A majority of transplanted organs come from donors after brain death (BD). Renal grafts from these donors have higher delayed graft function and lower long-term survival rates compared to living donors. We designed a novel porcine BD model to better delineate the incompletely understood inflammatory response to BD, hypothesizing that adhesion molecule pathways would be upregulated in BD.

Animals were anesthetized and instrumented with monitors and a balloon catheter, then randomized to control and BD groups. BD was induced by inflating the balloon catheter and animals were maintained for 6 hours. RNA was extracted from kidneys, and gene expression pattern was determined.

In total, 902 gene pairs were differently expressed between groups. Eleven selected pathways were upregulated after BD, including cell adhesion molecules.

These results should be confirmed in human organ donors. Treatment strategies should target involved pathways and lessen the negative effects of BD on transplantable organs.

Integrating community expertise into scientific teams and research endeavors can holistically address complex health challenges and grand societal problems. An in-depth understanding of the integration of team science and community engagement principles is needed. The purpose of this scoping review was to identify how and where team science and community engagement approaches are being used simultaneously in research.

We followed Levac’s enhancement of Arksey and O’Malley’s Scoping Review Framework and systematically searched PubMed, CINAHL, Scopus, ERIC, and Embase for team science and community engagement terms through January 2024.

Sixty-seven articles were reviewed. Publications describing integrated team science and community-engaged research have increased exponentially since 2004. Over half were conducted outside of the U.S., utilized qualitative methods, included community-researcher co-development of research question and study design, and described team partnership goals, roles, and management. Fewer studies evaluated partnership, built community capacity, described financial compensation to communities, or described team dynamics facilitation.

As researchers continue to integrate community engagement and team science, common criteria and strategies for integrating the approaches are needed. We provide 19 recommendations for research teams, research institutions, journals, and funding bodies in service of advancing the science and practice of this integration.

Real-world data, such as administrative claims and electronic health records, are increasingly used for safety monitoring and to help guide regulatory decision-making. In these settings, it is important to document analytic decisions transparently and objectively to assess and ensure that analyses meet their intended goals.

The Causal Roadmap is an established framework that can guide and document analytic decisions through each step of the analytic pipeline, which will help investigators generate high-quality real-world evidence.

In this paper, we illustrate the utility of the Causal Roadmap using two case studies previously led by workgroups sponsored by the Sentinel Initiative – a program for actively monitoring the safety of regulated medical products. Each case example focuses on different aspects of the analytic pipeline for drug safety monitoring. The first case study shows how the Causal Roadmap encourages transparency, reproducibility, and objective decision-making for causal analyses. The second case study highlights how this framework can guide analytic decisions beyond inference on causal parameters, improving outcome ascertainment in clinical phenotyping.

These examples provide a structured framework for implementing the Causal Roadmap in safety surveillance and guide transparent, reproducible, and objective analysis.

Although the involvement of citizen scientists in research can contribute to scientific benefits, much remains unknown about participants’ lived experiences in research. Thus, the purpose of this study was to explore how citizen scientists describe their role in, motivation for, and communication with researchers.

In-depth interviews (N = 9) were conducted with citizen scientists at a translational health research center.

Key results include that citizen scientists were invested in learning researchers’ discipline-specific language and viewed small group sizes as conducive to their active participation.

Programs can apply these findings in an effort to improve citizen scientists’ long-term engagement in research.

To conduct high-quality, rigorous research, and advance scientific knowledge, Translational Teams (TTs) engage in information behaviors, including seeking, using, creating, sharing, storing, and retrieving information, in ways specific to the translational context. Currently, little is known about TTs’ approach to information management. This qualitative pilot study explored how TTs at the University of Wisconsin-Madison interact with information, as well as the scientific and organizational impact of their interactions.

We conducted interviews with ten members of UW TTs. Interviews were transcribed and thematic analysis was conducted.

Four themes emerged: (1) TT members did not recognize the centrality of information or information behaviors to their scientific work; (2) TT members engaged in similar information behaviors and used similar tools across disciplines and topics; (3) TT members did not receive support or guidance from their institution in managing information; and (4) Individualized choices of TT members conflicted at the team level, causing confusion and increasing the potential for data and information loss. Acting as freelance information management agents, TT members made individualized decisions about what tools to use and how to use them, often in a piecemeal manner and without communicating these decisions to other team members.

Research institutions should both encourage teams to discuss their information management approaches at the beginning of a project and provide leaders with training on how to have these conversations and what topics should be included. Additionally, institutions can provide researchers with guidelines for using software platforms to help mitigate information management challenges.

Clinical and Translational Research (CTR) requires a team-based approach, with successful teams engaging in skilled management and use of information. Yet we know little about the ways that Translational Teams (TTs) engage with information across the lifecycle of CTR projects. This qualitative study explored the challenges that information management imposes on the conduct of team-based CTR.

