Using combination of albumin to fibrinogen ratio and prognostic nutritional index model for predicting disease activity in patients with systemic lupus erythematosus.

Background: Systemic lupus erythematosus (SLE) is chronic autoimmune disease with multiple organ damage and is associated with poor prognosis and high mortality. Identification of universal biomarkers to predict SLE activity is challenging due to the heterogeneity of the disease. This study aimed to identify the indicators that are sensitive and specific to predict activity of SLE.Methods: We retrospectively analyzed 108 patients with SLE. Patients were categorized into SLE with activity and without activity groups on the basis of SLE disease activity index. We analyzed the potential of routine and novel indicators in predicting the SLE activity using receiver operating characteristic curves and multivariate logistic regression. The Spearman method was used to understand the correlation between albumin to fibrinogen ratio (AFR), prognostic nutritional index (PNI), AFR-PNI model and disease activity.Results: SLE with activity group had higher ESR, CRP, D-dimer, fibrinogen, CRP to albumin ratio, positive rate of anti-dsDNA and ANUA, and lower C3, total bilirubin, total protein, albumin, albumin/globulin, creatinine, high density liptein cholesterol, hemoglobin, hematocrit, lymphocyte count, positive rate of anti-SSA, AFR, PNI than SLE without activity. A further established model based on combination of AFR and PNI (AFR-PNI model) showed prominent value in distinguishing SLE with activity patients from SLE without activity patients. In addition, the sensitivity and specificity of AFR-PNI model + anti-dsDNA combination model were superior to AFR-PNI model. AFR and PNI were risk factors for SLE activity. Moreover, AFR+PNI model correlated with disease activity and AFR-PNI model was associated with fever, pleurisy, pericarditis, renal involvement.Conclusion: These findings suggest that predictive model based on combination of AFR and PNI may be useful markers to identify active SLE in clinical practice.

Prognostic value of mid-term cardiovascular magnetic resonance follow-up in patients with non-ischemic dilated cardiomyopathy: a prospective cohort study.

BACKGROUND: The prognostic value of follow-up cardiovascular magnetic resonance (CMR) in dilated cardiomyopathy (DCM) patients is unclear. We aimed to investigate the prognostic value of cardiac function, structure, and tissue characteristics at mid-term CMR follow-up. METHODS: The study population was a prospectively enrolled cohort of DCM patients who underwent guideline-directed medical therapy with baseline and follow-up CMR, which included measurement of biventricular volume and ejection fraction, late gadolinium enhancement, native T1, native T2, and extracellular volume. During follow-up, major adverse cardiac events (MACE) were defined as a composite endpoint of cardiovascular death, heart transplantation, and heart-failure readmission. RESULTS: Among 235 DCM patients (median CMR interval: 15.3 months; interquartile range: 12.5-19.2 months), 54 (23.0%) experienced MACE during follow-up (median: 31.2 months; interquartile range: 20.8-50.0 months). In multivariable Cox regression, follow-up CMR models showed significantly superior predictive value than baseline CMR models. Stepwise multivariate Cox regression showed that follow-up left ventricular ejection fraction (LVEF; hazard ratio [HR], 0.93; 95% confidence interval [CI], 0.91-0.96; p < 0.001) and native T1 (HR, 1.01; 95% CI, 1.00-1.01; p = 0.030) were independent predictors of MACE. Follow-up LVEF ≥ 40% or stable LVEF < 40% with T1 ≤ 1273 ms indicated low risk (annual event rate < 4%), while stable LVEF < 40% and T1 > 1273 ms or LVEF < 40% with deterioration indicated high risk (annual event rate > 15%). CONCLUSIONS: Follow-up CMR provided better risk stratification than baseline CMR. Improvements in the LVEF and T1 mapping are associated with a lower risk of MACE.

Radiomic signatures reveal multiscale intratumor heterogeneity associated with tissue tolerance and survival in re-irradiated nasopharyngeal carcinoma: a multicenter study.

