Development and evaluation of an eHealth self-management intervention for patients with chronic kidney disease in China: protocol for a mixed-method hybrid type 2 trial

Background: Chronic kidney disease (CKD) is a significant public health concern. In patients with CKD, interventions that support disease self-management have shown to improve health status and quality of life. At the moment, the use of electronic health (eHealth) technology in self-management interventions is becoming more and more popular. Evidence suggests that eHealth-based self-management interventions can improve health-related outcomes of patients with CKD. However, knowledge of the implementation and effectiveness of such interventions in general, and in China in specific, is still limited. This study protocol aims to develop and tailor the evidence-based Dutch ‘Medical Dashboard’ eHealth self-management intervention for patients suffering from CKD in China and evaluate its implementation process and effectiveness. Methods: To develop and tailor a Medical Dashboard intervention for the Chinese context, we will use an Intervention Mapping (IM) approach. A literature review and mixed-method study will first be conducted to examine the needs, beliefs, perceptions of patients with CKD and care providers towards disease (self-management) and eHealth (self-management) interventions (IM step 1). Based on the results of step 1, we will specify outcomes, performance objectives, and determinants, select theory-based methods and practical strategies. Knowledge obtained from prior results and insights from stakeholders will be combined to tailor the core interventions components of the ‘Medical Dashboard’ self-management intervention to the Chinese context (IM step 2–5). Then, an intervention and implementation plan will be developed. Finally, a 9-month hybrid type 2 trial design will be employed to investigate the effectiveness of the intervention using a cluster randomized controlled trial with two parallel arms, and the implementation integrity (fidelity) and determinants of implementation (IM step 6). Discussion: Our study will result in the delivery of a culturally tailored, standardized eHealth self-management intervention for patients with CKD in China, which has the potential to optimize patients’ self-management skills and improve health status and quality of life. Moreover, it will inform futur

Bend resistant large mode area fiber with multi-trench in the core

A bend resistant large mode area fiber with multitrench in the core is proposed. Four layers of trenches with high refractive index are introduced to modulate the mode field distribution. Low refractive index trenches in traditional designs are replaced with pure silica trenches to reduce the difficulty of manufacture. Meanwhile, the core region with a refractive index higher than pure silica cladding conforms to the practical requirement for active fibers. Numerical investigations show that single mode operation with a mode field area of 1100 µm2 is achieved at a bend radius of 15 cm. This design shows the potential of mode field scaling for multitrench fibers and makes a contribution to compact high power fiber lasers

Resilience-Oriented Planning of Urban Distribution System Source–Network–Load–Storage in the Context of High-Penetrated Building-Integrated Resources

Building-integrated flexible resources can offer economical availability to accommodate high-penetrated renewable energy sources (RESs), which can be potentially coordinated to achieve cost-effective supply. This paper proposes a resilience-oriented planning model of urban distribution system source–network–load–storage in the context of high-penetrated building-integrated resources. In this model, source–network–load–storage resources are cost-optimally planned, including the lines, soft open point (SOP), building-integrated photovoltaics (BIPVs), building-integrated wind turbine (BIWT), building-integrated energy storage system (ESS), etc. To enhance fault recovery capability during extreme faults, fault scenarios are incorporated into the distribution system operation via coupled multiple recovery stages. The resilience-oriented planning is a thorny problem due to its source–network–load–storage couplings, normal-fault couplings, etc. The original resilience-oriented planning is reformulated as a mixed-integer linear programming (MILP) problem, which can then be solved with a two-stage method and evaluated via a multi-dimensional evaluation metrics. The proposed planning methodology is benchmarked over a Portugal 54-node urban distribution system to verify the superiority and effectiveness on the system economy and resilience levels. Case studies show that the proposed methodology can exploit the optimal synergies of different source–network–load–storage components and enhance system dispatchability

A Rigorously-Incremental Spatiotemporal Data Fusion Method for Fusing Remote Sensing Images

The spatiotemporal remote sensing images have significant importance in forest ecological monitoring, forest carbon management, and other related fields. Spatiotemporal data fusion technology of remote sensing images combines high spatiotemporal and high temporal resolution images to address the current limitation of single sensors in obtaining high spatiotemporal resolution. This technology has gained widespread attention in recent years. However, the current models still exhibit some shortcomings in dealing with land cover changes, such as poor clustering results, inaccurate incremental spatiotemporal calculations, and sensor differences. In this article, we propose a rigorously-incremental spatiotemporal data fusion method for fusing remote sensing images with different resolutions to address the aforementioned problems. The proposed method utilizes the particle swarm optimization Gaussian mixture model to extract endmembers and establishes a linear relationship between sensors to obtain accurate time increments. Furthermore, bicubic interpolation is used instead of thin plate spline interpolation for spatial interpolation, and also support vector regression is used to calculate weights for obtaining a weighted sum of temporal and spatial increments. In addition, sensor errors are allocated to the calculation of residuals. The experimental results show the efficacy of the proposed algorithm for fusing fine image Landsat with coarse image MODIS data and conclude that the proposed algorithm presents a better solution for heterogeneous data with strong phenological changes and regions with changes in surface types, which provides a better solution for remote sensing image fusion and, hence, improves the accuracy, stability, and robustness of data fusion

