Extracellular vesicles-based point-of-care testing for the diagnosis and monitoring of Alzheimer's disease.

Alzheimer's disease (AD) is a debilitating condition that affects millions of people worldwide. One promising strategy for detecting and monitoring AD early on is using extracellular vesicles (EVs)-based point-of-care testing; however, diagnosing AD using EVs poses a challenge due to the low abundance of EV-biomarkers. Here, we present a fully integrated organic electrochemical transistor (OECT) that enables high accuracy, speed, and convenience in the detection of EVs from AD patients. We incorporated self-aligned acoustoelectric enhancement of EVs on a chip that rapidly propels, enriches, and specifically binds EVs to the OECT detection area. With our enhancement of pre-concentration, we increased the sensitivity to a limit of detection of 500 EV particles/µL and reduced the required detection time to just two minutes. We also tested the sensor on an AD mouse model to monitor AD progression, examined mouse Aß EVs at different time courses, and compared them with intraneuronal Aß cumulation using MRI. This innovative technology has the potential to diagnose Alzheimer's and other neurodegenerative diseases accurately and quickly, enabling monitoring of disease progression and treatment response.

Human Eye Activity Monitoring Using Continuous Wave Doppler Radar: A Feasibility Study.

Human eye activity has been widely studied in many fields such as psychology, neuroscience, medicine, and human-computer interaction engineering. In previous studies, monitoring of human eye activity mainly depends on electrooculogram (EOG) that requires a contact sensor. This article proposes a novel eye movement monitoring method called continuous wave doppler oculogram (cDOG). Unlike the conventional EOG-based eye movement monitoring methods, cDOG based on continuous wave doppler radar sensor (cDRS) can remotely measure human eye activity without placing electrodes on the head. To verify the feasibility of using cDOG for eye movement monitoring, we first theoretically analyzed the association between the radar signal and the corresponding eye movements measured with EOG. Afterward, we conducted an experiment to compare EOG and cDOG measurements under the conditions of eyes closure and opening. In addition, different eye movement states were considered, including right-left saccade, up-down saccade, eye-blink, and fixation. Several representative time domain and frequency domain features obtained from cDOG and from EOG were compared in these states, allowing us to demonstrate the feasibility of using cDOG for monitoring eye movements. The experimental results show that there is a correlation between cDOG and EOG in the time and frequency domain features, the average time error of single eye movement is less than 280.5 ms, and the accuracy of cDOG in eye movement detection is higher than 92.35%, when the distance between the cDRS and the face is 10 cm and eyes is facing the radar directly.

Evaluation of skin sympathetic nervous activity for classification of intracerebral hemorrhage and outcome prediction.

Classification and outcome prediction of intracerebral hemorrhage (ICH) is critical for improving the survival rate of patients. Early or delayed neurological deterioration is common in ICH patients, which may lead to changes in the autonomic nervous system (ANS). Therefore, we proposed a new framework for ICH classification and outcome prediction based on skin sympathetic nervous activity (SKNA) signals. A customized measurement device presented in our previous papers was used to collect data. 117 subjects (50 healthy control subjects and 67 ICH patients) were recruited for this study to obtain their 5-min electrocardiogram (ECG) and SKNA signals. We extracted the signal's time-domain, frequency-domain, and nonlinear features and analyzed their differences between healthy control subjects and ICH patients. Subsequently, we established the ICH classification and outcome evaluation model based on the eXtreme Gradient Boosting (XGBoost). In addition, heart rate variability (HRV) as an ANS assessment method was also included as a comparison method in this study. The results showed significant differences in most features of the SKNA signal between healthy control subjects and ICH patients. The ICH patients with good outcomes have a higher change rate and complexity of SKNA signal than those with bad outcomes. In addition, the accuracy of the model for ICH classification and outcome prediction based on the SKNA signal was more than 91% and 83%, respectively. The ICH classification and outcome prediction based on the SKNA signal proved to be a feasible method in this study. Furthermore, the features of change rate and complexity, such as entropy measures, can be used to characterize the difference in SKNA signals of different groups. The method can potentially provide a new tool for rapid classification and outcome prediction of ICH patients. Index Terms-intracerebral hemorrhage (ICH), skin sympathetic nervous activity (SKNA), classification, outcome prediction, cardiovascular and cerebrovascular diseases.

Skin sympathetic nerve activity as a biomarker for outcomes in spontaneous intracerebral hemorrhage.

