ResearchMatch 3.0 initial submission

ResearchMatch 3.0 -  initial projects submissions

Please find below the list of ResearchMatch projects received from the McGill life science and clinical communities. The McGill data science community is now being asked to contact the individual researchers if interested to collaborate over the summer.

For additional information about each project proposal such as the Project description, Description of datasets to be used or generated, Why you see this as a collaborative research project and what you hope to gain from the collaboration, log in to the ResearchMatch portal.


Projects are split into 3 groups:

Clinical Research (7)  Life Science Research (11)  Population Health Research (2)

 

Clinical Research


Project title: Patient Predictors of Voice Disorders and Treatment Outcomes

The primary objective of this study is to determine the occurrence and common characteristics of patients with voice disorders as well as their treatment outcomes based on retrospective chart review. The research question is: Which sociodemographic factors, health conditions and lifestyle behaviours are associated with voice disorders and treatment outcomes in a treatment-seeking population in Montreal? This investigation is fundamental to improving treatment outcomes for voice rehabilitation.

Keywords: laryngology, high-dimensional data, risk stratification, personalized treatment, Big data analysis

PI: Nicole Li-Jessen (Assistant Professor/ Canada Research Chair)

Department: School of Communication Sciences and Disorders

email: nicole.li [at] mcgill.ca

 

Project title: AI-empowered Real-time COVID-19 Symptom Monitoring and Prediction among Long-Term Care Residents

COVID-19 severely affects the elderly population especially those residing in long-term care (LTC) homes. Early detection and prediction of asymptomatic changes in this group can facilitate rapid isolation and consequently has the potential to save thousands of lives. A system that will be able to immediately alert care providers when COVID-19 symptoms are identified/predicted and monitor any decompensation is essential in LTC facilities. Objective: We aim to determine if and how using the Internet of Medical Things (IoMT) technology along with artificial intelligence (AI) could improve LTC residents, staff, and organizational outcomes during a COVID-19 outbreak. In this project, we will conduct an implementation study. The IoMT technology along with the AI algorithm developed in this project will be used to monitor, detect and predict COVID-19 symptoms in the targeted LTC facilities. We hypothesized that integrating IoMT technology and AI will provide an effective and efficient way for monitoring and predicting COVID-19 symptoms of residents at LTC facilities during (and beyond) COVID-19 outbreak.

Keywords: COVID-19, elderly care, wearables, signal processing, artificial intelligence, IoMT, long term care facilities

PI: Samira Rahimi (Assistant Professor)

Department: Family Medicine

email: samira.rahimi [at] mcgill.ca

 

Project title: Developing a Simple Bedside Clinical Estimator of Extubation Readiness in Extremely Preterm Infants

Extremely preterm infants (born before 28 weeks gestation) are a highly vulnerable population that commonly requires intubation and mechanical ventilation after birth. Due to various complications associated with prolonged mechanical ventilation, clinicians try to extubate these infants as early as possible. However, in current practice, the decision to extubate is highly subjective and varies significantly from one clinician to the other. As a result, many infants are exposed to mechanical ventilation for unnecessarily prolonged periods of time, while others are prematurely disconnected from the ventilator and reintubated. For those reasons, it would be ideal to develop a simple clinical bedside tool that could accurately estimate an infant’s probability of having a successful or failed extubation at any given day while mechanically ventilated. Using data obtained from the largest prospective multicenter study about extubation, we propose applying machine-learning methodologies to develop a simple clinical estimator of extubation readiness in extremely preterm infants.

Keywords: precision medicine, pediatrics, risk prediction

PI: Wissam Shalish (Neonatologist)

Department: Pediatrics

emailwissamshalish [at] yahoo.ca

 

Project title: Patient-Specific Computational Modeling of the Heart Failure for COVID-19 Cases

Heart failure (HF) is a syndrome of ventricular dysfunction whereby the damaged or weakened heart cannot supplysufficient blood flow to the body. Pre-existing cardiovascular diseases (CVD), especially HF, is a risk factor for COVID-19 infection and those with CVD are at higher risk of severe cardiovascular complications such as acutemyocardial injury, arrhythmias, myocarditis, and have higher mortality. Among cardiovascular complications, cardiac arrhythmia has been noted in 16.7% of hospitalized patients while heart failure in 23.0% of patients. Computational modeling of the cardiovascular system using patient­ specific information will provide an effective, and more importantly non-invasive tool to the cardiovascular specialists using which they can offer better diagnosis and more successful treatment to patients with cardiovascular diseases. The first goal of this program is to automatically categorize our database of COVID-19 patients in different stages of HF from their cardiac images, using machine learning methods.

