e lundin phd

Johan Lundin, MD, PhD, is Research Director at FIMM and Professor of Medical Technology at Karolinska Institutet (KI), Stockholm. Johan Lundin is also Associate Professor in Biomedical Informatics, University of Helsinki. His overall research aims are to study the use of digital technologies and artificial intelligence (AI) for improvement of diagnostics and care of the individual patient. He has together with his research group at FIMM and researchers at KI developed technologies for diagnostic decision support, for example cloud-based and mobile solutions that allow the diagnostic process to be performed remotely, by a human observer or using AI. The methods can aid in diagnostics at the point-of-care, decrease the workload of local experts and enable task-shifting.

As a Research Director at FIMM Dr. Lundin facilitates research related to digital medicine grand challenges at the Meilahti Campus (e.g. HiLIFE, iCAN), on a national level and through international collaborations with both academia and industry. Lundin also is involved in development and maintenance of research infrastructures on a national (e.g. Biocenter Finland) and international international level (e.g. EATRIS). These infrastructure networks operate research services for scientists, clinicians and industry.

Nina Linder is a physician by training and received her MD and PhD from the University of Helsinki, Finland. Dr. Linder initially focused on molecular medicine and cancer biomarker research. She previously held a position as Medical Advisor within the pharmaceutical industry with involvement in early phase clinical trials for novel targeted molecular cancer therapeutics.

Nina Linder’s current research involves the development of novel artificial intelligence-based solutions for cancer and infectious disease diagnostics. Linder is also co-heading several projects developing artificial intelligence-based tools for point-of-care diagnostics in a global setting. The overall goal of Linder’s research is to promote the implementation of innovative decision-support solutions for precision medicine to improve the translation from basic medical research to the doctor and patient at the clinic.

Dmitrii Bychkov studied computer science in St.Petersburg, Russia and then did his masters studies in bioinformatic at Aalto University. His work focuses on image-based cancer diagnostics with machine learning. He has been developing and adapting computer vision approaches to mine information form basic morphology of microscopic cancer tissue specimens. Currently Dmitrii approaches final stages of his doctoral studies.

Oscar Holmström is a medical doctor who is currently completing his doctoral studies at FIMM. His work focuses on studying how digital microscopy diagnostics, supported by AI-based analysis of samples, can be moved from laboratory settings to the point of care. He has been involved in multiple field studies in e.g. Tanzania and Kenya to study how these methods can be utilized in areas with limited access to laboratories and medical experts to facilitate diagnostics of e.g. malaria, tropical parasites and cervical cancer.

Email: [email protected]

Sebastian Stenman is a medical doctor who is working on his doctoral thesis on thyroid cancer digital pathology. Currently, his work studies how novel digital methods, such as artificial-intelligence based algorithms, can be utilized to facilitate the sample analysis. These novel methods can provide effective tools for improving diagnostics by e.g. automatic detection of tumor-infiltrating lymphocytes and the percentage of tall cells in papillary thyroid cancer.

Email : [email protected]

Klaus Kessel is a medical student and from time to time works on research projects at FIMM. His work thus far has been about the application of machine learning in ophthalmic pathology.

Email: [email protected]

Hakan Kücükel is experienced in software development and programming, especially related to image management and processing pipelines. His knowledge on handling gigapixel-sized digital samples in the digital diagnostic projects is essential for the research unit at FIMM. Furthermore, he has advanced skills in development of backend solutions such as database servers and cloud computing environments and client-side user interfaces.

Email : [email protected]

Antti Suutala is a bioinformatician at FIMM. He received the M.Sc.Eng. (2007) in electronic engineering from University of Oulu, Finland. He is interested in solving problems related to mobile microscopy and its applications especially in resource poor settings. The main research topics cover the designing, developing and testing of mobile microscope along with the novel sample preparation techniques.

Email: [email protected]

Stanford University

The Program on Energy and Sustainable Development is part of the Freeman Spogli Institute for International Studies .

Erik Lundin

Erik Lundin, PhD

• Faculty Affiliate at PESD

Research Faculty Research Institute of Industrial Economics Stockholm, Sweden

• DOI: 10.1186/s12912-021-00566-7
• Corpus ID: 232289156

End-of-life care for people with advanced dementia and pain: a qualitative study in Swedish nursing homes

• E. Lundin , T. Godskesen
• Published in BMC Nursing 21 January 2020

15 Citations

Are nurse`s needs assessment methods robust enough to recognise palliative care needs in people with dementia a scoping review, registered nurses' knowledge, attitudes and practices of pain management for aged care residents with dementia: an integrative review., family involvement in pain management for people living with dementia: an integrative review.

