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.

The application of digital monitoring biomarkers in health, wellness and disease management is reviewed. Harnessing the near limitless capacity of these approaches in the managed healthcare continuum will benefit from a systems-based architecture which presents data quality, quantity, and ease of capture within a decision-making dashboard.

A framework was developed which stratifies key components and advances the concept of contextualized biomarkers. The framework codifies how direct, indirect, composite, and contextualized composite data can drive innovation for the application of digital biomarkers in healthcare.

The de novo framework implies consideration of physiological, behavioral, and environmental factors in the context of biomarker capture and analysis. Application in disease and wellness is highlighted, and incorporation in clinical feedback loops and closed-loop systems is illustrated.

The study of contextualized biomarkers has the potential to offer rich and insightful data for clinical decision making. Moreover, advancement of the field will benefit from innovation at the intersection of medicine, engineering, and science. Technological developments in this dynamic field will thus fuel its logical evolution guided by inputs from patients, physicians, healthcare providers, end-payors, actuarists, medical device manufacturers, and drug companies.

There is a clear need to educate and train the clinical research workforce to conduct scientifically sound clinical research. Meeting this need requires the creation of tools to assess both an individual’s preparedness to function efficiently in the clinical research enterprise and tools to evaluate the quality and effectiveness of programs that are designed to educate and train clinical research professionals. Here we report the development and validation of a competency self-assessment entitled the Competency Index for Clinical Research Professionals, version II (CICRP-II).

CICRP-II was developed using data collected from clinical research coordinators (CRCs) participating in the “Development, Implementation and Assessment of Novel Training In Domain-Based Competencies” (DIAMOND) project at four clinical and translational science award (CTSA) hubs and partnering institutions.

An exploratory factor analysis (EFA) identified a two-factor structure: the first factor measures self-reported competence to perform Routine clinical research functions (e.g., good clinical practice regulations (GCPs)), while the second factor measures competence to perform Advanced clinical functions (e.g., global regulatory affairs). We demonstrate the between groups validity by comparing CRCs working in different research settings.

The excellent psychometric properties of CICRP-II and its ability to distinguish between experienced CRCs at research-intensive CTSA hubs and CRCs working in less-intensive community-based sites coupled with the simplicity of alternative methods for scoring respondents make it a valuable tool for gauging an individual’s perceived preparedness to function in the role of CRC as well as an equally valuable tool to evaluate the value and effectiveness of clinical research education and training programs.

Implementation scientists increasingly recognize that the process of implementation is dynamic, leading to ad hoc modifications that may challenge fidelity in protocol-driven interventions. However, limited attention to ad hoc modifications impairs investigators’ ability to develop evidence-based hypotheses about how such modifications may impact intervention effectiveness and cost. We propose a multi-method process map methodology to facilitate the systematic data collection necessary to characterize ad hoc modifications that may impact primary intervention outcomes.

We employ process maps (drawn from systems science), as well as focus groups and semi-structured interviews (drawn from social sciences) to investigate ad hoc modifications. Focus groups are conducted with the protocol’s developers and/or planners (the implementation team) to characterize the protocol “as envisioned,” while interviews conducted with frontline administrators characterize the process “as realized in practice.” Process maps with both samples are used to identify when modifications occurred across a protocol-driven intervention. A case study investigating a multistage screening protocol for autism spectrum disorders (ASD) is presented to illustrate application and utility of the multi-method process maps.

In this case study, frontline administrators reported ad hoc modifications that potentially influenced the primary study outcome (e.g., time to ASD diagnosis). Ad hoc modifications occurred to accommodate (1) whether providers and/or parents were concerned about ASD, (2) perceptions of parental readiness to discuss ASD, and (3) perceptions of family service delivery needs and priorities.

Investigation of ad hoc modifications on primary outcomes offers new opportunities to develop empirically based adaptive interventions. Routine reporting standards are critical to provide full transparency when studying ad hoc modifications.

Trainees and investigators from underrepresented minority (URM) backgrounds face unique challenges to establishing successful careers in clinical and translational research. Structured training for mentors is an important mechanism to increase the diversity of the research workforce. This article presents data from an evaluation of the University of California, San Francisco (UCSF) Center for AIDS Research (CFAR) Mentoring the Mentors program aimed at improving mentors’ competency in working with diverse mentees in HIV research.

