospital, China

🎓 Dr. Zhenwei Shi is a distinguished Postdoctoral Fellow in Clinical Medicine at Guangdong Provincial People’s Hospital, bringing a wealth of knowledge cultivated through a stellar academic journey. Having earned a Ph.D. in Clinical Data Science from Maastricht University and a Master’s in artificial intelligence from the University of Groningen, Netherlands, Dr. Shi seamlessly blends clinical medicine, data science, and AI expertise. In his dynamic professional trajectory, he serves as an Assistant Researcher at Southern Medical University/Guangdong Provincial People’s Hospital, contributing significantly to medical research. As the Research PI at the Guangdong Key Laboratory of Medical Image Analysis and Application, Dr. Shi exhibits a keen focus on advancing healthcare through innovative technology. Adorned with prestigious awards, including a Special Award for digital health innovation, Dr. Shi’s commitment to excellence is evident. His research interests in deep learning, quantitative imaging analysis, and oncology data integration, underscored by a passion for federated learning, position him as a visionary in the evolving landscape of healthcare technology. 🌐👨‍⚕️🔬

🎓 Education :

👨‍🎓 Dr. Zhenwei Shi has embarked on an illustrious educational journey, culminating in his current role as a Postdoctoral Fellow in Clinical Medicine at Guangdong Provincial People’s Hospital (2021-2023). His academic pursuits took him to Maastricht University in the Netherlands, where he earned a Ph.D. in Clinical Data Science from 2016 to 2020. Prior to that, Dr. Shi delved into the realm of artificial intelligence at the University of Groningen, the Netherlands, where he successfully obtained a Master’s degree from 2013 to 2016. With a rich background spanning clinical medicine, data science, and artificial intelligence, Dr. Shi brings a diverse skill set and a passion for advancing healthcare through innovative research and technology. 🌐📚🔬

🌐 Professional Profiles : 

Google Scholar

Scopus

🔍 Experience :

👨‍🔬 Dr. Zhenwei Shi has seamlessly transitioned from his academic achievements to a dynamic professional trajectory. Currently serving as an Assistant Researcher at Southern Medical University/Guangdong Provincial People’s Hospital in Guangzhou, China, since 2023, Dr. Shi is actively contributing to the advancement of medical research. Simultaneously, he holds the position of Research PI at the Guangdong Key Laboratory of Medical Image Analysis and Application, based in Guangzhou, China, since 2020. In 2019, Dr. Shi broadened his expertise as a Visiting Scholar at the prestigious Dana-Farber Cancer Institute, affiliated with Harvard University in Boston, USA. With a diverse range of experiences, Dr. Zhenwei Shi continues to make impactful contributions to the fields of medical imaging, analysis, and application. 🌐💼

🏆Awards :

🏆 Dr. Zhenwei Shi stands adorned with accolades, showcasing his remarkable achievements in the realm of healthcare and digital innovation. His outstanding contributions were recognized with a Special Award at the First National Digital Health Innovation Application Competition, highlighting his prowess in leveraging technology for transformative healthcare solutions. Dr. Shi’s commitment to excellence is further underscored by his acknowledgment as a recipient of the High-level Talent Introduction at Guangdong Provincial People’s Hospital, reflecting his impact in the medical field.

Adding to his impressive list of honors, Dr. Shi has been selected as part of the Guangdong Provincial Overseas Postdoctoral Talent Support Program, affirming his status as a distinguished professional in his field. These awards not only acknowledge Dr. Zhenwei Shi’s dedication to advancing healthcare but also position him as a key figure in the integration of digital health innovations. 🌟💡👨‍⚕️

🧠 Research Interests 🔬🌐 :

🧠 Dr. Zhenwei Shi, with an insatiable curiosity and passion for innovation, delves into the forefront of cutting-edge research. His primary research interests span the expansive realms of deep learning, quantitative imaging analysis, and the integration of big data within the oncology domain. Dr. Shi is at the forefront of exploring the potential of federated learning, harnessing the power of decentralized data for collaborative advancements in healthcare. His expertise also extends to the intersection of deep learning and medicine, where he strives to unravel the transformative possibilities of artificial intelligence in shaping the future of medical practices. With an unwavering commitment to pushing the boundaries of knowledge, Dr. Zhenwei Shi stands as a visionary in the dynamic intersection of technology and healthcare. 🌐🔬🤖

📚 Publication Impact and Citations : 

Scopus Metrics:

  • 📝 Publications: 41 documents indexed in Scopus.
  • 📊 Citations: A total of 423 citations for his publications, reflecting the widespread impact and recognition of Dr. Zhenwei Shi’s research within the academic community.

Google Scholar Metrics:

  • All Time:
    • Citations: 641 📖
    • h-index: 15 📊
    • i10-index: 20 🔍
  • Since 2018:
    • Citations: 638 📖
    • h-index: 15 📊
    • i10-index: 20 🔍

👨‍🏫 A prolific researcher with significant impact and contributions in the field, as evidenced by citation metrics. 🌐🔬

Publications Top Notes  :

1.  Learning from scanners: Bias reduction and feature correction in radiomics

Published Year: 2019, Cited By: 65

Journal: Clinical and Translational Radiation Oncology

2.  Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing

Published Year: 2019, Cited By: 60

Journal: Physica Medica

3.  Distributed radiomics as a signature validation study using the Personal Health Train infrastructure

Published Year: 2019, Cited By: 53

Journal: Scientific Data

4.  A CT-based deep learning radiomics nomogram for predicting the response to neoadjuvant chemotherapy in patients with locally advanced gastric cancer: A multicenter cohort study

Published Year: 2022, Cited By: 40

Journal: EClinicalMedicine

5.  Multi-layer pseudo-supervision for histopathology tissue semantic segmentation using patch-level classification labels

Published Year: 2022, Cited By: 39

Journal: Medical Image Analysis

6.  Ontology‐guided radiomics analysis workflow (O‐RAW)

Published Year: 2019, Cited By: 39

Journal: Medical Physics

7.  Multicenter CT phantoms public dataset for radiomics reproducibility tests

Published Year: 2019, Cited By: 35

Journal: Medical Physics

8.  PDBL: Improving histopathological tissue classification with plug-and-play pyramidal deep-broad learning

Published Year: 2022, Cited By: 27

Journal: IEEE Transactions on Medical Imaging

9.  External validation of a prognostic model incorporating quantitative PET image features in oesophageal cancer

Published Year: 2019, Cited By: 27

Journal: Radiotherapy and Oncology

10.  FAIR‐compliant clinical, radiomics and DICOM metadata of RIDER, interobserver, Lung1 and head‐Neck1 TCIA collections

Published Year: 2020, Cited By: 23

Journal: Medical Physics