Real-world Data Collection Research Articles

Evaluating biosimilars: safety, efficacy, and regulatory considerations in clinical studies.

Biosimilars are a rapidly growing area of clinical research, yet they encounter significant challenges, especially in emerging markets where regulatory and clinical hurdles differ markedly from those in established regions like Europe and the US. This commentary addresses these unique challenges and offers new perspectives on the global adoption of biosimilars. It emphasizes the crucial role of real world evidence in supporting biosimilar approvals, an aspect often underrepresented in current literature. The commentary also provides a comparative analysis of the regulatory frameworks in China and Europe, highlighting how these differences shape biosimilar development and market approval processes. By focusing on the issues of indication extrapolation and immunogenicity, this commentary highlights the necessity of continuous real-world data collection to ensure the safety and efficacy of biosimilars across multiple indications. Our analysis enhances the understanding of biosimilar research and supports their broader adoption as safe, effective, and accessible healthcare solutions globally.

Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations - an ISPOR Working Group Report

ObjectiveTo provide an introduction to the uses of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), in the field of health technology assessment (HTA). MethodsWe reviewed applications of generative AI in three areas: systematic literature reviews, real world evidence (RWE) and health economic modeling. Results(1) Literature reviews: generative AI has the potential to assist in automating aspects of systematic literature reviews by proposing search terms, screening abstracts, extracting data and generating code for meta-analyses; (2) Real World Evidence (RWE): generative AI can facilitate automating processes and analyze large collections of real-world data (RWD) including unstructured clinical notes and imaging; (3) Health economic modeling: generative AI can aid in the development of health economic models, from conceptualization to validation. Limitations in the use of foundation models and LLMs include challenges surrounding their scientific rigor and reliability, the potential for bias, implications for equity, as well as nontrivial concerns regarding adherence to regulatory and ethical standards, particularly in terms of data privacy and security. Additionally, we survey the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools. ConclusionsWhile generative AI technology holds promise with respect to HTA applications, it is still undergoing rapid developments and improvements. Continued careful evaluation of their applications to HTA is required. Both developers and users of research incorporating these tools, should familiarize themselves with their current capabilities and limitations.

Microwave Digital Twin Prototype for Shoulder Injury Detection.

One of the most common shoulder injuries is the rotator cuff tear (RCT). The risk of RCTs increases with age, with a prevalence of 9.7% in those under 20 years old and up to 62% in individuals aged 80 years and older. In this article, we present first a microwave digital twin prototype (MDTP) for RCT detection, based on machine learning (ML) and advanced numerical modeling of the system. We generate a generalizable dataset of scattering parameters through flexible numerical modeling in order to bypass real-world data collection challenges. This involves solving the linear system as a result of finite element discretization of the forward problem with use of the domain decomposition method to accelerate the computations. We use a support vector machine (SVM) to differentiate between injured and healthy shoulder models. This approach is more efficient in terms of required memory resources and computing time compared with traditional imaging methods.

A Real-World Longitudinal Study in Non-Functioning Pituitary Incidentalomas: A PRECES Micro-Adenomas Sub-Analysis.

Background. Incidentalomas have an increasing incidence all over the world due to a larger access to imaging assessments, and endocrine incidentalomas make no exception in this matter, including pituitary incidentalomas (PIs). Objective. Our objective was to analyse the dynamic changes amid a second computed tomography (CT) scan after adult patients were initially confirmed with a PI (non-functioning micro-adenoma). Methods. This was a multi-centric, longitudinal, retrospective study in adults (aged between 20 and 70 y) amid real-world data collection. We excluded patients who experienced baseline pituitary hormonal excess or deficiency or those with tumours larger than 1 cm. Results. A total of 117 adults were included (94.02% females) with a mean age of 43.86 ± 11.99 years, followed between 6 and 156 months with a median (M) of 40 months (Q1 Q3: 13.50, 72.00). At the time of PI diagnosis, the transverse diameter had a mean value of 0.53 ± 0.16 cm, the longitudinal mean diameter was 0.41 ± 0.13 cm, and the largest diameter was 0.55 ± 0.16 cm. No PI became functioning during follow-up, neither associated hypopituitarism nor increased >1 cm diameter. A total of 46/117 (39.32%) patients had a larger diameter during follow-up (increase group = IG) versus a non-increase group (non-IG; N = 71, 60.68%) that included the subjects with stationary or decreased diameters. IG had lower initial transverse, longitudinal, and largest diameter versus non-IG: 0.45 ± 0.12 versus 0.57 ± 0.17 (p < 0.0001), 0.36 ± 0.11 versus 0.43 ± 0.13 (p = 0.004), respectively, 0.46 ± 0.12 versus 0.6 ± 0.16 (p < 0.0001). IG versus non-IG had a larger period of surveillance: M (Q1, Q3) of 48 (24, 84) versus 32.5 (12, 72) months (p = 0.045) and showed similar age, pituitary hormone profile, and tumour lateralisation at baseline and displayed a median diameter change of +0.14 cm versus -0.03 cm (p < 0.0001). To conclude, a rather high percent of patients might experience PI diameter increase during a longer period of follow-up, including those with a smaller initial size, while the age at diagnosis does not predict the tumour growth. This might help practitioners with further long-term surveillance protocols.

