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Propolis mouthwashes efficacy in managing gingivitis and periodontitis: a systematic review of the latest findings.
BDJ Open
January 2025
Department of Orthodontics, Institute of Dentistry, Medical Faculty, Jagiellonian University, Kraków, Poland.
Background And Objectives: Gingivitis and periodontitis are common periodontal diseases that can significantly harm overall oral health, affecting the teeth and their supporting tissues, along with the surrounding anatomical structures, and if left untreated, leading to the total destruction of the alveolar bone and the connective tissues, tooth loss, and other more serious systemic health issues. Numerous studies have shown that propolis can help reduce gum inflammation, inhibit the growth of pathogenic bacteria, and promote tissue regeneration, but with varying degrees of success reported. For this reason, this comprehensive systematic review aims at finding out the truth concerning the efficacy of propolis mouthwashes in treating gingivitis and periodontitis, as its main objective.
View Article and Find Full Text PDFAssessment of deep learning technique for fully automated mandibular segmentation.
Am J Orthod Dentofacial Orthop
February 2025
Department of Orthodontics, Faculty of Dentistry, Çanakkale Onsekiz Mart University, Çanakkale, Turkey.
Introduction: This study aimed to assess the precision of an open-source, clinician-trained, and user-friendly convolutional neural network-based model for automatically segmenting the mandible.
Methods: A total of 55 cone-beam computed tomography scans that met the inclusion criteria were collected and divided into test and training groups. The MONAI (Medical Open Network for Artificial Intelligence) Label active learning tool extension was used to train the automatic model.
Improving MRI turbulence quantification by addressing the measurement errors caused by the derivatives of the turbulent velocity field - Sequence development and in-vitro validation.
Magn Reson Imaging
January 2025
Institute of Fluid Mechanics, University of Rostock, Rostock, Germany.
Purpose: To improve the current method for MRI turbulence quantification which is the intravoxel phase dispersion (IVPD) method. Turbulence is commonly characterized by the Reynolds stress tensor (RST) which describes the velocity covariance matrix. A major source for systematic errors in MRI is the sequence's sensitivity to the variance of the derivatives of velocity, such as the acceleration variance, which can lead to a substantial measurement bias.
View Article and Find Full Text PDFReducing bias in source-free unsupervised domain adaptation for regression.
Neural Netw
January 2025
Luca Healthcare R&D, Shanghai, 200000, China. Electronic address:
Due to data privacy and storage concerns, Source-Free Unsupervised Domain Adaptation (SFUDA) focuses on improving an unlabelled target domain by leveraging a pre-trained source model without access to source data. While existing studies attempt to train target models by mitigating biases induced by noisy pseudo labels, they often lack theoretical guarantees for fully reducing biases and have predominantly addressed classification tasks rather than regression ones. To address these gaps, our analysis delves into the generalisation error bound of the target model, aiming to understand the intrinsic limitations of pseudo-label-based SFUDA methods.
View Article and Find Full Text PDFClimate Change-Induced Decline in Succulent in Namibia's Arid Regions.
Plants (Basel)
January 2025
Department of Plant and Soil Sciences, University of Pretoria, Hatfield, Pretoria P.O. Box X20, South Africa.
The global rise in temperatures due to climate change has made it difficult even for specialised desert-adapted plant species to survive on sandy desert soils. Two of Namibia's iconic desert-adapted plant species, and the quiver tree , have recently been shown to be under threat because of climate change. In the current study, three ecologically important Namibian milk bushes were evaluated for their climate change response.
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