Medical School Winter Research projects
MED#1: Can Digital Innovations Transform Rural Primary Health Care? A Systematic Review
Project title: MED#1 | Can Digital Innovations Transform Rural Primary Health Care? A Systematic Review |
Project duration, hours of engagement | 4 weeks from 24th June to 19th July 2024 Hours expected per week: 35 hours |
Location: | Toowoomba: Rural Clinical School, Boyce Gardens |
Description: | You will participate in an ongoing systematic review, by contributing towards a specific step in the review process. |
Expected outcomes and deliverables: | Contribution to a journal manuscript and/or conference presentation. |
Suitable for: | Students with prior experience conducting systematic reviews or other types of research reviews are encouraged to apply. |
Primary Supervisor: | Dr Bushra Nasir |
Contact info: | The supervisor CAN be contacted by students prior to submission of an application. |
MED#2: A systematic review of factors that influence consumer involvement in rural health practice, research and education
Project title: MED#2 | A systematic review of factors that influence consumer involvement in rural health practice, research and education |
Project duration, hours of engagement | 4 weeks from 24th June to 19th July 2024 Hours expected per week: 30 hours |
Location: | Toowoomba: Boyce Gardens |
Description: | There is an increasing push to involve patients and consumers (i.e., patient, carer, family) in healthcare. This systematic review will examine the international evidence on consumer involvement in rural health practice, education and research. It will follow the PRISMA guidelines. Further information about this systematic review can be found in the registered PROSPERO protocol. |
Expected outcomes and deliverables: | Participation in this research will enable students to achieve the following learning outcomes: 1) Learn to undertake systematic searching of the literature. 2) Apply the PRISMA guidelines in practice. 3) Collaborative working skills. |
Suitable for: | It is desirable for students participating in this research to have undertaken literature reviews and to have a demonstrated ability to work with others towards a common goal. |
Primary Supervisor: | |
Contact info: | The supervisor CAN be contacted by students prior to submission of an application. |
MED#3: Artificial intelligence for predicting COPD exacerbations
Project title: MED#3 | Artificial intelligence for predicting COPD exacerbations | |
Project duration, hours of engagement | 4 weeks from 24th June to 19th July 2024 Hours expected per week: negotiable up to 36 hours per week | |
Location: | UQ Thoracic Research Centre, The Prince Charles Hospital | |
Description: | Chronic Obstructive Pulmonary disease (COPD) is a chronic lung disease that requires complex management. Major challenges exist for effective care of patients with COPD in healthcare organisations. Artificial intelligence (AI) has the potential to deliver risk prediction models for patients at the highest risk of deterioration, to personalise preventative measures for these patients. The aim of this literature review project is to analyse recent studies of AI for health services improvements in COPD management, especially predicting risk of exacerbations and hospitalisations. | |
Expected outcomes and deliverables: | The project involves the following activities: | |
Suitable for: | Students with an interest in learning: • Evidenced-based approaches • Respiratory management and prognosis • Digital health and artificial intelligence • Health system improvements | |
Additional requirements: | This project requires evidence of vaccination or non-susceptibility for vaccine preventable diseases. Student will be provided with details to obtain evidence before commencing (information will be provided with an offer of placement). | |
Primary Supervisor: | Professor Ian Yang or | |
Contact info: | The supervisor MUST be contacted by students prior to submission of an application. | |
MED#4: Lung Cancer in Never Smokers
Project title: MED#4 | Lung Cancer in Never Smokers | |
Project duration, hours of engagement | 4 weeks from 24th June to 19th July 2024 Hours expected per week: negotiable up to 36 hours per week | |
Location: | UQ Thoracic Research Centre, The Prince Charles Hospital | |
Description: | Lung cancer is the most commonly occurring cancer in men and the third most commonly occurring cancer in women with a history of smoking being the main risk factor. Lung cancer in never-smokers appears to be a distinct disease. Environmental tobacco smoke is a relatively weak carcinogen and can account for only a minority of lung cancers arising in never-smokers. Air pollution is a known risk factor which we are studying with a particular interest in carbon black in lung tissues. | |
Expected outcomes and deliverables: | Learning and Activities: Literature review for potential biomarkers in lung tissue that is a surrogate for exposure to environmental carcinogens e.g. asbestos fibres, carbon black particles, DNA adducts and mutations, and then evaluating methods. | |
