Prof. Bairong Shen

West China Hospital, Sichuan University, China

Bairong Shen, Professor of the Institute of System Genetics at West China Hospital, Sichuan University. Currently also holds positions as Professor at the Institute for Systems Biology in Seattle and the University of the Basque Country in Spain. Serves as the leader of the virtual teaching and research group for the "101 Plan" of the Ministry of Education in the field of medical data collection and analysis. Since returning to China in 2008, has led over 10 projects funded by the National Natural Science Foundation and other institutions, and has supervised the training of over 90 graduate students. Has published over 200 papers in various international journals across disciplines such as Intensive Care Medicine, International Journal of Surgery, Bioinformatics, International Journal of Medical Informatics, Genome Biology, and Nucleic Acids Research. Has edited 10 books in both Chinese and English related to the field of translational informatics. His research interests include the theoretical aspects of biomarker discovery, biomedical data sharing and security, and intelligent management of chronic diseases.

 

Speech Title: "Digital Medicine & Digital Prescription"

 

Abstract: In this presentation, I will delve into the transformative potential of digital technologies in healthcare. We begin by introducing the concept of digital medicine and its implications for the future of healthcare delivery. Then I will focus on digital prescriptions, exploring how they revolutionize medication management and enhance patient care, discussing the benefits of digital prescriptions, such as improved accuracy, accessibility, and convenience for both patients and healthcare providers. I will present compelling case studies showcasing the application of digital medicine in the treatment of prevalent conditions like Diabetes, Cardiovascular Diseases (CVDs), and Prostate Cancer. These case studies shed light on the successful integration of digital technologies into existing healthcare systems, highlighting the positive outcomes and challenges encountered during implementation. Lastly, I will look towards the future and discuss the exciting prospects and potential advancements in digital medicine, explore emerging trends, such as remote patient monitoring, telemedicine, and artificial intelligence-driven diagnostics. We also touch upon the importance of data privacy, security, and regulatory considerations in the digital medicine landscape.

Prof. Yang Zhan

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, China

Yang Zhan a Professor at the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. He is the director of the Center for Brain Intelligence and Brain Cognition. He is a distinguished core researcher of the Chinese Academy of Sciences. His main research interests are brain neural information processing and brain-computer interaction. He received his bachelor's degree in electronic information engineering from Dalian University of Technology in 2003 and his Ph.D. degree from the University of Cambridge in 2010. In 2014, he joined the Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences. The research results have been published in high-level journals such as Neuron, Nature Neuroscience, Nature Communications, NeuroImage, and IEEE Trans. As the project leader, he has been funded by the Talent Program of the Chinese Academy of Sciences, the Key R&D Program of the Ministry of Science and Technology, the Talent Team Project of Guangdong Province, and the National Natural Science Foundation of China.

 

 

 

Assoc. Prof. Minmin Wang

Westlake University, China

Minmin Wang holds a Ph.D. in Biomedical Engineering from Zhejiang University and is a associate professor at the Institute of Optoelectronics, Westlake University. He is a member of the Neuromodulation Committee of the Zhejiang Rehabilitation Medical Association. Her research areas include neural engineering, brain-computer interfaces, medical image processing, and physiological modeling. He has developed several transcranial electrical stimulators certified by the Zhejiang Medical Device Testing Institute, achieving international standards, involving more than 1,200 subjects, with significant clinical outcomes. He has published over 20 papers and holds more than 10 granted invention patents. He has received the Lin Baixin High-Tech Award from Zhejiang University.

 

Speech Title: "Quantitative Assessment and Optimization of Brain Stimulation"

 

Abstract: Non-invasive brain stimulation techniques such as transcranial electrical stimulation (tES) and transcranial magnetic stimulation (TMS) have been widely used in the treatment of neurological and psychiatric disorders. However, the effects of brain stimulation vary significantly among individuals, and reproducibility is often low. This presentation will discuss the biophysical effects of transcranial electromagnetic stimulation, evaluated through modeling and in vivo measurements. It will also explore optimization methods to tailor transcranial electromagnetic stimulation to individual needs, providing valuable insights for the technical application and individual optimization of neuromodulation.

