ACNN-BiLSTM: A Deep Learning Approach for Continuous Noninvasive Blood Pressure Measurement Using Multi-Wavelength PPG Fusion.

As an essential physiological indicator within the human body, noninvasive continuous blood pressure (BP) measurement is critical in the prevention and treatment of cardiovascular disease. However, traditional methods of blood pressure prediction using a single-wavelength Photoplethysmographic (PPG) have bottlenecks in further improving BP prediction accuracy, which limits their development in clinical application and dissemination. To this end, this study proposed a method to fuse a four-wavelength PPG and a BP prediction model based on the attention mechanism of a convolutional neural network and bidirectional long- and short-term memory (ACNN-BiLSTM). The effectiveness of a multi-wavelength PPG fusion method for blood pressure prediction was evaluated by processing PPG signals from 162 volunteers. The study compared the performance of the PPG signals with different individual wavelengths and using a multi-wavelength PPG fusion method in blood pressure prediction, assessed using mean absolute error (MAE), root mean squared error (RMSE) and AAMI-related criteria. The experimental results showed that the ACNN-BiLSTM model achieved a better MAE ± RMSE for a systolic BP and diastolic BP of 1.67 ± 5.28 and 1.15 ± 2.53 mmHg, respectively, when using the multi-wavelength PPG fusion method. As a result, the ACNN-BiLSTM blood pressure model based on multi-wavelength PPG fusion could be considered a promising method for noninvasive continuous BP measurement.

Passive leg raising modulates low-frequency oscillation propagation in peripheral atherosclerosis: A pilot study.

OBJECTIVE: Low-frequency oscillations (LFOs) observed in the periphery may reflect physiological processes. The aim of this study was to investigate these processes' effects on LFOs and the differences between healthy subjects and those with peripheral arteriosclerosis disease (PAD). METHODS: 14 PAD patients and 25 healthy controls were studied in resting (RS) and passive leg raising (PLR) states. We simultaneously measured LFOs at the peripheral left earlobes (LE), right earlobes (RE), left fingertips (LF), right fingertips (RF), left toes (LT), and right toes (RT), along with coherence and phase shift analysis processing. RESULTS: The coherence coefficients in the PAD group were lower than those in the healthy group (p < .01), and the phase shifts in the PAD group were higher than those in the healthy group (p < .01) in a resting state. Mild to moderate PAD patients had greater coherence coefficients and smaller phase shifts than severe PAD patients. 0.05 Hz PLR LFOs originating in the LT can be observed in other peripheral positions. The proportion of occurrence times for 0.05 Hz PLR LFOs peaks observed at different peripheral positions was different in healthy subjects, patients with bilateral multiple lower limb arteriosclerosis, and those with left or right lower limb arteriosclerosis. CONCLUSION: The coherence coefficient and phase shift characteristics of LFOs were different between healthy subjects and PAD patients. LFOs have the potential to provide valuable physiological process information associated with atherosclerosis in the periphery.

Enriched environment ameliorates fear memory impairments induced by sleep deprivation via inhibiting PIEZO1/calpain/autophagy signaling pathway in the basal forebrain.

AIMS: To verify the hypothesis that an enriched environment (EE) alleviates sleep deprivation-induced fear memory impairment by modulating the basal forebrain (BF) PIEZO1/calpain/autophagy pathway. METHODS: Eight-week-old male mice were housed in a closed, isolated environment (CE) or an EE, before 6-h total sleep deprivation. Changes in fear memory after sleep deprivation were observed using an inhibitory avoidance test. Alterations in BF PIEZO1/calpain/autophagy signaling were detected. The PIEZO1 agonist Yoda1 or inhibitor GsMTx4, the calpain inhibitor PD151746, and the autophagy inducer rapamycin or inhibitor 3-MA were injected into the bilateral BF to investigate the pathways involved in the memory-maintaining role of EE in sleep-deprived mice. RESULTS: Mice housed in EE performed better than CE mice in short- and long-term fear memory tests after sleep deprivation. Sleep deprivation resulted in increased PIEZO1 expression, full-length tropomyosin receptor kinase B (TrkB-FL) degradation, and autophagy, as reflected by increased LC3 II/I ratio, enhanced p62 degradation, increased TFEB expression and nuclear translocation, and decreased TFEB phosphorylation. These molecular changes were partially reversed by EE treatment. Microinjection of Yoda1 or rapamycin into the bilateral basal forebrain induced excessive autophagy and eliminated the cognition-protective effects of EE. Bilateral basal forebrain microinjection of GsMTx4, PD151746, or 3-MA mimicked the cognitive protective and autophagy inhibitory effects of EE in sleep-deprived mice. CONCLUSIONS: EE combats sleep deprivation-induced fear memory impairments by inhibiting the BF PIEZO1/calpain/autophagy pathway.