BACKGROUND: Post-radiation nasopharyngeal necrosis (PRNN) is a severe adverse event following re-radiotherapy for patients with locally recurrent nasopharyngeal carcinoma (LRNPC) and associated with decreased survival. Biological heterogeneity in recurrent tumors contributes to the different risks of PRNN. Radiomics can be used to mine high-throughput non-invasive image features to predict clinical outcomes and capture underlying biological functions. We aimed to develop a radiogenomic signature for the pre-treatment prediction of PRNN to guide re-radiotherapy in patients with LRNPC. METHODS: This multicenter study included 761 re-irradiated patients with LRNPC at four centers in NPC endemic area and divided them into training, internal validation, and external validation cohorts. We built a machine learning (random forest) radiomic signature based on the pre-treatment multiparametric magnetic resonance images for predicting PRNN following re-radiotherapy. We comprehensively assessed the performance of the radiomic signature. Transcriptomic sequencing and gene set enrichment analyses were conducted to identify the associated biological processes. RESULTS: The radiomic signature showed discrimination of 1-year PRNN in the training, internal validation, and external validation cohorts (area under the curve (AUC) 0.713-0.756). Stratified by a cutoff score of 0.735, patients with high-risk signature had higher incidences of PRNN than patients with low-risk signature (1-year PRNN rates 42.2-62.5% vs. 16.3-18.8%, P < 0.001). The signature significantly outperformed the clinical model (P < 0.05) and was generalizable across different centers, imaging parameters, and patient subgroups. The radiomic signature had prognostic value concerning its correlation with PRNN-related deaths (hazard ratio (HR) 3.07-6.75, P < 0.001) and all causes of deaths (HR 1.53-2.30, P < 0.01). Radiogenomics analyses revealed associations between the radiomic signature and signaling pathways involved in tissue fibrosis and vascularity. CONCLUSIONS: We present a radiomic signature for the individualized risk assessment of PRNN following re-radiotherapy, which may serve as a noninvasive radio-biomarker of radiation injury-associated processes and a useful clinical tool to personalize treatment recommendations for patients with LANPC.

Optimal dispatching of regional power grid considering vehicle network interaction.

When large-scale electric vehicles are connected to the grid for unordered charging, it will seriously affect the stability and security of the power system. To solve this problem, this paper proposes a regional power network optimization scheduling method considering vehicle network interaction. Initially, based on the user behavior characteristics and charging and discharging characteristics of electric vehicles, a charging and discharging behavior model of electric vehicles was established. Based on the Monte Carlo sampling algorithm, the scheduling upper and lower limits of each scheduling cycle of electric vehicles were described, and the scheduling potential of each scheduling cycle of electric vehicles was obtained. Then, the electricity price is then used as an incentive parameter to guide EV users to charge during periods of low electricity prices and participate in discharge during periods of peak electricity prices. Aiming at the highest economic efficiency, the best consumption effect of new energy and the smoothest demand-side power curve of regional power grid, a three-objective optimal dispatching model was established. In the later stage, uncertainty factors are taken into consideration by introducing the concept of interval numbers, and an interval multi-objective optimization dispatching model is established. The two dispatching models are solved by NSGA-II algorithm and improved NSGA-II algorithm, and the Pareto solution set is obtained. Finally, based on the analytic Hierarchy Process (AHP), the optimal scheduling scheme is determined. The Monte Carlo sampling method is used to simulate the user side charging demand, and the effectiveness of this method is verified. In addition, the results of the interval multi-objective optimization model and the deterministic multi-objective optimization model are compared, and it is proved that the solution results of the interval multi-objective model are more adaptive, practical and robust to the uncertain factors.

Decreased expression of NAT10 in peripheral blood mononuclear cells from new-onset ankylosing spondylitis and its clinical significance.

