Translational medicine and biotechnologies 1
A.A. 2024/2025
Obiettivi formativi
The course aims to apply a multidisciplinary approach to human health problems to transfer new findings from the basic sciences 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.
Risultati apprendimento attesi
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
Fundamental Knowledge: Develop a thorough understanding of the fundamental aspects of sample collection, proper storage techniques, and the role of biobanks.
Interpretation Skills:Acquire the skills to interpret reference values accurately.Understand and interpret the sensitivity and specificity of various diagnostic tests.
Point-of-Care Testing (POCT): Become confident and proficient in the use and interpretation of Point-of-Care Testing (POCT) devices.
Clinical Enzymology: Gain knowledge of the main enzymes quantified in clinical laboratories for the diagnosis of liver and cardiac disorders.
Protein Serum Profiles: Develop skills in interpreting protein serum profiles.
Iron Monitoring:Become skilled in interpreting tests required for iron monitoring in a clinical laboratory setting.
Hemoglobin Disorders: Acquire knowledge of the tests required for monitoring hemoglobin, porphyrins, hemoglobinopathies, and thalassemias.
Therapeutic Drug Monitoring (TDM):Develop the ability to interpret therapeutic drug monitoring results effectively.
BLOCK 3: METHODOLOGY AND APPLICATION OF TRANSLATIONAL MICROBIOLOGY
Define correctly outbreak and cluster in bacterial surveillance
Describe the methodology and outcomes of phylogenetic studies in viruses and bacteria
Provide a proper characterization of microbiota
Illustrate the methodologies used in fast microbiology and their application in respiratory/gastrointestinal diseases and sepsis/septic shock
Illustrate the main applications of ultrasensitive methodologies in clinical microbiology
Be aware of the contribution of the microbiology lab to the diagnosis and management of bacterial infections.
BLOCK 4: HEALTH INFORMATICS
Recognise real-word scenarios where statistical and automatic learning tools can provide advantages for the analysis.
Recognise the difference between descriptive and predictive analysis.
Identify proper methods suited for specific research questions.
Compute statistical measures and implement simple statistical methods and prediction models.
Interpret the results of the statistical analysis/models respect to the input data and the modelling assumptions.
Identify and properly communicate results of the statistical analysis.
BLOCK 5: BASIC PRINCIPLES OF NGS AND SINGLE CELL ANALYSIS
Students will be able to;
Explain the principles and basic concepts of Next-Generation Sequencing (NGS) technologies.
Gain familiarity with single-cell analysis techniques, including single-cell RNA sequencing (scRNA-seq) and single-cell ATAC-seq
Understand the importance of experimental design considerations in NGS and single-cell studies to address specific questions in biological research, precision medicine and clinical settings
Gain basic proficiency in analyzing NGS and single-cell sequencing data
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
Fundamental Knowledge: Develop a thorough understanding of the fundamental aspects of sample collection, proper storage techniques, and the role of biobanks.
Interpretation Skills:Acquire the skills to interpret reference values accurately.Understand and interpret the sensitivity and specificity of various diagnostic tests.
Point-of-Care Testing (POCT): Become confident and proficient in the use and interpretation of Point-of-Care Testing (POCT) devices.
Clinical Enzymology: Gain knowledge of the main enzymes quantified in clinical laboratories for the diagnosis of liver and cardiac disorders.
Protein Serum Profiles: Develop skills in interpreting protein serum profiles.
Iron Monitoring:Become skilled in interpreting tests required for iron monitoring in a clinical laboratory setting.
Hemoglobin Disorders: Acquire knowledge of the tests required for monitoring hemoglobin, porphyrins, hemoglobinopathies, and thalassemias.
Therapeutic Drug Monitoring (TDM):Develop the ability to interpret therapeutic drug monitoring results effectively.
