Two new computerized tomography (CT) systems have been installed in UHN’s core facilities to support the research community.
The STTARR Innovation Centre has acquired a 128-slice, spectral (dual-energy) CT system. The model that was acquired is the GE Healthcare Revolution Gen 2 ES, which, relative to previous models, has enhanced small lesion detection, improved tissue characterization and metal artefact reduction. It comes with a Stellant integrated contrast injector and fluoroscopy capabilities, and post-processing software packages, which allow segmentation and quantitative analysis of conventional mono-energetic and dual-energy acquisitions. A 70-cm tilt-gantry aperture provides the flexibility to image a wide range of large animal models and ex vivo specimens.
The second system is housed at the Guided Therapeutics (GTx) Surgery facility. The new system is a Siemens Cios Spin cone-beam CT, which replaces an aging C-arm prototype system. The new CT has improved image quality and superior reliability. It is also highly mobile and can be used to support guidance technology studies, and with pre-clinical animal models and patent analogs. Thus, the new cone-beam CT represents a key resource for image-guided surgery in head, neck, thoracic and orthopedic procedures, while making use of biocompatible three-dimensional (3D) scaffolds and custom 3D-printed cutting guides. Recently, the new system was integrated into a cutting edge projection-based augmented reality surgical navigation platform, which can be used to project pre-surgery plans and tumors onto the surgical site with sub-millimeter accuracy.
Both of these systems greatly advance the ability of STTARR and GTx clients to conduct imaging research and compliments the wide range of other imaging resources located at these facilities. The purchase of this state-of-the-art equipment was completed by Junior Project Manager Peter Ashton at Research Facilities Planning & Implementation and was made possible by funding from the Canada Foundation for Innovation, the Province of Ontario, The Princess Margaret Cancer Foundation and UNIFOR.
Millions of photoreceptor cells in the eye are responsible for converting light into signals that can be transmitted to the brain and processed. When these cells malfunction or are damaged, vision loss often results.
Laboratory tests over the past two decades have shown that when photoreceptors are transplanted into the retina (i.e., the light-sensitive layer of tissue at the back of the eye) vision loss can be reversed. Dr. Valerie Wallace, a Senior Scientist at the Donald K. Johnson Eye Institute, and her research team have observed a phenomenon that helps explain how these cells restore vision.
“Transplanted cells rarely integrate into the retina, yet they are somehow still able to rescue vision,” says Dr. Arturo Ortin-Martinez, a Scientific Associate in Dr. Wallace’s lab and the lead author of the study. “Understanding this process will enable us to improve the success of transplants.”
The researchers found that donor cells form microscopic, tunnel-like bridges—called nanotubes—with the host photoreceptors. Materials that are essential to photoreceptor function can move between the cells via these nanotubes.
Using microscopy techniques that can track proteins and other components of cells, the team observed the nanotubes forming and extending as protrusions from the donor cells towards the recipient cells. They then saw materials moving between the cells.
Among the techniques used to gain this insight was an exciting method developed by Dr. Wallace’s group called InVision. This method makes eye tissue transparent, enabling researchers to image whole eyes, rather than thin sections of tissue. After examining the eye at this scale, the team concluded that around 80% of the donated photoreceptors formed nanotubes with host photoreceptors.
A 3D reconstruction of immunofluorescence images captured from a whole, intact eye that was prepared using the InVision method. Donor and host photoreceptors (green) are localized in the same region and are connected by nanotubes. |
When tracking the movement of cellular materials, the researchers found that the amount of material transferred to the host cells depended on the persistence of the donor cells—more material was transferred when the cells survived longer. The research team also discovered that materials were only transferred when the donor cells were photoreceptors; materials were rarely transferred from other types of cells, such as brain neurons.
“We think that this exchange helps restore vision in transplant recipients by supplying materials that are missing in the damaged retina,” says Dr. Wallace. “Next we need to explore what cargo is moving between cells and how we can optimize the transfer process.”
Because photoreceptors are a type of nerve cell, these new observations may impact the design of cell-based strategies for treating degeneration in other parts of the nervous system.
This work was supported by the Ontario Institute of Regenerative Medicine, Medicine by Design at the University of Toronto, the Krembil Foundation and the UHN Foundation. V Wallace holds a Tier 1 Canada Research Chair in Retina Regeneration.
Ortin-Martinez A, Yan NE, Tsai ELS, Comanita L, Gurdita A, Tachibana N, Liu ZC, Lu S, Dolati P, Pokrajac NT, El-Sehemy A, Nickerson PEB, Schuurmans C, Bremner R, Wallace VA. Photoreceptor nanotubes mediate the in vivo exchange of intracellular material. EMBO J. 2021 Sep 8. doi: 10.15252/embj.2020107264.
Science is more important than ever.
COVID-19 has thrust science into the spotlight and shown the world that research is critical for solving global challenges.
From the outset of the pandemic, the Krembil community has adapted to change and pushed forward—steadfast in its mission to develop cures for diseases of the brain and spine, bones and joints, and eyes.