• Highly Influenced

Cultural Influence on Nurses' Pain Observations Related to Dementia: An Integrative Review.

Registered nurses’ experiences of end-of-life care in nursing homes of south korea: a qualitative study, association between pain with disability in the elderly with dementia, factors related to person-centered care for older patients with cancer and dementia in designated cancer hospitals., challenges in recognizing and discussing changes in a resident’s condition in the palliative phase: focus group discussions with nursing staff working in nursing homes about their experiences, outpatient opioid prescribing by alzheimer’s diagnosis among older adults with pain in united states, caring to the end: an empirical application of swanson's caring theory to end-of-life care., 59 references, nurses' experiences of pain management for people with advanced dementia approaching the end of life: a qualitative study, quality of end-of-life care in patients with dementia compared to patients with cancer: a population-based register study, nursing home residents dying with dementia in flanders, belgium: a nationwide postmortem study on clinical characteristics and quality of dying., pain relief at the end of life: nurses' experiences regarding end-of-life pain relief in patients with dementia., decision factors nurses use to assess pain in nursing home residents with dementia., death with dementia--the need for better care., caring for dying patients in the nursing home: voices from frontline nursing home staff., understanding nurses' decisions to treat pain in nursing home residents with dementia., symptoms and treatment when death is expected in dementia patients in long-term care facilities, pain treatments for nursing home residents with advanced dementia and substantial impaired communication: a cross-sectional analysis at baseline of a cluster randomized controlled trial, related papers.

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Gene Transfer to Mouse Heart and Skeletal Muscles Using a Minicircle Expressing Human Vascular Endothelial Growth Factor

2009, Journal of Cardiovascular Pharmacology

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• Bull World Health Organ
• v.98(4); 2020 Apr 1

Artificial intelligence, diagnostic imaging and neglected tropical diseases: ethical implications

Alon vaisman.

a Division Infectious Diseases, Toronto General Hospital, University of Toronto, 14EN 209, 200 Elizabeth Street, Toronto, Ontario M5G 2C4, Canada.

Nina Linder

b Department of Women's and Children's Health, International Maternal and Child health, Uppsala University, Sweden

Johan Lundin

c Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden

Ani Orchanian-Cheff

d Health Sciences Library, University Health Network, Toronto, Canada.

Jean T Coulibaly

e Unité de Formation et de Recherche Biosciences, Université Félix Houphouët-Boigny, Abidjan, Côte d'Ivoire.

Richard KD Ephraim

f Department of Medical Laboratory Sciences, University of Cape Coast, Cape Coast, Ghana.

Isaac I Bogoch

Artificial intelligence, defined as a system capable of interpreting and learning from data to produce a specific goal, 1 has made significant advances in the field of neglected tropical diseases. Specifically, artificial intelligence is increasingly applied to the task of interpreting images of such diseases and generating accurate and reliable diagnoses that may ultimately inform management of these conditions. Neglected tropical diseases affect over a billion people globally and are a significant source of morbidity and mortality in low- and middle-income countries. 2 Artificial intelligence has the potential to transform how such diseases are diagnosed and may contribute to enabling clinical and public health delivery in low- and middle-income countries. For example, artificial intelligence applied to neglected tropical disease diagnosis may help drive point-of-care clinical decision-making, identify outbreaks before they spread and help map these diseases to guide public health surveillance and control efforts. The latest research in this field demonstrates that novel diagnostic tools, such as mobile phone microscopes have rapidly improved diagnostic characteristics and broadened the scope of pathogens tested, and have excellent functionality in neglected tropical disease-endemic settings. 3 , 4 Such devices are already being field tested and implemented on a limited scale, for example in Côte d’Ivoire. 5

However, careful consideration to several ethical concerns arising from artificial intelligence-driven diagnoses of neglected tropical diseases in low-resource settings is critical for maximizing the benefit of this technology. 6 Artificial intelligence applications focused on image-based diagnoses is still in its infancy and therefore, now is an opportune time to ensure that these applications develop within an ethical framework. Here, we outline important ethical challenges faced by low- and middle-income countries that may benefit from the implementation of these technologies. Key issues discussed include the interrelationships between stakeholder engagement, consent, data security, accessibility of technology, adhering to current and evolving care standards and deciding how to effectively use resources. Addressing these issues during the design phase of artificial intelligence technology will facilitate its timely implementation and maximize public health benefit.

