Translational medicine and biotechnologies 1
A.Y. 2025/2026
Learning objectives
The course aims to apply a multidisciplinary approach to human health problems to transfer new findings from the basicsciences to clinical practice. The purpose of the course is to train students to familiarize themselves with methods, using an interdisciplinary approach, of biotechnology research with an impact on Human Health.
Expected learning outcomes
BLOCK 1: BIOTECHNOLOGY APPROACHES IN TRANSLATIONAL MEDICINE
At the end of this course, the student will acquire knowledge on different biotechnological techniques used in both clinics and biomedical translational research.
BLOCK 2: CLINICAL BIOCHEMISTRY
At the end of the course, students will learn the pivotal role and limits of laboratory tests in preventing diseases, defining diagnoses, and establishing and monitoring treatments. Students will know when to prescribe and how to interpret some common laboratory biochemical profiles.
BLOCK 3: DIAGNOSTIC MICROBIOLOGY
At the end of the course, students will learn the methodologies to be employed for the main microbiological investigations; when prescribe microbiological investigations; how to interpret some common laboratory microbiological profiles.
BLOCK 4: HEALTH INFORMATICS
1. Recognise real-word scenarios where statistical and automatic learning tools can provide advantages for the analysis.
2. Recognise the difference between descriptive and predictive analysis.
3. Identify proper methods suited for specific research questions.
4. Compute statistical measures, and implement simple statistical methods and prediction models.
5. Interpret the results of the statistical analysis/models respect to the input data and the modelling assumptions.
6. Identify and properly communicate results of the statistical analysis.
BLOCK 5: BASIC PRINCIPLES OF NGS AND SINGLE CELL ANALYSIS
Students will be able to:
1. Describe the principles and basic concepts of Next-Generation Sequencing (NGS) technologies.
2. Understand and interpret biomedical research findings derived from NGS and single-cell approaches, including single-cell RNA sequencing (scRNA-seq).
3. Understand the importance of experimental design considerations in NGS and single-cell studies to address specific questions in biological research and clinical settings.
At the end of this course, the student will acquire knowledge on different biotechnological techniques used in both clinics and biomedical translational research.
BLOCK 2: CLINICAL BIOCHEMISTRY
At the end of the course, students will learn the pivotal role and limits of laboratory tests in preventing diseases, defining diagnoses, and establishing and monitoring treatments. Students will know when to prescribe and how to interpret some common laboratory biochemical profiles.
BLOCK 3: DIAGNOSTIC MICROBIOLOGY
At the end of the course, students will learn the methodologies to be employed for the main microbiological investigations; when prescribe microbiological investigations; how to interpret some common laboratory microbiological profiles.
BLOCK 4: HEALTH INFORMATICS
1. Recognise real-word scenarios where statistical and automatic learning tools can provide advantages for the analysis.
2. Recognise the difference between descriptive and predictive analysis.
3. Identify proper methods suited for specific research questions.
4. Compute statistical measures, and implement simple statistical methods and prediction models.
5. Interpret the results of the statistical analysis/models respect to the input data and the modelling assumptions.
6. Identify and properly communicate results of the statistical analysis.
BLOCK 5: BASIC PRINCIPLES OF NGS AND SINGLE CELL ANALYSIS
Students will be able to:
1. Describe the principles and basic concepts of Next-Generation Sequencing (NGS) technologies.
2. Understand and interpret biomedical research findings derived from NGS and single-cell approaches, including single-cell RNA sequencing (scRNA-seq).
3. Understand the importance of experimental design considerations in NGS and single-cell studies to address specific questions in biological research and clinical settings.
