European heart journal. Digital health最新文献

{"title":"Correspondence to the European Heart Journal-digital health in response to the paper by Attia <i>et al.</i> 2022.","authors":"Nishil Patel,&nbsp;Salaheldin Agamy,&nbsp;Mahmood Ahmad","doi":"10.1093/ehjdh/ztac053","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac053","url":null,"abstract":"We were interested to read the paper by Attia et al. 1 which demon-strated the value of electrocardiogram enabled stethoscopes (ECG-Scope). Their findings show potential in the utilization of artificial intelligence (AI) algorithms in conjunction with a single lead ECG-Scope to identify left ventricular dysfunction (LVSD). A clinical pathway such as this may speed up diagnosis and potentially improve patient outcomes.","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/3e/53/ztac053.PMC9779798.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10734970","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":"Clinical validation of a 13-lead electrocardiogram derived from a self-applicable 3-lead recording for diagnosis of myocardial supply ischaemia and common non-ischaemic electrocardiogram abnormalities at rest.","authors":"Frederic Van Heuverswyn,&nbsp;Céline De Schepper,&nbsp;Marc De Buyzere,&nbsp;Mathieu Coeman,&nbsp;Jan De Pooter,&nbsp;Benny Drieghe,&nbsp;Peter Kayaert,&nbsp;Liesbeth Timmers,&nbsp;Sofie Gevaert,&nbsp;Simon Calle,&nbsp;Victor Kamoen,&nbsp;Anthony Demolder,&nbsp;Milad El Haddad,&nbsp;Peter Gheeraert","doi":"10.1093/ehjdh/ztac062","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac062","url":null,"abstract":"<p><strong>Aims: </strong>In this study, we compare the diagnostic accuracy of a standard 12-lead electrocardiogram (ECG) with a novel 13-lead ECG derived from a self-applicable 3-lead ECG recorded with the right exploratory left foot (RELF) device. The 13th lead is a novel age and sex orthonormalized computed ST (ASO-ST) lead to increase the sensitivity for detecting ischaemia during acute coronary artery occlusion.</p><p><strong>Methods and results: </strong>A database of simultaneously recorded 12-lead ECGs and RELF recordings from 110 patients undergoing coronary angioplasty and 30 healthy subjects was used. Five cardiologists scored the learning data set and five other cardiologists scored the validation data set. In addition, the presence of non-ischaemic ECG abnormalities was compared. The accuracy for detection of myocardial supply ischaemia with the derived 12 leads was comparable with that of the standard 12-lead ECG (<i>P</i> = 0.126). By adding the ASO-ST lead, the accuracy increased to 77.4% [95% confidence interval (CI): 72.4-82.3; <i>P</i> < 0.001], which was attributed to a higher sensitivity of 81.9% (95% CI: 74.8-89.1) for the RELF 13-lead ECG compared with a sensitivity of 76.8% (95% CI: 71.9-81.7; <i>P</i> < 0.001) for the 12-lead ECG. There was no significant difference in the diagnosis of non-ischaemic ECG abnormalities, except for Q-waves that were more frequently detected on the standard ECG compared with the derived ECG (25.9 vs. 13.8%; <i>P</i> < 0.001).</p><p><strong>Conclusion: </strong>A self-applicable and easy-to-use 3-lead RELF device can compute a 12-lead ECG plus an ischaemia-specific 13th lead that is, compared with the standard 12-lead ECG, more accurate for the visual diagnosis of myocardial supply ischaemia by cardiologists.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/ca/41/ztac062.PMC9779790.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10732811","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":"Validation of risk prediction models applied to longitudinal electronic health record data for the prediction of major cardiovascular events in the presence of data shifts.","authors":"Yikuan Li,&nbsp;Gholamreza Salimi-Khorshidi,&nbsp;Shishir Rao,&nbsp;Dexter Canoy,&nbsp;Abdelaali Hassaine,&nbsp;Thomas Lukasiewicz,&nbsp;Kazem Rahimi,&nbsp;Mohammad Mamouei","doi":"10.1093/ehjdh/ztac061","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac061","url":null,"abstract":"Abstract Aims Deep learning has dominated predictive modelling across different fields, but in medicine it has been met with mixed reception. In clinical practice, simple, statistical models and risk scores continue to inform cardiovascular disease risk predictions. This is due in part to the knowledge gap about how deep learning models perform in practice when they are subject to dynamic data shifts; a key criterion that common internal validation procedures do not address. We evaluated the performance of a novel deep learning model, BEHRT, under data shifts and compared it with several ML-based and established risk models. Methods and results Using linked electronic health records of 1.1 million patients across England aged at least 35 years between 1985 and 2015, we replicated three established statistical models for predicting 5-year risk of incident heart