Frontiers in digital health最新文献

{"title":"Integrated portable ECG monitoring system with CNN classification for early arrhythmia detection.","authors":"Aayush Panwar, Modigari Narendra, Arnav Arya, Rohan Raj, Arnab Kumar","doi":"10.3389/fdgth.2025.1535335","DOIUrl":"10.3389/fdgth.2025.1535335","url":null,"abstract":"<p><strong>Introduction: </strong>Electrocardiograms (ECGs) play a crucial role in diagnosing heart diseases by capturing the electrical activity of the heart. With the rising need for real-time cardiac monitoring, portable solutions have gained significance for timely detection and intervention. This study presents a portable ECG monitoring system incorporating Convolutional Neural Networks (CNNs) for accurate classification of cardiac abnormalities, including arrhythmias.</p><p><strong>Methods: </strong>The proposed system consists of an Arduino Nano microcontroller interfaced with an AD8232 ECG sensor for real-time ECG signal acquisition. The collected ECG data undergoes preprocessing before being fed into CNN models trained on the MIT-BIH Arrhythmia dataset. The model is designed for both binary and multi-class classification, distinguishing normal and abnormal heart rhythms. Performance metrics, including accuracy, were evaluated against state-of-the-art approaches to assess classification effectiveness.</p><p><strong>Results: </strong>Experimental evaluations demonstrate the CNN model's high classification accuracy, achieving 98.35% in binary classification and 99.3% in multi-class classification. These results surpass existing benchmarks, highlighting the efficiency of the proposed system. The system's low-cost hardware and real-time classification capabilities enhance its suitability for continuous cardiac monitoring.</p><p><strong>Discussion: </strong>The proposed ECG monitoring system presents a reliable and cost-effective solution for early arrhythmia detection. By leveraging CNNs, it ensures accurate classification of cardiac abnormalities, making it a promising tool for both clinical and remote healthcare settings. Its potential impact extends to real-time monitoring, early diagnosis, and personalized healthcare, contributing to improved cardiovascular health management.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1535335"},"PeriodicalIF":3.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11959012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Immersive solutions: South African community service nurses' perspectives on virtual reality potential in hypertension management.","authors":"Jayd Brittany Vitorino Clara, Charlene Downing, Patrick Ndayizigamiye, Pieter Herman Myburgh","doi":"10.3389/fdgth.2025.1430438","DOIUrl":"10.3389/fdgth.2025.1430438","url":null,"abstract":"<p><strong>Introduction: </strong>With the rapid development of information technology globally and the scarcity of educators in higher education institutions, educational reforms are crucial to prepare students for an advancing and complex work environment. Virtual reality (VR) makes education widely available as it bridges the gap between students and educators, as educators and students enter an immersive world where educators can guide students.</p><p><strong>Aim: </strong>The researchers' aim for this study was to explore community service nurses' (CSN's) experiences with a VR prototype when managing a hypertensive patient.</p><p><strong>Method: </strong>The study comprised nine CSN with varied knowledge, skills, experiences, and who have been allocated to certain disciplines within a public hospital. The study was split into three phases: phase one, focus group and individual interviews were used to gain an understanding of the CSN's current knowledge and experiences regarding the assessment and implementation of nursing interventions used in the management of hypertensive patients. In the second phase, participants were exposed to the VR environment, where they were prompted by the programmed patient avatar to perform several nursing diagnostic procedures and interpret the clinical data provided in order to formulate a nursing diagnosis. During the third phase, the researchers conducted focus groups and individual interviews to acquire and comprehend the participants experiences regarding their interaction with the VR prototype and describe the benefits and drawbacks of the prototype they encountered.</p><p><strong>Results: </strong>Constructive feedback and recommendations were provided by participants regarding the VR program's interactiveness and the accuracy of diagnostic tests. Participants claimed the experience was enjoyable, and based on the researchers' observations, the VR program stimulated critical thinking as well as clinical reasoning as intended. Their feedback was used to alter the VR prototype before the main study's commencement.