Sharing data from clinical studies can accelerate scientific progress, improve transparency, and increase the potential for innovation and collaboration. However, privacy concerns remain a barrier to data sharing. Certain concerns, such as reidentification risk, can be addressed through the application of anonymization algorithms, whereby data are altered so that it is no longer reasonably related to a person. Yet, such alterations have the potential to influence the data set\'s statistical properties, such that the privacy-utility trade-off must be considered. This has been studied in theory, but evidence based on real-world individual-level clinical data is rare, and anonymization has not broadly been adopted in clinical practice.
The goal of this study is to contribute to a better understanding of anonymization in the real world by comprehensively evaluating the privacy-utility trade-off of differently anonymized data using data and scientific results from the German Chronic Kidney Disease (GCKD) study.
The GCKD data set extracted for this study consists of 5217 records and 70 variables. A 2-step procedure was followed to determine which variables constituted reidentification risks. To capture a large portion of the risk-utility space, we decided on risk thresholds ranging from 0.02 to 1. The data were then transformed via generalization and suppression, and the anonymization process was varied using a generic and a use case-specific configuration. To assess the utility of the anonymized GCKD data, general-purpose metrics (ie, data granularity and entropy), as well as use case-specific metrics (ie, reproducibility), were applied. Reproducibility was assessed by measuring the overlap of the 95% CI lengths between anonymized and original results.
Reproducibility measured by 95% CI overlap was higher than utility obtained from general-purpose metrics. For example, granularity varied between 68.2% and 87.6%, and entropy varied between 25.5% and 46.2%, whereas the average 95% CI overlap was above 90% for all risk thresholds applied. A nonoverlapping 95% CI was detected in 6 estimates across all analyses, but the overwhelming majority of estimates exhibited an overlap over 50%. The use case-specific configuration outperformed the generic one in terms of actual utility (ie, reproducibility) at the same level of privacy.
Our results illustrate the challenges that anonymization faces when aiming to support multiple likely and possibly competing uses, while use case-specific anonymization can provide greater utility. This aspect should be taken into account when evaluating the associated costs of anonymized data and attempting to maintain sufficiently high levels of privacy for anonymized data.
German Clinical Trials Register DRKS00003971; https://drks.de/search/en/trial/DRKS00003971.
RR2-10.1093/ndt/gfr456.

Data-driven expert team formation is a complicated and multifaceted process that requires access to accurate information to identify researchers\' areas and level of expertise and their collaborative prospects. In this respect, bibliometric data represents a valuable and reliable source of information that can be effectively employed in revealing key insights regarding candidates. Due to its complex and complete structure of publication metadata records, IEEE Xplore database may offer the possibility to compute an extensive set of indicators about researchers\' publication production and how they have interacted during time. Considering the case of Politehnica University of Timisoara scholars for the interval 2010-2022, current dataset encapsulates relevant and rich information for assembling multidisciplinary research teams, being also a testing ground for experimenting and calibrating the expert team formation methods and mechanisms.

Social media is increasingly used for health queries and subspecialist selection, but physicians receive little training in its use. This case study describes use of the free data tool Facebook Audience Insights to understand population demographics relevant to an orthopedic practice. Facebook Audience Insights was used to compare demographics and activity patterns of two patient samples typical of total joint arthroplasty (TJA)-young TJA (ages 55-64 years) and Medicare TJA (age ≥65 years)-in May 2020. Creation of a professional Facebook page is described accompanied by the demonstration of Audience Insights to analyze regional user patterns. A local sample of Facebook users was then compared with a single orthopedic practice\'s Facebook traffic. Facebook use is common among patients undergoing TJA, and the proportion of self-identified women increases with age (young TJA, 53% women; Medicare TJA, 63% women). Women are more interactive Facebook users across all age ranges, with more frequent comments, \"Liked\" pages, and advertisement clicks. Analysis of a local Facebook population revealed a lower proportion of TJA-aged patients than the national cohort; however, TJA-aged patients represented 38% of the practice\'s Facebook page traffic, with a predominance of visitors being women (26% women, 12% men). Facebook demonstrates a high prevalence of users in the typical age range for TJA. Those users were common on an orthopedic practice Facebook page, suggesting social media may be an effective medium for engaging patients. [Orthopedics. 2024;47(2):e79-e84.].

