Background: Achieving the well-being of individuals with multimorbidity is a critical concern in global health. Self-efficacy and self-management ability are recognized as key factors in mitigating the impact of illness, yet how they influence the relationship betweensomatic symptomburden and health-related quality of life(HRQoL) remains to be elucidated. Moreover, previous research on self-management ability has often assumed it to be homogeneous, overlooking potential latent profiles. The present study aimed to characterize the association between somatic symptom burden and HRQoL and the mediating role of self-efficacy and self-management ability. Additionally, to explore the latent profiles of self-management ability. Methods: This multicenter cross-sectional study was conducted in seven communities and four hospitals across three cities in China, involving 1472 multimorbid older adults. Data were collected using self-reported questionnaires that assessed clinical and sociodemographic characteristics, somatic symptom burden, self-efficacy, self-management ability, and HRQoL. The mediating effects of self-efficacy and self-management were estimated using the bootstrap method in IBM SPSS 26.0. Latent Profile Analysis was employed via Mplus 8 to delineate latent subgroups among multimorbidity based on four dimensions from self-management ability. Results: The findings supported the hypothesized model based on Lazarus and Folkman’s stress and coping theory. Mediation analysis indicated that higher self-efficacy and self-management ability were associated with better HRQoL among participants with lower somatic symptom burden. Three latent profiles were identified: ‘poor self-management’, ‘intermediate self-management’, and ‘high self-management’. Conclusions: Our findings partially support the stress and coping theory. Boosting self-efficacy and self-management ability may improve HRQoL. Importantly, self-management ability exhibits clinical heterogeneity, necessitating targeted interventions tailored to the critical needs of different patient subgroups to effectively enhance HRQoL in the context of multimorbidity. Clinical trial number: Not applicable.

Background:Right ventricular pacing (RVP) therapy is the conventional approach for atrioventricular block despite its propensity to cause electrical and mechanical dyssynchrony. This dyssynchrony increases the risk of atrial fibrillation and heart failure, eventually leading to left ventricular dysfunction. Left bundle branch pacing (LBBP) has recently emerged as a novel physiological pacing method. This study utilizes conventional ultrasound cardiography (UCG), two-dimensional speckle tracking imaging (2D-STI), and tissue Doppler imaging (TDI) to investigate the disparities in electrical and mechanical cardiac synchrony between LBBP and RVP patients. Methods:The retrospective analysis includes data from patients who underwent LBBP (n=50) and RVP (n=50) in Zhangjiagang First People's Hospital between January 2019 and June 2020, meeting the stipulated inclusion criteria. The study compares pacing parameters, UCG metrics, cardiac electrical and mechanical synchrony, pacing success rates, and safety events both pre-operation and at 3, 6, 12, and 24 months post-operation. Results:Implantation success rates for both RVP and LBBP groups were 100%, with 100% and 100% pacing success rates, respectively [P = .001 RR (95% CI) : 2.5 (1.5, 3.5)]. The LBBP group exhibited significant advantages over the RVP group throughout the follow-up period. LBBP patients displayed shortened QRS duration, reduced pacing thresholds and impedance, improved sensory function, lower serum NT-proBNP levels, and an increased proportion of NYHA class I patients [P = .003 RR (95% CI) : 1.6 (1.1, 2.3)]. Furthermore, left ventricular ejection fraction increased significantly, while left ventricular diastolic and end-systolic diameters decreased in the LBBP group compared to the RVP group [P = .004 RR (95% CI) : 1.7 (1.3, 2.2)]. The LBBP group also demonstrated shorter ventricular systolic synchrony parameters, including Tls-Dif, PSD, Trs-SD, Tas-SD, Tas-post, Ts-SD, and Ts-DIf, compared to the RVP group [P = .005 RR (95% CI) : 1.5 (1.2, 2.0)]. Notably, no postoperative complications occurred in either group, such as electrode displacement, lead thrombus attachment, incision bleeding, pocket hemorrhage, or infection. However, the readmission rates for heart failure were 16% in the RVP group and 2% in the LBBP group. Conclusion:LBBP achieves physiological cardiac pacing, leading to significant improvements in serum NT-proBNP levels and cardiac function and enhanced ventricular contraction synchrony. Utilizing UCG, 2D-STI, and TDI for quantitative evaluation of cardiac electrical and mechanical synchrony proves to be a valuable clinical approach.

