European Journal of Clinical Investigation最新文献

{"title":"Total lesion glycolysis is a promising predictor of chemo-response in pancreatic cancer patients treated with neoadjuvant chemotherapy prior to surgery.","authors":"Sooin Byeon, Bridget Abbott, Paul Roach, Dale L Bailey, Angela Chou, Sarah Maloney, Anthony J Gill, Jaswinder Samra, Anubhav Mittal, Sumit Sahni","doi":"10.1111/eci.70046","DOIUrl":"https://doi.org/10.1111/eci.70046","url":null,"abstract":"<p><strong>Background: </strong>There has been increased use of neoadjuvant chemotherapy (NAC) in resectable pancreatic ductal adenocarcinoma (PDAC) patients. [¹⁸F]fluoro-2-deoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) scan is being frequently used to determine treatment response in PDAC patients undergoing NAC. Maximum standardized uptake value (SUV_max) is conventionally used as an FDG-PET/CT parameter, but there are emerging parameters, such as total lesion glycolysis (TLG), which take into account mean standardized uptake (SUV_mean) and metabolic tumour volume (MTV). This study compared the ability of emerging FDG-PET/CT parameters (i.e. SUV_mean, MTV and TLG) to predict chemo-response compared to SUV_max.</p><p><strong>Methods: </strong>In this single centre, retrospective study, NAC-treated PDAC patients (n = 74) for whom both pre- and post-NAC FDG-PET/CT scans were available were recruited. All scans were imported to a single analysis platform and reanalysed. Chemo-response was determined by the assessment of percentage viable tumour cells in the tumour bed. Statistical analysis was performed on the data.</p><p><strong>Results: </strong>A significant correlation was observed between post-treatment FDG-PET/CT scan parameters and viable cancer cells in the tumour bed, with TLG showing a higher degree of correlation (r = .3131) compared to all other parameters (r = .2722-.3008). The percentage decrease in the TLG (post-NAC scan vs. pre-NAC scan) demonstrated the highest degree of correlation with viable cancer cells in the tumour bed (r = -.3444) and had a statistically significant (p = .0157) effect between NAC responders (Median = 80.57) and non-responders (Median = 65.16). The difference between TLG (post-NAC scan vs. pre-NAC scan) was shown to be an independent prognostic indicator for overall survival (hazard ratio = .5033, p = .0361).</p><p><strong>Conclusion: </strong>TLG was shown to be a superior predictor of chemo-response and patient prognosis compared to all other FDG-PET/CT parameters in PDAC patients treated with NAC.</p>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":" ","pages":"e70046"},"PeriodicalIF":4.4,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143802849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Localization of accessory pathways in Wolff-Parkinson-white syndrome using ECG-based multi-task deep learning","authors":"Jasper Hennecken,&nbsp;Bauke K. O. Arends,&nbsp;Thomas Mast,&nbsp;Lukas Dekker,&nbsp;Pim van der Harst,&nbsp;Yuri Blaauw,&nbsp;Wolfgang Dichtl,&nbsp;Thomas Senoner,&nbsp;Rutger J. Hassink,&nbsp;Peter Loh,&nbsp;René van Es,&nbsp;Rutger R. van de Leur","doi":"10.1111/eci.14385","DOIUrl":"https://doi.org/10.1111/eci.14385","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Wolff-Parkinson-White syndrome is characterized by accessory atrioventricular pathways (AP) and atrio-ventricular re-entry arrhythmias. Catheter ablation approach and success are determined by AP location. Existing rule-based algorithms based on the electrocardiogram (ECG) are time consuming, prone to inter-observer variability and use delta wave polarity as a binary variable. To overcome these challenges, we propose a model based on a deep neural network (DNN).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Patients with concealed pathways, multiple antegrade conducting pathways or without any sinus rhythm ECGs were excluded. AP location was determined based on electrophysiological testing during catheter ablation and categorized into right-sided, septal and left-sided APs. Multi-task learning with auxiliary identification of the presence of pre-excitation, parahisian pathways and locations where a transseptal puncture is potentially required was used to increase usability and performance. The DNN was compared to the Milstein and Arruda algorithms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Between 1997 and 2023, 645 patients who underwent catheter ablation for an AP were included in the study. The model was developed using 1.394 ECGs from 567 patients. The DNN was tested using 78 ECGs in two independent cohorts. The model outperformed both the Milstein and Arruda algorithms with an area under the receiver operating characteristic curve (AUROC) of .92 (95% confidence interval: .88–.96) compared to the Arruda algorithm (AUROC of .80; <i>p</i> &lt;.001) and the Milstein algorithm (AUROC of .81; <i>p</i> &lt;.001).