127P UK real-world longitudinal data collection and analysis in adult SMA: the Adult SMA REACH Data collection study

127P UK real-world longitudinal data collection and analysis in adult SMA: the Adult SMA REACH Data collection study

Managing transit-oriented development: A comparative analysis of expert groups and multi-criteria decision making methods

A key challenge for transport managers and planners in sustainable development is evaluating transit facilities' performance. While Multi-Criteria Decision-Making (MCDM) tools are often used, they can be influenced by experts' subjective biases. This study applies MCDM to assess Mass Rapid Transit (MRT) stations in Jakarta, Indonesia, focusing on Transit-Oriented Development (TOD). The primary goal is to compare stakeholder perspectives and MCDM methods, complemented by a sensitivity analysis and validation with real-world smart card data. The findings reveal significant differences in criteria weighting between Indonesian and non-Indonesian experts, and between academic and non-academic experts, especially in transit connectivity and land use diversity. The study also shows variations in station rankings across different MCDM methods. Sensitivity analysis identifies transit connectivity as the most critical criterion. Simple Additive Weighting (SAW) with linear normalisation aligns well with actual usage data and shows robustness in sensitivity analysis, making it the most reliable method for TOD evaluation. The study highlights the need for continuous TOD performance monitoring and the regular collection of real-world data on ridership and TOD indicators.

608P Collection of real-world data for Duchenne muscular dystrophy patients through a national registry: description of the current cohort in the UK

608P Collection of real-world data for Duchenne muscular dystrophy patients through a national registry: description of the current cohort in the UK

The study protocol for a multicenter observational headache registry: Brazilian Headache Registry - REBRACEF II

ObjectiveThis article presents the protocol for the Brazilian Headache Registry (REBRACEF), the first national registry designed to systematically collect real-world data on patients with primary and secondary headaches at specialized centers in Brazil. The main objective is to describe the methods for patient selection, the registry infrastructure, data collection, and the standardized instruments used.MethodologyREBRACEF is a prospective, observational, multicenter cohort study conducted in real-world settings.Expected resultsThis protocol aims to standardize and enable the structuring of the registry, allowing its implementation in other centers across Brazil. It also ensures external feasibility by establishing a robust framework that can be replicated in other national and international healthcare institutions.ConclusionThe registry protocol provides a standardized framework for the collection of real-world data on patients with primary and secondary headaches, facilitating the improvement of headache management and research in Brazil.

SATURN: assessing the feasibility of utilising existing registries for real-world evidence data collection to meet patients, regulatory, health technology assessment and payer requirements

