Reliability and validity of a graphical computerized adaptive test Longshi scale for rapid assessment of activities of daily living in stroke survivors.

Stroke survivors frequently experience difficulties in daily activities, such as bathing, feeding, and mobility. This study aimed to evaluate the reliability and validity of a computer-adaptive test-Longshi scale (CAT-LS) for assessing activities of daily living (ADL) in stroke survivors. This cross-sectional study collected data using an electronic application. The ADL function of stroke survivors in rehabilitation departments of hospitals was assessed using both the CAT-LS and BI. Correlations between the CAT-LS and Barthel index (BI) and concurrent validity were evaluated using Pearson's correlation test and multiple linear regression. Interrater reliability was evaluated using the intraclass correlation coefficient based on a two-way random effect. The internal consistency of the CAT-LS was assessed using Cronbach's coefficient (α) and corrected item-total correlations. Overall, 103 medical institutions in China were used in this study. In total, 7151 patients with stroke were included in this study. The CAT-LS classified patients into three ADL groups (bedridden, domestic, and community) with significantly different BI scores (P < 0.05). The CAT-LS results obtained using the decision-tree scoring model were consistent with the scores for each BI item. A strong correlation was observed between CAT-LS and BI (Pearson's r: 0.6-0.894, P < 0.001). The CAT-LS demonstrated good internal consistency (Cronbach's α, 0.803-0.894) and interrater reliability (ICC, 0.928-0.979). CAT-LS is time-efficient and requires < 1 min to administer. The CAT-LS is a reliable and valid tool for assessing ADL function in stroke survivors and can provide rapid and accurate assessments that reduce the burden on healthcare professionals. Further validation of this tool in other populations and settings is necessary.Study registration number: No.: ChiCTR2000034067; http://www.chictr.org.cn/showproj.aspx?proj=54770 .

State Evaluation of Self-Powered Wireless Sensors Based on a Fuzzy Comprehensive Evaluation Model.

The energy harvesters used in self-powered wireless sensing technology, which has the potential to completely solve the power supply problem of the sensing nodes from the source, usually require mechanical movement or operate in harsh environments, resulting in a significant reduction in device lifespan and reliability. Therefore, the influencing factors and failure mechanisms of the operating status of self-powered wireless sensors were analyzed, and an innovative evaluation index system was proposed, which includes 4 primary indexes and 13 secondary indexes, including energy harvesters, energy management circuits, wireless communication units, and sensors. Next, the weights obtained from the subjective analytic hierarchy process (AHP) and objective CRITIC weight method were fused to obtain the weights of each index. A self-powered sensor evaluation scheme (FE-SPS) based on fuzzy comprehensive evaluation was implemented by constructing a fuzzy evaluation model. The advantage of this scheme is that it can determine the current health status of the system based on its output characteristics. Finally, taking vibration energy as an example, the operational status of the self-powered wireless sensors after 200 h of operation was comprehensively evaluated. The experimental results show that the test self-powered wireless sensor had the highest score of "normal", which is 0.4847, so the evaluation result was "normal". In this article, a reliability evaluation strategy for self-powered wireless sensor was constructed to ensure the reliable operation of self-powered wireless sensors.

Reliability of Longshi scale with remote assessment of smartphone video calls for stroke patients' activities of daily living.

OBJECTIVES: The impairment of activities of daily living (ADL) has brought a heavy burden to the life of patients after stroke. Timely and accurate assessment of patients' activities of daily living is very necessary, which determines the whole process of diagnosis, rehabilitation and prognosis of patients. However, the patients with limited mobility or transportation are difficulted to gain high quality assessment services. This study aimed to explore the reliability of remote Longshi Assessment Scale of Activities of Daily Life (Longshi Scale) with smartphone video calls by comparing the consistency of remote assessment and bedside assessment, as well as the test-retest reliability of the remote assessment. The evaluation duration of these two methods was recorded and the level of satisfaction of patients was investigated. METHODS: In this prospective study, a total of 129 stroke survivors were recruited and accepted a bedside face-to-face assessment and a remote assessment by video calls. The Longshi Scale was used for both bedside and remote assessment and conducted with the inquiry between patients and evaluators. A satisfaction questionnaire was also launched. RESULTS: The result of disability level evaluated from the bedside and remote assessments was highly consistent. The intraclass correlation coefficient with weighted kappa (wK) value was 0.86 (95% confidence interval, 0.80∼0.92). And test-retest of the remote assessment indicated an excellent agreement beyond chance, and its correlation coefficient with wK value was 0.96 (95% confidence interval, 0.92∼1.00). It took 74.44±55.3 s to complete the bedside assessment and 90.86±63.30 s to complete the remote assessment of Longshi Scale. There was no statistical significance in the assessment duration between these two methods (P = 0.056). Satisfaction surveys showed more than 85% of participants were satisfied or very satisfied with remote Longshi scale assessment. CONCLUSIONS: The remote assessment of Longshi Scale with smartphone video calls is reliable and has high acceptance. This method can be readily implemented to evaluate the ADL of stroke patients to improve the capacity of rehabilitation and health services in remote areas.