Most published studies focusing on the development of artificial intelligence tools for image-based diagnoses are conducted in laboratories based in high-income countries. Consequently, the limited engagement of scientists and clinicians from endemic regions may restrict the utility, and eventually scale, of these technologies in precisely the countries that would benefit the most. Therefore, several stakeholders should be involved from the earliest phases in the development of artificial intelligence tools. 7 These stakeholders include data scientists and engineers from both low- and middle-income countries affected by neglected tropical diseases and those from high-income countries currently working in artificial intelligence diagnostics. Pairing teams of data scientists and engineers would enable capacity building in low- and middle-income countries where there is currently limited infrastructure to develop such diagnostic tools. Other important stakeholders include clinical, public health, governmental and citizen representation from low- and middle-income countries affected by these diseases. Such groups are critical in the identification of priority areas, shaping research questions and implementing the technology into routine health-care use. Private industry and governmental bodies should also be instrumental in the scale-up, licensing and regulation of new diagnostic tools, and their involvement and support during concept development may help streamline product development. 8

Addressing ethical issues surrounding informed consent, an issue closely intertwined with data security, is vital in the development of artificial intelligence image-based diagnostic tools for neglected tropical diseases. Diagnostic tests inherently involve some form of biologic sample collection from a patient and this procedure is frequently connected to patient-identifying information. Although these diagnoses may be performed at the point of care, 9 the collected specimens and images may be subsequently used to train and improve machine-learning algorithms. Individuals providing samples must consent to their biologic sample, and perhaps other personal data. Similarly, individuals must be notified of which personal information is being used and stored, where it is being stored, who has access to this data, how it is being accessed and how this personal information is being used or may be used in the future. 1 , 6 Given that much of this diagnosis technology has been developed in high-income countries for use in low- and middle-income countries, special attention is required. Therefore, the informed consent process in low- and middle-income countries must adhere to the highest standards that are respectful and inclusive of culture, language, religion, gender, age and socioeconomic status. 10

Ethical concerns may also arise due to the accessibility of artificial intelligence technology for neglected tropical disease diagnoses. Since this technology can be transformative to communities burdened by such diseases, its accessibility to all affected populations, including to those in underserviced and remote communities, must be insured. Early and broad stakeholder engagement in project development can ensure that artificial intelligence diagnostics tools tailored for low- and middle-income countries will be accessible and barriers, such as affordability and scale will be considered in countries burdened by these diseases. Examples of mitigating access issues include facilitating the development and use of open-source software in the early stages of product development to help lower implementation costs and share information. 11

We must also examine how these technologies affect the standards of care as both the technology developed and local care standards will continue to evolve. While artificial intelligence-based diagnoses of neglected tropical diseases may evolve into preferred methods for diagnosis, further advances in this field and others will inevitably take place, and appropriate regulation and oversight will be essential to ensure that diagnostic tools continue to adhere to local standards of care. 12 Hence, early and continuous involvement of government, industry and health-care teams is essential for the continuous maintenance of diagnostic quality. Furthermore, as these technologies continue to evolve, low- and middle-income countries must not be left behind as image-based artificial intelligence diagnostic tools improve and expand in the health-care sector.

Lastly, there may be unintended consequences for implementing these diagnostic tools in low- and middle-income countries. While most intentions seem to be laudable, such as facilitating care in low-resource settings, many of these countries have significant limitations in health-care infrastructure and challenges in the provision of health care to large segments of their population. The implementation of artificial intelligence diagnostics could unintentionally draw vital resources from other programmes. Ensuring broad engagement from the outset may help mitigate these issues by identifying priority areas specific to particular countries. Stakeholders can conduct current-state assessments for future innovations to determine the impact, both positive and negative, of artificial intelligence diagnostic implementation, and decide how and if such innovations can be used locally and the appropriate timing of implementation.