Initially, research disciplines operated independently, but the emergence of trans-disciplinary sciences led to convergence research, impacting graduate programs and research laboratories, especially in bioengineering and material engineering as presented here. Current graduate curriculum fails to efficiently prepare students for multidisciplinary and convergence research, thus creating a gap between the students and research laboratory expectations. We present a convergence training framework for graduate students, incorporating problem-based learning under the guidance of senior scientists and collaboration with postdoctoral researchers. This case study serves as a template for transdisciplinary convergent training projects - bridging the expertise gap and fostering successful convergence learning experiences in computational biointerface (material-biology interface). The 18-month Advanced Data Science Workshop, initiated in 2019, involves project-based learning, online training modules, and data collection. A pilot solution utilized Jupyter notebook on Google collaborator and culminated in a face-to-face workshop where project presentations and finalization occurred. The program started with 9 experts in the four diverse fields creating 14 curated projects in data science (Artificial Intelligence/Machine Learning), material science, biofilm engineering, and biointerface. These were integrated into convergence research through webinars by the experts. The experts chose 8 of the 14 projects to be part of an all-day in-person workshop, where over 20 learners formed eight teams that tackled complex problems at the interface of digital image processing, gene expression analysis, and material prediction. Each team was comprised of students and postdoctoral researchers or research scientists from diverse domains including computer science, materials science, and biofilm research. Some projects were selected for presentation at the international IEEE Bioinformatics conference in 2022, with three resulting Machine Learning (ML) models submitted as a journal paper. Students engaged in problem discussions, collaborated with experts from different disciplines, and received guidance in decomposing learning objectives. Based on learner feedback, this successful experience allows for consolidation and integration of convergence research via problem-based learning into the curriculum. Three bioengineering participants, who received training in data science and engineering, have received bioinformatics jobs in biotechnology industries.

UNASSIGNED: Pathology reports are stored as unstructured, ungrammatical, fragmented, and abbreviated free text with linguistic variability among pathologists. For this reason, tumor information extraction requires a significant human effort. Recording data in an efficient and high-quality format is essential in implementing and establishing a hospital-based-cancer registry.
UNASSIGNED: This study aimed to describe implementing a natural language processing algorithm for oncology pathology reports.
UNASSIGNED: An algorithm was developed to process oncology pathology reports in Spanish to extract 20 medical descriptors. The approach is based on the successive coincidence of regular expressions.
UNASSIGNED: The validation was performed with 140 pathological reports. The topography identification was performed manually by humans and the algorithm in all reports. The human identified morphology in 138 reports and by the algorithm in 137. The average fuzzy matching score was 68.3 for Topography and 89.5 for Morphology.
UNASSIGNED: A preliminary algorithm validation against human extraction was performed over a small set of reports with satisfactory results. This shows that a regular-expression approach can accurately and precisely extract multiple specimen attributes from free-text Spanish pathology reports. Additionally, we developed a website to facilitate collaborative validation at a larger scale which may be helpful for future research on the subject.
UNASSIGNED: Los reportes de patología están almacenados como texto libre sin estructura, gramática, fragmentados o abreviados, con variabilidad lingüística entre patólogos. Por esta razón, la extracción de información de tumores requiere un esfuerzo humano significativo. Almacenar información en un formato eficiente y de alta calidad es esencial para implementar y establecer un registro hospitalario de cáncer.
UNASSIGNED: Este estudio busca describir la implementación de un algoritmo de Procesamiento de Lenguaje Natural para reportes de patología oncológica.
UNASSIGNED: Desarrollamos un algoritmo para procesar reportes de patología oncológica en Español, con el objetivo de extraer 20 descriptores médicos. El abordaje se basa en la coincidencia sucesiva de expresiones regulares.
UNASSIGNED: La validación se hizo con 140 reportes de patología. La identificación topográfica se realizó por humanos y por el algoritmo en todos los reportes. La morfología fue identificada por humanos en 138 reportes y por el algoritmo en 137. El valor de coincidencias parciales (fuzzy matches) promedio fue de 68.3 para Topografía y 89.5 para Morfología.
UNASSIGNED: Se hizo una validación preliminar del algoritmo contra extracción humana sobre un pequeño grupo de reportes, con resultados satisfactorios. Esto muestra que múltiples atributos del espécimen pueden ser extraídos de manera precisa de texto libre de reportes de patología en Español, usando un abordaje de expresiones regulares. Adicionalmente, desarrollamos una página web para facilitar la validación colaborativa a gran escala, lo que puede ser beneficioso para futuras investigaciones en el tema.

