Neuroplasticity in rehabilitation: Roadmap to functional recovery after stroke

Cover story for Pulse@UM Year 2021 Issue 2: Searching for Silver Bullets in Healthcare

Neuroplasticity in rehabilitation: Roadmap to functional recovery after stroke

Reha Technology’s G-EO systems, a body weight suspended, end effector walking system utilised in the Physiotherapy gym, 11th floor, Menara Selatan.

By Dr Chung Tze Yang, Dr Chan Soo Chin, Associate Prof Dr Mazlina Mazlan

We used to learn that our brain cells stopped dividing with age and that our brains are thus static, hence there will be limited chances of recovery after any injury to our adult brains. However, this is no longer true. The concept of neuroplasticity is now often quoted in neuroscience and neurorehabilitation. In simple terms, neuroplasticity can be described as the brain’s neural network’s ability to change, reorganise and adapt to changes such as experience, stimulation or pathology.

Fourier M2 Motus, upper limb robotic rehabilitation platform incorporating gamification. Occupational therapy, 10th floor Menara Selatan.

In rehabilitation medicine, this concept is used to encourage recovery from stroke or brain injuries, via the concept of specific practice, high repetition and intensity. For example, a patient with adequate motor recovery is prescribed with robotic exoskeletons to deliver the high number of steps/minute repetition to enhance neuroplasticity. When coupled with an enriched environment of adequate stimulation and novelty, with achievable but challenging tasks, such rehabilitation program can facilitate further synaptic re-connections. This concept allows incorporation of virtual reality, interaction and gamification into rehabilitation as well. 

On top of the behavioural approaches, direct neural stimulation with non-invasive brain stimulation (neuromodulation) is also being investigated to facilitate neuroplasticity. As our neurons function via an electrical gradient across the cell membrane, using an external physical agent can influence the brain’s cellular function. An example is the Transcranial Magnetic Stimulation (TMS) procedure which induces an electrical counter current in the cortical neurons below the coil when applied on the cranium. At submaximal power, a repetitive TMS (rTMS) can stimulate or inhibit those neurons. Therefore, rTMS can prime the brain neural network for adapting and relearning and further augment the effects of behavioral rehabilitation therapy. 

Another neuromodulatory approach is applying a small constant electrical current (2 miliAmp) via two sponge electrodes on the skull. This is termed Transcranial Direct Current Stimulation (tDCS). The cortical neurons under the anode electrode are excited whilst that under the cathode is inhibited. Combined with behavioural therapy, this procedure may enhance learning. We have found that both 5-Hz rTMS and anodal tDCS induced effects on corticospinal excitability in persons with chronic stroke lasting at least 1 hour after stimulation (1). Our latest research project is to investigate the role of TMS to obtain the lower limb motor evoked potentials, which can potentially guide the neurorehabilitation of gait impairment after stroke. This project is funded by a Fundamental Research Grant Scheme grant with co-researchers in the Faculty of Engineering (2). 

Repetitive Transcranial Magnetic Stimulation (rTMS) inhibitive stimulation on the contralesional hemisphere in a stroke patient. This is to prime the brain to be more responsive to the subsequent rehabilitation therapies. Specialist Rehabilitation Clinic, 1st floor Menara Selatan.


1. Goh, H. T., Chan, H. Y., & Abdul-Latif, L. (2015). Aftereffects of 2 noninvasive brain stimulation techniques on corticospinal excitability in persons with chronic stroke: a pilot study. Journal of neurologic physical therapy : JNPT, 39(1), 15–22. 

2. Mapping the corticomotor pathway of different lower limbs musculature from deep penetration transcranial magnetic stimulation responses. FRGS (FP002-2020)

For more on translating neuroscience, technology or neuromodulation into clinical practice, do get in touch with us at

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Development of Imaging Data Repository of Coronavirus Disease 2019…

Cover story for Pulse@UM Year 2021 Issue 2: Searching for Silver Bullets in Healthcare

Development of Imaging Data Repository of Coronavirus Disease 2019 (COVID-19) in University Malaya Medical Centre

Heatmap for visual understanding of CNN interpretation of CXR with pneumonic changes.