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our model showed excellent discriminatory performance in predicting the location of an accessory pathway while outperforming conventional techniques. Clinically, this tool can improve preoperative planning and risk stratification.</p>\u0000 </section>\u0000 </div>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":"55 S1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eci.14385","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for electrocardiogram interpretation: Bench to bedside","authors":"Bas B. S. Schots,&nbsp;Camila S. Pizarro,&nbsp;Bauke K. O. Arends,&nbsp;Marish I. F. J. Oerlemans,&nbsp;Dino Ahmetagić,&nbsp;Pim van der Harst,&nbsp;René van Es","doi":"10.1111/eci.70002","DOIUrl":"https://doi.org/10.1111/eci.70002","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Recent advancements in deep learning (DL), a subset of artificial intelligence, have shown the potential to automate and improve disease recognition, phenotyping and prediction of disease onset and outcomes by analysing various sources of medical data. The electrocardiogram (ECG) is a valuable tool for diagnosing and monitoring cardiovascular conditions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The implementation of DL in ECG analysis has been used to detect and predict rhythm abnormalities and conduction abnormalities, ischemic and structural heart diseases, with performance comparable to physicians. However, despite promising development of DL algorithms for automatic ECG analysis, the integration of DL-based ECG analysis and deployment of medical devices incorporating these algorithms into routine clinical practice remains limited.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>This narrative review highlights the applications of DL in 12-lead ECG analysis. Furthermore, we review randomized controlled trials that assess the clinical effectiveness of these DL tools. Finally, it addresses different key barriers to widespread implementation in clinical practice, including regulatory hurdles, algorithm transparency and data privacy concerns.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>By outlining both the progress and the obstacles in this field, this review aims to provide insights into how DL could shape the future of ECG analysis and enhance cardiovascular care in daily clinical practice.</p>\u0000 </section>\u0000 </div>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":"55 S1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eci.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications, challenges and future directions of artificial intelligence in cardio-oncology","authors":"Francesco Ravera,&nbsp;Nicolò Gilardi,&nbsp;Alberto Ballestrero,&nbsp;Gabriele Zoppoli","doi":"10.1111/eci.14370","DOIUrl":"https://doi.org/10.1111/eci.14370","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The management of cardiotoxicity related to cancer therapies has emerged as a significant clinical challenge, prompting the rapid growth of cardio-oncology. As cancer treatments become more complex, there is an increasing need to enhance diagnostic and therapeutic strategies for managing their cardiovascular side effects.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This review investigates the potential of artificial intelligence (AI) to revolutionize cardio-oncology by integrating diverse data sources to address the challenges of cardiotoxicity management.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We explore applications of AI in cardio-oncology, focusing on its ability to leverage multiple data sources, including electronic health records, electrocardiograms, imaging modalities, wearable sensors, and circulating serum biomarkers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>AI has demonstrated significant potential in improving risk stratification and longitudinal monitoring of cardiotoxicity. By optimizing the use of electrocardiograms, non-invasive imaging, and circulating biomarkers, AI facilitates earlier detection, better prediction of outcomes, and more personalized therapeutic interventions. These advancements are poised to enhance patient outcomes and streamline clinical decision-making.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>AI represents a transformative opportunity in cardio-oncology by advancing diagnostic and therapeutic capabilities. However, successful implementation requires addressing practical challenges such as data integration, model interpretability, and clinician training. Continued collaboration between clinicians and AI developers will be essential to fully integrate AI into routine clinical workflows.