We conducted interviews with ten members of TTs at University of Wisconsin. Interviews were transcribed and thematic analysis was conducted.

TTs’ piecemeal and reactive approaches to information management created conflict within the team and slowed scientific progress. The lack of cohesive information management strategies made it more difficult for teams to develop strong team processes like communication, scientific coordination, and project management. While TTs’ research was hindered by the institutional challenges of interdisciplinary team information sharing, TTs who had developed shared approaches to information management that foregrounded transparency, accountability, and trust, described substantial benefits to their teamwork.

We propose a new model for the Science of Team Science field – a Translational Team Science Hierarchy of Needs – that suggests interventions should be targeted at the appropriate stage of team development in order to maximize a team’s scientific potential.

In Michigan, the COVID-19 pandemic severely impacted Black and Latinx communities. These communities experienced higher rates of exposure, hospitalizations, and deaths compared to Whites. We examine the impact of the pandemic and reasons for the higher burden on communities of color from the perspectives of Black and Latinx community members across four Michigan counties and discuss recommendations to better prepare for future public health emergencies.

Using a community-based participatory research approach, we conducted semi-structured interviews (n = 40) with Black and Latinx individuals across the four counties. Interviews focused on knowledge related to the pandemic, the impact of the pandemic on their lives, sources of information, attitudes toward vaccination and participation in vaccine trials, and perspectives on the pandemic’s higher impact on communities of color.

Participants reported overwhelming effects of the pandemic in terms of worsened physical and mental health, financial difficulties, and lifestyle changes. They also reported some unexpected positive effects. They expressed awareness of the disproportionate burden among Black and Latinx populations and attributed this to a wide range of disparities in Social Determinants of Health. These included racism and systemic inequities, lack of access to information and language support, cultural practices, medical mistrust, and varied individual responses to the pandemic.

Examining perspectives and experiences of those most impacted by the pandemic is essential for preparing for and effectively responding to public health emergencies in the future. Public health messaging and crisis response strategies must acknowledge the concerns and cultural needs of underrepresented populations.

Academic health systems and their investigators are challenged to systematically assure clinical research regulatory compliance. This challenge is heightened in the emerging era of centralized single Institutional Review Boards for multicenter studies, which rely on monitoring programs at each participating site.

To describe the development, implementation, and outcome measurement of an institution-wide paired training curriculum and internal monitoring program for clinical research regulatory compliance.

Standard operating procedures (SOPs) were developed to facilitate investigator and research professional adherence to institutional policies, federal guidelines, and international standards. An SOP training curriculum was developed and implemented institution-wide. An internal monitoring program was launched, utilizing risk-based monitoring plans of pre-specified frequency and intensity, assessed upon Institutional Review Boards approval of each prospective study. Monitoring plans were executed according to an additional SOP on internal monitoring, with monitoring findings captured in a REDCap database.

We observed few major violations across 3 key domains of clinical research conduct and demonstrated a meaningful decrease in the rates of nonmajor violations in each, over the course of 2 years.

The paired training curriculum and monitoring program is a successful institution-wide clinical research regulatory compliance model that will continue to be refined.

Incorporating real-world data using “big data” analysis in healthcare are useful to extract specific information for healthcare delivery system improvement. All-cause mortality is an essential measure to enhance patient safety in clinical trial research, especially for underrepresented pediatric participants.

This study aimed to determine the associations between pediatric mortality and patient-specific factors using the Healthcare Cost and Utilization Project (HCUP) database.

Data from the 2019 the HCUP Kids’ Inpatient Database (KID) were used to conduct a logistic regression analysis to determine associations between pediatric patients’ the chance of survival and their demographic and socioeconomic background, discharge records, and hospital information.

Total number of diagnoses (OR = 0.84), total number of procedures (OR = 0.86), length of stay (OR = 1.04), age intervals greater than 1 year (OR > 1.0), transfer into the hospital from a different acute care (OR = 0.34), major diagnoses of multiple significant trauma (OR = 0.03) or hepatobiliary system and pancreas (OR = 0.10), region of hospital – west and midwest (OR > 1.0), and medium or larger hospital bed size (OR > 1.0) were all significantly associated with the chance of survival for patients participating in pediatric clinical trials (p < 0.05).

Real-world clinical trial data analysis showed the potential improvement area including reallocating trial resources to promote trial quality and safe participation for pediatric patients. Pediatric trials need tools that are developed using user-centered design approaches to satisfy the unique needs and requirements of pediatric patients and their caregivers. Safe intrahospital transfer procedures and active dissemination of successful trial best practices are crucial to trial management, adherence, quality, and safety.