failure, stroke, and coronary heart disease. The results were compared with a widely accepted machine learning model (random forests), and a novel deep learning model (BEHRT). In addition to internal validation, we investigated how data shifts affect model discrimination and calibration. To this end, we tested the models on cohorts from (i) distinct geographical regions; (ii) different periods. Using internal validation, the deep learning models substantially outperformed the best statistical models by 6%, 8%, and 11% in heart failure, stroke, and coronary heart disease, respectively, in terms of the area under the receiver operating characteristic curve. Conclusion The performance of all models declined as a result of data shifts; despite this, the deep learning models maintained the best performance in all risk prediction tasks. Updating the model with the latest information can improve discrimination but if the prior distribution changes, the model may remain miscalibrated.","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779795/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10233201","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":"Corrigendum to: 2021 ISHNE / HRS / EHRA / APHRS Collaborative Statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals: From the International Society for Holter and Noninvasive Electrocardiology / Heart Rhythm Society / European Heart Rhythm Association / Asia Pacific Heart Rhythm Society.","authors":"","doi":"10.1093/ehjdh/ztac060","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac060","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.1093/ehjdh/ztab001.].</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10586481","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 artificial intelligence-based platform for automatically estimating time-averaged wall shear stress in the ascending aorta.","authors":"Lei Lv,&nbsp;Haotian Li,&nbsp;Zonglv Wu,&nbsp;Weike Zeng,&nbsp;Ping Hua,&nbsp;Songran Yang","doi":"10.1093/ehjdh/ztac058","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac058","url":null,"abstract":"<p><strong>Aims: </strong>Aortopathies are a series of disorders requiring multiple indicators to assess risk. Time-averaged wall shear stress (TAWSS) is currently considered as the primary indicator of aortopathies progression, which can only be calculated by Computational Fluid Dynamics (CFD). However, CFD's complexity and high computational cost, greatly limit its application. The study aimed to construct a deep learning platform which could accurately estimate TAWSS in ascending aorta.</p><p><strong>Methods and results: </strong>A total of 154 patients who had thoracic computed tomography angiography were included and randomly divided into two parts: training set (90%, <i>n</i> = 139) and testing set (10%, <i>n</i> = 15). TAWSS were calculated via CFD. The artificial intelligence (AI)-based model was trained and assessed using the dice coefficient (DC), normalized mean absolute error (NMAE), and root mean square error (RMSE). Our AI platform brought into correspondence with the manual segmentation (DC = 0.86) and the CFD findings (NMAE, 7.8773% ± 4.7144%; RMSE, 0.0098 ± 0.0097), while saving 12000-fold computational cost.</p><p><strong>Conclusion: </strong>The high-efficiency and robust AI platform can automatically estimate value and distribution of TAWSS in ascending aorta, which may be suitable for clinical applications and provide potential ideas for CFD-based problem solving.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/bf/01/ztac058.PMC9779925.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9310428","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":"Meet Key Digital Health thought Leaders: Gerhard Hindricks.","authors":"Nico Bruining","doi":"10.1093/ehjdh/ztac064","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac064","url":null,"abstract":"of novel process and diagnostic/ therapeutic pathways","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/de/33/ztac064.PMC9779917.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10635291","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":"Regional implementation of atrial fibrillation screening: benefits and pitfalls.","authors":"Luc J H J Theunissen,&nbsp;Reyan B E M Abdalrahim,&nbsp;Lukas R C Dekker,&nbsp;Eric J M Thijssen,&nbsp;Sylvie F A M S de Jong,&nbsp;Peter E Polak,&nbsp;Pepijn H van de Voort,&nbsp;Geert Smits,&nbsp;Karin Scheele,&nbsp;Annelies Lucas,&nbsp;Dennis P A van Veghel,&nbsp;Henricus-Paul Cremers,&nbsp;Jeroen A A van de Pol,&nbsp;Hareld M C Kemps","doi":"10.1093/ehjdh/ztac055","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac055","url":null,"abstract":"<p><strong>Aims: </strong>Despite general awareness that screening for atrial fibrillation (AF) could reduce health hazards, large-scale implementation is lagging behind technological developments. As the successful implementation of a screening programme remains challenging, this study aims to identify facilitating and inhibiting factors from healthcare providers' perspectives.