One of the main characteristics of health systems and pharmaceutical supply chains is their significant costs in the public sector, which has forced governments and companies active in this field to find ways to reduce costs. In this paper, the deterioration of imported pharmaceutical items is investigated as one of the challenges of the supply chain of pharmaceutical firms. Specifically, the micro, small medium enterprise (MSME), and a collaborative strategy to reduce its costs is presented. The technical solution of the cooperative strategy is the formation of a partnership alliance between the foreign patent holder of brand drugs and a domestic manufacturer through an exclusive license contract in the local country. This leads to a significant reduction of costs in the distribution network of the pharmaceutical supply chain. On the other hand, supply chain management techniques in the cooperative strategy provide the necessary motivation for its practical implementation by splitting fair profits between producers and other members, namely local government, distributors, and pharmacies. For these purposes, a cooperative game theory-based contract is utilized to set the parameters of the license agreement, and then a profit-sharing mechanism is introduced that splits the benefits of cooperation among the supply chain members based on their afforded costs. The most important contribution of the current research is to propose an integrated framework that combines the logistics network models, valuation methods, and profit split mechanisms that embody more facts from real-world problems than separate models in this regard in previous studies. Moreover, results of the proposed strategy in the supply chain of a drug for thalassemia patients in Iran indicate the effectiveness of the proposed strategy in reducing costs and deterioration. Further, it is shown that the higher the ordering costs of the imported drugs, the lower the market share of the patent holder, and the lower the financing expenses of the cooperative alliance, the more efficient is the proposed strategy.

The increasing sophistication of mobile and sensing technology has enabled the collection of intensive longitudinal data (ILD) concerning dynamic changes in an individual\'s state and context. ILD can be used to develop dynamic theories of behavior change which, in turn, can be used to provide a conceptual framework for the development of just-in-time adaptive interventions (JITAIs) that leverage advances in mobile and sensing technology to determine when and how to intervene. As such, JITAIs hold tremendous potential in addressing major public health concerns such as cigarette smoking, which can recur and arise unexpectedly. In tandem, a growing number of studies have utilized multiple methods to collect data on a particular dynamic construct of interest from the same individual. This approach holds promise in providing investigators with a significantly more detailed view of how a behavior change processes unfold within the same individual than ever before. However, nuanced challenges relating to coarse data, noisy data, and incoherence among data sources are introduced. In this manuscript, we use a mobile health (mHealth) study on smokers motivated to quit (Break Free; R01MD010362) to illustrate these challenges. Practical approaches to integrate multiple data sources are discussed within the greater scientific context of developing dynamic theories of behavior change and JITAIs.

As data grows exponentially across diverse fields, the ability to effectively leverage big data has become increasingly crucial. In the field of data science, however, minority groups, including African Americans, are significantly underrepresented. With the strategic role of minority-serving institutions to enhance diversity in the data science workforce and apply data science to health disparities, the National Institute for Minority Health Disparities (NIMHD) provided funding in September 2021 to six Research Centers in Minority Institutions (RCMI) to improve their data science capacity and foster collaborations with data scientists. Meharry Medical College (MMC), a historically Black College/University (HBCU), was among the six awardees. This paper summarizes the NIMHD-funded efforts at MMC, which include offering mini-grants to collaborative research groups, surveys to understand the needs of the community to guide project implementation, and data science training to enhance the data analytics skills of the RCMI investigators, staff, medical residents, and graduate students. This study is innovative as it addressed the urgent need to enhance the data science capacity of the RCMI program at MMC, build a diverse data science workforce, and develop collaborations between the RCMI and MMC\'s newly established School of Applied Computational Science. This paper presents the progress of this NIMHD-funded project, which clearly shows its positive impact on the local community.

Big data analytics (BDA), along with the resource efficiency and sustainability perspectives of a circular economy, supports the transition to circular agri-food supply chains (AFSCs), contributing to a country\'s achievement of the United Nations\' Sustainable Development Goals. However, there is still limited research demonstrating the importance and awareness of BDA implementation in circular AFSCs in developing countries. As a result of the barriers to BDA adoption in these regions, circular AFSCs in developing countries are still in their infancies. This study sought to identify the barriers to BDA adoption in circular AFSCs in Turkey using a Delphi-based Pythagorean fuzzy analytic hierarchy process. The proposed method removes the potential for bias and produces consensus among managers of companies in various AFSCs in Turkey. The findings of this study show that the most impactful barriers to BDA are technical, economic and social, followed by environmental and organisational. The most crucial sub-barriers to BDA adoption are \"lack of trust, privacy and security\", \"lack of financial resources\" and \"lack of skilled human resources\". This research can guide industry managers and policymakers in the development of strategies for overcoming barriers to BDA adoption in circular AFSCs in developing nations.

Data science, bioinformatics, and machine learning are the advent and progression of the fourth paradigm of exploratory science. The need for human-supported algorithms to capture patterns in big data is at the center of personalized healthcare and directly related to translational research. This paper argues that hypothesis-driven and data-driven research work together to inform the research process. At the core of these approaches are theoretical underpinnings that drive progress in the field. Here, we present several exemplars of research on the gut-brain axis that outline the innate values and challenges of these approaches. As nurses are trained to integrate multiple body systems to inform holistic human health promotion and disease prevention, nurses and nurse scientists serve an important role as mediators between this advancing technology and the patients. At the center of person-knowing, nurses need to be aware of the data revolution and use their unique skills to supplement the data science cycle from data to knowledge to insight.