Conectar
Neurodegenerative disorders (NDDs) represent an unprecedented public health burden. These disorders are clinically heterogeneous and therapeutically challenging, but advances in discovery science and trial methodology offer hope for translation to new treatments. Against this background, there is an urgent unmet need for biomarkers to aid with early and accurate diagnosis, prognosis and monitoring throughout the care pathway and in clinical trials.
Investigations routinely used in clinical care and trials are often invasive, expensive, time-consuming, subjective and ordinal. Speech data represent a potentially scalable, non-invasive, objective and quantifiable digital biomarker that can be acquired remotely and cost-efficiently using mobile devices, and analysed using state-of-the-art speech signal processing and machine learning approaches. This prospective case–control observational study of multiple NDDs aims to deliver a deeply clinically phenotyped longitudinal speech dataset to facilitate development and evaluation of speech biomarkers.
People living with dementia, motor neuron disease, multiple sclerosis and Parkinson’s disease are eligible to participate. Healthy individuals (including relatives or carers of participants with neurological disease) are also eligible to participate as controls. Participants complete a study app with standardised speech recording tasks (including reading, free speech, picture description and verbal fluency tasks) and patient-reported outcome measures of quality of life and mood (EuroQol-5 Dimension-5 Level, Patient Health Questionnaire 2) every 2 months at home or in clinic. Participants also complete disease severity scales, cognitive screening tests and provide optional samples for blood-based biomarkers at baseline and then 6-monthly. Follow-up is scheduled for up to 24 months. Initially, 30 participants will be recruited to each group. Speech recordings and contemporaneous clinical data will be used to create a dataset for development and evaluation of novel speech-based diagnosis and monitoring algorithms.
Digital App for Speech and Health Monitoring Study was approved by the South Central—Hampshire B Ethics Committee (REC ref. 24/SC/0067), NHS Lothian (R&D ref. 2024/0034) and NHS Forth Valley (R&D ref. FV1494). Results of the study will be submitted for publication in peer-reviewed journals and conferences. Data from the study will be shared with other researchers and used to facilitate speech processing challenges for neurological disorders. Regular updates will be provided on the Anne Rowling Regenerative Neurology Clinic web page and social media platforms.
ClinicalTrials.gov NCT06450418 (pre-results).
Clinical research nurses work at the fulcrum of clinical trials with clearly defined roles and responsibilities. In England, the National Institute for Health Research (the main funder of health research) has broadened its scope to encompass social care research. The expectation is that clinical research nurses will expand their skill set to support these new studies, many of which will employ qualitative and mixed methods. This discussion paper explores the challenges of facilitating this clinical academic workforce development through a case study of a homeless health and social care research project. This was one of the first studies to engage clinical research nurses in this new and expanded role.
Much of what is known about the research nurse workforce has been generated through studies of clinical trials in oncology. The ‘caring-recruiting’ dichotomy has been used as a heuristic device for identifying workforce issues that can impact on study delivery such as how intense pressure to recruit study participants leads to low job satisfaction.
This case study reflects on the authors' experiences of employing a clinical research nurse in a social care research project concerned with the discharge of homeless people from hospital. The ‘caring-recruiting’ dichotomy is used to generate new information about the relationship between workforce development and the successful delivery of social care research.
The case study illuminates how social care research can generate different pressures and ethical challenges for research nurses. The time and skill it took to recruit study participants identified as ‘hard to reach’ was suggestive of the need to move beyond performance measures that prioritise recruitment metrics. The need for different types of staff supervision and training was also warranted as supporting study participants who were homeless was often distressing, leading to professional boundary issues.
This study highlights that performance management, training and supervisory arrangements must be tailored to the characteristics of each new study coming onto the portfolio to ensure research nurses are fully supported in this new and expanded role.
Blue light (peak wavelength 442 nm) has been shown to modulate the immune response in preclinical models of intra-abdominal sepsis and pneumonia. In vivo pathways involve optic nerve stimulation with transmission to the central nervous system, activation of parasympathetic pathways terminating at the spleen, and downstream immune effects including decreased inflammatory tissue damage and improved pathogen clearance. Related effects on pain mediators including proinflammatory cytokines (interleukin 6, TNF- α) and autonomic tone (increased parasympathetic outflow) suggest possible analgesic properties that would be highly relevant to a trauma population.
This is a randomised controlled trial in which adult trauma inpatients (
Full ethical approval for this trial has been granted by the University of Pittsburgh Institutional Review Board. On study completion, results will be published in the peer-reviewed literature and at ClinicalTrials.gov.
Intrathoracic cancers, such as lung cancer, mesothelioma and thymoma, represent diagnostic challenges in primary care. We aimed to summarise evidence on the performance of imaging techniques that could aid the detection of intrathoracic cancers in low prevalence settings.
Systematic review and quality appraisal using Quality Assessment of Diagnostic Accuracy Studies-2 and Grading of Recommendations Assessment, Development and Evaluation.
MEDLINE, Embase and Web of Science were searched with a predesigned search strategy for articles from January 2000 to January 2024.
We included studies relevant for primary care, where participants were suspected of having intrathoracic cancer and reported on at least one diagnostic performance measure. We excluded studies where the cancer diagnosis was already established. Data extraction and synthesis screening were conducted independently by two reviewers. Data extraction and quality appraisal were conducted by one reviewer and checked by a second reviewer.
Out of 30 539 records identified by the database searches, 13 studies were included. There was heterogeneity in the types of cancers, populations included and reported diagnosis pathways for suspected cancers. Imaging modalities investigated included chest X-ray (three studies), computer tomography (CT, six studies), magnetic resonance imaging (two studies), positron emission tomography CT (two studies), ultrasound (two studies) and scintigraphy (one study). Chest X-ray sensitivity reported for lung cancer ranged from 33.3% to 75.9%, with specificity ranging from 83.2% to 95.5%. For CT, reported sensitivity varied from 58% for pleural malignancy to 100% for lung cancer. One study investigating an artificial intelligence tool to detect lung cancer found poor detection performance in a real-world patient cohort.
We found a limited number of studies reporting on the diagnostic performance of usual imaging techniques when used in unselected primary care settings for the diagnosis of intrathoracic cancer in symptomatic patients. There is a need for more studies evaluating such techniques in the general population presenting in primary care, where the prevalence is relatively low. A better understanding of the performance could lead to better detection strategies for intrathoracic cancers in primary care. Intrathoracic cancers, such as lung cancer, mesothelioma and thymoma, represent diagnostic challenges in primary care. We aimed to summarise evidence on the performance of imaging techniques that could aid the detection of intrathoracic cancers in low prevalence settings.
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