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Azathioprine (AZA) is commonly used for many autoimmune disorders; however, the limitation of its clinical use is due to potential toxicities, including severe leukopenia. Recent studies have identified genetic NUDT15 variants strongly associated with AZA-induced leukopenia in Asian patients. This study aimed to investigate the strength of above genetic association and evaluate the usefulness of prospective screening of the NUDT15 variants to prevent AZA-induced leukopenia in Chinese patients. AZA-induced leukopenia in patients with autoimmune disorders were enrolled from multiple medical centers in Taiwan/China between 2012 and 2017 to determine the strength of genetic association of NUDT15 or TPMT variants by whole exome sequencing (WES). Furthermore, a prospective study was conducted between 2018 and 2021 to investigate the incidence of AZA-induced leukopenia with and without genetic screening. The WES result showed the genetic variants of NUDT15 R139C (rs116855232) (P = 3.7 × 10-25 , odds ratio (OR) = 21.7, 95% confidence interval (95% CI) = 12.1-38.8) and NUDT15 rs746071566 (P = 4.2 × 10-9 , OR = 7.1, 95% CI = 3.7-13.7), but not TPMT, were associated with AZA-induced leukopenia and NUDT15 R139C variant shows the highest sensitivity with 92.5%. Furthermore, the targeted screening of 1,013 participants for NUDT15 R139C enabled those identified as carriers to use alternative immunosuppressants. This strategy resulted in a significant decrease in the incidence of AZA-induced leukopenia compared with historical incidence (incidence rate = from 7.6% decreased to 0.4%; P = 9.3 × 10-20 ). In conclusion, the NUDT15 R139C variant was strongly associated with AZA-induced leukopenia in Chinese patients. The genetic screening of NUDT15 R139C followed by use of alternative immunosuppressants in identified carriers effectively decreased the incidence of AZA leukopenia for patients with autoimmune disorders.

Value co-creation profoundly affects brand experience, human interaction rules, and customer behavior performance and is a recurring theme in research in the field of virtual brand communities. Studies have determined an influence model for value co-creation, but there is a dearth of research on the impacts of value co-creation activities on brand attachment. This study built a mediation theoretical research framework on value co-creation theory, brand experience, and brand attachment. The purpose of this study is to explore the significance of value co-creation activities on brand experience. Furthermore, this study intends to measure the impact of brand experience on brand attachment. Additionally, this study intends to investigate the mediation role of brand experience in the relationship between value co-creation and brand attachment. This study analyzed 512 data collected by structural equation modeling using registered users of the OPPO community as participants. The results of this empirical test show that the three dimensions of value co-creation (interpersonal interaction, feedback, and advocacy) have a positive effect on brand experience and that brand experience has a positive and significant impact on brand attachment and mediates the relationship between value co-creation and brand attachment. The findings of significance for management are the identification of factors that enhance value co-creation in virtual brand communities. Doi: 10.28991/ESJ-2023-07-04-014 Full Text: PDF

A personal-computer-based and a Raspberry Pi single-board computer-based virtual force sensor with EtherCAT communication for a six-axis robotic arm are proposed in this paper. Both traditional mathematical modeling and machine learning techniques are used in the establishment of the dynamic model of the robotic arm. Thanks to the high updating rate of EtherCAT, the machine learning-based dynamic model on a personal computer achieved an average correlation coefficient between the estimated torque and the actual torque feedback from the motor driver of about 0.99. The dynamic model created using traditional mathematical modeling and the Raspberry Pi single-board computer demonstrates an approximate correlation coefficient of 0.988 between the estimated torque and the actual torque. The external torque observer is established by calculating the difference between the actual torque and the estimated torque, and the virtual force sensor converts the externally applied torques calculated for each axis to the end effector of the robotic arm. When detecting external forces applied to the end effector, the virtual force sensor demonstrates a correlation coefficient of 0.75 and a Root Mean Square Error of 12.93 N, proving its fundamental competence for force measurement. In this paper, both the external torque observer and the virtual force control are applied to applications related to sensing external forces of the robotic arm. The external torque observer is utilized in the safety collision detection mechanism. Based on experimental results, the system can halt the motion of the robotic arm using the minimum external force that the human body can endure, thereby ensuring the operator’s safety. The virtual force control is utilized to implement a position and force hybrid controller. The experimental results demonstrate that, under identical control conditions, the position and force hybrid controller established by the Raspberry Pi single-board computer achieves superior control outcomes in a constant force control scenario with a pressure of 40 N. The average absolute error is 9.62 N, and the root mean square error is 11.16 N when compared to the target pressure. From the analysis of the results, it can be concluded that the Raspberry Pi system implemented in this paper can achieve a higher control command update rate compared to personal computers. As a result, it can provide greater control benefits in position and force hybrid control.

Background Perturbation of gut microbiota has been linked to chronic kidney disease (CKD), which was correlated with a sophisticated milieu of metabolic and immune dysregulation. Methods To clarify the underlying host–microbe interaction in CKD, we performed multi-omics measurements, including systems-level gut microbiome, targeted serum metabolome and deep immunotyping, in a cohort of patients and non-CKD controls. Results Our analyses on functional profiles of the gut microbiome showed a decrease in the diversity and abundance of carbohydrate-active enzyme (CAZyme) genes but an increase in the abundance of antibiotic resistance, nitrogen cycling enzyme and virulence factor genes in CKD. Moreover, models generated using measurements of serum metabolites (amino acids, bile acids and short-chain fatty acids) or immunotypes were predictive of renal impairment but less so than many of the functional profiles derived from gut microbiota, with the CAZyme genes being the top-performing model to accurately predict the early stage of diseases. In addition, co-occurrence analyses revealed coordinated host–microbe relationships in CKD. Specifically, the highest fractions of significant correlations were identified with circulating metabolites by several taxonomic and functional profiles of gut microbiome, while immunotype features were moderately associated with the abundance of microbiome-encoded metabolic pathways and serum levels of amino acids (e.g. B cell cluster tryptophan and B cell cluster tryptophan metabolism). Conclusion Overall, our multi-omics integration revealed several signatures of systems-level gut microbiome in robust associations with host–microbe co-metabolites and renal function, which may have aetiological and diagnostic implications in CKD. [ABSTRACT FROM AUTHOR]