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Reducing Quotation Errors in Scientific Manuscripts: A Novel Approach from the Global Andrology Forum.
World J Mens Health
January 2025
Global Andrology Forum, Moreland Hills, OH, USA.
Purpose: This study investigated 1) the frequency of quotation errors in multi-authored medical manuscripts in andrology, 2) analyzed common types of quotation errors and the methods used to rectify them, and 3) evaluated their impact on manuscript accuracy, credibility, and research conclusions.
Materials And Methods: Twelve manuscripts written by the Global Andrology Forum (GAF) members between 2023 and 2024 were randomly selected for this study. The manuscripts and "Quotation Verification Sheets" were analyzed by senior GAF researchers to detect the number and types of quotation errors.
WGAN-GP for Synthetic Retinal Image Generation: Enhancing Sensor-Based Medical Imaging for Classification Models.
Sensors (Basel)
December 2024
Computer Science Department, Instituto Nacional de Astrofísica Óptica y Electrónica, Luis Enrrique Erro No. 1, Sta. María Tonantzintla, Puebla 72840, Mexico.
Accurate synthetic image generation is crucial for addressing data scarcity challenges in medical image classification tasks, particularly in sensor-derived medical imaging. In this work, we propose a novel method using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and nearest-neighbor interpolation to generate high-quality synthetic images for diabetic retinopathy classification. Our approach enhances training datasets by generating realistic retinal images that retain critical pathological features.
View Article and Find Full Text PDFKnowledge graph-based thought: a knowledge graph-enhanced LLM framework for pan-cancer question answering.
Gigascience
January 2025
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, 310024 Hangzhou, China.
Article Synopsis
- Large language models (LLMs) have potential in biomedical sciences but often produce errors and unreliable outputs.
- A new approach called the knowledge graph-based thought (KGT) framework combines LLMs with knowledge graphs (KGs) to enhance accuracy and reduce factual mistakes.
- The KGT framework shows versatility across different LLMs, helping in drug discovery and resistance prediction, and sets a new benchmark for biomedical question answering.
Artificial Intelligence-Powered Training Database for Clinical Thinking: App Development Study.
JMIR Form Res
January 2025
Centre for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing, China.
Background: With the development of artificial intelligence (AI), medicine has entered the era of intelligent medicine, and various aspects, such as medical education and talent cultivation, are also being redefined. The cultivation of clinical thinking abilities poses a formidable challenge even for seasoned clinical educators, as offline training modalities often fall short in bridging the divide between current practice and the desired ideal. Consequently, there arises an imperative need for the expeditious development of a web-based database, tailored to empower physicians in their quest to learn and hone their clinical reasoning skills.
View Article and Find Full Text PDFA Perspective on the MARS2 Trial.
J Thorac Oncol
January 2025
Department of Cardiothoracic Surgery, New York University Langone Health, New York, New York. Electronic address:
Introduction: The phase 3 randomized controlled trial of extended pleurectomy decortication and chemotherapy versus chemotherapy alone for pleural mesothelioma (PM) (MARS2) reported "extended pleurectomy decortication was associated with worse survival to 2 years, and more serious adverse events for individuals with resectable PM, compared with chemotherapy alone." These results have led to considerable discourse regarding the future role of surgery for PM, and there has not been unanimity in the mesothelioma surgical community regarding the trial interpretation. This "perspective" evaluates MARS2 using internationally renowned PM experts who either agreed with the trial interpretation or who found issues with its conduct which may have influenced the results.
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