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Background: Superagers, older adults with exceptional cognitive abilities, show preserved brain structure compared to typical older adults. We investigated whether superagers have biologically younger brains based on their structural integrity.

Methods: A cohort of 153 older adults (aged 61-93) was recruited, with 63 classified as superagers based on superior episodic memory and 90 as typical older adults, of whom 64 were followed up after two years.

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Objectives: This study evaluates the potential of pulp volume/total tooth-volume measurements of canine teeth in relation to chronologic age in patients with cleft lip and palate (CLP). The significance of this study lies in its exploration of the usability of these measurements for age determination in CLP patients, providing a novel perspective to the existing literature.

Methods: Cone beam computed tomography images of 33 patients (16 females, 17 males) with unilateral CLP aged 14-45 years and 33 age- and sex-matched healthy individuals (16 females, 17 males) were retrospectively evaluated.

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In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks.

J Imaging Inform Med

January 2025

Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Disease, Shanghai, 200080, China.

The objectives of this study are to construct a deep convolutional neural network (DCNN) model to diagnose and classify meibomian gland dysfunction (MGD) based on the in vivo confocal microscope (IVCM) images and to evaluate the performance of the DCNN model and its auxiliary significance for clinical diagnosis and treatment. We extracted 6643 IVCM images from the three hospitals' IVCM database as the training set for the DCNN model and 1661 IVCM images from the other two hospitals' IVCM database as the test set to examine the performance of the model. Construction of the DCNN model was performed using DenseNet-169.

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Multi-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers.

J Imaging Inform Med

January 2025

College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.

This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.

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Introduction: A novel antifungal formulation combining zinc oxide nanoparticles and Whitfield's spirit solution (ZnO-WFs) was developed to enhance the treatment of superficial fungal foot infections.

Methods: This 8-week, randomized, double-blinded controlled trial compared the efficacy, safety, and cost-effectiveness of ZnO-WFs with those of Whitfield's spirit solution (WFs) alone and a zinc oxide nanoparticle solution (ZnOs). Seventy of the 84 enrolled patients completed the trial.

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