The second goal is to perform Finite Element Analysis on a modified Living Heart Model to predict heart failure progression for each COVID-19 patient. But for collaboration during the summer, we propose a retrospective study conducted on two patients admitted to any McGill affiliated hospital with myocarditis or acute-onset heart failure.Patients presenting with acute heart failure secondary to myocarditis, myocardial ischemia or infarction, in the absence of coronary artery dis-ease, were included. Two patients, a 65-year­ old woman presenting with hemodynamic instability, fever, and infected pacemaker lead, as well as a 24- year-old woman in septic shock, both COVID-19 positive and requiring intensive care unit (ICU) admission, were included and during summer are evaluated. Computational modeling has been an increasingly used tool to create patient-specific mechanistic models and predict macroscopic phenomena. Such models have been emerging in the past decades as an effort to, among others, potentiate individualized therapies and model disease progression. In order to understand suchprocess, quantitative prediction of wall stress and strain states in cardiac tissues are of paramount importance. It is thus vital to clinically determine the critical point at which irreversible changes in the myocardial fibers necessitate intervention. In this study, an attempt is made to investigate cardiac ventricular dilation using combined patient-specific finite element analysis and statistical techniques.

Keywords: Image analysis and machine learning, Myocarditis, Covid-19, Heart Failure, Hypertrophy and Remodelling, Finite Element Analysis

PI: Alireza Heidari 

Department: Mathematics and Statistics

emailalireza.heidari [at] mcgill.ca

 

Project title: Investigating the alkali cation/proton exchanger SLC9A6/NHE6 in comorbidity of epilepsy and autism

Investigating the role of the alkali cation/proton exchanger SLC9A6/NHE6 in comorbidity of epilepsy and autism Genetic studies have implicated a heterogeneous group of loci and genes in epilepsy and autism. Environment may be an additional factor conferring susceptibility to these disorders. While the precise pathophysiological mechanisms remain unclear, converging evidence from both animal and human studies suggests that many of these factors may disrupt maturation of GABAergic interneurons, especially the fast spiking parvalbumin (PV) interneurons, as a common and possibly unifying pathway for some forms of epilepsy and autism spectrum disorder (ASD). Recently we found that the endosomal alkali cation/proton exchanger SLC9A6/NHE6, which has been linked with epilepsy and ASD, is highly expressed in GABAergic interneurons and involved in trafficking of cargo such as neurotrophins and receptors important for the survival, development, and function of neurons. Dr Orlowski and Dr McKinney have been working on NHE6 together for a number of years and would like to collaborate with computational biologist to perform alternate gene set enrichment analyses using differentially expressed genes from previously published RNA-seq studies of post-mortem epilepsy and ASD in the cerebral cortex. We envisage these studies may pinpoint genes linking with NHE6 relevant to pathophysiology yet circumvent the need to understand genetic architecture or gene-environment interactions leading to disease.

PI: Anne McKinney (Professor)

Department: Pharmacology and Therapeutics

email: anne.mckinney [at] mcgill.ca

 

Project title: NLP and the development of a quality of life classification system based on patients' perspective

This study aims to capture and encode the life areas that matter to people living with severe obesity obtained from unstructured data. This study attempts to fill a gap in the way we measure and treat obesity: the current tools used to measure the impact of a weight-management intervention on a person's quality of life (QOL) do not consider people's preferences for different outcomes. This analysis is fundamental for the development of a preference-based measure of Weight-Related Quality of life-based on life areas that matter to people living with obesity. Such a measure will help inform treatment-decision making.