By Dr Ng Wei Lin, Professor Dr Kartini Rahmat, Dr Nadia Fareeda Muhammad Gowdh

The current Coronavirus disease 2019 (COVID-19) pandemic has increased the burden on the healthcare system. COVID-19 commonly manifests as fever, cough and shortness of breath and   varies in severity from asymptomatic to critical illness. Confirmatory diagnosis of COVID-19 is by real-time reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid. Chest x-ray (CXR) and computer tomography (CT) of the chest are part of the investigations used in diagnosis and prognostication of COVID-19. Based on certain features, multiple severity scoring systems have been published to assess disease severity based on imaging to aid patient management.

With the aim of developing deep learning algorithms to assist in diagnostic interpretation and disease monitoring, a data repository has been set up by the Department of Biomedical Imaging, University Malaya Medical Centre (Figure 1).This data is preserved for future studies, education, support and design of future policies.  

Figure 1: Workflow of developing an imaging data repository of CXR and CT chest in COVID-19.

Deep learning tools on CXR and chest CT have shown positive results in detection and differentiating COVID-19 from other lung pathologies.  Deep learning tools objectively assess images compared to subjective visual assessments by the radiologist. Previous study found that among the prominent 10 convolutional neural networks (CNN) on diagnosis of COVID-19, the best performance which are comparable to the moderate performance of radiologists are set by ResNet-101 and Xception tools1. Our collaborators include eminent scientists, Professor Rajendra U Acharya from Ngee Ann Polytechnic, Singapore and Prof Chow Li Sze from UCSI University.  Over 500 normal and COVID-19 pneumonia CXRs from this repository have been used alongside over 10000 public domain CXRs for development of deep learning systems to improve confidence in diagnosing COVID-19 changes on CXR (Top Figure & Figure 3). To date multiple works are underway, focusing on automated identification of Covid-19 pneumonia using deep learning algorithms2.

Figure 3: An overview of convolutional neural network architecture process of interpreting images.

The application of deep learning curated from a data repository in the current COVID 19 clinical setting is crucial for accurate, fast diagnosis and prognostication of the disease.


1. Ardakani, A. A., Kanafi, A. R., Acharya, U. R., Khadem, N., & Mohammadi, A. (2020). Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Computers in biology and medicine, 121, 103795. 

2. Barua, P. D., Muhammad Gowdh, N. F., Rahmat, K., Ramli, N., Ng, W. L., Chan, W. Y., Kuluozturk, M., Dogan, S., Baygin, M., Yaman, O., Tuncer, T., Wen, T., Cheong, K. H., & Acharya, U. R. (2021). Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images. International journal of environmental research and public health, 18(15), 8052.

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Deciphering Radiomics Signatures of Breast Cancer – the BREOMICS…

Cover story for Pulse@UM Year 2021 Issue 1: Big Data & Artificial Intelligence in Medicine and Healthcare Research

Deciphering Radiomics Signatures of Breast Cancer – the BREOMICS approach

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By Associate Professor Jeannie Wong Hsiu Ding, Professor Dr Kartini Rahmat, Professor Dr Ng Kwan Hoong & Associate Professor Dr Chan Chee Seng

According to GLOBOCAN 2018, 18.1 million new breast cancer cases and 9.6 million breast cancer deaths were reported worldwide. It is the second most common cancer, representing 11.6% of all cancers in females after lung cancer. Breast cancer, if detected and treated early, would result in a better prognosis for patients. Whilst mammography has been proven to reduce breast cancer mortality, its sensitivity decreases in younger women and women with mammographically dense breasts (Figure 1). 

Figure 1: Mammographic
images of a dense breast with
an ill-defined tumour
Figure 1: Mammographic images of a dense breast with an ill-defined tumour

Currently, histopathological diagnosis from a biopsy or surgical specimen is the gold standard for the determination of breast cancer subtypes. However, it has been associated with adverse clinical outcomes and variable responses between patients with the same diagnosis and treatment. Furthermore, biopsy is prone to under-sampling due to the heterogeneous nature of the lesion. 

