[Wearable devices: Perspectives on assessing and monitoring human physiological status].

This review article aims to explore the major challenges that the healthcare system is currently facing and propose a new paradigm shift that harnesses the potential of wearable devices and novel theoretical frameworks on health and disease. Lifestyle-induced diseases currently account for a significant portion of all healthcare spending, with this proportion projected to increase with population aging. Wearable devices have emerged as a key technology for implementing large-scale healthcare systems focused on disease prevention and management. Advancements in miniaturized sensors, system integration, the Internet of Things, artificial intelligence, 5G, and other technologies have enabled wearable devices to perform high-quality measurements comparable to medical devices. Through various physical, chemical, and biological sensors, wearable devices can continuously monitor physiological status information in a non-invasive or minimally invasive way, including electrocardiography, electroencephalography, respiration, blood oxygen, blood pressure, blood glucose, activity, and more. Furthermore, by combining concepts and methods from complex systems and nonlinear dynamics, we developed a novel theory of continuous dynamic physiological signal analysis-dynamical complexity. The results of dynamic signal analyses can provide crucial information for disease prevention, diagnosis, treatment, and management. Wearable devices can also serve as an important bridge connecting doctors and patients by tracking, storing, and sharing patient data with medical institutions, enabling remote or real-time health assessments of patients, and providing a basis for precision medicine and personalized treatment. Wearable devices have a promising future in the healthcare field and will be an important driving force for the transformation of the healthcare system, while also improving the health experience for individuals.

Associations of Reduced Sympathetic Neural Activity and Elevated Baroreflex Sensitivity With Non-Rapid Eye Movement Sleep: Evidence From Electroencephalogram- and Electrocardiogram-Based Sleep Staging.

OBJECTIVE: Autonomic neural controls in sleep regulation have been previously demonstrated; however, whether these alternations can be observed by different sleep staging approaches remains unclear. Two established methods for sleep staging-the standardized visual scoring and the cardiopulmonary coupling (CPC) analysis based on electrocardiogram-were used to explore the cardiovascular profiles of sleep. METHODS: Overnight polysomnography was recorded together with continuous beat-to-beat blood pressure. Cortical activity, heart rate variability, blood pressure variability, and baroreflex sensitivity during sleep stages from 24 nights of sleep were obtained from 15 normotensive participants and analyzed. RESULTS: Non-rapid eye movement sleep (NREM) from visual scoring and restful sleep (RS) of CPC both showed the highest delta power of electroencephalogram (EEG) and lowest beta activity of EEG in comparison with other sleep stages (p < .001); likewise, the lowest total power of heart rate variability and suppressed vascular-sympathetic activity, reflected by low-frequency power of blood pressure variability, as well as a trend in elevated baroreflex sensitivity, were observed in the NREM or RS. This suppressed vascular-sympathetic activity during stable sleep further exhibited a significant correlation with increased slow-wave activity (NREM: r = -0.292 ± 0.34, p = .002; RS: r = -0.209 ± 0.30, p = .010). CONCLUSIONS: Autonomic nervous system is evidently associated with stable sleep, as indicated by the similar findings obtained from sleep stages categorized by standardized visual scoring or CPC analysis. Such association between cardiovascular neural activity and sleep EEGs can be observed regardless of the sleep staging approach followed.

The Value of Heart Rhythm Complexity in Identifying High-Risk Pulmonary Hypertension Patients.

Pulmonary hypertension (PH) is a fatal disease-even with state-of-the-art medical treatment. Non-invasive clinical tools for risk stratification are still lacking. The aim of this study was to investigate the clinical utility of heart rhythm complexity in risk stratification for PH patients. We prospectively enrolled 54 PH patients, including 20 high-risk patients (group A; defined as WHO functional class IV or class III with severely compromised hemodynamics), and 34 low-risk patients (group B). Both linear and non-linear heart rate variability (HRV) variables, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE), were analyzed. In linear and non-linear HRV analysis, low frequency and high frequency ratio, DFAα1, MSE slope 5, scale 5, and area 6-20 were significantly lower in group A. Among all HRV variables, MSE scale 5 (AUC: 0.758) had the best predictive power to discriminate the two groups. In multivariable analysis, MSE scale 5 (p = 0.010) was the only significantly predictor of severe PH in all HRV variables. In conclusion, the patients with severe PH had worse heart rhythm complexity. MSE parameters, especially scale 5, can help to identify high-risk PH patients.

