Bioelectrical impedance phase angle combined with physical function predicts pre-frailty in maintenance hemodialysis patients: a prospective study.

BACKGROUND: The prevalence of pre-frailty is notably high among maintenance hemodialysis (MHD) patients. Pre-frailty, an early and reversible condition between non-frailty and frailty, can lead to adverse outcomes such as increased unplanned hospital admissions and a higher risk of other chronic diseases. Early identification and intervention of pre-frailty in MHD patients are crucial. This study aimed to establish a simple and effective model for screening and identifying MHD patients at high risk of pre-frailty by using 50 kHz-Whole Body Phase Angle (PhA) measured by bioelectrical impedance analysis (BIA), hand grip strength (HGS), the Five-Times-Sit-to-Stand Test (FTSST), and laboratory parameters, with a specific focus on gender differences. METHODS: This prospective cross-sectional study was conducted from September to December 2023 at the Wenjiang Hemodialysis Center in the Department of Nephrology, West China Hospital, Sichuan University, Chengdu, China. A total of 244 MHD patients, including 130 males and 114 females, were enrolled, comprising 128 non-frail and 116 pre-frail individuals. Data were collected prospectively, including demographic information, physical measurements, and laboratory test results. All participants provided informed consent before enrollment. The FRAIL scale (FS) was used to assess pre-frailty in MHD patients. Grip strength was measured using an electronic grip strength tester, physical function was assessed using the Five-Times-Sit-to-Stand Test, and whole-body phase angle was measured using the InBody S10 device. RESULTS: A total of 244 MHD patients with a mean age of 53.75 ± 0.90 years were enrolled, including 130 males with a mean age of 54.12 ± 1.26 years and 114 females with a mean age of 53.32 ± 1.29 years. ROC curve analysis showed that in male patients, the AUC of PhA for predicting pre-frailty was 0.919, with a sensitivity of 94.5% and specificity of 91.3%, and a cutoff value of 6.05°; in female patients, the AUC of PhA was 0.870, with a sensitivity of 70.5% and specificity of 90.6%, and a cutoff value of 5.25°. The AUC of FTSST for screening pre-frailty in male patients was 0.827, with a sensitivity of 62.3% and specificity of 96.2%, and a cutoff value of 12.95 s; in female patients, the AUC of FTSST was 0.784, with a sensitivity of 67.3% and specificity of 84.0%, and a cutoff value of 12.95 s. Additionally, in male patients, the combination of PhA and FTSST resulted in an AUC of 0.930, with a sensitivity of 96.4% and specificity of 81.3%; in female patients, the AUC was 0.911, with a sensitivity of 78.7% and specificity of 92.5%. CONCLUSION: PhA measured by BIA, in combination with the Five-Times-Sit-to-Stand Test, serves as an effective screening tool and predictor of pre-frailty in MHD patients. The combination of PhA and FTSST shows enhanced diagnostic value in female patients, while PhA alone is sufficient for predicting pre-frailty in male patients. TRIAL REGISTRATION: Chinese Clinical Trial Registry (ChiCTR2100051111), registered on 2021-09-13.

A machine learning-based assistant tool for early frailty screening of patients receiving maintenance hemodialysis.

PURPOSE: To develop an assistant tool based on machine learning for early frailty screening in patients receiving maintenance hemodialysis. METHODS: This is a single-center retrospective study. 141 participants' basic information, scale results and laboratory findings were collected and the FRAIL scale was used to assess frailty. Then participants were divided into the frailty group (n = 84) and control group (n = 57). After feature selection, data split and oversampling, ten commonly used binary machine learning methods were performed and a voting classifier was developed. RESULTS: The grade results of Clinical Frailty Scale, age, serum magnesium, lactate dehydrogenase, comorbidity and fast blood glucose were considered to be the best feature set for early frailty screening. After abandoning models with overfitting or poor performance, the voting classifier based on Support Vector Machine, Adaptive Boosting and Naive Bayes achieved a good screening performance (sensitivity: 68.24% ± 8.40%, specificity:72.50% ± 11.81%, F1 score: 72.55% ± 4.65%, AUC:78.38% ± 6.94%). CONCLUSION: A simple and efficient early frailty screening assistant tool for patients receiving maintenance hemodialysis based on machine learning was developed. It can provide assistance on frailty, especially pre-frailty screening and decision-making tasks.

Evaluating the Field Emission Properties of N-Type Black Silicon Cold Cathodes Based on a Three-Dimensional Model.

Black silicon (BS), a nanostructured silicon surface containing highly roughened surface morphology, has recently emerged as a promising candidate for field emission (FE) cathodes in novel electron sources due to its huge number of sharp tips with ease of large-scale fabrication and controllable geometrical shapes. However, evaluating the FE performance of BS-based nanostructures with high accuracy is still a challenge due to the increasing complexity in the surface morphology. Here, we demonstrate a 3D modeling methodology to fully characterize highly disordered BS-based field emitters randomly distributed on a roughened nonflat surface. We fabricated BS cathode samples with different morphological features to demonstrate the validity of this method. We utilize parametrized scanning electron microscopy images that provide high-precision morphology details, successfully describing the electric field distribution in field emitters and linking the theoretical analysis with the measured FE property of the complex nanostructures with high precision. The 3D model developed here reveals a relationship between the field emission performance and the density of the cones, successfully reproducing the classical relationship between current density J and electric field E (J-E curve). The proposed modeling approach is expected to offer a powerful tool to accurately describe the field emission properties of large-scale, disordered nano cold cathodes, thus serving as a guide for the design and application of BS as a field electron emission material.

A Study on the Field Emission Characteristics of High-Quality Wrinkled Multilayer Graphene Cathodes.

Field emission (FE) necessitates cathode materials with low work function and high thermal and electrical conductivity and stability. To meet these requirements, we developed FE cathodes based on high-quality wrinkled multilayer graphene (MLG) prepared using the bubble-assisted chemical vapor deposition (B-CVD) method and investigated their emission characteristics. The result showed that MLG cathodes prepared using the spin-coating method exhibited a high field emission current density (~7.9 mA/cm2), indicating the excellent intrinsic emission performance of the MLG. However, the weak adhesion between the MLG and the substrate led to the poor stability of the cathode. Screen printing was employed to prepare the cathode to improve stability, and the influence of a silver buffer layer was explored on the cathode's performance. The results demonstrated that these cathodes exhibited better emission stability, and the silver buffer layer further enhanced the comprehensive field emission performance. The optimized cathode possesses low turn-on field strength (~1.5 V/µm), low threshold field strength (~2.65 V/µm), high current density (~10.5 mA/cm2), and good emission uniformity. Moreover, the cathode also exhibits excellent emission stability, with a current fluctuation of only 6.28% during a 4-h test at 1530 V.