Multiple guidelines for thyroid nodule management have been developed by endocrinologists, often in collaboration with surgeons and radiologists. While there is now a lot of scientific information available to meet the needs of healthcare providers, there is not always uniformity and standardization among recommendations. Consequently, the interpretation and application of guidelines in clinical practice remain somewhat limited. In this context, the management of "small" thyroid nodule warrants full discussion. Looking at treatment guidelines, surgery is the first-line option and the risk of cancer relapse can be assessed only after at least thyroidectomy; in addition, according to guidelines of minimally invasive treatment, thermal ablation may be considered for patients with small classical papillary carcinoma. However, the Thyroid Imaging Reporting And Data Systems do not recommend biopsy in nodules less than 1 cm; and performing biopsy may yield a result that is suspicious or consistent with malignancy without specifying the cancer subtype. With these premises, facing cases of "small" nodule less than 1 cm is challenging. Even if the recommendations of guidelines sound singularly appropriate, they may seem conflicting. Coordinated guidelines are needed.
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http://dx.doi.org/10.1007/s11154-025-09950-z | DOI Listing |
Natl Sci Rev
April 2025
Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China.
With the adoption of foundation models (FMs), artificial intelligence (AI) has become increasingly significant in bioinformatics and has successfully addressed many historical challenges, such as pre-training frameworks, model evaluation and interpretability. FMs demonstrate notable proficiency in managing large-scale, unlabeled datasets, because experimental procedures are costly and labor intensive. In various downstream tasks, FMs have consistently achieved noteworthy results, demonstrating high levels of accuracy in representing biological entities.
View Article and Find Full Text PDFInt J Qual Stud Health Well-being
December 2025
Humanism and Social Resilience, University of Humanistic Studies (UvH), Utrecht, The Netherlands.
Older people often face drastic life events, such as spousal loss, that profoundly affect their daily lives. Consequently, resilience-how one navigates life's changes to avoid further adverse outcomes-is increasingly relevant in ageing studies. Although understanding older adults' resilience is key to preventing adverse outcomes, the complexity of loss-related events and everyday resilience in later life is underexplored from a process-based perspective.
View Article and Find Full Text PDFObjectives: This study assessed the utility of Cameron's Illness Risk Representation (IRR) framework in understanding how women interpret their breast cancer risk after receiving a clinically derived estimate.
Design: Secondary qualitative analysis of two studies within the BC-Predict trial, using semi-structured telephone interviews with women aged 47-74 who received breast cancer risk estimates via population screening.
Methods: Forty-eight women were informed of their 10-year breast cancer risk (low (<1.
Materials (Basel)
February 2025
Centro de Investigación en Ciencias Físico-Matemáticas (CICFIM), Facultad de Ciencias Físico-Matemáticas, Universidad Autónoma de Nuevo León, San Nicolás de los Garza 66455, Mexico.
The thermal stability of bimetallic nanoparticles plays a crucial role in their performance in applications in catalysis, biotechnology, and materials science. In this study, we employ molecular dynamics simulations to investigate the melting behavior of Au-Pd nanoparticles with cuboctahedral, icosahedral, and decahedral geometries. Using a tight-binding potential, we systematically explore the effects of particle size and composition on the melting transition.
View Article and Find Full Text PDFMaterials (Basel)
February 2025
Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China.
This study proposes a novel framework using graph convolutional networks to analyze and interpret X-ray diffraction patterns, addressing challenges in phase identification for multi-phase materials. By representing X-ray diffraction patterns as graphs, the framework captures both local and global relationships between diffraction peaks, enabling accurate phase identification even in the presence of overlapping peaks and noisy data. The framework outperforms traditional machine learning models, achieving a precision of 0.
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