Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea magistrale in Molecular and Medical Biotechnology - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
| Modules | Credits | TAF | SSD |
|---|
One course to be chosen among the followingOne course to be chosen among the followingTwo courses to be chosen among the followingThree courses to be chosen among the following2° Year activated in the A.Y. 2020/2021
| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
One course to be chosen among the followingOne course to be chosen among the followingTwo courses to be chosen among the followingThree courses to be chosen among the following| Modules | Credits | TAF | SSD |
|---|
| Modules | Credits | TAF | SSD |
|---|
Two courses to be chosen among the following ("Biotechnology in Neuroscience" and "Clinical proteomics" 1st and 2nd year; the other courses 2nd year only)Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Clinical proteomics (2019/2020)
Teaching code
4S003688
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
CHIM/01 - ANALYTICAL CHEMISTRY
Period
I semestre dal Oct 1, 2019 al Jan 31, 2020.
Learning outcomes
The aim of clinical proteomics is actually to find molecular signatures, to describe affected pathways and possibly identify candidate biomarkers that can help in the diagnosis, prognosis and prediction of therapeutic outcomes and elucidate pathogenic mechanisms. Upon completing the course, students will have the knowledge necessary to recognize the strengthens and weakness of the different proteomics methodologies and of their application to current areas of clinical investigation.
Program
• Introduction to clinical proteomics
• Strategies for protein sample preparation
• Gel-based and gel-free clinical proteomics analyses
• Protein Identification by Tandem Mass Spectrometry
• Label-Based and Label-free MS clinical proteomic approaches
• Differential profiling of Breast Cancer plasma proteome for biomarkers identification
• Clinical Proteomics to study Pancreatic Cancer Stem Cells
• Brain tissue proteomic analysis to identify biomarkers of Alzheimer’s Disease
• Evaluation of therapeutic effects of neural stem cells therapy in Parkinson’s disease
• Proteomics of cerebrospinal fluid to identify biomarkers for amyotrophic lateral sclerosis
• Pharmacoproteomics for elucidating the mechanism of action of anticancer drugs
| Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|
| Josip Lovric | Introducing Proteomics: From concepts to sample separation, mass spectrometry and data analysis | Wiley | 2011 | 978-0-470-03524-5 |
Examination Methods
The NON-attending students must contact the coordinator of the course within the first two weeks to be included in the schedule of presentations and to have the scientific paper assigned. It is suggested to attend at least 30% of lessons.
It is mandatory the participation to the entire lesson in which the student exposes the article. It is mandatory (except for non-attending students) also the participation to the lessons in which the colleagues discuss their paper.
The final exam includes the presentation of a scientific paper and a written exam (open questions) that will cover all the topics of the program.
The final vote is obtained from the following formula: Vote = Vote_exam + max of 2 points per paper presentation.
