Journal of Clinical and Translational Science最新文献

Introduction: Endometriosis and chronic pelvic pain (CPP) are complex conditions that significantly impact quality of life. Few tools systematically capture patient-reported outcomes in this population. This pilot study evaluated patients' experiences and the perceived usability of an electronic Patient-Reported Outcome (ePRO) tool to assess its feasibility in supporting a clinical data registry. Associations between demographic/clinical characteristics and ePRO usability were also explored.

Methods: This prospective observational study included patients enrolled at a tertiary endometriosis and CPP clinic who completed a REDCap-based ePRO survey remotely. The survey included demographic items and 13 validated instruments assessing pain, psychological distress, sensory processing, and quality of life. Usability was evaluated through an Online Questionnaire-Experiences Survey (OQES), covering accessibility, completion experience, redundancy, and content relevance. Descriptive statistics, t-tests, and Hedges' g were used for analysis; open-ended responses were thematically reviewed.

Results: Fourteen patients were invited; 11 (78.6%) completed the full ePRO. Most found it easy to access (90.9%) with stable internet (100%). While 63.6% reported some redundancy, none reported discomfort, and 90.9% agreed the survey captured relevant experiences. Participants with higher Central Sensitization Inventory (CSI) and Generalized Anxiety Disorder-7 (GAD-7) scores were more likely to complete all items (P = 0.042 and .047). Those who did not perceive redundancy scored significantly higher on the Pain Catastrophizing Scale (P = .048) and Endometriosis Health Profile-30 (P = .016).

Conclusion: The ePRO tool showed high feasibility. Patients with higher symptom burden were more likely to find it useful. Future improvements should reduce redundancy and clarify survey instructions.

Reference standards are vital for harmonizing heterogeneous clinical data for research, but little is known about their implementations or costs. We surveyed NIH Clinical and Translational Science Awards (CTSA) hubs to assess operational dimensions of institutional implementation, maintenance, and use of the Logical Observation Identifiers, Names, and Codes (LOINC) standard. Respondents (n = 19,30%) exhibited substantial variability in approaches to implementation. Differences in number and training of staff and frequency of mapping updates make it difficult to estimate costs and comparability of data across sites. CTSA and other multi-site research can benefit from operational definitions and recommended processes for LOINC implementation.

Background: Artificial intelligence (AI) technology is rapidly entering biomedical research, and there is a need to assess and develop curricula that address trainees' learning objectives and interests. Studies of biomedical workforce development show that the prospective engagement of students in formulating educational objectives and activities improves motivation and learning outcomes. This study aimed to explore the educational applications of a novel AI-powered technology in undergraduate education.

Methods: A mixed-methods approach using elicitation interviews and cultural domain analysis was applied to identify the salience of ideas around the educational uses of Functional Unit State Identification & Navigation with Whole Slide Images (FUSION), an AI-powered cell-visualization technology. Interviews from 21 students were reduced to learning application statements and assessed for cultural salience and clustering for potential educational applications.

Results: Saturation was reached after 11 interviews, and analysis resulted in eight clusters of 25 unique consensus-based statements. Students thought of the technology as a tool for cell analysis and measuring, but they also viewed applications for medical and K-12 education, public engagement, and note-keeping for technology research. Methodologically, our study demonstrates the potential of cultural consensus for learner-centered curriculum development.

Conclusions: Our findings suggest that trainees perceive many educational uses of FUSION, including those that fit traditional biomedical research curricula and translational applications. Trainees should be engaged in co-design to support and guide technology translation for educational use.

Advancing community engagement and participatory research approaches necessitates shifting cultural norms. The paper describes a program designed to explicitly embed and reinforce a culture of engagement through resource allocation, modeling, and recognition that was initiated by a Clinical and Translational Science Institute Community Engagement Program CE Program. Resources were allocated to the relationship development process between researchers and community partners. Funded partnerships were provided with guidance to support the equitable distribution of resources. Partnerships received additional reinforcement through participation in a learning collaborative, intended to support community partnership development, model best practices in community engagement and to build a network of community engaged, and participatory researchers at the institution. Investigators reported the learning collaborative "gave them permission" to focus on the process. Overall, lessons learned indicate embedding and reinforcing practices that center relationship and reward time spent building partnerships is a promising strategy to buffer against cultural norms that favor outcomes and over process.

Introduction: Mentoring is an important developmental tool for academic scientists. The match between mentor and mentee is a critical factor than can influence outcomes for mentees. However, we know little about what factors should be considered when matching mentors and mentees. We draw on person-environment fit theory to examine how different factors related to the fit between mentors and mentees may influence outcomes for mentees in health-related scientific disciplines.

Methods: Data were collected from 76 mentor-mentee pairs who participated in a nine-month mentoring program for scientists interested in clinical and translational research. An index of supplementary fit was calculated to reflect mentor-mentee similarity in terms of race/ethnicity, gender, academic discipline, professional track, percent time allocated to research, and type of research. Complementary fit reflected the proportion of skills mentees identified as needs that their mentor felt comfortable providing. Mentee outcomes assessed included satisfaction with one's mentor, learning and development experiences, and short-term research output.

