Biotechnologies in Molecular Diagnostics and Fundamental of Statistics
A.Y. 2025/2026
Learning objectives
Acquire knowledge on the organization of the clinical diagnostic laboratory and of principles and methodologies associated to the most advanced diagnostic techniques.
Definition of diagnostic biomarker, characteristics of the main biomarkers currently used in clinical diagnostics of different human diseases and in clinical and traslational research.
Acquire the knowledge related to the basic principles of the main non-invasive diagnostic techniques (RX, TAC, Nuclear-based techniques, Ultrasound, MRI, Optical imaging) and the biomarkers assessed by using these procedures
Understand the potential of different imaging techniques and potential applications in clinical and preclinical / experimental settings.
Acquire basic knowledge on the relationship between scientific and statistical method with reference to the problems of measurement in biomedicine with the basics of the planning of experiments.
Definition of diagnostic biomarker, characteristics of the main biomarkers currently used in clinical diagnostics of different human diseases and in clinical and traslational research.
Acquire the knowledge related to the basic principles of the main non-invasive diagnostic techniques (RX, TAC, Nuclear-based techniques, Ultrasound, MRI, Optical imaging) and the biomarkers assessed by using these procedures
Understand the potential of different imaging techniques and potential applications in clinical and preclinical / experimental settings.
Acquire basic knowledge on the relationship between scientific and statistical method with reference to the problems of measurement in biomedicine with the basics of the planning of experiments.
Expected learning outcomes
To know the limits and applicability of the different methods used in advanced clinical diagnostics. To be able to choose between different methodologies.
To apply the use of different biomarkers and diagnostic criteria related to different human diseases.
To understand the potential of different imaging techniques and potential applications in clinical and preclinical / experimental settings.
Knowing how to interpret the results of the different clinical diagnostic methods on a statistical basis. Knowing how to use the basic statistical methods for data analysis in biomedical research.
To apply the use of different biomarkers and diagnostic criteria related to different human diseases.
To understand the potential of different imaging techniques and potential applications in clinical and preclinical / experimental settings.
Knowing how to interpret the results of the different clinical diagnostic methods on a statistical basis. Knowing how to use the basic statistical methods for data analysis in biomedical research.
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
Lesson period
First semester
Prerequisites for admission
Students must have fulfilled all the prerequisite requirements indicated in the study plan: Biochemistry and Fundamentals of Human Biochemistry, Techniques in molecular and cellular biology, General Pathology and Immunology.
Assessment methods and Criteria
The level of learning will only be tested at the end of the course, without any intermediate or pre-testing. The examination consists of two written tests, both compulsory and taken on the same day.
During the first written test, which lasts 1 hour, students must answer multiple-choice questions or short answers. For the multiple-choice questions, with 5 possible options, it will be indicated whether there can be one or more correct answers.
The second written test for the medical statistics part, lasting 1 hour, consists of 30 questions (20 for statistics and evaluation of evidence in medicine and 10 for imaging diagnostics, comprising:
1) open-ended questions with analysis of statistical problems (50-60 %);
2) multiple-choice questions with a brief explanation of the choice (40-50 %);
Each question allows 1 point. The written test is passed if the total score is equal to or higher than 18.
Both tests aim to ascertain that the student has theoretical knowledge of the main methodologies of biochemistry and clinical diagnostics and of the principles underlying advanced diagnostic techniques, as well as the necessary basic statistical skills for observation and measurement in the presence of experimental uncertainty and the evaluation of scientific evidence from data analysis in biomedical research. The following assessment parameters will be taken into account: correctness and completeness of the answers; in addition, for the open-ended part, the ability to discursively organise knowledge, critical reasoning skills and competence in the use of specialist vocabulary will be assessed.
The final examination grade, expressed in thirtieths, will result from the credit-weighted average of the marks obtained in the first and second written examinations approximated to the integer.
The results of the test will be communicated electronically via the official UNIMI platform and the student may accept or reject the grade.
During the first written test, which lasts 1 hour, students must answer multiple-choice questions or short answers. For the multiple-choice questions, with 5 possible options, it will be indicated whether there can be one or more correct answers.
The second written test for the medical statistics part, lasting 1 hour, consists of 30 questions (20 for statistics and evaluation of evidence in medicine and 10 for imaging diagnostics, comprising:
1) open-ended questions with analysis of statistical problems (50-60 %);
2) multiple-choice questions with a brief explanation of the choice (40-50 %);
Each question allows 1 point. The written test is passed if the total score is equal to or higher than 18.
