Elective courses

Course dates

Autumn 2024

In English: 21-23/10 + 5/11
In Swedish: 18-20/11 + 6/12 

Course Leaders

Jan Lexell jan [dot] lexell [at] med [dot] lu [dot] se (jan[dot]lexell[at]med[dot]lu[dot]se),
Christina Brogårdh christina [dot] brogardh [at] med [dot] lu [dot] se (christina[dot]brogardh[at]med[dot]lu[dot]se)

Target group

PhD students at the Faculty of Medicine, with priority given to those who have passed their halfway review.

Purpose

The aim of the course is for the doctoral student to deepen their knowledge and skills around the publication process and how to write and review a scientific manuscript.

Outline

The teaching takes place mainly through interactive educational activities. The course includes lectures, reviewing of scientific articles, group work, discussions, practical applications and independent study. The course is given during five days and starts with three course days, then one day of independent work, and ends 2 weeks later with one course day.

Course dates 

Autumn 2024:

Week 47, 18-22 November. Mornings are in class and the afternoons consist of individual work.

Course organizers 

Matteo Bruschettini matteo [dot] bruschettini [at] med [dot] lu [dot] se (matteo[dot]bruschettini[at]med[dot]lu[dot]se)

Martin Ringsten martin [dot] ringsten [at] med [dot] lu [dot] se (martin[dot]ringsten[at]med[dot]lu[dot]se)

Examinator

Stefan Hansson stefan [dot] hansson [at] med [dot] lu [dot] se (stefan[dot]hansson[at]med[dot]lu[dot]se)

Target group 

The one week course is aimed towards PhD students and researchers at the Faculty of Medicine.
Participation is free for PhD students from European Economic Area (EEA) and Switzerland. Other external participants might require a fee for participation, see more on Cochrane Sweden’s website for this course.

Description

The course is aimed at PhD students and researchers who wants to increase their knowledge about how to conduct a systematic review or evidence synthesis. The course is also relevant for people who will use systematic reviews, evidence synthesis or results from randomized trials to inform decisions in healthcare (clinicians, decision makers, guideline developers, or policy makers).

The course aims to introduce and increase participants knowledge about the Cochrane methodology to systematic reviews with a focus on systematic reviews of interventions. During the week we will go through the process from the initial idea and research question that can be explored in a systematic review, tools to support the systematic review process, risk of bias, meta-analysis, the GRADE-approach to judge uncertainty, best practice reporting of results in reviews, and the use of systematic reviews in guidelines and decision making.
The course will include lecturers and facilitators from several Cochrane Centers, each within their expert area. Lectures will be mixed with discussions and working in groups with exercises in the mornings, and after lunch participants will work individually within the Cochrane Interactive Learning-modules. There will be time to ask individual questions to our lecturers and facilitators about your own potential reviews or other evidence-related questions during the week.

Location

The course will be aimed to be conducted on campus in Lund for all days.

Examination
To pass the course you will need to attend the days in class, have an active participation in discussions and teamwork during these days, and completion of the module 1-8 and quizzes in Cochrane Interactive Learning.

Credits

The course is rewarded with 1,5 ECTS credits (equal to one week full time studies) for enrolled PhD students. All participants will receive a certificate of attendance for the course.

Resources and literature

Cochrane Interactive Learning modules, available from https://training.cochrane.org/interactivelearning 

Cochrane Handbook for Systematic Reviews of Interventions, available for free from https://training.cochrane.org/handbook

Additional articles, books and some pre-course work will be handed out before the course starts.

Registration

You can register through the link in the right hand margin. Chose the correct date of the course. If you are an external participant (outside of Lund University), please clearly state this and your affiliation and professional title in “Other comments”, and try to fill the other information in as good as possible (if not relevant leave blank)

Course leader

Lena Uller, Docent, Respiratorisk Immunofarmakologi, Institutionen för experimentell medicinsk vetenskap, Lund

Examiner

Lena Uller

Target Group

This is a compulsory course for doctoral students at Lunds University who aim to work with animals. You will register specifically for the species you aim to work with. No previous qualifications required. The course is equivalent to a FELASA B level but not yet formally certified by Felasa.