Keywords: natural language processing, obesity, quality of life, personalized treatment, artificial intelligence, quantitative medicine, statistical analyses, precision medicine, data mining

PI: Ana Moga (Faculty Lecturer/ PhD Student)

Department: Rehabilitation Science

email: ana.moga [at] mcgill.ca

 

Project title: Development of a Severity Scoring System for Intermediate/Mild Zellweger Spectrum Disorders

Zellweger Spectrum Disorders (ZSD) are a group of rare, autosomal recessive, multisystemic disorders characterized by a defect in peroxisome biogenesis. ZSD is a phenotypic continuum ranging from severe to mild, however, severity of symptoms cannot be entirely predicted by genotype or biochemical abnormalities. The lack of a robust tool to assess disease severity is a major limitation in the management and treatment of this disorder. Our objective is to develop and validate a robust and quantitative severity scoring system based on the body systems that can be affected in individuals with intermediate/mild ZSD. A validated ZSD severity scoring system would allow clinicians to identify prognostic features in ZSD patients and reliably quantify disease burden. Moreover, it could be used for classification and stratification of patients in clinical trials that seek to evaluate potential treatments for ZSD, to distinguish clinical endpoints, and for monitoring disease progression and treatment responses.

Keywords: Zellweger Spectrum Disorder, Severity scoring system, Peroxisome biogenesis disorders, Rare diseases, Disease management, Prognosis, Clinical trial stratification

PI: Nancy Braverman (Professor)

Department: Department of Paediatrics

email: pbd.genetics [at] mcgill.ca

 

Life Science Research



Project title: Establishing the role of the aryl hydrocarbon receptor (AhR) in pulmonary fibrosis

Worldwide, fibrotic diseases account for approximately 45% of total deaths, of which fibrosis of the kidney, heart and lung are the most common. Pulmonary fibrosis refers to a group of interstitial lung diseases characterized by irreversible stiffening of the lung due to scar formation, which disrupts oxygen exchange. This causes irreversible decline in lung function and leads to death from respiratory failure. Most of the time, the cause of pulmonary fibrosis is not known, in which case the disease is called idiopathic pulmonary fibrosis (IPF). IPF is an incurable disease with a median survival of only 3-5 years from diagnosis with no viable therapies to reverse the fibrotic process. A major impediment to the development of effective therapies for IPF is our limited understanding of the molecular pathways that contribute to the disease process. We have identified the aryl hydrocarbon receptor (AhR) as a potential new target, as we propose that the AhR integrates numerous pathogenic mechanisms that drive fibrosis, including fibroblast differentiation and extracellular matrix (ECM) production. The AhR is a ubiquitously-expressed transcription factor historically associated with the metabolism of xenobiotic and aromatic hydrocarbons. However, studies over the last several years suggest that the AhR controls numerous biological processes such as cell proliferation and differentiation in response to endogenously-produced ligands. Our central hypothesis is that aberrant AhR activation in response to fibrotic stimuli drives the development of pulmonary fibrosis by integrating multiple pathogenic mechanisms. Our preliminary data show that the AhR, elevates collagen production by human lung fibroblasts. However, additional studies are required to better understand the mechanism by wich AhR reduces these features of fibrosis. The findings that will be obtained from this project will be built into future grant applications and further support translational aims to determine if the AhR reduces pulmonary fibrosis in vivo. Overall, the final outcome will be to determine the utility of targeting the AhR pathway as a new therapeutic option in pulmonary fibrosis

Keywords: Pulmonary fibrosis, IPF, Mass Spectrometry, AhR, Aryl hydrocarbon receptor, Lung diseases

PI: Hussein Traboulsi

email: Hussein.Traboulsi [at] mail.mcgill.ca

 

Project title: A complex systems approach to study the circadian regulation of signalling pathways in T cells  

Circadian clocks control various aspects of physiology. The circadian control of T cells, which are at the core of immune responses to pathogens, is unclear. We showed a role for circadian clocks in controlling the magnitude of T cell responses, for example in response to vaccination. To decipher the mechanisms involved, we study the signalling pathways showing 24 h rhythms in T cells. Although we have identified some pathways that appear to exhibit some level of differential activity according to the time of day, whether and how these underlie the rhythm of response of these cells to vaccination, remains to be determined. We aim to use computational and complex systems approaches to find what molecular aspects of T cell function are key in their circadian regulation. This work will contribute to better understanding the role of circadian clocks in the immune response, and open the door to new T cell-based therapies.