This year’s Krembil Annual Report highlights a selection of our greatest research achievements over the past year, including:
● advancing our understanding of why men and women experience chronic pain differently;
● validating new classification criteria for systemic lupus; and
● developing a molecular model of the brain connections that are involved in visual processing.
The report also highlights recent events at which Krembil scientists and trainees shared their expertise with the community to promote public engagement and ensure continued support for research.
Click here to read the report.
Health care can seem like a sea of specialized terms. In hospitals, medical, scientific and regulatory terms are brought together and often shortened in clinical notes, which are used by the care team to monitor and treat patients.
Hard to interpret acronyms can make it more challenging for researchers to extract usable medical data, hampering the progress research that aims to improve care.
“Expanding the abbreviations in clinical notes can be a difficult problem without expert knowledge. For example, ‘RA’ could mean right atrium, rheumatoid arthritis or room air, depending on the context,” Dr. Michael Brudno explains. Determining what an abbreviation means is usually simple for a human expert, but is a challenging task for automated systems.
To address this issue, Dr. Brudno led a team of researchers to build a machine learning approach to automatically identify the proper meaning of abbreviations in medical notes. Machine learning is an approach through which a computer algorithm can be ‘taught’ to solve complex problems, such as spotting patterns in large sets of data. However, in order to ‘teach’ the algorithm, large amounts of high-quality data are needed.
To overcome this issue, and the potential costs of creating this dataset (e.g., paying experts go back over clinical notes to expand any abbreviations), the research team customized their machine learning system so that it could overcome ambiguity in the clinical notes.
“One of the keys to interpreting shortened medical terms is context. Context is everything,” says Marta Skreta, the first author of the study. “For this reason, we taught our system to scan the entire clinical note to establish a global context. For example, if the clinical note was about a heart condition, the system would be able to correctly identify ‘RA’ as ‘right atrium’. Our system also uses related concepts from sentences close to the unknown abbreviation to further help build the context.”
The team also incorporated “ontologies”—structured sets of medical language terms—to help identify related terms. Specifically, the system can pull information from NIH’s Unified Medical Language System to identify related terms, their synonyms and to identify common abbreviations of these terms.
Once completed, the machine learning system was able to automatically scan medical notes and identify the terms that abbreviations referred with high accuracy.
The main application of the system will be to identify any abbreviations and create unambiguous data sets that can be better used by researchers and are more suitable for training other machine learning systems.
This work was supported by The Princess Margaret Cancer Foundation.
Skreta M, Arbabi A, Wang J, Drysdale E, Kelly J, Singh D, Brudno M. Automatically disambiguating medical acronyms with ontology-aware deep learning. Nat Commun. 2021 Sep 7. doi: 10.1038/s41467-021-25578-4
Researchers at UHN have uncovered a new classification system for meningiomas—the most common type of brain cancer, accounting for 30% of all brain tumours. The work was led by Dr. Gelareh Zadeh, Senior Scientist at the Princess Margaret Cancer Centre and Head of Neurosurgery at Toronto Western Hospital. It will be presented during the Plenary Session of the Society for Neuro-Oncology Annual Scientific Meeting.
Currently, brain tumours are classified using criteria published by the World Health Organization (WHO), which categorizes them into three grades based on histopathological features by examining them under a microscope. However, this classification system does not mirror the clinical behaviour of all meningiomas.
“The field is in urgent need of new methods to classify all forms of meningioma that not only predict how they will behave, but will ultimately help inform therapy,” says Dr. Zadeh.
To do this, the team developed an integrative approach that involved generating and combining genetic and epigenetic data from tumour samples from 121 patients. By integrating these different data types in a single analysis, they found that meningioma could classified into four molecular groups, which also generated new biological insight into how these tumours behave.
“In comparison to current classification systems, including the WHO grading criteria, the four molecular groups that we identified were more accurate in predicting clinical outcomes—particularly the time it took for cancer to return after treatment,” says Dr. Farshad Nassiri, the first author of the study.
The team took these results further and developed a test that can be used to classify tumours in a clinical setting. They were also able to uncover potential treatment options based on biological characteristics of the tumour.
“This work represents a pivotal step in redefining the current classification of meningiomas, which has changed very little in three decades,” says Dr. Zadeh. “Being able to classify tumours using molecular features that reflect their clinical behaviour will pave the path forward for deciding the best course of therapy for these patients.”
This work was supported by the Canadian Institutes of Health Research, the AANS/CNS Section on Tumors, the Neurosurgery Research & Education Foundation, Hold’em for Life, the Canadian Cancer Society, the Brain Tumour Charity U.K., the National Institutes of Health, the Mary Hunter Meningioma Program, The Princess Margaret Cancer Foundation and the UHN Foundation.
Nassiri F, Liu J, Patil V, Mamatjan Y, Wang JZ, Hugh-White R, Macklin AM, Khan S, Singh O, Karimi S, Corona RI, Liu LY, Chen CY, Chakravarthy A, Wei Q, Mehani B, Suppiah S, Gao A, Workewych AM, Tabatabai G, Boutros PC, Bader GD, de Carvalho DD, Kislinger T, Aldape K, Zadeh G. A clinically applicable integrative molecular classification of meningiomas. Nature. 2021 Sep;597(7874):119-125. doi: 10.1038/s41586-021-03850-3.