Credits 

3 University credits for the full course, 2 credits when the practical part is not completed.

Time & Place 

This is a web-based education using Canvas Catalog. You work on your own time at your own computer.

Content of the course

The course is in English and contains 15 modules

• Module 1: Ethics and Animal Use
• Module 2: Swedish Legislation
• Module 3: Animal Records
• Module 4: Identification Methods
• Module 5: Humane Endpoints
• Self-assessments Legislation, Animal Records, ID & Humane Endpoints
• Module 6: Biology
• Module 7: Ethology
• Module 8: Husbandry
• Module 9: Animal Care and Supervision
• Self-assessments Husbandry, Animal Care and Supervision
• Module 10: Anaesthesia, Analgesia and Euthanasia
• Module 11: Diseases in Laboratory Animals
• Module 12: Animal Experimental Methodology
• Module 13: Genetically Modified Organisms
• Module 14: Alternative Methods
• Module 15: Safety in Biomedical Facilities

To complete the course

Estimated time to complete the course is 40 h. The different modules will be examined continuously with self-assessments. Upon completing the theoretical part, there is a practical part which extent depends on your planned upcoming practical activities. Upon this you will receive a certificate valid for operate with animals.

Course literature

All literature is available on Canvas Catalog with additional links to Internet sites, which contain further information.
If you have questions about the course, please contact: djurutbildning [at] med [dot] lu [dot] se (djurutbildning[at]med[dot]lu[dot]se) 

Training in Laboratory Animal Science - to apply (Lund University Staff Pages)

Course leaders

Patrik Önnerfjord (patrik [dot] onnerfjord [at] med [dot] lu [dot] se)

Lotta Happonen (lotta [dot] happonen [at] med [dot] lu [dot] se)

Examiner

Prof. Johan Malmström (johan [dot] malmstrom [at] med [dot] lu [dot] se)

Target group

PhD students at the Faculty of Medicine

Scope

The course equals one week (1.5 ECTS credits). Five days are scheduled as well as some some self-studies.

Place

This course will be given physically and partly digitally.

Time

Autumn 2024. Week 49 (Dec2-Dec6). Self-studies the week before the start of the course are included (about 4 hours), consisting of reading course literature and part one of the assignment. Compulsory attendance all days from 8-16.

Number of participants

Max 12

Language

English

General information

This is an electable course for PhD students who are interested in learning more about biological mass spectrometry and clinical proteomics. Mass spectrometry (MS) is a technique to measure the molecular weight (m/z) of biomolecules such as proteins or peptides. Proteomics describes the large-scale analysis of proteins in a biological sample and MS based proteomics is used extensively in the life science area with numerous applications spanning from basic research questions to precision medicine e.g. in the hospital where MS is used for bacterial phenotyping in acute sepsis to select the effective drug treatment and thereby save lives. Clinical proteomics focus on clinical samples such as tissues, cells and various biological fluids, that need special considerations to be successful. There are local infrastructures for biological mass spectrometry available at the Medical Faculty: translational proteomics (CTP), structural proteomics (SciLifelab) as well as the national resource for biological MS (BioMS).

The aim of the course is to provide the students with an introduction to current methodologies in the field of MS-based proteomics. The students should obtain an overview of typical proteomics applications and be introduced to proteomics experimental workflows to enable the technology to be included in their own research project.

Learning objectives: On completion of the course, the student will be able to:

• explain the basic principles of mass spectrometry and proteomics
• understand  how biological MS can be used in a wider perspective as for example to obtain critical sequence information from unknown proteins, and how MS can be used to investigate protein structure and protein-protein interactions
• perform  basic MS data analysis including identification and quantification of proteins
• describe and suggest analytical approaches to biological questions – discuss advantages and limitations
• participate in scientific discussions regarding proteomics technologies and critically evaluate scientific results
• plan how to incorporate mass spectrometry/proteomics into your own PhD work and enable this versatile high-performance technology to potentially improve your individual research project

Content and design

The course will cover basic principles of mass spectrometry, separation of proteins and peptides, sample preparation techniques, data acquisition methods, data analysis, analysis of post-translational modifications (PTMs) and bioinformatics analysis. There will also be invited lectures (senior researchers within biological MS and clinical proteomics) that will include specific applications in various research areas. 