HLA-G 14bp Ins/Del Polymorphism in the 3'UTR Region and Acute Rejection in Kidney Transplant Recipients: An Updated Meta-Analysis.

Background and Objectives: Acute kidney injury (AKI) affects the survival rate of kidney transplant organs and patients. Acute rejection (AR) due to AKI may lead to kidney transplantation failure. It is known that there is a relationship between human leukocyte antigen-G (HLA-G), which is involved in immune regulation, and AR in transplant patients. Moreover, 14-bp insertion/deletion polymorphism in the 3' untranslated region (UTR) region of the HLA-G gene is known to affect HLA-G expression. However, its relationship to AR is still controversial. The aim of this study was to investigate whether HLA-G 14-bp insertion/deletion polymorphism contributed to the development of AR in kidney transplant patients using a meta-analysis. Materials and Methods: To perform our meta-analysis, eligible studies about HLA-G 14-bp insertion/deletion polymorphism and AR were searched in electronic databases until 1 June 2021. Finally, a total of 336 patients with AR and 952 patients without AR in relation to kidney transplantation were analyzed from a total of nine studies. Results: In our results, the Del allele and Ins/Del+Del/Del and Del/Del genotypes significantly increased susceptibility of AR in Asian populations [odds ratio (OR) = 2.359, 95% confidence interval (CI) = 1.568-3.550, p = 3.8 × 10-5; OR = 3.357, 95% CI = 1.769-6.370, p = 0.002; OR = 2.750, 95% CI = 1.354-5.587, p = 0.0052 in each model, respectively]. Conclusions: Evidence of the present results indicate that HLA-G 14-bp insertion/deletion polymorphism is associated with susceptibility to AR in the Asian population.

Electrocardiogram-based sleep analysis for sleep apnea screening and diagnosis.

PURPOSE: Despite the increasing number of research studies of cardiopulmonary coupling (CPC) analysis, an electrocardiogram-based technique, the use of CPC in underserved population remains underexplored. This study aimed to first evaluate the reliability of CPC analysis for the detection of obstructive sleep apnea (OSA) by comparing with polysomnography (PSG)-derived sleep outcomes. METHODS: Two hundred five PSG data (149 males, age 46.8 ± 12.8 years) were used for the evaluation of CPC regarding the detection of OSA. Automated CPC analyses were based on ECG signals only. Respiratory event index (REI) derived from CPC and apnea-hypopnea index (AHI) derived from PSG were compared for agreement tests. RESULTS: CPC-REI positively correlated with PSG-AHI (r = 0.851, p < 0.001). After adjusting for age and gender, CPC-REI and PSG-AHI were still significantly correlated (r = 0.840, p < 0.001). The overall results of sensitivity and specificity of CPC-REI were good. CONCLUSION: Compared with the gold standard PSG, CPC approach yielded acceptable results among OSA patients. ECG recording can be used for the screening or diagnosis of OSA in the general population.

Heart rate variability and surgical pleth index under anesthesia in poor and normal sleepers.

Poor sleep quality is associated with autonomic dysfunctions and altered pain perception and tolerance. To investigate whether autonomic dysregulations related to insomnia would still exist under general anesthesia, we adopt heart rate variability (HRV) analysis to evaluate ANS activity and surgical pleth index (SPI) to compare nociceptive/anti-nociceptive balance. We enrolled 61 adult females scheduled for gynecological surgeries under general anesthesia. All the subjects were ASA Class I to III without using medicines affecting HRV. We used the Insomnia Severity Index to evaluate sleep qualities. ECG data were recorded and signals which denote four different surgical stages were extracted (baseline, incision, mid-surgery, and end of surgery). We analyzed the HRV changes across the whole surgical period and differences among good and poor sleepers. We also compared the SPI differences among groups. For baseline HRV analysis, we found significant differences in the RMSSD (p = 0.043), pNN50 (p = 0.029), VLF power (p = 0.035), LF power (p = 0.004), and HF power (p = 0.037) between the good and poor sleeper groups. However, all intergroup differences disappeared after anesthesia induction. Temporal HRV changes significantly among different perioperative stages (RMSSD, p < 0.001; pNN50, p = 0.004; LF, p < 0.001; and HF, p < 0.001). Patients with different sleep qualities did not exhibit different SPI levels in all four periods. Poor sleepers exhibited attenuated parasympathetic activities at the baseline but no differences after the induction. Nociceptive/anti-nociceptive balance seems not be altered by poor sleep condition under general anesthesia.

PTSD Predicts Smoking Cessation Failure in a Trauma-Exposed Population.