Keywords: N/A

PI: Anmar Khadra (Associate Professor)

Department: Physiology

emailAnmar.Khadra [at] mcgill.ca

 

Project title: Onco-gDriver v. OvCa: Interactive online ovarian cancer genetics tool integrating clinical metrics

Ovarian cancer is the most lethal gynecologic malignancy. Currently, there are no reliable cancer detection screening technologies akin to what is available for other cancers such as breast and colorectal. Consequently, women often present with advanced stage disease where treatment regimens are less effective. There is a high heritable component for ovarian cancer, where women heterozygous for germline pathogenic variants in BRCA1 and BRCA2, are at significantly increased risk of developing ovarian cancer. Variants in these genes are actionable and women can be offered risk reducing surgery to significantly reduce their risk for this disease. However, variants in these genes do not account for all hereditary cases, especially where familial aggregation of ovarian cancer occurs. Therefore, identification of new risk genes could help detect women at increased risk. Identifying women at increased risk of heritable ovarian cancer can aid in implementing risk assessment and management and thus impact on high morbidity and mortality associated with this lethal disease. As the number of estimated newly diagnosed cases of ovarian cancer in Canada (2.7% of all cancer cases) is lower than other cancers, sample size has often been a barrier in studies investigating new risk genes. We propose to use our own in-house whole exome sequencing data of over 100 ovarian cancer cases, with defined clinical characteristics, and publicly available resources to characterize the germline genomic landscape of ovarian cancers using a web-based interactive tool. This interactive database we proposed to call “Onco-gDriver v. OvCa” (in anticipation of generating other such tools for other cancer types) linked to other publicly available databases could generate numerous visual outputs depending on the research question and help with building or confirming hypothesizes based on computational driven research of complex multi-dimensional genetic and clinical metrics associated data.

Keywords: candidate genes, interactive tool, human genetics, big data analysis, data mining, breast cancer, ovarian cancer, next-generation sequencing, hypothesis building

PI: Patricia Tonin (Full Professor)

Department: Medicine & Human Genetics

email: patricia.tonin [at] mcgill.ca

 

Project title: Genome-wide assessment of the transcription/chromatin relationship

Gene regulatory defects underlie virtually every complex human disease, making the understanding of fundamental mechanisms of transcriptional regulation a key imperative. The role of chromatin structure in regulating access of the transcriptional machinery to gene promoters is well-established. The interplay between chromatin and transcription in gene bodies is less well understood, and has the potential to impact polymerase pausing and elongation, as well as mRNA alternative splicing and 3’ end processing. In this project we will use strand-specific RNA-seq, RNA polymerase ChIP-seq, and MNase-seq datasets (generated by our lab and publicly available) to comprehensively analyze transcription-chromatin relationships in a simple eukaryote model system.

Keywords: chromatin structure, MNase-seq, transcription, RNAseq

PI: Jason Tanny (Associate Professor)

Department: Pharmacology and Therapeutics

email: jason.tanny [at] mcgill.ca

 

Project title: The generalizability of connectivity-Based PD prediction model: large open to small local datasets

Featured by bradykinesia, rigidity and resting tremor symptoms, Parkinson’s disease (PD) is the second most common movement disorder. The accumulation of open disease-oriented Magnetic Resonance Imaging (MRI) databases such as the Parkinson’s Progression Markers Initiative (PPMI) and other high-quality MRI datasets for the health population like HCP, allows us to build better prediction models as well as testing the generalizability and reproducibility of such models. It will be an advantage for the research community if we are able to improve the modeling and analysis practices of local data with the information obtained from larger open databases. In this project, we would like to test the generalizability of the PD prediction models based on functional and structural connectivity (FC, SC), using both the large open PPMI dataset and our smaller local anonymous clinical dataset (SLCD). We are interested in testing: 1)How different SC and FC measures perform in PPMI and SLCD; 2) Whether SC and FC independent predictors or their interactions contribute more to the prediction power; 3) How will the prediction models trained from PPMI perform in SLCD and vice versa. We will analyze the reasons behind such differences, and give recommendations for how to use large open datasets to analyze smaller local datasets datasets.