BLOCK 3: METHODOLOGY AND APPLICATION OF TRANSLATIONAL MICROBIOLOGY
Define correctly outbreak and cluster in bacterial surveillance
Describe the methodology and outcomes of phylogenetic studies in viruses and bacteria
Provide a proper characterization of microbiota
Illustrate the methodologies used in fast microbiology and their application in respiratory/gastrointestinal diseases and sepsis/septic shock
Illustrate the main applications of ultrasensitive methodologies in clinical microbiology
Be aware of the contribution of the microbiology lab to the diagnosis and management of bacterial infections.
BLOCK 4: HEALTH INFORMATICS
Recognise real-word scenarios where statistical and automatic learning tools can provide advantages for the analysis.
Recognise the difference between descriptive and predictive analysis.
Identify proper methods suited for specific research questions.
Compute statistical measures and implement simple statistical methods and prediction models.
Interpret the results of the statistical analysis/models respect to the input data and the modelling assumptions.
Identify and properly communicate results of the statistical analysis.
BLOCK 5: BASIC PRINCIPLES OF NGS AND SINGLE CELL ANALYSIS
Students will be able to;
Explain the principles and basic concepts of Next-Generation Sequencing (NGS) technologies.
Gain familiarity with single-cell analysis techniques, including single-cell RNA sequencing (scRNA-seq) and single-cell ATAC-seq
Understand the importance of experimental design considerations in NGS and single-cell studies to address specific questions in biological research, precision medicine and clinical settings
Gain basic proficiency in analyzing NGS and single-cell sequencing data
Periodo: Primo semestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Prerequisiti
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
Modalità di verifica dell’apprendimento e criteri di valutazione
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
Programma
Data exploration and descriptive analysis
The first part will be very strictly connected with the biostatistics course and will involve using excel for presenting patient data and making statistical comparison.
- Types of Research problems: Exploratory, Descriptive, Predictive <-> Causality, Prescriptive
- Structured, Semi structured data source (tables, text, images)
- Tables: Records and features
- Data Types (nominal, ordinal, ranked, discrete, continuous)
- Exploratory Data Analysis
- Statistics (mean, std, )
- Graphical representation (pie plot, histograms, boxplot, catplots(violin), scatter, cumulative, Line graphs)
- References:
[4] Chapter 2,3
[1] Chapter 1
[3] Chapter 2,3
2. Data preprocessing for predictive models
- Graphical Programming Tool : Orange
Installation and tool exploration
Simple use cases (exploratory analysis examples)
- Data Preprocessing
Outliers
Missing Values
Data Representation: Numerical, Categorical (one-hot encode)
Standardization
Discretization
Feature Engineering
Unbalance data
- References
[4] Chapter 2, 4
3. Introduction to statistical learning
- Supervised/Unsupervised Learning
Classification
Regression
Clustering
- Model Workflow
Bias/Variance and overfitting
Holdout method
- Supervised Learning
Classification
Classification Tree
Logistic Regression
Linear Regression
- Unsupervised Learning
Dimensionality Reduction
Data embedding
Clustering
PCA
- References
[4] Chapter 19,20
[5] Chapter 2, 3.1-3.3, 4.1-4.3, 5, 8, (12)
The first part will be very strictly connected with the biostatistics course and will involve using excel for presenting patient data and making statistical comparison.
- Types of Research problems: Exploratory, Descriptive, Predictive <-> Causality, Prescriptive
- Structured, Semi structured data source (tables, text, images)
- Tables: Records and features
- Data Types (nominal, ordinal, ranked, discrete, continuous)
- Exploratory Data Analysis
- Statistics (mean, std, )
- Graphical representation (pie plot, histograms, boxplot, catplots(violin), scatter, cumulative, Line graphs)
- References:
[4] Chapter 2,3
[1] Chapter 1
[3] Chapter 2,3
2. Data preprocessing for predictive models
- Graphical Programming Tool : Orange
Installation and tool exploration
Simple use cases (exploratory analysis examples)
- Data Preprocessing
Outliers
Missing Values
Data Representation: Numerical, Categorical (one-hot encode)
Standardization
Discretization
Feature Engineering
Unbalance data
- References
[4] Chapter 2, 4
3. Introduction to statistical learning
- Supervised/Unsupervised Learning
Classification
Regression
Clustering
- Model Workflow
Bias/Variance and overfitting
Holdout method
- Supervised Learning
Classification
Classification Tree
Logistic Regression
Linear Regression
- Unsupervised Learning
Dimensionality Reduction
Data embedding
Clustering
PCA
- References
[4] Chapter 19,20
[5] Chapter 2, 3.1-3.3, 4.1-4.3, 5, 8, (12)
Metodi didattici
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.