</p><p><strong>Methods and results: </strong>A mixed-methods approach was used to gather data among practice nurses in primary care in the southern region of the Netherlands to evaluate the implementation of an ongoing single-lead electrocardiogram (ECG)-based AF screening programme. Potential facilitating and inhibiting factors were evaluated using online questionnaires (N = 74/75%) and 14 (of 24) semi-structured in-depth interviews (58.3%). All analyses were performed using SPSS 26.0. In total, 16 682 screenings were performed on an eligible population of 64 000, and 100 new AF cases were detected. Facilitating factors included 'receiving clear instructions' (mean ± SD; 4.12 ± 1.05), 'easy use of the ECG-based device' (4.58 ± 0.68), and 'patient satisfaction' (4.22 ± 0.65). Inhibiting factors were 'time availability' (3.20 ± 1.10), 'insufficient feedback to the practice nurse' (2.15 ± 0.89), 'absence of coordination' (54%), and the 'lack of fitting policy' (32%).</p><p><strong>Conclusion: </strong>Large-scale regional implementation of an AF screening programme in primary care resulted in a low participation of all eligible patients. Based on the perceived barriers by healthcare providers, future AF screening programmes should create preconditions to fit the intervention into daily routines, appointing an overall project lead and a General Practitioner (GP) as a coordinator within every GP practice.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/79/26/ztac055.PMC9779812.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10747229","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":"Smartphone and wearable detected atrial arrhythmias in Older Adults: Results of a fully digital European Case finding study.","authors":"L Fabritz,&nbsp;D L Connolly,&nbsp;E Czarnecki,&nbsp;D Dudek,&nbsp;E Guasch,&nbsp;D Haase,&nbsp;T Huebner,&nbsp;A Zlahoda-Huzior,&nbsp;K Jolly,&nbsp;P Kirchhof,&nbsp;J Obergassel,&nbsp;U Schotten,&nbsp;E Vettorazzi,&nbsp;S J Winkelmann,&nbsp;A Zapf,&nbsp;R B Schnabel","doi":"10.1093/ehjdh/ztac067","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac067","url":null,"abstract":"<p><strong>Aims: </strong>Simplified detection of atrial arrhythmias via consumer-electronics would enable earlier therapy in at-risk populations. Whether this is feasible and effective in older populations is not known.</p><p><strong>Methods and results: </strong>The fully remote, investigator-initiated <b>Smart</b>phone and wearable detected atrial arrhythmia <b>in O</b>lder <b>A</b>dults <b>C</b>ase finding study (Smart in OAC-AFNET 9) digitally enrolled participants ≥65 years without known atrial fibrillation, not receiving oral anticoagulation in Germany, Poland, and Spain for 8 weeks. Participants were invited by media communications and direct contacts. Study procedures adhered to European data protection. Consenting participants received a wristband with a photoplethysmography sensor to be coupled to their smartphone. The primary outcome was the detection of atrial arrhythmias lasting 6 min or longer in the first 4 weeks of monitoring. Eight hundred and eighty-two older persons (age 71 ± 5 years, range 65-90, 500 (57%) women, 414 (47%) hypertension, and 97 (11%) diabetes) recorded signals. Most participants (72%) responded to adverts or word of mouth, leaflets (11%) or general practitioners (9%). Participation was completely remote in 469/882 persons (53%). During the first 4 weeks, participants transmitted PPG signals for 533/696 h (77% of the maximum possible time). Atrial arrhythmias were detected in 44 participants (5%) within 28 days, and in 53 (6%) within 8 weeks. Detection was highest in the first monitoring week [incidence rates: 1st week: 3.4% (95% confidence interval 2.4-4.9); 2nd-4th week: 0.55% (0.33-0.93)].</p><p><strong>Conclusion: </strong>Remote, digitally supported consumer-electronics-based screening is feasible in older European adults and identifies atrial arrhythmias in 5% of participants within 4 weeks of monitoring (NCT04579159).</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779806/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10784585","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":"The effectiveness of a telemedical program for COVID-19 positive high-risk patients in domestic isolation","authors":"L. Brunelli, L. Poelzl, J. Hirsch, C. Engler, F. Naegele, T. Egelseer-Bruendl, T. Scheffauer, C. Rassel, C. Schmit, F. Nawabi, A. Luckner-Hornischer, A. Bauer, G. Poelzl","doi":"10.1093/ehjdh/ztac076.2802","DOIUrl":"https://doi.org/10.1093/ehjdh/ztac076.2802","url":null,"abstract":"Abstract Background For almost two years, the Covid-19 pandemic has posed an enormous challenge to healthcare systems. Recurrent waves of disease brought the health systems to