OBJECTIVE: The objective of the study was to investigate whether a diagnosis of posttraumatic stress disorder (PTSD; full or partial) or specific PTSD symptom clusters predicted failure in quitting smoking in a trauma-exposed population. Methods: Participants were 310 smokers who attempted quitting smoking, either successfully (quitters, n = 213) or not (relapsers, n = 97), who lived in slums and were attending a family doctor program. Measurements included a general questionnaire covering sociodemographic characteristics, clinical status and life habits, and the Posttraumatic Stress Disorder Checklist - Civilian Version. Differences in sociodemographic, clinical and lifestyle characteristics between quitters and relapsers were compared using a chi-square test. Because of the small sample size, full and partial PTSD were collapsed into a single category. Results: Significant differences (p ≤ .15) between quitters and relapsers were found in age, body mass index (BMI), income, alcohol consumption, and in the presence of full/partial PTSD diagnosis and of all three symptom clusters separately. Four logistic regression models predicting smoking cessation were modeled to control for confounding factors and included as independent variables a full/partial PTSD diagnosis and the three posttraumatic symptom clusters. The avoidance/numbing cluster presented the strongest association with relapse status (ORa 2.04, 95% CI [1.15, 3.63], p = .015), followed by the full/partial PTSD (ORa 1.80, 95% CI [1.04, 3.14], p = .038). The re-experiencing and the hyperarousal clusters were non-significantly associated with smoking cessation (ORa 1.34, 95% CI [0.80, 2.31], ns and ORa 1.65, 95% CI [0.96, 2.84], ns, respectively). Conclusions: Full/partial PTSD and posttraumatic symptom clusters uniquely predict risk for smoking relapse and thus may be a useful therapeutic target in trauma-exposed smokers.

On the Variability of Heart Rate Variability-Evidence from Prospective Study of Healthy Young College Students.

Heart rate variability (HRV) has been widely used as indices for autonomic regulation, including linear analyses, entropy and multi-scale entropy based nonlinear analyses, and however, it is strongly influenced by the conditions under which the signal is being recorded. To investigate the variability of healthy HRV under different settings, we recorded electrocardiograph (ECG) signals from 56 healthy young college students (20 h for each participant) at campus using wearable single-lead ECG device. Accurate R peak to R peak (RR) intervals were extracted by combing the advantages of five commonly used R-peak detection algorithms to eliminate data quality influence. Thorough and detailed linear and nonlinear HRV analyses were performed. Variability of HRV metrics were evaluated from five categories: (1) different states of daily activities; (2) different recording time period in the same day during free-running daily activities; (3) body postures of sitting and lying; (4) lying on the left, right and back; and (5) gender influence. For most of the analyzed HRV metrics, significant differences (p < 0.05) were found among different recording conditions within the five categories except lying on different positions. Results suggested that the standardization of ECG data collection and HRV analysis should be implemented in HRV related studies, especially for entropy and multi-scale entropy based analyses. Furthermore, this preliminary study provides reference values of HRV indices under various recording conditions of healthy young subjects that could be useful information for different applications (e.g., health monitoring and management).

Dynamic Cerebral Autoregulation Is an Independent Functional Outcome Predictor of Mild Acute Ischemic Stroke.

Background and Purpose- Cerebral autoregulation is impaired in patients with acute ischemic stroke. The purpose of this study was to investigate whether dynamic cerebral autoregulation (dCA) indices constitute an independent functional outcome predictor of acute ischemic stroke. Methods- In this study, 86 patients at days 3 to 7 after acute ischemic stroke and 40 age- and sex-matched controls were enrolled for assessing their dCA indices under spontaneous hemodynamic fluctuations. The dCA indices of patients with favorable outcomes (modified Rankin Scale score ≤1 at 3 months, n=65), patients with unfavorable outcomes (modified Rankin Scale score ≥2 at 3 months, n=21), and controls were compared. Results- The dCA indices, namely the phase shift at very low frequency band (phase_VLF), in the patients with unfavorable outcomes were significantly worse than those in the patients with favorable outcomes. However, the phase_VLF in the patients with favorable outcomes did not differ from those in the controls. Impaired dCA was associated with elevated mean arterial pressure and large infarction volume but was also present in patients with normal mean arterial pressure or small infarction volume. Phase_VLF was a predictor of outcomes in the receiver operating characteristic analysis (area under the curve: 0.722; P<0.001). Multivariate analysis revealed that a phase_VLF value of <61° was independently associated with unfavorable outcomes (odds ratio=4.90; P=0.024). Conclusions- Phase_VLF is an independent functional outcome predictor of acute ischemic stroke.