</p>\u0000 </section>\u0000 </div>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":"55 S1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eci.14370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Barriers, facilitators and strategies for the implementation of artificial intelligence-based electrocardiogram interpretation: A mixed-methods study","authors":"Bauke K. O. Arends,&nbsp;Jenna M. McCormick,&nbsp;Pim van der Harst,&nbsp;Pauline Heus,&nbsp;René van Es","doi":"10.1111/eci.14387","DOIUrl":"https://doi.org/10.1111/eci.14387","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The implementation of artificial intelligence-based electrocardiogram interpretation (AI-ECG) algorithms relies heavily on end-user acceptance and a well-designed implementation plan. This study aimed to identify the key barriers, facilitators and strategies for the successful adoption of AI-ECG in clinical practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A sequential explanatory mixed-methods study was conducted among future AI-ECG end-users in the Netherlands, including doctors, nurses, and ambulance professionals, using a clinical scenario involving chest pain. Quantitative data were collected through a three-round Delphi survey (<i>n</i> = 25) to identify key barriers and facilitators. Building on these findings, qualitative data were gathered through semi-structured interviews (<i>n</i> = 7) and focus groups (<i>n</i> = 12) to further explain the barriers and facilitators, and discuss relevant implementation strategies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Participants expressed a general openness to working with AI-ECG. Four key barriers and twelve facilitators were identified in the quantitative phase. Participants mentioned the relative advantage of AI-ECG in the context of recognizing subtle, or rare, ECG abnormalities and assisting in patient triage. However, successful implementation requires end-users to have trust in the algorithm, clear protocols, actionable model output, integration with existing clinical systems and multidisciplinary implementation teams. Several strategies were proposed to address these challenges, including conducting local consensus discussions, identifying and preparing local champions and revising professional roles.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This mixed-methods study grounded in established theoretical frameworks identified several barriers and facilitators to AI-ECG implementation and proposed strategies to address these challenges. These findings provide valuable insights for developing effective implementation plans for AI-ECG in clinical practice.</p>\u0000 </section>\u0000 </div>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":"55 S1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eci.14387","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deciphering immune dynamics in atherosclerosis: Inflammatory mediators as biomarkers and therapeutic target.","authors":"Paolo Magni, Tijana Mitić, Yvan Devaux, Philippe Pierre, Miron Sopić, Fernando de la Cuesta, Rui Vitorino","doi":"10.1111/eci.70043","DOIUrl":"https://doi.org/10.1111/eci.70043","url":null,"abstract":"<p><strong>Background: </strong>Atherosclerosis, one of the main causes of cardiovascular disease, is driven by complex interactions between lipid metabolism and immune mechanisms in the vascular system. Regulatory molecules, particularly protein fragments derived from cytokines, chemokines and other immune-related proteins, play a central role in modulating inflammation and immune responses in atherosclerotic plaques.</p><p><strong>Results: </strong>Recent advances in peptidomics have revealed the dual role of immune system-derived peptides as indicators and effectors of atherosclerotic cardiovascular disease (ASCVD). Certain subsets of immune cells, such as pro-inflammatory monocytes and regulatory T cells, contribute to this peptide-mediated regulation. New findings suggest that these peptides may serve as diagnostic biomarkers and therapeutic targets in atherosclerosis.</p><p><strong>Conclusion: </strong>This review highlights the translational relevance of immune-mediated peptides in ASCVD and emphasizes their diagnostic and therapeutic potential. By integrating peptidomics with immunology research, a new framework for understanding and targeting inflammation in atherosclerosis is proposed, opening new avenues for precision medicine in cardiovascular care.</p>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":" ","pages":"e70043"},"PeriodicalIF":4.