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This study evaluated the association between planned anterior tooth movements, attachment use, and the need for refinement in clear aligner (CA) therapy using logistic regression. A retrospective analysis was conducted on 116 patients and 696 anterior teeth treated with Invisalign. For each tooth, planned movement magnitudes (extrusion, intrusion, rotation, angulation, inclination) and the presence of attachments were recorded. Refinement was defined as a binary outcome. Multivariable logistic regression and independent t-tests were used to assess associations and compare movement magnitudes between refined and non-refined teeth. In tooth 11, greater planned rotation was associated with a modest but statistically significant reduction in refinement likelihood (OR = 0.92, p < .05). Attachment use emerged as a significant factor for teeth 12 and 21, decreasing refinement risk in tooth 21 (OR = 0.05, p < .01), yet increasing it in tooth 12 (OR = 4.95, p < .05). For tooth 22, increased planned extrusion reduced the probability of refinement (OR = 0.34, p < .05), whereas higher degrees of rotation and inclination were associated with increased refinement rates (OR = 1.13 and OR = 1.52, respectively; p < .05). No statistically significant predictors were identified for canines (teeth 13 and 23). These findings underscore the heterogeneity in biomechanical response among maxillary anterior teeth during CA. Specifically, lateral incisors demonstrated a greater susceptibility to refinement needs in the presence of complex movements such as rotation and inclination. These results support the adoption of individualized, tooth-specific treatment planning strategies to enhance predictability and minimize the need for refinement in CA therapy.
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http://dx.doi.org/10.1038/s41598-025-10801-9 | DOI Listing |
Cancer Med
September 2025
Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
Background: Patients with clear cell renal cell carcinoma (ccRCC) often undergo organ resection, with treatment strategies based on recurrence risk. Current metastatic potential assessments rely on the WHO/ISUP grading system, which is subject to interobserver variability.
Methods: We developed an artificial intelligence (AI) model to classify cells according to contemporary grading rules and evaluated the prognostic significance of tumor cell profiles, particularly focusing on cells with prominent nucleoli.
Curr Atheroscler Rep
September 2025
Department of Medicine, Division of Cardiovascular Disease, University of Alabama at Birmingham, 521 19th Street South-GSB 444, Birmingham, AL, 35233, USA.
Purpose Of Review: This review examines cardiovascular disease (CVD) risk prediction models relevant to older adults, a rapidly expanding population with elevated CVD risk. It discusses model characteristics, performance metrics, and clinical implications.
Recent Findings: Some models have been developed specifically for older adults, while several others consider a broader age range, including some older individuals.
JMIR Hum Factors
September 2025
College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
Background: The rapid advancement of next-generation sequencing has significantly expanded the landscape of precision medicine. However, health care professionals face increasing challenges in keeping pace with the growing body of oncological knowledge and integrating it effectively into clinical workflows. Precision oncology decision support (PODS) tools aim to assist clinicians in navigating this complexity, yet their current functionalities only partially address clinical needs.
View Article and Find Full Text PDFPLoS One
September 2025
Clinical Microbiology and Parasitology Department, Hospital Universitario 12 de Octubre, Madrid, Spain.
The Quantiferon Gold Plus (QFT) test, a widely used interferon-γ release assay (IGRA), diagnoses latent tuberculosis infection (LTBI) with a positivity threshold of ≥0.35 IU/mL. Results near this cut-off can be challenging to interpret due to variability from immunological, pre-analytical, and technical factors, prompting recommendations for a borderline range to refine diagnosis and reduce overtreatment.
View Article and Find Full Text PDFFront Med (Lausanne)
August 2025
Universidad Internacional Iberoamericana, Arecibo, PR, United States.
Electrocardiogram (ECG) classification plays a critical role in early detection and trocardiogram (ECG) classification plays a critical role in early detection and monitoring cardiovascular diseases. This study presents a Transformer-based deep learning framework for automated ECG classification, integrating advanced preprocessing, feature selection, and dimensionality reduction techniques to improve model performance. The pipeline begins with signal preprocessing, where raw ECG data are denoised, normalized, and relabeled for compatibility with attention-based architectures.
View Article and Find Full Text PDF