In contrast, imaging modalities such as mammography, ultrasound, and magnetic resonance imaging (MRI) can visualise the whole breast lesion and its heterogeneous nature. Conventional radiological practise involves subjective perceptual skills in the diagnosis and stratification of breast cancer risks, leading to variations across interpreters and the adverse effect of fatigue.

In addressing subjective radiological interpreters’ limitations, computer-aided detection /diagnosis (CAD) systems based on different machine learning algorithms have been developed and deployed in some commercial mammography systems since 1998. In the last decade, deep learning-based artificial intelligence (AI) systems in medicine have also gained momentum.

Here, researchers from the Faculty of Medicine, in collaboration with the Faculty of Computer Science & Information Technology, took on the approach of exploring radiomics features of breast cancer. This project, code-named “BREOMICS” stands for (BREast radiOMICS), attempts to decipher breast cancer through the revolutionary use of radiology image datasets.

Radiomics is the extraction of a vast amount of quantitative features from digital medical images, not usually visible to the naked human eye, through high output computing to produce image-based tumour phenotyping. The central idea behind the concept of radiomics is that the quantitative image features (also called image phenotypes) reflect the tissue or disease. In other words, medical images are not just images; they are data that can be mined. 

Radiomics involves image acquisition -> image segmentation -> radiomics feature extractions -> feature reduction -> radiomics modelling. There are two different approaches to radiomics, the conventional handcraft radiomics and the deep learning-based approach (Figure 2). 

Figure 2: Deep learning-based radiomics
Figure 2: Deep learning-based radiomics

Handcraft or conventional radiomics mainly extract and select features such as histogram, morphology, wavelet feature from the medical images after the pre-processing steps such as denoising, normalisation and segmentation. Accurate segmentation is usually performed by radiologists. Hence, it is very human-dependent. Deep learning radiomics, on the other hand, employs end-to-end learning, a learning mechanism where the training of a system is represented by only a single model, bypassing the intermediate layers usually presented in conventional handcraft radiomics pipeline designs. However, the full advantage of deep learning radiomics needs to leverage on big data availability, the close inter-discipline collaboration between clinical experts, medical physicists and computer science specialists. 

Recent improvements in deep learning radiomics (DLR) extracting high-level features from medical imaging could promote the performance of computer-aided diagnosis (CAD) for cancer. Deep learning-based approach has been utilised in different parts of the radiomics process. In the era of precision medicine, BREOMICS aims for a total understanding of breast cancer which will uncover the intrinsic relationship between various radiologic imaging phenotypes with other breast cancer biomarkers, able to interrogate the whole breast lesion with a single “virtual biopsy”, providing an accurate differential diagnosis of breast lesions, towards a more precise and personalised treatment. 


1. Bray, F., et al. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 68(6): p. 394-424. 

2. Freer, P.E. (2015). Mammographic Breast Density: Impact on Breast Cancer Risk and Implications for Screening. 35(2): p. 302-315. 

3. Pesapane, F., et al. (2018). Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. European radiology experimental. 2(1): p. 35-35. 

4. Acharya, U.R., et al. (2018). Towards precision medicine: from quantitative imaging to radiomics. Journal of Zhejiang University. Science. B. 19(1): p. 6-24. 

5. Crivelli, P., et al. (2018). A New Challenge for Radiologists: Radiomics in Breast Cancer BioMed Research International. 2018: p. 10. 

6. Gillies, R.J., et al. (2016). Radiomics: Images Are More Than Pictures, They Are Data. 278(2): p. 563-577. 

7. Pang, T., et al. (2020). Deep learning radiomics in breast cancer with different modalities: Overview and future. Expert Systems with Applications. 158: p. 113501. 