Materiale di riferimento
1. Leslie E. Daly and Geoffrey J. Bourke, "Interpretation and Uses of Medical Statistics". (5th edition). Available online in Unimi library.
2. J. Mark Elwood, "Critical Appraisal of Epidemiological Studies and Clinical Trials", 3rd Edition, Oxford University Press
3. Douglas G. Altman, "Practical statistics for medical research". Chapman and Hall
4. Marcello Pagano, Kimberlee Gauvreau, "Principles of Biostatistics", 2000, Duxbury Press
5. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. "An Introduction to Statistical Learning : with Applications in R". New York :Springer, 2013. (available online)
Softwares
· Microsoft Excel
· Orange Data Mining https://orangedatamining.com/
2. J. Mark Elwood, "Critical Appraisal of Epidemiological Studies and Clinical Trials", 3rd Edition, Oxford University Press
3. Douglas G. Altman, "Practical statistics for medical research". Chapman and Hall
4. Marcello Pagano, Kimberlee Gauvreau, "Principles of Biostatistics", 2000, Duxbury Press
5. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. "An Introduction to Statistical Learning : with Applications in R". New York :Springer, 2013. (available online)
Softwares
· Microsoft Excel
· Orange Data Mining https://orangedatamining.com/
Molecular biology
Programma
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. Define single-cell technologies and their significance in understanding cellular heterogeneity
5. Compare single-cell RNA sequencing (scRNA-seq), single-cell ATAC-seq and other single-cell omics techniques
6. Learn basic data analysis methods for NGS and single-cell sequencing data, including quality control, alignment, and differential expression analysis
7. Interpret and visualize NGS and single-cell sequencing data using basics bioinformatics tools and software
2. Identify different NGS platforms and their strengths and limitations
3. Discuss the diverse applications of NGS in genomics, transcriptomics and epigenomics
4. Define single-cell technologies and their significance in understanding cellular heterogeneity
5. Compare single-cell RNA sequencing (scRNA-seq), single-cell ATAC-seq and other single-cell omics techniques
6. Learn basic data analysis methods for NGS and single-cell sequencing data, including quality control, alignment, and differential expression analysis
7. Interpret and visualize NGS and single-cell sequencing data using basics bioinformatics tools and software
Metodi didattici
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.
Materiale di riferimento
Lectures and suggested papers will be uploaded on the myAriel platform
Clinical Biochemistry
Programma
1. Biological specimen collection and storage
2. Standardization and automation in laboratory medicine
3. Methods evaluation and reference values, specificity and sensitivity, predictive values
4. Biobanks
5. POCT
6. Enzymology in clinical laboratory
7. Plasma proteins and acute phase response
8. Iron monitoring
9. Hemoglobin, porphyrin, hemoglobinopathies and thalassemia monitoring
10. Therapeutic drug monitoring
2. Standardization and automation in laboratory medicine
3. Methods evaluation and reference values, specificity and sensitivity, predictive values
4. Biobanks
5. POCT
6. Enzymology in clinical laboratory
7. Plasma proteins and acute phase response
8. Iron monitoring
9. Hemoglobin, porphyrin, hemoglobinopathies and thalassemia monitoring
10. Therapeutic drug monitoring
Metodi didattici
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.
