Are the primary cell cultures and cell lines used in endometriosis research of sufficient quality? Primary cells used in endometriosis research lack purity and phenotypic characterisation, and cell lines are not genotypically authenticated. The poor reproducibility of in vitro research and the lack of authenticity of the cell lines used represent reasons of concern in the field of reproductive biology and endometriosis research. In the present study, past in vitro research in the field of endometriosis was systematically reviewed to determine whether the appropriate quality controls were considered. In addition, we explored the performance of Paired Box 2 (Pax2) as an endometrium specific marker in endometrial and endometriotic primary cell cultures; we also characterised the most diffused endometriosis cell lines with respect to important markers including the short tandem repeat (STR) profile. Literature review part: almost 300 published protocols describing the isolation and creation of primary cell cultures from endometriosis were reviewed. Wet-lab part: primary cells isolated from 13 endometriosis patients were analysed by immunohistochemistry, immunofluorescence and FACS for the expression of Pax2. Cell lines Z11 and Z12, the most diffused endometriosis cell lines, were characterised with respect to the expression of Pax2, steroid hormone receptors and STR profile. From the literature review work, we underscored the lack of sufficient cell purity and phenotypic characterisation of primary cell cultures, which present high risk of contaminations from surrounding non-endometriotic tissues. Past work based on the use of cell lines was reviewed as well, and it emerged that cell line authentication was never performed. In an effort to address these weaknesses for future research, we present data on the performance of Pax2, a suitable marker to exclude ovarian (and other non-endometrial) cell contaminations from primary cell cultures; STR profiles of cell lines Z11 and Z12 were analysed and indicated that the cells were authentic. These profiles are now available for authentication purposes to researchers wishing to perform experiments with these cells. A quality control pipeline to assure sufficient quality of in vitro research in the field of reproductive biology and endometriosis is proposed. We encourage scientists, research institutes, journal reviewers, editors and funding bodies to raise awareness of the problem and adopt appropriate policies to solve it in the future. STR profiles of cell lines Z11 and Z12 are deposited at the Cellosaurus database—web.expasy.org. There may be additional markers suitable to assess cell quality. Future in vitro research in endometriosis and the reliability of outcomes can be improved by using the recommendations presented in this study. The study was partly financed by the ‘Stichting Fertility Foundation’ (The Netherlands). The authors declare no existing conflict of interest. Non-applicable.

Background: The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place. Few published descriptions of centralized digital research platforms provide a framework for their development. Objective: This study aims to serve as a road map for those seeking to develop a centralized digital research platform. We describe the technical and functional aspects of the ehive app, the centralized digital research platform of the Hasso Plattner Institute for Digital Health at Mount Sinai Hospital, New York, New York. We then provide information about ongoing studies hosted on ehive, including usership statistics and data infrastructure. Finally, we discuss our experience with ehive in the broader context of the current landscape of digital health research platforms. Methods: The ehive app is a multifaceted and patient-facing central digital research platform that permits the collection of e-consent for digital health studies. An overview of its development, its e-consent process, and the tools it uses for participant recruitment and retention are provided. Data integration with the platform and the infrastructure supporting its operations are discussed; furthermore, a description of its participant- and researcher-facing dashboard interfaces and the e-consent architecture is provided. Results: The ehive platform was launched in 2020 and has successfully hosted 8 studies, namely 6 observational studies and 2 clinical trials. Approximately 1484 participants downloaded the app across 36 states in the United States. The use of recruitment methods such as bulk messaging through the EPIC electronic health records and standard email portals enables broad recruitment. Light-touch engagement methods, used in an automated fashion through the platform, maintain high degrees of engagement and retention. The ehive platform demonstrates the successful deployment of a central digital research platform that can be modified across study designs. Conclusions: Centralized digital research platforms such as ehive provide a novel tool that allows investigators to expand their research beyond their institution, engage in large-scale longitudinal studies, and combine multimodal data streams. The ehive platform serves as a model for groups seeking to develop similar digital health research programs. International Registered Report Identifier (IRRID): DERR1-10.2196/49204