BACKGROUND: NAT10 is the firstly recognized RNA acetyltransferase that participates in multiple cellular biological processes and human disease. However, the role of N-acetyltransferase 10 (NAT10) in ankylosing spondylitis (AS) is still poorly elaborated. METHODS: Fifty-six patients with New-Onset AS, 52 healthy controls (HC), 20 patients with rheumatoid arthritis (RA) and 16 patients with systemic lupus erythematosus (SLE) were recruited from The First Afliated Hospital of Nanchang University, and their clinical characteristics were recorded. The expression level of NAT10 in peripheral blood mononuclear cell (PBMC) was examined using reverse transcription-quantitative PCR analysis. The correlations between the expression level of NAT10 in the New-Onset AS patients and disease activity of AS were examined, and receiver operating characteristic (ROC) curves were built to evaluate predictive value in AS. Univariate analysis and multivariate regression analysis were used to analyze the risk factors and construct predictive model. RESULTS: The mRNA expressions of NAT10 in PBMC from new-onset AS patients were significantly low and there were negative correlation between mRNA NAT10 and ASDAS-CRP, BASDIA in new-onset AS patients. ROC analysis suggested that mRNA NAT10 has value in distinguishing new-onset AS patients from HC, RA and SLE. Furthermore, a novel predictive model based on mRNA NAT10 and neutrophil percentages (N%) was constructed for distinguishing new-onset AS patients from HC (AUC = 0.880, sensitivity = 84.62%, specificity = 76.92%) and the predictive model correlated with the activity of new-onset AS. Furthermore, the predictive model could distinguish new-onset AS patients from RA and SLE (AUC = 0.661, sensitivity = 90.38%, specificity = 47.22%). Moreover, the potential predictive value of the combination of predictive model-HLA-B27 for AS vs. HC with a sensitivity of 92.86% (39/42), a specificity of 100.00% (52/52) and an accuracy of 96.81% (91/94) was superior to that of HLA-B27, which in turn had a sensitivity of 84.44% (38/45), a specificity of 100.00% (52/52) and an accuracy of 92.78% (90/97). CONCLUSION: The present study suggested that the decreased mRNA NAT10 may play a role in AS pathogenesis and predictive model based on mRNA NAT10 and N% act as bioindicator for forecast and progression of diseases.

Identifying Key Factors for Burnout Among Orthopedic Surgeons Using the Analytic Hierarchy Process Method.

Objectives: To develop an evaluation model for, and identify key factors contributing to, burnout in orthopedic surgeons, providing a reference for the management of burnout among orthopedic surgeons in hospitals. Methods: We developed an analytic hierarchy process (AHP) model with 3 dimensions and 10 sub-criteria based on an extensive literature review and expert assessment. We used expert and purposive sampling and 17 orthopedic surgeons were selected as research subjects. The AHP process was then used to obtain the weights and to prioritize the dimensions and criteria for burnout in orthopedic surgeons. Results: The dimension of C 1 (personal/family) was the key factor affecting burnout in orthopedic surgeons, and in the sub-criteria, the top four sub-criteria were C 11 (little time for family), C 31 (anxiety about clinical competence), C 12 (work-family conflict), and C 22 (heavy work load). Conclusion: This model was effective in analyzing the key factors contributing to job burnout risk, and the results can inform improved management of the levels of burnout affecting orthopedic surgeons in hospitals.

Applying a multi-criteria decision-making approach to identify key satisfaction gaps in hospital nurses' work environment.