OBJECTIVE: A rapid and accurate forecast for the early prognosis of ICH patients is challenging. This study investigated whether heart rate variability (HRV) and skin sympathetic nerve activity (SKNA) could prognosticate poor neurological outcomes in ICH patients. METHODS: Between November 2020 and November 2021, we studied 92 spontaneous ICH patients in the First Affiliated Hospital of Nanjing Medical University. Glasgow Outcome Scale (GOS) score at 2 weeks after the ICH was used to categorize patients into good and poor outcome groups. The modified Rankin Scale (mRS) assessed patients' ability to live independently for 1 year. We utilized a portable high-frequency electrocardiogram (ECG) recording system to record the HRV and SKNA information in ICH patients and control participants. RESULTS: 77 patients were eligible for the prediction of neurological outcome and were allocated into the good (n = 22) or poor (n = 55) outcome groups based on the GOS grade. In univariate logistic regression analysis, significant variables that could differentiate the outcomes were age, hypertension, tracheal intubation, Glasgow Coma Scale (GCS) score, existing intraventricular hemorrhage, white blood cells, neutrophil, lnVLF, lnTP, and aSKNA. Variables in the best fit multivariable logistic regression model were age, hypertension, GCS score, neutrophils, and aSKNA. The GCS score was the only independent risk factor for poor outcomes. At 30 days and 1 year of follow-up, patients with lower aSKNA had poor outcomes. INTERPRETATION: ICH patients had reduced aSKNA, which could be a prognostic indicator. A lower aSKNA suggested a worse prognosis. The present data indicate that ECG signals could be helpful for prognosticating ICH patients.

Evaluation of autonomic nervous system activity in intradialytic hypotension using entropy and skin sympathetic nerve activity.

Introduction: The function of the autonomic nervous system (ANS) is crucial in the development of intradialytic hypotension (IDH). This study introduced the entropy of heart rate variability (HRV) and skin sympathetic nerve activity (SKNA) to provide a complementary nonlinear and dynamic perspective for evaluating ANS function concerning IDH. Methods: 93 patients undergoing hemodialysis (HD) were enrolled, and the baseline data, electrocardiogram (ECG), and SKNA were collected. The patients were separated into the IDH and nonIDH groups based on the thresholds, which were characterized as reductions in systolic blood pressure (SBP) of at least 20 mm Hg or mean arterial pressure (MAP) of at least 10 mm Hg. We developed a logistic regression model for IDH after analyzing the changes in the time domain, frequency domain, the entropy of HRV, and SKNA indices during HD. Results: After 4-h HD, the detected results for heart rate, the ratio of low frequency and high frequency (LF/HF), and average SKNA (aSKNA) all increased in both groups. Nine out of the ten HRV indices and aSKNA in the nonIDH group were higher than those in the IDH group at most moments. aSKNA was positively correlated with heart rate (p = 0.0001) and LF/HF (p = 0.0005) in the nonIDH group, while the correlation disappeared in the IDH group, which indicated a worse ANS response in IDH patients. The logistic regression model exhibited the results of initial SBP [odds ratio (OR) 1.076; p = 0.001], and the difference between the last and first segments (DLF) of heart rate [OR 1.101; p =0.012] and LF/HF [OR 0.209; p =0.034], as well as the extreme value of the difference between other segments and the first segments (EOF) of aSKNA [OR 2.908; p =0.017], which were independent indicators for IDH. Discussion: The new nonlinear and dynamic assessment perspectives provided by the entropy of HRV and SKNA help to distinguish differences in ANS patterns between IDH patients and nonIDH patients and have the potential to be used in clinical monitoring for HD patients.

Function and inhibition of Haspin kinase: targeting multiple cancer therapies by antimitosis.

OBJECTIVES: Haploid germ cell-specific nuclear protein kinase (Haspin) is a serine/threonine kinase as an atypical kinase, which is structurally distinct from conventional protein kinases. KEY FINDINGS: Functionally, Haspin is involved in important cell cycle progression, particularly in critical mitosis regulating centromeric sister chromatid cohesion during prophase and prometaphase, and subsequently ensuring proper chromosome alignment during metaphase and the normal chromosome segregation during anaphase. However, increasing evidence has demonstrated that Haspin is significantly upregulated in a variety of cancer cells in addition to normal proliferating somatic cells. Its knockdown or small molecule inhibition could prevent cancer cell growth and induce apoptosis by disrupting the regular mitotic progression. Given the specificity of its expressed tissues or cells and the uniqueness of its current known substrate, Haspin can be a promising target against cancer. Consequently, selective synthetic and natural inhibitors of Haspin have been widely developed to determine their inhibitory power for various cancer cells in vivo and in vitro. SUMMARY: Here our perspective includes a comprehensive review of the roles and structure of Haspin, its relatively potent and selective inhibitors and Haspin's preliminary studies in a variety of cancers.