BackgroundSATURN (Systematic Accumulation of Treatment practices and Utilisation, Real world evidence, and Natural history data) for the rare condition osteogenesis imperfecta (OI) has the objective to create a common core dataset by utilising existing, well-established data sources to meet the needs of the various stakeholders (physicians, registry/dataset owners, patients and patient associations, OI community leaders, European [EU] policymakers, regulators, health technology assessments [HTA]s, and healthcare systems including payers). This paper describes the steps taken to assess the feasibility of one existing OI registry (i.e., the Registry of OI [ROI]) as a candidate for SATURN. The same methodology will be applied to other existing OI registries in the future and this same concept could be utilised for other rare disease registries.MethodsThe approach to assessing the feasibility of the ROI registry consisted of three steps: (1) an assessment of the registry characteristics using the Registry Evaluation and Quality Standards Tool (REQueST); (2) a gap analysis comparing SATURN required Core Variables to those being captured in the registry’s Case Report Form (CRF); and (3) a compliance check on the data exchange process following the Title 21 of Code of Federal Regulations (CFR) Part 11/EudraLex Annex 11 Compliance Checklist. The first registry that SATURN has assessed is the ROI database at the Istituto Ortopedico Rizzoli (IOR) in Italy.ResultsThe results from the ROI REQueST have demonstrated satisfactory complete responses in terms of methodology, essential standards, interpretability, and interoperability—readiness for data linkage, data sources, and ethics to meet the needs of data customers. However, the ROI data is from a tertiary referral centre which may limit the ability to understand the full patient journey. The gap analysis has revealed that an exact or logical match between SATURN requested variables and the ROI current variables exists for the following items: patient characteristics, treatment of OI (medical and surgical) and treatment of pain (with the exception of frequency of treatment and reasons for discontinuation), fracture history and bone density. However, data on safety was missing. The compliance check has implied that the ROI implemented appropriate controls for the web-based platform (i.e., Genotype–phenotype Data Integration Platform [GeDI]) that is involved in processing the electronic patient data, and GeDI is a validated/compliant application that follows relevant 21 CFR Part 11/EudraLex Annex 11 regulations.ConclusionsThis robust feasibility process highlights potential limitations and opportunities to develop and to refine the collaboration with the ROI as the SATURN programme moves forward. It also ensures that the existing datasets in the rare condition OI are being maximised to respond to the needs of patients, data customers and decision-makers. This feasibility process has allowed SATURN to build a compliant methodology that aligns with the requirements from the European Medicines Agency (EMA) and HTAs. More data variables will continue to be developed and refined along the way with more registries participating in SATURN. As a result, SATURN will become a meaningful and truly collaborative core dataset, which will also contribute to advancing understanding of OI diagnosis, treatment, and care.

Generating synthetic identifiers to support development and evaluation of data linkage methods

We aimed to develop a framework for generating synthetic identifier datasets to support development and evaluation of data linkage methods. We evaluated whether replicating associations between attributes and identifiers improved the utility of the synthetic data for assessing linkage error. We determined the steps required to generate synthetic identifiers that replicate the properties of real-world data collection. We generated synthetic versions of a large United Kingdom cohort study (the Avon Longitudinal Study of Parents and Children), according to the quality and completeness of identifiers recorded over several waves of the cohort. We evaluated the utility of the synthetic identifier data in terms of assessing linkage quality (false matches and missed matches). Comparing data from two collection points in ALSPAC, we found within-person disagreement in identifiers (differences in recording due to both natural change and non-valid entries) in 18% of surnames and 12% of forenames. Rates of disagreement varied by maternal age and ethnic group. Synthetic data provided accurate estimates of linkage quality metrics compared with the original data (within 0.13-0.55% for missed matches and 0.00-0.04% for false matches). Incorporating associations between identifier errors and maternal age/ethnicity improved synthetic data utility. Replicating dependencies between attribute values (e.g. ethnicity), values of identifiers (e.g. name), identifier disagreements (e.g. missing values, errors or changes over time), and their patterns and distribution structure enables generation of realistic synthetic data that can be used for robust evaluation of linkage methods. Our framework provides a novel and generalisable mechanism for developing and benchmarking record linkage algorithms.

Generating 3D Models for UAV-Based Detection of Riparian PET Plastic Bottle Waste: Integrating Local Social Media and InstantMesh