the limit of their resilience. Purpose The Tele-Covid telemedicine care program was installed in December 2020 to monitor high-risk patients in home isolation. Close monitoring allows early detection of disease deterioration and timely intensification of therapy, ideally avoiding intensive care. Conversely, if the course of the disease is stable, unnecessary hospitalisation can be avoided, thus reducing the burden on the healthcare system. Methods Patient acquisition was performed in collaboration with the local public health service and primary care physicians. Covid-19 positive high-risk patients (age >65 years and/or severe comorbidities) from the greater Innsbruck area were fitted with an ear sensor-based home monitoring system. The ear sensor measures SpO2, respiratory rate, body temperature and heart rate. The monitoring team (25 medical students supervised by 6 physicians) provided continuous monitoring of vital signs (24/7). After validation of the measurements, the collected parameters were evaluated using a specially developed risk score. If a defined risk score was exceeded, the patient was contacted by telephone. The combination of the clinical condition and the risk score determined the further course of action: (a) wait and see, (b) notify the primary care physician, or (c) refer for inpatient admission. The program was active from December 2020 to March 2022. In Summer 2021, the program was temporarily paused due to the epidemiological situation. Results A total of 132 patients (59.8% women) were monitored. The median age was 74 years (IQR: [67.3–80.8]). 91 patients (68.9%) had at least one relevant comorbidity. During the monitoring period, hospitalisation was required in 20 patients (15.2%), 3 of whom were transferred to the intensive care unit. Of the hospitalised patients, 3 (15%) patients died. During the same monitoring period, the Austrian Ministry of Health reported a mortality rate of 20.5% of all hospitalised patients in Austria aged 70–79 years. Subjectively, the patients felt safe due to close monitoring. Conclusion The Tele-Covid program is the successful implementation of a remote monitoring system in a pandemic situation. In the future, a broad application of the program is feasible. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Funded by the Region of the Tyrol","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88488707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What helps the successful implementation of digital decision aids supporting shared decision-making in cardiovascular diseases? A systematic review.","authors":"Loes J Peters, Alezandra Torres-Castaño, Faridi S van Etten-Jamaludin, Lilisbeth Perestelo Perez, Dirk T Ubbink","doi":"10.1093/ehjdh/ztac070","DOIUrl":"10.1093/ehjdh/ztac070","url":null,"abstract":"<p><strong>Aims: </strong>Although digital decision aids (DAs) have been developed to improve shared decision-making (SDM), also in the cardiovascular realm, its implementation seems challenging. This study aims to systematically review the predictors of successful implementation of digital DAs for cardiovascular diseases.</p><p><strong>Methods and results: </strong>Searches were conducted in MEDLINE, Embase, PsycInfo, CINAHL, and the Cochrane Library from inception to November 2021. Two reviewers independently assessed study eligibility and risk of bias. Data were extracted by using a predefined list of variables. Five good-quality studies were included, involving data of 215 patients and 235 clinicians. Studies focused on DAs for coronary artery disease, atrial fibrillation, and end-stage heart failure patients. Clinicians reported DA content, its effectivity, and a lack of knowledge on SDM and DA use as implementation barriers. Patients reported preference for another format, the way clinicians used the DA and anxiety for the upcoming intervention as barriers. In addition, barriers were related to the timing and Information and Communication Technology (ICT) integration of the DA, the limited duration of a consultation, a lack of communication among the team members, and maintaining the hospital's number of treatments. Clinicians' positive attitude towards preference elicitation and implementation of DAs in existing structures were reported as facilitators.</p><p><strong>Conclusion: </strong>To improve digital DA use in cardiovascular diseases, the optimum timing of the DA, training healthcare professionals in SDM and DA usage, and integrating DAs into existing ICT structures need special effort. Current evidence, albeit limited, already offers advice on how to improve DA implementation in cardiovascular medicine.</p>","PeriodicalId":72965,"journal":{"name":"European heart journal. Digital health","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/23/a8/ztac070.PMC9890083.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10663669","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}