Differences in symmetrical low-frequency oscillations among healthy subjects, and those with stroke or peripheral arterial disease.

Low-frequency oscillations (LFOs) observed in near-infrared spectroscopy (NIRS) reflect autonomic physiological processes, and may serve as useful indicators for detecting and monitoring circulatory dysfunction. The aim of this study was to reveal whether LFOs can be used as vascular perfusion biomarkers to differentiate different types and degrees of vascular lesions based on clinical patient data. Materials and Methods: In this study, healthy controls, ischemic stroke patients and peripheral atherosclerosis patients completed a resting-state LFO detection experiment. LFOs were collected simultaneously at peripheral right and left earlobes, fingertips and toes, along with coherence and phase shift analyses processing. Results: The results showed that the coherence coefficients of symmetric peripheral positions and the absolute value-phase shifts of fingers and toes can be used to distinguish healthy individuals, ischemic stroke patients and peripheral atherosclerosis patients. The symmetric earlobes' absolute value-phase shifts could be used to differentiate mild and severe ischemic stroke patients; the coherence coefficients and absolute value-phase shifts of the symmetric toes could be used to differentiate mild and severe peripheral arteriosclerosis patients. The accuracy of differentiating between types of patients was 70%; those with different degrees of peripheral atherosclerosis was 85%, and those with different degrees of ischemic stroke was 72%. Conclusions: LFOs can serve as vascular perfusion biomarkers to differentiate types and degrees of vascular lesions. Therefore, LFOs have the potential to provide valuable patient information to assist researchers and clinicians in identifying specific peripheral circulatory damage subgroups.

Granulomatous myopathy co-existent immune-mediated necrotizing myopathy: A case report.

Granulomatous myopathy (GM) is a rare disease characterized by non-caseating inflammation of the skeletal muscle, with sarcoidosis as a common cause. Here, we report a case of GM co-existent immune-mediated necrotizing myopathy (IMNM) in which an anti-signal recognition particle (SRP) antibody was positive and a muscle biopsy showed a non-caseating granulomatous structure, along with myofiber necrosis and inflammatory cell infiltration.

Effect of prophylactic antiviral intervention on T cell immunity in hepatitis B virus-infected pregnant women.

BACKGROUND: Antiviral intervention in hepatitis B virus (HBV)-infected pregnant women can effectively reduce mother-to-child transmission. However, the immunological characteristics of pregnant women with chronic HBV infection and the effects of antiviral intervention during pregnancy on maternal immune response remain unknown. We aimed to investigate these effects by comparing mothers who received antiviral intervention during pregnancy with those who did not. METHODS: Pregnant women positive for hepatitis B surface antigen and hepatitis B e-antigen (HBsAg+ HBeAg+) were enrolled at delivery, including 34 received prophylactic antiviral intervention during pregnancy (AVI mothers) and 15 did not (NAVI mothers). T lymphocyte phenotypes and functions were analysed using flow cytometry. RESULTS: At delivery, maternal regulatory T cell (Treg) frequency in AVI mothers was significantly higher than that in NAVI mothers (P < 0.002), and CD4+ T cells in AVI mothers displayed a decreased ability to secrete IFN-γ (P = 0.005) and IL-21 (P = 0.043), but an increased ability to secrete IL-10 and IL-4 (P = 0.040 and P = 0.036), which represented a higher Treg frequency, enhanced Th2 response and suppressed Th1 response. Treg frequency among AVI mothers was correlated negatively with serum HBsAg and HBeAg levels. After delivery, the ability of CD4+ T cells or CD8+ T cells to secrete IFN-γ or IL-10 was similar and no significant difference in Treg frequency was found between the two groups. CONCLUSIONS: Prophylactic antiviral intervention during pregnancy has an effect on T cell immunity in pregnant women, which was characterised by increased maternal Treg frequency, enhanced Th2 response and suppressed Th1 response at delivery.