Results: As predicted, we found that supplementary fit was positively related to mentee satisfaction with the mentor. We found no support for the expected relationship between complementary fit and mentee learning development experiences or short-term research output. Supplementary analyses explored other non-hypothesized relationships among study variables.

Conclusions: This research underscores the need to consider different types of fit when matching academic mentors and mentees in clinical and translational science-related disciplines. Our results can be leveraged during the matching process in academic mentoring programs to maximize the success of mentoring relationships for scientists in health-related fields.

Evaluation teams have been critical to the success of Clinical and Translational Science Award (CTSA) programs funded by the National Center for Advancing Translational Sciences (NCATS). Given the limited resources often available to evaluation teams and the growing emphasis on impact evaluation and continuous quality improvement (CQI), CTSA programs may need to develop innovative strategies to build capacity for effectively implementing CQI and impact evaluation, while still tracking commonly reported metrics. To address this challenge, the Boston University (BU) Clinical and Translational Science Institute (CTSI) partnered with the BU Hariri's Software and Application Innovation Lab (SAIL) to develop a web-based digital tool, known as TrackImpact, that streamlines data collection, saving significant time and resources, and increasing evaluation team capacity for other activities. Time and cost saving analyses are used to demonstrate how we increased evaluation team capacity by using this innovative digital tool.

Numerous symposia and conferences have been held to discuss the promise of Artificial Intelligence (AI). Many center on its potential to transform fields like health and medicine, law, education, business, and more. Further, while many AI-focused events include those data scientists involved in developing foundational models, to our knowledge, there has been little attention on AI's role for data science and the data scientist. In a new symposium series with its inaugural debut in December 2024 titled AI for Data Science, thought leaders convened to discuss both the promises and challenges of integrating AI into the workflows of data scientists. A keynote address by Michael Pencina from Duke University together with contributions from three panels covered a wide range of topics including rigor, reproducibility, the training of current and future data scientists, and the potential of AI's integration in public health.

Introduction: Decentralized clinical trials (DCTs) are often hindered by challenges in remotely capturing biomarkers. To address this gap, we developed MyTrials, a mobile application integrated with REDCap, designed to facilitate the remote capture of biomarkers via Bluetooth-enabled remote patient monitoring (RPM) devices. The purpose of the present study was to evaluate the feasibility and acceptability of MyTrials among participants within a DCT design.

Methods: In this four-arm randomized trial, 47 participants were allocated to receive zero, one, two, or three RPM devices. Participants were asked to use their devices once per week for a total of four weeks to remotely provide biomarkers via MyTrials. Feasibility was assessed using objective metrics of successful biomarker submission (i.e., valid device data accompanied by a video confirming participant identity) alongside the participant-reported Feasibility of Intervention Measure (FIM). Acceptability was evaluated via the Acceptability of Intervention Measure (AIM) and the System Usability Scale (SUS).

Results: Among participants assigned at least one device, the successful biomarker submission rate was 74% across all study weeks. FIM and AIM scores exceeded prespecified feasibility benchmarks across all conditions except the zero-device condition. SUS scores consistently indicated high usability across all conditions (range: 77.29-94.29).

Conclusions: The MyTrials platform is a feasible and acceptable solution for remote biomarker capture in DCTs. These findings support the potential of MyTrials to advance remote data collection in clinical research.

Introduction: Traumatic brain injury (TBI) is a leading cause of disability and death. Both repetitive transcranial magnetic stimulation (rTMS) and Cerebrolysin (CRB) are promising therapies regulating neural plasticity. This study aimed to assess the changes in resting-state brain activity following CRB, rTMS, or combined CRB-rTMS therapy.

Methods: This secondary analysis of the CAPTAIN-rTMS trial analyzed eyes-closed segments from EEG recordings at 30 days (baseline) and 180 days (after treatment) respectively. We computed relative power spectral densities for delta, theta, alpha and beta frequency bands, for the entire scalp and different regions. We conducted neuropsychological assessments and evaluated the correlations between resting-state relative power spectral density values and neuropsychological assessment performance.

Results: We analyzed a total of 50 patients. For the entire scalp, we found statistically significant decreases in relative alpha power (p = 0.02) and significant increases in relative delta power (p = 0.02), further subgroup analysis showing differences between visits in the CRB + sham group (paired Cliff's δ = 0.6, p = 0.012 for Delta band, δ = 0.6, p = 0.064 for Alpha band). The differences were higher in the central (alpha p = 0.004, delta p = 0.002) and parietal (alpha p = 0.012, delta p = 0.03), and lower in the frontal (alpha p = 0.05, delta p = 0.026), temporal (alpha p = 0.065, delta p = 0.077), and occipital (alpha p = 0.064, delta p = 0.084) regions. Neuropsychological tests performance was negatively correlated with resting-state relative delta power, and positively correlated with alpha power.

Conclusion: We found overall slowing of brain electrical activity during recovery after TBI, which was further influenced by rTMS and CRB treatment. Resting-state relative power spectral densities correlate with neuropsychological measurements.