Stroke volume and cardiac output during 6 minute-walk tests are strong predictors of maximal oxygen uptake in people after stroke.

BACKGROUND AND OBJECTIVES: The 6-minute walk test (6MWT) is a field test commonly used to predict peak oxygen consumption (VO2peak) in people after stroke. Inclusion of cardiodynamic variables measured by impedance cardiography (ICG) during a 6MWT has been shown to improve prediction of VO2peak in healthy adults but these data have not been considered in people after stroke. This study investigates whether the prediction of VO2peak can be improved by the inclusion of cardiovascular indices derived by impedance cardiography (ICG) during the 6MWT in people after stroke. METHODS: This was a cross-sectional study. Patients diagnosed with stroke underwent in random order, a maximal cardiopulmonary exercise test (CPET) and 6MWT in separate dates. Heart rate (HR), stroke volume (SV) and cardiac output (CO) were measured by ICG during all tests. Oxygen consumption was recorded by a metabolic cart during the CPET. Recorded data were subjected to multiple regression analyses to generate VO2peak prediction equations. RESULTS: Fifty-nine patients, mean age 50.0±11.7 years were included in the analysis. The mean distance covered in the 6MWT (6MWD) was 294±13 m, VO2peak was 19.2±3.2 ml/min/kg. Mean peak HR, SV and CO recorded during 6MWT were 109±6 bpm, 86.3±8.8 ml, 9.4±1.2 L/min and during CPET were 135±14 bpm, 86.6±9 ml, 11.7±2 L/min respectively. The prediction equation with inclusion of cardiodynamic variables: 16.855 + (-0.060 x age) + (0.196 x BMI) + (0.01 x 6MWD) + (-0.416 x SV6MWT) + (3.587 x CO 6MWT) has a higher squared multiple correlation (R2) and a lower standard error of estimate (SEE) and SEE% compared to the equation using 6MWD as the only predictor. CONCLUSION: Inclusion of SV and CO measured during the 6MWT in stroke patients further improved the VO2peak prediction power compared to using 6MWD as a lone predictor.

Decoding the Immune Microenvironment of Clear Cell Renal Cell Carcinoma by Single-Cell Profiling to Aid Immunotherapy.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer, and it is the major cause of kidney cancer death. Understanding tumor immune microenvironments (TMEs) is critical in cancer immunotherapies. Here, we studied the immune characterization at single-cell resolution by integrating public data of ccRCC across different tissue types, and comparing the transcriptome features and tumor TME differences in tumors, normal adjacent tissue, and peripheral blood. A total of 16 different types of cell components of ccRCC were identified. We revealed that there is an overall increase in T-cell and myeloid populations in tumor-infiltrated immune cells compared to normal renal tissue, and the B-cell population in the tumor showed a sharp decrease, which indicates that the cells in tumor tissue undergo strong immune stress. In addition, the cell-cell communication analysis revealed specific or conserved signals in different tissue types, which may aid to uncover the distinct immune response. By combining and analyzing publicly available ccRCC bulk RNA-seq datasets, 10 genes were identified as marker genes in specific cell types, which were significantly associated with poor prognosis. Of note, UBE2C, which may be a good indicator of tumor proliferation, is positively associated with reductions in overall survival and highly associated with tumor grade. Our integrated analysis provides single-cell transcriptomic profiling of ccRCC and their TME, and it unmasked new correlations between gene expression, survival outcomes, and immune cell-type components, enabling us to dissect the dynamic variables in the tumor development process. This resource provides deeper insight into the transcriptome features and immune response of ccRCC and will be helpful in kidney cancer immunotherapy.

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions.

