We are developing the novel αIIbβ3 antagonist, RUC-4, for subcutaneously (SC)-administered first-point-of-medical-contact treatment for ST segment elevation myocardial infarction (STEMI).

We studied the (1) pharmacokinetics (PK) of RUC-4 at 1.0, 1.93, and 3.86 mg/kg intravenous (IV), intramuscular (IM), and SC in non-human primates (NHPs); (2) impact of aspirin on RUC-4 IC50 in human platelet-rich plasma (PRP); (3) effect of different anticoagulants on the RUC-4 IC50 in human PRP; and (4) relationship between αIIbβ3 receptor blockade by RUC-4 and inhibition of ADP-induced platelet aggregation.

(1) All doses of RUC-4 were well tolerated, but animals demonstrated variable temporary bruising. IM and SC RUC-4 reached dose-dependent peak levels within 5–15 minutes, with T1/2 s between 0.28 and 0.56 hours. Platelet aggregation studies in NHPs receiving IM RUC-4 demonstrated >80% inhibition of the initial slope of ADP-induced aggregation with all three doses 30 minutes post-dosing, with subsequent dose-dependent loss of inhibition over 4–5 hours. (2) The RUC-4 IC50 for ADP-induced platelet aggregation was unaffected by aspirin treatment (40±9 nM vs 37±5 nM; p = 0.39). (3) The RUC-4 IC50 was significantly higher in PRP prepared from D-phenylalanyl-prolyl-arginyl chloromethyl ketone (PPACK)-anticoagulated blood compared to citrate-anticoagulated blood using either thrombin receptor activating peptide (TRAP) (122±17 vs 66±25 nM; p = 0.05; n = 4) or ADP (102±22 vs 54±13; p<0.001; n = 5). (4) There was a close correspondence between receptor blockade and inhibition of ADP-induced platelet aggregation, with aggregation inhibition beginning with ~40% receptor blockade and becoming nearly complete at >80% receptor blockade.

Based on these results and others, RUC-4 has now progressed to formal preclinical toxicology studies.

Dissemination and implementation (D&I) science is not a formal element of the Clinical Translational Science Award (CTSA) Program, and D&I science activities across the CTSA Consortium are largely unknown.

The CTSA Dissemination, Implementation, and Knowledge Translation Working Group surveyed CTSA leaders to explore D&I science-related activities, barriers, and needed supports, then conducted univariate and qualitative analyses of the data.

Out of 67 CTSA leaders, 55.2% responded. CTSAs reported directly funding D&I programs (54.1%), training (51.4%), and projects (59.5%). Indirect support (e.g., promoted by CTSA without direct funding) for D&I activities was higher – programs (70.3%), training (64.9%), and projects (54.1%). Top barriers included funding (39.4%), limited D&I science faculty (30.3%), and lack of D&I science understanding (27.3%). Respondents (63.4%) noted the importance of D&I training and recommended coordination of D&I activities across CTSAs hubs (33.3%).

These findings should guide CTSA leadership in efforts to raise awareness and advance the role of D&I science in improving population health.

Poor clinical trial (CT) recruitment is a significant barrier to translating basic science discoveries into medical practice. Improving support for primary care provider (PCP) referral of patients to CTs may be an important part of the solution. However, implementing CT referral support in primary care is not only technically challenging, but also presents challenges at the person and organization levels.

The objectives of this study were (1) to characterize provider and clinical supervisor attitudes and perceptions regarding CT research, recruitment, and referrals in primary care and (2) to identify perceived workflow strategies and facilitators relevant to designing a technology-supported primary care CT referral program. Focus groups were conducted with PCPs, directors, and supervisors.

Analysis indicated widespread support for the intrinsic scientific value of CTs, while at the same time deep concerns regarding protecting patient well-being, perceived loss of control when patients participate in trials, concern about the impact of point-of-care referrals on clinic workflow, the need for standard processes, and the need for CT information that enables referring providers to quickly confirm that the burdens are justified by the benefits at both patient and provider levels. PCP suggestions pertinent to implementing a CT referral decision support system are reported.

The results from this work contribute to developing an implementation approach to support increased referral of patients to CTs.

Electronic health record (EHR) data have emerged as an important resource for population health and clinical research. There have been significant efforts to leverage EHR data for research; however, given data security concerns and the complexity of the data, EHR data are frequently difficult to access and use for clinical studies. We describe the development of a Clinical Research Datamart (CRDM) that was developed to provide well-curated and easily accessible EHR data to Duke University investigators.