Image-based artificial intelligence for the diagnoses of neglected tropical diseases has the potential to transform health care in low- and middle-income countries affected by these diseases. While this field is still in its early stages, there is potential to bring quality diagnostic tools to clinical and public health settings in the most underserved regions. As this field evolves, integrating an ethical framework for the development of these tools will enable their sustainability and utility. Broad stakeholder engagement, a focus on consent and data security, and balancing the use of limited public health resources are important principles that can be introduced early in the development of this technology. Doing so will ensure the most impactful use of artificial intelligence diagnoses for neglected tropical diseases and its ultimate long-term success and sustainability.

Acknowledgements

NL is also affiliated with the Institute for Molecular Medicine Finland, University of Helsinki, Helsinki, Finland.

Competing interests:

None declared.

Prof. Johan Lundin

Professor of Medical Technology at Karolinska Institutet, Stockholm and a Research Director at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki

Johan Lundin, MD, PhD is a Professor of Medical Technology at Karolinska Institutet, Stockholm and a Research Director at the Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Finland. His overall research aims are to study the use of digital technologies and artificial intelligence for improvement of diagnostics and care of the individual patient. In addition to the research, Dr. Lundin has together with his research group at FIMM and researchers at Karolinska Institutet developed technologies for diagnostic decision support and AI-based solutions that improve access to diagnostics at the point-of-care, both in high and low resource settings.

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View Scopus Profile

Johan Lundin

• Research Director , Institute for Molecular Medicine Finland
• Title of Docent , Clinicum
• Supervisor for doctoral programme , Doctoral Programme in Biomedicine
• Supervisor for doctoral programme , Doctoral Programme in Clinical Research
• Phone +358504155459, +358294125783

PL 20 (Tukholmankatu 8)

Research activity per year

Personal profile

External positions.

Professor, Karolinska Institutet

9 Jan 2019 → …

Guest Professor, Karolinska Institutet

1 Nov 2012 → 31 Oct 2017

Fields of Science

• 3111 Biomedicine
• 3122 Cancers
• 318 Medical biotechnology
• 3142 Public health care science, environmental and occupational health
• 113 Computer and information sciences
• 217 Medical engineering

International and National Collaboration

Dive into details.

Select a country/territory to view shared publications and projects

Publications

• 144 Article
• 12 Conference article
• 5 Meeting Abstract
• 3 Conference contribution
• 1 Editorial
• 1 Review Article
• 1 Other contribution

Research output per year

Diagnosis of soil-transmitted helminth infections with digital mobile microscopy and artificial intelligence in a resource-limited setting

Research output : Contribution to journal › Article › Scientific › peer-review

External validation of a deep learning-based algorithm for detection of tall cells in papillary thyroid carcinoma: A multicenter study

A novel deep learning-based point-of-care diagnostic method for detecting plasmodium falciparum with fluorescence digital microscopy.

Research output : Chapter in Book/Report/Conference proceeding › Chapter › Scientific › peer-review

Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery

A deep learning–based algorithm for tall cell detection in papillary thyroid carcinoma.

Projects per year

LOH 2024-2026, Lundin

Medicinska understödsföreningen Liv och Hälsa RF

01/06/2024 → 31/05/2026

Project : Foundations (Private Foundations, Non-Profit Foundations, Charitable Trusts)

FLS, Lundin 2023

Lundin, J. , Bychkov, D. , Kücükel, H. & Suutala, A.

Finska Läkaresällskapet rf Handlingar

01/01/2023 → 31/08/2025

Sigrid Juselius foundation grant 2016

01/04/2016 → 31/03/2017

Project : Research project

Targovax research collaboration

01/02/2016 → 30/06/2018

Tekes Mobile Micrcoscopy

01/10/2015 → 30/04/2017

Best Presentation at a Scientific Session: Diagnostic Image Analysis

Lundin, Johan (Recipient), 20 Sept 2010

Prize : Prizes and awards

Top influencer within healthcare in Finland in the category of reformers

Lundin, Johan (Recipient), 2021

• 174 Organisation and participation in conferences, workshops, courses, seminars
• 48 Invited talk
• 22 Oral presentation
• 15 Types for other activities - Other
• 15 Academic visit to other institution
• 6 Public Talks
• 4 Supervisor or co-supervisor of doctoral thesis
• 3 Member of doctoral thesis committee / follow-up group
• 3 Pre-examiner of doctoral thesis
• 3 Membership or other role in review committee
• 2 Peer review of manuscripts
• 2 Opponent of doctoral dissertation
• 2 Academic visit at UH
• 2 Membership or other role in national/international committee, council, board
• 1 Editor of research journal
• 1 Editor of special theme number
• 1 Supervision of other thesis (Master's, Licentiate)