Heart failure is a prevalent and severe cardiovascular disease with high morbidity, disability, and mortality rates, imposing substantial burdens on global healthcare systems. Early and accurate prediction of heart failure is crucial for improving patient outcomes and reducing medical costs. However, clinical diagnosis relies on integrating rich multimodal patient information, including physiological signals, electronic health records, and clinical texts, which are high-dimensional and heterogeneous, limiting the efficiency and accuracy of manual analysis. To address these challenges, we propose a Contrastive and Adversarial Representation Enhancement framework (CARE) for heart failure prediction. The framework jointly optimizes a cross-modal contrastive objective to explicitly align semantically related modalities and constrains distributional discrepancies via adversarial learning, producing modality-invariant and highly complementary embeddings. A cross-modal attention mechanism further captures semantic correspondences and enables end-toend integration of structured electronic health records (EHRs), signal annotation reports, and clinical texts. Experimental results on real-world medical datasets demonstrate that CARE outperforms existing approaches, improving performance with AUROC improved by 0.038 and AUPRC improved by 0.055 compared to baseline methods.

Background: Patients with normal resting left ventricular ejection fraction (LVEF) without either coronary artery disease (CAD) or a hypertensive response can paradoxically develop reduced LVEF during exercise stress echocardiography (ESE). The clinical phenotype and outcomes of these patients is unknown. Hypothesis: Patients who paradoxically develop reduced LVEF during exercise may represent a sub-phenotype of heart failure with preserved LVEF (HFpEF). Aims: To describe the baseline characteristics and clinical outcomes of this unique patient population. Methods: Among all ESE performed between January 2003 and December 2022, patients without a hypertensive response to exercise and without CAD by angiogram within 90 days of ESE, who had a resting LVEF ≥50% with a ≥5% LVEF decrease during ESE were identified. All-cause mortality, HF hospitalization, and atrial fibrillation (AF) outcomes were assessed. Kaplan-Meier and Cox regression methods were used to analyze time-to-event outcomes. Results: Among 213,643 stress echocardiograms performed, 134 patients met eligibility criteria (Table 1). The mean age of the population was 66±10 years, 76% were women, and 16% had AF at baseline. Mean LVEF was 58±4% at rest and 43±4% at peak stress. Stress electrocardiogram met criteria for ischemia in 14%. The 10-year all-cause mortality risk was 12.9% (95% CI 5.5-20.3) with 10 (37%) of 27 deaths due to cancer (Figure 1, Panel A). The 10-year estimated risk of HF hospitalization was 17.6% (95% CI 9.0-26.2) (Figure 1, Panel B). Among 112 patients without AF at baseline, the 10-year risk of developing AF was 23.4% (95% CI 13.4-33.4). Conclusions: Patients with exercise-induced reduced LVEF in the absence of obstructive CAD have a high incidence of HF hospitalizations and AF. Given the preponderance of women, risk for AF, HF hospitalizations, and cancer-related deaths, the possibility that this condition is an early sub-phenotype of HFpEF should be investigated.

Background: CDCP1 has been associated with reverse remodeling in human dilated cardiomyopathy (DCM) mediating its effect by reducing cardiac fibrosis which has been demonstrated in-vitro using human cardiac fibroblasts (CF) and in-vivo histologically in mice. However, the role of and the molecular mechanisms by which CDCP1 attenuates cardiac fibrosis in-vivo is unknown. Methods: To characterize the transcriptomic profiles of CDCP1 in cardiac fibrosis, Cdcp1 KO FVB/NJ mice were generated, and implanted with osmotic minipumps containing angiotensin II and phenylephrine (Ang II/PE) or saline at age of 10 weeks. There were 4 experimental groups (6 mice each), Saline_WT, Saline_KO, AngII/PE_WT, and AngII/PE_KO. After 4 weeks of AngII/PE induction, mice were euthanized, RNA was extracted from their heart tissue followed by RNA-seq to explore transcriptomic profiles. Fibrosis was histologically determined using picosirius (PSR) staining and was quantified by percentage of fibrosis area. Results: Histological analysis demonstrated that Cdcp1 KO attenuated severe cardiac fibrosis by 32.7% determined by quantification of percentage of fibrosis area. When comparing the transcriptome-wide gene expression in WT mice hearts from Saline to AngII/PE induction, a total of 316 differentially expressed genes (DEGs) were identified ( Fig. 1A ). Expression of Nppb (encodes ProBNP), a marker for heart failure, was significantly upregulated. The most significantly upregulated DEG after AngII/PE induction was Crlf1 ( Fig. 1A ), a gene predominantly expressed in CF, consistent with previous findings. We next compared transcriptomic profiles of AngII/PE induction WT and Cdcp1 KO mice hearts, to explore the role of CDCP1 in cardiac fibrosis. A total of 163 DEGs were identified ( Fig. 1B ). Notably, Cdcp1 -mediated DEGs enriched GO Pathways associated with extracellular matrix organization (Biological Process) and collagen-containing extracellular matrix (Cellular Component) ( Figs. 1C, 1D ), pivotal processes in cardiac fibrosis. In addition, Cdcp1 KO resulted in several “top” DEGs (including Rnase1, Ccl7, Ccl12 Il21r , and Il6 ) which have known function in inflammation, suggesting that CDCP1 might attenuate cardiac fibrosis through immune regulation . Conclusion: Our findings underscore the pivotal role of CDCP1 in modulating cardiac fibrosis in-vivo, potentially through immune regulation. Targeting CDCP1 may offer promising therapeutic avenues for mitigating myocardial fibrosis.

The diversity of anxiety emotions and individual differences among different athletes have increased the difficulty of emotion recognition. To address this, a rapid recognition method of athlete's anxiety emotion based on multimodal fusion is proposed. Wireless sensor networks are used to collect facial expression images of athletes, and wavelet transform is applied for denoising the collected images. Image features are extracted using grey-level co-occurrence matrix, and the athlete's facial expression images are normalised. Features related to the athlete's emotions, such as voice characteristics, facial expression features, and physiological indicators, are obtained. These features from different perceptual modalities are fused to achieve rapid recognition of athletes' anxiety emotions. The test results demonstrate that this method not only improves the image denoising effect but also achieves high accuracy and efficiency in emotion recognition, enabling accurate and real-time recognition of athletes' emotions.

Background: Electrocardiography-based artificial intelligence (AI-ECG) validated models that detect cardiac disease are increasingly being applied in clinical practice. The utility of such tools to detect insidious cardiac disease in patients with normal baseline left ventricular ejection fraction (LVEF) who paradoxically develop reduced LVEF during exercise stress echocardiography (ESE), and do not have coronary artery disease (CAD) or hypertension, is unknown. Hypothesis: AI-ECG is useful to diagnose insidious cardiac disease and predict clinical outcomes in patients with exercise-induced cardiomyopathy. Aims: To assess the utility of AI ECG in patients with exercise-induced cardiomyopathy. Methods: Among all ESE performed between January 2003 and December 2022, patients without a hypertensive response to exercise and without CAD (confirmed by coronary angiography within 90 days after ESE), with resting LVEF ≥50% and a paradoxical ≥5% LVEF decrease during ESE were identified. A previously validated AI-ECG algorithm that predicts atrial fibrillation (AF), reduced LVEF, and cardiac amyloidosis (CA) was applied to the baseline ECG closest to the time of ESE. The predicted probability of AF, reduced LVEF, and CA if above the published thresholds, was determined. Results: There were 134 patients with exercise-induced cardiomyopathy who were identified. The mean age of this cohort was 66±10 years, 76% were women and 16% had AF at baseline. Mean LVEF was 58±4% at rest and 43±4% at peak stress. The median follow-up period was 6.8 years (IQR 3.0-12.2). Among patients without a baseline history of AF (n=112), AI-ECG identified 29% with a significant probability of AF, which was associated with a subsequent AF diagnosis at univariable analysis (HR 2.505, 95%CI 1.016-6.177, p=0.046). There were 10 patients with an AI-ECG prediction of reduced LVEF among whom 3 were subsequently hospitalized with HF. AI-ECG was positive for CA in 8 and 3 amongst these had subsequent HF hospitalizations (Table). Conclusions: Baseline AI-ECG may help predict subsequent AF and diagnose preclinical amyloid cardiomyopathy in patients who by resting echocardiography do not seem to have cardiac disease but with exercise develop reduced LVEF.