In recent years, waste pollution has become a severe threat to riparian environments worldwide. Along with the advancement of deep learning (DL) algorithms (i.e., object detection models), related techniques have become useful for practical applications. This work attempts to develop a data generation approach to generate datasets for small target recognition, especially for recognition in remote sensing images. A relevant point is that similarity between data used for model training and data used for testing is crucially important for object detection model performance. Therefore, obtaining training data with high similarity to the monitored objects is a key objective of this study. Currently, Artificial Intelligence Generated Content (AIGC), such as single target objects generated by Luma AI, is a promising data source for DL-based object detection models. However, most of the training data supporting the generated results are not from Japan. Consequently, the generated data are less similar to monitored objects in Japan, having, for example, different label colors, shapes, and designs. For this study, the authors developed a data generation approach by combining social media (Clean-Up Okayama) and single-image-based 3D model generation algorithms (e.g., InstantMesh) to provide a reliable reference for future generations of localized data. The trained YOLOv8 model in this research, obtained from the S2PS (Similar to Practical Situation) AIGC dataset, produced encouraging results (high F1 scores, approximately 0.9) in scenario-controlled UAV-based riparian PET bottle waste identification tasks. The results of this study show the potential of AIGC to supplement or replace real-world data collection and reduce the on-site work load.

1871P Temperature-controlled hand-foot cooling combined with compression prevents chemotherapy-induced polyneuropathy (CIPN): A single center, prospective, real-world data collection

1871P Temperature-controlled hand-foot cooling combined with compression prevents chemotherapy-induced polyneuropathy (CIPN): A single center, prospective, real-world data collection

Energy Analysis &amp; Optimization of A Residential Structure

This project delves into the critical realm of energy analysis and optimization for residential buildings. With global energy demand surging and environmental concerns mounting, the need for efficient energy consumption is paramount. The study employs a multidisciplinary approach, combining architectural design, data-driven insights, and advanced technologies. Real-world data collection, including smart meter readings and sensors, forms the foundation for evaluating energy usage trends, peak demands, and seasonal fluctuations. Building Energy Modeling (BEM) software aids in simulating diverse scenarios, enabling accurate predictions of energy performance. Key Words: Autodesk Revit, Autodesk Insight, (EUI) Energy Use Intensity. BIM (Building Information Modelling).

Automated data collection tool for real-world cohort studies of chronic hepatitis B: Leveraging OCR and NLP technologies for improved efficiency

BackgroundCollecting and standardizing clinical research data is a very tedious task. This study is to develop an intelligent data collection tool, named CHB-EDC, for real-world cohort studies of chronic hepatitis B (CHB), which can assist in standardized and efficient data collection. MethodsCHB_EDC is capable of automatically processing various formats of data, including raw data in image format, using internationally recognized data standards, OCR, and NLP models. It can automatically populate the data into eCRFs designed in the REDCap system, supporting the integration of patient data from electronic medical record systems through commonly used web application interfaces. This tool enables intelligent extraction and aggregation of data, as well as secure and anonymous data sharing. ResultsFor non-electronic data collection, the average accuracy of manual collection was 98.65 %, with an average time of 63.64 min to collect information for one patient. The average accuracy CHB-EDC was 98.66 %, with an average time of 3.57 min to collect information for one patient. In the same data collection task, CHB-EDC achieved a comparable average accuracy to manual collection. However, in terms of time, CHB-EDC significantly outperformed manual collection (p < 0.05). Our research has significantly reduced the required collection time and lowered the cost of data collection while ensuring accuracy. ConclusionThe tool has significantly improved the efficiency of data collection while ensuring accuracy, enabling standardized collection of real-world data.

Smoothing and approximation of grassland fire loading data for engineering structures by Capped Extended Support Vector Regression

This research proposes a machine learning-aided data smoothing and approximation scheme to investigate grassland fire loading for engineering structures. The investigations on grassland fire loading have significant impacts on wildfire-resistant design, building codes, regulations, and the like in specific regions. Real-world data collection systems inevitably introduce imperfections, such as noise, outliers, and the like. Furthermore, the collected data are normally discretized on time and location, and thus the information between them is completely absent. Achieving a smooth fit to data and reducing these imperfections are desired to facilitate subsequent analyses. Accordingly, a supervised machine learning algorithm is developed to smooth and approximate grassland fire loading data. Within the proposed algorithm, a convex optimization programming is established, which provides theoretical support to the accuracy of the algorithm. Then, through the proposed scheme, the grassland fire loading data can be approximated into an explicit regression model, which continuously completes the missing information during the observations. Moreover, attributed to the sparsity feature of the developed algorithm, the model can be simplified in its expression to further improve its applicability in engineering. Finally, real-world experimental data and numerical simulation results are separately investigated to demonstrate the effectiveness of the proposed scheme.

