Diagnosis of neurodegenerative diseases with a refined Lempel-Ziv complexity.

The investigation into the distinctive difference of gait is of significance for the clinical diagnosis of neurodegenerative diseases. However, human gait is affected by many factors like behavior, occupation and so on, and they may confuse the gait differences among Parkinson's disease, amyotrophic lateral sclerosis, and Huntington's disease. For the purpose of examining distinctive gait differences of neurodegenerative diseases, this study extracts various features from both vertical ground reaction force and time intervals. Moreover, refined Lempel-Ziv complexity is proposed considering the detailed distribution of signals based on the median and quartiles. Basic features (mean, coefficient of variance, and the asymmetry index), nonlinear dynamic features (Hurst exponent, correlation dimension, largest Lyapunov exponent), and refined Lempel-Ziv complexity of different neurodegenerative diseases are compared statistically by violin plot and Kruskal-Wallis test to reveal distinction and regularities. The comparative analysis results illustrate the gait differences across these neurodegenerative diseases by basic features and nonlinear dynamic features. Classification results by random forest indicate that the refined Lempel-Ziv complexity can robustly enhance the diagnosis accuracy when combined with basic features.

Efficacy of ß2-adrenergic receptor agonist combined with corticosteroid in the treatment of children with cough variant asthma.

BACKGROUND: Cough variant asthma (CVA) is one of the most common respiratory diseases in children, which has a serious impact on the quality of life and daily activities of children. For severe CVA, immunomodulatory drugs are needed. AIM: To evaluate the efficacy of salmeterol combined with budesonide in the treatment of pediatric CVA. METHODS: 130 children with CVA from January 2020 to December 2022 were prospectively selected and randomly divided into an observation group (salmeterol combined with budesonide) and a control group (budesonide combined with a placebo). Compare the clinical efficacy of two groups before and after intervention. The evaluation parameters include cough frequency score, nocturnal cough arousal, and lung function indicators. Serum inflammatory markers, immune function markers and airway anatomical indicators were also measured. RESULTS: After the intervention, the total effective rate of the observation group was significantly higher than that of the control group, and the cough frequency score and the night cough wake rate of the observation group were lower than that of the control group, with a statistically significant difference. In addition, the changes of lung function indicators, serum markers and immune function markers in the observation group were better than those in the control group. CONCLUSION: The clinical efficacy of salmeterol combined with Budesonide in the treatment of CVA is better than that of Budesonide alone.

Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA.

Background: Dilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM. Methods: The hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells. Results: A total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples. Conclusion: The current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.

A novel web-based calculator to predict 30-day all-cause in-hospital mortality for 7,202 elderly patients with heart failure in ICUs: a multicenter retrospective cohort study in the United States.

Background and aims: Heart failure (HF) is a significant cause of in-hospital mortality, especially for the elderly admitted to intensive care units (ICUs). This study aimed to develop a web-based calculator to predict 30-day in-hospital mortality for elderly patients with HF in the ICU and found a relationship between risk factors and the predicted probability of death. Methods and results: Data (N = 4450) from the MIMIC-III/IV database were used for model training and internal testing. Data (N = 2,752) from the eICU-CRD database were used for external validation. The Brier score and area under the curve (AUC) were employed for the assessment of the proposed nomogram. Restrictive cubic splines (RCSs) found the cutoff values of variables. The smooth curve showed the relationship between the variables and the predicted probability of death. A total of 7,202 elderly patients with HF were included in the study, of which 1,212 died. Multivariate logistic regression analysis showed that 30-day mortality of HF patients in ICU was significantly associated with heart rate (HR), 24-h urine output (24h UOP), serum calcium, blood urea nitrogen (BUN), NT-proBNP, SpO2, systolic blood pressure (SBP), and temperature (P < 0.01). The AUC and Brier score of the nomogram were 0.71 (0.67, 0.75) and 0.12 (0.11, 0.15) in the testing set and 0.73 (0.70, 0.75), 0.13 (0.12, 0.15), 0.65 (0.62, 0.68), and 0.13 (0.12, 0.13) in the external validation set, respectively. The RCS plot showed that the cutoff values of variables were HR of 96 bmp, 24h UOP of 1.2 L, serum calcium of 8.7 mg/dL, BUN of 30 mg/dL, NT-pro-BNP of 5121 pg/mL, SpO2 of 93%, SBP of 137 mmHg, and a temperature of 36.4°C. Conclusion: Decreased temperature, decreased SpO2, decreased 24h UOP, increased NT-proBNP, increased serum BUN, increased or decreased SBP, fast HR, and increased or decreased serum calcium increase the predicted probability of death. The web-based nomogram developed in this study showed good performance in predicting 30-day in-hospital mortality for elderly HF patients in the ICU.