Learning activities include lectures, group exercises, instrument demos (to generate MS data) and a round tour at BMC D13. Participants are expected to have access to a laptop. 

Furthermore, the course includes one compulsory assignment, in which the doctoral student is to reflect on a research situation (preferably from their own PhD-project) where biological mass spectrometry might be used, present it for the course participants and finally propose in writing how this technology can be used to enrich the previously described research situation.

Assessment

To pass the course, approved individual assignment is needed in addition to active participation in all course events. 

Grades

The grades awarded are Pass and Fail.

Admission requirements

Applicants admitted to research studies at the Faculty of Medicine in Lund are prioritized.

Literature

Selected research articles and other study materials will be made available before and during the course.

Course leader

Fredrik Ek (Fredrik [dot] Ek [at] med [dot] lu [dot] se (fredrik[dot]ek[at]med[dot]lu[dot]se))

Ana Carneiro (Ana [dot] Carneiro [at] med [dot] lu [dot] se (ana[dot]carneiro[at]med[dot]lu[dot]se))

Examiner

Marcus Järås (Marcus [dot] Jaras [at] med [dot] lu [dot] se (marcus[dot]jaras[at]med[dot]lu[dot]se)) 

Target group

Ph.D. students at a Faculty of Medicine

Language

English

Scope

The course equals two week (3 hp/credits). 10 days are scheduled, as well as self-studies.

Place

See schedule

Number of participants

15

Course content

The course covers the general process for development of a new drug from preclinical discovery via pre-clinical development and clinical trials. The course will address scientific, strategic and regulatory challenges from discovery to approval of a new drug and also includes key methods and terminology. It also covers the importance of the professional groups that are involved in the different phases of the development of a new drug. This course will prepare you for future work in the pharmaceutical industry as well as work in academia regarding innovations, early drug development and entrepreneurship.

Learning outcomes

• give an account of drug development from discovery to registration of a new
• drug and describe commonly occurring methods associated with drug development.
• give an account of important breakpoints in the drug development process
• give an account of different types of patents that occur in drug development
• describe the laws and rules that control the development of a new drug in the pre-clinical and clinical phase
• describe the different phases of the clinical development
• give an account of different possibilities for funding drug development in the preclinical and clinical phase
• participate in teamwork to provide theoretical and practical solutions for assignments related to drug development and clinical trials
• behave with a professional approach, respect others' opinions in discussions of drug development and clinical trials and meet set deadlines
• reflect on ethical considerations during the drug development process
• reflect on different strategies to achieve scientific, market-related and regulatory goals during drug development

 Course design

The fundament in the course is seminars by experts mainly from the Life science industry, but also from Region Skåne and Lund University. In addition to seminars, the students will carry out team-based activities. The working methods in the course mostly involve active learning, requiring the students to prepare before each teaching component. The students are expected to behave professionally and, just as in a future work situation, participate constructively in team-based activities.

Assessment

Multiple-choice questions to test learning outcomes for knowledge and understanding. If there are special reasons, other forms of assessment may apply. The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Grades

Marking scale: Fail or Pass.

To achieve the grade of Pass as a final grade, the grade of Pass is required on the multiple-choice question test.

Entry requirements

PhD students at a Medical faculty.

Literature

Relevant articles will be distributed to the students during the course. The students will also search for relevant information themselves.

Course leader 
Karin Engström (karin [dot] engstrom [at] med [dot] lu [dot] se)

Examiner
Helena Persson

Target group: 
Ph.D. students at the Faculty of Medicine

Scope 
The course equals one week (1.5 ECTS credits). Five days are scheduled, as well as self-studies.

Place
BMC E11073 Rådslaget

Time
Autumn 2024 - Week 38 (September 16-20)
Monday-Thursday 9-16. For the last day, attendance on-site is not compulsory.