Natural disease course of chronic visceral acid sphingomyelinase deficiency in adults: A first step toward treatment criteria.

Acid sphingomyelinase deficiency (ASMD) is an ultra-rare lysosomal storage disease with a broad spectrum of manifestations ranging from severe neuropathic forms to attenuated, chronic visceral forms. Manifestations of the chronic visceral subtype are variable and encompass different degrees of hepatosplenomegaly, pulmonary disease and dyslipidemia. The aim of this study was to provide insights into the natural course of adult patients with the chronic visceral subtype. Based on these insights, we proposed tentative criteria for initiation and follow-up of enzyme replacement therapy (ERT). The data of 23 adult patients were collected in a prospective study. Clinical, genetic and demographic data, plasma measurements, abdominal imaging, pulmonary imaging, pulmonary function tests and quality of life questionnaires were collected. Stability of disease based on several clinical, biochemical and radiological markers (i.e., spleen volume, platelet levels, liver volume, alanine aminotransferase [ALT] levels, diffusion capacity of the lungs for carbon monoxide [DLCO] chitotriosidase activity and lysosphingomyelin [LSM]) was assessed. Cardiovascular risk was estimated based on sex, age, smoking, systolic blood pressure and lipid profile. Quality of life was evaluated with the 36-Item Short Form Health Survey and the Health Assessment Questionnaire. Median follow-up was 6.1 years (range 1.3-19.5 years). The most common manifestations were splenomegaly (100%), decreased high-density lipoprotein cholesterol (HDL-C) plasma levels (83%), (signs of) steatosis measured with transient elastography (82%), thrombocytopenia (64%), hepatomegaly (52%) and decreased diffusion capacity (45%). The majority of markers remained stable during follow-up. Twelve patients showed progression of disease: four for spleen volume, two for liver volume, three for DLCO, seven for chitotriosidase activity and three for LSM. One patient showed progression of disease based on four markers, although this patient did not report any problems at the last visit. Cardiovascular risk was estimated and was increased in half of the patients older than 40 years. Patient-reported quality of life did not differ from the general population, but differences in median 36-Item Short Form Health Survey (SF-36) scores of patients with severe pulmonary involvement and those of patients without pulmonary involvement were observed. Tentative criteria for initiation and effect of therapy were proposed. In conclusion, the chronic visceral subtype of ASMD showed a predominantly stable disease course in this cohort. We propose that ERT should be initiated on an individual basis and only in case of progression or symptomatic disease. Collection and analysis of real world data are necessary to refine start, stop and follow-up criteria in the future.

Monitoring solar irradiance and PV module performance in mobile applications

The paper presents a comprehensive evaluation of a PV module attached to an electric vehicle using a custom developed PV monitoring system to estimate the effect of PV energy production on the driving range.A rigid crystalline silicon photovoltaic (PV) module with a nominal peak power of 192.4 W accompanied with four irradiance sensors (positioned horizontally and vertically to the left, right and back) was mounted on an electric car, and various driving scenarios were executed, with data recorded for analysis. The results of a two-month continuous monitoring of the car's usage mainly in the Central Slovenia region in late winter and early springtime, show that the daily energy generation by the installed PV module contributed an additional driving range of up to 5 km per day, totalling 93 km of additional range in the entire monitoring period. Based on irradiance monitoring results applied to a hypothetical case where all sides would be covered with a state-of-the-art PV, the driving range would extent to 471 km in total for the same period. When compared to a fixed, ideally installed PV module at our outdoor PV monitoring site in Ljubljana, the average performance ratio (PR) of the mobile PV module in the two-month period was 87 %, whereas the PR of the fixed installed PV module was 83 %. The energy yield varied significantly under the varying conditions resulting from different car usage scenarios and on average it reached 41 % of the fixed PV module's energy yield. Through real-world experiments and data collection, different car use cases under various driving and parking conditions are presented and evaluated in detail. From these, the base specifications of a dedicated mobile VIPV monitoring system are identified.