Lesson period: First semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Prerequisites for admission
To take the Translational medicine and biotechnologies 1 exam, students must have already passed all the exams of the first year (Fundamentals of Basic Sciences, Cells, Molecules and Genes , Histology, Anatomy, Biochemistry, Fundamentals of biomedical imaging) and all the exams of second years (Functions 1 and 2, Microbiology, Genetics and Mechanisms of diseases
For HEALTH INFORMATICS is required a good knowledge of Excel and basic knowledge in analysis and statistical. Suggested material: https://support.office.com/en-us/article/introduction-to-excel-starter601794a9-b73d-4d04-b2d4-eed4c40f98be
For HEALTH INFORMATICS is required a good knowledge of Excel and basic knowledge in analysis and statistical. Suggested material: https://support.office.com/en-us/article/introduction-to-excel-starter601794a9-b73d-4d04-b2d4-eed4c40f98be
Assessment methods and Criteria
The assessment of learning takes place through a written test lasting 60 minutes.
The test will be based on the Moodle platform basically with multiple-choice and short answer questions (n=32) distributed among the 5 different blocks.
The student earns:
1 point for each correct answer
0.5 points for each partial or incomplete answer
0 points for each incorrect answer or unanswered question.
The exam will be considered successfully completed if the student has acquired a minimum score of 18/30.
In the event of a score of 31-32, honors (lode) will be granted.
Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Registration through SIFA is mandatory
The test will be based on the Moodle platform basically with multiple-choice and short answer questions (n=32) distributed among the 5 different blocks.
The student earns:
1 point for each correct answer
0.5 points for each partial or incomplete answer
0 points for each incorrect answer or unanswered question.
The exam will be considered successfully completed if the student has acquired a minimum score of 18/30.
In the event of a score of 31-32, honors (lode) will be granted.
Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Registration through SIFA is mandatory
Informatics
Course syllabus
1. Discuss the main properties of the Statistical Learning paradigm
2. Recognize and apply techniques for data exploration and descriptive analysis
3. Identify and apply data preprocessing techniques for the construction of predictive models
4. Identify the main differences and application domains of supervised and unsupervised learning models
5. Know the foundations of the main methods for supervised/unsupervised learning and implement them using basic tools and
software
6. Evaluate and interpret the output of supervised/unsupervised learning models
7. Communicate effectively the results of predictive models
2. Recognize and apply techniques for data exploration and descriptive analysis
3. Identify and apply data preprocessing techniques for the construction of predictive models
4. Identify the main differences and application domains of supervised and unsupervised learning models
5. Know the foundations of the main methods for supervised/unsupervised learning and implement them using basic tools and
software
6. Evaluate and interpret the output of supervised/unsupervised learning models
7. Communicate effectively the results of predictive models
Teaching methods
Synchronous learning: class-room lectures
Asynchronous learning: audio-video lectures, question and answers, video-tutorials, articles and book chapters for further exploring the proposed topics will be suggested.
ATTENDANCE: Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Asynchronous learning: audio-video lectures, question and answers, video-tutorials, articles and book chapters for further exploring the proposed topics will be suggested.
ATTENDANCE: Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Teaching Resources
Marcello Pagano, Kimberlee Gauvreau, "Principles of Biostatistics", 2000, Duxbury Press
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. "An Introduction to Statistical Learning : with
Applications in R". New York :Springer, 2013. (available online)
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. "An Introduction to Statistical Learning : with
Applications in R". New York :Springer, 2013. (available online)
Molecular biology
Course syllabus
1. Define the principles and fundamental concepts underlying NGS technologies
2. Identify different NGS platforms and their strengths and limitations
3. Discuss the diverse applications of NGS in genomics, transcriptomics and epigenomics
4. Single-cell technologies and their significance in understanding cellular complexity
5. Introductory data analysis and versatile uses of single-cell RNA sequencing data
6. Clinical and research uses of NGS in the biomedical field
2. Identify different NGS platforms and their strengths and limitations
3. Discuss the diverse applications of NGS in genomics, transcriptomics and epigenomics
4. Single-cell technologies and their significance in understanding cellular complexity
5. Introductory data analysis and versatile uses of single-cell RNA sequencing data
6. Clinical and research uses of NGS in the biomedical field
Teaching methods
Synchronous learning: class-room lectures
Asynchronous learning: audio-video lectures, question and answers, video-tutorials, articles and book chapters for further exploring the proposed topics will be suggested.