 

 

Dr. Peng Zhang
Huazhong University of Science and Technology, China

Peng Zhang received the B.S. degree in biomedical engineering and the Ph.D. degree in control science and engineering from Huazhong University of Science and Technology, Wuhan, China, in 2011 and 2018, respectively. He joined Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, as a post-doctoral fellow in 2018. His research areas include intracortical brain-machine interface, biomedical signal analysis, and deep learning. He has published 25 SCI papers in high-level journals such as Med, Nature Chemistry, and IEEE Trans. He also authored a chapter in the book "Neural Interface: Frontiers and Applications" and applied for 16 patents (8 granted and one patent transformation with 1.27 million).

 

Speech Title: "Automatic detection and risk prediction of atrial fibrillation"

 

Abstract: Atrial fibrillation is the most common arrhythmia in the general population, and can lead to dangerous complications. Effective automatic detection and risk prediction of atrial fibrillation are crucial for its prevention and treatment. This presentation will first introduce the research on atrial fibrillation detection, which involves developing a deep learning-based AI algorithm to accurately and automatically detect atrial fibrillation episodes from clinical 24-hour Holter ECG data. Subsequently, the presentation will present the research on atrial fibrillation risk prediction, where a deep learning-based AI algorithm is developed to effectively predict individual atrial fibrillation risks using only heartbeat information during sinus rhythm. Both studies have undergone comprehensive performance evaluations on large-scale real-world clinical datasets. Additionally, the presentation will explore how clinicians can utilize these AI tools to enhance their atrial fibrillation detection and risk prediction capabilities in real clinical practice.

 

 

Dr. Zahra Batool

West China Hospital, Sichuan University, China

Dr. Zahra Batool academic journey has been marked by rigorous study, deep contemplation, and a relentless pursuit of knowledge. Dr. Zahra systematically delved into various fields, mastering disciplines such as carcinogenicity, environmental chemistry, food nutrition, microbiology, antimicrobial drug resistance, molecular biology, bioscience, and biotechnology. This interdisciplinary knowledge base has provided her with a comprehensive understanding of complex scientific phenomena. During Bachelors, she performed rigorous study on computational study of biological sciences with completion of B.S Hons (Bio-informatics). During Master's studies in Biotechnology, she focused on medical biotechnology and oncogenes, achieving a thorough grasp of bioscience and biotechnology principles. Subsequently, in Ph.D She uniquely bridged food science disciplines, to delve into the intricate relationship between carcinogens, nutrition, and health outcomes. Her research in this area led me to explore the characterization of novel medicinal polysaccharides, elucidating their medicinal and anti-inflammatory properties. In her post-doctoral research, She also focused on renal cancer and its therapeutic interventions. This endeavor provided her with invaluable insights into cancer biology, bioscience, and molecular mechanisms. As a result, she has authored several publications as the first and contributing author, contributing to the advancement of scientific knowledge in these fields.

 

Speech Title: "Revolutionizing Triple-Negative Breast Cancer: AI-Assisted Sub-typing, Diagnosis, and Treatment"

 

Abstract: Triple negative breast cancer (TNBC) is most aggressive type of breast cancer with multiple invasive sub-types and leading cause of women's death worldwide. Lack of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2) causes it to spread rapidly making its treatment challenging due to unresponsiveness towards anti-HER and endocrine therapy. Hence, needing advanced therapeutic treatments and strategies in order to get better recovery from TNBC. Artificial intelligence (AI) has been emerged by giving its high inputs in the automated diagnosis as well as treatment of several diseases, particularly TNBC. Presently, AI is primarily composed of two key components: machine learning (ML) and deep learning (DL). ML utilizes specific algorithms to learn from vast datasets, enabling the creation of decision rules, enhancing the identification of common characteristics across different data types, thereby improving diagnostic accuracy. AI based TNBC molecular sub-typing, diagnosis as well as therapeutic treatment has become successful now days. These advancements led to the widespread application of DL-based AI in cancer pathology. Therefore, in this presentation we will discuss recent advancements in the role and assistance of AI particularly focusing on molecular sub-typing, diagnosis as well as treatment of TNBC by employing different AI based algorithms.

