Download full-text PDF |
Source |
|---|
Publication Analysis
Top Keywords
Similar Publications
Nelonemdaz and Patients With Acute Ischemic Stroke and Mechanical Reperfusion: The RODIN Randomized Clinical Trial.
JAMA Netw Open
January 2025
Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
Importance: Nelonemdaz selectively antagonizes the 2B subunit of the N-methyl-d-aspartate glutamate receptor and scavenges free radical species.
Objective: To evaluate whether nelonemdaz enhances the clinical outcomes of patients with acute ischemic stroke undergoing emergent reperfusion therapy.
Design, Setting, And Participants: This multicenter double-blind placebo-controlled randomized phase 3 trial (December 25, 2021, to June 30, 2023, in South Korea) recruited patients with acute ischemic stroke who met the following criteria: National Institutes of Health Stroke Scale score greater than or equal to 8, Alberta Stroke Program Early Computed Tomography score greater than or equal to 4, and endovascular thrombectomy within 12 hours after stroke onset.
Photon mini-GRID therapy for preoperative breast cancer tumor treatment: A treatment plan study.
Med Phys
January 2025
Institut Curie, Université PSL, CNRS UMR3347, Inserm U1021, Signalisation Radiobiologie et Cancer, Orsay, France.
Background: Breast cancer is the leading cause of female cancer mortality worldwide, accounting for 1 in 6 cancer deaths. Surgery, radiation, and systemic therapy are the three pillars of breast cancer treatment, with several strategies developed to combine them. The association of preoperative radiotherapy with immunotherapy may improve breast cancer tumor control by exploiting the tumor radio-induced immune priming.
View Article and Find Full Text PDFFast and interpretable mortality risk scores for critical care patients.
J Am Med Inform Assoc
January 2025
Department of Computer Science, Duke University, Durham, NC 27708, United States.
Objective: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.
Material And Methods: We developed a new algorithm, GroupFasterRisk, which has several important benefits: it uses both hard and soft direct sparsity regularization, it incorporates group sparsity to allow more cohesive models, it allows for monotonicity constraint to include domain knowledge, and it produces many equally good models, which allows domain experts to choose among them.
Age-invariant genes: multi-tissue identification and characterization of murine reference genes.
Aging (Albany NY)
January 2025
Department of Pathology, Yale University School of Medicine, New Haven, CT 06519, USA.
Studies of the aging transcriptome focus on genes that change with age. But what can we learn from age-invariant genes-those that remain unchanged throughout the aging process? These genes also have a practical application: they can serve as reference genes in expression studies. Reference genes have mostly been identified and validated in young organisms, and no systematic investigation has been done across the lifespan.
View Article and Find Full Text PDFUse of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.
Radiology
January 2025
From the Department of Radiology, University of Washington, UW Medical Center-Montlake, Seattle, Wash (D.M.); Department of Radiology, OncoRad/Tumor Imaging Metrics Core (TIMC), University of Washington, Seattle, Wash (D.M.); Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (M.v.A.); Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, the Netherlands (M.H.); Department of Radiology, Mayo Clinic, Rochester, Minn (T.L., E.E.W.); Departments of Cardiology and Radiology, Royal Brompton Hospital, London, United Kingdom (E.D.N.); School of Biomedical Engineering and Imaging Sciences, King's College, London, United Kingdom (E.D.N.); Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Ill (B.D.A.); Department of Radiology, University of Cagliari, Cagliari, Italy (L.S.); Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1 Postbus 30 001, 9700 RB Groningen, the Netherlands (R.V.); Department of Medical Imaging, University Medical Imaging Toronto, University of Toronto, Toronto, Ontario, Canada (K.H.); and Toronto General Hospital Research Institute, University Health Network, University of Toronto, Toronto, Ontario, Canada (K.H.).
Artificial intelligence (AI) offers promising solutions for many steps of the cardiac imaging workflow, from patient and test selection through image acquisition, reconstruction, and interpretation, extending to prognostication and reporting. Despite the development of many cardiac imaging AI algorithms, AI tools are at various stages of development and face challenges for clinical implementation. This scientific statement, endorsed by several societies in the field, provides an overview of the current landscape and challenges of AI applications in cardiac CT and MRI.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!