Niccola Funel | Rehabilitation | Excellence in Innovation

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Dr. Niccola Funel | Rehabilitation | Excellence in Innovation

Lab Manager, Azienda Ospedaliera USL Nordovest; Department of Laboratory Diagnostics; Division of Immunohematology; Section of Laboratory Analysis, Italy

Dr. Niccola Funel is a distinguished molecular biologist and researcher specializing in clinical pathology, with a focus on personalized medicine. He holds two PhDs: one in Molecular and Experimental Oncology and the other in Translational Clinical Medicine. With over 250 scientific contributions, Dr. Funel is a prolific researcher and laboratory manager at Azienda Ospedaliera USL Nordovest in Italy. His expertise spans molecular biology, genetics, and epigenomics. He has also held advisory positions within the Italian Society of Pancreatology. Dr. Funel is a highly respected academic editor and reviewer for several international journals, contributing significantly to the medical and scientific communities. His work in the COVID-19 field since 2021 demonstrates his dedication to advancing medical research for practical, patient-centered outcomes.

Profile

Orcid

Education

Dr. Niccola Funel’s academic journey has been marked by his commitment to advanced scientific research. He first completed his Bachelor’s in Molecular Biology, followed by specialization in Clinical Pathology. His pursuit of knowledge led him to earn two prestigious PhDs: the first in Molecular and Experimental Oncology and the second in Translational Clinical Medicine. This robust education foundation has equipped Dr. Funel with expertise in various molecular and clinical domains. His academic career has been further enriched through collaborations with renowned institutions, such as VU University (Amsterdam), Imperial College (London), and Karolinska Institute (Sweden). These educational experiences have allowed him to remain at the forefront of research in molecular biology, genetic expression, and epigenomics, reinforcing his significant contributions to the scientific and medical fields.

Experience

Dr. Niccola Funel’s extensive career spans roles as a molecular biologist, laboratory manager, and researcher. Since 2021, he has been serving as a biologist and research manager focused on COVID-19 research at Azienda Ospedaliera USL Nordovest in Italy. With over 250 published scientific contributions, he has made significant advancements in molecular biology and clinical applications, particularly in personalized medicine. Additionally, Dr. Funel has consulted for companies like Laica Microsystems and Menarini Diagnostics. His research collaborations span across leading universities such as VU University, Imperial College, and Karolinska Institute. Beyond research, Dr. Funel has held influential editorial roles, including academic editor, associate editor, and reviewer for top international journals. His experience has led to key patents and book publications, including Molecular Diagnostics and Treatment of Pancreatic Cancer, solidifying his reputation as a thought leader in molecular biology.

Research Focus

Dr. Niccola Funel’s primary research focus lies in molecular biology, genetic expression, and epigenomics, with particular emphasis on cancer biology and personalized medicine. He has dedicated much of his career to advancing the understanding of pancreatic cancer, seeking innovative diagnostic and therapeutic strategies. His research also explores the molecular mechanisms of tendinitis, where his work on the Polynucleotides High Purification Technology (PN HPT™) has garnered significant attention for improving treatment outcomes. Additionally, Dr. Funel is involved in exploring genetic determinants and epigenetic regulation in cancer, contributing to the emerging field of liquid biopsy for diagnosis, prognosis, and treatment monitoring. His extensive research collaborations with institutions such as the Karolinska Institute, Imperial College, and VU University have bolstered his position as a leader in molecular medicine, focusing on both basic science and translational clinical applications.

Publication Top Notes

  • Polynucleotides High Purification Technology (PN HPTâ„¢) Injection Improves Pain Status and Functional Impairment in Hip and Shoulder Tendinitis
  • Silver Nanoparticle-Coated Polyhydroxyalkanoate Based Electrospun Fibers for Wound Dressing Applications
  • Zebrafish Patient-Derived Xenografts Identify Chemo-Response in Pancreatic Ductal Adenocarcinoma Patients
  • Microdissected pancreatic cancer proteomes reveal tumor heterogeneity and therapeutic targets
  • A Model of a Zebrafish Avatar for Co-Clinical Trials
  • Triticum vulgare Extract Modulates Protein-Kinase B and Matrix Metalloproteinases 9 Protein Expression in BV-2 Cells: Bioactivity on Inflammatory Pathway Associated with Molecular Mechanism Wound Healing
  • A propensity score-matched analysis of robotic versus open pancreatoduodenectomy for pancreatic cancer based on margin status
  • Decrease in phospho-PRAS40 plays a role in the synergy between erlotinib and crizotinib in an EGFR and cMET wild-type squamous non-small cell lung cancer cell line
  • Genetic determinants of telomere length and risk of pancreatic cancer: A PANDoRA study
  • Proteomic analysis of gemcitabine-resistant pancreatic cancer cells reveals that microtubule-associated protein 2 upregulation associates with taxane treatment
  • Role of c-MET inhibitors in overcoming drug resistance in spheroid models of primary human pancreatic cancer and stellate cells
  • Splicing modulation as novel therapeutic strategy against diffuse malignant peritoneal mesothelioma
  • The emerging role of liquid biopsy in diagnosis, prognosis and treatment monitoring of pancreatic cancer

 

 

Lilyana Khatib | Rehabilitation | Best Researcher Award

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Ms. Lilyana Khatib | Rehabilitation | Best Researcher Award

Researcher, University of Haifa, Israel.