Activities per year

Digital microscopy combined with artificial intelligence for point-of-care diagnostics in resource-limited settings

Johan Lundin (Invited speaker)

Activity : Talk or presentation types › Invited talk

Artificial Intelligence and Mobile Microscopy for Cancer and Infectious Disease Diagnostics- The MoMic Project

Johan Lundin (Speaker)

Activity : Talk or presentation types › Oral presentation

Artificial intelligence for image-based diagnostics

Artificial intelligence for cancer diagnostics from a global health perspective, press/media, at the crossroads of research and business.

1 Media contribution

Press/Media : Press / Media

Developing widely available diagnostics

Finland/sweden: research team uses ai for remote smear diagnostics.

1 item of Media coverage

AI delivers cervical cancer screening to rural areas of Kenya

Ai supported diagnostics can facilitate screening for cervical cancer in resource-poor settings.

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Ph.D. Student Visiting Friend During School Break Is Accused of Murdering Baby, Abusing Slain Child's Twin

Nicole Virzi is charged with criminal homicide

Allegheny County Jail; Isabella Kahhale and Sam Plate/GoFundMe

A Ph.D. student is accused of murdering an infant boy and abusing his twin brother while babysitting for a family friend in Pittsburgh.

Nicole Virzi, 29, is pursuing a doctorate in San Diego. She was staying in Pittsburgh at the time of the baby’s death, according to reports from The San Diego Union-Tribune , WTAE and WPXI .

Virzi was arrested on June 16 and has been charged with criminal homicide, three counts of aggravated assault and one count of endangering the welfare of a child, according to court records. She has been denied bail and is due for a preliminary hearing on June 28.

Citing the criminal complaint, WTAE reported that Virzi was babysitting newborn Leon Katz while his parents brought his twin brother to the hospital after she had informed them about injuries to the baby’s genital area.

The Union-Tribune, citing the same complaint, reported that before the baby was taken to the hospital, Virzi had allegedly been alone with the child.

After Leon was left in Virzi’s care so his parents could take their other son to the hospital, Leon suffered an ultimately fatal skull fracture, according to the reported complaint.

WTAE reported that a doctor who examined the twins told police that the injuries they suffered were consistent with child abuse and “not accidental,” according to the complaint.

Want to keep up with the latest crime coverage? Sign up for  PEOPLE's free True Crime newsletter  for breaking crime news, ongoing trial coverage and details of intriguing unsolved cases. 

Virzi’s defense attorney David Schrager told the Union-Tribune that his client did not commit a crime and said the baby fell out of a bouncing chair while she was out of the room.

“We are at the beginning of our investigation, but it’s important to note that my client has no criminal history whatsoever,” Shrager said, according to the paper. “She is a Ph.D. student and was a longtime friend of the family.” 

According to her bio page on the school’s website, Virzi is pursuing a Ph.D. in clinical psychology at San Diego State University and UC San Diego.

A GoFundMe started to benefit Leon’s family referred to Virzi as a “trusted family friend.” So far it has raised over $60,000.

If you suspect child abuse, call the Childhelp National Child Abuse Hotline at 1-800-4-A-Child or 1-800-422-4453, or go to www.childhelp.org. All calls are toll-free and confidential. The hotline is available 24/7 in more than 170 languages.

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Robert Folke Lundin, Ph.D. Publications

Robert Folke Lundin, Ph.D.

Lundin first published as a graduate student. Upon graduation he also published with his own lab resulting in many additional publications.

Lundin and his collaborators published the following publications.

If you are aware of any additional publications or reports/projects please add them in our Collection Forum or contact us to let us know.

