Transgender health research needed.

Ever since the Cass Review was released in April-an evaluation by the United Kingdom's National Health Service that has led to a ban on pubertysuppressing medication for transgender and gender diverse (TGD) youth-there has been criticism of the assessment and its recommendations, and concern about how it could be leveraged to more broadly affect public health care. In response, physicians, researchers, and major medical organizations worldwide have emphasized that scientific studies point to the benefits of medical interventions supporting gender affirmation. Indeed, more care innovation is needed, driven by community-led research, to improve the well-being of TGD people in ways that can benefit all of society.

Antibody characterization is critical to enhance reproducibility in biomedical research.

Antibodies are used in many areas of biomedical and clinical research, but many of these antibodies have not been adequately characterized, which casts doubt on the results reported in many scientific papers. This problem is compounded by a lack of suitable control experiments in many studies. In this article we review the history of the 'antibody characterization crisis', and we document efforts and initiatives to address the problem, notably for antibodies that target human proteins. We also present recommendations for a range of stakeholders - researchers, universities, journals, antibody vendors and repositories, scientific societies and funders - to increase the reproducibility of studies that rely on antibodies.

Negligence in biomedical research: an anti-racist approach for substance use researchers.

Racism is embedded in the fabric of society at structural, disciplinary, hegemonic, and interpersonal levels, working as a mechanism that drives health disparities. In particular, stigmatized views of substance use get entangled with racialization, serving as a tool to uphold oppressive systems. While national health institutions have made commitments to dismantle these systems in the United States, anti-racism has not been integrated into biomedical research practice. The ways in which substance use researchers use and interpret race data-without engaging in structural racism as a mechanism of health inequity-can only be described as inadequate. Drawing upon concepts from the Public Health Critical Race praxis, QuantCrit, and an anti-racism research framework, we recommend a set of guidelines to help biomedical researchers conceptualize and engage with race more responsibly in substance use research.

Biospecimen research and the law.

In the well-known case of Henrietta Lacks, cells from her tumor were taken without consent and used more than 70 years ago to create the first immortal human cell line ("HeLa" cells). That event led to many scientific breakthroughs and to the debate about the ethics of consent and requirements for compensation. May 2024 saw two decisions by US federal courts-one related to Lacks-that could narrow the scope of research allowed on tissues obtained without consent and on nonidentified tissues, with implications for biomedicine.

An LLM-Based Visualization and Analysis Aid for a Secondary Use Clinical Data Platform.

This work introduces a novel approach to facilitate clinical research on secondary clinical data by integrating an LLM-based chatbot within a specialized platform called data hotel. The platform is designed to empower clinical researchers within our institution by enabling the generation of research hypotheses from secondary use patient data sources. Our focus in this work is on the deployment and functionality of the LLM-based chatbot within the data hotel ecosystem. The aim is to aid medical experts in visualizing and analyzing data sourced from the platform but also to enable the seamless storage of the generated code, enhancing the efficiency and reproducibility of the research process. This integration represents a significant advancement in leveraging LLM capabilities to enhance the utility and accessibility of clinical research platforms.

The Ouest Data Hub: An Interregional Health Data Sharing Ecosystem for Research.

The Ouest Data Hub (ODH) a project lead by GCS HUGO which is a cooperation group of University Hospitals in the French Grand Ouest region represents a groundbreaking initiative in this territory, advancing health data sharing and reuse to support research driven by real-world health data. Central to its structure are the Clinical Data Warehouses (CDWs) and Clinical Data Centers (CDCs), essential for analytics and as the linchpin of the ODH's status as an interregional Learning Health System. Aimed at fostering innovation and research, the ODH's collaborative and multi-institutional model effectively utilizes both local and shared resources. Yet, the path is not without challenges, especially in data quality and interoperability, where ongoing harmonization and standard adherence are critical. In 2023, this facilitated access to extensive health data from over 9.3 million patient records, demonstrating the ODH's capacity for both monocentric and multicentric research across various clinical fields, in close collaboration with physicians. The integration of healthcare professionals is crucial, ensuring data's clinical relevance and guiding accurate interpretations. Future expansions of the ODH to new hospitals and data types promise to enhance its model further, already inspiring similar frameworks across France. This scalable model for health data ecosystems showcases the ODH's potential as a foundation for national and supranational data sharing efforts.

Germany's Approach for the Secondary Use of Federated Real-World Data in the German Portal for Medical Research Data.

In the light of big data driven clinical research, fair access to real world clinical health data enables evidence to improve patient care. Germany's healthcare system provides an abundant data resource but unique challenges due to its federated nature, heterogeneity and high data-protection standards. The Medical Informatics Initiative (MII) developed concepts that are being implemented in the German Portal for Medical Research Data (FDPG) to grant access to distributed data-sources across state borders. The portal currently provides access to more than 10 million patient resources containing hundreds of millions of laboratory parameters, diagnostic reports, administered medications, procedures and specimens. Upcoming datasets include among others oncological data, molecular analysis results and microbiological findings. Here, we describe the philosophy, implementation and experience behind the framework: standardized access processes, interoperable fair data, software for in depth feasibility requests, tools to support researchers and hospital stakeholders alike as well as transparency measures to provide data use information for patients. Challenges remain to improve data quality and automatization of technical and organizational processes.

Rapid Deployment of a Pseudonymization Service in a Distributed Research Data Infrastructure - Lessons Learned.