Introduction: Patient-reported outcome measures (PROMs) represent important endpoints in metastatic prostate cancer (mPCa). However, the clinically valid and accurate measurement of health-related quality of life depends on the psychometric properties of the PROMs considered. Objective: To appraise, compare, and summarize the properties of PROMs in mPCa. Evidence acquisition: We performed a review of PROMs used in RCTs, including patients with mPCa, using Medline in September 2021, according to the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) criteria. This systematic review is part of PIONEER (an IMI2 European network of excellence for big data in PCa). Results: The most frequently used PROMs in RCTs of patients with mPCa were the Functional Assessment for Cancer Therapy—Prostate (FACT-P) (n = 18), the Brief Pain Inventory—Short Form (BPI-SF) (n = 8), and the European Organization for Research and Treatment of Cancer quality of life core 30 (EORTC QLQ-C30) (n = 6). A total of 283 abstracts were screened and 12 full-text studies were evaluated. A total of two, one, and two studies reported the psychometric proprieties of FACT-P, Brief Pain Inventory (BPI), and BPI-SF, respectively. FACT-P and BPI showed a high content validity, while BPI-SF showed a moderate content validity. FACT-P and BPI showed a high internal consistency (summarized by Cronbach’s α 0.70–0.95). Conclusions: The use of BPI and FACT-P in mPCa patients is supported by their high content validity and internal consistency. Since BPI is focused on pain assessment, we recommend FACT-P, which provides a broader assessment of QoL and wellbeing, for the clinical evaluation of mPCa patients. However, these considerations have been elaborated on in a very limited number of studies. Patient summary: In this paper, we review the psychometric properties of PROMs used with patients with mPCa to find the questionnaires that best assess patients’ QoL, in order to help professionals in their intervention and improve patients’ QoL. We recommend the use of BPI and FACT-P for their high content validity and internal consistency despite the limited number of studies considered.

Wearable devices are becoming widely applied in healthcare to enable continuous, noninvasive monitoring, but their use in pediatric populations remains relatively underexplored. This review synthesizes 36 clinical studies focused on pediatric hospital and outpatient wearables published between 2014 and 2025. Devices included wrist-worn trackers, adhesive biosensors, and more, capturing electrocardiography, photoplethysmography, accelerometry, and other signals. Clinical applications spanned a variety of care settings. Artificial intelligence (AI) partially enhanced interpretation for the early detection of conditions such as postoperative complications and sepsis. Despite their promising accuracy, most studies remain small, single-center pilots focused on feasibility and signal validity rather than outcomes such as mortality, readmission, or long-term recovery. Key barriers include pediatric-specific device design, motion-robust signal quality, regulatory clearance, workflow integration, and equitable adoption in low-resource settings. Ethical concerns such as privacy, consent, and incidental findings and regulatory constraints, particularly the lack of pediatric labeling and approval for consumer and AI-driven devices, further limit translation into practice. Future work should prioritize multi-center studies, multimodal analytics, explainable AI, and seamless integration into clinical pathways. With these advances, wearables can move beyond feasibility to become reliable, personalized tools that improve pediatric monitoring and care.

Clinical note classification is a common clinical NLP task. However, annotated data-sets are scarse. Prompt-based learning has recently emerged as an effective method to adapt pre-trained models for text classification using only few training examples. A critical component of prompt design is the definition of the template (i.e. prompt text). The effect of template position, however, has been insufficiently investigated. This seems particularly important in the clinical setting, where task-relevant information is usually sparse in clinical notes. In this study we develop a keyword-optimized template insertion method (KOTI) and show how optimizing position can improve performance on several clinical tasks in a zero-shot and few-shot training setting.

Objective: To present the Artificial Intelligence-Ready Mount Sinai (AIR·MS) platform-unified access to diverse clinical datasets from the Mount Sinai Health System (MSHS), along with computational infrastructure for AI-driven research and demonstrate its utility with 3 research projects. Materials and Methods: AIR·MS integrates structured and unstructured data from multiple MSHS sources via the OMOP Common Data Model on an in-memory columnar database. Unstructured pathology and radiology data are integrated through metadata extracted from and linking the raw source data. Data access and analytics are supported from the HIPAA-compliant Azure cloud and the on-premises Minerva High-Performance Computing (HPC) environment. Results: AIR·MS provides access to structured electronic health records, clinical notes, and metadata for pathology and radiology images, covering over 12M patients. The platform enables interactive cohort building and AI model training. Experimentation with complex cohort queries confirm a high system performance. Three use cases demonstrate, risk-factor discovery, and federated cardiovascular risk modeling. Discussion: AIR·MS demonstrates how clinical data and infrastructure can be integrated to support large-scale AI-based research. The platform’s performance, scale, and cross-institutional design position it as a model for similar initiatives. Conclusion: AIR·MS provides a scalable, secure, and collaborative platform for AI-enabled healthcare research on multimodal clinical data.