2024 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024)
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Prof. Pier Paolo Piccaluga

University of Bologna, Italy

ProfilePier Paolo Piccaluga, MD, Ph.D., currently holds the position of Associate Professor of Pathology at the Department of Experimental, Diagnostic and Specialty Medicine, Bologna University School of Medicine—Institute of Hematology and Medical Oncology. He also serves as an executive physician at The Biobank of Research, IRCCS S. Orsola-Malpighi Hospital. In 2018, he was appointed to teaching positions at Queen Mary University of London and Jomo Kenyatta University of Agriculture and Technology in Nairobi, Kenya. Dr. Piccaluga has authored numerous international publications in prestigious journals such as Nature Medicine, Journal of Clinical Investigation, Journal of Experimental Medicine, Journal of Clinical Oncology, Blood, Lancet Oncology,and Lancet Infectious Diseases. His contributions have earned him recognition as a Top Italian Scientist (TIS) by VIA-Academy.

Speech Title: The new classification of hematopoietic and lymphoid tumors: the role of new technologies

Abstract: Hematopoietic and lymphoid tumors encompass a wide spectrum of malignancies arising from cells of the hematopoietic and lymphoid system. These tumors can originate from various cell types, including myeloid cells, lymphoid cells, and precursor cells, leading to a diverse range of clinical presentations and outcomes. The classification of these tumors is critical for accurate diagnosis, prognosis determination, and treatment planning. 

The classification of hematopoietic and lymphoid tumors has undergone significant evolution over time, reflecting advances in scientific knowledge and technological capabilities. Early classification systems, such as the French-American-British (FAB) classification, were based primarily on morphological features, cytochemistry, and immunophenotype. These systems provided a foundation for understanding and categorizing acute leukemias but had limitations in capturing the full spectrum of hematopoietic and lymphoid malignancies.

Advancements in molecular biology, cytogenetics, and high-throughput sequencing technologies have revolutionized the classification of hematopoietic and lymphoid tumors. The integration of genetic and molecular data into classification systems has enabled a more precise and biologically driven approach to categorizing these diseases, leading to improved diagnostic accuracy and personalized treatment strategies.

The World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues serves as a globally recognized and authoritative system for categorizing hematopoietic and lymphoid malignancies. The latest iteration of the WHO classification, as of 2022, provides a comprehensive framework for classifying these tumors based on morphological, immunophenotypic, genetic, and clinical characteristics. The WHO classification organizes hematopoietic and lymphoid tumors into distinct entities, including leukemias, lymphomas, myeloproliferative neoplasms, myelodysplastic syndromes, and related disorders. Each entity is further subdivided into specific subtypes based on criteria such as cell lineage, genetic alterations, and clinical behavior, allowing for a more nuanced and clinically relevant classification scheme.

Recent advances in molecular profiling techniques, such as next-generation sequencing and gene expression profiling, have enabled a deeper understanding of the genetic landscape of hematopoietic and lymphoid tumors. The identification of recurrent genetic mutations, chromosomal rearrangements, and gene fusions has not only refined the classification of these diseases but also paved the way for targeted therapies and personalized treatment approaches.

By integrating molecular information with traditional morphological and clinical data, healthcare providers can tailor treatment strategies to individual patients based on the specific genetic alterations driving their disease. This personalized medicine approach holds great promise for improving outcomes and reducing treatment-related toxicities in patients with hematopoietic and lymphoid tumors.

In conclusion, the classification of hematopoietic and lymphoid tumors is a dynamic and evolving field that plays a crucial role in guiding clinical decision-making and improving patient care. The WHO classification provides a robust framework for categorizing these malignancies based on a comprehensive set of criteria, including morphology, immunophenotype, genetics, and clinical features. Advances in molecular classification and personalized medicine have further enhanced our understanding of these diseases and have opened up new avenues for targeted therapies and precision medicine interventions. Staying abreast of the latest developments in the classification of hematopoietic and lymphoid tumors is essential for healthcare professionals involved in the diagnosis and management of patients with these complex and heterogeneous malignancies.

Zulqarnain Baloch.jpg

Prof. Zulqarnain Baloch

Kunming University of Science and Technology, China

ProfileDr. Zulqarnain Baloch served as an associate professor at the Biomedical Center of Northwest Minzu University in Lanzhou from 2019 to 2020. Since 2020, he has held the position of a professor and master tutor at the School of Life Science and Technology, Kunming University of Science and Technology. His research expertise lies in the field of molecular epidemiology pertaining to infectious diseases, pharmaceuticals, and traditional Chinese medicine.


  • Yunnan Provincial government awarded Outstanding Ph.D. Thesis award of 2017

  • Kunming University of Science and Technology 2017 Graduate Outstanding Thesis

  • Excellent International Graduate of 2016 at Kunming University of science and Technology(Gold Medal)


  • Member of Asian Council of Science Editors

  • Member of World Society for Virology

  • Member of Alumni of Kunming University of Science and Technology, Yunnan Province, China

  • Member of Pakistan Veterinary Medical Council


  • Molecular Medicine Reports

  • Evidence-based Complementary and Alternative Medicine (Gust Editor)

Speech Title: Confronting Antimicrobial Resistance: A Global Call to Action for  Sustainable Development.