The efficacy and neural mechanism of acupuncture therapy in the treatment of visceral hypersensitivity in irritable bowel syndrome.

Irritable Bowel Syndrome (IBS) is a complex functional gastrointestinal disorder primarily characterized by chronic abdominal pain, bloating, and altered bowel habits. Chronic abdominal pain caused by visceral Hypersensitivity (VH) is the main reason why patients with IBS seek medication. Significant research effort has been devoted to the efficacy of acupuncture as a non-drug alternative therapy for visceral-hyperalgesia-induced IBS. Herein, we examined the central and peripheral analgesic mechanisms of acupuncture in IBS treatment. Acupuncture can improve inflammation and relieve pain by reducing 5-hydroxytryptamine and 5-HT3A receptor expression and increasing 5-HT4 receptor expression in peripheral intestinal sensory endings. Moreover, acupuncture can also activate the transient receptor potential vanillin 1 channel, block the activity of intestinal glial cells, and reduce the secretion of local pain-related neurotransmitters, thereby weakening peripheral sensitization. Moreover, by inhibiting the activation of N-methyl-D-aspartate receptor ion channels in the dorsal horn of the spinal cord and anterior cingulate cortex or releasing opioids, acupuncture can block excessive stimulation of abnormal pain signals in the brain and spinal cord. It can also stimulate glial cells (through the P2X7 and prokinetic protein pathways) to block VH pain perception and cognition. Furthermore, acupuncture can regulate the emotional components of IBS by targeting hypothalamic-pituitary-adrenal axis-related hormones and neurotransmitters via relevant brain nuclei, hence improving the IBS-induced VH response. These findings provide a scientific basis for acupuncture as an effective clinical adjuvant therapy for IBS pain.

Local tumor destruction and liver resection increase overall survival in intermediate/advanced hepatocellular carcinoma patients: evidence from a population-based study.

Background: Liver resection (LR) and local tumor destruction (LTD) are effective treatments, but not commonly recommended for patients with intermediate/advanced hepatocellular carcinoma (HCC). This study aimed to explore whether LR/LTD could improve overall survival (OS) of these patients, and to identify the patients who will most likely benefit from LR/LTD. Methods: Data of patients with intermediate/advanced HCC between 2001 and 2018 were extracted from Surveillance, Epidemiology, and End Results database. OS was compared between HCC patients who received LR/LTD and those who did not. A nomogram was constructed for predicting OS, and it was then validated. Results: A total of 535 eligible patients were included, among which 128 received LR/LTD while 407 did not. Significantly higher OS in patients who received LR/LTD was observed (P<0.001). Based on independent prognostic factors obtained from univariate and multivariate analyses, a nomogram was constructed. The C-indices of nomogram were higher than those of the TNM staging system (training cohort: 0.74 vs. 0.59; validation cohort: 0.78 vs. 0.61). Similarly, areas under receiver operating characteristic curves and calibration curves indicated good accuracy of the nomogram. Decision curve analysis curves revealed good clinical practicability of the nomogram. Furthermore, low-risk patients (nomogram score: 0-221.9) had higher OS compared with high-risk patients (nomogram score: higher than 221.9) (P<0.001). Conclusion: LR/LTD significantly improves OS in patients with intermediate/advanced HCC. The nomogram developed in the present study shows high predicating value for OS in patients with intermediate/advanced HCC, which might be useful in selecting patients who are most suitable for LR/LTD.

Wearable multisource quantitative gait analysis of Parkinson's diseases.

As the motor symptoms of Parkinson's disease (PD) are complex and influenced by many factors, it is challenging to quantify gait abnormalities adequately using a single type of signal. Therefore, a wearable multisource gait monitoring system is developed to perform a quantitative analysis of gait abnormalities for improving the effectiveness of the clinical diagnosis. To detect multisource gait data for an accurate evaluation of gait abnormalities, force sensitive sensors, piezoelectric sensors, and inertial measurement units are integrated into the devised device. The modulation circuits and wireless framework are designed to simultaneously collect plantar pressure, dynamic deformation, and postural angle of the foot and then wirelessly transmit these collected data. With the designed system, multisource gait data from PD patients and healthy controls are collected. Multisource features for quantifying gait abnormalities are extracted and evaluated by a significance test of difference and correlation analysis. The results show that the features extracted from every single type of data are able to quantify the health status of the subjects (p < 0.001, ρ > 0.50). More importantly, the validity of multisource gait data is verified. The results demonstrate that the gait feature fusing multisource data achieves a maximum correlation coefficient of 0.831, a maximum Area Under Curve of 0.9206, and a maximum feature-based classification accuracy of 88.3%. The system proposed in this study can be applied to the gait analysis and objective evaluation of PD.

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review.

Objective: Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods: A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results: The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions: This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

Simultaneous Estimation of the Vertical Stiffness in the Knee and Hip for Healthy Human Subjects during Walking.

The stiffness of lower limb joints is a critical characteristic of walking. To investigate the potential of establishing a simple and universal model to describe the characteristics related to vertical vibration during human walking, vertical stiffness is introduced at the knee and hip. A multi-mass-spring model of the human body is established in the vertical direction. In the Fourier form, results of experiments on 14 healthy adults show that the vertical displacements of joints are a function of the leg length and walking cadence, while the ground reaction force is a function of the body weight and walking cadence. The obtained universal equations of vertical displacement and ground reaction force are employed as the input parameters to the proposed multi-mass-spring model. Thus, the vertical stiffness in the knee and hip can then be estimated simultaneously by the subject's weight, leg length, and walking cadence. The variation of vertical stiffness shows different time-varying trends in different gait phases across the entire gait cycle. Finally, the proposed model for vertical stiffness estimation is validated by the vertical oscillation of the pelvis. The average error across three gait cycles for all subjects is 20.48%, with a standard deviation of 5.44%. These results display that the vertical stiffness of knee and hip across the entire gait cycle can be directly estimated by individual parameters that are easy to measure. It provides a different view of human walking analysis and may be applied in future pathological gait recognition, bipedal robots, and lower limb exoskeletons.

A new prediction model for overall survival of elderly patients with solitary bone plasmacytoma: A population-based study.

Background: Comprehensive studies on the prognosis of solitary bone plasmacytoma (SPB) are lacking, especially in elderly patients with SPB. This study aims to establish a novel nomogram and risk stratification system to predict the overall survival (OS) of elderly patients with SPB. Methods: The data of elderly patients with SPB from 2000 to 2017 were identified in the SEER database. SPB patients were randomly assigned to the training set (n = 825) and validation set (n = 354). The Cox regression analysis was used to determine the independent risk factors for OS in elderly SPB patients. The nomogram was established and assessed by the area under the receiver operating curve (AUC), the consistency index (C-index), and the calibration plot. Patients were divided into low-, medium-, and high-risk groups based on the score of the nomogram. The Kaplan-Meier (K-M) curve was used to verify the differences in overall survival among the three groups. Result: A total of 1,179 elderly patients with SPB were included in the study. Age at diagnosis, prior cancer before SPB, marital status, radiotherapy, and chemotherapy were independent risk factors of OS. The AUC of the 3, 5, and 8-year OS in the training and validation sets were between 0.707 and 0.860. The C-index and calibration plot also indicated that the nomogram has great predictive accuracy and robustness. After risk stratification, patients in the high-risk group had the worst OS. Conclusion: A novel nomogram was built to predict the OS of elderly patients with SPB. It will help clinicians formulate more reasonable and personalized treatment strategies.

Gait Synergy Analysis and Modeling on Amputees and Stroke Patients for Lower Limb Assistive Devices.

The concept of synergy has drawn attention and been applied to lower limb assistive devices such as exoskeletons and prostheses for improving human-machine interaction. A better understanding of the influence of gait kinematics on synergies and a better synergy-modeling method are important for device design and improvement. To this end, gait data from healthy, amputee, and stroke subjects were collected. First, continuous relative phase (CRP) was used to quantify their synergies and explore the influence of kinematics. Second, long short-term memory (LSTM) and principal component analysis (PCA) were adopted to model interlimb synergy and intralimb synergy, respectively. The results indicate that the limited hip and knee range of motions (RoMs) in stroke patients and amputees significantly influence their synergies in different ways. In interlimb synergy modeling, LSTM (RMSE: 0.798° (hip) and 1.963° (knee)) has lower errors than PCA (RMSE: 5.050° (hip) and 10.353° (knee)), which is frequently used in the literature. Further, in intralimb synergy modeling, LSTM (RMSE: 3.894°) enables better synergy modeling than PCA (RMSE: 10.312°). In conclusion, stroke patients and amputees perform different compensatory mechanisms to adapt to new interlimb and intralimb synergies different from healthy people. LSTM has better synergy modeling and shows a promise for generating trajectories in line with the wearer's motion for lower limb assistive devices.

A Visualized Dynamic Prediction Model for Overall Survival in Elderly Patients With Pancreatic Cancer for Smart Medical Services.

Background: Pancreatic cancer (PC) is a highly malignant tumor of the digestive system. The number of elderly patients with PC is increasing, and older age is related to a worse prognosis. Accurate prognostication is crucial in treatment decisions made for people diagnosed with PC. However, an accurate predictive model for the prognosis of these patients is still lacking. We aimed to construct nomograms for predicting the overall survival (OS) of elderly patients with PC. Methods: Patients with PC, older than 65 years old from 2010 to 2015 in the Surveillance, Epidemiology, and End Results database, were selected and randomly divided into training cohort (n = 4,586) and validation cohort (n = 1,966). Data of patients in 2016-2018 (n = 1,761) were used for external validation. Univariable and forward stepwise multivariable Cox analysis was used to determine the independent prognostic factors. We used significant variables in the training set to construct nomograms predicting prognosis. The performance of the models was evaluated for their discrimination and calibration power based on the concordance index (C-index), calibration curve, and the decision curve analysis (DCA). Results: Age, insurance, grade, surgery, radiation, chemotherapy, T, N, and American Joint Commission on Cancer were independent predictors for OS and thus were included in our nomogram. In the training cohort and validation cohort, the C-indices of our nomogram were 0.725 (95%CI: 0.715-0.735) and 0.711 (95%CI: 0.695-0.727), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves showed similar results. The calibration curves showed a high consensus between observations and predictions. In the external validation cohort, C-index (0.797, 95%CI: 0.778-0.816) and calibration curves also revealed high consistency between observations and predictions. The nomogram-related DCA curves showed better clinical utility compared to tumor-node-metastasis staging. In addition, we have developed an online prediction tool for OS. Conclusions: A web-based prediction model for OS in elderly patients with PC was constructed and validated, which may be useful for prognostic assessment, treatment strategy selection, and follow-up management of these patients.

Therapeutic effects of acupuncture in rheumatoid arthritis are associated with centromere protein F expression.