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Using Google Analytics for Research: A Case Study

Cover story for Pulse@UM Year 2021 Issue 1: Big Data & Artificial Intelligence in Medicine and Healthcare Research

Using Google Analytics for Research: A Case Study

By Professor Dr Ng Chirk Jenn, Dr Ooi Chor Yau & Dr William Khoo Swee Keong

Web-based apps have been widely used as a form of health intervention. Studies have shown that web-based apps are effective in changing health behaviour. However, to understand how to implement web-based apps in a real-world setting, a form of monitoring is necessary. One of the tools that is currently available for free is Google Analytics (GA). GA is a free tool that can be used to monitor web-traffic by tracking and analysing web-traffic data. It provides some insights into the behaviour of people that access the website by monitoring data such as number of visits, duration, pages accessed and user location. 

In this case study, we used ScreenMen, a web-based app that was developed to increase the uptake of screening in men. ScreenMen undertook a rigorous and systematic development process based on theories, evidence and needs of men. ScreenMen targets men between 20 and 50 years of age as this group of men usually do not go for health screening. ScreenMen can be accessed easily via various platforms including laptop, desktop and mobile devices, as long as a web-browser and internet connection are available. A pilot study is currently being conducted to implement ScreenMen in a government health clinic. By using GA, we are able to determine the number of patients who accessed and completed ScreenMen; and the time taken to complete the screening process using ScreenMen. A unique QR code is generated for each promotional material (e.g., pamphlet, bunting, banner) in the health clinic so that it allows the researcher to identify how men prefer to access ScreenMen. 

The main strength of using GA as a data collection method is that it can easily capture comprehensive data on user behaviour. For example, we are able to track every single user who accessed ScreenMen: the time, duration and each web page accessed in the web-based app. We are also able track the users based on their location, the types of platform, and number of times they accessed ScreenMen. However, GA has its limitations; ascertaining the validity of the data can be a problem. For example, in our study, we are looking at men using ScreenMen but we cannot be certain if the users are men or women. Another validity issue is the duration of using ScreenMen; if the user is idle while accessing ScreenMen, the duration of completing the web-based app will be longer than expected. 

Overall, GA is a good tool to collect data on web-traffic and user behaviour in using a web-based app. However, other forms of evaluation are necessary to complement GA as a data collection and analytic tool. Future studies should look into the effectiveness of using GA as part of the process evaluation of web-based apps. 


1. Wantland DJ, Portillo CJ, Holzemer WL, Slaughter R, McGhee EM. (2004). The effectiveness of Web-based vs. non-Web-based interventions: a meta-analysis of behavioral change outcomes. J Med Internet Res. 6(4):e40. 

2. Teo CH. (2019). A Mobile Web App to Improve Health Screening Uptake in Men (ScreenMen): Utility and Usability Evaluation Study. JMIR mHealth and uHealth. 7(4):e10216.

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The Democratization of Global Infectious Disease Surveillance Data: Practical…

Cover story for Pulse@UM Year 2021 Issue 1: Big Data & Artificial Intelligence in Medicine and Healthcare Research

The Democratization of Global Infectious Disease Surveillance Data: Practical Considerations, Concerns and Opportunities

By Dr Vivek Jason Jayaraj & Professor Dr Sanjay Rampal

The rapid emergence and integration of the internet, mobile phones, satellites, and sensors into everyday human life has ensured that data continues to be increasingly intertwined into the fabric of our postmodern society. In the 1990s, a single 3.5-inch floppy disk with a capacity of 1.44MB could perhaps carry a fair amount of data one generated overtime periods of days to weeks. Fast forward 30 years, and each human on the planet now produces 1.7MB of data per second. Such is the ubiquity of data within our day-to-day existence that terms such as “big data” are now viewed in an almost banal perspective.

This data revolution has in no doubt had ripples in all fields including in the surveillance of disease. The great American epidemiologist, Alexander Langmuir, defined surveillance in 1963 as a “continued watchfulness of trend in all relevant data”- foreshadowing the concepts of velocity, volume, variety, veracity, variability and value that have become the cornerstone of big data. This intersection of big data and surveillance has led to the discipline of digital epidemiology, which promises increased precision in the identification of at-risk populations, increased efficiency of surveillance, and more targeted interventions.