Aim: A decision analysis model was constructed to explore the key factors affecting the job satisfaction of hospital nurses and to analyze the key satisfaction gaps in the case hospital. Background: In China, medical institutions are facing greater pressure and challenges in the normalization of epidemic prevention and control. Nurses play a critical role in the delivery of medical care services. Past studies have shown that improving job satisfaction among hospital nurses is important for both reducing nurse turnover and improving the quality of care. Methods: McCloskey/Mueller satisfaction scale (MMSS-31) was used to survey 25 nursing specialists in a case hospital in Zhejiang. Then, the Consistent Fuzzy Preference Relation (CFPR) method was used to analyze the degree of importance of dimensions and corresponding sub-criteria. Finally, the importance-performance analysis method was applied to identify critical satisfaction gaps for the case hospital. Results: In terms of local weight for dimensions, "Control/Responsibility (C 8)" ≻ "Praise/Recognition (C 7)" ≻ "Extrinsic Rewards (C 1)" are the top three key factors for nurses' work environment satisfaction in the case of a hospital. In addition, the sub-criteria "Salary (C 11)", "Benefits (C 13)", "Child care (C 33)", "Recognition-peers (C 73)", "Encouragement/feedback (C 74)", and "Decision making (C 85)" are the key factors for improving clinical nursing satisfaction in the case hospital. Conclusion: The issues that nurses care about but for which they have not attained expectations mainly involved extrinsic rewards, recognition/encouragement and control over their working process. The findings of this study could offer an academic reference for management and remind them to consider the above factors in exploring future reform, further improving nurses' job satisfaction and motivating them to provide better nursing services.

Impact of salvage radiotherapy on survival of patients with advanced locally recurrent nasopharyngeal carcinoma: Derivation and validation of a predictive model.

BACKGROUND: Salvage radiotherapy (RT) is a potentially curative approach for advanced locally recurrent nasopharyngeal carcinoma (NPC), but it is associated with severe toxicities. We aimed to develop a model to predict which patients would benefit from salvage RT. METHODS: A total of 809 patients who were diagnosed with advanced locally recurrent NPC and treated with salvage RT or palliative chemotherapy (CT) at a high-volume cancer center were included. Patients were randomly split into a training and validation set and matched using inverse probability of treatment weighting. The primary outcome was overall survival (OS). Candidate variables associated with heterogeneous treatment effects were identified with interaction terms in Cox model and incorporated into Salvage Radiotherapy Outcome Score (SARTOS). RESULTS: The final model included five interaction terms indicating that female sex, presence of prior RT-induced grade ≥ 3 late toxicities and suboptimal performance status were associated with less benefit from salvage RT. SARTOS from the model significantly predicted treatment effects of salvage RT in matched training (Pinteration < 0.001) and validation cohorts (Pinteration = 0.027). Of patients in high SARTOS subgroup, salvage RT significantly improved survival versus palliative CT in matched training (3-year OS 67.3% vs. 42.0%, HR 0.51, 95% CI 0.32-0.82, P = 0.005) and validation cohorts (3-year OS 71.8% vs. 22.8%, HR 0.40, 95% CI 0.17-0.97, P = 0.042); in low SARTOS subgroup, salvage RT failed to induce survival benefit. CONCLUSIONS: We found that the SARTOS model could identify a subgroup of patients who benefit from salvage RT versus palliative CT, which helps personalize treatment recommendations for patients with recurrent NPC.

Influence of Paternalistic Leadership Style on Innovation Performance Based on the Research Perspective of the Mediating Effect of the Constructive Deviance of Employees.

Innovation is the primary driving force behind the development of China as a modern economic power. This study examines the impact of paternalistic leadership on innovation, proposing a theoretical model using the three dimensions of paternalistic leadership (i.e., benevolence, morality, and authoritarianism) as independent variables, constructive deviance as a mediating variable, and innovation performance as the dependent variable. Empirical results showed that benevolent and moral leadership has a positive impact on innovation performance while authoritarian leadership has a negative impact. Constructive deviance by employees has a positive impact on innovation performance. Benevolent and moral leadership has a positive impact on the constructive deviance of employees, while authoritarian leadership has a negative impact on constructive deviance. In addition, benevolent and moral leadership has a positive impact on innovation performance through the constructive deviance of employees, while the impact of authoritarian leadership is negative. In practice, leaders should recognize that constructive deviance is a double-edged sword and guide employees to engage in reasonable constructive deviant behavior, thereby creating sound organizational environments to foster innovation, eliminate barriers, and benefit from the positive impact of the constructive deviance of employees to enhance innovation performance.