Suitable for: | Students wishing to: • improve or gain skills in research methodology, and literature review. • Students interested in respiratory diseases and lung cancer screening. | |
Additional requirements: | This project requires evidence of vaccination or non-susceptibility for vaccine preventable diseases. Student will be provided with details to obtain evidence before commencing (information will be provided with an offer of placement). | |
Primary Supervisor: | Professor Kwun Fong or | |
Contact info: | The supervisor MUST be contacted by students prior to submission of an application. | |
MED#5: Human vs Artificial Intelligence: a comparative analysis of transcription accuracy of Quitline vaping cessation conversations
Project title: MED#5 | Human vs Artificial Intelligence: a comparative analysis of transcription accuracy of Quitline vaping cessation conversations | |
Project duration, hours of engagement | 4 weeks from 24th June to 19th July 2024 Hours expected per week: 36 hours | |
Location: | Northside Clinical Unit: UQ Thoracic Research Centre, The Prince Charles Hospital | |
Description: | Technological advancements in artificial intelligence (AI) have allowed us to automate processes, such as speech recognition, to reduce financial and human resource burdens. Quitline has recently implemented an automatic transcription system to document and provide important insights into conversations between counsellors and clients, which may be used for quality assessments, training, and research to ultimately improve service effectiveness. While these systems are potentially useful, they often focus on individual dictation rather than conversational discourse and nuance. Furthermore, previous estimates of word error rates in automatic speech recognition systems for medical conversations are variable and are worse for ethnic minorities. The feasibility and accuracy of automatic transcription systems in smoking product cessation counselling has not been explored. Therefore, this project aims to compare the accuracy of an AI transcription service against ‘gold standard’ human transcription of Quitline vaping cessation conversations. | |
Expected outcomes and deliverables: | With support from the supervisory team, scholars will gain skills in data analysis and manuscript writing. The scholar will also provide a report and short presentation to Quitline managers outlining study findings. | |
Suitable for: | This project is open to students with a background in health science, computer science, pre-medical students, and students considering a PhD. | |
Additional requirements: | This project might require evidence of vaccination or non-susceptibility for vaccine preventable diseases. Student will be provided with details to obtain evidence before commencing (information will be provided with an offer of placement). | |
Primary Supervisor: | Ms Hollie Bendotti | |
Contact info: | The supervisor CAN be contacted by students prior to submission of an application. | |
MED#6: The role of ferroptosis in co-toxic liver disease
Project title: MED#6 | The role of ferroptosis in co-toxic liver disease | |
Project duration, hours of engagement | 4 weeks from 24th June to 19th July 2024 Hours expected per week: 30 hours | |
Location: | Greenslopes Private Hospital | |
Description: | Patients with liver disease often have multiple co-toxins underlying the pathogenesis of their hepatic injury. Alcoholic liver disease (ALD) and metabolic associated fatty liver disease (MAFLD) account for most liver diseases in the Western world and are common causes of morbidity and mortality. Therapeutic advances in ALD and MAFLD are urgently needed. Both conditions are frequently accompanied by perturbations in iron metabolism including elevations in serum ferritin, increased hepatic iron concentration and a rise in the highly toxic non-transferrin bound iron with increasing severity of liver disease. Ferroptosis is a form of cell death driven by iron and lipid peroxidation and has been shown to be involved in liver disease progression. The overall goal of this project is to investigate pathways involved in ferroptosis in co-toxic liver disease progression and to determine if reducing hepatic iron in co-toxic liver disease can inhibit ferroptosis. | |
Expected outcomes and deliverables: | Students will gain experience in laboratory techniques and develop scientific writing, analysis and presentation skills. Laboratory techniques may include cell culture, immunohistochemistry, Western blot, microscopy, real-time PCR, ELISA. | |
Suitable for: | This project is suitable for students with a background in Biomedical Sciences considering Honours or a PhD. | |
Additional requirements: | This project might require evidence of vaccination or non-susceptibility for vaccine preventable diseases. Student will be provided with details to obtain evidence before commencing (information will be provided with an offer of placement). | |
Primary Supervisor: | Dr Afolabi Akanbi or | |
Contact info: | The supervisor CAN be contacted by students prior to submission of an application. | |