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Kirchner J, Gerçek M, Gesch J, Omran H, Friedrichs K, Rudolph F, Ivannikova M, Rossnagel T, Piran M, Pfister R, Blanke P, Rudolph V, Rudolph TK. Artificial intelligence-analyzed computed tomography in patients undergoing transcatheter tricuspid valve repair. Int J Cardiol 2024; 411:132233. [PMID: 38848770 DOI: 10.1016/j.ijcard.2024.132233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/26/2024] [Accepted: 06/03/2024] [Indexed: 06/09/2024]
Abstract
BACKGROUND Baseline right ventricular (RV) function derived from 3-dimensional analyses has been demonstrated to be predictive in patients undergoing transcatheter tricuspid valve repair (TTVR). The complex nature of these cumbersome analyses makes patient selection based on established imaging methods challenging. Artificial intelligence (AI)-driven computed tomography (CT) segmentation of the RV might serve as a fast and predictive tool for evaluating patients prior to TTVR. METHODS Patients suffering from severe tricuspid regurgitation underwent full cycle cardiac CT. AI-driven analyses were compared to conventional CT analyses. Outcome measures were correlated with survival free of rehospitalization for heart-failure or death after TTVR as the primary endpoint. RESULTS Automated AI-based image CT-analysis from 100 patients (mean age 77 ± 8 years, 63% female) showed excellent correlation for chamber quantification compared to conventional, core-lab evaluated CT analysis (R 0.963-0.966; p < 0.001). At 1 year (mean follow-up 229 ± 134 days) the primary endpoint occurred significantly more frequently in patients with reduced RV ejection fraction (EF) <50% (36.6% vs. 13.7%; HR 2.864, CI 1.212-6.763; p = 0.016). Furthermore, patients with dysfunctional RVs defined as end-diastolic RV volume > 210 ml and RV EF <50% demonstrated worse outcome than patients with functional RVs (43.7% vs. 12.2%; HR 3.753, CI 1.621-8.693; p = 0.002). CONCLUSIONS Derived RVEF and dysfunctional RV were predictors for death and hospitalization after TTVR. AI-facilitated CT analysis serves as an inter- and intra-observer independent and time-effective tool which may thus aid in optimizing patient selection prior to TTVR in clinical routine and in trials.
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Cirulli F, Spencer SJ, Zhang C. Chatting with AI: ChatGPT, Where are we at 18 Months on and What Should we be Doing About it? Neuroscience 2024; 552:112-114. [PMID: 38925471 DOI: 10.1016/j.neuroscience.2024.06.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/28/2024]
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Stewart J, Innes M, Goudie A. The potential impact of artificial intelligence on emergency department overcrowding and access block. Emerg Med Australas 2024; 36:632-634. [PMID: 39013803 DOI: 10.1111/1742-6723.14461] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2024] [Accepted: 06/14/2024] [Indexed: 07/18/2024]
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Salwei ME, Weinger MB. Artificial Intelligence in Anesthesiology: Field of Dreams or Fire Swamp? Preemptive Strategies for Optimizing Our Inevitable Future. Anesthesiology 2024; 141:217-221. [PMID: 38980165 DOI: 10.1097/aln.0000000000005046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
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Fishe JN. Implementation and Equity Are the Keys for the Future of Artificial Intelligence in Emergency Medicine. Ann Emerg Med 2024; 84:157-158. [PMID: 38691063 DOI: 10.1016/j.annemergmed.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/26/2024] [Accepted: 04/01/2024] [Indexed: 05/03/2024]
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Metcalfe R. Trainee Focus debate: Artificial intelligence will have a positive impact on emergency medicine. Emerg Med Australas 2024; 36:637-638. [PMID: 39013800 DOI: 10.1111/1742-6723.14458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 07/18/2024]
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Rodoplu Solovchuk D. Advances in AI-assisted biochip technology for biomedicine. Biomed Pharmacother 2024; 177:116997. [PMID: 38943990 DOI: 10.1016/j.biopha.2024.116997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 06/13/2024] [Accepted: 06/15/2024] [Indexed: 07/01/2024] Open
Abstract
The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.
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Daungsupawong H, Wiwanitkit V. Large language model, AI and scientific research. J Neurosurg Sci 2024; 68:500. [PMID: 38949059 DOI: 10.23736/s0390-5616.24.06233-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
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Marmagkiolis K, Monlezun DJ, Caballero J, Cilingiroglu M, Brown MN, Ninios V, Ali A, Iliescu CA. Prevalence, mortality, cost, and disparities in transcatheter mitral valve repair and replacement in cancer patients: Artificial intelligence and propensity score national 5-year analysis of 7495 procedures. Int J Cardiol 2024; 408:132091. [PMID: 38663811 DOI: 10.1016/j.ijcard.2024.132091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/26/2024] [Accepted: 04/22/2024] [Indexed: 05/19/2024]
Abstract
INTRODUCTION We conducted the first comprehensive evaluation of the therapeutic value and safety profile of transcatheter mitral edge-to-edge repair (TEER) and transcatheter mitral valve replacement (TMVR) in individuals concurrently afflicted with cancer. METHODS Utilizing the National Inpatient Sample (NIS) dataset, we analyzed all adult hospitalizations between 2016 and 2020 (n = 148,755,036). The inclusion criteria for this retrospectively analyzed prospective cohort study were all adult hospitalizations (age 18 years and older). Regression and machine learning analyses in addition to model optimization were conducted using ML-PSr (Machine Learning-augmented Propensity Score adjusted multivariable regression) and BAyesian Machine learning-augmented Propensity Score (BAM-PS) multivariable regression. RESULTS Of all adult hospitalizations, there were 5790 (0.004%) TMVRs and 1705 (0.001%) TEERs. Of the total TMVRs, 160 (2.76%) were done in active cancer. Of the total TEERs, 30 (1.76%) were done in active cancer. After the comparable rates of TEER/TMVR in active cancer in 2016, the prevalence of TEER/TMVR was significantly less in active cancer from 2017 to 2020 (2.61% versus 7.28% p < 0.001). From 2017 to 2020, active cancer significantly decreased the odds of receiving TEER or TMVR (OR 0.28, 95%CI 0.13-0.68, p = 0.008). In patients with active cancer who underwent TMVR/TEER, there were no significant differences in socio-economic disparities, mortality or total hospitalization costs. CONCLUSION The presence of malignancy does not contribute to increased mortality, length of stay or procedural costs in TMVR or TEER. Whereas the prevalence of TMVR has increased in patients with active cancer, the utilization of TEER in the context of active cancer is declining despite a growing patient population.
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Colalillo JM, Smith J. Artificial intelligence in medicine: The rise of machine learning. Emerg Med Australas 2024; 36:628-631. [PMID: 39013808 DOI: 10.1111/1742-6723.14459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Accepted: 06/11/2024] [Indexed: 07/18/2024]
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Petrella RJ. The AI Future of Emergency Medicine. Ann Emerg Med 2024; 84:139-153. [PMID: 38795081 DOI: 10.1016/j.annemergmed.2024.01.031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 05/27/2024]
Abstract
In the coming years, artificial intelligence (AI) and machine learning will likely give rise to profound changes in the field of emergency medicine, and medicine more broadly. This article discusses these anticipated changes in terms of 3 overlapping yet distinct stages of AI development. It reviews some fundamental concepts in AI and explores their relation to clinical practice, with a focus on emergency medicine. In addition, it describes some of the applications of AI in disease diagnosis, prognosis, and treatment, as well as some of the practical issues that they raise, the barriers to their implementation, and some of the legal and regulatory challenges they create.
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Sheng B, Pushpanathan K, Guan Z, Lim QH, Lim ZW, Yew SME, Goh JHL, Bee YM, Sabanayagam C, Sevdalis N, Lim CC, Lim CT, Shaw J, Jia W, Ekinci EI, Simó R, Lim LL, Li H, Tham YC. Artificial intelligence for diabetes care: current and future prospects. Lancet Diabetes Endocrinol 2024; 12:569-595. [PMID: 39054035 DOI: 10.1016/s2213-8587(24)00154-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/28/2024] [Accepted: 05/16/2024] [Indexed: 07/27/2024]
Abstract
Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition. In this Review, we explore and summarise the current and future prospects of AI across the diabetes care continuum, from enhancing screening and diagnosis to optimising treatment and predicting and managing complications.
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Kulkarni S. Three ways AI is changing the 2024 Olympics for athletes and fans. Nature 2024; 632:20. [PMID: 39054366 DOI: 10.1038/d41586-024-02427-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
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Han L, Char DS, Aghaeepour N. Artificial Intelligence in Perioperative Care: Opportunities and Challenges. Anesthesiology 2024; 141:379-387. [PMID: 38980160 PMCID: PMC11239120 DOI: 10.1097/aln.0000000000005013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Artificial intelligence (AI) applications have great potential to enhance perioperative care. This paper explores promising areas for AI in anesthesiology; expertise, stakeholders, and infrastructure for development; and barriers and challenges to implementation.
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Hilbig A. Trainee Focus debate: Artificial intelligence will have a negative impact on emergency medicine. Emerg Med Australas 2024; 36:639-640. [PMID: 39013801 DOI: 10.1111/1742-6723.14460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 07/18/2024]
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Cecconi M, Greco M, Shickel B, Vincent JL, Bihorac A. Artificial intelligence in acute medicine: a call to action. Crit Care 2024; 28:258. [PMID: 39075468 PMCID: PMC11285390 DOI: 10.1186/s13054-024-05034-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Accepted: 07/12/2024] [Indexed: 07/31/2024] Open
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Sarıkaya Solak S, Göktay F. The Promising Role of Artificial Intelligence in Nail Diseases. Balkan Med J 2024; 41:234-235. [PMID: 38767411 DOI: 10.4274/balkanmedj.galenos.2024.2024-010424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/06/2024] Open
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Guleria A, Krishan K, Sharma V, Kanchan T. Global adoption of facial recognition technology with special reference to India-Present status and future recommendations. MEDICINE, SCIENCE, AND THE LAW 2024; 64:236-244. [PMID: 38263636 DOI: 10.1177/00258024241227717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
The face is the most essential part of the human body, and because of its distinctive traits, it is crucial for recognizing people. Facial recognition technology (FRT) is one of the most successful and fascinating technologies of the modern times. The world is moving towards contactless FRT after the COVID-19 pandemic. Due to its contactless biometric characteristics, FRT is becoming quite popular worldwide. Businesses are replacing conventional fingerprint scanners with artificial intelligence-based FRT, opening up enormous commercial prospects. Security and surveillance, authentication/access control systems, digital healthcare, photo retrieval, etc., are some sectors where its use has become essential. In the present communication, we presented the global adoption of FRT, its rising trend in the market, utilization of the technology in various sectors, its challenges and rising concerns with special reference to India and worldwide.
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Dowden JJ, Pretty RW, Shea JM, Dermody M, Doyle G, Antle S, Bond D. A novel technology for harmonizing and analyzing cancer data. Observations from integrating health connect in Newfoundland and Labrador, Canada. Health Informatics J 2024; 30:14604582241267792. [PMID: 39056109 DOI: 10.1177/14604582241267792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024]
Abstract
Objective: This article aims to describe the implementation of a new health information technology system called Health Connect that is harmonizing cancer data in the Canadian province of Newfoundland and Labrador; explain high-level technical details of this technology; provide concrete examples of how this technology is helping to improve cancer care in the province, and to discuss its future expansion and implications. Methods: We give a technical description of the Health Connect architecture, how it integrated numerous data sources into a single, scalable health information system for cancer data and highlight its artificial intelligence and analytics capacity. Results: We illustrated two practical achievements of Health Connect. First, an analytical dashboard that was used to pinpoint variations in colon cancer screening uptake in small defined geographic regions of the province; and second, a natural language processing algorithm that provided AI-assisted decision support in interpreting appropriate follow-up action based on assessments of breast mammography reports. Conclusion: Health Connect is a cutting-edge, health systems solution for harmonizing cancer screening data for practical decision-making. The long term goal is to integrate all cancer care data holdings into Health Connect to build a comprehensive health information system for cancer care in the province.
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Zheng H, Hu X. Computational intelligence in bioinformatics and biomedicine. Methods 2024; 227:58-59. [PMID: 38729457 DOI: 10.1016/j.ymeth.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2024] Open
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Leff LE, Koperwas ML. Calculated Medicine: Seven Decades of Accelerating Growth. Am J Med 2024; 137:582-588. [PMID: 38556036 DOI: 10.1016/j.amjmed.2024.03.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 03/11/2024] [Accepted: 03/11/2024] [Indexed: 04/02/2024]
Abstract
The field of Calculated Medicine has grown substantially over the last 7 decades. Comprised of objective, evidence-based medical decision tools, Calculated Medicine has broad application in medical practice, medical research, and health care management. This article reviews the history and varied methodologies of Calculated Medicine, starting with the 1953 Apgar score and concluding with a look into modern computational tools of the field: machine learning, natural language processing, artificial intelligence, and in silico research techniques. We'll also review and quantify the rapidly accelerating growth of Calculated Medicine in the medical literature. Our database of journal articles referring to the field has accumulated over 1.8 million citations, with more than 460 new citations (on average) posted every day. Using natural language processing, we examine and analyze this burgeoning database. Lastly, we examine an important new direction of Calculated Medicine: self-reflection on its potential effect on racial and ethnic disparities in health care. Our field is making great strides promoting health care egality, and some of the most prominent contributions will be reviewed.
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Healy WJ, Musani A, Fallaw DJ, Islam SU. Emerging Role of Artificial Intelligence in Academic Pulmonary Medicine. South Med J 2024; 117:369-370. [PMID: 38959964 DOI: 10.14423/smj.0000000000001704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
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Byrne AL, Mulvogue J, Adhikari S, Cutmore E. Discriminative and exploitive stereotypes: Artificial intelligence generated images of aged care nurses and the impacts on recruitment and retention. Nurs Inq 2024; 31:e12651. [PMID: 38940314 DOI: 10.1111/nin.12651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Revised: 06/11/2024] [Accepted: 06/17/2024] [Indexed: 06/29/2024]
Abstract
This article uses critical discourse analysis to investigate artificial intelligence (AI) generated images of aged care nurses and considers how perspectives and perceptions impact upon the recruitment and retention of nurses. The article demonstrates a recontextualization of aged care nursing, giving rise to hidden ideologies including harmful stereotypes which allow for discrimination and exploitation. It is argued that this may imply that nurses require fewer clinical skills in aged care, diminishing the value of working in this area. AI relies on existing data sets, and thus represent existing stereotypes and biases. The discourse analysis has highlighted key issues which may further impact upon nursing recruitment and retention, and advocates for stronger ethical consideration, including the use of experts in data validation, for the way that aged care services and nurses are depicted and thus valued.
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Collino F, Gattinoni L, Camporota L. Are we ready to harness AI and digital modelling for precision in PEEP settings? Intensive Care Med 2024; 50:1177-1178. [PMID: 38832993 DOI: 10.1007/s00134-024-07465-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/23/2024] [Indexed: 06/06/2024]
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Brooks M. Inside the maths that drives AI. Nature 2024; 631:244-246. [PMID: 38961154 DOI: 10.1038/d41586-024-02185-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2024]
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