We have increasingly witnessed the democratization of data in the last decade. Examples of this are 1) democratization of surveillance data – Project Tycho ( and 2) use of novel surveillance methodologies such as the mining of social media data – Twitter API. These efforts, however, are dwarfed by the sheer scale at which the COVID-19 data machinery has so rapidly developed. In just a year, organisations such as the John-Hopkins Coronavirus resource centre, Worldometers, and Our World in Data have all assembled global data collation networks of basic surveillance aggregates. This global democratization of COVID-19 associated data has allowed for rapid development and dissemination of data visualisation and data analytics.

Nonetheless, in mining and utilising these data sources there remain several important practical considerations. Despite movements within the open data space within the last decade- COVID-19 has again highlighted the reluctance of authorities to report data with complete transparency (Neill, 2020). Instead, data has been reported in portions, in inconsistent locations, in non- standard formats, contravening typical conventions of data storage and with no detailed descriptions of the data structure.

Data, be it aggregates of cases or deaths, is eventually a reflection of a public health apparatus which are dynamic both across national boundaries and time. It is useful to think of a disease as an iceberg- with the public health apparatus a camera capturing a picture of the said iceberg. A more efficient camera would have the capacity to capture images in greater resolution and size- possibly even capturing portions of the iceberg that are underwater.

Just as the internet has served as an information superhighway that can be accessed from the comfort of your home, the accessibility of surveillance data within the COVID-19 epidemic has been almost universal. Early in the pandemic, this led to many non-experts wading into the field of infectious disease, and epidemiology earning themselves the moniker of “armchair epidemiologist”. This led to a deluge of data visualisation and analytics creating an “infodemic”- an environment that very quickly led to factions and followings on social media with a host of narratives and counter-narratives.

The Covid-19 pandemic is far from over. There is also the threat of larger future pandemics by other emerging and re-emerging infectious diseases. We need further democratization of data and further evolution of the digital epidemiology discipline to be better prepared.

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Big Data in Diabetes Care: Advancing Quality and Improving…

Cover story for Pulse@UM Year 2021 Issue 1: Big Data & Artificial Intelligence in Medicine and Healthcare Research

Big Data in Diabetes Care: Advancing Quality and Improving Outcomes

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By Associate Professor Dr Lim Lee Ling, Dr Lim Quan Hziung & Dr Nurul Aida Binti Mohamad Zulkafli

Big Data, put simply, is the analysis of information, not only at massive volumes but of great variety and high velocity, in order to yield outcomes of substantial veracity and values. In 2012, the International Data Corporation
projected the value of the digital universe to reach a mind-boggling of 40,000 Exabytes (40 × 1018 bytes) by the year 2020. Leveraging on this, Big Data has been utilised by industries worldwide for different purposes. Advancements in data science have enabled the development of machine learning and artificial intelligence that can facilitate health predictions.

Figure 1: Workflow of Big Data analytics.

In health care, Big Data is implemented by digitally consolidating different parameters of health information (Figure 1). This information is gathered from complex and heterogeneous platforms, including but is not limited to, electronic health records, prescription information, geospatial mobility patterns, and data from clinical trials, laboratory, imaging, digital health technology, and multi-omics analytics. This massive volume of data is compiled in a data warehouse or registry, and subsequently processed via data wrangling in order to deliver key metrics to multi- level stakeholders for health services planning, improving health outcomes and reducing health care costs in the long run.

The care of people with diabetes stands to benefit the most from the implementation of data science. In addition to genetic predisposition, rapid globalisation, urbanisation, and lifestyle transitions have accelerated the diabetes epidemic in Asian countries including Malaysia. The understanding of current diagnostic, prognostic, and therapeutic areas in diabetes has undergone evolutionary changes over the years.

To complement the findings of randomised clinical trials, there are growing demands for high-quality real-world evidence to provide insights into diabetes epidemiology, treatment effectiveness and safety, and health economic impact. Integrating population- and individual-level data on preventive medicine; systems biology, information technology, and ecological models will enhance the implementation of precision medicine in diabetes (Figure 2). This data-driven approach is necessary to guide policy, treatment and reimbursement decision-making.

