Search

IntroductionDigitisation of patient records, coupled with a moral imperative to use routinely collected data for research, necessitate effective data governance that both facilitates evidence-based research and minimises associated risks. The Generalisable Overview of Study Risk for Lead Investigators Needing Guidance (GOSLING) provides the first quantitative risk-measure for assessing the data-related risks of clinical research projects.MethodsGOSLING employs a self-assessment designed to standardise risk assessment, considering various domains, including data type, security measures, and public co-production. The tool categorises projects into low, medium, and high-risk tiers based on a scoring system developed with the input of patient and public members. It was validated using both real and synthesised project proposals to ensure its effectiveness at triaging the risk of requests for health data.ResultsThe tool effectively distinguished between fifteen low, medium, and high-risk projects in testing, aligning with subjective expert assessments. An interactive interface and an open-access policy for the tool encourage researchers to self-evaluate and mitigate risks prior to submission for data governance review. Initial testing demonstrated its potential to streamline the review process by identifying projects that may require less scrutiny or those that pose significant risks.DiscussionGOSLING represents the first quantitative approach to measuring study risk, answering calls for standardised risk assessments in using health data for research. Its implementation could contribute to advancing ethical data use, enhancing research transparency, and promoting public trust. Future work will focus on expanding its applicability and exploring its impact on research efficiency and data governance practices.

Abstract Background The transition of the Ruhr region in Germany from a hard coal belt into a knowledge-based economy with a dynamic service sector and state of the art universities over the past 60–80 years has been widely touted as a successful example of how just and fair low carbon energy transitions can unfold. Methods This paper leverages documentary analysis of data across a wide array of sources to test these claims and identify lessons by creating a novel just energy transition framework. Results The study finds economic motivation, mindset and reorientation—not environmental concerns—to be the defining features for at least the first two decades of this shift. The lack of willingness to acknowledge environmental impacts and market realities has delayed the transition and led to wasteful allocation of resources towards supporting the hard coal mining industry. The prominence given to distributional justice cushions the victims of this transition financially, but does not allow a broad based coalition to advance the transition process. It is in the second phase (mid-1980s onwards that we see procedural aspects of justice come forth and support greater ownership and sustainability of the transition to emerge, while the evidence of recognition justice continues to be scant. Conclusions There are many nuanced successes in the Ruhr’s example, along with some failures worth highlighting. It is in the breakdown of this transition into two distinct phases and their nuances (particularly in the domain of justice) that fresh insights emerge and allow for a better understanding of what constitutes a suitable energy transition in a particular socio-economic and political context. As the international community embarks on ambitious greenhouse gas reduction targets, it can maximise the benefits and minimise the damages and costs by considering these realities on the ground.

Alcoholic fatty liver disease (AFLD) is characterized by lipid accumulation and inflammation and can progress to cirrhosis and cancer in the liver. AFLD diagnosis currently relies on histological analysis of liver biopsies. Early detection permits interventions that would prevent progression to cirrhosis or later stages of the disease. Herein, we have conducted the first comprehensive time‐course study of lipids using novel state‐of‐the art lipidomics methods in plasma and liver in the early stages of a mouse model of AFLD, i.e., Lieber‐DeCarli diet model. In ethanol‐treated mice, changes in liver tissue included up‐regulation of triglycerides (TGs) and oxidized TGs and down‐regulation of phosphatidylcholine, lysophosphatidylcholine, and 20‐22‐carbon‐containing lipid‐mediator precursors. An increase in oxidized TGs preceded histological signs of early AFLD, i.e., steatosis, with these changes observed in both the liver and plasma. The major lipid classes dysregulated by ethanol play important roles in hepatic inflammation, steatosis, and oxidative damage. Conclusion: Alcohol consumption alters the liver lipidome before overt histological markers of early AFLD. This introduces the exciting possibility that specific lipids may serve as earlier biomarkers of AFLD than those currently being used.

IntroductionThe potential for synthetic data to act as a replacement for real data in research has attracted attention in recent months due to the prospect of increasing access to data and overcoming data privacy concerns when sharing data. The field of generative artificial intelligence and synthetic data is still early in its development, with a research gap evidencing that synthetic data can adequately be used to train algorithms that can be used on real data. This study compares the performance of a series machine learning models trained on real data and synthetic data, based on the National Diet and Nutrition Survey (NDNS).MethodsFeatures identified to be potentially of relevance by directed acyclic graphs were isolated from the NDNS dataset and used to construct synthetic datasets and impute missing data. Recursive feature elimination identified only four variables needed to predict mean arterial blood pressure: age, sex, weight and height. Bayesian generalised linear regression, random forest and neural network models were constructed based on these four variables to predict blood pressure. Models were trained on the real data training set (n = 2408), a synthetic data training set (n = 2408) and larger synthetic data training set (n = 4816) and a combination of the real and synthetic data training set (n = 4816). The same test set (n = 424) was used for each model.ResultsSynthetic datasets demonstrated a high degree of fidelity with the real dataset. There was no significant difference between the performance of models trained on real, synthetic or combined datasets. Mean average error across all models and all training data ranged from 8.12 To 8.33. This indicates that synthetic data was capable of training equally accurate machine learning models as real data.DiscussionFurther research is needed on a variety of datasets to confirm the utility of synthetic data to replace the use of potentially identifiable patient data. There is also further urgent research needed into evidencing that synthetic data can truly protect patient privacy against adversarial attempts to re-identify real individuals from the synthetic dataset.