Lilyana Khatib is a passionate and skilled Machine Learning (ML) Algorithm Engineer with a focus on machine and deep learning algorithms. Holding a Master’s degree in Computer Science, she specializes in applying advanced machine learning techniques to real-world challenges, particularly in the healthcare and medical fields. She is known for her quick learning ability and disciplined approach to problem-solving. In her current role at Biosense Webster, Lilyana is involved in developing algorithms for electrophysiology and cardiac rhythm, contributing to the advancement of medical technology. Beyond her professional work, she is actively engaged in volunteering efforts, including mentoring and empowering Arab women in STEM and exposing high-school students to the world of technology. Her research interests also extend to adaptive testing systems and computer vision applications.

Profile

Scopus

Education 

Lilyana Khatib completed her M.Sc. in Computer Science at the University of Haifa, graduating with a GPA of 95, cum laude. During her studies, she focused on machine learning and its applications in various domains, including healthcare. She pursued a Deep Learning course at the Technion, where she achieved an impressive grade of 97, demonstrating her mastery in the field. Her academic career was marked by a commitment to excellence, combining theoretical knowledge with practical research. Her B.Sc. in Computer Science from the University of Haifa, with a GPA of 83, laid the foundation for her deep interest in artificial intelligence and machine learning. Lilyana’s academic training enabled her to conduct high-impact research, such as her thesis on adaptive testing for fall risk assessments. She continues to build on this foundation through her professional work and volunteer initiatives.

Experience 

Lilyana Khatib’s professional experience spans across various aspects of machine learning and algorithm development. Currently working as a Machine Learning Algorithm Engineer at Biosense Webster since 2022, Lilyana designs and implements ML algorithms, addressing challenges in electrophysiology and cardiac rhythm. She manages end-to-end ML pipelines, including data collection, feature engineering, model development, and evaluation. In this role, she collaborates with multidisciplinary teams, including hardware, software, and clinical experts, to integrate algorithms seamlessly. Prior to this, she worked as a Research Assistant at Bar Ilan University, contributing to ML research on sign language recognition and motion capture analysis. Additionally, she served as a Teaching Assistant at the University of Haifa, tutoring computer science students and creating AI lab exercises. Lilyana’s diverse experiences allow her to approach problems with both academic rigor and practical insight, making her a versatile contributor to machine learning projects.

Awards and Honors 

Lilyana Khatib has received several academic and professional accolades throughout her career. She graduated with distinction (cum laude) from the University of Haifa with a Master’s degree in Computer Science, reflecting her strong academic performance and dedication to excellence. Her research contributions have also earned recognition in the field of machine learning, with her work on adaptive testing algorithms for older adults being published in Applied Sciences. In addition, her participation in international conferences such as the Language Resources and Evaluation Conference (LREC) in Marseilles, France, where she co-authored a paper on sentiment analysis, further highlights her standing in the research community. Beyond academic awards, Lilyana is also celebrated for her leadership roles, especially in mentoring and empowering underrepresented groups in STEM, such as her involvement as a team lead at AWSc and a mentor at Tsofen, showcasing her commitment to fostering diversity and inclusion in technology.

Research Focus 

Lilyana Khatib’s research focuses on the intersection of machine learning, healthcare, and computer vision. Her primary area of interest lies in developing advanced algorithms for healthcare applications, specifically in cardiac rhythm analysis, electrophysiology, and fall risk assessments. Through her work at Biosense Webster, she applies both classic machine learning and deep learning techniques to real-world challenges in the medical field, improving patient outcomes through more accurate diagnostics and assessments. Lilyana’s M.Sc. thesis, which explored a machine learning-based computerized adaptive testing algorithm for fall risk, demonstrates her dedication to healthcare technology. Additionally, she has worked on projects such as the Crying Detection Application, which uses computer vision to detect emotional states without relying on audio. Her research is highly interdisciplinary, integrating computer science, healthcare, and AI, and aims to make a meaningful impact on medical practices and patient care, particularly for vulnerable populations such as older adults.

Publications

  • Using Machine Learning to Shorten and Adapt Fall Risk Assessments for Older Adults 🧠📊 (Applied Sciences, 2025)
  • Capturing Distalization 📖 (Workshop on Sentiment Analysis and Linguistic, LREC, Marseilles, France, 2022)