Declined donor organs and explanted recipient organs may hold considerable value for biomedical research, particularly in advancing knowledge of disease mechanisms and supporting drug development. However, public perceptions of such use, and preferences for how consent should be obtained, remain underexplored.

Four workshops were held across the UK to examine the views of organ donor families and transplant recipients regarding the use of human organs in research, with a focus on myocardial regeneration. Each workshop included three brief presentations on transplantation and cardiac regeneration, followed by facilitated small-group discussions. Observational notes were taken to capture participants’ perspectives on the use of organs unsuitable for transplantation. A follow-up survey generated both quantitative and qualitative data, the latter analysed using thematic analysis.

Participants expressed strong support for the use of declined donor and explanted recipient organs in research. Transplant recipients frequently cited a desire to give back to the National Health Service (NHS), while donor families viewed research use as a meaningful way to honour their loved ones when transplantation was not possible.

This exploratory study highlights widespread support for using non-transplantable organs in research among individuals with personal experience of transplantation. The findings suggest a need for further research into how best to support and inform potential donors and families. Participants emphasised the importance of sensitive communication, clear consent processes and transparency regarding the use of donated organs.

We aim to use an agent-based model to accurately predict the spread of COVID-19 within multiple US state prisons.

We developed a semistochastic transmission model of COVID-19.

Five regional state-owned prisons within North Carolina.

Several thousand incarcerated individuals.

We measured (1) the observed and simulated average daily infection rate of COVID-19 for each prison studied in 30-day intervals, (2) the observed and simulated average daily recovery rate from COVID-19 for each prison studied in 30-day intervals, (3) the mean absolute percentage error (MAPE) of each prison’s summary statistics and the simulated results and (4) the parameter estimates of key predictors used in the model.

The COVID-19 pandemic disparately affected incarcerated populations in the USA, with severe morbidity and infection rates across the country. In response, many predictive models were developed to help mitigate risk. However, these models did not feature the systemic factors of prisons, such as vaccination rates, populations and capacities (to determine overcrowding) and design and were not generalisable to other prisons.

An agent-based model that used geospatial contact networks and compartmental transmission dynamics was built to create predictive microsimulations that simulated COVID-19 outbreaks within five North Carolinian regional prisons between July 2020 and June 2021. The model used the characteristics of an outbreak’s initial case size, a given facility’s capacity and its incarcerated vaccination rate as additional parameters alongside traditional susceptible-exposed-infected-recovered transmission dynamics. By fitting the model to each prison’s data using approximate Bayesian computation methods, we derived parameter estimates that reasonably modelled real-world results. These individualised estimates were then averaged to produce generalised parameter estimates for North Carolina state prisons overall.

Our model had a mean average MAPE score of 23.0 across all facilities, meaning that it reasonably forecasted facilities’ average daily positive and recovery rates of COVID-19. Our model estimated an average incarcerated vaccination rate of 54% across all prisons (with a 95% CI of ±0.12). In addition, the prisons of this study were estimated to be operating at 90% of their capacity on average (95% CI ±0.16). Given the high levels of COVID-19 observed in these prisons, which averaged over one-third positive tests on respective 1-day maxima, we conclude that vaccination levels were not sufficient in curbing COVID-19 outbreaks, and high occupancy levels likely exacerbated the spread of COVID-19 within prisons.

In addition, data gaps in facilities without recorded daily testing resulted in poor spread predictions, demonstrating how important consistent data release practices are in incarcerated settings for accurate tracking and prediction of outbreaks.

The findings of this study better quantify how spatial contact networks and facility-level characteristics unique to congregate living facilities can be used to predict infectious disease spread. Our approach also highlights the need for increased vaccination efforts and potential capacity reductions to mitigate COVID-19 transmission in prisons.

Early-life exposures, such as nutritional deficiencies, stress, smoking, toxins, medications, diseases, infections and inflammation may affect multiple physiological and metabolic systems in the offspring, including hormonal regulation, bone metabolism and mineralisation, and body composition. Moreover, the effect of these early-life exposures on later health may potentially be mediated through adverse neonatal epigenetic reprogramming of bone-related genes affecting health later in life, especially skeletal development and bone density. Thus, to advance this research further, the overall aim of the project is to investigate if (a) neonatal epigenetic and genetic signature; (b) maternal risk factors during preconception and pregnancy, such as medicine use, diseases, socioeconomic status, major life events, weight, growth and lifestyle; (c) risk factors at birth, such as instrumental delivery, mode of delivery, medicine use, injuries, diseases, weight, size for gestational age, ponderal index, gestational age; and (d) childhood risk factors, such as diseases, medicine use, major life events, weight, growth and lifestyle are associated with hormonal status, lipids, bone turnover markers, bone mineral density, fat mass and lean body mass at age 18–19 years.

