A polarization double-enhancement strategy to achieve super low energy consumption with ultra-high energy storage capacity in BCZT-based relaxor ferroelectrics.

Due to dielectric capacitors' already-obtained fast charge-discharge speed, research has been focused on improving their Wrec. Increasing the polarization and enhancing the voltage endurance are efficient ways to reach higher Wrec, however simultaneous modification still seems a paradox. For example, in the ferroelectric-to-relaxor ferroelectric (FE-to-RFE) phase transition strategy, which has been widely used in the latest decade, electric breakdown strength (Eb) and energy storage efficiency (η) always increase, while at the same time, the maximum polarization (Pmax) inevitably decreases. The solution to this problem can be obtained from another degree of freedom, like defect engineering. By incorporating Bi(Zn2/3Ta1/3)O3 (BZT) into the Ba0.15Ca0.85Zr0.1Ti0.9O3 (BCZT) lattice to form (1 - x)Ba0.15Ca0.85Zr0.1Ti0.9O3-xBi(Zn2/3Ta1/3)O3 (BCZT-xBZT) solid-solution ceramics, in this work, ultrahigh ferroelectric polarization was achieved in BCZT-0.15BZT, which is caused by the polarization double-enhancement, comprising the contribution of interfacial and dipole polarization. In addition, due to the electron compensation, a Schottky contact formed at the interface between the electrode and the ceramic, which in the meantime, enhanced its Eb. A Wrec of 8.03 J cm-3, which is the highest among the BCZT-based ceramics reported so far, with an extremely low energy consumption, was finally achieved. BCZT-0.15BZT also has relatively good polarization fatigue after long-term use, good energy storage frequency stability and thermal stability, as well as excellent discharge properties.

1,8-Cineole Ameliorates Advanced Glycation End Products-Induced Alzheimer's Disease-like Pathology In Vitro and In Vivo.

Advanced glycation end products (AGEs) are stable products produced by the reaction of macromolecules such as proteins, lipids or nucleic acids with glucose or other reducing monosaccharides, which can be identified by immunohistochemistry in the senile plaques and neurofibrillary tangles of Alzheimer's disease (AD) patients. Growing evidence suggests that AGEs are important risk factors for the development and progression of AD. 1,8-cineole (CIN) is a monoterpenoid compound which exists in many plant essential oils and has been proven to have neuroprotective activity, but its specific effect and molecular mechanisms are not clear. In this study, AGEs-induced neuronal injury and intracerebroventricular-AGE animals as the possible models for AD were employed to investigate the effects of CIN on AD pathology as well as the molecular mechanisms involved both in vivo and in vitro. Our study demonstrated that CIN could ameliorate tau phosphorylation by down-regulating the activity of GSK-3ß and reducing Aß production by inhibiting the activity of BACE-1 both in vivo and in vitro. It is suggested that CIN has certain therapeutic value in the treatment of AD.

Lighting Up Electrochemiluminescence-Inactive Dyes via Grafting Enabled by Intramolecular Resonance Energy Transfer.

Due to near-zero optical background and photobleaching, electrochemiluminescence (ECL), an optical phenomenon excited by electrochemical reactions, has drawn extensive attention, especially for ultrasensitive bioassays. Developing diverse ECL emitters is crucial to unlocking their multiformity and performances but remains a formidable challenge due to the rigorous requirements for ECL. Herein, we report a general strategy to light up ECL-inactive dyes in an aqueous solution via grafting, a well-developed concept for plant propagation since 500 BCE. As a proof of concept, a series of luminol donor-dye acceptor-based ECL emitters were grafted with near-unity resonance energy transfer (RET) efficiency and coarse/fine-tunable emission wavelengths. Rather than the sophisticated design of new skeleton-based molecules to meet all of the prerequisites for ECL in a constrained manner, each unit in the proposed ECL ensemble performed its functions maximally. As a result, beyond traditional two-dimensional (2D) ones, a three-dimensional (3D) coordinate biosensing system, simultaneously showing a calibration curve and selectivity, was established using the new ECL emitter. This lighting up strategy would generally address the scarcity of ECL emitters and enable unprecedented functions.

Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach.

BACKGROUND: Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. OBJECTIVE: This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. METHODS: A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients' CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. RESULTS: We present a prediction model combining patients' radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients' laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. CONCLUSIONS: To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

Optimization of apparent diffusion coefficient measured by diffusion-weighted MRI for diagnosis of breast lesions presenting as mass and non-mass-like enhancement.

The purpose of this study was to investigate the diagnostic value of the apparent diffusion coefficient (ADC), measured by diffusion-weighted magnetic resonance imaging (MRI), for the diagnosis of breast lesions presenting as mass and non-mass-like enhancement (NMLE). The breast MRI studies of 174 patients were reviewed retrospectively. A total of 188 histologically confirmed lesions were analyzed and classified into 127 mass enhancement (86 malignant and 41 benign) and 61 NMLE (42 malignant and 19 benign). The ADC values were measured using a spin-echo echo-planner-imaging (SE-EPI) sequence with b=1,000 s/mm(2). Diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. The mean ADC was 0.99 ± 0.22 × 10(-3)mm(2)/s for invasive cancer, 1.23 ± 0.33 × 10(-3)mm(2)/s for ductal carcinoma in situ (DCIS), and 1.52 ± 0.35 × 10(-3)mm(2)/s for benign adenosis. The mean ADC of all NMLE lesions was 1.44 ± 0.41 × 10(-3)mm(2)/s, which is higher than the mean ADC of all mass lesions, 1.12 ± 0.33 × 10(-3)mm(2)/s. In the ROC analysis, the optimal cutoff ADC value for differentiating benign from malignant lesions was 1.05 × 10(-3)mm(2)/s for mass lesions and 1.35 × 10(-3)mm(2)/s for NMLE. In conclusion, ADC values can be used for the diagnosis of invasive and DCIS as well as benign tumors. The NMLE lesions tend to have higher ADC values than mass lesions; therefore, the morphological appearance of a lesion needs to be considered when using the ADC value for diagnosis.