Which Multivariate Multi-Scale Entropy Algorithm Is More Suitable for Analyzing the EEG Characteristics of Mild Cognitive Impairment?

So far, most articles using the multivariate multi-scale entropy algorithm mainly use algorithms to analyze the multivariable signal complexity without clearly describing what characteristics of signals these algorithms measure and what factors affect these algorithms. This paper analyzes six commonly used multivariate multi-scale entropy algorithms from a new perspective. It clarifies for the first time what characteristics of signals these algorithms measure and which factors affect them. It also studies which algorithm is more suitable for analyzing mild cognitive impairment (MCI) electroencephalograph (EEG) signals. The simulation results show that the multivariate multi-scale sample entropy (mvMSE), multivariate multi-scale fuzzy entropy (mvMFE), and refined composite multivariate multi-scale fuzzy entropy (RCmvMFE) algorithms can measure intra- and inter-channel correlation and multivariable signal complexity. In the joint analysis of coupling and complexity, they all decrease with the decrease in signal complexity and coupling strength, highlighting their advantages in processing related multi-channel signals, which is a discovery in the simulation. Among them, the RCmvMFE algorithm can better distinguish different complexity signals and correlations between channels. It also performs well in anti-noise and length analysis of multi-channel data simultaneously. Therefore, we use the RCmvMFE algorithm to analyze EEG signals from twenty subjects (eight control subjects and twelve MCI subjects). The results show that the MCI group had lower entropy than the control group on the short scale and the opposite on the long scale. Moreover, frontal entropy correlates significantly positively with the Montreal Cognitive Assessment score and Auditory Verbal Learning Test delayed recall score on the short scale.

Blood pressure stratification using photoplethysmography and light gradient boosting machine.

Introduction: Globally, hypertension (HT) is a substantial risk factor for cardiovascular disease and mortality; hence, rapid identification and treatment of HT is crucial. In this study, we tested the light gradient boosting machine (LightGBM) machine learning method for blood pressure stratification based on photoplethysmography (PPG), which is used in most wearable devices. Methods: We used 121 records of PPG and arterial blood pressure (ABP) signals from the Medical Information Mart for Intensive Care III public database. PPG, velocity plethysmography, and acceleration plethysmography were used to estimate blood pressure; the ABP signals were used to determine the blood pressure stratification categories. Seven feature sets were established and used to train the Optuna-tuned LightGBM model. Three trials compared normotension (NT) vs. prehypertension (PHT), NT vs. HT, and NT + PHT vs. HT. Results: The F1 scores for these three classification trials were 90.18%, 97.51%, and 92.77%, respectively. The results showed that combining multiple features from PPG and its derivative led to a more accurate classification of HT classes than using features from only the PPG signal. Discussion: The proposed method showed high accuracy in stratifying HT risks, providing a noninvasive, rapid, and robust method for the early detection of HT, with promising applications in the field of wearable cuffless blood pressure measurement.

Analysis of the neural mechanism of spectra decrease in MCI by a thalamo-cortical coupled neural mass model.

