Factors impacting participation in research during the COVID-19 pandemic: results from a survey of patients in the ophthalmology outpatient department.

BACKGROUND: Understanding public and patient attitudes to clinical research is paramount to successful recruitment. The COVID-19 pandemic has led to additional hurdles in achieving this. Our aim is to understand the current factors and attitudes towards clinical trial participation in order to assist in recruitment to clinical trials. METHODS: We conducted face-to-face interviews with patients in the outpatient department at a tertiary eye hospital facilitated by a 32-item questionnaire developed by the research team. Patient characteristics were correlated with their responses, in addition to qualitative thematic text analysis. RESULTS: A total of 53 patients were interviewed. Forty per cent indicated that they would be willing to participate in clinical research in the current climate. General motivating factors for involvement in research included personal gain, altruism and contribution to innovation. Factors limiting participation included concerns regarding own safety, inconvenience, accessibility and lack of benefit. 22.6% of participants felt that the COVID-19 pandemic has changed their outlook on research. These were categorised into positive (increased awareness of the importance and need for research, altruism) and negative (increased anxiety, need to minimise exposure to the hospital environment) influences. CONCLUSIONS: Factors influencing patients' decisions to participate in trials are similar to those observed prior to COVID-19 but with an increased focus on the environment the research is conducted in. The COVID-19 pandemic has had positive and negative impacts on patient attitudes towards research. Trial design, with a particular focus on setting and safety measures, in reassuring patients is increasingly important.

Transcutaneous fluorescence spectroscopy as a tool for non-invasive monitoring of gut function: first clinical experiences.

Gastro-intestinal function plays a vital role in conditions ranging from inflammatory bowel disease and HIV through to sepsis and malnutrition. However, the techniques that are currently used to assess gut function are either highly invasive or unreliable. Here we present an alternative, non-invasive sensing modality for assessment of gut function based on fluorescence spectroscopy. In this approach, patients receive an oral dose of a fluorescent contrast agent and a fibre-optic probe is used to make fluorescence measurements through the skin. This provides a readout of the degree to which fluorescent dyes have permeated from the gut into the blood stream. We present preliminary results from our first measurements in human volunteers demonstrating the potential of the technique for non-invasive monitoring of multiple aspects of gastro-intestinal health.

A CNN-aided method to predict glaucoma progression using DARC (Detection of Apoptosing Retinal Cells).

BACKGROUND: A key objective in glaucoma is to identify those at risk of rapid progression and blindness. Recently, a novel first-in-man method for visualising apoptotic retinal cells called DARC (Detection-of-Apoptosing-Retinal-Cells) was reported. The aim was to develop an automatic CNN-aided method of DARC spot detection to enable prediction of glaucoma progression. METHODS: Anonymised DARC images were acquired from healthy control (n=40) and glaucoma (n=20) Phase 2 clinical trial subjects (ISRCTN10751859) from which 5 observers manually counted spots. The CNN-aided algorithm was trained and validated using manual counts from control subjects, and then tested on glaucoma eyes. RESULTS: The algorithm had 97.0% accuracy, 91.1% sensitivity and 97.1% specificity to spot detection when compared to manual grading of 50% controls.  It was next tested on glaucoma patient eyes defined as progressing or stable based on a significant (p<0.05) rate of progression using OCT-retinal nerve fibre layer measurements at 18 months. It demonstrated 85.7% sensitivity, 91.7% specificity with AUC of 0.89, and a significantly (p=0.0044) greater DARC count in those patients who later progressed. CONCLUSION: This CNN-enabled algorithm provides an automated and objective measure of DARC, promoting its use as an AI-aided biomarker for predicting glaucoma progression and testing new drugs.