Interpretable Machine Learning Model Predicting Early Neurological Deterioration in Ischemic Stroke Patients Treated with Mechanical Thrombectomy: A Retrospective Study

Early neurologic deterioration (END) is a common and feared complication for acute ischemic stroke (AIS) patients treated with mechanical thrombectomy (MT). This study aimed to develop an interpretable machine learning (ML) model for individualized prediction to predict END in AIS patients treated with MT. The retrospective cohort of AIS patients who underwent MT was from two hospitals. ML methods applied include logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The area under the receiver operating characteristic curve (AUC) was the main evaluation metric used. We also used Shapley Additive Explanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) to interpret the result of the prediction model. A total of 985 patients were enrolled in this study, and the development of END was noted in 157 patients (15.9%). Among the used models, XGBoost had the highest prediction power (AUC = 0.826, 95% CI 0.781–0.871). The Delong test and calibration curve indicated that XGBoost significantly surpassed those of the other models in prediction. In addition, the AUC in the validating set was 0.846, which showed a good performance of the XGBoost. The SHAP method revealed that blood glucose was the most important predictor variable. The constructed interpretable ML model can be used to predict the risk probability of END after MT in AIS patients. It may help clinical decision making in the perioperative period of AIS patients treated with MT

Chemical Transformation of Lead Halide Perovskite into Insoluble, Less Cytotoxic, and Brightly Luminescent CsPbBr 3 /CsPb 2 Br 5 Composite Nanocrystals for Cell Imaging

International audienc

Genetic background of hematological parameters in Holstein cattle based on genome-wide association and RNA sequencing analyses

ABSTRACT: Hematological parameters refer to the assessment of changes in the number and distribution of blood cells, including leukocytes (LES), erythrocytes (ERS), and platelets (PLS), which are essential for the early diagnosis of hematological system disorders and other systemic diseases in livestock. In this context, the primary objectives of this study were to investigate the genomic background of 19 hematological parameters in Holstein cattle, focusing on LES, ERS, and PLS blood components. Genetic and phenotypic (co)variances of hematological parameters were calculated based on the average information restricted maximum likelihood method and 1,610 genotyped individuals and 5,499 hematological parameter records from 4,543 cows. Furthermore, we assessed the genetic relationship between these hematological parameters and other economically important traits in dairy cattle breeding programs. We also carried out genome-wide association studies and candidate gene analyses. Blood samples from 21 primiparous cows were used to identify candidate genes further through RNA sequencing (RNA-seq) analyses. Hematological parameters generally exhibited low-to-moderate heritabilities ranging from 0.01 to 0.29, with genetic correlations between them ranging from −0.88 ± 0.09 (between mononuclear cell ratio and lymphocyte cell ratio) to 0.99 ± 0.01 (between white blood cell count and granulocyte cell count). Furthermore, low-to-moderate approximate genetic correlations between hematological parameters with one longevity, 4 fertility, and 5 health traits were observed. One hundred ninety-nine significant SNP located primarily on the Bos taurus autosomes (BTA) BTA4, BTA6, and BTA8 were associated with 16 hematological parameters. Based on the RNA-seq analyses, 6,687 genes were significantly downregulated and 4,119 genes were upregulated when comparing 2 groups of cows with high and low phenotypic values. By integrating genome-wide association studies (GWAS), RNA-seq, and previously published results, the main candidate genes associated with hematological parameters in Holstein cattle were ACRBP, ADAMTS3, CANT1, CCM2L, CNN3, CPLANE1, GPAT3, GRIP2, PLAGL2, RTL6, SOX4, WDFY3, and ZNF614. Hematological parameters are heritable and moderately to highly genetically correlated among themselves. The large number of candidate genes identified based on GWAS and RNA-seq indicate the polygenic nature and complex genetic determinism of hematological parameters in Holstein cattle

Electric Field–Controlled Multistep Proton Evolution in HxSrCoO2.5 with Formation of H–H Dimer

Ionic evolution–induced phase transformation can lead to wide ranges of novel material functionalities with promising applications. Here, using the gating voltage during ionic liquid gating as a tuning knob, the brownmillerite SrCoO2.5 is transformed into a series of protonated HxSrCoO2.5 phases with distinct hydrogen contents. The unexpected electron to charge‐neutral doping crossover along with the increase of proton concentration from x = 1 to 2 suggests the formation of exotic charge neutral H–H dimers for higher proton concentration, which is directly visualized at the vacant tetrahedron by scanning transmission electron microscopy and then further supported by first principles calculations. Although the H–H dimers cause no change of the valency of Co2+ ions, they result in clear enhancement of electronic bandgap and suppression of magnetization through lattice expansion. These results not only reveal a hydrogen chemical state beyond anion and cation within the complex oxides, but also suggest an effective pathway to design functional materials through tunable ionic evolution