Figure 2: Transforming fragmented care into a multi-component integrated care system to support the delivery of high-quality diabetes care.

In the current unprecedented COVID-19 pandemic, real-world evidence through robust data analytics has shown that people with diabetes have 1.4 to 2.4 times increased risk of death or intensive care admission, suggesting
fundamental issues in the organisation and delivery of diabetes care. Given the complex care needs of people with diabetes, a multi-level and multi-component strategy is crucial to improve diabetes prevention and management (Figure 2 and Figure 3). Several high-income countries and regions including the USA, United Kingdom, Hong Kong and Singapore have successfully leveraged data to drive actions at policy, system, provider and individual levels for tackling the syndemic of type 2 diabetesand obesity (Figure 3). This nation- and territory-wide initiatives,include but are not limited to, diet-related policies (e.g., food labelling regulations, sugar-sweetened beverage taxes, healthy school food), diabetes prevention programmes, risk assessment and management programmes, and patient support programmes.

Figure 3: A conceptual framework for a multi-level and multi-component society–community–individual strategy to integrate primary and secondary prevention of diabetes.

Closer to home, there are ongoing efforts to utilise data to improve disease surveillance including the setting up of MeLODY (Multi-ethnic Lifestyle, Obesity, and Diabetes Registry in MalaYsia; contact
my) and MeMORY (Multi-ethnic Maternal Offspring Registry in MalaYsia; contact These approaches are timely in Malaysia, given the unabated rise of diabetes prevalence to 18.3% in 2019.

To this end, Big Data is a revolutionary tool to facilitate precision medicine in diabetes. Multi-level stakeholders including policymakers, payers, communities, health care providers, and people with diabetes must join efforts to unlock key discoveries and outline the opportunities for winning the battle against diabetes.

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Breast Milk Sharing in Ethnically Diverse Malaysia

Cover story for Pulse@UM Year 2020 Issue 3: Health at the Crossroads – An Interdisciplinary Approach

Breast Milk Sharing in Ethnically Diverse Malaysia

By Tik Maimunah, Prof Dr Maznah Dahlui and Dr. Nik Daliana Nik Farid

Breastfeeding has well-established health benefits to mothers and infants. The World Health Organization (WHO) recommends that infants who are not able to receive breast milk from their own mothers should receive breast milk from others as an alternative. This programme will emphasize the socio-medical and scientific basis of breastfeeding and breast milk sharing from the perspective of religious teaching, in a setting of a multicultural society in Malaysia. Therefore, the project will primarily examine the level of obligation of breastfeeding as well as the permissibility of alternatives to breastfeeding that are found to be beneficial in certain circumstances such as in premature newborn and critically ill condition. However, in a Muslim-majority country and a multiracial background, milk sharing practices remains controversial due to the concept of “milk kinship” in Islam.  This requires in-depth analysis from the Islamic legal principles and maxims. Therefore, these premises of the project will be covered by the sub-programme one. 

Meanwhile, the scientific basis of the influence of breast milk on growth and development of the newborn also needs an empirical evidence to reinforce its acceptance, practice and sharing. Hence, scientific evidence on the nutritional aspects especially in protein composition and immuno-protective components of the breast milk will be investigated in sub-programme two. This would hopefully provide important insights on whether the profiles of biomolecules in human milk differ from the first to the second year of lactation, as well as their roles in the development of the infant’s immune system and growth. 

Our IIRG team joined the Gift of Love event in conjunction with World Breastfeeding Week
The event organized by The Breastfeeding Advocates Network (TBAN) on 4th August 2019.

In order to translate the outcomes from the two sub-programmes at the level of the community, recommendation and implementation of the practices will require further analysis especially on the perception and acceptance en masse. The scope of this objective will come under the investigation of the sub-programme three. 