Keywords: Prediction, neuroimaging, Parkinson Disease

Trainee: Qing Wang

email: vincent.w.qing [at] gmail.com

 

Project title: Transcriptomics to predict developmental dynamics, function and heterogeneity of ependymal cells

Adipose tissue plays a central role in maintenance of healthy metabolism. In obesity, its capacity to expand via extracellular matrix remodeling, cell growth and lipid storage fails. This leads to ectopic lipid accumulation, whole-body low-grade inflammation, insulin resistance and ultimately to type 2 diabetes. This project examines the detailed profile of the extracellular matrix and inflammatory changes in adipose tissue that disrupt normal expansion during weight gain. The project uses whole adipose tissue transcriptome data set from a rare monozygotic, weight discordant twins as well as adipose tissue RNAseq data from longitudinal mouse weight gain study. Focus will be on adipose tissue matrisome alterations and changes in inflammatory profile during adipose tissue expansion. Ultimate goal is to identify molecular targets that would allow maintenance of healthy metabolism in obesity. 

Keywords: brain research, single cell RNA-sequencing, Prediction

PI: Jo Stratton (Assistant Professor)

Department: Neurology and Neurosurgery

email: jo.stratton [at] mcgill.ca

 

Project title: Exploring the role of Synaptopodin and Spine Apparatus in synaptic plasticity and learning

For a long time, it was believed that the connections in the brain become fixed upon maturation, but this dogma has simply faded over the years. Indeed, recent experimental evidence shows that the brain never stops changing through learning. Plasticity is the capacity of the brain to change with learning. Yet some synapses or connections in the brain are more likely to undergo plasticity while others become lost.

Interestingly, a particular structure, called the Spine Apparatus (SA), is found in a subset of synapses. The SA contains a protein called synaptopodin (SP). Genetic depletion of SP/SA in the mature brain prevents synaptic plasticity. The precise role of SA/SP in calcium dynamics is still poorly understood. Although we know which players are involved at the molecular level, from glutamate receptors to calcium binding proteins, we do not know how they are interconnected with SP. Most, if not all, computational models of spine plasticity do not take into account SA and SP. How their presence influences calcium signalling (amplitude, time decay) coming from an EPSP or an action potential and how they change the interaction between these two signals which are at the heart of synaptic plasticity phenomena remain largely unexplored. Development of new computational model that takes into account the calcium flux-balance equation will allow us to explore spine dynamics and plasticity in response to various stimuli, make new hypotheses and help guide future experiments

Keywords: N/A

PI: Anne McKinney (Professor)

Department: Pharmacology

emailanne.mckinney [at] mcgill.ca

 

Project title: Defining a polygenic risk score for a novel pathway in Alzheimer's disease.

Alzheimer's disease is a progressive neurodegenerative disorder affecting 35 million people worldwide and ranked as the 7th leading cause of death in Canada. To date, no effective treatment or cure exists. Alzheimer's disease is regarded as a multifactorial disease and many pathological changes are established such as inflammation, aggregation of proteins and peptides, vascular impairment, hypoglycemia and others. Driven by our biochemical research, we hypothesize that several of the key proteins linked to Alzheimer's disease line up in one cellular pathway. We would like to use the power of large data analyses to support the hypothesis and further inform us about important proteins that we may have missed in the biochemical work.

Keywords: GWAS, Polygenic risk scores, brain research

PI: Lisa Munter (Associate Professor)

Department: Pharmacology and Therapeutics

email: lisa.munter [at] mcgill.ca

 

Project title: Bioinformatic analysis to stratify histological growth patterns in colorectal liver metastasis

Colorectal cancer is the second leading cause of cancer in Canadians with liver metastases being the major cause of death. Three distinct histological growth patterns of colon cancer liver metastases have been described that affect patients' prognosis. Dr. Metrakos and his colleagues are using bioinformatic-based tools to generate a multi-modality signature based on RNA sequencing data form a resected tumor and background liver human specimens, to identify Colorectal Cancer Liver Metastases (CRCLM) patients who will 1) respond to Angiogenic inhibitor-based therapies and 2) monitor the development of drug resistance (non-responders). It is expected that this knowledge will lead to the optimization of current treatment strategies, a precision therapy approach to the management of metastatic disease, and cost savings for the Canadian health care system.