ATTENDANCE: Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Asynchronous learning: audio-video lectures, question and answers, video-tutorials, articles and book chapters for further exploring the proposed topics will be suggested.
ATTENDANCE: Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Teaching Resources
Lectures and suggested papers will be uploaded on the myAriel platform
Clinical Biochemistry
Course syllabus
1. The organization of the biomedical laboratory
2. Point-of-care-testing
3. The concept of precision medicine and the role of the biomedical laboratory in disease risk prediction, diagnosis, and
therapy. Big data
4. The laboratory diagnostic process and the evidence-based medicine
5. Biological specimens' collection and preanalytical variable influence on laboratory results
6. Interpreting the laboratory data.
7. Basic haematology: the blood panel, hemoglobinopathies, and iron metabolism
8. Protein and inflammatory profiles
9. Enzymology
2. Point-of-care-testing
3. The concept of precision medicine and the role of the biomedical laboratory in disease risk prediction, diagnosis, and
therapy. Big data
4. The laboratory diagnostic process and the evidence-based medicine
5. Biological specimens' collection and preanalytical variable influence on laboratory results
6. Interpreting the laboratory data.
7. Basic haematology: the blood panel, hemoglobinopathies, and iron metabolism
8. Protein and inflammatory profiles
9. Enzymology
Teaching methods
Synchronous learning: class-room lectures
Asynchronous learning: audio-video lectures, question and answers, video-tutorials, articles and book chapters for further exploring the proposed topics will be suggested.
ATTENDANCE: Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Asynchronous learning: audio-video lectures, question and answers, video-tutorials, articles and book chapters for further exploring the proposed topics will be suggested.
ATTENDANCE: Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Teaching Resources
Rifai N. Tietz Fundamentals of Clinical Chemistry and Molecular Diagnostics. Elsevier
Microbiology
Course syllabus
1. Diagnosis of infection:
- Concept of direct and indirect diagnosis of infection
- Importance of the diagnostic suspicion for the request of microbiological investigation
- Influence of the characteristics of infectious agents for a correct execution of microbiological samples
- Knowledge of the main methods of direct and indirect microbiological investigations
- Interpretation of the microbiological report in relation to the phase of infection (recent, previous or chronic for serological
investigations) and therapeutic orientation (susceptibility assays to antimicrobial drugs) and bacterial resistance to
antibiotics.
2. Direct and indirect diagnostic investigations and interpretation of the report for:
- Viral hepatitis (HBV, HCV, HAV, HEV, HDV)
- HIV and related opportunistic infections
- Meningitis
- Sexually transmitted infections
- Respiratory and genitourinary tract infections
- TORCH complex and infections in pregnancy
- Concept of direct and indirect diagnosis of infection
- Importance of the diagnostic suspicion for the request of microbiological investigation
- Influence of the characteristics of infectious agents for a correct execution of microbiological samples
- Knowledge of the main methods of direct and indirect microbiological investigations
- Interpretation of the microbiological report in relation to the phase of infection (recent, previous or chronic for serological
investigations) and therapeutic orientation (susceptibility assays to antimicrobial drugs) and bacterial resistance to
antibiotics.
2. Direct and indirect diagnostic investigations and interpretation of the report for:
- Viral hepatitis (HBV, HCV, HAV, HEV, HDV)
- HIV and related opportunistic infections
- Meningitis
- Sexually transmitted infections
- Respiratory and genitourinary tract infections
- TORCH complex and infections in pregnancy
Teaching methods
Synchronous learning: class-room lectures
Asynchronous learning: audio-video lectures, question and answers, video-tutorials, articles and book chapters for further exploring the proposed topics will be suggested.
ATTENDANCE: Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Asynchronous learning: audio-video lectures, question and answers, video-tutorials, articles and book chapters for further exploring the proposed topics will be suggested.
ATTENDANCE: Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Teaching Resources
Connie R. Mahon, Donald C. Lehman, George Manuselis, "Textbook of Diagnostic Microbiology" Elsevier
James J. Dunn, Marc Roger Couturier, Audrey N. Schuetz, "Challenging cases in Diagnostic Clinical Microbiology -
Advanced Strategies and Techniques", Springer
James J. Dunn, Marc Roger Couturier, Audrey N. Schuetz, "Challenging cases in Diagnostic Clinical Microbiology -
Advanced Strategies and Techniques", Springer
Laboratory medicine
Course syllabus
1. Public health programs in medicine
2. Regulation and ethics of modern technology in modern medicine
3. Flow Cytometry - Part 1 (Principles and Technology)
4. Flow Cytometry - Part 2 (Applications in Medicine)
5. Introduction to animal models
6. Animal Models to study human diseases
7. Stemness and technological tools
8. Principle of Molecular biology techniques in medical applications (NO NGS) - Part 1
9. Principle of Molecular biology techniques in medical applications (NO NGS) - Part 2
10. Imaging (no radiology or radioimaging)
11. Principle of proteomics
12. Principle of deep-learning and artificial intelligence
2. Regulation and ethics of modern technology in modern medicine
3. Flow Cytometry - Part 1 (Principles and Technology)
4. Flow Cytometry - Part 2 (Applications in Medicine)
5. Introduction to animal models
6. Animal Models to study human diseases
7. Stemness and technological tools
8. Principle of Molecular biology techniques in medical applications (NO NGS) - Part 1
9. Principle of Molecular biology techniques in medical applications (NO NGS) - Part 2
10. Imaging (no radiology or radioimaging)
11. Principle of proteomics
12. Principle of deep-learning and artificial intelligence
Teaching methods
Synchronous learning: class-room lectures
Asynchronous learning: audio-video lectures, question and answers, video-tutorials, articles and book chapters for further exploring the proposed topics will be suggested.
ATTENDANCE: Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Asynchronous learning: audio-video lectures, question and answers, video-tutorials, articles and book chapters for further exploring the proposed topics will be suggested.
ATTENDANCE: Attendance is required to be allowed to take the exam. Unexcused absence is tolerated up to 34% of the course activities. University policy regarding excused illness is followed.
Teaching Resources
The lectures slides and the pdf of paper presented during the lessons will be uploaded on the Ariel website of the University of Milan.
Modules or teaching units
Clinical Biochemistry
BIO/12 - CLINICAL BIOCHEMISTRY AND MOLECULAR BIOLOGY - University credits: 2
Lessons: 16 hours
Lessons - Innovative Teaching: 8 hours
Lessons - Innovative Teaching: 8 hours
Professor:
Tringali Cristina Alessandra
Informatics
INF/01 - INFORMATICS - University credits: 2
Lessons: 16 hours
Lessons - Innovative Teaching: 8 hours
Lessons - Innovative Teaching: 8 hours
Professor:
Soto Gomez Mauricio Abel
Laboratory medicine
MED/46 - BIOTECHNOLOGY AND METHODS IN LABORATORY MEDICINE - University credits: 2
Lessons: 16 hours
Lessons - Innovative Teaching: 8 hours
Lessons - Innovative Teaching: 8 hours
Professors:
Di Vito Clara, Mavilio Domenico
Microbiology
MED/07 - MICROBIOLOGY AND CLINICAL MICROBIOLOGY - University credits: 2
Lessons: 16 hours
Lessons - Innovative Teaching: 8 hours
Lessons - Innovative Teaching: 8 hours
Professor:
Delbue Serena
Molecular biology
BIO/11 - MOLECULAR BIOLOGY - University credits: 1
Lessons: 8 hours
E-learning: 4 hours
E-learning: 4 hours
Professor:
Fakiola Michaela
Professor(s)
Reception:
Professor shoud be contacted via e-mail to arrange day and time
In presence (LITA, Segrate) or via Teams
Reception:
On Appointment
Istituto Clinico Humanitas, Via A. ;anzoni 113, Rozzano, Milano
Reception:
Monday 14:00 - 16:00 (send a confirmation e-mail )
Room 3019, piano 3
Reception:
By appointment by mail/phone
via F.lli Cervi 93-LITA Segrate