A Comparison of Control Strategies in Commercial and Research Knee Prostheses.

GOAL: To provide an overview of control strategies in commercial and research microprocessor-controlled prosthetic knees (MPKs). METHODS: Five commercially available MPKs described in patents, and five research MPKs reported in scientific literature were compared. Their working principles, intent recognition, and walking controller were analyzed. Speed and slope adaptability of the walking controller was considered as well. RESULTS: Whereas commercial MPKs are mostly passive, i.e., do not inject energy in the system, and employ heuristic rule-based intent classifiers, research MPKs are all powered and often utilize machine learning algorithms for intention detection. Both commercial and research MPKs rely on finite state machine impedance controllers for walking. Yet while commercial MPKs require a prosthetist to adjust impedance settings, scientific research is focused on reducing the tunable parameter space and developing unified controllers, independent of subject anthropometrics, walking speed, and ground slope. CONCLUSION: The main challenges in the field of powered, active MPKs (A-MPKs) to boost commercial viability are first to demonstrate the benefit of A-MPKs compared to passive MPKs or mechanical non-microprocessor knees using biomechanical, performance-based and patient-reported metrics. Second, to evaluate control strategies and intent recognition in an uncontrolled environment, preferably outside the laboratory setting. And third, even though research MPKs favor sophisticated algorithms, to maintain the possibility of practical and comprehensible tuning of control parameters, considering optimal control cannot be known a priori. SIGNIFICANCE: This review identifies main challenges in the development of A-MPKs, which have thus far hindered their broad availability on the market.

Design, Modelling, and Experimental Evaluation of a Compact Elastic Actuator for a Gait Assisting Exoskeleton.

This paper presents a Transmissive Force Sensing Elastic Actuator (TFSEA) for exoskeleton applications. Exoskeletons can serve as orthotic or rehabilitative devices enabling people with paraplegia to walk. Several exoskeletons have been commercialized, most of which are above 23 kg, making them too heavy and bulky for people with paraplegia to put on and take off by themselves. One of the bottlenecks of achieving lightweight exoskeleton design is actuation. This work focused on developing a compact, lightweight and high-performance actuator. Using the differential property of harmonic drive, a new elastic actuator configuration was created. A dynamic model was developed for the proposed design and a model-based controller was implemented. Various tests were done to evaluate the performance of the actuator. The results showed that the torsional spring exhibits linearity of 99.99%, with no backlash or hysteresis. Thejoint can output 100 Nm peak torque with a large-torque bandwidth of 5 Hz. Moreover, it weighs only 1.56 kg, leading to a torque density of 64 Nm/kg and a power density of 360 W/kg, the highest published to date in the same torque and power rating.

Design and control of the MINDWALKER exoskeleton.

Powered exoskeletons can empower paraplegics to stand and walk. Actively controlled hip ab/adduction (HAA) is needed for weight shift and for lateral foot placement to support dynamic balance control and to counteract disturbances in the frontal plane. Here, we describe the design, control, and preliminary evaluation of a novel exoskeleton, MINDWALKER. Besides powered hip flexion/extension and knee flexion/extension, it also has powered HAA. Each of the powered joints has a series elastic actuator, which can deliver 100 Nm torque and 1 kW power. A finite-state machine based controller provides gait assistance in both the sagittal and frontal planes. State transitions, such as stepping, can be triggered by the displacement of the Center of Mass (CoM). A novel step-width adaptation algorithm was proposed to stabilize lateral balance. We tested this exoskeleton on both healthy subjects and paraplegics. Experimental results showed that all users could successfully trigger steps by CoM displacement. The step-width adaptation algorithm could actively counteract disturbances, such as pushes. With the current implementations, stable walking without crutches has been achieved for healthy subjects but not yet for SCI paraplegics. More research and development is needed to improve the gait stability.

