Research hotspots and trends of artificial intelligence in rheumatoid arthritis: A bibliometric and visualized study

Artificial intelligence (AI) applications on rheumatoid arthritis (RA) are becoming increasingly popular. In this bibliometric study, we aimed to analyze the characteristics of publications relevant to the research of AI in RA, thereby developing a thorough overview of this research topic. Web of Science was used to retrieve publications on the application of AI in RA from 2003 to 2022. Bibliometric analysis and visualization were performed using Microsoft Excel (2019), R software (4.2.2) and VOSviewer (1.6.18). The overall distribution of yearly outputs, leading countries, top institutions and authors, active journals, co-cited references and keywords were analyzed. A total of 859 relevant articles were identified in the Web of Science with an increasing trend. USA and China were the leading countries in this field, accounting for 71.59% of publications in total. Harvard University was the most influential institution. Arthritis Research & Therapy was the most active journal. Primary topics in this field focused on estimating the risk of developing RA, diagnosing RA using sensor, clinical, imaging and omics data, identifying the phenotype of RA patients using electronic health records, predicting treatment response, tracking the progression of the disease and predicting prognosis and developing new drugs. Machine learning and deep learning algorithms were the recent research hotspots and trends in this field. AI has potential applications in various fields of RA, including the risk assessment, screening, early diagnosis, monitoring, prognosis determination, achieving optimal therapeutic outcomes and new drug development for RA patients. Incorporating machine learning and deep learning algorithms into real-world clinical practice will be a future research hotspot and trend for AI in RA. Extensive collaboration to improve model maturity and robustness will be a critical step in the advancement of AI in healthcare

Risk prediction model establishment with tri-phasic CT image features for differential diagnosis of adrenal pheochromocytomas and lipid-poor adenomas: Grouping method

ObjectivesThe purpose of this study was to establish a risk prediction model for differential diagnosis of pheochromocytomas (PCCs) from lipid-poor adenomas (LPAs) using a grouping method based on tri-phasic CT image features.MethodsIn this retrospective study, we enrolled patients that were assigned to a training set (136 PCCs and 183 LPAs) from two medical centers, along with an external independent validation set (30 PCCs and 54 LPAs) from another center. According to the attenuation values in unenhanced CT (CTu), the lesions were divided into three groups: group 1, 10 HU < CTu ≤ 25 HU; group 2, 25 HU < CTu ≤ 40 HU; and group 3, CTu > 40 HU. Quantitative and qualitative CT imaging features were calculated and evaluated. Univariate, ROC, and binary logistic regression analyses were applied to compare these features.ResultsCystic degeneration, CTu, and the peak value of enhancement in the arterial and venous phase (DEpeak) were independent risk factors for differential diagnosis of adrenal PCCs from LPAs. In all subjects (groups 1, 2, and 3), the model formula for the differentiation of PCCs was as follows: Y = -7.709 + 3.617*(cystic degeneration) + 0.175*(CTu ≥ 35.55 HU) + 0.068*(DEpeak ≥ 51.35 HU). ROC curves were drawn with an AUC of 0.95 (95% CI: 0.927–0.973) in the training set and 0.91 (95% CI: 0.860–0.929) in the external validation set.ConclusionA reliable and practical prediction model for differential diagnosis of adrenal PCCs and LPAs was established using a grouping method

The Chinese version of the Health Professional Communication Skills Scale: Psychometric evaluation

ObjectiveThis study aims to translate the Health Professional Communication Skills Scale (HP-CSS) into Chinese and assess its psychometric properties.MethodsA total of 836 healthcare professionals were recruited. The demographic characteristics form and HP-CSS were used for data collection. The psychometric properties of HP-CSS were evaluated by examining item analysis, construct validity, known-group discriminant validity, internal consistency, and split-half reliability.ResultsIn terms of item analysis, the critical ratio (CR) of 18 items was both >3 (CR ranging from 9.937 to 28.816), and the score of each item was positively correlated with the total score (r ranging from 0.357 to 0.778, P < 0.001). The fit indices showed that the original correlated four-factor model of HP-CSS was adequate: χ2 =722.801; df = 126; χ2/df = 5.737; RMSEA = 0.075; CFI = 0.923; NNFI = 0.908; TLI = 0.906; IFI = 0.923. In terms of known-group discriminant validity, the HP-CSS total score was related to gender, occupation, work years, and communication skill training. Cronbach's α coefficient was 0.922, and the split-half reliability was 0.865 for the total scale.ConclusionThe Chinese version of the HP-CSS is a reliable and valid instrument to evaluate communication skills among healthcare professionals in China

Unidirectional Invisibility in PT-Symmetric Cantor Photonic Crystals

In this paper, we investigate the nonreciprocity of reflection in parity-time−symmetric (PT-symmetric) Cantor photonic crystals (PCs). Two one-dimensional PCs abiding by the Cantor sequence are PT-symmetric about the center. The PT symmetry and defect cavities in Cantor PCs can induce optical fractal states which are transmission modes. Subsequently, the left and right reflectionless states are located on both sides of a transmission peak. The invisible effect depends on the incident direction and the invisible wavelength can be modulated by the gain–loss factor. This study has potential applications in tunable optical reflectors and invisible cloaks