Real-world data show sustained therapeutic effects of dupilumab in chronic rhinosinusitis with nasal polyps (CRSwNP) over 3 years.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a prevalent chronic inflammatory condition affecting the nose and paranasal sinuses, posing a significant socio-economic impact with substantial challenges in management. Biologics targeting type 2 inflammation such as dupilumab, have emerged as promising options. This study addresses a critical knowledge gap by comprehensively evaluating the 3-year impact of sustained dupilumab therapy in CRSwNP. A multicentric, retrospective collection of real-world data from five tertiary referral centers in Germany was conducted, enrolling 150 adult patients. The study investigated patient-reported outcomes, disease-specific indices and clinical measures, focusing on therapeutic response persistence, adverse events, and factors influencing treatment continuity. Results indicate significant improvements in clinical parameters from baseline (n = 150) with sustained effectiveness after 36 months (n = 138) as indicated in mean score ± standard deviation. Dupilumab treatment significantly improved overall disease-related impairment (VAS score: 7.5 ± 2.5 to 1.6 ± 1.3) and rhinosinusitis symptoms (SNOT-22: 59.4 ± 19.4 to 18.0 ± 15.0). Nasal Polyp Scores (NPS) decreased (5.3 ± 1.8 to 0.7 ± 1.1), and olfactory function improved (3.2 ± 2.5 to 8.4 ± 2.8), with three out of four patients achieving normosmia or hyposmia after 36 months. An "Excellent" treatment response according to EUFOREA23 criteria was observed in 76.5% of patients after 36 months. Sixteen patients discontinued Dupilumab, 12 permanently. Adverse events totaled 69 in 48 patients, commonly self-limiting. The study highlights the enduring effectiveness and lack of habituation to dupilumab after a sustained therapy of 3 years, providing valuable insights into its long-term therapeutic implications for CRSwNP patients.

Patient-Reported Outcome Measures in Chronic Spontaneous Urticaria, Angioedema, and Atopic Dermatitis

Reducing the burden of disease for patients and families requires being able to measure health status changes related to disease severity, control, and response to treatment over time. Patient-reported outcomes are patient perceptions of their health status. Such perceptions are critical to decision making. Some patient-reported outcome measures (PROMs) are extensive and often intended to be used only for detailed research assessments. Many PROMs, however, form critical components of valid, reliable, and responsive assessments in clinical research and routine clinical practice. The smallest score change in a PROM that would lead to different decision making by patients is called the minimally important difference. Using PROMs may also offer advantages over general questions or unvalidated tools. With the innovation of technology, the ability to chronicle disease symptoms using communication technology (mobile phone applications) has become increasingly available. Collection of real-world data in this capacity will be very useful for identifying more precise phenotypes/endotypes necessary for investigation of tailored therapies for chronic spontaneous and inducible urticaria, angioedema, and atopic dermatitis. Here, we provide an overview of PROMs that have been developed for the assessment of disease severity, control, and quality of life and that have been validated for the use of adults and children with these skin disorders.

Guiding Principles for Evaluating Vaccines in Joint Health Technology Assessment in the European Union: Preparing for the European Union’s Regulation on Health Technology Assessment for Vaccines

ObjectivesThe appraisal of vaccines in the European Union (EU) currently involves many different decision-making bodies and processes. The objective of this study was to help inform the development of standardized methodology and vaccine-specific processes for use in the EU Regulation on health technology assessment (HTA). MethodsLiterature reviews and expert consultation were conducted to identify current practices and gaps related to vaccine appraisals and to develop guiding principles for the joint clinical assessment of vaccines. ResultsWe found that significant variation exists across the EU member states in the decision-making processes when clinically evaluating vaccines. Three guiding principles consisting of 13 recommendations were developed to help inform the development of decision-making frameworks for the joint clinical assessment of vaccines in the EU: (1) support the creation of appropriate terminology and measurements for clinical appraisals of vaccines; (2) develop inclusive, timely, and transparent vaccine appraisal processes to support stronger evidence generation for vaccine decision making and appraisal; and (3) improve the collection and interoperability of real-world data, including robust surveillance, to foster evidence generation and support the standardization of vaccine clinical appraisals. ConclusionsGiven the significance of vaccines for public health, there is an urgency to develop standardized and vaccine-specific methodologies and processes for use in the EU joint HTA framework. The proposed guiding principles could support the effective implementation of the EU Regulation on HTA for vaccines and have the potential to ensure consistent, transparent, and timely access to new vaccines in the EU.