Publications

Publications are listed in order they appeared and subdivided by decades. (Quick Links: 1960s , 1970s , 1980s , 1990s , 2000s )

Lundin, Robert Folke, 1961. Morphology and Ontogeny of Phanassymetria Roth from the Haragan Formation (Devonian) of Oklahoma: Urbana-Champaign, Illinois, United States, University of Illinois, M.S. (Masters) – Geology, 53 p. [SNOMNH_Pbot-00000000]

Lundin, Robert Folke, 1962. Ostracodes From the Henryhouse Formation (Silurian) of Oklahoma: Urbana-Champaign, Illinois, United States, University of Illinois, Ph. D. – Geology, 99 p. [SNOMNH_Pbot-00000000]

Lundin, Robert Folke, and Scott, Harold W., 1963. Morphology and Ontogeny of Phanassymetria Roth: Journal of Paleontology v. 37 no. 6, p. 1272–1283. [SNOMNH_Pbot-00000000]

Lundin, Robert Folke, 1965. Ostracodes of the Henryhouse Formation (Silurian) in Oklahoma: Oklahoma Geological Survey Bulletin, v. 108, p. 1-104. [SNOMNH_Pbot-00000017]

Lundin, Robert Folke, 1965. A New Name for Leperdita symmetrica Lundin, 1965 (Not Leperdita symmetrica Holtedahl, 1914): Journal of Paleontology, v. 39, no. 6, p. 1221. [SNOMNH_Pbot-00000464]

Lundin, Robert Folke, 1968. Ostracodes of the Haragan Formation (Devonian) in Oklahoma: Oklahoma Geological Survey Bulletin, v. 116, p. 1-121. [SNOMNH_Pbot-00000185]

Lundin, Robert Folke, and Newton, George D., 1970. Ostracoda and the Silurian Stratigraphy of Northwestern Alabama: Geological Survey of Alabama Bulletin v. 95. p. x-x. [SNOMNH_Pbot-00000000]

Lindsay, Everett H., and Lundin, Robert Folke, 1972. An Oligocene Oreodont (Mammalia: Artiodactyla) from Central Arizona: Journal of Paleontology, v. 46, no. 1, p. 115-119. [SNOMNH_Pbot-00000000]

Petersen, Lee E., and Lundin, Robert Folke, 1974. Thlipsura martinssoni : A new Ostracode Species from the Silurian of England: Journal of Paleontology, v. 48, no. 2, p. 357-359. [SNOMNH_Pbot-00000000]

Swain, Frederick M., Kornicker, Louis S., and Lundin, Robert Folke, eds., 1975. Biology and Paleobiology of Ostracoda: A Symposium at University of Delaware in 1972: Bulletin of American Paleontology v. 65 no. 282, p. x-x. [SNOMNH_Pbot-00000000]

Lundin, Robert Folke, Williams, Mark, and Siveter, David J., 1995. Domatial Dimorphism Occurs in Leperditellid and Monotiopleurid Ostracodes: Journal of Paleontology, v. 69, no. 5, p. 886-896. [SNOMNH_Pbot-00000000]

Birkmann, Harry, and Lundin, Robert Folke, 1996. Confocal Microscopy: Potential Applications in Micropaleontology: Journal of Paleontology, v. 70, no. 6, p. 1084-1087. [SNOMNH_Pbot-00000000]

Friedman, Gerald M., and Lundin, Robert Folke, 1998. Freshwater Ostracodes from Upper Middle Devonian Fluvial Facies, Catskill Mountains, New York: Journal of Paleontology, v. 72, no. 3, p. 485-490. [SNOMNH_Pbot-00000000]

Lundin, Robert Folke, and Sumrall, Colin D., 1999. Ostracodes from the Naco Formation (Upper Carboniferous) at the Kohl Ranch Locality, Central Arizona: Journal of Paleontology, v. 73, no. 2, p. 454-460. [SNOMNH_Pbot-00000000]

Friedman, Gerald M., and Lundin, Robert Folke, 2001. Ostracodes as Indicators of Brackish Water Environments in the Catskill Magnafacies (Devonian) of New York State: Discussion: Palaeogeography, Palaeoclimatology, Palaeoecology, v. 171, no. 2001, p. 73-79. [SNOMNH_Pbot-00000000]

Shang-Qi, Wang, and Lundin, Robert Folke, 2004. Sinoleperditiini (Ostracoda) from the Lower Emisian Shanglun Formation at the Shanglun Village, Central Guangxi, China: Journal of Paleontology, v. 78, no. 2, p. 349-358. [SNOMNH_Pbot-00000000]

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Blood Engineering – Vanessa Lundin group

Our work aims to uncover biophysical regulators of human hematopoiesis and lies at the interface of stem cell biology, biomaterial science and tissue engineering.