Mentors from around the USA who had in one of seven separate 2-day training workshops conducted from 2013 to 2020 were invited to participate in an online evaluation survey of their experiences with the training and their subsequent mentoring activities.

There was a high response rate (80%) among the 226 mentors invited to complete the survey. The 180 respondents were diverse in demographics, professional disciplines, and geographic distribution. Quantitative and qualitative data indicate a lasting positive impact of the training, with sustained improvements documented on a validated measure of self-appraised mentoring competency. Respondents also endorsed high interest in future, follow-up training with continued focus on topics related to mentoring in the context of diversity.

The evaluation of the UCSF CFAR Mentoring the Mentors program showed lasting impact in improving mentoring practices, coupled with high interest in continued in-depth training in areas focused on diversity, equity, and inclusion.

Lack of participation in clinical trials (CTs) is a major barrier for the evaluation of new pharmaceuticals and devices. Here we report the results of the analysis of a dataset from ResearchMatch, an online clinical registry, using supervised machine learning approaches and a deep learning approach to discover characteristics of individuals more likely to show an interest in participating in CTs.

We trained six supervised machine learning classifiers (Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor Classifier (KNC), Adaboost Classifier (ABC) and a Random Forest Classifier (RFC)), as well as a deep learning method, Convolutional Neural Network (CNN), using a dataset of 841,377 instances and 20 features, including demographic data, geographic constraints, medical conditions and ResearchMatch visit history. Our outcome variable consisted of responses showing specific participant interest when presented with specific clinical trial opportunity invitations (‘yes’ or ‘no’). Furthermore, we created four subsets from this dataset based on top self-reported medical conditions and gender, which were separately analysed.

The deep learning model outperformed the machine learning classifiers, achieving an area under the curve (AUC) of 0.8105.

The results show sufficient evidence that there are meaningful correlations amongst predictor variables and outcome variable in the datasets analysed using the supervised machine learning classifiers. These approaches show promise in identifying individuals who may be more likely to participate when offered an opportunity for a clinical trial.

In response to a call issued by the National Research Council to investigate the knowledge, skills, and attitudes of effective science teams, we designed a team training program for conducting science in collaborative contexts.

We reviewed the literature to develop an evidence-based competency model for effective science teams along with exemplary behaviors that can be used for founding team training and evaluation. We discuss the progress of teamwork and team development research that serves as a foundation for this work, as well as previous research involving team-based competencies.

Three overarching competencies emerged from the literature as key for science team effectiveness: psychological safety, awareness and exchange, and self-correction and adaptation. These competencies are fully described, including their evidence base.

We developed a competency model and implementation plan for a team training program specific to science teams – TeamMAPPS (Team Methods to Advance Processes and Performance in Science). This paper details steps in the implementation process, including plans for consortia dissemination, evaluation, and future development.

Clinical and Translational Science Award Program (CTSA)-funded institutions were charged with developing clinical and translational science programs and transforming clinical research at their institutions. Community engagement (CE) was recognized as a key component and catalyst of that transformation. CE hub capacities for working with communities and translating knowledge into practice have been illustrated through their COVID-19 responses.

CE hub leaders met and discussed their CTSA’s early responses regarding the COVID-19 pandemic. The 2-hour discussion was distilled into themes which were sent to the CE hub leaders with a request for written accounts describing actions taken to engage local partners, communities, and institutions. The written reports form the basis for this compendium.

Eighteen institutions submitted written reports describing activities in relation to six themes: (1) listen to the community and respond to concerns, (2) collect data to understand the impact of COVID-19 on distinct communities and groups, (3) communicate science and address misinformation, (4) collaborate with health departments, (5) engage hubs and underrepresented populations in COVID-19 research, and (6) support our own well-being and that of others.

Bidirectional interactions comprise the foundation of CE, which requires trusted partnerships that sustain communication through a series of activities and goals. The nimble responses to the pandemic substantiate the need for CE programs to maintain the infrastructure necessary to achieve the primary CTSA goals of improving health within and across communities and localities as well as expanding research participation of community members.