Unidirectional Invisibility in PT-Symmetric Cantor Photonic Crystals

In this paper, we investigate the nonreciprocity of reflection in parity-time−symmetric (PT-symmetric) Cantor photonic crystals (PCs). Two one-dimensional PCs abiding by the Cantor sequence are PT-symmetric about the center. The PT symmetry and defect cavities in Cantor PCs can induce optical fractal states which are transmission modes. Subsequently, the left and right reflectionless states are located on both sides of a transmission peak. The invisible effect depends on the incident direction and the invisible wavelength can be modulated by the gain–loss factor. This study has potential applications in tunable optical reflectors and invisible cloaks

Grid−Connected Microbial Fuel Cell Modeling and Control in Distributed Generation

Water shortages and water pollution have seriously threatened the sustainable development of the community. The grid−connected microbial fuel cell is an effective way to control the cost of wastewater treatment plants. Moreover, it solves the problem of low efficiency and high energy consumption. In view of the characteristics of strong coupling, non−linearity, and internal load in the process of microbial fuel cell grid connection, it is necessary to design the grid−connected unit of power electronic device. Based on the establishment of the microbial fuel cell stack model, the stability control and the constant power control scheme were designed for the chopper and inverter, respectively. The simulation results showed that the control strategy with the combination of voltage stabilizer and constant power can make a grid−connected system of all phase voltage and frequency output. The three−phase voltage Uabc was steady at 7 h and the voltage amplitude was controlled at roughly 380 V, according to the output voltage waveform. The value was 50 Hz, which satisfies the criteria for grid connection

Recognizing the level of organizational commitment based on deep learning methods and EEG

In recent years, the application scenarios for Electroencephalogram (EEG) research have become increasingly extensive. Compared to other tasks, using EEG to recognize the difference in the levels of subjects’ personality traits is a greater challenge to some extent. In this paper, we propose a new task of recognizing the level of people’s Organizational Commitment based on EEG signals and Deep Learning methods. Aiming at this goal, we constructed a graph convolutional neural network structure (EEG-GCN) based on the topological graph of EEG features, and compared it with other deep learning model frameworks such as one-dimensional convolutional neural network (1D-CNN), two-dimensional convolutional neural network (2D-CNN), and LSTM. Meanwhile, we have studied the construction of the adjacency matrix of the EEG feature topology map, and finally found that the combination of Pairwise Phase Consistency (PPC) and geodetic distance is the best choice. The model we constructed can achieve an average accuracy of 79.1%. Furthermore, after expanding the size of our dataset, our model is able to achieve an overall average accuracy of 81.9%. Therefore, it can be seen that the combination of resting-state EEG and deep learning method is effective in recognizing organizational commitment personality traits

Grid-Connected Microbial Fuel Cell Modeling and Control in Distributed Generation

Water shortages and water pollution have seriously threatened the sustainable development of the community. The grid-connected microbial fuel cell is an effective way to control the cost of wastewater treatment plants. Moreover, it solves the problem of low efficiency and high energy consumption. In view of the characteristics of strong coupling, non-linearity, and internal load in the process of microbial fuel cell grid connection, it is necessary to design the grid-connected unit of power electronic device. Based on the establishment of the microbial fuel cell stack model, the stability control and the constant power control scheme were designed for the chopper and inverter, respectively. The simulation results showed that the control strategy with the combination of voltage stabilizer and constant power can make a grid-connected system of all phase voltage and frequency output. The three-phase voltage Uabc was steady at 7 h and the voltage amplitude was controlled at roughly 380 V, according to the output voltage waveform. The value was 50 Hz, which satisfies the criteria for grid connection

IL-1β Pretreatment Improves the Efficacy of Mesenchymal Stem Cells on Acute Liver Failure by Enhancing CXCR4 Expression

Background. Mesenchymal stem cells (MSCs), with the powerful metabolic and functional supporting abilities for inflammatory diseases, may be an effective therapeutic strategy for acute liver failure (ALF). However, the efficacy of MSCs can still be promoted if pretreatment is applied to enhance their poor migration towards the damaged liver. The purpose of this study is to determine the effect of IL-1β pretreatment on the efficacy and homing ability of MSCs in ALF. Methods. MSCs were isolated by the whole bone marrow adherence method and characterized. The efficacy and homing ability of IL-1β-pretreated MSCs (Pre-MSCs) were examined in a rat ALF model and compared with that of MSCs and normal saline. Then, Western blot was performed to detect the c-Met and CXCR4 expression of MSCs and Pre-MSCs and followed by flow cytometry to detect the meaningful indicators. Finally, the migration abilities of different cells and different conditions were tested by the Transwell migration assay. Results. MSCs of ideal purity were successfully isolated and cultured. Comparing with MSCs, Pre-MSCs had significantly better efficacy on improving the survival rate and liver function of ALF rats. Further analyses of damaged liver tissues showed that IL-1β pretreatment significantly enhanced the efficacy of MSCs on suppressing liver necrosis. Besides, Pre-MSCs exhibited better effects in inhibiting apoptosis and activating proliferation. The results of tracing experiments with CM-Dil-labeled cells confirmed that more cells migrated to the damaged liver in the Pre-MSC group. In terms of mechanism, the CXCR4 expression was significantly enhanced by IL-1β pretreatment, and an increased migration ability towards SDF-1 that could be reversed by AMD3100 was found in Pre-MSCs. Conclusion. IL-1β pretreatment could enhance the homing ability of MSCs at least partially by increasing the expression of CXCR4 and further improve the efficacy of MSCs on ALF