Publications

Staff and contact, about our research group.

The group is part of the Center for Hematology and Regenerative Medicine (HERM) .

Mission of the research group

Hematopoietic stem and progenitor cells (HSPCs) are the key players in hematopoiesis and provide life-long production of blood in the bone marrow. Clonal blood disorders, such as myelodysplastic neoplasms (MDS) and acute myeloid leukemia (AML), arise in HSPCs and are characterized by disrupted hematopoiesis and cytopenia. Our research aims at understanding human hematopoietic development and disease using induced pluripotent stem cells (iPSCs) and lies at the interface of stem cell biology, biomaterial science and genome engineering. iPSCs can be derived from individuals with certain gene variants, expanded indefinitely, and specified into HSPCs and various hematopoietic lineages, such as erythrocytes, megakaryocytes and NK cells. Furthermore, these cells allow for genome editing, functional readouts, and validation of drug candidates in vitro . 

Understanding the genetic predisposition and pathogenesis of myeloid neoplasms is crucial to be able to prevent and block disease. Utilizing personalized cell models that harbor specific genetic lesions provides us an opportunity to study a continuum of disease clinically, transcriptionally, and functionally. In our lab, we are working on cell lines with specific mutations to model a spectrum of clinical conditions, from predisposition syndromes to low-risk MDS and high-risk AML. We are leveraging our patient-derived iPSCs with CRISPR/Cas9- and multiomics technologies in order to identify mechanisms underlying myeloid neoplasm development. Moreover, we are passionate about applying interdisciplinary methodology, such as 3D scaffold culture, to better recapitulate in vivo hematopoiesis and thereby enhance adult blood maturation and disease modeling in vitro . Our ultimate goal is to uncover the molecular pathogenesis of myeloid disease that may aid in treatment development. Collaborators include Eva Hellström-Lindberg and Andreas Lennartsson from KI; Kasper Karlsson at SciLifeLab; and Trista North and George Daley from BCH, USA. 

Selected publications

• Article: GENES CHROMOSOMES & CANCER. 2023;62(11):672-677 The clinical phenotype of germline RUNX1 mutations in relation to the accompanying somatic variants and RUNX1 isoform expression. Cabrerizo Granados D; Barbosa I; Baliakas P; Hellström-Lindberg E; Lundin V
• Article: DEVELOPMENTAL CELL. 2020;52(4):446-460.e5 YAP Regulates Hematopoietic Stem Cell Formation in Response to the Biomechanical Forces of Blood Flow. Lundin V; Sugden WW; Theodore LN; Sousa PM; Han A; Chou S; Wrighton PJ; Cox AG; Ingber DE; Goessling W; Daley GQ; North TE
• Article: NATURE COMMUNICATIONS. 2018;9(1):892 Reconstruction of complex single-cell trajectories using CellRouter. Lummertz da Rocha E; Rowe RG; Lundin V; Malleshaiah M; Jha DK; Rambo CR; Li H; North TE; Collins JJ; Daley GQ
• Article: MOLECULAR METABOLISM. 2018;7:12-22 Neurturin is a PGC-1α1-controlled myokine that promotes motor neuron recruitment and neuromuscular junction formation. Mills R; Taylor-Weiner H; Correia JC; Agudelo LZ; Allodi I; Kolonelou C; Martinez-Redondo V; Ferreira DMS; Nichterwitz S; Comley LH; Lundin V; Hedlund E; Ruas JL; Teixeira AI
• Article: CELL REPORTS. 2014;8(3):665-670 CtBPs sense microenvironmental oxygen levels to regulate neural stem cell state. Dias JM; Ilkhanizadeh S; Karaca E; Duckworth JK; Lundin V; Rosenfeld MG; Ericson J; Hermanson O; Teixeira AI
• Article: NATURE METHODS. 2014;11(8):841-846 Spatial control of membrane receptor function using ligand nanocalipers. Shaw A; Lundin V; Petrova E; Fördős F; Benson E; Al-Amin A; Herland A; Blokzijl A; Högberg B; Teixeira AI
• Article: PLOS ONE. 2011;6(4):e18624 Control of neural stem cell survival by electroactive polymer substrates. Lundin V; Herland A; Berggren M; Jager EWH; Teixeira AI

Group leader

All members of the group

• David Cabrerizo Granados, Post Doc