Population-based, nationwide, cross-sectional clinical study with potential for longitudinal reassessment. Danish women and men aged 18–19 years old will be selected at random from the Danish National Population Registry and invited if they have available neonatal dried blood spot cards. A total of 2000 individuals will be enrolled. The study combines register data, and neonatal epigenetic and genetic analyses from stored blood with clinical and survey data. Body composition will be measured using dual-energy X-ray absorptiometry. Adult blood and hair samples will be obtained to assess hormonal status, lipids and bone turnover markers. Height, weight, waist and hip circumference, and blood pressure will be measured. Questionnaires on well-being, sleep patterns, dietary and exercise habits, onset of puberty, use of cannabis, nicotine, alcohol and pain medication will be included. Information on medicine use, diseases, socioeconomic status, major life events, weight, growth and lifestyle will be obtained from the national administrative and health registers at the time of conception and during pregnancy for the parents, as well as from the participants throughout their lifetime. Health registries include the Danish Medical Birth Register, the National Patient Register, the Danish National Prescription Register, the National Child Health Register and Statistics Denmark. Multivariate regression analyses will be performed.

This nationwide study has been approved by the Regional Committees on Health Research Ethics for Southern Denmark (S-20230105). The study participants will be enrolled in the study following their informed written consent. Results will be submitted for publication. The Strengthening the Reporting of Observational Studies in Epidemiology Statement guidelines will be used for reporting.

NCT06509776.

Postpartum depression (PPD) is a debilitating condition affecting over 20% of postpartum women, with disproportionately higher rates among black and Latina women compared with their white counterparts. Current recommendations for PPD prevention demand significant healthcare system resources, highlighting the need for alternative, evidence-based interventions that minimise strain on these systems. Mindfulness has been shown to effectively reduce depressive symptoms and prevent relapse across various populations. However, no studies to date have evaluated the efficacy of a digitally delivered mindfulness intervention specifically for black and Latina women at increased risk of PPD.

This article presents the protocol for the Healthy Mama and Baby study, a randomised controlled trial (RCT). This trial evaluates whether a mobile-based (mHealth) mindfulness intervention tailored for pregnant women reduces depressive symptoms among pregnant black and Latina women at high risk for PPD.

We are conducting a fully remote RCT, recruiting 600 pregnant black and/or Latina women at risk of PPD from Kaiser Permanente Northern California (KPNC), an integrated healthcare delivery system. Participants are enrolled before 30 weeks’ gestation. They are randomised into either an mHealth mindfulness intervention arm, which receives access to a mindfulness app tailored specifically for pregnant and postpartum women, or a time-matched and attention-matched active control arm, which receives access to an online program of calming nature sounds. Both arms are instructed to engage in their assigned program for 5–20 min per day for 6 weeks. Outcome assessments are conducted online at baseline, post intervention and post partum (~7 weeks post partum) using validated questionnaires. Outcomes include depressive symptoms (primary) and anxiety, sleep and perceived stress (secondary).

All study procedures have been approved by the KPNC Institutional Review Board. The findings will be disseminated widely through peer-reviewed publications and conference presentations.

NCT05186272.

To explore patient perspectives on using a digital adherence technology (DAT) for tuberculosis (TB) treatment, specifically, the TB Treatment Support Tools (TB-TST) intervention, which integrates a mobile app designed to enhance patient-centred support, monitoring and communication, alongside a drug metabolite test.

Qualitative study conducted as part of a pragmatic randomised controlled trial.

Four public reference hospitals in Argentina. All patients in the intervention group were invited to participate; 33 patients in the intervention group and five treatment supporters were included.

Data collection and analysis: semistructured interviews were conducted. The normalisation process theory guided analysis to understand factors that enable or hinder the intervention’s integration into routine practice for TB treatment medication adherence.

Patients identified medication reminders, educational messages and direct communication with treatment supporters (TSs) as the most helpful components of the intervention. Many reported using the app to ask TSs questions they felt uncomfortable raising with physicians in person. Initially, many patients did not fully understand the purpose and use of the metabolite test. Over time, their understanding of the app improved, though some continued to misinterpret the test results. Motivation to adhere to TB treatment was primarily driven by a desire to protect family members and resume normal daily activities. Reported barriers to app use included time constraints due to work, technical issues, limited internet connectivity and the burden of medication side effects. While the intervention was generally perceived as supportive and user-friendly, patients suggested improvements such as faster response times from TSs, expanded availability and better technical reliability and internet access.

These findings highlight the importance of tailoring digital adherence interventions to meet the diverse needs of patients and reinforce the pivotal role of the TS as a trusted and accessible source of guidance throughout TB treatment.

NCT04221789; https://clinicaltrials.gov/ct2/show/NCT04221789.