The CRDM was designed to (1) contain most of the patient-level data elements needed for research studies; (2) be directly accessible by individuals conducting statistical analyses (including Biostatistics, Epidemiology, and Research Design (BERD) core members); (3) be queried via a code-based system to promote reproducibility and consistency across studies; and (4) utilize a secure protected analytic workspace in which sensitive EHR data can be stored and analyzed. The CRDM utilizes data transformed for the PCORnet data network, and was augmented with additional data tables containing site-specific data elements to provide additional contextual information.

We provide descriptions of ideal use cases and discuss dissemination and evaluation methods, including future work to expand the user base and track the use and impact of this data resource.

The CRDM utilizes resources developed as part of the Clinical and Translational Science Awards (CTSAs) program and could be replicated by other institutions with CTSAs.

Mentorship is critical for faculty success, satisfaction, and engagement. However, many faculty, particularly underrepresented racial/ethnic (UR) faculty, lack access to high-quality mentoring. In an effort to improve mentoring for all faculty, we developed and implemented a formally structured faculty mentor training program (FMTP) across UC San Diego Health Sciences, which included institutional support, mentorship training, and department/division mentorship programs.

FMTP impact was evaluated using three primary outcome variables: mentoring quality, mentoring behaviors, and institutional climate. Participants’ self-assessed mentoring competencies were measured using validated instruments.

A total of 391 (23%) of Health Sciences faculty participated in FMTP. Participation rate was higher for women than men (30% versus 17%) and highest for UR faculty (39%). FMTP was implemented in 16 of 19 departments. Self-reported mentoring improved for FMTP participants with mentoring quality (p = 0.009) and meeting mentees’ expectations (p = 0.01) continuing to improve for up to 2 years after training. However, participants were unsure if they were meeting UR mentees’ expectations. FMTP participants were significantly more satisfied with mentoring quality (p < 0.001) compared to non-participants, with the greatest increase in satisfaction reported by UR faculty (38–61%). UR faculty reported improved overall morale (51–61%) and a perception that the environment was supportive for UR faculty (48–70%).

The implementation of a system-wide formal structured FMTP was associated with improved faculty satisfaction, quality of mentoring, and institutional climate, especially for UR faculty.

Scientific endeavors are increasingly carried out by teams of scientists. While there is growing literature on factors associated with effective science teams, little is known about processes that facilitate the success of dissemination and implementation (D&I) teams studying the uptake of healthcare innovations. This study aimed to identify strategies used by D&I scientists to promote team science.

Using a nominal group technique, a sample of 27 D&I scholars responded to the question, “What strategies have you or others used to promote team science?” Participants were asked to individually respond and then discuss within a small group to determine the group’s top three strategies. Through a facilitated consensus discussion with the full sample, a rank-ordered list of three strategies was determined.

A total of 126 individual responses (M = 9; SD = 4.88) were submitted. Through small group discussion, six groups ranked their top three strategies to promote team science. The final ranked list of strategies determined by the full sample included: (1) developing and maintaining clear expectations, (2) promoting and modeling effective communication, and (3) establishing shared goals and a mission of the work to be accomplished.

Because of its goal of translating knowledge to practice, D&I research necessitates the use of team science. The top strategies are in line with those found to be effective for teams in other fields and hold promise for improving D&I team cohesion and innovation, which may ultimately accelerate the translation of health innovations and the improvement of care quality and outcomes.

The purpose of this paper is to describe the formation, operation, and evaluation of a Community Engagement Advisory Board (CEAB) that serves as a resource of the University of Illinois at Chicago’s (UIC) Center for Clinical and Translational Sciences (CCTS).

Current CEAB roles and functions, operating procedures for research consultations and program evaluation strategies were described. Investigators receiving a consultation from 2009 to 2017 (n=91, response rate 78%) were surveyed via an online survey immediately after the consultation and at 12-month follow-up.

Overall, CEAB members were viewed as having sufficient information (92%) and expertise (79%) to provide consultation. Satisfaction levels with the specific consultation received and the overall consultation service were high. The majority of investigators indicated that they would come back to the CEAB for a future consultation, if needed, and would recommend a consultation to others (93% and 96%, respectively). At 12-months, 87% of respondents indicated they had implemented at least some of the recommendations received and 93% said that the consultation influenced their subsequent research.

Data from recent annual evaluations highlight the benefits of CEAB for consulting investigators. Our model can be used to inform the development of future CEAB boards.

To enhance the performance evaluation of Clinical and Translational Science Award (CTSA) hubs, we examined the utility of advanced bibliometric measures that go beyond simple publication counts to demonstrate the impact of translational research output.

