The aim of this study was to evaluate the impact of different file sizes on the accuracy of two electronic apex locators (EALs). Thirty extracted human single-rooted permanent mandibular incisors were used. A #10 K-file was inserted in the root canal until its end could be observed (using a light microscope) through the apical foramen. One millimetre was subtracted to establish working length (WL). Electronic readings were performed using MiniApex Locator or Root ZX II, from #10 K-file to #130 K-file. Statistical analysis was performed by two-way anova and Tukey test (P ≤ 0.05). From #60 to #130 K-file, observed differences were noted between the values obtained with both EALs and WL (P ≤ 0.05). The MiniApex Locator showed increased means when measurements were made with #50 to #70 and with #120 (P = 0.008) and #130 (P = 0.005) K-files. File sizes influenced the accuracy of EALs - the greater the instrumentation size, the higher mean differences compared to WL.
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http://dx.doi.org/10.1111/aej.12235 | DOI Listing |
Hypertension
January 2025
Clinical Research Institute, Institute of Advanced Clinical Medicine, Peking University, Beijing, China (X.Z., W.X., Y.W.).
Background: Although the information on the validation status of electronic sphygmomanometer (ES) devices in use in health care institutions and households is much more clinically relevant than that of ES models available on the market, it remains insufficient.
Methods: A national survey was conducted across all administrative regions of mainland China to assess the validation status of ESs. Fifty-eight cities were selected with stratification by municipality, provincial capital, and other cities, and health care institutions and households in each city were chosen by convenience to identify ES devices in use according to the study protocol.
Vis Intell
December 2024
Department of Information Technology and Electrical Engineering, ETH Zurich, Sternwartstrasse 7, Zürich, Switzerland.
The LLaMA family, a collection of foundation language models ranging from 7B to 65B parameters, has become one of the most powerful open-source large language models (LLMs) and the popular LLM backbone of multi-modal large language models (MLLMs), widely used in computer vision and natural language understanding tasks. In particular, LLaMA3 models have recently been released and have achieved impressive performance in various domains with super-large scale pre-training on over 15T tokens of data. Given the wide application of low-bit quantization for LLMs in resource-constrained scenarios, we explore LLaMA3's capabilities when quantized to low bit-width.
View Article and Find Full Text PDFMath Biosci Eng
December 2024
Department of Electronics and Communication Engineering, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India.
The hippocampus is a small, yet intricate seahorse-shaped tiny structure located deep within the brain's medial temporal lobe. It is a crucial component of the limbic system, which is responsible for regulating emotions, memory, and spatial navigation. This research focuses on automatic hippocampus segmentation from Magnetic Resonance (MR) images of a human head with high accuracy and fewer false positive and false negative rates.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
School of Life Sciences, Tiangong University, Tianjin 300387, China.
Objective: The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.
Methods: The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model.
Int J Retina Vitreous
January 2025
Department of Retina and Vitreous, Narayana Nethralaya, #121/C, 1st R Block, Chord Road, Rajaji Nagar, Bengaluru, 560010, India.
Purpose: To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters.
Methods: This retrospective study included 158 eyes (151 patients) with full-thickness MHs treated between 2017 and 2023 by the same surgeon and using the same intraoperative surgical technique. Data from electronic medical records and OCT scans were extracted, with OCT-derived qualitative and quantitative MH characteristics recorded.
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