Abstract Background The coronavirus disease 2019 pandemic has led to global disruption of healthcare. Many students volunteered to provide clinical support. Volunteering to work in a clinical capacity was a unique medical education opportunity; however, it is unknown whether this was a positive learning experience or which volunteering roles were of most benefit to students. Methods The COVIDReady2 study is a national cross-sectional study of all medical students at medical schools in the United Kingdom. The primary outcome is to explore the experiences of medical students who volunteered during the pandemic in comparison to those who did not. We will compare responses to determine the educational benefit and issues they faced. In addition to quantitative analysis, thematic analysis will be used to identify themes in qualitative responses. Discussion There is a growing body of evidence to suggest that service roles have potential to enhance medical education; yet, there is a shortage of studies able to offer practical advice for how these roles may be incorporated in future medical education. We anticipate that this study will help to identify volunteer structures that have been beneficial for students, so that similar infrastructures can be used in the future, and help inform medical education in a non-pandemic setting. Trial registration Not Applicable.

IntroductionDuring the course of the COVID-19 pandemic, there have been suggestions that various techniques could be employed to improve the fit and, therefore, the effectiveness of face masks. It is well recognized that improving fit tends to improve mask effectiveness, but whether these fit modifiers are reliable remains unexplored. In this study, we assess a range of common "fit hacks" to determine their ability to improve mask performance.MethodsBetween July and September 2020, qualitative fit testing was performed in an indoor living space. We used quantitative fit testing to assess the fit of both surgical masks and KN95 masks, with and without 'fit hacks', on four participants. Seven fit hacks were evaluated to assess impact on fit. Additionally, one participant applied each fit hack multiple times to assess how reliable hacks were when reapplied. A convenience of four participants took part in the study, three females and one male with a head circumference range of 54 to 60 centimetres.Results and discussionThe use of pantyhose, tape, and rubber bands were effective for most participants. A pantyhose overlayer was observed to be the most effective hack. High degrees of variation were noted between participants. However, little variation was noted within participants, with hacks generally showing similar benefit each time they were applied on a single participant. An inspection of the fit hacks once applied showed that individual facial features may have a significant impact on fit, especially the nose bridge.ConclusionsFit hacks can be used to effectively improve the fit of surgical and KN95 masks, enhancing the protection provided to the wearer. However, many of the most effective hacks are very uncomfortable and unlikely to be tolerated for extended periods of time. The development of effective fit-improvement solutions remains a critical issue in need of further development.

IntroductionThe COVID-19 pandemic has made well-fitting face masks a critical piece of protective equipment for healthcare workers and civilians. While the importance of wearing face masks has been acknowledged, there remains a lack of understanding about the role of good fit in rendering protective equipment useful. In addition, supply chain constraints have caused some organizations to abandon traditional quantitative or/and qualitative fit testing, and instead, have implemented subjective fit checking. Our study seeks to quantitatively evaluate the level of fit offered by various types of masks, and most importantly, assess the accuracy of implementing fit checks by comparing fit check results to quantitative fit testing results.MethodsSeven participants first evaluated N95 and KN95 respirators by performing a fit check. Participants then underwent quantitative fit testing wearing five N95 respirators, a KN95 respirator, a surgical mask, and fabric masks.ResultsN95 respirators offered higher degrees of protection than the other categories of masks tested; however, it should be noted that most N95 respirators failed to fit the participants adequately. Fit check responses had poor correlation with quantitative fit factor scores. KN95, surgical, and fabric masks achieved low fit factor scores, with little protective difference recorded between respiratory protection options. In addition, small facial differences were observed to have a significant impact on quantitative fit.ConclusionFit is critical to the level of protection offered by respirators. For an N95 respirator to provide the promised protection, it must fit the participant. Performing a fit check via NHS self-assessment guidelines was an unreliable way of determining fit.

BackgroundDespite a growing body of evidence suggesting that short sleep duration may be linked to adverse metabolic outcomes, how these associations differ between age groups remains unclear. We use eight years of data from the UK National Diet and Nutritional Survey (NDNS) (2008-2016) to analyse cross-sectional relationships between sleep duration and metabolic risk in participants aged 11-70 years.MethodsParticipants (n = 2008) who provided both metabolic risk and sleep duration data were included. Self-reported sleep duration was standardised by age, to account for differences in age-related sleep requirements. A standardised metabolic risk score was constructed, comprising: waist circumference, blood pressure, serum triglycerides, serum high-density lipoprotein cholesterol, and fasting plasma glucose. Regression models were constructed across four age groups from adolescents to older adults.ResultsOverall, decreased sleep duration (hrs) was associated with an increased metabolic risk (standard deviations) with significant quadratic (B:0.028 [95%CI: 0.007, 0.050]) and linear (B:-0.061 [95%CI: -0.111, -0.011]) sleep duration coefficients. When separated by age group, stronger associations were seen among mid-aged adults (36-50y) (quadratic coefficient: 0.038 [95%CI: 0.002, 0.074]) compared to other age groups (e.g. adolescents (11-18y), quadratic coefficient: -0.009 [95%CI: -0.042, 0.025]). An increased difference between weekend and weekday sleep was only associated with increased metabolic risk in adults aged 51-70 years (B:0.18 [95%CI: 0.005, 0.348]).ConclusionsOur results indicate that sleep duration is linked to adverse metabolic risk and suggest heterogeneity between age groups. Longitudinal studies with larger sample sizes are required to explore long-term effects of abnormal sleep and potential remedial benefits.