Number of participants 
24

Language
English

Objectives
The purpose of the course is to provide basic knowledge of the programming language R to facilitate independent future use of applications written and / or implemented in this language, such as statistical analysis programs. 

Learning outcomes
On completion of the course, the student shall be able to:
• perform basic data operations using R
• identify potential pitfalls when handling data with R
• create basic and visually appealing diagrams using R
• identify online resources to independently answer questions and troubleshoot when programming in R

Content 
This course introduces students to the basic terms and concepts used within programming. The focus is on the handling of data with R, e.g., importing it into RStudio/Posit, summarising, cross-referencing, merging, creating new data. Different forms of relevant and graphically appealing visualisations will also be covered, as well as exporting the data and diagrams created in different formats. Packages in the Tidyverse software collection are used.

Design
A pre-course assignment involves installing RStudio/Posit and studying basic programming concepts and terminology with the help of provided course literature. Access to a laptop computer is required. The course consists of four compulsory full days. Teaching methods include lectures, programming demonstrations, and individual exercises. On the final day of the course, students work independently on the examination, with teachers available online. On-site participation is not compulsory during the final day of the course. Students who are unable to participate in the compulsory classes have the opportunity to work on the specified exercises on their own and to contact the teachers according to their scheduled availability.

Assessment
The examination consists of solving assignments using programming in R. The programming scripts that are used to answer the questions are then sent to the teachers for evaluation.

Grades
The grades awarded are Pass and Fail.

Admission requirements
Applicants who are admitted to postgraduate studies at Lund University are given priority. Other applicants affiliated with the Faculty of Medicine can be accepted if there are vacancies.

Required reading 
Literature on programming concepts distributed before the start of the course. 

3 credits (half-time)

Dates: November 4–29, 2024

Course content and aim: The course provides the participants with in-depth knowledge of different methods in regression analysis and how these methods can be applied in medical research. 

The course consists of the following five themes:

• Introduction to theory and methods of regression analysis
• Linear regression for continuous outcomes: analysis, diagnostics, and robust methods. Analysis of variance (ANOVA).
• Logistic regression for binary outcomes: analysis, interpretation, and diagnostics. Prediction of outcome probabilities and transformation of parameter estimates into risk ratios and risk differences.
• Ordinal and multinomial logistic regression for categorical outcomes: analysis, interpretation, and diagnostics. Prediction av outcome probabilities. 
• Poisson regression and other methods for count data: analysis, interpretation, and diagnostics. Prediction of outcome probabilities.

Schedule:

Monday November 4: Individual preparations, such as learning basic Stata commands. (Note: Participants can choose between the statistical programs Stata and R.) An introductory material to Stata for beginners will be sent out.

Tuesday November 5: Lectures and exercises 9–16

Thursday November 7: Lectures and exercises 9–16

Tuesday November 12: Lectures and exercises 9–16

Thursday November 14: Lectures and exercises 9–16

Monday November 18: Lectures and exercises 9–16

Tuesday November 19–Thursday November 21: Group projects (expected time to spend: 2 days)

Friday November 22: Project presentations 9–12

Monday November 25 – Friday November 29: Take-home exam (expected time to spend: 1–2 days)

Course teachers:

Anton Nilsson, associate professor, PhD, Department of Laboratory Medicine, Lund University (anton [dot] nilsson [at] med [dot] lu [dot] se (anton[dot]nilsson[at]med[dot]lu[dot]se))

Pär-Ola Bendahl, associate professor, PhD, Department of Clinical Sciences Lund, Lund University (par-ola [dot] bendahl [at] med [dot] lu [dot] se (par-ola[dot]bendahl[at]med[dot]lu[dot]se)) 

Sara Ekberg, PhD, Red Door Analytics (sara [at] reddooranalytics [dot] se) 

Course examiner:

Jonas Björk, professor, PhD, Department of Laboratory Medicine, Lund University (Jonas [dot] Bjork [at] med [dot] lu [dot] se (jonas[dot]bjork[at]med[dot]lu[dot]se))

Language: English

Target group: PhD students in medicine. Participants should have passed Applied Statistics I and II, or equivalent.

