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Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

Exploiting the Bragg mirror effect of photonic crystal photoelectrode is desperately desired for photoelectrochemical water splitting. Herein, a novel TiO2 nanotube photonic crystal bi-layer structure consisting of a top nanotube layer and a bottom nanotube photonic crystal layer is presented. In this architecture, the photonic bandgap of bottom TiO2 nanotube photonic crystals can be precisely adjusted by modulating the anodization parameters. When the photonic bandgap of bottom TiO2 nanotube photonic crystals overlaps with the electronic bandgap of TiO2, the bottom TiO2 nanotube photonic crystal layer will act as a Bragg mirror, leading to the boosted ultraviolet light absorption of the top TiO2 nanotube layer. Benefiting from the promoted UV light absorption, the TiO2 NT-115-NTPC yields a photocurrent density of 1.4 mA/cm2 at 0.22 V vs. Ag/AgCl with a Faradic efficiency of 100%, nearly two times higher than that of conventional TiO2 nanotube arrays. Furthermore, incident photon-to-current conversion efficiency is also promoted within ultraviolet light region. This research offers an effective strategy for improving the performance of photoelectrochemical water splitting through intensifying the light–matter interaction. [ABSTRACT FROM AUTHOR]

Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) is clinically important. In this study, we propose a dual-model radiomic analysis with multivariate Cox proportional hazards regression models to investigate promising risk factors associated with MCI conversion to AD. T1 structural magnetic resonance imaging (MRI) and 18F-Fluorodeoxyglucose (FDG) positron emission tomography (PET) data, from the AD Neuroimaging Initiative database, were collected from 131 patients with MCI who converted to AD within 3 years and 132 patients with MCI without conversion within 3 years. These subjects were randomly partition into 70% training dataset and 30% test dataset with multiple times. We fused MRI and PET images by wavelet method. In a subset of subjects, a group comparison was performed using a two-sample t-test to determine regions of interest (ROIs) associated with MCI conversion. 172 radiomic features from ROIs for each individual were established using a published radiomics tool. Finally, L1-penalized Cox model was constructed and Harrell's C index (C-index) was used to evaluate prediction accuracy of the model. To evaluate the efficacy of our proposed method, we used a same analysis framework to evaluate MRI and PET data separately. We constructed prognostic Cox models with: clinical data, MRI images, PET images, fused MRI/PET images, and clinical variables and fused MRI/PET images in combination. The experimental results showed that captured ROIs significantly associated with conversion to AD, such as gray matter atrophy in the bilateral hippocampus and hypometabolism in the temporoparietal cortex. Imaging model (MRI/PET/fused) provided significant enhancement in prediction of conversion compared to clinical models, especially the fused-modality Cox model. Moreover, the combination of fused-modality imaging and clinical variables resulted in the greatest accuracy of prediction. The average C-index for the clinical/MRI/PET/fused/combined model in the test dataset was 0.69, 0.73, 0.73 and 0.75, and 0.78, respectively. These results suggested that a combination of radiomic analysis and Cox model analyses could be used successfully in survival analysis and may be powerful tools for personalized precision medicine patients with potential to undergo conversion from MCI to AD.

Summary: Zn2GeO4 and Zn2GeO4:Mn2+ were synthesized by a hydrothermal method. The phase, microstructure, and optical and photoelectrochemical properties were investigated. The photocatalytic activities were evaluated by the photocatalytic hydrogen generation. The X‐ray diffraction (XRD), scanning electron microscopy (SEM), and Brunauer‐Emmett‐Teller (BET) results indicate the Mn2+ doping has no obvious influence on the phase and microstructure of Zn2GeO4. But the Mn2+ doping influences the optical and photoelectrochemical properties obviously. The Mn2+ doping leads to the red shift of band gap edge and the light absorption in visible region. The Zn2GeO4:Mn2+ has the greater light absorption efficiency, more efficient e−‐h+ pair separation and promoting charge transfer across the electrode/electrolyte interface. These characteristics of Zn2GeO4:Mn2+ suggest that it has higher activity than that of Zn2GeO4 and this is confirmed by the highly efficient hydrogen generation. [ABSTRACT FROM AUTHOR]

Objectives. 18F-FDG PET scan is one of the most frequently used neural imaging scans. However, the influence of age has proven to be the greatest interfering factor for many clinical dementia diagnoses when analyzing 18F-FDG PET images, since radiologists encounter difficulties when deciding whether the abnormalities in specific regions correlate with normal aging, disease, or both. In the present paper, the authors aimed to define specific brain regions and determine an age-correction mathematical model. Methods. A data-driven approach was used based on 255 healthy subjects. Results. The inferior frontal gyrus, the left medial part and the left medial orbital part of superior frontal gyrus, the right insula, the left anterior cingulate, the left median cingulate, and paracingulate gyri, and bilateral superior temporal gyri were found to have a strong negative correlation with age. For evaluation, an age-correction model was applied to 262 healthy subjects and 50 AD subjects selected from the ADNI database, and partial correlations between SUVR mean and three clinical results were carried out before and after age correction. Conclusion. All correlation coefficients were significantly improved after the age correction. The proposed model was effective in the age correction of both healthy and AD subjects. [ABSTRACT FROM AUTHOR]