Quan Zhang | Pharmaceutics | Research Excellence Award

Dr. Quan Zhang | Pharmaceutics | Research Excellence Award

Chengdu Medical College | China

Dr. Quan Zhang is an accomplished pharmaceutical scientist recognized for his contributions to targeted drug delivery and drugability optimization. His research focuses on developing advanced delivery platforms for tumor immunotherapy, brain disorders, and arthritis, along with improving the pharmacokinetic and therapeutic profiles of polyphenolic compounds and traditional Chinese medicine formulations. He has led numerous competitive research projects, including national-level grants and industry collaborations, and has played a key role in advancing both basic and applied pharmaceutical studies. His work has resulted in a substantial body of high-impact publications, several invention patents, and contributions to academic textbooks. Dr. Zhang has made notable advances in biomimetic nanomedicine, tumor microenvironment–responsive therapeutics, and innovative formulations for alcohol intoxication management, including the discovery of novel active compounds and the development of commercial health products. His research outcomes have influenced the fields of cancer therapy, neurodegenerative disease treatment, and anti-inflammatory drug strategies, integrating molecular design with translational pharmaceutical technology. He has been recognized through talent programs and scientific awards, reflecting his leadership in pharmaceutics and his impact on interdisciplinary drug research.

Profile: Scopus

Featured Publications

Zhang, Q., et al. (2024). Current advances in biomimetic drug delivery system for targeted therapy of rheumatoid arthritis. Acta Pharmaceutica Sinica B.

Zhang, Q., et al. (2024). Co-delivery nanoparticle targeting CAF for simultaneous activating T cell plus NK cell attack in solid tumor. Journal of Controlled Release.

Deng, C., Zhang, Q., Jia, M., Zhao, J., Sun, X., Gong, T., & Zhang, Z. (2019). Tumors and their microenvironment dual-targeting chemotherapy with local immune adjuvant therapy for effective antitumor immunity against breast cancer. Advanced Science, 6, 1801868.

Deng, C., Zhang, Q., He, P., Zhou, B., He, K., Sun, X., Lei, G., Gong, T., & Zhang, Z. (2021). Targeted apoptosis of macrophages and osteoclasts in arthritic joints is effective against advanced inflammatory arthritis. Nature Communications, 12, 2174.

Mao, J., Liu, X., Zhang, L., Chen, Y., Zhou, S., Liu, Y., Ye, J., Xu, X., & Zhang, Q. (2024). Self-nanoemulsifying drug delivery system of morin: A new approach for combating acute alcohol intoxication. International Journal of Nanomedicine, 19, 10569–10588.

Belal Hamed | Computer Science | Best Researcher Award

Dr. Belal Hamed | Computer Science | Best Researcher Award

Assistant Lecturer at Department of Computer Science, Faculty of Science, Minia University, Egypt

Belal Ahmed Mohammed Hamed is an Assistant Lecturer at the Department of Computer Science, Faculty of Science, Minia University, and at the Department of Artificial Intelligence, Minia National University, Egypt. He holds a master’s degree in Computer Science, with research expertise in bioinformatics, machine learning, and graph-based disease prediction models. His work focuses on developing advanced algorithms for pattern recognition in DNA sequences and medical data analysis. He has published in Scopus and SCI-indexed journals, and contributed to six research projects, including two funded ones. He also serves as a reviewer for journals such as Scientific Reports and The Journal of Supercomputing. His notable contributions include a high-accuracy Graph Convolutional Network model for Alzheimer’s gene prediction.

Profile:

Academic Background:

Belal holds a Master’s degree in Computer Science. His academic training and research work are rooted in computer science, with a focus on interdisciplinary applications in healthcare and genomics.

Research Areas:

  • Bioinformatics

  • Machine Learning

  • SNP-based Disease Prediction

  • Graph Neural Networks

  • DNA Pattern Matching Algorithms

Research Contributions:

Belal developed a deep learning model that integrates SNP data and Graph Convolutional Networks (GCNs) to predict gene-disease associations, specifically in Alzheimer’s disease. The model achieved 98.04% accuracy and AUROC of 0.996, identifying both known and novel genes. His framework is adaptable for use in other diseases, supporting personalized medicine and clinical research.

Publications & Impact:

  • 4 research papers in SCI/Scopus-indexed journals (Springer Nature, Wiley)

  • Google Scholar Citations: 73

  • h-index: 3

Research & Projects:

  • Participated in 6 research projects, including 2 funded

  • Contributed to 1 industry-academic collaboration in medical data analysis

Editorial Roles:

  • Reviewer for The Journal of Supercomputing, Scientific Reports, and Medical Data Mining Journal

  • Young Scientist – Medical Data Mining Journal

Collaborations:

Active in interdisciplinary research teams, particularly in genomics and artificial intelligence.

Publication Top Notes: