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http://dx.doi.org/10.1111/cpf.12758 | DOI Listing |
J Fluoresc
January 2025
Materials Science Lab (1), Physics Department, Faculty of Science, Cairo University, Giza, Egypt.
This study reports the synthesis, characterization, and optical properties of ZnO, ZnCeO, and ZnNdO nanoparticles and their interactions with lead acetate solutions. X-ray diffraction (XRD) confirmed that the nanoparticles were synthesized in a single-phase hexagonal structure, with crystallite sizes of 12.48 nm, 50.
View Article and Find Full Text PDFHeart Int
August 2024
National Amyloidosis Centre, University College London, Royal Free Campus, London, UK.
[This corrects the article DOI: 10.17925/HI.2024.
View Article and Find Full Text PDFCureus
December 2024
Orthodontics and Dentofacial Orthopaedics, Kalinga Institute of Dental Sciences, Bhubaneswar, IND.
Vertical maxillary excess presents a complex challenge in orthodontic treatment, necessitating effective anchorage systems for optimal correction. This research is useful to assess the skeletal anchorage system's (SAS) effectiveness in correcting the vertical maxillary excess among adult patients presenting with gummy smiles. This study includes case reports with English full text and examines the global general adult (18+) human population with vertical maxillary excess (VME).
View Article and Find Full Text PDFPLoS One
January 2025
Faculty of Dentistry, PHENIKAA University, Hanoi, Vietnam.
Objectives: This study aims to evaluate the performance of the latest large language models (LLMs) in answering dental multiple choice questions (MCQs), including both text-based and image-based questions.
Material And Methods: A total of 1490 MCQs from two board review books for the United States National Board Dental Examination were selected. This study evaluated six of the latest LLMs as of August 2024, including ChatGPT 4.
AJR Am J Roentgenol
January 2025
Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Boston, MA 02120 Phone: 617-525-9702.
Automated extraction of actionable details of recommendations for additional imaging (RAIs) from radiology reports could facilitate tracking and timely completion of clinically necessary RAIs and thereby potentially reduce diagnostic delays. To assess the performance of large-language models (LLMs) in extracting actionable details of RAIs from radiology reports. This retrospective single-center study evaluated reports of diagnostic radiology examinations performed across modalities and care settings within five subspecialties (abdominal imaging, musculoskeletal imaging, neuroradiology, nuclear medicine, thoracic imaging) in August 2023.
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