Keywords: RNAseq, biomarkers , cancer, personalized treatment

Trainee: Audrey Kapelanski-Lamoureux

email: audrey.k.lamoureux [at] gmail.com

 

Project title: 4D Heart Development

In recent years, various stem cell engineering approaches have shown substantial promise to repair lost or damaged tissue. However, generating representative stem cell-derived, three-dimensional structures remains complicated due to insufficient understanding of the in vivo signalling pathways that are needed to drive directed differentiation of stem cells. Ideally, engineered structures also recapitulate the native tissue architecture, but this highly depends on a better understanding of how different cell types and sublineages contribute to these structures in situ throughout development. In order to define the cellular lineages, their derivatives, and cell-cell interactions that drive heart formation, we have generated a single cell transcriptomic atlas from early to postnatal heart development. We will integrate our single cell dataset with spatial expression maps of relevant stages in order to establish three-dimensional single cell atlases of each developmental stage. Combining these individual timepoints into one larger dataset will establish a four-dimensional map of heart development. This 4D map can be used to define cell lineages, as well as gene regulatory networks and cell-cell interactions that drive differentiation and maturation of all cells in the heart. For this project, we seek computational collaborators with experience in single cell analysis, integrating complex datasets, and modelling three- and four-dimensional representations of these data. We have already established the required stem cell technology to experimentally test and validate computational models and, in the long-term, aim to apply complementary approaches to better align true heart development with directed stem cell differentiation protocols.

PI: Piet van Vliet (Research Associate)

Department: CHU Ste Justine, Cardiovascular Genetics

email: pvanvliet [at] gmail.com

 

Project title: Measuring the Relationship between Cytoskeleton Organization and Force at Adhesions in Cancer Cells

Cancer cells from primary tumors rely on cell migration to metastasize to distance organs. Metastatic disease is what primarily kills cancer patients. Cell-matrix adhesions act as points of contact between the cytoskeleton inside cells and their external environment. They act as traction points for cells to generate force and move and thus play a critical role in regulating cell migration. Determining molecular mechanisms that regulate the cell-matrix adhesions and regulate tension will lead to potential new therapeutic targets to treat cancer metastasis. Cellular heterogeneity and complexity of cytoskeleton organization and regulate of adhesion tension make this a difficult problem to understand. We would like to develop and train machine learning algorithms to pull out unique phenotypes, expand them to many different applications and identify novel therapeutic targets.

Keywords: cancer, machine learning, Image analysis and machine learning, breast cancer, cytoskeleton, cell migration, actin

PI: Claire Brown (Associate Professor)

Department: Physiology

email: claire.brown [at] mcgill.ca

 

Population Health Research


 

Project title: Characterizing People Living with HIV for Frailty

The survival rate for people with HIV has dramatically increased with the effective antiretroviral treatment, and today people living with HIV can expect a normal life expectancy. Considering this increased life expectancy, people living with HIV are facing age related conditions such as frailty, even earlier than people without HIV. Frailty is an impediment to active ageing in the general population and its prevalence is 4 to 5 times higher in people living with HIV. As people are no longer dying of HIV but of age-related co-morbidities, we queried whether the same could be true for frailty. Are the causes of frailty HIV related or are they co-morbidity, personal, life-style and/or environmental related. The objective of this study is to identify the factors that distinguish people with and without frailty in a cohort of Canadians of middle or older age men and women living with HIV using machine learning methods.

Keywords: machine learning, Patient-oriented research, HIV infection, Frailty, statistical analyses, Big data analysis, artificial intelligence

Trainee: Mehmet Inceer

email: mehmet.inceer [at] mail.mcgill.ca

 

Project title: Relevance of Estradiol for the Brain's Functional Organization Across the Adult Female Lifespan

Studies investigating the relevance of sex hormones in trajectories of brain aging and cognitive decline represent just a small portion of the scientific literature on aging. This is alarming considering that roughly half the human population undergoes a major shift in hormonal levels at midlife during the menopausal transition. The known distributions of receptors for one of these sex hormones in particular, estradiol, exist in key functional network hub areas of the brain. Recent work from another lab, which densely sampled an individual woman across a 30 day period, has revealed using time-lagged analyses that fluctuations in endogenous estradiol predicted changes in functional connectivity network topology. Following these results, the project proposed here aims to generalize this relationship between estradiol and intrinsic functional brain network organization by examining data from the UK Biobank, one of the only open access datasets currently available which has collected both imaging and sex hormone data from tens of thousands of participants.

Keywords: neural circuits, Big data analysis, neural dynamics, high-dimensional data, neuroimaging, aging

Trainee: Myles LoParco

email: mloparco2018 [at] gmail.com

 

 

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