EMG patterns during assisted walking in the exoskeleton.

Neuroprosthetic technology and robotic exoskeletons are being developed to facilitate stepping, reduce muscle efforts, and promote motor recovery. Nevertheless, the guidance forces of an exoskeleton may influence the sensory inputs, sensorimotor interactions and resulting muscle activity patterns during stepping. The aim of this study was to report the muscle activation patterns in a sample of intact and injured subjects while walking with a robotic exoskeleton and, in particular, to quantify the level of muscle activity during assisted gait. We recorded electromyographic (EMG) activity of different leg and arm muscles during overground walking in an exoskeleton in six healthy individuals and four spinal cord injury (SCI) participants. In SCI patients, EMG activity of the upper limb muscles was augmented while activation of leg muscles was typically small. Contrary to our expectations, however, in neurologically intact subjects, EMG activity of leg muscles was similar or even larger during exoskeleton-assisted walking compared to normal overground walking. In addition, significant variations in the EMG waveforms were found across different walking conditions. The most variable pattern was observed in the hamstring muscles. Overall, the results are consistent with a non-linear reorganization of the locomotor output when using the robotic stepping devices. The findings may contribute to our understanding of human-machine interactions and adaptation of locomotor activity patterns.

Modeling, design, and optimization of Mindwalker series elastic joint.

Weight and power autonomy are limiting the daily use of wearable exoskeleton. Lightweight, efficient and powerful actuation system are not easy to achieve. Choosing the right combinations of existing technologies, such as battery, gear and motor is not a trivial task. In this paper, we propose an optimization framework by setting up a power-based quasi-static model of the exoskeleton joint drivetrain. The goal is to find the most efficient and lightweight combinations. This framework can be generalized for other similar applications by extending or accommodating the model to their own needs. We also present the Mindwalker exoskeleton joint, for which a novel series elastic actuator, consisting of a ballscrew-driven linear actuator and a double spiral spring, was developed and tested. This linear actuator is capable of outputting 960 W power and the exoskeleton joint can output 100 Nm peak torque continuously. The double spiral spring can sense torque between 0.08Nm and 100 Nm and it exhibits linearity of 99.99%, with no backlash or hysteresis. The series elastic joint can track a chirp torque profile with amplitude of 100 Nm over 6 Hz (large torque bandwidth) and for small torque (2 Nm peak-to-peak), it has a bandwidth over 38 Hz. The integrated exoskeleton joint, including the ballscrew-driven linear actuator, the series spring, electronics and the metal housing which hosts these components, weighs 2.9 kg.

Spring uses in exoskeleton actuation design.

An exoskeleton has to be lightweight, compliant, yet powerful to fulfill the demanding task of walking. This imposes a great challenge for the actuator design. Electric motors, by far the most common actuator in robotic, orthotic, and prosthetic devices, cannot provide sufficiently high peak and average power and force/torque output, and they normally require high-ratio, heavy reducer to produce the speeds and high torques needed for human locomotion. Studies on the human muscle-tendon system have shown that muscles (including tendons and ligaments) function as a spring, and by storing energy and releasing it at a proper moment, locomotion becomes more energy efficient. Inspired by the muscle behavior, we propose a novel actuation strategy for exoskeleton design. In this paper, the collected gait data are analyzed to identify the spring property of the human muscle-tendon system. Theoretical optimization results show that adding parallel springs can reduce the peak torque by 66%, 53%, and 48% for hip flexion/extension (F/E), hip abduction/adduction (A/A), and ankle dorsi/plantar flexion (D/PF), respectively, and the rms power by 50%, 45%, and 61%, respectively. Adding a series spring (forming a Series Elastic Actuator, SEA) reduces the peak power by 79% for ankle D/PF, and by 60% for hip A/A. A SEA does not reduce the peak power demand at other joints. The optimization approach can be used for designing other wearable robots as well.