BACKGROUND: Generalized regression neural network (GRNN) and logistic regression (LR) are extensively used in the medical field; however, the better model for predicting stroke outcome has not been established. The primary goal of this study was to compare the accuracies of GRNN and LR models to identify the most optimal model for the prediction of acute stroke outcome, as well as explore useful biomarkers for predicting the prognosis of acute stroke patients. METHOD: In a single-center study, 216 (80% for the training set and 20% for the test set) acute stroke patients admitted to the Shenzhen Second People's Hospital between December 2019 to June 2021 were retrospectively recruited. The functional outcomes of the patients were measured using Barthel Index (BI) on discharge. A training set was used to optimize the GRNN and LR models. The test set was utilized to validate and compare the performances of GRNN and LR in predicting acute stroke outcome based on the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and the Kappa value. RESULT: The LR analysis showed that age, the National Institute Health Stroke Scale score, BI index, hemoglobin, and albumin were independently associated with stroke outcome. After validating in test set using these variables, we found that the GRNN model showed a better performance based on AUROC (0.931 vs 0.702), sensitivity (0.933 vs 0.700), specificity (0.889 vs 0.722), accuracy (0.896 vs 0.729), and the Kappa value (0.775 vs 0.416) than the LR model. CONCLUSION: Overall, the GRNN model demonstrated superior performance to the LR model in predicting the prognosis of acute stroke patients. In addition to its advantage in not affected by implicit interactions and complex relationship in the data. Thus, we suggested that GRNN could be served as the optimal statistical model for acute stroke outcome prediction. Simultaneously, prospective validation based on more variables of the GRNN model for the prediction is required in future studies.

Free-Standing N-Doped Porous Carbon Fiber Membrane Derived From Zn-MOF-74: Synthesis and Application as Anode for Sodium-Ion Battery With an Excellent Performance.

It is important to develop new energy storage and conversion technology to mitigate the energy crisis for the sustainable development of human society. In this study, free-standing porous nitrogen-doped carbon fiber (PN-CF) membranes were obtained from the pyrolysis of Zn-MOF-74/polyacrylonitrile (PAN) composite fibers, which were fabricated in situ by an electrospinning technology. The resulting free-standing fibers can be cut into membrane disks and directly used as an anode electrode without the addition of any binder or additive. The PN-CFs showed great reversible capacities of 210 mAh g-1 at a current density of 0.05 A g-1 and excellent cyclic stability of 170.5 mAh g-1 at a current density of 0.2 A g-1 after 600 cycles in sodium ion batteries (SIBs). The improved electrochemical performance of PN-CFs can be attributed to the rich porous structure derived by the incorporation of Zn-MOF-74 and nitrogen doping to promote sodium ion transportation.

Cardiodynamic variables measured by impedance cardiography during a 6-minute walk test are reliable predictors of peak oxygen consumption in young healthy adults.

Accurate prediction of aerobic capacity is necessary to guide appropriate exercise prescription. It is common to use 6-minute walk distance (6MWD) to predict peak oxygen uptake (VO2peak) in the clinical environment. The aim of this study was to determine whether prediction of VO2peak can be improved by the inclusion of cardiovascular indices derived by impedance cardiography (ICG) during the 6MWT. A total of 62 healthy university students aged 21±1 years completed in separate days, a cardiopulmonary exercise test (CPET) and two 6MWTs (30 min apart), during which heart rate (HR), stroke volume (SV) and cardiac output (CO) were measured by ICG (PhysioFlow® PF07 EnduroTM). The CPET was conducted with the Ergoselect 200 Ergoline and oxygen consumption measured by a MasterScreenTM CPX breath-by-breath metabolic cart. Multiple regression analyses were conducted to generate VO2peak prediction equations using 6MWD with, or without the cardiovascular indices recorded at the end of the best performed 6MWT as predictor variables. The mean peak HR (bpm), SV (ml) and CO (L/min) recorded during 6MWT were 156±18, 95.6±9, 15±2.8 and during CPET were 176±16, 91.3±8, 16.2±2.7, respectively. Analyses revealed the following VO2peak prediction equation: VO2peak = 100.297+(0.019x6MWD)+(-0.598xHR6MWT)+(-1.236xSV6MWT) + (8.671 x CO6MWT). This equation has a squared multiple correlation (R2) of 0.866, standard error of the estimate (SEE) of 2.28 mL/kg/min and SEE:VO2peak (SEE%) of 7.2%. Cross-validation of equation stability using predicted residual sum of squares (PRESS) statistics showed a R2 (Rp2), SEE (SEEp) and SEEp% of 0.842, 2.38 mL/kg/min and 7.6% respectively. The minimal shrinkage of R2 implied regression model stability. Correlation between measured and predicted VO2peak using this equation was strong (r = 0.931, p<0.001). When 6MWD alone was used as the predictor for VO2peak, the generated equation had a lower R2 (0.549), and a higher SEE (4.08 mL/kg/min) and SEE% (12.9%). This is the first study which included cardiac indices during a 6MWT as variables for VO2peak prediction. Our results suggest that inclusion of cardiac indices measured during the 6MWT more accurately predicts VO2peak than using 6MWD data alone.