BACKGROUND His bundle pacing（HBP） and left bundle branch pacing（LBBP） both provide physiologic pacing which maintain left ventricular synchrony. They both improve heart failure（HF） symptoms in atrial fibrillation（AF） patients. We aimed to assess the intra-patient comparison of ventricular function and remodeling as well as leads parameters corresponding to two pacing modalities in AF patients referred for pacing in intermediate term.METHODS Uncontrolled tachycardia AF patients with both leads implantation successfully were randomized to either modality. Echocardiographic measurements, New York Heart Association（NYHA） classification, quality-of-life assessments and leads parameters were obtained at baseline and at each 6-month follow up. Left ventricular function including the left ventricular endosystolic volume（LVESV）, left ventricular ejection fraction（LVEF） and right ventricular（RV） function quantified by tricuspid annular plane systolic excursion（TAPSE） were all assessed.RESULTS Consecutively twenty-eight patients implanted with both HBP and LBBP leads successfully were enrolled（69.1 ± 8.1 years, 53.6% male, LVEF 59.2% ± 13.7%）. The LVESV was improved by both pacing modalities in all patients（n = 23） and the LVEF was improved in patients with baseline LVEF at less than 50%（n = 6）. The TAPSE was improved by HBP but not LBBP（n =23）.CONCLUSION In this crossover comparison between HBP and LBBP, LBBP was found to have an equivalent effect on LV function and remodeling but better and more stable parameters in AF patients with uncontrolled ventricular rates referred for atrioventricular node（AVN） ablation. HBP could be preferred in patients with reduced TAPSE at baseline rather than LBBP.

Transcranial focused ultrasound is a novel technique for the noninvasive treatment of brain diseases. The success of the treatment greatly depends on achieving precise and efficient intraoperative focus. However, compensating for aberrated ultrasound waves caused by the skull through numerical simulation-based phase corrections is a challenging task due to the significant computational burden involved in solving the acoustic wave equation. In this article, we propose a promising strategy using the coupling of the boundary integral equation method (BIEM) and the finite element method (FEM) to overcome the above limitation. Specifically, we adopt the BIEM to obtain the Robin-to-Dirichlet maps on the boundaries of the skull and then couple the maps to the FEM matrices via a dual interpolation technique, resulting in a computational domain including only the skull. Three simulation experiments were conducted to evaluate the effectiveness of the proposed method, including a convergence test and two skull-induced aberration corrections in 2D and 3D ultrasound. The results show that the method's convergence is guaranteed as the element size decreases, leading to a decrease in pressure error. The computation times for simulating a 500 kHz ultrasound field on a regular desktop computer were found to be 0.47 ± 0.01 s in the 2D case and 43.72 ± 1.49 s in the 3D case, provided that lower-upper decomposition (approximately 13 s in 2D and 2.5 h in 3D) was implemented in advance. We also demonstrated that more accurate transcranial focusing can be achieved by phase correction compared to the noncorrected results (with errors of 1.02 mm vs. 6.45 mm in 2D and 0.28 mm vs. 3.07 mm in 3D). The proposed strategy is valuable for enabling online ultrasound simulations during treatment, facilitating real-time adjustments and interventions.

The monitoring of physiological parameters is a crucial topic in promoting human health and an indispensable approach for assessing physiological status and diagnosing diseases. Particularly, it holds significant value for patients who require long-term monitoring or with underlying cardiovascular disease. To this end, Visual Contactless Physiological Monitoring (VCPM) is capable of using videos recorded by a consumer camera to monitor blood volume pulse (BVP) signal, heart rate (HR), respiratory rate (RR), oxygen saturation (SpO 2 ) and blood pressure (BP). Recently, deep learning-based pipelines have attracted numerous scholars and achieved unprecedented development. Although VCPM is still an emerging digital medical technology and presents many challenges and opportunities, it has the potential to revolutionize clinical medicine, digital health, telemedicine as well as other areas. The VCPM technology presents a viable solution that can be integrated into these systems for measuring vital parameters during video consultation, owing to its merits of contactless measurement, cost-effectiveness, user-friendly passive monitoring and the sole requirement of an off-the-shelf camera. In fact, the studies of VCPM technologies have been rocketing recently, particularly AI-based approaches, but few are employed in clinical settings. Here we provide a comprehensive overview of the applications, challenges, and prospects of VCPM from the perspective of clinical settings and AI technologies for the first time. The thorough exploration and analysis of clinical scenarios will provide profound guidance for the research and development of VCPM technologies in clinical settings.