Dr. Siba Haykal, Affiliate Scientist at Toronto General Hospital Research Institute, has been named a recipient of Canada’s Top 40 Under 40® for 2021.
The annual award was founded by Caldwell and recognizes 40 outstanding Canadian leaders under the age of 40 who inspire others, are visionaries and creative problem-solvers, and give back to their communities.
Dr. Haykal and her peers were selected from hundreds of nominees by a respected and independent Advisory Board, comprising 30 diverse leaders from across Canada. Four key criteria were assessed to select the recipients: Vision & Innovation, Leadership, Impact & Influence and Social Responsibility.
Upon hearing about the award, Dr. Haykal was thankful. “It’s such a huge honour. It is one of the few times where I was speechless,” she said. “This was thanks to the inspiration of my mentors over the years.”
Dr. Haykal is a Plastic and Reconstructive Surgeon at UHN with a subspecialty in microsurgery. She is also an Assistant Professor at the University of Toronto and the Director of Senior Resident Education in the University’s Division of Plastic & Reconstructive Surgery.
She received her MD training from the University of Ottawa and residency training at the University of Toronto. During her residency, she also obtained a PhD in tissue engineering, regenerative medicine and immunology with a focus on the reconstruction of non-solid organs such as the trachea. Dr. Haykal then pursued fellowship training in microsurgical reconstruction at the Albany Medical Centre in New York where she was subsequently appointed as an Assistant Professor.
Dr. Haykal joined UHN in 2018. Her clinical practice focuses on complex cancer reconstruction and microsurgical reconstruction of the breast, head and neck, and extremities. Her research centers on two areas: tissue-engineered techniques for tracheal reconstruction, and the body’s immune response to the transplantation of multiple tissues in individuals with cancers and severe injuries. Through her clinical practice and research, she is able to improve the quality of life of patients who often have no other options.
Congratulations Dr. Haykal!
Eight world-class scientists and clinicians at the University Health Network (UHN) have been recognized in Clarivate’s list of Highly Cited Researchers for 2021.
Each year, Clarivate identifies the world’s most influential researchers. The ranking is determined by the number of highly cited papers—those within the top one percent by citations—that are published in a researcher’s field over the last decade.
The researchers on the list are one in a thousand: for every researcher that makes the list, there are 1,000 researchers that do not meet the threshold. Because of this, the selected researchers are truly exceptional at communicating their findings and have extraordinary impact in their field and peer communities.
Congratulations to the following UHN scientists who were included among 6,602 of the most impactful researchers in the world:
● Dr. Gary D. Bader (Affiliate Scientist at the Princess Margaret Cancer Centre), a leading computational biologist with expertise in bridging molecular and clinical datasets to identify clinically relevant targets of disease
● Dr. Sidney H. Kennedy (Senior Scientist at the Krembil), a renowned researcher that is investigating treatments for major depressive and bipolar disorders
● Dr. Anthony E. Lang (Senior Scientist at Krembil), an influential researcher in the field of movement disorders, most notably Parkinson disease
● Dr. Andres M. Lozano* (Senior Scientist at Krembil), an innovator in surgical approaches to treat depression, Parkinson and Alzheimer disease
● Dr. Tak W. Mak* (Senior Scientist at the Princess Margaret Cancer Centre), a world-leading pioneer in cancer immunotherapy
● Dr. Roger S. McIntyre (Clinician Investigator at Krembil), an expert in cognitive impairment associated with mood disorders
● Dr. Frances A. Shepherd (Senior Scientist at the Princess Margaret Cancer Centre), a renowned oncologist who conducts research on lung cancer and who is a leader in the design, development and conduct of clinical trials
● Dr. Ming-Sound Tsao* (Senior Scientist at the Princess Margaret Cancer Centre), a preeminent authority in translational lung cancer research
*Note that this year, Drs. Lozano, Mak and Tsao were included in the Cross-Field category for excelling at interdisciplinary research and having impact in multiple areas of scientific discovery.
These researchers join 196 other Canadians who made the list. This total places Canada as the seventh top country in the world in terms of highly cited researchers, after Australia, Germany and the Netherlands.
David Pendlebury, Senior Citation Analyst at the Institute for Scientific Information at Clarivate said, “It is increasingly important for nations and institutions to recognize and support the exceptional researchers who are driving the expansion of the world’s knowledge. This list identifies and celebrates exceptional individual researchers at the University Health Network who are having a significant impact on the research community as evidenced by the rate at which their work is being cited by their peers. The research they have contributed is fueling the innovation, sustainability, health and security that is key for our society’s future.”
The methodology to determine the list of influential researchers draws on the data and analysis performed by bibliometric experts and data scientists at the Institute for Scientific Information at Clarivate. The full list, as well as a detailed explanation of the analysis methodology, is available here.
Research conducted at UHN's research institutes spans the full spectrum of diseases and disciplines, including cancer, cardiovascular sciences, transplantation, neural and sensory sciences, musculoskeletal health, rehabilitation sciences, and community and population health.
Learn more about our institutes by clicking below:
