Effect of a novel telehealth device for dietary cognitive behavioral intervention in overweight or obesity care.

Obesity has become a major public health issue which relate to numerous physical problems and highly comorbid with depression and anxiety. Recently, some studies of technology-based interventions for weight reduction emerged to overcome the barriers from time, cost and distance. Mood component and eating behavior related to obesity are less discussed so far with technology-based intervention though. This pilot study was aimed to investigate the effect of telehealth assisted intervention on weight reduction, mood status, and eating behavior change under a smartphone application (app) with novel 3D food picture recognition and incorporated with cognitive behavioral training programs. Adult aged 30-60 years old with overweight were recruited and randomly assigned to control-first group and intervention-first group. In period 1, control-first group had regular life and intervention-first group underwent app intervention; in period 2, two groups went crossover. Body composition and psychological/behavioral questionnaires were collected at baseline, end of period 1, and end of period 2. Nonparametric statistics was performed for data analyzing. A total of 20 participants were enrolled. In control-first group, there were statistically significant reduction in body weight (- 0.55 kg, p = 0.02) and change of body weight percentage (- 0.6%, p = 0.02) after App use. In intervention-first group, the fat percentage decreased by 0.4% after App use in period 1, and increased by 0.05% in period 2. The integrated crossover data revealed that subjects of App group had significant improvements in mindful eating behavior. This pilot study showed the effectiveness in using CogniNU app for weight control and eating behavior. The difference of short-term and long-term effectiveness of technology-based weight control intervention deserves more investigation in the future.Clinical Trial Registration: ISRCTN16082909.

Neuroimage Biomarker Identification of the Conversion of Mild Cognitive Impairment to Alzheimer's Disease.

An efficient method to identify whether mild cognitive impairment (MCI) has progressed to Alzheimer's disease (AD) will be beneficial to patient care. Previous studies have shown that magnetic resonance imaging (MRI) has enabled the assessment of AD progression based on imaging findings. The present work aimed to establish an algorithm based on three features, namely, volume, surface area, and surface curvature within the hippocampal subfields, to model variations, including atrophy and structural changes to the cortical surface. In this study, a new biomarker, the ratio of principal curvatures (RPC), was proposed to characterize the folding patterns of the cortical gyrus and sulcus. Along with volumes and surface areas, these morphological features associated with the hippocampal subfields were assessed in terms of their sensitivity to the changes in cognitive capacity by two different feature selection methods. Either the extracted features were statistically significantly different, or the features were selected through a random forest model. The identified subfields and their structural indices that are sensitive to the changes characteristic of the progression from MCI to AD were further assessed with a multilayer perceptron classifier to help facilitate the diagnosis. The accuracy of the classification based on the proposed method to distinguish whether a MCI patient enters the AD stage amounted to 79.95%, solely using the information from the features selected by a logical feature selection method.

Quantitative analysis of intrinsic skin aging in dermal papillae by in vivo harmonic generation microscopy.

Chronological skin aging is associated with flattening of the dermal-epidermal junction (DEJ), but to date no quantitative analysis focusing on the aging changes in the dermal papillae (DP) has been performed. The aim of the study is to determine the architectural changes and the collagen density related to chronological aging in the dermal papilla zone (DPZ) by in vivo harmonic generation microscopy (HGM) with a sub-femtoliter spatial resolution. We recruited 48 Asian subjects and obtained in vivo images on the sun-protected volar forearm. Six parameters were defined to quantify 3D morphological changes of the DPZ, which we analyzed both manually and computationally to study their correlation with age. The depth of DPZ, the average height of isolated DP, and the 3D interdigitation index decreased with age, while DP number density, DP volume, and the collagen density in DP remained constant over time. In vivo high-resolution HGM technology has uncovered chronological aging-related variations in DP, and sheds light on real-time quantitative skin fragility assessment and disease diagnostics based on collagen density and morphology.

Automatic cell segmentation and nuclear-to-cytoplasmic ratio analysis for third harmonic generated microscopy medical images.

Traditional biopsy procedures require invasive tissue removal from a living subject, followed by time-consuming and complicated processes, so noninvasive in vivo virtual biopsy, which possesses the ability to obtain exhaustive tissue images without removing tissues, is highly desired. Some sets of in vivo virtual biopsy images provided by healthy volunteers were processed by the proposed cell segmentation approach, which is based on the watershed-based approach and the concept of convergence index filter for automatic cell segmentation. Experimental results suggest that the proposed algorithm not only reveals high accuracy for cell segmentation but also has dramatic potential for noninvasive analysis of cell nuclear-to-cytoplasmic ratio (NC ratio), which is important in identifying or detecting early symptoms of diseases with abnormal NC ratios, such as skin cancers during clinical diagnosis via medical imaging analysis.