ObjectivePermanent pacemaker implantation (PPI) is a common complication after transcatheter aortic valve replacement (TAVR). Recently, the cusp-overlap projection (COP) technique was thought to be a feasible method to reduce PPI risk. However, the evidence is still relatively scarce. Therefore, this meta-analysis was performed to compare COP and standard three-cusp coplanar (TCC) projection technique.MethodsPubMed and EMBASE databases were systematically searched for relevant literature published from the inception (EMBASE from 1974 and PubMed from 1966) to 16 April 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The primary outcome of interest was post-operative (including in-hospital and 30-day) PPI.ResultsTotal of 3,647 subjects from 11 studies were included in this meta-analysis. Of those, 1,453 underwent self-expanding TAVR using COP and 2,194 using TCC technique. In a pooled analysis, the cumulative PPI incidence was 9.3% [95% confidence interval (CI): 6.9–11.7%] and 18.9% (95% CI: 15.5–22.3%) in the COP group and TCC group, respectively. The application of the COP technique was associated with a significant PPI risk reduction (I2 = 40.3% and heterogeneity Chi-square p = 0.070, random-effects OR: 0.49, 95% CI: 0.36–0.66, p < 0.001). A higher implantation depth was achieved in the COP group compared with the TCC group [standardized mean difference (SMD) = −0.324, 95% CI: (−0.469, −0.180)]. There was no significant difference between the two groups in second valve implantation, prosthesis pop-out, fluoroscopic time, post-operative left bundle branch block, mortality, stroke, moderate/severe paravalvular leakage, mean gradient, and length of hospital stay. However, radiation doses were higher in the COP group [SMD = 0.394, 95% CI: (0.216, 0.572), p < 0.001].ConclusionIn self-expanding TAVR, the application of the cusp overlap projection technique was associated with a lower risk of PPI compared with the standard TCC technique.Systematic review registration[https://inplasy.com/inplasy-2022-4-0092/], identifier [INPLASY202240092].

BACKGROUND:The imaging photoplethysmography (IPPG) technology has been demonstrated to be an effective method for heart rate (HR) monitoring. However, some interference caused by the ambient illumination variation and facial motion severely influences the accuracy of the HR measurement. Some color spaces and color formats are assumed to reduce the interference, and enhance the accuracy of HR estimation. OBJECTIVE:The aim is to identify the optimal color space and format for IPPG based HR measurement. METHODS:Six color spaces and 3 color formats are compared in this study, based on an IPPG based HR measurement system. 424 pieces of videos captured by the system are used for the selection of the optimal color channel and color space; while 10 pieces of videos are for the identification of the optimal color format. RESULTS:The results shows that the green channel of RGB space is the optimal color channel, and RGB is the optimal color space, in respect of the mean squared error of HR estimation. BayerBG 8bit is found to be the optimal color format for video recording, which can significantly reduce the HR estimation error. CONCLUSIONS:BayerBG 8bit color format for video recording, and RGB color space for video analysis is suggested for the IPPG based HR measurement system. The suitable configuration of color space and format could enhance the accuracy of HR measurement.

目的 跌倒是导致老年人意外死亡的第二大因素,通过快捷的实验方法和步态参数来区分老年人中的易跌倒人群,加以精准干预,可以显著降低老年人跌倒发生概率,具有重要的社会意义.方法 招募54名不同跌倒史老年人参与增强的起立行走实验(Enhanced-TUG),使用三维运动捕捉系统收集三维时空数据.计算转身前、转身和转身后的整个过程中的三维步态数据加速度和速度值,并通过离散傅立叶变换将其转换为时域和频域数据,以确定不同行走阶段和不同跌倒史次数人群的步态特征差异.结果 随着跌倒次数的增加,老年人步速逐渐降低;跌倒组老年人转身速度较慢;跌倒组老年人加速度与速度信号的幅值四分位数、均值大于非跌倒组,低频高幅信号段为跌倒组的主要特征.结论 利用增强的起立行走实验结合简单的时频步态参数分析可快速的对老年人步态稳定性进行区分,识别易跌倒老年人群.