Both tests aim to ascertain that the student has theoretical knowledge of the main methodologies of biochemistry and clinical diagnostics and of the principles underlying advanced diagnostic techniques, as well as the necessary basic statistical skills for observation and measurement in the presence of experimental uncertainty and the evaluation of scientific evidence from data analysis in biomedical research. The following assessment parameters will be taken into account: correctness and completeness of the answers; in addition, for the open-ended part, the ability to discursively organise knowledge, critical reasoning skills and competence in the use of specialist vocabulary will be assessed.
The final examination grade, expressed in thirtieths, will result from the credit-weighted average of the marks obtained in the first and second written examinations approximated to the integer.
The results of the test will be communicated electronically via the official UNIMI platform and the student may accept or reject the grade.
Biotechnology in diagnostics
Course syllabus
Frontal teaching:
L1: What clinical biochemistry is about and relevance to diagnostics. Pre-analytical phase collection, preparation and storage of biological samples of blood, urine, CSF, saliva and the role of biobanks.
L2: Analytical phase: and post-analytical phase: Variability and quality control and interpretation of results
L3: Point of care testing: experimental design and applications
L4:Capillary electrophoresis in laboratory diagnostics: principles and applications
L5: HPLC in laboratory diagnostics: principles and applications
L6: Immunoassay in laboratory diagnostics: principles, applications and critical issues.
L7: Mass Spectrometry in laboratory diagnostics: GC-MS, LC-MS, MALDI, principles and characteristics.
L8: Mass spectrometry in laboratory diagnostics: main applications and criticalities.
Exercises:
Separative science concepts, bottom-up proteomics approach (sample preparation, HPLC, mass spectrometry). Introduction to enrichment and pathway analysis tools.
Top-down' proteomics approach (2D-SDS-PAGE, 2D-DIGE, MALDI mass spectrometry, peptide fingerprinting). Introduction to software for data analysis and variation representation.
L1: What clinical biochemistry is about and relevance to diagnostics. Pre-analytical phase collection, preparation and storage of biological samples of blood, urine, CSF, saliva and the role of biobanks.
L2: Analytical phase: and post-analytical phase: Variability and quality control and interpretation of results
L3: Point of care testing: experimental design and applications
L4:Capillary electrophoresis in laboratory diagnostics: principles and applications
L5: HPLC in laboratory diagnostics: principles and applications
L6: Immunoassay in laboratory diagnostics: principles, applications and critical issues.
L7: Mass Spectrometry in laboratory diagnostics: GC-MS, LC-MS, MALDI, principles and characteristics.
L8: Mass spectrometry in laboratory diagnostics: main applications and criticalities.
Exercises:
Separative science concepts, bottom-up proteomics approach (sample preparation, HPLC, mass spectrometry). Introduction to enrichment and pathway analysis tools.
Top-down' proteomics approach (2D-SDS-PAGE, 2D-DIGE, MALDI mass spectrometry, peptide fingerprinting). Introduction to software for data analysis and variation representation.
Teaching methods
The majority of the course consists of lectures. The teaching material consists of presentations in PDF format and is made available at the end of the lecture on the myAriel platform.
An exercise activity is also planned, during which non-formal teaching is carried out. Students are given questionnaires to assess their acquired knowledge and are given commented video tutorials on the main experimental procedures required to perform a proteomics/mass spectrometry experiment. The students, divided into small groups, must carry out exercises relevant to what is followed in the videos.
Attendance of the course is compulsory.
An exercise activity is also planned, during which non-formal teaching is carried out. Students are given questionnaires to assess their acquired knowledge and are given commented video tutorials on the main experimental procedures required to perform a proteomics/mass spectrometry experiment. The students, divided into small groups, must carry out exercises relevant to what is followed in the videos.
Attendance of the course is compulsory.
Teaching Resources
Tietz Fundamentals of Clinical Chemistry and Molecular Diagnostics
9th Edition - September 2, 2023 Editor: Nader Rifai
Biochimica Clinica e Medicina di Laboratorio
M. Ciaccio, G. Lippi 3a edizione. 2020
9th Edition - September 2, 2023 Editor: Nader Rifai
Biochimica Clinica e Medicina di Laboratorio
M. Ciaccio, G. Lippi 3a edizione. 2020
Statistics in biomedical experimentation
Course syllabus
Diagnostic Imaging
Non-invasive techniques: anatomical and functional studies, difference between structure and function, advantages and disadvantages, possible combinations.