Number of participants: 20

Location: Lund 

Literature: 

• Vach, W. Regression models as a tool in medical research. CRC Press, 1st ed., 2013. (Available as an e-book at the Lund University Library.)
• Additional readings that will be made available to the participants.

Points: 1.5 hp (fulltime)

Dates: 13^th May – 17^th May 2024

General information

The course provides a background to the issue of missing data and to the consequences of simple ad hoc methods to address the issue. The advantages and shortcomings of different methods will be discussed. The method in focus on the course is multiplied imputation (MI), which participants will have the opportunity to test in the laboratory components.

Objective: The aim of the course is to make participants aware of the consequences of incorrect handling of missing data in medical research in general and to provide them with tools for correct handling of missing data in their own research.

The course content covers the following themes:

• Introduction to missing data
  • Identifying missing data
  • Potential consequences of missing data
  • Mechanisms for the generation of missing data
  • Brief overview of methods for handling missing data
• Multiple imputation
  • Brief theoretical background to MI
  • The chained equations method
  • Constructing an imputation model
  • Analysing imputed data
  • Diagnosis of the MI model (model validation)
• Reporting MI results and the limitations of the method
  • Guidelines for reporting analyses of MI-generated data
  • Limitations of the MI method

Schedule:

Monday, Tuesday (morning), Wednesday and Friday (morning) – in-class activities, Tuesday (afternoon) and Thursday – own work, Friday (afternoon) - examination.

Teachers:

Aleksandra Turkiewicz, docent, CStat, Enheter för klinisk epidemiologi, Kliniska Vetenskaper, Lund

Pär-Ola Bendahl, docent, fil. dr., Institutionen för kliniska vetenskaper Lund, Lunds universitet (par-ola [dot] bendahl [at] med [dot] lu [dot] se (par-ola[dot]bendahl[at]med[dot]lu[dot]se))

Examiner:

Jonas Björk, professor, fil. dr., Institutionen för laboratoriemedicin, Lunds universitet (jonas [dot] bjork [at] med [dot] lu [dot] se (jonas[dot]bjork[at]med[dot]lu[dot]se))

Language: The course is given in English

Target group and requirements: To be admitted to the course, applicants must have prior knowledge equivalent to Applied Statistics I and II. In particular, they are to be familiar with the theory of linear and logistic regression models and be able to adapt these models and interpret corresponding output from their statistics software. The choice of software is optional, but the participants are required to be familiar with the program they choose and that it has the method multiple imputation via chained equations implemented (e.g. R, Stata or SPSS). The participants are responsible for ensuring that they have a suitable and working statistics program installed on their computer before the start of the course.

Number of participants: 20

Location: Lund

• Activity balance during health, ill health and sickness
• Collecting and using biobank samples in research
• Applied Epidemiology and statistics III: Causal inference with non-randomized data 
• Approaches to handling of missing data (samarbete med GU - online course)
• Basic Data Handling and Visualization with R
• Clinical proteomics and biological mass spectrometry
• Complex interventions in health care with a special focus on the care of adults and older persons
• Diabetes research
• Drug development and clinical trials 
• Epidemiology I - Introduction to Epidemiology
• Flow cytometry, introductory course
• Flow cytometry, continuation course
• Glycobiology
• Health and Environment with special focus on climate change and sustainability
• Introduction to programming
• MAX IV/ESS-based imaging for medical and biomedical research, experimental setup
• Medical Bioinformatics, Introduction
• Neutron scattering for medical and biomedical research, experimental part.
• Perspectives on gender and intersectionality in medical and health research
• Preclinical imaging
• Applied Epidemiology and Statistics III – Causal inference with non-randomised data
• Applied Qualitative Methodology II 
• Applied Statistics III – Statistical methods for repeated measurements
• Applied Statistics III – Time Series Analysis in Clinical and Environmental Epidemiology
• Applied Statistics III – Survival Analysis
• X-ray micro- and nanoimaging for medical and biomedical research, experimental part