Stroke outcome assessment: Optimizing cutoff scores for the Longshi Scale, modified Rankin Scale and Barthel Index.

The Longshi Scale, a visual-based scale, is reliable and valid in activity assessment, but lacks cutoff definition corresponding to classical scales such as the modified Rankin Scale and Barthel Index. Therefore, this study aimed to investigate the relationships of the Longshi Scale with the modified Rankin Scale and Barthel Index and optimize cutoff scores of these scales in stroke outcomes assessment. This is a cross-sectional study. Stroke patients were measured concurrently by the Longshi scale, modified Rankin Scale and Barthel Index. Kruskal-Wallis test and Spearman correlation analysis were used to analyze the differences and associations among the three scales. The receiver operating characteristic curve was performed to determine the optimal cutoff scores. A total of 5475 stroke patients (67.3% ischemic) were included in this study. There are close relationships of the Longshi Scale with adjusted modified Rankin Scale and Barthel Index (r = -0.861, 0.922; p<0.001, <0.001; respectively). The activity levels assessed by adjusted modified Rankin Scale and Barthel Index among different Longshi scale grades were significantly different (χ2:4217.27, 4676.55; p<0.001, <0.001; respectively). The optimal cutoff scores were adjusted modified Rankin Scale 4, 3, 3, 3, 2 for Longshi scale grade 2 to 6 (sensitivity%: 96.12, 70.24, 89.10, 96.80, 86.23, specificity%: 72.72, 98.29, 93.81, 79.82, 92.89, respectively), and Barthel Index 15, 45, 60, 75, 80 for Longshi scale grade 2 to 6 (sensitivity%: 92.54, 89.28, 91.32, 90.30, 95.65, specificity%: 95.48, 89.51, 94.02, 90.41, 90.62, respectively). In conclusion, the classification of Longshi Scale is consistent with those of modified Rankin Scale and Barthel Index. We recommend the Longshi Scale as an effective supplement for modified Rankin Scale and Barthel Index in assessing the outcome in acute stroke patients.

Enteropathogenic Escherichia coli Mediates CoCrMo Particle-Induced Peri-Implant Osteolysis by Increasing Peripheral 5-HT.

The human gut microbiota has been proven to have great effects on the regulation of bone health. However, the association between gut microbiota and particle-induced osteolysis, which is the primary cause of aseptic loosening, is still unknown. In this study, we used a combination of wide-spectrum antibiotics to eliminate the majority of gut microbiota and found that reduction of gut commensal bacteria significantly alleviated the progression of osteolysis, in which anaerobe was the biggest culprit in the exacerbation of osteolysis. Furthermore, colonization of enteropathogenic Escherichia coli (EPEC), a subspecies of anaerobe, could promote the development of particle-induced osteolysis by increasing the secretion of peripheral 5-hydroxytryptamine (5-HT) from the colon. Elevated 5-HT level decreased the phosphorylation of CREB and inhibited the proliferation of osteoblasts. Collectively, these results indicated EPEC colonization suppressed the bone formation and aggravated particle-induced osteolysis in vivo. Thus, clearance of EPEC is expected to become a potential preventive approach to treat debris-induced osteolysis and aseptic loosening.

Hydrogen treatment reduces tendon adhesion and inflammatory response.

A rat model of tendon repair was established to investigate the effects of hydrogen water on tendon adhesion reduction. Thirty-six Sprague Dawley rats were randomly divided into a normal saline (NS) group and a hydrogen water (HS) group according to the processing reagents after a tendon repairing operation. Pre- and postoperative superoxide dismutase (SOD), malondialdehyde (MDA), and glutathione (GSH) in subjects' serum were observed. Skin fibroblasts were grouped into an NS group, H2 O2 group, H2 group, and H2 O2 H2 group. Expressions of Nrf2, CATA, and γ-GCS were also tested by Western blot analysis. 8-OHdG, GSH, MDA, and SOD of the cells were analyzed by the enzyme-linked immunosorbent assay method. The postoperative SOD activity and GSH contents were significantly reduced (P < 0.05), whereas the postoperative MDA level was significantly increased (P < 0.05). Similarly, the postoperative HS group showed significantly higher SOD activity and GSH contents (P < 0.05) but lower MDA (P < 0.05) compared with the postoperative NS group. MDA and 8-OHdG were significantly decreased in hydrogen-rich medium, while SOD and GSH were increased. The expression of Nrf2, CATA, and γ-GCS in antioxidant system were reduced after H2 O2 processing, which were restored after the application of hydrogen-rich medium. Hydrogen water can reduce tendon adhesion after tendon repairing and prohibit excessive inflammatory response, which could be associated with the activation of the Nrf2 pathway.