OBJECTIVE: The objective of this study is to study the critical role of CENPF in regulation of rheumatoid arthritis with acupuncture treatment. METHODS: PCA was used to analyze the different expression genes between AP treatment group and control group. Volcano plot and random forest model were used to analyze the decreased and increased expression genes. RT-qPCR and IF were used to measure the expression of CENPF in CIA model rat with or without AP treatment. The expression of MCP-1, TNF-α and IL-6 was measured by western blotting. The pathology character and arthritis index were used to analyze the severity of joint injury. RESULTS: PCA data showed that the expression of genes was different between AP treatment group and control group from GEO datasets. Volcano plot and random forest model analysis indicated that CENPF is the most significantly increased expression gene after AP treatment. RT-qPCR and IF assay showed that CENPF is reduced expression in CIA model rat, while CENPF is upregulated expression in CIA model rat with AP treatment. Furthermore, overexpression of CENPF reduced the increasing of MCP-1, TNF-α and IL-6 in CIA model rat. On the contrary, CENPF deficiency induced the expression of MCP-1, TNF-α and IL-6 in CIA model rat. Additionally, the expression of MCP-1, TNF-α and IL-6 in CIA model rat was suppressed, whereas knockdown of CENPF antagonized the decrease of MCP-1, TNF-α and IL-6 in CIA model rat with AP treatment. CONCLUSIONS: CENPF may be a key gene in regulation of the therapeutic effects of acupuncture in rheumatoid arthritis. Rheumatoid arthritis is a globally common autoimmune inflammatory disease found especially in China. Acupuncture (AP), a traditional Chinese medicine (TCM) treatment method, is commonly used for treating rheumatoid arthritis. Many studies have demonstrated that acupuncture alone or in combination with other treatments is beneficial to treat clinical situation of rheumatoid arthritis, thus improving function and quality of life. In this study, we found that centromere protein F (CENPF) is a key gene in rheumatoid arthritis with acupuncture treatment by using differentially expressed genes (DEGs) and random forest model analysis of GSE57983 and GSE77298. Acupuncture helps to up-regulate the expression of CENPF in tissues in rheumatoid arthritis. Functionally, overexpression of CENPF inhibits monocyte chemoattractant protein (MCP)-1, tumor necrosis factor (TNF)-α, and Interleukin (IL)-6 expressions whereas deficiency of CENPF facilitates MCP-1, TNF-α, and IL-6 expressions in a collagen-induced arthritis (CIA) rat model. Furthermore, knocked down CENPF with acupuncture treatment antagonizes the inhibition of MCP-1, TNF-α, and IL-6 expressions in a CIA rat model. CENPF could be a crucial biomarker in regulating function of acupuncture in treating rheumatoid arthritis.

Efficacy of continuous positive airway pressure in neonates with respiratory distress syndrome: A protocol for systematic review and meta-analysis.

BACKGROUND: Respiratory distress syndrome (RDS) is a condition caused by a deficiency in pulmonary surfactant. Many interventions, including pulmonary surfactant, non-invasive respiratory support, and other supportive treatments have been used to prevent RDS. However, recent studies have focused on the continuous positive airway pressure as a significant potential agent for preventing RDS. However, its safety and effectiveness are yet to be assessed. To this end, the current study aims to perform to explore the safety and effectiveness of continuous positive airways in treating neonates with RDS. METHODS: We will conduct comprehensive literature searches on MEDLINE, Cochrane Library, EMBASE, Chinese National Knowledge Infrastructure, and Chinese BioMedical Literature from their inception to April 2021. The search aims to identify all the randomized controlled studies on continuous positive airway pressure in treating neonates with RDS. In addition, we aim to search the gray literature to establish any available potential studies. We will use 2 independent authors to determine study eligibility, extract data using the structured pro-forma table, analyze data, and utilize suitable tools in assessing the risk of bias in the selected studies. Accordingly, we will conduct all statistical analyses using RevMan 5.3 software. RESULTS: The current study aims to provide high-quality synthesis of existing evidence concerning the continuous positive airway pressure to treat neonates suffering from RDS. CONCLUSION: Our findings seek to provide evidence to establish whether continuous positive airway pressure can ascertain safety and effectiveness for neonates with RDS. ETHICS AND DISSEMINATION: The study will require ethical approval. OSF REGISTRATION NUMBER: May 20, 2021.osf.io/7nj8s. (https://osf.io/7nj8s/).

Evaluation of efficiency and safety of combined montelukast sodium and budesonide in children with cough variant asthma: A protocol for systematic review and meta-analysis.