Autonomic nervous activity analysis based on visibility graph complex networks and skin sympathetic nerve activity.

Background: Autonomic nerve system (ANS) plays an important role in regulating cardiovascular function and cerebrovascular function. Traditional heart rate variation (HRV) and emerging skin sympathetic nerve activity (SKNA) analyses from ultra-short-time (UST) data cannot fully reveal neural activity, thereby quantitatively reflect ANS intensity. Methods: Electrocardiogram and SKNA from sixteen patients (seven cerebral hemorrhage (CH) patients and nine control group (CO) patients) were recorded using a portable device. Ten derived HRV (mean, standard deviation and root mean square difference of sinus RR intervals (NNmean, SDNN and RMSSD), ultra-low frequency (<0.003 Hz, uLF), very low frequency ([0.003 Hz, 0.04 Hz), vLF), low frequency ([0.04 Hz, 0.15 Hz), LF) and high frequency power ([0.15 Hz, 0.4 Hz), HF), ratio of LF to HF (LF/HF), the standard deviation of instantaneous beat-to-beat R-R interval variability (SD1), and approximate entropy (ApEn)) and ten visibility graph (VG) features (diameter (Dia), average node degree (aND), average shortest-path length (aSPL), clustering coefficient (CC), average closeness centrality (aCC), transitivity (Trans), average degree centrality (aDC), link density (LD), sMetric (sM) and graph energy (GE) of the constructed complex network) were compared on 5-min and UST segments to verify their validity and robustness in discriminating CH and CO under different data lengths. Besides, their potential for quantifying ANS-Load were also investigated. Results: The validation results of HRV and VG features in discriminating CH from CO showed that VG features were more clearly distinguishable between the two groups than HRV features. For effectiveness evaluation of analyzing ANS on UST segment, the NNmean, SDNN, RMSSD, LF, HF and LF/HF in HRV features and the CC, Trans, Dia and GE of VG features remained stable in both activated and inactivated segments across all data lengths. The capability of HRV and VG features in quantifying ANS-Load were evaluated and compared under different ANS-Load, the results showed that most HRV features (SDNN, LFHF, RMSSD, vLF, LF and HF) and almost all VG features were correlated to sympathetic nerve activity intensity. Conclusions: The proposed autonomic nervous activity analysis method based on VG and SKNA offers a new insight into ANS assessment in UST segments and ANS-Load quantification.

Efficacy, safety and immunogenicity of hexavalent rotavirus vaccine in Chinese infants.

A randomized, double-blind, placebo-controlled multicenter trial was conducted in healthy Chinese infants to assess the efficacy and safety of a hexavalent live human-bovine reassortant rotavirus vaccine (HRV) against rotavirus gastroenteritis (RVGE). A total of 6400 participants aged 6-12 weeks were enrolled and randomly assigned to either HRV (n â€‹= â€‹3200) or placebo (n â€‹= â€‹3200) group. All the subjects received three oral doses of vaccine four weeks apart. The vaccine efficacy (VE) against RVGE caused by rotavirus serotypes contained in HRV was evaluated from 14 days after three doses of administration up until the end of the second rotavirus season. VE against severe RVGE, VE against RVGE hospitalization caused by serotypes contained in HRV, and VE against RVGE, severe RVGE, and RVGE hospitalization caused by natural infection of any serotype of rotavirus were also investigated. All adverse events (AEs) were collected for 30 days after each dose. Serious AEs (SAEs) and intussusception cases were collected during the entire study. Our data showed that VE against RVGE caused by serotypes contained in HRV was 69.21% (95%CI: 53.31-79.69). VE against severe RVGE and RVGE hospitalization caused by serotypes contained in HRV were 91.36% (95%CI: 78.45-96.53) and 89.21% (95%CI: 64.51-96.72) respectively. VE against RVGE, severe RVGE, and RVGE hospitalization caused by natural infection of any serotype of rotavirus were 62.88% (95%CI: 49.11-72.92), 85.51% (95%CI: 72.74-92.30) and 83.68% (95%CI: 61.34-93.11). Incidences of AEs from the first dose to one month post the third dose in HRV and placebo groups were comparable. There was no significant difference in incidences of SAEs in HRV and placebo groups. This study shows that this hexavalent reassortant rotavirus vaccine is an effective, well-tolerated, and safe vaccine for Chinese infants.

Wearable Fetal ECG Monitoring System from Abdominal Electrocardiography Recording.