The sampled data included North Carolina Translational and Clinical Science Institute (NC TraCS)-supported publications produced between September 2008 and March 2017. We adopted advanced bibliometric measures and a state-of-the-art bibliometric network analysis tool to assess research productivity, citation impact, the scope of research collaboration, and the clusters of research topics.

Totally, 754 NC TraCS-supported publications generated over 24,000 citation counts by April 2017 with an average of 33 cites per article. NC TraCS-supported research papers received more than twice as many cites per year as the average National Institute of Health-funded research publications from the same field and time. We identified the top productive researchers and their networks within the CTSA hub. Findings demonstrated the impact of NC TraCS in facilitating interdisciplinary collaborations within the CTSA hub and across the CTSA consortium and connecting researchers with right peers and organizations.

Both improved bibliometrics measures and bibliometric network analysis can bring new perspectives to CTSA evaluation via citation influence and the scope of research collaborations.

Non-alcoholic fatty liver disease (NAFLD) is an increasing cause of chronic liver disease that accompanies obesity and the metabolic syndrome. Excess fructose consumption can initiate or exacerbate NAFLD in part due to a consequence of impaired hepatic fructose metabolism. Preclinical data emphasized that fructose-induced altered gut microbiome, increased gut permeability, and endotoxemia play an important role in NAFLD, but human studies are sparse. The present study aimed to determine if two weeks of excess fructose consumption significantly alters gut microbiota or permeability in humans.

We performed a pilot double-blind, cross-over, metabolic unit study in 10 subjects with obesity (body mass index [BMI] 30–40 mg/kg/m2). Each arm provided 75 grams of either fructose or glucose added to subjects’ individual diets for 14 days, substituted isocalorically for complex carbohydrates, with a 19-day wash-out period between arms. Total fructose intake provided in the fructose arm of the study totaled a mean of 20.1% of calories. Outcome measures included fecal microbiota distribution, fecal metabolites, intestinal permeability, markers of endotoxemia, and plasma metabolites.

Routine blood, uric acid, liver function, and lipid measurements were unaffected by the fructose intervention. The fecal microbiome (including Akkermansia muciniphilia), fecal metabolites, gut permeability, indices of endotoxemia, gut damage or inflammation, and plasma metabolites were essentially unchanged by either intervention.

In contrast to rodent preclinical findings, excess fructose did not cause changes in the gut microbiome, metabolome, and permeability as well as endotoxemia in humans with obesity fed fructose for 14 days in amounts known to enhance NAFLD.

Although classical randomized clinical trials (RCTs) are the gold standard for proof of drug efficacy, randomized discontinuation trials (RDTs), sometimes called “enriched” trials, are used increasingly, especially in psychiatric maintenance studies.

A narrative review of two decades of experience with RDTs.

RDTs in psychiatric maintenance trials tend to use a dependent variable as a predictor: treatment response. Treatment responders are assessed for treatment response. This tautology in the logic of RDTs renders them invalid, since the predictor and the outcome are the same variable. Although RDTs can be designed to avoid this tautologous state of affairs, like using independent predictors of outcomes, such is not the case with psychiatric maintenance studies

Further, purported benefits of RDTs regarding feasibility were found to be questionable. Specifically, RDTs do not enhance statistical power in many settings, and, because of high dropout rates, produce results of questionable validity. Any claimed benefits come with notably reduced generalizability.

RDTs appear to be scientifically invalid as used in psychiatric maintenance designs. Their purported feasibility benefits are not seen in actual trials for psychotropic drugs. There is warrant for changes in federal policy regarding marketing indications for maintenance efficacy using the RDT design.

Personalized medicine has exposed wearable sensors as new sources of biomedical data which are expected to accrue annual data storage costs of approximately $7.2 trillion by 2020 (>2000 exabytes). To improve the usability of wearable devices in healthcare, it is necessary to determine the minimum amount of data needed for accurate health assessment.

Here, we present a generalizable optimization framework for determining the minimum necessary sampling rate for wearable sensors and apply our method to determine optimal optical blood volume pulse sampling rate. We implement t-tests, Bland–Altman analysis, and regression-based visualizations to identify optimal sampling rates of wrist-worn optical sensors.

We determine the optimal sampling rate of wrist-worn optical sensors for heart rate and heart rate variability monitoring to be 21–64 Hz, depending on the metric.

Determining the optimal sampling rate allows us to compress biomedical data and reduce storage needs and financial costs. We have used optical heart rate sensors as a case study for the connection between data volumes and resource requirements to develop methodology for determining the optimal sampling rate for clinical relevance that minimizes resource utilization. This methodology is extensible to other wearable sensors.