The emerging cell membrane (CM)-camouflaged poly(lactide-co-glycolide) (PLGA) nanoparticles (NPs) (CM@PLGA NPs) have witnessed tremendous developments since coming to the limelight. Donning a novel membrane coat on traditional PLGA carriers enables combining the strengths of PLGA with cell-like behavior, including inherently interacting with the surrounding environment. Thereby, the in vivo defects of PLGA (such as drug leakage and poor specific distribution) can be overcome, its therapeutic potential can be amplified, and additional novel functions beyond drug delivery can be conferred. To elucidate the development and promote the clinical transformation of CM@PLGA NPs, the commonly used anucleate and eukaryotic CMs have been described first. Then, CM engineering strategies, such as genetic and nongenetic engineering methods and hybrid membrane technology, have been discussed. The reviewed CM engineering technologies are expected to enrich the functions of CM@PLGA for diverse therapeutic purposes. Third, this article highlights the therapeutic and diagnostic applications and action mechanisms of PLGA biomimetic systems for cancer, cardiovascular diseases, virus infection, and eye diseases. Finally, future expectations and challenges are spotlighted in the concept of translational medicine.

Electro-Larynx can help the laryngectomees re-pronounce the voice, while the Electro-Laryngeal (EL) speech has a poor intelligibility and naturalness. Recently, voice conversion (VC) has been applied to enhance the EL speech, which achieves a good result. However, the complicated tone variation rule of continuous Mandarin EL speech takes a new challenge into enhancement of EL speech by VC. In this paper, a novel framework combining manual tone control (MTC) and statistical VC is proposed to enhance the continuous Mandarin EL speech. As statistical VC methods, GMM-based VC and CLDNN-based VC are implemented for the proposed framework. The objective and subj ective evaluations are designed to validate the proposed framework. The experimental results have demonstrated that 1) the combination of MTC and statistical VC yields significant improvements in both naturalness and intelligibility of the enhanced Mandarin EL speech, 2) the word perception error rates of the enhanced Mandarin EL speech is decreased from 11.35% of Mandarin EL speech with MTC to 5.61 % by using statistical VC, and 3) the proposed framework achieves the average tone accuracy of 26.59% higher than that of original continuous Mandarin EL speech.

More and more hybrid brain-computer interfaces (BCI) supplement traditional single-modality BCI in practical applications. Combinations based on steady-state visual evoked potential (SSVEP) and electromyography (EMG) are the widely used hybrid BCIs. The EMG of jaw clench is commonly used together with SSVEP. This article explored the interference with SSVEP from occipital electrodes by the jaw clench-related EMG so that SSVEP with specific frequency can be identified even during occlusal movements. The experiment was divided into three sets base on the jaw clench patterns (no clenches, chew, and long clench). In each set, the subjects used the same visual stimuli, which were realized by the three flashing targets at different frequencies (6.2Hz, 9.8Hz, and 14.6Hz). After collecting the SSVEP at 4 sites in the occipital region, the SSVEP response spectrum of each stimulus was observed under the three jaw clench patterns. Then, the SSVEP signal was identified by the canonical correlation analysis method for accuracy statistics. Spectrum responses showed that the interference of the jaw clench EMG on SSVEP could be avoided when the stimulation frequency is lower than 20Hz. SSVEP could be identified based on the frequency domain characteristics of these signals. During steady-state visual stimulation with jaw clenches, the recognition rate of SSVEP was still high (no clenches: 100.0%, chew: 94.7%, and long clench: 100.0%). Through reasonable frequency selecting and signal processing, the influence of the jaw clench movement on the SSVEP could be reduced and a high recognition accuracy could be achieved, even the jaw clench actions and the SSVEP stimulation occur simultaneously.

Recent advancements in the field of artificial intelligence have demonstrated success in a variety of clinical tasks secondary to the development and application of big data, supercomputing, sensor networks, brain science, and other technologies. However, no projects can yet be used on a large scale in real clinical practice because of the lack of standardized processes, lack of ethical and legal supervision, and other issues. We analyzed the existing problems in the field of artificial intelligence and herein propose possible solutions. We call for the establishment of a process framework to ensure the safety and orderly development of artificial intelligence in the medical industry. This will facilitate the design and implementation of artificial intelligence products, promote better management via regulatory authorities, and ensure that reliable and safe artificial intelligence products are selected for application.