Materiale di riferimento
Rifai N. Tietz Fundamentals of Clinical Chemistry and Molecular Diagnostics. Elsevier
Microbiology
Programma
METHODOLOGY AND APPLICATION OF TRANSLATIONAL MICROBIOLOGY
1. Genomic features and transmission dynamics in bacteria as part of surveillance procedure
2. Genomic features and transmission dynamics in emerging and reemerging viruses
3. The microbiota in health and pathogenic conditions; the fecal transplantation
4. Fast microbiology in respiratory/gastrointestinal diseases
5. Fast microbiology in sepsis and septic/shock
8. The application of ultrasensitive methodologies in clinical microbiology
9. Next-generation sequencing methods (A)
10. Basics of genomic reconstruction and phylogeny (A)
11. Molecular assays and quantification methods (A)
12. Workflows for bacteremia (A)
1. Genomic features and transmission dynamics in bacteria as part of surveillance procedure
2. Genomic features and transmission dynamics in emerging and reemerging viruses
3. The microbiota in health and pathogenic conditions; the fecal transplantation
4. Fast microbiology in respiratory/gastrointestinal diseases
5. Fast microbiology in sepsis and septic/shock
8. The application of ultrasensitive methodologies in clinical microbiology
9. Next-generation sequencing methods (A)
10. Basics of genomic reconstruction and phylogeny (A)
11. Molecular assays and quantification methods (A)
12. Workflows for bacteremia (A)
Metodi didattici
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.
Materiale di riferimento
P Murray, K Rosenthal, M Pfaller. MEDICAL MICROBIOLOGY. 9th ed. Elsevier 2020 or later
Laboratory medicine
Programma
BIOTECHNOLOGY APPROACHES IN TRANSLATIONAL MEDICINE
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
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
Metodi didattici
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.
Materiale di riferimento
The lectures slides and the pdf of paper presented during the lessons will be uploaded on the Ariel website of the University of Milan.
Moduli o unità didattiche
Clinical Biochemistry
BIO/12 - BIOCHIMICA CLINICA E BIOLOGIA MOLECOLARE CLINICA - CFU: 2
Lezioni: 16 ore
: 8 ore
: 8 ore
Docente:
Gelfi Cecilia
Turni:
Turno
Docente:
Gelfi Cecilia
Informatics
INF/01 - INFORMATICA - CFU: 2
Lezioni: 16 ore
: 8 ore
: 8 ore
Docente:
Soto Gomez Mauricio Abel
Turni:
Turno
Docente:
Soto Gomez Mauricio Abel
Laboratory medicine
MED/46 - SCIENZE TECNICHE DI MEDICINA DI LABORATORIO - CFU: 2
Lezioni: 16 ore
: 8 ore
: 8 ore
Docenti:
Di Vito Clara, Mavilio Domenico
Turni:
Microbiology
MED/07 - MICROBIOLOGIA E MICROBIOLOGIA CLINICA - CFU: 2
Lezioni: 16 ore
: 8 ore
: 8 ore
Docente:
Alteri Claudia
Turni:
Turno
Docente:
Alteri Claudia
Molecular biology
BIO/11 - BIOLOGIA MOLECOLARE - CFU: 1
Lezioni: 8 ore
Lezioni in teledidattica: 4 ore
Lezioni in teledidattica: 4 ore
Docente:
Pagani Massimiliano
Turni:
Turno
Docente:
Pagani MassimilianoSiti didattici
Docente/i
Ricevimento:
Disponibile su Teams ed in presenza; giorno ed ora sono da concordare con il docente via email.
Teams or in presence
Ricevimento:
Giorno ed ora sono da concordare con il docente via email
In presenza (LITA, Segrate) o via Teams
Ricevimento:
Lunedì dalle 10 alle 13
LITA
Ricevimento:
Previo Appuntamento
Istituto Clinico Humanitas, Via Alessandro Manzoni 113, Rozzano, Milano