The SARS-CoV-2 pandemic highlighted the importance of fast, collaborative research in biomedicine. Within the ORCHESTRA consortium, we rapidly deployed a pseudonymization service with minimal training and maintenance efforts under time-critical conditions to support a complex, multi-site research project. Over two years, the service was deployed in 13 sites across 11 countries to register more than 10,000 study participants and 15,000 biosamples. In this work, we present lessons learned as part of this process. Most importantly, we learned that common challenges can be overcome by creatively utilizing widely available tools and that having a dedicated partner to manage software rollout and pre-configure software packages for each site fosters the effective implementation.

Integration of Oncological Data into openEHR: A Path Towards Improved Cancer Care and Research.

The integration of tumor-related diagnosis and therapy data is a key factor for cancer-related collaborative projects and research projects on-site. The Medical Data Integration Center (MeDIC) of the University Hospital Schleswig-Holstein, resulting from the Medical Informatics Initiative and Network University Medicine in Germany, has agreed on an openEHR-based data management based on a centralized repository with harmonized annotated data. Consequently, the oncological data should be integrated into the MeDIC to interconnect the information and thus gain added value. A uniform national data set for tumor-related reports is already defined for the cancer registries. Therefore, this work aims to transform the national oncological basis data set for tumor documentation (oBDS) so that it can be stored and utilized properly in the openEHR repository of the MeDIC. In a previous work openEHR templates representing the oncological basis data set were modeled. These templates were used to implement a processing pipeline including a metadata repository, which defines the mappings between the elements, a FHIR terminology service for annotation and validation, resulting in a tool to automatically build openEHR compositions from oBDS data. The prototype proved the feasibility of the referred mapping, integration into the MeDIC is straightforward and the architecture introduced is adaptable to future needs by design.

Data Standardization in the Medical Field Through FHIR and FAIR Implementation: A Systematic Review.

The authors investigate in this paper the current situation of the FHIR resources adoption in order to FAIRify data in the medical research field. By aligning with the FAIR data principles, data becomes easier to share and reuse. This review aims to analyze how integrating the FHIR resources improved the findability, accessibility, interoperability, and reusability of datasets. By searching for the state-of-art situation in this field, we want to emphasize the significant role that FAIR data occupies in the medical research community, by also providing directions for further development and improved interoperability.

Data and Process Harmonisation of Multi-National, Multi-Site Research Data.

To achieve a single fully harmonised research data set suitable for analysis from data collected at multiple sites requires not only semantic integration of collection concepts and convergence onto single collection units, but harmonisation of data collection processes. We describe our experience of identifying harmonisation challenges in the Precision ALS project, with particular focus on process alignment challenges in a multi-site multi-national research data collection project.

Smart FOX - Enabling Citizen-Based Donation of EHR-Standardised Data for Clinical Research in Austria.

Access to healthcare data for secondary use in clinical research is often restricted due to privacy concerns or business interests, hindering comprehensive analysis across patient pathways. The Smart FOX project seeks to address this challenge by developing concepts, methods, and tools to facilitate citizen/patient-driven donations of health data for clinical research. Leveraging the groundwork, laid by the national Electronic Health Record implementation in Austria (called ELGA), Smart FOX aims to harness structured datasets from ELGA for research purposes through an opt-in approach. With funding secured from the Austrian Research Promotion Agency, the project embarks on innovative solutions encompassing governance frameworks, community engagement, and technical infrastructure. The Smart FOX consortium, comprising key stakeholders across various healthcare-associated domains, will evaluate these efforts through demonstrators focusing on clinical registries, patient-generated data, and recruitment services. The project targets to accompany the development of future data donation infrastructure while ultimately advancing clinical research efficiency and bolstering Austria's preparedness for the European Health Data Space. This paper presents the first systematic evaluation of the technical concept and proposal for the federated system architecture of the Austrian Health Data Donation Space, which is the socio-technical goal of Smart FOX.

FoPraNet-BW: An Infrastructure for Clinical Studies in Practice-Based Research Networks in the German Health System.

General practice-based research networks have become an integral tool to gain medical knowledge from primary care in many countries. For this purpose, a scalable IT-infrastructure is presented considering the limiting peculiarities in the German health system and enabling GPs to participate in clinical studies based on their patient population. The infrastructure consists of a central study management server and local clients for each practice. It adopts to the currently limited digital connectivity of GP practices, data protection regulations for clinical data and the needs of the medical staff to manage a clinical study. The infrastructure is in production at the four university hospitals in the state of Baden-Württemberg. Until now three clinical studies with over 70 GPs and 350 Participants are successfully conducted or have been finished. Further clinical studies are in the planning stages.

HL7 FHIR in Health Research: A FHIR Specification for Metadata in Clinical, Epidemiological, and Public Health Studies.

This work presents the Fast Healthcare Interoperability Resources (FHIR®) specification of the NFDI4Health Metadata schema based on FHIR Version 4: We created 16 profiles to facilitate the integration of clinical, epidemiological, and public health study data. Despite challenges arising from the extensive MDS as well as missing concepts in semantic standards, it marks a significant advance in applying information technology standards to health research.

Unveiling Medical Insights: Advanced Topic Extraction from Scientific Articles.

In the ever-evolving landscape of medical research and healthcare, the abundance of scientific articles presents both a treasure trove of knowledge and a daunting challenge. Researchers, clinicians, and data scientists grapple with vast amounts of unstructured information, seeking to extract meaningful insights that can drive advancements in the biomedical domain including, research trends, patient care, drug discovery, and disease understanding. This paper utilizes the topic extraction algorithms on Breast Cancer Research to shed light on the current trends and the path to follow in this field. We utilized TextRank and Large Language Models (LLM) using the TripleA tool to extract topics in the field, analyzing and comparing the results.