Fetal electrocardiography (ECG) monitoring during pregnancy can provide crucial information for assessing the fetus's health status and making timely decisions. This paper proposes a portable ECG monitoring system to record the abdominal ECG (AECG) of the pregnant woman, comprising both maternal ECG (MECG) and fetal ECG (FECG), which could be applied to fetal heart rate (FHR) monitoring at the home setting. The ECG monitoring system is based on data acquisition circuits, data transmission module, and signal analysis platform, which consists of low input-referred noise, high input impedance, and high resolution. The combination of the adaptive dual threshold (ADT) and the independent component analysis (ICA) algorithm is employed to extract the FECG from the AECG signals. To validate the performance of the proposed system, AECG is recorded and analyzed of pregnant women in three different postures (supine, seated, and standing). The result shows that the proposed system can record the AECG in different postures with good signal quality and high accuracy in fetal ECG and heart rate information. Sensitivity (Se), positive predictive accuracy (PPV), accuracy (ACC), and their harmonic mean (F1) are utilized as the metrics to evaluate the performance of the fetal QRS (fQRS) complexes extraction. The average Se, PPV, ACC, and F1 score are 99.62%, 97.90%, 97.40%, and 98.66% for the fQRS complexes extraction,, respectively. This paper shows the proposed system has a promising application in fetal health monitoring.

Dynamics of Cardiac Autonomic Responses During Hemodialysis Measured by Heart Rate Variability and Skin Sympathetic Nerve Activity: The Impact of Interdialytic Weight Gain.

Background: Autonomic nervous regulation plays a critical role in end-stage kidney disease (ESKD) patients with cardiovascular complications. However, studies on autonomic regulation in ESKD patients are limited to heart rate variability (HRV) analysis. Skin sympathetic nerve activity (SKNA), which noninvasively reflects the sympathetic nerve activity, has not been used in ESKD patients. Methods: Seventy-six patients on maintenance hemodialysis (MHD) treatment (a 4-h HD session, three times a week) were enrolled. Utilizing a noninvasive, single-lead, high-frequency recording system, we analyzed the dynamic change in HRV parameters and SKNA during HD. The different characteristics between the subgroups divided based on interdialytic weight gain (IDWG, <3 kg or ≥3 kg) were also demonstrated. Results: After the HD, values for heart rate (75.1 ± 11.3 to 80.3 ± 12.3 bpm, p < 0.001) and LF/HF (1.92 ± 1.67 to 2.18 ± 2.17, p = 0.013) were significantly higher than baseline. In subgroup analysis, average voltage of skin sympathetic nerve activity (aSKNA) in IDWG ≥3 kg group was lower than the IDWG <3 kg group at the end of MHD (1.06 ± 0.30 vs 1.32 ± 0.61 µV, p = 0.046). Moreover, there was a linear correlation between mean heart rate (HR) and aSKNA in low IDWG patients (p < 0.001), which was not found in high IDWG patients. At the 1-year follow-up, high IDWG patients had a higher incidence of cardiovascular hospitalization (p = 0.046). Conclusions: In MHD patients, a gradual activation of sympathetic nerve activity could be measured by HRV and aSKNA. A lower aSKNA at the end of HD and a loss of HR-aSKNA correlation in overhydrated patients were observed. Extensive volume control is promising to improve the autonomic nervous function and clinical outcomes in this population.

An Artifact-Resistant Feature SKNAER for Quantifying the Burst of Skin Sympathetic Nerve Activity Signal.

Evaluation of sympathetic nerve activity (SNA) using skin sympathetic nerve activity (SKNA) signal has attracted interest in recent studies. However, signal noises may obstruct the accurate location for the burst of SKNA, leading to the quantification error of the signal. In this study, we use the Teager−Kaiser energy (TKE) operator to preprocess the SKNA signal, and then candidates of burst areas were segmented by an envelope-based method. Since the burst of SKNA can also be discriminated by the high-frequency component in QRS complexes of electrocardiogram (ECG), a strategy was designed to reject their influence. Finally, a feature of the SKNA energy ratio (SKNAER) was proposed for quantifying the SKNA. The method was verified by both sympathetic nerve stimulation and hemodialysis experiments compared with traditional heart rate variability (HRV) and a recently developed integral skin sympathetic nerve activity (iSKNA) method. The results showed that SKNAER correlated well with HRV features (r = 0.60 with the standard deviation of NN intervals, 0.67 with low frequency/high frequency, 0.47 with very low frequency) and the average of iSKNA (r = 0.67). SKNAER improved the detection accuracy for the burst of SKNA, with 98.2% for detection rate and 91.9% for precision, inducing increases of 3.7% and 29.1% compared with iSKNA (detection rate: 94.5% (p < 0.01), precision: 62.8% (p < 0.001)). The results from the hemodialysis experiment showed that SKNAER had more significant differences than aSKNA in the long-term SNA evaluation (p < 0.001 vs. p = 0.07 in the fourth period, p < 0.01 vs. p = 0.11 in the sixth period). The newly developed feature may play an important role in continuously monitoring SNA and keeping potential for further clinical tests.