Principles and applications of X-Ray based techniques: principles, imaging biomarkers, instrumentation, applications
Ultrasound imaging: principles and applications: principles, Doppler effect, imaging biomarkers, available instrumentation, applications
Principles and applications of nuclear imaging techniques: principles about radioactivity, radioactive decays, decay products, instrumentation available, uses in diagnostics and therapy
Principles and applications of Imaging with Magnetic Resonance: principles of the technique, available instrumentation, possible applications
Principles and applications of optical imging: Bioluminescence and fluorescence concept, available instrumentation, preclinical applications
Overview of molecular and cellular imaging: tracer concept, reporter genes for non-invasive imaging, cell labelling.
Foundations of Statistics
Statistics and Scientific Method
The problem of measurement: variables and scales
Data summaries: central tendency and variability indices (descriptive statistics).
Measurements: measurement errors (Accuracy and Precision).
Measurements and statistical models (statistical sampling)
Statistics as a measurement method: estimation (Statistical Inference 1)
Confidence intervals
Testing hypotheses on a statistical basis (Statistical Inference 2)
Association and dependence, correlation and regression (the linear model)
Diagnostic test: sensitivity, specificity and ROC, predictive values and diagnostic likelihood ratios.
The use of software for statistical analysis.
Non-invasive techniques: anatomical and functional studies, difference between structure and function, advantages and disadvantages, possible combinations.
Principles and applications of X-Ray based techniques: principles, imaging biomarkers, instrumentation, applications
Ultrasound imaging: principles and applications: principles, Doppler effect, imaging biomarkers, available instrumentation, applications
Principles and applications of nuclear imaging techniques: principles about radioactivity, radioactive decays, decay products, instrumentation available, uses in diagnostics and therapy
Principles and applications of Imaging with Magnetic Resonance: principles of the technique, available instrumentation, possible applications
Principles and applications of optical imging: Bioluminescence and fluorescence concept, available instrumentation, preclinical applications
Overview of molecular and cellular imaging: tracer concept, reporter genes for non-invasive imaging, cell labelling.
Foundations of Statistics
Statistics and Scientific Method
The problem of measurement: variables and scales
Data summaries: central tendency and variability indices (descriptive statistics).
Measurements: measurement errors (Accuracy and Precision).
Measurements and statistical models (statistical sampling)
Statistics as a measurement method: estimation (Statistical Inference 1)
Confidence intervals
Testing hypotheses on a statistical basis (Statistical Inference 2)
Association and dependence, correlation and regression (the linear model)
Diagnostic test: sensitivity, specificity and ROC, predictive values and diagnostic likelihood ratios.
The use of software for statistical analysis.
Teaching methods
The teachers will use frontal lessons and practical exercise activities (4 CFU).
The teachers will use the Ariel platform to provide the teaching material which consists of: a) a copy of the presentations used in the lectures; b) supplementary bibliographic material and programming code for statistical analyses.
Attendance to lectures and exercise activities is mandatory.
The teachers will use the Ariel platform to provide the teaching material which consists of: a) a copy of the presentations used in the lectures; b) supplementary bibliographic material and programming code for statistical analyses.
Attendance to lectures and exercise activities is mandatory.
Teaching Resources
Any in-depth analysis with reviews and articles provided in class and mentioned in the lessons.
Biotechnology in diagnostics
BIO/12 - CLINICAL BIOCHEMISTRY AND MOLECULAR BIOLOGY
MED/05 - CLINICAL PATHOLOGY
MED/05 - CLINICAL PATHOLOGY
Practicals: 16 hours
Lessons: 32 hours
Lessons: 32 hours
Statistics in biomedical experimentation
MED/01 - MEDICAL STATISTICS
MED/36 - IMAGING AND RADIOTHERAPY
MED/36 - IMAGING AND RADIOTHERAPY
Practicals: 8 hours
Lessons: 28 hours
Lessons: 28 hours
Professors:
Orenti Annalisa, Ottobrini Luisa
Professor(s)
Reception:
By appointment via e-mail
LITA Segrate - Via F.lli Cervi 93, 20090 Segrate (MI)
Reception:
Monday 10am-13pm
LITA Segrate