Objective.In order to deeply understand the neurophysiological mechanism of the spectra decrease in mild cognitive impairment (MCI), this paper studies a new neural mass model, which can simulate various intracerebral electrophysiological activities.Approach. In this study, a thalamo-cortical coupled neural mass model (TCC-NMM) is proposed. The influences of the coupling coefficients and other key parameters on the model spectra are simulated. Then, the unscented Kalman filter (UKF) algorithm is used to reversely identify the parameters in the TCC-NMM. Furthermore, the TCC-NMM and UKF are combined to analyze the spectra reduction mechanism of electroencephalogram (EEG) signals in MCI patients. The independent sample t-test is carried out to statistical analyze the differences of the identified parameters between MCI and normal controls. The Pearson correlation analysis is used to analyze the intrinsic relationship between parameters and the scores of the comprehensive competence assessment scale.Main results.The simulation results show that the decreased cortical synaptic connectivity constantsC1can result in spectra decrease of the TCC-NMM outputs. The real EEG analysis results show that the identified values of parameterC1are significant lower in the MCI group than in control group in frontal and occipital areas and the parametersC1are positively correlated with the Montreal Cognitive Assessment (MoCA) scores in the two areas. This consistency suggests that the cortical synaptic connectivity loss from pyramidal cells to excitatory interneurons (eIN) may be one of the neural mechanisms of EEG spectra decrease in MCI.Significance. (a) In this study, a new mathematical model TCCNMM based on anatomy and neurophysiology is proposed. (b) All key parameters in TCC-NMM are studied in detail through the forward and reverse analysis and the influence of these parameters on the output spectra of the model is pointed out. (c) The possible neural mechanism of the decreased spectra in MCI patients is pointed out by the joint analysis of simulation in forward with TCC-NMM and analysis of the actual EEG signals in reverse with UKF identification algorithm. (d) We find that the identified parameter C1 of MCI patients is significantly lower than that of the control group, which is consistent with the simulation analysis of TCC-NMM. So, we suggest that the decreased MCI alpha power spectrum is likely related to the cortical synaptic connection loss from pyramidal cells to eIN.

Validation of the EDAN SA-10 ambulatory blood pressure monitor in general population according to the Association for the Advancement of Medical Instrumentation/European Society of Hypertension/International Organization for Standardization Universal Standard.

OBJECTIVE: To evaluate the accuracy of the EDAN SA-10 oscillometric upper-arm professional office ambulatory blood pressure (BP) monitor in general population according to the Association for the Advancement of Medical Instrumentation (AAMI)/European Society of Hypertension (ESH)/International Organization for Standardization (ISO) Universal Standard (ISO 81060-2:2018). METHODS: Subjects were recruited according to the AAMI/ESH/ISO Universal Standard using the same arm sequential BP measurement method. Four cuffs of the test device were used for arm circumference 16-21.5 cm (extra small), 20.5-28 cm (small), 27-35 cm (medium), and 34-43 cm (large). RESULTS: A total of 105 subjects were recruited, and 97 subjects were included in the final analysis. For validation criterion 1, the mean ± SD of the differences between the test device and reference BP readings was -0.59 ± 4.04/-1.79 ± 4.39 mmHg (systolic/diastolic). For criterion 2, the SD of the averaged BP differences between the test device and reference BP per subject was 3.10/3.80 mmHg (systolic/diastolic). CONCLUSION: The EDAN SA-10 upper-arm ambulatory BP monitor has passed all the requirements of the AAMI/ESH/ISO Universal Standard (ISO 81060-2:2018) in general population and can be recommended for clinical use.

Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information.

This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (WPCMI) was proposed in this research. With the WPCMI method, coupling feature strength of two time series in Alpha1, Alpha2, Beta1, Beta2 and Gamma bands for aMCI with T2DM and NC with T2DM could be extracted respectively. Then selected three frequency bands coupling feature matrix with the help of multi-spectral image transformation method to map it as spectral image characteristics. And finally classified these characteristics through the convolution neural network method(CNN). For aMCI with T2DM and NC with T2DM, the highest classification accuracy of 96%, 95%, 95% could be achieved respectively in the combination of three frequency bands (Alpha1, Alpha2, Gamma), (Beta1, Beta2 and Gamma) and (Alpha2, Beta1, Beta2). This WPCMI method highlighted the coupling dynamic characteristics of EEG signals, and its classification performance was better than all previous methods in aMCI with T2DM diagnosis field. WPCMI method could be used as an effective biomarker to distinguish EEG signals of aMCI with T2DM and NC with T2DM. The coupling feature extraction method used in this paper provided a new perspective for the EEG analysis of aMCI with T2DM.