Robust PVC Identification by Fusing Expert System and Deep Learning.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

Potent Anti-SARS-CoV-2 Efficacy of COVID-19 Hyperimmune Globulin from Vaccine-Immunized Plasma.

Coronavirus disease 2019 (COVID-19) remains a global public health threat. Hence, more effective and specific antivirals are urgently needed. Here, COVID-19 hyperimmune globulin (COVID-HIG), a passive immunotherapy, is prepared from the plasma of healthy donors vaccinated with BBIBP-CorV (Sinopharm COVID-19 vaccine). COVID-HIG shows high-affinity binding to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike (S) protein, the receptor-binding domain (RBD), the N-terminal domain of the S protein, and the nucleocapsid protein; and blocks RBD binding to human angiotensin-converting enzyme 2 (hACE2). Pseudotyped and authentic virus-based assays show that COVID-HIG displays broad-spectrum neutralization effects on a wide variety of SARS-CoV-2 variants, including D614G, Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Kappa (B.1.617.1), Delta (B.1.617.2), and Omicron (B.1.1.529) in vitro. However, a significant reduction in the neutralization titer is detected against Beta, Delta, and Omicron variants. Additionally, assessments of the prophylactic and treatment efficacy of COVID-HIG in an Adv5-hACE2-transduced IFNAR-/- mouse model of SARS-CoV-2 infection show significantly reduced weight loss, lung viral loads, and lung pathological injury. Moreover, COVID-HIG exhibits neutralization potency similar to that of anti-SARS-CoV-2 hyperimmune globulin from pooled convalescent plasma. Overall, the results demonstrate the potential of COVID-HIG against SARS-CoV-2 infection and provide reference for subsequent clinical trials.

An atrial fibrillation detection system based on machine learning algorithm with mix-domain features and hardware acceleration.

This paper presents a real-time electrocardiogram (ECG) analysis system that can detect atrial fibrillation (AF) using machine learning algorithms without a cloud server. The system takes advantage of the heterogeneous structure of the Zynq system-on-chip (SoC) to optimize the tasks of local implementation of AF detection. The features extraction is based on multi-domain features including entropy features and RR interval features, which is conducted using the embedded micro controller to generate significant features for AF detection. An AF classifier based on artificial neural network (ANN) algorithm is then implemented in the programmable logic of the SoC for acceleration. The validation of the proposed system is performed by using the real-world ECG data from MIT-BIH database and CPSC 2018 database. The experimental results show an accuracy 93.60% and 97.78% when tested on these two databases respectively. The AF detection performance of the embedded algorithm is majorly identical to that of the PC-based algorithm, indicating a robust performance of hardware implementation of the AF detection.

A novel single-lead handheld atrial fibrillation detection system.

Objective. The single-lead handheld atrial fibrillation (AF) detection device is suitable for daily monitoring or early screening of AF in the hospital. However, the signal quality and the reliability of AF detection algorithm still need to be improved. This study proposed a novel AF detection system with a user-friendly interaction and a lightweight and accurate AF detection algorithm.Approach. The system consisted of a single-lead handheld electrocardiogram device with a novel appearance like a gaming handle and a smartphone terminal embedded with AF detection. After feature optimization, the rule-based multi-feature AF detection algorithm had relatively good AF detection ability. Three types of experiments were designed to test the performance of the system. (1) Test the accuracy and time/memory cost of the AF detection algorithm. (2) Compare the proposed device with the standard device Shimmer. (3) Use the simulator to test the effectiveness of the system.Main results.The percentage of differences of successive RR intervals larger than 50 ms (PNN50), minimum value of RR intervals (minRR), and coefficient of sample entropy (COSEn) were features chosen for AF detection. (1) The sensitivity, specificity, and accuracy were 96.00%, 99.75%, 97.88% on the MIT-BIH AF database, and 98.50%, 94.50%, 96.50% on the clinical database we founded. The time/memory cost of the proposed algorithm was much smaller than that of support vector machine. (2) The mean correlation coefficient of RR was 0.9950, indicating a high degree of consistency. (3) This system showed the effectiveness of AF detection.Significance. The proposed single-lead handheld AF detection system is demonstrated to be accurate, lightweight, consistent with the standard device, and efficient for AF detection.