Probiotics protect mice from CoCrMo particles-induced osteolysis.

Wear particle-induced inflammatory osteolysis is the primary cause of aseptic loosening, which is the most common reason for total hip arthroplasty (THA) failure in the med- and long term. Recent studies have suggested an important role of gut microbiota (GM) in modulating the host metabolism and immune system, leading to alterations in bone mass. Probiotic bacteria administered in adequate amounts can alter the composition of GM and confer health benefits to the host. Given the inflammatory osteolysis that occurs in wear debris-induced prosthesis loosening, we examined whether the probiotic Lactobacillus casei could reduce osteolysis in a mouse calvarial resorption model. In this study, L. casei markedly protected mice from CoCrMo particles (CoPs)-induced osteolysis. Osteoclast gene markers and the number of osteoclasts were significantly decreased in L. casei-treated mice. Probiotic treatment decreased the M1-like macrophage phenotype indicated by downregulation of tumor necrosis factor α (TNF-α), interleukin (IL)-6 and inducible nitric oxide synthase (iNOS) and increased the M2-like macrophage phenotype indicated by upregulation of IL-4, IL-10 and arginase. Collectively, these results indicated that the L. casei treatment modulated the immune status and suppressed wear particle-induced osteolysis in vivo. Thus, probiotic treatment may represent a potential preventive and therapeutic approach to reduced wear debris-induced osteolysis.

Sodium dodecyl benzene sulfonate functionalized graphene for confined electrochemical growth of metal/oxide nanocomposites for sensing application.

The electrochemical fructose sensor attracts considerable attention in the food industry and for clinical applications. Here, a novel fructose biosensor was developed based on immobilization of highly dispersed CuO-Cu nanocomposites on Graphene that was non-covalently functionalized by sodium dodecyl benzene sulfonate (SDBS) (denoted briefly as SDBS/GR/CuO-Cu). The structure and morphology of SDBS/GR/CuO-Cu were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The electrochemistry and electrocatalysis were evaluated by cyclic voltammetry (CV). The fructose sensing performances were evaluated by chronoamperometry (i-t). Those properties were also compared with that of CuO-Cu. Results revealed the distinctly enhanced sensing properties of SDBS/GR/CuOCu towards fructose, showing significantly lowered overpotential of +0.40V, ultrafast (<1s) and ultra-sensitive current response (932 µAm M(-1)cm(-2)) in a wide linear range of 3-1000 µM, with satisfactory reproducibility and stability. Those could be ascribed to the good electrical conductivity, large specific surface area, high dispersing ability and chemical stability of GR upon being functionalized non-covalently by SDBS, as well as the outstanding cation anchoring ability of SDBS on GR to resist aggregation among Cu-based nanoparticles during electro-reduction. More importantly, an improved selectivity in fructose detection was achieved. SDBS/GR/CuO-Cu is one of the promising electrode materials for electrochemical detection of fructose.

Design of templated nanoporous carbon electrode materials with substantial high specific surface area for simultaneous determination of biomolecules.

Nanoporous carbon materials have attracted significant interests in the design of electrodes for electrocatalysis and biosensors. Here, three templated nanoporous carbons (TNCs) materials with substantial different specific surface area were designed and synthesized by a nanocasting method, in which mesoporous silicates and acid were used as template and catalyst, respectively. The TNCs were then used as electrode materials for simultaneous detection of dopamine (DA), ascorbic acid (AA) and uric acid (UA) at physiological pH. The correlations between specific surface area, edge-plane defect sites in TNCs and their distinguishing ability towards AA, DA, and UA were investigated. For TNCs with substantial larger specific surface area and more defect sites, the oxidation peaks of AA, DA and UA were separated well and their oxidation currents increased remarkably. A highly sensitive electrochemical sensor for simultaneous detection of those biomolecules was achieved by designing TNCs1 with the largest specific surface area and the most defect sites as the electrode material. The sensitivity of AA, DA and UA at the sensor is 0.012, 4.031, 0.605 µA/µM respectively. Results suggest that TNCs1 is promising in biomolecules simultaneous detection. This work may also be valuable for scientists who search for excellent carbon materials for biosensing and electrocatalysis.