Diel rhythm in activity of antioxidant enzymes, as well as contents of glutathione and lipid peroxides, has been intensively investigated in Mammalia and Aves, however, the relevant studies about fish are few. In the present study, we examined variation in contents of cortisol, glucose and lactic acid in plasma of black sea bass Centropristis striata under natural photoperiod during a 24-h period. In addition, variation in activity of antioxidant enzymes, such as superoxide dismutase (SOD), glutathione peroxidase (GSH-PX), catalase (CAT) and glutathione reductase (GR) as well as contents of total glutathione (T-GSH), reduced glutathione (GSH), oxidized glutathione (GSSG) and malondialdehyde (MDA) in liver and plasma of the fish were also determined. The plasma and liver samples were collected from the test fish at 3 h intervals during a 24-h cycle, with the first sampling time set at 03:00 h. No significant differences were found in glucose content and activities of GSH-PX and GR in plasma, as well as activities of SOD and GR in liver among different sampling times. In contrast, apparent variation was observed in contents of cortisol, lactic acid and MDA in plasma, activities of SOD and CAT in plasma, contents of MDA, T-GSH, GSH and GSSG in liver and activities of GSH-PX and CAT in liver between different sampling times. Moreover, contents of cortisol and MDA in plasma, SOD activity in plasma, and contents of MDA, GSH and GSSG in liver exhibited circadian rhythm, and their acrophases occurred at 06:08 h, 18:38 h, 15:09 h, 09:57 h, 23:36 h and 07:30 h, respectively. The present study indicates that some physiological parameters relating to stress response, such as cortisol and MDA contents in plasma, MDA, GSH and GSSG contents in liver and SOD activity in plasma changed at different time throughout a day in black sea bass. Therefore, caution should be taken when evaluating stress response in fish with these physiological parameters measured at different times.

Bioelectrical signals can be divided into continuous signals and pulsesignals.Conventional bioelectrical signal signals include incoherent pulse signals,coherent pulse signals, staggered periodic pulse signals, stepping frequency pulsesignals, linear frequency modulation signals, nonlinear frequency modulation signals,phase coded signals, etc. Here we mainly introduce commonly used chirped signals,nonlinear frequency modulation signals, phase coded signals, etc.In order to furtherimprove the processing capacity of bioelectrical signal, the detailed analysis algorithmbased on the bioelectrical signal processing model is used to implement the simulationverification model of the algorithm, such as pulse pressure, MTI, MTD, CFAR, ranging,velocity measurement, and measurement.

Background: Falls are one of the major causes of injury in the elderly. Obesity may be related to the risk of falling. Understanding the dynamic stability mechanisms of obese elderly people during gait is important as it may be associated with fall protection. Research question: Does obesity affect the dynamic walking stability of elderly people? Methods: This is a prospective study. Fifty-three elderly participants, aged 60-82 years, were categorized into body mass index (BMI) groups. In single-limb support experiments, the center of mass velocity (COMv), center of mass acceleration (COMa), region of velocity stability (ROSv) and region of acceleration stability (ROSa) were calculated using kinematic data sampled from a motion analysis system. In addition, all participants were assessed for the dynamic balance ability test scale (DBATS). Statistical analyses were performed by one-way ANOVA, Kruskal-Wallis/Wilcoxon nonparametric tests, or bivariate Pearson/Spearman correlation analysis. Results: During walking, peak COMv and COMa decreased with increasing BMI (Normal BMI: 1.20 +/- 0.14 m/s, 1.66 +/- 0.36 m/s(2); High BMI: 1.14 +/- 0.11 m/s, 1.56 +/- 0.30 m/s(2); Higher BMI: 1.04 +/- 0.15 m/s, 1.47 +/- 0.25 m/s(2)). M toe-off (TO), the normalized participants' center of mass (COM) is significantly more anterior in the Higher BMI group (Normal BMI: -0.30 +/- 0.09, High BMI: -0.23 +/- 0.07, Higher BMI: -0.16 +/- 0.10), their normalized COMv and COMa (Normal BMI: 1.40 +/- 0.16, 0.53 +/- 0.11; High BMI: 1.33 +/- 0.13, 0.49 +/- 0.11; Higher BMI: 1.21 +/- 0.16, 0.46 +/- 0.11) are slower. The mean DBATS score of the Higher BMI group was the highest, indicating the weakest dynamic balance ability. Significance: The COM dynamic stability parameters indicate that obesity may worsen balance, with the peak COMv and ROSv most affected. With increasing BMI, the dynamic stability and balance of elderly people both decreased.