 

 

 

Dr. Rajeev K. Singla

West China Hospital, Sichuan University, China

Dr. Rajeev K. Singla has done his bachelors (B.Pharm) in 2007 from Maharshi Dayanand University, Rohtak, India and M.Pharm (Pharmaceutical Chemistry) in 2009 from Manipal University, Karnataka, India. He has submitted his PhD thesis on May, 2018 and received his PhD (Doctorate) degree from Faculty of Technology, University of Delhi, India in year 2019. Since January 2020, he is associated with Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, Sichuan (China) as Assistant Researcher with a broad experience in natural products, metabolic disorders, neurological disorders, infectious diseases, cheminformatics, and public health. He is also Founder and Chief Editor of Indo Global Journal of Pharmaceutical Sciences (https://igjps.com/index.php/IGJPS). He is also serving as Associate Editor of Frontiers in Pharmacology and Frontiers in Nutrition, as well as Topical Advisory Panel Member of Cancers (MDPI), Advisory Board Member- Heliyon (Genetics Section, Cell Press, Elsevier), and Editorial Board Member- BMC Complementary Medicine and Therapies. He has published more than 120 papers indexed in Web of Science Core Collection. As per Web of Science verified records, he had participated in 429 verified peer reviews and 267 verified editorial activities. As per Google Scholar statistics, citations to his work are above 3750 with h-index of 31 and i10-index of 104. His cumulative impact factor as per Web of Science records will be around 450. He has also published 4 books and 16 book chapters so far.

 

Speech Title: "Natural Products Against Neurological and Metabolic Disorders: Role of Computational Studies in Exploring Their Translational Potential"

 

Abstract: Since ancient times, natural products have been used for the treatment and management of various diseases and disorders. Using multidirectional approaches, we have showcased the therapeutic potential of natural products in combating neurological and metabolic disorders, and the transformative impact of computational techniques on this endeavor. By harnessing the power of cheminformatics, bioinformatics, artificial intelligence, machine learning, and related methods, researchers can efficiently identify, optimize, and repurpose natural products as effective therapeutic agents. These cutting-edge approaches enable the rapid analysis of vast datasets, prediction of drug-target interactions, and prediction of pharmacokinetic and toxicological properties, thereby revolutionizing the drug discovery pipeline. We aimed to have a thorough analysis of the current landscape, addressing challenges and opportunities in this rapidly evolving field, and highlighting the exciting potential of natural products and computational techniques in driving personalized medicine and improving human health outcomes.

 

 

Dr. Amin Ullah

West China Hospital, Sichuan University, China

I completed my Ph.D. in reproductive immunology at Chongqing Medical University. Now, I am a postdoctoral researcher at West China Hospital, Sichuan University. I study inflammatory biomarkers, including cytokines (interleukins) and CXC chemokines linked to metabolic disorders during obesity, such as diabetes, NAFLD, breast, ovarian, and prostate cancers, PCOS, endometriosis, and preeclampsia. I have published extensively in this field and possess deep knowledge of cytokines and CXC chemokines, small molecules crucial in metabolic disorder development and inflammation. Understanding these inflammatory mediators' activities is crucial for targeting medicines and improving clinical outcomes as obesity rates rise worldwide.  My research focuses on pathways and immune cell recruitment of CXC chemokines (CXCL1-CXCL16 with receptors CXCRs), cytokines(interleukins), and metabolic problems in pediatric obesity. This understanding is vital for developing interventions and enhancing clinical outcomes, particularly as childhood obesity increases. By exploring the complex functions of these mediators in childhood obesity-related metabolic disorders, we aim to identify targeted treatments to modulate inflammation and reduce metabolic dysfunction, insulin sensitivity issues, diabetes, and NAFLD progression. Discovering these biological pathways will help us find new therapeutic targets and diagnostic indicators to prevent and treat metabolic problems in obese children.