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1430438"},"PeriodicalIF":3.2,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrative review on children's perceived and experienced subjective digital well-being.","authors":"Halla Björk Holmarsdottir, Idunn Seland, Liudmila Zinoveva, Monica Barbovschi, Alina Bărbuță, Dimitris Parsanoglou, Maria Symeonaki","doi":"10.3389/fdgth.2025.1410609","DOIUrl":"10.3389/fdgth.2025.1410609","url":null,"abstract":"<p><p>This review examines children's perceived and experienced subjective digital well-being by investigating their digital activities, behaviours and online relationships across three domains (Family, Leisure, and Education) presenting children's own perspectives. The included studies are limited to research published between 2011 and 2021 using European samples incorporating children aged 5-17 years. While research on children's digital well-being has expanded over the last two decades, the novelty of this review is that it presents research across all activity domains, representing an ecological approach to child development, one that aims to capture children's own views. The 23 studies identified for the review show, first, an apparent shortage of studies on children's well-being involving digital technologies that incorporate children's own perspectives on their situation. Second, the review shows that these studies relate primarily to well-being outcomes categorised as either social, emotional and cultural outcomes or as cognitive development and educational outcomes. Directions for further research on children's digital well-being are suggested.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1410609"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949879/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approaches to identify scenarios for data science implementations within healthcare settings: recommendations based on experiences at multiple academic institutions.","authors":"Lillian Sung, Michael Brudno, Michael C W Caesar, Amol A Verma, Brad Buchsbaum, Ravi Retnakaran, Vasily Giannakeas, Azadeh Kushki, Gary D Bader, Helen Lasthiotakis, Muhammad Mamdani, Lisa Strug","doi":"10.3389/fdgth.2025.1511943","DOIUrl":"10.3389/fdgth.2025.1511943","url":null,"abstract":"<p><strong>Objectives: </strong>To describe successful and unsuccessful approaches to identify scenarios for data science implementations within healthcare settings and to provide recommendations for future scenario identification procedures.</p><p><strong>Materials and methods: </strong>Representatives from seven Toronto academic healthcare institutions participated in a one-day workshop. Each institution was asked to provide an introduction to their clinical data science program and to provide an example of a successful and unsuccessful approach to scenario identification at their institution. Using content analysis, common observations were summarized.</p><p><strong>Results: </strong>Observations were coalesced to idea generation and value proposition, prioritization, approval and champions. Successful experiences included promoting a portfolio of ideas, articulating value proposition, ensuring alignment with organization priorities, ensuring approvers can adjudicate feasibility and identifying champions willing to take ownership over the projects.</p><p><strong>Conclusion: </strong>Based on academic healthcare data science program experiences, we provided recommendations for approaches to identify scenarios for data science implementations within healthcare settings.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1511943"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949942/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physicians and AI in healthcare: insights from a mixed-methods study in Poland on adoption and challenges.","authors":"Ewelina Kowalewska","doi":"10.3389/fdgth.2025.1556921","DOIUrl":"10.3389/fdgth.2025.1556921","url":null,"abstract":"<p><strong>Introduction: </strong>Understanding healthcare professionals' attitudes towards artificial intelligence (AI) in medicine is crucial for improving patient care and clinical practice. This study combines a systematic review and a survey targeting Polish physicians to explore these attitudes. While many healthcare professionals express enthusiasm and readiness for AI integration, others remain skeptical due to concerns about reliability, ethical implications, and legal accountability. The systematic review highlighted AI's potential benefits, such as improved diagnostic accuracy and workflow efficiency, alongside challenges like data privacy and the need for validation in atypical scenarios.</p><p><strong>Materials and methods: </strong>This study combines insights from a systematic review and a targeted survey to assess healthcare professionals' attitudes toward AI. The survey focused on Polish physicians, a group uniquely positioned to provide insights due to their healthcare system's specific challenges.</p><p><strong>Results: </strong>The survey revealed optimism among Polish physicians (n86), with 68% ready to adopt AI tools, but underscored the necessity of tailored education and clear implementation guidelines.</p><p><strong>Discussion: </strong>This study provides valuable insights into the dual narrative of optimism and skepticism surrounding AI in healthcare, emphasizing the importance of addressing barriers to maximize its benefits globally.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1556921"},"PeriodicalIF":3.2,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11949901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143756373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated inflammatory bowel disease detection using wearable bowel sound event spotting.","authors":"Annalisa Baronetto, Sarah Fischer, Markus F Neurath, Oliver Amft","doi":"10.3389/fdgth.2025.1514757","DOIUrl":"10.3389/fdgth.2025.1514757","url":null,"abstract":"<p><strong>Introduction: </strong>Inflammatory bowel disorders may result in abnormal Bowel Sound (BS) characteristics during auscultation. We employ pattern spotting to detect rare bowel BS events in continuous abdominal recordings using a smart T-shirt with embedded miniaturised microphones. Subsequently, we investigate the clinical relevance of BS spotting in a classification task to distinguish patients diagnosed with inflammatory bowel disease (IBD) and healthy controls.