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143794780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of artificial intelligence in cardiovascular research: Fear less and live bolder","authors":"Alessandro Scuricini,&nbsp;Davide Ramoni,&nbsp;Luca Liberale,&nbsp;Fabrizio Montecucco,&nbsp;Federico Carbone","doi":"10.1111/eci.14364","DOIUrl":"https://doi.org/10.1111/eci.14364","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Artificial intelligence (AI) has captured the attention of everyone, including cardiovascular (CV) clinicians and scientists. Moving beyond philosophical debates, modern cardiology cannot overlook AI's growing influence but must actively explore its potential applications in clinical practice and research methodology.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods and Results</h3>\u0000 \u0000 <p>AI offers exciting possibilities for advancing CV medicine by uncovering disease heterogeneity, integrating complex multimodal data, and enhancing treatment strategies. In this review, we discuss the innovative applications of AI in cardiac electrophysiology, imaging, angiography, biomarkers, and genomic data, as well as emerging tools like face recognition and speech analysis. Furthermore, we focus on the expanding role of machine learning (ML) in predicting CV risk and outcomes, outlining a roadmap for the implementation of AI in CV care delivery. While the future of AI holds great promise, technical limitations and ethical challenges remain significant barriers to its widespread clinical adoption.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Addressing these issues through the development of high-quality standards and involving key stakeholders will be essential for AI to transform cardiovascular care safely and effectively.</p>\u0000 </section>\u0000 </div>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":"55 S1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eci.14364","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The reach and leading end of modern machine learning-based artificial intelligence in cardiovascular medicine","authors":"Luis Eduardo Juarez-Orozco","doi":"10.1111/eci.70004","DOIUrl":"https://doi.org/10.1111/eci.70004","url":null,"abstract":"","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":"55 S1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding interpretability through complexity reduction in machine learning-based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study","authors":"Eero Lehtonen,&nbsp;Jarmo Teuho,&nbsp;Monire Vatandoust,&nbsp;Juhani Knuuti,&nbsp;Remco J. J. Knol,&nbsp;Friso M. van der Zant,&nbsp;Luis Eduardo Juárez-Orozco,&nbsp;Riku Klén","doi":"10.1111/eci.14391","DOIUrl":"https://doi.org/10.1111/eci.14391","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Machine learning-based analysis can be used in myocardial perfusion imaging data to improve risk stratification and the prediction of major adverse cardiovascular events for patients with suspected or established coronary artery disease. We present a new machine learning approach for the identification of patients who develop major adverse cardiovascular events. The new method is robust against the deleterious effect of outliers in the training set stratification and training process.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The proposed sum-of-sigmoids model is obtained by averaging the contributions of various input variables in an ensemble of XGBoost models. To illustrate its performance, we have applied it to predict major adverse cardiovascular events from advanced imaging data extracted from rest and adenosine stress ¹³N-ammonia positron emission tomography myocardial perfusion imaging polar maps. There were 1185 individual studies performed, and the event occurrence was tracked over a follow-up period of 2 years.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The sum-of-sigmoids model achieved a prediction accuracy of .83 on the test set, matching the performance of significantly more complex and less interpretable models (whose accuracies were .83–.84).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The sum-of-sigmoids model is interpretable and simple, while achieving similar prediction accuracy to significantly more complex machine learning models in the considered prediction task. It should be suitable for applications such as automated clinical risk stratification, where clear and explicit justification of the classification procedure is highly pertinent.</p>\u0000 </section>\u0000 </div>","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":"55 S1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/eci.14391","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial Introduction","authors":"Luis Eduardo Juarez-Orozco,&nbsp;Hendrik Nathoe","doi":"10.1111/eci.70029","DOIUrl":"https://doi.org/10.1111/eci.70029","url":null,"abstract":"","PeriodicalId":12013,"journal":{"name":"European Journal of Clinical Investigation","volume":"55 S1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}