Cohort 3 student, Tim Arueyingho publishes a paper in JMIR entitled ‘Insights Into Sociodemographic Influences on Type 2 Diabetes Care and Opportunities for Digital Health Promotion in Port Harcourt, Nigeria: Quantitative Study’.

Tim had the following to say about the publication: ‘Using Port Harcourt as a case study, we explored the sociodemographic profile of people with T2D through traditional and automated quantitative approaches. This informed the design of digital health promotion strategies and the adoption opportunities we identified for this demographic. From this study, we can assert that people with T2D residing in the city of Port Harcourt are receptive to technological interventions for managing their condition. Although the specific nature of this intervention remains uncertain, we are confident that, with the right approach, issues related to use, adoption, compliance, and referral will not pose significant challenges. We advocate for the replication of this study in similar global South contexts, incorporating any overlooked questions, exploring alternative platforms to Twilio, and refining sampling or recruitment practices’.

Link to the paper: https://diabetes.jmir.org/2024/1/e56756

Cohort 2 student Neshika Wijewardhane publishes a paper in the Journal of Micro and Bio Robotics entitled ‘Long-term imaging and spatio-temporal control of living cells using targeted light based on closed-loop feedback’.

Neshika had the following to say about her publication: ‘This publication showcases the 2nd iteration of the DOME in my PhD project. We present a robotic device that is able to image and illuminate living cells with light at a higher intensity, with additional fluorescence imaging (Fluoro-DOME). We can image cells long-term, illuminate the leading edge of a wound over time, and autonomously alter the light pattern in real time to restructure the illumination on the wound edge.

With this iteration, we could set up the autonomous real-time frontier illumination of the leading edge of the wound. Currently, I am optimising the light irradiation regime to aid in accelerated wound healing and closure.’

Link to the paper: https://link.springer.com/article/10.1007/s12213-024-00165-0

Cohort 3 student Tim Arueyingho publishes a paper in AfriCHI ’23: Proceedings of the 4th African Human Computer Interaction Conference entitled: ‘Exploring the nexus of Social Media Networks and Instant Messengers in Collaborative Type 2 Diabetes care: A Case Study of Port Harcourt, Nigeria’.

Tim had the following to say about the publication:

‘Having identified limitations in the use of technology for Type 2 Diabetes (T2D) care, as well as the need for context-specific T2D self- and collaborative care technologies, I designed a mixed-methods study to explore the interpersonal relationships between people with T2D, caregivers, and community pharmacists, and how contextual factors affect these relationships in T2D care. The essence of this study is to identify context-specific design opportunities for emerging T2D self- and collaborative technologies, and to discover new ways of conducting remote health research and codesign activities in global southern community contexts. The first phase of this study resulted in the collection of 110 questionnaire responses and the conducting of 51 interviews using WhatsApp and traditional methods of data collection. While this study generated significant insights, an empirical contribution that could not be overlooked was the use of social media (SM) and instant messengers (IM) for T2D care in this context. This short paper describes these unique findings, exploring SM and IM for self- and collaborative T2D care in Port Harcourt, Nigeria.’

Link to Paper: https://dl.acm.org/doi/10.1145/3628096.3628744

Cohort 1 student Marceli Wac publishes a journal article in SoftwareX (Elsevier) entitled: ‘CATS: Cloud-native time-series data annotation tool for intensive care.’

Marceli had the following to say about the publication: ‘Intensive care units are complex, data-rich healthcare environments which provide substantial opportunities for applications in machine learning. While certain solutions can be derived directly from data, complex problems require additional human input provided in the form of data annotations. Due to the large size and complexities associated with healthcare data, the existing software packages for time-series data annotation are infeasible for effective use in the clinical setting and frequently require significant time commitments and technical expertise.
Our software provides a comprehensive, end-to-end solution to the time-series data annotation and proposes a novel approach for a semi-automated annotation in the cloud. It allows for conducting large-scale, asynchronous data annotation activities across multiple, geographically distributed users. The adoption of our software could benefit the wider research community by enhancing existing datasets, creating novel avenues for research that uses them and allowing for meaningful data annotation within smaller and highly specialised populations.

Produced software has the potential to be used in variety of domains and in particular, research involving the clinical time-series data. It allows the staff non-technically trained in the domain of computer science (such as clinicians) to enhance the existing datasets by creating annotations and providing additional context to data, which could be used to build novel machnie learning algorithms and apply them to the previously unexplored problems. The tool is cloud based allows for large-scale, distributed annotation, meaning that once deployed – it could be used by any group of clinicians across the world simultaneously.’

Link to Paper: https://www.softxjournal.com/article/S2352-7110(23)00289-3/fulltext#secd1e1291

Cohort 2 student Neshika Wijewardhane publishes in the International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS) entitled ‘Modular Wavelength Adaptation of the Dynamic Optical MicroEnvironment’.

Neshika had the following to say about the experience: ‘The DOME is a powerful and adaptable platform that facilitates the study of light-reactive systems at the microscale. While the projection module of the DOME can produce light patterns with high spatial and temporal resolution, the maximum irradiance (incident electromagnetic energy per unit area) that can be generated by its native LEDs is limited. Increasing the irradiance is crucial to enabling new biomedical applications such as inducing DNA damage. In this paper, we present a modular solution to allow general light sources to be used with the DOME. By switching to a high-powered near-UV light source, we show that DNA damage can be caused by the Epi-DOME’s projection system at a targeted location.

In a previous paper, I showed that I could image and identify the leading edge of a wound and then project light onto the wound edge. With this paper, I showed that I can target the light to cause low levels of DNA damage in a select population of cells. By putting these two pieces of work together, I should be able to selectively damage cells at the leading edge of the wound, thus initiating specific pathways that increase the migration of cells. Enabling faster wound closure.’