BACKGROUND: Cough variant asthma (CVA) is classified as a distinct form of asthma. As the primary or only symptom, cough is the leading cause for the most prevalent chronic cough among kids. The American College of Clinical Pharmacy, British Thoracic Society, and Chinese guidelines established for diagnosing and treating chronic cough in kids recommend inhaled corticosteroids, combined with leukotriene receptor antagonists when necessary. METHODS: We will conduct a comprehensive search in major databases using keywords to find studies related to the analysis of montelukast sodium and budesonide for treating CVA in kids. Two reviewers will independently assess the quality of the selected research articles and perform data extraction. Next, we will use the RevMan software (version: 5.3) to conduct the statistical analysis of the present study. RESULTS: This study will assess the efficacy and safeness of using montelukast sodium and budesonide to treat kids with CVA by pooling the results of individual studies. CONCLUSION: Our findings will provide vigorous evidence to judge whether montelukast sodium and budesonide therapy is an efficient form of therapy for CVA patients. ETHICS AND DISSEMINATION: Ethics approval is not needed for the present meta-analysis. OSF REGISTRATION NUMBER: May 17, 2021.osf.io/cuvjz (https://osf.io/cuvjz/).

A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database.

Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking. Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort (n = 1093) and validation cohort (n = 461). Patients' data in 2016 (n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan-Meier curves and log-rank tests. Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714-0.764) and 0.756 (95% CI: 0.719-0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778-0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan-Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed. Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients.

Self-Powered Smart Insole for Monitoring Human Gait Signals.

With the rapid development of low-power consumption wireless sensors and wearable electronics, harvesting energy from human motion to enable self-powered sensing is becoming desirable. Herein, a pair of smart insoles integrated with piezoelectric poly(vinylidene fluoride) (PVDF) nanogenerators (NGs) are fabricated to simultaneously harvest energy from human motion and monitor human gait signals. Multi-target magnetron sputtering technology is applied to form the aluminum electrode layers on the surface of the PVDF film and the self-powered insoles are fabricated through advanced 3D seamless flat-bed knitting technology. Output responses of the NGs are measured at different motion speeds and a maximum value of 41 V is obtained, corresponding to an output power of 168.1 µW. By connecting one NG with an external circuit, the influence of external resistance, capacitor, and motion speed on the charging characteristics of the system is systematically investigated. To demonstrate the potential of the smart insoles for monitoring human gait signals, two subjects were asked to walk on a treadmill at different speeds or with a limp. The results show that one can clearly distinguish walking with a limp from regular slow, normal, and fast walking states by using multiscale entropy analysis of the stride intervals.

Development of a multistage compliant mechanism with new boundary constraint.

This paper presents a piezo-actuated compliant mechanism with a new boundary constraint to provide concurrent large workspace and high dynamic frequency for precision positioning or other flexible manipulation applications. A two-stage rhombus-type displacement amplifier with the "sliding-sliding" boundary constraint is presented to maximize the dynamic frequency while retaining a large output displacement. The vibration mode is also improved by the designed boundary constraint. A theoretical kinematic model of the compliant mechanism is established to optimize the geometric parameters, and a prototype is fabricated with a compact dimension of 60 mm × 60 mm × 12 mm. The experimental testing shows that the maximum stroke is approximately 0.6 mm and the output stiffness is 1.1 N/µm with the fundamental frequency of larger than 2.2 kHz. Lastly, the excellent performance of the presented compliant mechanism is compared with several mechanisms in the previous literature. As a conclusion, the presented boundary constraint strategy provides a new way to balance the trade-off between the frequency response and the stroke range widely existed in compliant mechanisms.

Evaluation strategy of regenerative braking energy for supercapacitor vehicle.

In order to improve the efficiency of energy conversion and increase the driving range of electric vehicles, the regenerative energy captured during braking process is stored in the energy storage devices and then will be re-used. Due to the high power density of supercapacitors, they are employed to withstand high current in the short time and essentially capture more regenerative energy. The measuring methods for regenerative energy should be investigated to estimate the energy conversion efficiency and performance of electric vehicles. Based on the analysis of the regenerative braking energy system of a supercapacitor vehicle, an evaluation system for energy recovery in the braking process is established using USB portable data-acquisition devices. Experiments under various braking conditions are carried out. The results verify the higher efficiency of energy regeneration system using supercapacitors and the effectiveness of the proposed measurement method. It is also demonstrated that the maximum regenerative energy conversion efficiency can reach to 88%.