Abstract: Antimicrobial resistance (AMR) leads to severe consequences, including more severe infections, increased healthcare costs, prolonged hospital stays, unresponsive treatments, and elevated fatality rates. Its impact extends to the Sustainable Development Goals (SDGs), particularly affecting underprivileged populations and jeopardizing sustainable agriculture and livelihoods. The emergence of antibiotic-resistant bacteria in underprivileged areas exacerbates complications and mortality risks, compounded by climate change-induced factors such as foodborne illnesses. Addressing AMR requires collaboration among governments, entrepreneurs, and the public sector to establish policies, surveillance systems, and investment in research. Expanding SDG 17, focusing on partnerships for sustainable development, would facilitate global antimicrobial stewardship initiatives. The World Bank's SDG database reveals a concerning picture with only a 15% success rate till 2023 and 48% showing  deviation, further exacerbated by the COVID-19 pandemic. Tackling AMR's global impact demands international cooperation, robust monitoring, and evaluation methods. The outlined priorities guide SDG implementation, with impoverished countries facing specific challenges in their efforts. In conclusion, addressing AMR and its impact on the SDGs requires comprehensive and collaborative solutions on a global scale, emphasizing the urgent need for concerted action to safeguard public health and sustainable development. 


Prof. Ming Chen

Zhejiang University, China

ProfileProf. Ming Chen is the director of the Bioinformatics Laboratory at the College of Life Sciences, and is the leading figure in the field of Bioinformatics at Zhejiang University, China. He obtained his Ph.D. in Bioinformatics from Bielefeld University, Germany, in 2004. He was seconded to the Fundamental Research Department of the Ministry of Science and Technology, served as president assistant and specially appointed dean at Inner Mongolia Minzu University.Prof. Chen's research work covers bioinformatics, systems biology, non-coding RNA transcriptomics, and precision medicine. He has published over 200 academic papers in peer-viewed journals such as Cell, Nature, Nucleic Acids Research, Bioinformatics, etc. with his Google Scholar H-index over 50. He has been included in the list of top 2% scientists of the world.Prof. Ming Chen is the President of the Bioinformatics Society of Zhejiang Province, China; Committee Director of Chinese Society for "Multi-Omics and Integrative Biology", Committee Deputy Director of Chinese Society for "Modeling and Simulation of Biological Systems"; Committee executive member of Chinese Societies for "Computational Systems Biology" and  "Functional Genomics & Systems Biology"; and Committee member of Chinese Societies for "Biomedical Information Technology" and "Biophysics (Bioinformatics). "

Speech Title: Big data-based studies on neurodegenerative-related interactome, aging and longevity

Abstract: Latest trends in bioinformatics and computational biology play a crucial role in analyzing large and complex biological datasets, understanding molecular mechanisms of life and disease, and accelerating the development of novel therapies. This talk focuses on neurodegenerative-related interactome, machine learning-based biological age prediction, and human aging and longevity knowledge graph. We conducted a comprehensive analysis of neurodegenerative disease-related proteins and their interactions, generating a high-resolution network with structural information. The Neurodegenerative Disease Atlas (NDAtlas) was developed, allowing for 3D molecular graphics beyond traditional 2D network information. We proposed a composite machine learning-based biological age (ML-BA) model based on biomarkers obtained from medical examination data. The composite ML-BA strongly associated with healthy risk indicators and various diseases, providing improved aging measurement capabilities and supporting the application potential of machine learning in aging research. We introduced HALD, a text mining-based human aging and longevity knowledge graph containing essential entities in the field of aging and longevity and related literature curated from PubMed. HALD enables comprehensive understanding of aging and longevity mechanisms, providing a foundation for developing anti-aging therapies for aging-related diseases.


Assoc. Prof. Ata Jahangir Moshayedi

Jiangxi University of Science and Technology, China

ProfileDr. Ata Jahangir Moshayedi, an Associate Professor at Jiangxi University of Science and Technology in China, holds a PhD in Electronic Science from Savitribai Phule Pune University in India. He is a distinguished member of IEEE ( senior member ) and ACM, as well as a Life Member of the Instrument Society of India and a Lifetime Member of the Speed Society of India. Additionally, he contributes to the academic community as a valued member of various editorial teams for international conferences and journals. Dr. Moshayedi's academic achievements are, marked by a portfolio of over 90 papers published across esteemed national and international journals and conferences along with 3 books on robotics (VR and mobile olfaction) and embedded systems. In addition to his scholarly publications, he has authored three books and is credited with two patents and nine copyrights, emblematic of his pioneering contributions to the field. His research interest includes Robotics and Automation/ Sensor modelling/Bio-inspired robot, Biomedical/ Mobile Robot Olfaction/Plume Tracking, Embedded Systems / Machine vision-based Systems/Virtual reality, and Machine vision/Artificial Intelligence. Currently, Dr. Moshayedi is actively engaged in pioneering work at Jiangxi University, where he is developing a model for Automated Guided Vehicles (AGVs) and advancing the realm of Food Delivery Service Robots.

Speech Title: The potential applications of E-Nose on Medicine

Abstract: The study of creature olfaction systems since 1982 has inspired the development of Electronic Nose (E-Nose) technology, marking a significant step in mimicking nature's sensing abilities. From its origins in food quality testing to applications in bomb detection and agriculture, E-Nose has expanded into medicine, including recent use in identifying COVID-19 infections. This talk delves into E-Nose's structure, sensor behavior, and its growing role in medical research, highlighting its potential for disease diagnosis, drug monitoring, environmental assessment, and rapid point-of-care testing, shaping new avenues for healthcare innovation.