There is increasing pressure on the federal research budget and shifting public opinions about the value of the academic enterprise. We must develop and apply metrics that demonstrate the broad benefits of research for health and society. The Translational Science Benefits Model (TSBM) measures the impact of large-scale translational science initiatives, such as the National Cancer Institute’s Cancer Moonshot. TSBM provides the scaffolding to illustrate how science has real-world health impacts. We propose an expansion of the TSBM to explicitly include implementation-focused outcomes.

TSBM includes four categories of benefits, including (1) clinical and medical, (2) community and public health, (3) economic, and (4) policy and legislative. Implementation science outcomes serve as a precursor to the model’s established domains of impact and can help to sharpen focus on the translational steps needed to achieve a broad range of impacts. We provide several examples of studies that illustrate these implementation outcomes and other clinical and community benefits.

It is important to consider a broad range of scientific impacts and the conditions that are necessary to achieve them. The expansion of the TSBM to include implementation science outcomes may help to accelerate the cancer community’s ability to achieve the goal of preventing 4 million cancer deaths by 2047.

As posthumous data use policy within the broader scope of navigating postmortem data privacy is a procedurally complex landscape, our study addresses this by exploring patterns in individuals’ willingness to donate data with health researchers after death and developing practical recommendations.

An electronic survey was conducted in April 2021 among adults (≥18 years of age) registered in ResearchMatch (www.researchmatch.org), a national health research registry. Descriptive statistics were used to observe trends in, and multinomial logistic regression analyses were conducted at a 95% confidence interval to determine the association between, willingness to donate some, all, or no data after death with researchers based on the participants’ demographics (education level, age range, duration of using online medical websites, and annual frequency of getting ill).

Of 399 responses, most participants were willing to donate health data (electronic medical record data [67%], prescription history data [63%], genetic data [54%], and fitness tracker data [53%]) after death. Among 397 respondents, we identified that individuals were more likely to donate some data after death (vs. no data) if they had longer duration of using online medical websites (adjusted relative risk ratio = 1.22, p= 0.04, 95% CI: 1.01 to 1.48). No additional significant findings were observed between willingness to donate all, some, or none of their data after death and other demographic factors.

Engaging patients in online medical websites may be one potential mechanism to encourage or inspire individuals to participate in posthumous data donation for health research purposes.

Individuals who play video games on computers and cellphones may have better psychomotor skills. It is unknown whether simulated driving performance varies between individuals who play video games more per week compared to individuals who play less. This study investigates whether initial simulated driving performance differs between high and low gamers during a brief (e.g., 10 minutes) driving simulation.

Data for this study were collected at baseline during enrollment for a randomized clinical trial (n = 40). Participants playing video games for > 10 hours/week were categorized as the high gaming group; others were in the low gaming group. Each participant drove the same simulation on the STISIM M1000 simulator, which recorded various driving performance metrics (e.g., driving out of lane and time to initial collision). Data between the groups were compared using Cox proportional hazards and analysis of covariance regression models.

The average age of participants was 21 ± 2.7 years and 48% were male. After adjusting for age, sex, and miles driven per week, the high gaming group spent a mean 4% less time driving out of lane compared to the low gaming group (β = –4.03, SD = 1.32, p ≤ 0.05). No other differences were observed between groups for any other outcome.

With the exception of percentage time driving out of lane, the number of hours gaming per week does not seem to impact an individual’s initial driving performance on a driving simulator. These findings may inform future driving simulation research methodology.

The Fibrosis-4 (FIB-4) index, a simple index that includes age, liver enzymes, and platelet count has been studied as a tool to identify patients at a risk of requiring mechanical ventilation due to its high negative predictive value. It is unknown if FIB-4 remains useful to predict the severity of respiratory disease requiring mechanical ventilation amongst new Coronavirus disease 2019 (COVID-19) variants and whether a relationship also exists between FIB-4 and 30-day mortality. The main objective was to determine if FIB-4 can predict mechanical ventilation requirements and 30-day mortality from COVID-19 across variants including Alpha, Delta, and Omicron.

This was a population-based, retrospective cohort analysis of 232,364 hospitalized patients in the National COVID-19 Cohort Collaborative between the age of 18–90 who tested positive for COVID-19 between April 27, 2020 and June 25, 2022. The primary outcome was association between FIB-4 and need for mechanical ventilation. Secondary measures included the association of FIB-4 with 30-day mortality.

A FIB-4 > 2.67 had 1.8 times higher odds of requiring mechanical ventilation across all variants of COVID-19 (OR 1.81; 95% CI: [1.76, 1.86]). The area under the ROC curve showed high diagnostic accuracy with values ranging between 0.79 (Omicron wave) and 0.97 (delta wave). Increased FIB-4 was associated with 30-day mortality across the variates.

The FIB-4 was consistently associated with both increased utilization of mechanical ventilation and 30-day mortality among COVID-19 patients across all waves in both adjusted and unadjusted models. This provides a simple tool for risk-stratification for front-line health care professionals.