Classification of ERP signal from amnestic mild cognitive impairment with type 2 diabetes mellitus using single-scale multi-input convolution neural network.

BACKGROUND: The application of deep learning models to electroencephalogram (EEG) signal classification has recently become a popular research topic. Several deep learning models have been proposed to classify EEG signals in patients with various neurological diseases. However, no effective deep learning model for event-related potential (ERP) signal classification is yet available for amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM). METHOD: This study proposed a single-scale multi-input convolutional neural network (SSMICNN) method to classify ERP signals between aMCI patients with T2DM and the control group. Firstly, the 18-electrode ERP signal on alpha, beta, and theta frequency bands was extracted by using the fast Fourier transform, and then the mean, sum of squares, and absolute value feature of each frequency band were calculated. Finally, these three features are converted into multispectral images respectively and used as the input of the SSMICNN network to realize the classification task. RESULTS: The results show that the SSMICNN can fuse MSI formed by different features, SSMICNN enriches the feature quantity of the neural network input layer and has excellent robustness, and the errors of SSMICNN can be simultaneously transmitted to the three convolution channels in the back-propagation phase. Comparison with Existing Method(s): SSMICNN could more effectively identify ERP signals from aMCI with T2DM from the control group compared to existing classification methods, including convolution neural network, support vector machine, and logistic regression. CONCLUSIONS: The combination of SSMICNN and MSI can be used as an effective biological marker to distinguish aMCI patients with T2DM from the control group.

Nonlinear Phase Synchronization Analysis of EEG Signals in Amnesic Mild Cognitive Impairment with Type 2 Diabetes Mellitus.

Studying the nonlinear synchronization of electroencephalogram (EEG) in type 2 diabetic mellitus (T2DM) to find the EEG characteristics related to cognitive impairment is beneficial to the early prevention and diagnosis of mild cognitive impairment. Correlation between probabilities of recurrence (CPR) is a nonlinear phase synchronization method based on recurrence and recurrence probability, which had shown its superiority in detecting epilepsy. In this study, CPR method was used for the first time to analyze the synchronization of eye-closed resting EEG signals with T2DM. The 27 participants were divided into amnesic mild cognitive impairment (aMCI) group (17 case) and control group (10 cases with age and education matched). The CPR values in two groups were statistically analyzed by Mann-Whitney U test, and the correlation between EEG synchronization and cognitive function was studied by Spearman's correlation. The results showed that aMCI group had lower CPR values at each electrode pair than control group, and two groups had decreased CPR values with the increase of the spatial distance of the electrode pair in inter hemispheric. The CPR values were significantly different in frontal, parietal and temporal regions in intra hemispheric between two groups. The CPR values of C3-F7, F4-C4 and FP2-T6 were significantly positively correlated with the MOCA values. This study showed that the synchronization values of EEG signals obtained by the CPR method were significantly different between aMCI and control group, and they were the EEG characteristics associated with cognitive impairment in T2DM.

Deep Learning Algorithm Classifies Heartbeat Events Based on Electrocardiogram Signals.

Cardiovascular diseases (CVDs) have become the number 1 threat to human health. Their numerous complications mean that many countries remain unable to prevent the rapid growth of such diseases, although significant health resources have been invested toward their prevention and management. Electrocardiogram (ECG) is the most important non-invasive physiological signal for CVD screening and diagnosis. For exploring the heartbeat event classification model using single- or multiple-lead ECG signals, we proposed a novel deep learning algorithm and conducted a systemic comparison based on the different methods and databases. This new approach aims to improve accuracy and reduce training time by combining the convolutional neural network (CNN) with the bidirectional long short-term memory (BiLSTM). To our knowledge, this approach has not been investigated to date. In this study, Database I with single-lead ECG and Database II with 12-lead ECG were used to explore a practical and viable heartbeat event classification model. An evolutionary neural system approach (Method I) and a deep learning approach (Method II) that combines CNN with BiLSTM network were compared and evaluated in processing heartbeat event classification. Overall, Method I achieved slightly better performance than Method II. However, Method I took, on average, 28.3 h to train the model, whereas Method II needed only 1 h. Method II achieved an accuracy of 80, 82.6, and 85% compared with the China Physiological Signal Challenge 2018, PhysioNet Challenge 2017, and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia datasets, respectively. These results are impressive compared with the performance of state-of-the-art algorithms used for the same purpose.

Therapeutic Effects of Simultaneous Delivery of Nerve Growth Factor mRNA and Protein via Exosomes on Cerebral Ischemia.

Stroke is the leading neurological cause of death and disability all over the world, with few effective drugs. Nerve growth factor (NGF) is well known for its multifaceted neuroprotective functions post-ischemia. However, the lack of an efficient approach to systemically deliver bioactive NGF into ischemic region hinders its clinical application. In this study, we engineered the exosomes with RVG peptide on the surface for neuron targeting and loaded NGF into exosomes simultaneously, with the resultant exosomes denoted as NGF@ExoRVG. By systemic administration of NGF@ExoRVG, NGF was efficiently delivered into ischemic cortex, with a burst release of encapsulated NGF protein and de novo NGF protein translated from the delivered mRNA. Moreover, NGF@ExoRVG was found to be highly stable for preservation and function efficiently for a long time in vivo. Functional study revealed that the delivered NGF reduced inflammation by reshaping microglia polarization, promoted cell survival, and increased the population of doublecortin-positive cells, a marker of neuroblast. The results of our study suggest the potential therapeutic effects of NGF@ExoRVG for stroke. Moreover, the strategy proposed in our study may shed light on the clinical application of other neurotrophic factors for central nervous system diseases.

Asymmetry of peripheral vascular biomarkers in ischemic stroke patients, assessed using NIRS.

SIGNIFICANCE: Low-frequency oscillations (LFOs) ranging from 0.01 to 0.15 Hz are common in functional imaging studies. Some of these LFOs are non-neuronal and are correlated with autonomic physiological processes. AIM: We investigate the relationships between systemic low-frequency oscillations (sLFOs) measured at different peripheral sites during resting states in ischemic stroke patients. APPROACH: Twenty-seven ischemic stroke patients (ages 44 to 90; 20 male and 7 female) were recruited for the study. During the experiments, fluctuations in oxyhemoglobin concentration were measured in the left and right toes, fingertips, and earlobes using a multichannel near-infrared spectroscopy instrument. We applied cross-correlation and frequency component analyses on the sLFO data. RESULTS: The results showed that embolization broke the symmetry of the sLFO transmission and that the damage was not limited to the local area but spread throughout the body. Among six peripheral sites, the power spectrum width of the earlobes was significantly larger than that of the fingers and toes. This indicates that the earlobes may contain more physiological information. Finally, the results of fuzzy clustering verified that sLFOs can serve as perfusion biomarkers to differentiate stroke from healthy subjects. CONCLUSIONS: The high correlation values and corresponding delays in sLFOs support the hypothesis that (1) the correlation characteristics of sLFOs in stroke patients are different from those of healthy subjects. These characteristics can reflect patient condition, to an extent. Embolization in ischemic stroke patients breaks the symmetry of the body's sLFO transmission, disrupting the balance of blood circulation. (2) sLFOs can be used as perfusion biomarkers to differentiate ischemic stroke patients from healthy subjects. Studying these signals can explicate the overall feedback/influence of pericentral interactions. Finally, peripheral sLFOs have been shown to be an effective and accurate tool for assessing peripheral blood circulation and vascular integrity in ischemic stroke patients.

Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network.