Overall, the findings obtained from this study will be important in giving a new insight into the perspective of breast milk sharing in Malaysia especially in developing a policy that address the ethical and safety issues while ensuring that strict guidelines are adhered and according to the teachings of Islam.

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Helicobacter pylori Eradication: A Frontier Digital Health System for…

Cover story for Pulse@UM Year 2020 Issue 3: Health at the crossroads – An interdisciplinary approach

Helicobacter pylori Eradication: A Frontier Digital Health System for Treatment Management via Antibiotic Resistance Profiles

By Prof. Chua Kek Heng

Helicobacter pylori is a bacterium that has quietly lived in the gut of nearly two-thirds of the world population, however many people are asymptomatic. Having this bacterium, a silent killer, living in the human body can lead to stomach ulcers and further increase the risk of developing stomach cancer up to six times higher compared to an uninfected individual. In the early 1990s, H. pylori was successfully eradicated in nearly 90 percent of the cases using 7-day first-line antibiotics. However, it has now become a challenging infection to treat as this bacterium has changed and resists the killing effects of commonly used antibiotics. Hence, to develop a more effective treatment regimen for H. pylori infection, it is crucial to gain insights into the antibiotic resistance profiles of the bacterium.

The assessment of antibiotic resistance profiles in clinical settings often involves the cultivation of bacteria from infected patients, which requires a longer turnaround time for results.  However, H. pylori is hard to be grown in a laboratory; therefore, in vitro cultivation is not a wise approach to track the infection. In this cross-disciplinary collaborative project, we aim to study the changes of bacterium’s genetic materials related to drugs commonly used in local clinical practice, especially clarithromycin against H. pylori using extracted DNA from biopsy samples. This could directly guide the researchers or medical personnel to identify an appropriate drug to eliminate and control the growth of H. pylori in the human body while bypassing the bacterial cultivation step. 

Overall workflow – integration of different disciplines towards the development of antibiotic resistance profiles in H. pylori.

The antibiotic resistance profiles obtained from this research will further be utilized to establish a molecular screening panel for use in the local community. The information generated from the screening panel will be subsequently subjected to suitable ruled-based methods, e.g. what-if models to suggest appropriate drug treatment. The output of this project certainly will benefit physicians in decision-making as well as patients to ensure they receive the effective antibiotic for treatment. 

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Virtual Exercises with Older Adults: The CAMHEP-PISA initiative

Cover story for Pulse@UM Year 2020 Issue 3: Health at the crossroads – An interdisciplinary approach

Virtual Exercises with Older Adults: The CAMHEP-PISA initiative

By Professor Dr Tan Maw Pin

With the COVID-19 movement restrictions orders, many of us are having difficulties juggling work and household responsibilities- childcare, virtual classes or loss of paid helpers. Others are more unfortunate, especially those working with the private sector who are experiencing loss of income. However, it is at times like this that society pulls together, despite having challenges of our own. This is the time to have more people to volunteer and among those who are still employed or with savings, to donate goods and money. At the same time,  everyone should stay as healthy as possible, to avoid burdening the healthcare system. 

However, little is known of the potential impact of our movement restriction orders on the  psychosocial health of our older community. The heightened risk of severe COVID-19 illness for older persons will undoubtedly lead to fear amongst the older population and those who look out for them. This may be further compounded by both self-imposed and enforced isolation from our various categories of movement restriction. While the general public are advised to practice physical distancing, older people are advised to stay at home to avoid physical contact since they are the most vulnerable group. This is where #UMPrihatin or the “Caring For Mental Health During the COVID-19 Pandemic (CaMHeP)” project with an extension of a Seniors arm comes in. The Ageing and Age-Associated Disorders Research group was invited to contribute to this important research initiative which began in April 2020 to mitigate the potential psychological effect of the pandemic on our general population.

We re-engaged our Promoting Independence in our Seniors with Arthritis (PISA) cohort by recruiting them into an exercise study. Individuals were given a choice of conducting home based exercise using an exercise booklet and video created for the PISA study by Prof Selina Khoo and her team from the Sports Centre and her team. Alternatively, they were given the choice to connect using Google Meet at 7.30am every morning to conduct virtual exercises. The exercises were led by Amira, our PhD student and Jarvin, a physiotherapist volunteer. 