</p><p><strong>Methods: </strong>Abdominal recordings were obtained from 24 patients with IBD with varying disease activity and 21 healthy controls across different digestive phases. In total, approximately 281 h of audio data were inspected by expert raters and thereof 136 h were manually annotated for BS events. A deep-learning-based audio pattern spotting algorithm was trained to retrieve BS events. Subsequently, features were extracted around detected BS events and a Gradient Boosting Classifier was trained to classify patients with IBD vs. healthy controls. We further explored classification window size, feature relevance, and the link between BS-based IBD classification performance and IBD activity.</p><p><strong>Results: </strong>Stratified group K-fold cross-validation experiments yielded a mean area under the receiver operating characteristic curve ≥0.83 regardless of whether BS were manually annotated or detected by the BS spotting algorithm.</p><p><strong>Discussion: </strong>Automated BS retrieval and our BS event classification approach have the potential to support diagnosis and treatment of patients with IBD.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1514757"},"PeriodicalIF":3.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11965935/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Promoting mental health in the age of new digital tools: balancing challenges and opportunities of social media, chatbots, and wearables.","authors":"Julien Coelho, Florian Pécune, Jean-Arthur Micoulaud-Franchi, Bernard Bioulac, Pierre Philip","doi":"10.3389/fdgth.2025.1560580","DOIUrl":"10.3389/fdgth.2025.1560580","url":null,"abstract":"<p><p>The promotion of mental health is essential for global health, affecting millions with disorders such as anxiety and depression. Although stigma and discrimination hinder progress, these conditions are often preventable or manageable at minimal cost. The adoption of digital tools in mental health promotion, including telemedicine, online therapy, social media, and wearables, offers promising new avenues to address these issues. This review proposes a framework that focuses on the use of digital tools to enhance health literacy, foster behavioral change, and support sustained positive health behaviors. Platforms such as TikTok, Facebook, and Instagram can effectively disseminate health information, increase awareness, and enhance social accountability. Artificial intelligence-driven virtual agents offer personalised mental health interventions, providing motivational support and customised advice. Additionally, wearable technology (e.g., fitness trackers and smartwatches) enables real-time monitoring of vital health metrics, encouraging ongoing healthy activities. Nonetheless, these technologies introduce challenges including privacy issues, data security, and equitable access to digital resources, raising a new class of rights to protect mental privacy, guard against algorithm bias, and prevent personality-changing manipulations. The absence of human interaction in fully digital solutions also raises concerns about a lack of empathy and emotional connection. For optimal use of digital tools in mental health, integration with conventional care practices and adaptation to diverse cultural and social backgrounds are necessary. The results of this review suggest that digital tools, when carefully implemented, can significantly improve mental health outcomes by making care more accessible, tailored, and effective, especially for underserved communities.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1560580"},"PeriodicalIF":3.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11965895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-initiated humour protocol: a pilot study with an AI agent.","authors":"Abbas Edalat, Ruoyu Hu, Zeena Patel, Neophytos Polydorou, Frank Ryan, Dasha Nicholls","doi":"10.3389/fdgth.2025.1530131","DOIUrl":"10.3389/fdgth.2025.1530131","url":null,"abstract":"<p><strong>Introduction: </strong>Non-hostile humour and laughter have been known for therapeutic benefits in an individual's mental health and wellbeing. To this end, we evaluated the Self-Initiated Humour Protocol (SIHP), a new type of self-administrable laughter intervention that utilises spontaneous and self-induced laughter. Rooted in the core principles of the Self-Attachment Technique-in which an individual creates an affectional bond with their childhood self as represented by their childhood photo or personalised childhood avatar-SIHP provides an algorithmic framework for individuals to learn to laugh in a non-hostile manner and develop a sense of humour in all possible life contexts. This allows SIHP to be self-administered by interacting with an AI agent.</p><p><strong>Methods: </strong>An 8-week intervention was conducted with N = 27 adult participants. Exclusion criteria: severe depression or anxiety (PHQ-9 and GAD-7 scores above 15). Participants' measurements were collected in the areas of wellbeing, use of different humour styles, emotional self-regulation, self-compassion and psychological capital, and analysed to understand any changes over time. Measurements were taken immediately before, after the intervention, and at the 3-month follow-up. Throughout the intervention, participants were required to practise SIHP 20 min a day with the aid of an emotionally intelligent chatbot and their personalised child avatar in virtual reality (VR).