OBJECTIVE: The purpose of this study is to judge whether this combination method of multispectral image and convolutional neural network (CNN) method can be used to distinguish amnestic mild cognitive impairment (aMCI) with Type 2 diabetes mellitus (T2DM) and normal controls (NC) with T2DM effectively. APPROACH: In this study, the authors first combined EEG signals from aMCI patients with T2DM and NC with T2DM on five different frequency bands, including Theta, Alpha1, Alpha2, Beta1, and Beta2. Then, the authors converted these time series into a series of multispectral images. Finally, the images data were classified with the CNN method. MAIN RESULTS: The classification effects of up to 89%, 91%, and 92% are obtained on the three combinations of frequency bands: Theta, Alpha1, and Alpha2; Alpha1, Alpha2, and Beta1; and Alpha2, Beta1, and Beta2. The spatial properties of EEG signals are highlighted, and its classification performance is found to be better than all the previous methods in the field of aMCI and T2DM diagnosis. The combination of multispectral images and CNN can be used as an effective biomarker for distinguishing the EEG signals in patients with aMCI and T2DM and in patients with NC with T2DM. SIGNIFICANCE: The combined approach used in this paper provides a new perspective for the analysis of EEG signals in patients with aMCI and T2DM.

The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method.

Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognitive impairment. However, it remains unclear which feature of EEG signals is best effective in assessing amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) when combining one classifier. This study proposed a novel feature extraction method of EEG signals named feature-fusion multispectral image method (FMIM) for diagnosis of aMCI with T2DM. The FMIM was integrated with convolutional neural network (CNN) to classify the processed multispectral image data. The results showed that FMIM could effectively identify aMCI with T2DM from the control group compared to existing multispectral image method (MIM), with improvements including the type and quantity of feature extraction. Meanwhile, part of the invalid calculation could be avoided during the classification process. In addition, the classification evaluation indexes were best under the combination of Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-1, and were also best under the combination of the Theta-Alpha1-Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-2. Therefore, FMIM can be used as an effective feature extraction method of aMCI with T2DM, and as a valuable biomarker in clinical applications.

Disposable Amperometric Immunosensor for Hepatitis B Antigen Detection Based on Multiwalled Carbon Nanotubes and Ferrocene Decorated Screen Printed Electrode.

In this work, we report an amperometric immunosensor for detecting Hepatitis B surface antigen based on ChitosanFerrocene-Ammoniated multiwalled carbon nanotubes (CS-Fc-AMWNTs). The CS-Fc-AMWNTs nanocomposites were produced by Schiff base reaction, providing not only large specific surface area, but also favorable conductivity and outstanding biocompatibility. HBsAb was modified on electrode surface by glutaraldehyde cross-linking. The immunocomplex was produced after incubation with HBsAg, which hindered the electron transfer and concentration of antibody was observed to change in the current. The immunosensor showed great linear range from 1-250 ng/mL. Results from this study show that the immunosensor has good reliability and high sensitivity for detection of HBsAg in certain clinical application value.

Estimating coupling strength between multivariate neural series with multivariate permutation conditional mutual information.

Recently, coupling between groups of neurons or different brain regions has been widely studied to provide insights into underlying mechanisms of brain functions. To comprehensively understand the effect of such coupling, it is necessary to accurately extract the coupling strength information among multivariate neural signals from the whole brain. This study proposed a new method named multivariate permutation conditional mutual information (MPCMI) to quantitatively estimate the coupling strength of multivariate neural signals (MNS). The performance of the MPCMI method was validated on the simulated MNS generated by multi-channel neural mass model (MNMM). The coupling strength feature of simulated MNS extracted by MPCMI showed better performance compared with standard methods, such as permutation conditional mutual information (PCMI), multivariate Granger causality (MVGC), and Granger causality analysis (GCA). Furthermore, the MPCMI was applied to estimate the coupling strengths of two-channel resting-state electroencephalographic (rsEEG) signals from different brain regions of 19 patients with amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) and 20 normal control (NC) with T2DM in Alpha1 and Alpha2 frequency bands. Empirical results showed that the MPCMI could effectively extract the coupling strength features that were significantly different between the aMCI and the NC. Hence, the proposed MPCMI method could be an effective estimate of coupling strengths of MNS, and might be a viable biomarker for clinical applications.