Participants performing online-guided exercise.

Forty-three older adults agreed to take part. Compared to their scores at the latest visit in PISA in 2018, there was a significant increase in anxiety scores.. Participation was great, with 70% electing to come into our virtual exercise sessions, while others preferred to exercise on their own at home. Data analysis on their adherence and effects of psychological health is now complete after the one-month intervention.

As the saying goes, it only takes a spark to get the fire going. The group has stayed together, and continues meeting every morning to this day. They have also agreed to share their story with the press, not once but twice!

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Healthy Adult Life Following Childhood Cancer

Cover story for Pulse@UM Year 2020 Issue 3: Health at the crossroads – An interdisciplinary approach

Healthy Adult Life Following Childhood Cancer

By Professor Dr Hany Ariffin, Associate Professor Dr Tengku Ain Kamalden & Dr Norsafatul Aznin A. Razak

Large epidemiological studies have shown that nearly two-thirds of childhood cancer survivors (CCS) develop at least one serious health condition, i.e. late effects, in their lifetime (Oeffinger, 2006). In 2015, the paediatric oncology research group conducted a surveillance study on long-term survivors treated at the University of Malaya Medical Centre (UMMC). It is found that young adult survivors of childhood leukemia had a higher prevalence of metabolic syndrome compared to controls (18.4% vs 4.6%). Additionally, approximately 50% of CCS had >1 criteria for metabolic syndrome despite only being in their midtwenties
(Ariffin, 2017).

As >80% of children are expected to survive cancer in the modern era, concrete steps are needed to: [1] address areas of physical and cognitive deficit; [2] develop effective health screening tools and [3] develop better therapies which limit treatment-related organ damage.

Harnessing the Power of Educational Psychology
Since 1980, over 200 children with brain tumours have been treated in UMMC with a survival rate of 60-70%, comparable to developed countries. Unfortunately, therapies for brain tumours lead to many long-term and devastating side-effects. Notably, cranial irradiation is associated with neurological deficits such as deterioration of IQ and poor memory (Ries, 2008). Additionally, as the child with brain tumour transitions into adolescence and adulthood, he/she often faces academic and psychosocial challenges (Langeveld, 2004).

In collaboration with researchers from the Faculty of Education and funded by a UM grant (IIRG-021B), an intervention programme for survivors of brain tumours was launched in 2019. The multi-faceted programme includes individual and family therapy and counselling, psychological rehabilitation and cognitive behavioural based interventions. Of note, due to the restrictions arising from the Covid-19 pandemic, these programmes have shifted to the online platform, ensuring continuity of care for the patients. 

Eyes as Windows to the Heart
The eye is the only privileged organ where blood vessels and unmyelinated nerve fibers can be seen and examined directly. The eyes have proverbially been touted as ‘windows to the soul’ but now also serve as a screening tool for heart disease. Working together with the UM Eye Research Centre and collaborators from Universities of Edinburgh and Dundee, retinal vessel analysis using software named VAMPIRE (Vascular Assay and Measurement Platform for Images of the Retina) was performed to identify CCS at increased risk for developing cardiovascular disease (Azanan, 2020). At-risk survivors were identified using images taken via a retinal camera to seek evidence of microvasculopathy (vascular attenuation). This, in addition to physical and biochemical evidence of endothelial dysfunction, facilitated identification of at-risk patients and allowed implementation of appropriate intervention measures.

Developing Better and Safer Therapies
Treatment protocols for childhood cancers have focused on achieving an optimal balance between cure and toxicities. Modern treatment protocols such as the sequential Malaysia-Singapore leukaemia (MaSpore-ALL) clinical trials are risk-adapted based on better understanding of tumour biology and treatment response to improve therapeutic precision. Hopefully, these efforts will yield the ultimate prize of excellent cure rates with minimal longterm side-effects; thus allowing childhood cancer survivors to lead healthy and productive adult lives. 

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