</p><p><strong>Results: </strong>Analysis of results at the 3-month follow-up showed significant improvements in the primary outcome of wellbeing with large effect size ( <math><mi>r</mi> <mo>=</mo> <mn>0.92</mn></math> ), as well as a range of secondary outcomes with large effect sizes, self-compassion ( <math><mi>r</mi> <mo>=</mo> <mn>0.93</mn></math> ), use of self-enhancing humour ( <math><mi>d</mi> <mo>=</mo> <mn>0.80</mn></math> ), and emotion regulation ( <math><mi>d</mi> <mo>=</mo> <mn>0.87</mn></math> ); the results also showed improvement to participant's psychological capital with moderate effect size ( <math><mi>d</mi> <mo>=</mo> <mn>0.56</mn></math> ).</p><p><strong>Discussion: </strong>This study shows the potential for the practice of SIHP as supported by an emotionally intelligent chatbot and personalised child avatar to have medium-term positive effects, which should be validated through future randomised trials.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1530131"},"PeriodicalIF":3.2,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11965911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SympCoughNet: symptom assisted audio-based COVID-19 detection.","authors":"Yuhao Lin, Xiu Weng, Bolun Zheng, Weiwei Zhang, Zhanjun Bu, Yu Zhou","doi":"10.3389/fdgth.2025.1551298","DOIUrl":"10.3389/fdgth.2025.1551298","url":null,"abstract":"<p><p>COVID-19 remains a significant global public health challenge. While nucleic acid tests, antigen tests, and CT imaging provide high accuracy, they face inefficiencies and limited accessibility, making rapid and convenient testing difficult. Recent studies have explored COVID-19 detection using acoustic health signals, such as cough and breathing sounds. However, most existing approaches focus solely on audio classification, often leading to suboptimal accuracy while neglecting valuable prior information, such as clinical symptoms. To address this limitation, we propose SympCoughNet, a deep learning-based COVID-19 audio classification network that integrates cough sounds with clinical symptom data. Our model employs symptom-encoded channel weighting to enhance feature processing, making it more attentive to symptom information. We also conducted an ablation study to assess the impact of symptom integration by removing the symptom-attention mechanism and instead using symptoms as classification labels within a CNN-based architecture. We trained and evaluated SympCoughNet on the UK COVID-19 Vocal Audio Dataset. Our model demonstrated significant performance improvements over traditional audio-only approaches, achieving 89.30% accuracy, 94.74% AUROC, and 91.62% PR on the test set. The results confirm that incorporating symptom data enhances COVID-19 detection performance. Additionally, we found that incorrect symptom inputs could influence predictions. Our ablation study validated that even when symptoms are treated as classification labels, the network can still effectively leverage cough audio to infer symptom-related information.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1551298"},"PeriodicalIF":3.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11936986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact on women's body satisfaction of exposure to postpartum imagery on social media.","authors":"Megan L Gow, Maddison Henderson, Amanda Henry, Lynne Roberts, Heike Roth","doi":"10.3389/fdgth.2025.1379337","DOIUrl":"10.3389/fdgth.2025.1379337","url":null,"abstract":"<p><strong>Background: </strong>Social networking sites may be a convenient, accessible and low-cost option for delivering health information at scale to postpartum women. However, social media use is associated with decreased body satisfaction and may contribute to psychological ill-health. Our study aimed to determine whether exposure to body-focused imagery, typical of imagery targeting postpartum women on Instagram, is associated with a reduction in state body satisfaction and state body appreciation. Secondly, we aimed to determine whether including postpartum-health-focused imagery, in conjunction with body-focused imagery, is associated with improving state body satisfaction/appreciation, compared with no postpartum health content.</p><p><strong>Methods: </strong>A single blinded quasi-experimental survey study, recruiting women who had given birth in the previous 2-years, asked participants about key demographic information, social media use and assessed thin-ideal internalization and media appearance pressures using validated tools. Participants were then exposed to either (1) 15 body-focused images of women with a thin-average level of adiposity; (2) as per (1) PLUS 5 postpartum-health-focused images; or (3) as per (1) PLUS 15 postpartum-health-focused images. State body satisfaction/appreciation were assessed before and after image exposure.</p><p><strong>Results: </strong>State body satisfaction/appreciation did not change from pre- to post-image exposure in any groups and measures were not different between groups at any time point.</p><p><strong>Discussion: </strong>Short-term exposure to body-focused imagery typical of Instagram content targeting postpartum women may not alter state body satisfaction or state body appreciation. Furthermore, incorporating postpartum-health-focused imagery did not alter results. Further research investigating whether an intervention providing health information to postpartum women via social media platforms improves health outcomes may be warranted.</p>","PeriodicalId":73078,"journal":{"name":"Frontiers in digital health","volume":"7 ","pages":"1379337"},"PeriodicalIF":3.2,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11937002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143722888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}