Preparation of Fe-Carbon Nanocomposite by Pyrolyzing Fe Adsorbed 1,8-Diamminonaphthalene for the Adsorption of Cr (VI).

In the present work, a facile method for the preparation of nanocomposite adsorbents composed of carbon and iron compounds was demonstrated. The adsorbents were produced by pyrolyzing an iron-coordinated 1,8-diaminonaphthalene at various temperatures under an N2 stream (FeDN X, where X represents the pyrolysis temperatures 600, 700, and 800 °C). Prepared FeDNs were employed as adsorbents for the removal of Cr (VI). The Cr (VI)-adsorption behavior of FeDNs were well-fitted to a Langmuir isotherm model. Among the samples prepared, FeDN 700 showed the best performance for the removal of Cr (VI). In particular, the maximum adsorption capacity of FeDN 700 was evaluated to be 34.81 mg/g. A variety of characterizations were carried out to elucidate the relationship between physical properties of adsorbents and their adsorption behaviors.

The Effects of Tai Chi on Sleep Quality in Chinese American Patients With Major Depressive Disorder: A Pilot Study.

OBJECTIVE: This pilot study evaluated the effects of Tai Chi training on sleep quality (primary outcomes), and depression and social functioning levels (secondary outcomes) among patients with depression. PARTICIPANTS: Sixteen depressed Chinese patients. METHODS: Participants received 1-hr Tai Chi training sessions 2 times per week for 10 weeks. Patients' subjective sleep quality ratings, objective sleep quality measurements, and depression and social functioning levels were measured before, during, and after the intervention. RESULTS: Sleep quality and depression outcomes improved significantly. Patients reported improved Pittsburgh Sleep Quality Index (PSQI) scores (9.6 ± 3.3 to 6.6 ± 5.2, p = 0.016), and cardiopulmonary coupling (CPC) analysis of electrocardiogram (ECG) showed decreased stable sleep onset latency (75.7 ± 100.6 to 20.9 ± 18.0, p = 0.014), increased stable sleep percentages (31.5 ± 18.7 to 46.3 ± 16.9, p = 0.016), and decreased unstable sleep percentages (45.3 ± 20.1 to 30.6 ± 16.5, p = 0.003). Patients also reported decreased Hamilton Rating Scale for Depression (HAM-D-17; 20.1 ± 3.7 to 7.8 ± 5.9, p < 0.001) and Beck Depression Inventory (BDI) scores (22.3 ± 9.1 to 11.1 ± 10.6, p = 0.006). Significant correlations were found between the changes in subjective sleep assessments ΔPSQI and ΔHAM-D-17 (r = 0.6, p = 0.014), and ΔPSQI and ΔBDI (r = 0.62, p = 0.010). Correlations between changes in objective sleep measurements and changes in depression symptoms were low and not significant. CONCLUSIONS: Tai Chi training improved sleep quality and mood symptoms among depressed patients.

Preparation of highly stable zeolite-alginate foam composite for strontium(90Sr) removal from seawater and evaluation of Sr adsorption performance.

Alginate bead is a promising strontium (Sr) adsorbent in seawater, but highly concentrated Na ions caused over-swelling and damaged the hydrogel bead. To improve the mechanical stability of alginate bead, flexible foam-type zeolite-alginate composite was synthesized and Sr adsorption performance was evaluated in seawater; 1-10% zeolite immobilized alginate foams were prepared by freeze-dry technique. Immobilization of zeolite into alginate foam converted macro-pores to meso-pores which lead to more compact structure. It resulted in less swollen composite in seawater medium and exhibited highly improved mechanical stability compared with alginate bead. Besides, Sr adsorption efficiency and selectivity were enhanced by immobilization of zeolite in alginate foam due to the increase of Sr binding sites (zeolite). In particular, Sr selectivity against Na was highly improved. The 10% zeolite-alginate foam exhibited a higher log Kd of 3.3, while the pure alginate foam exhibited 2.7 in the presence of 0.1 M Na. Finally, in the real seawater, the 10% zeolite-alginate foam exhibited 1.5 times higher Sr adsorption efficiency than the pure alginate foam. This result reveals that zeolite-alginate foam composite is appropriate material for Sr removal in seawater due to its swelling resistance as well as improved Sr adsorption performance in complex media.

Roles of preoperative C-reactive protein are more relevant in buccal cancer than other subsites.

BACKGROUND: C-reactive protein (CRP) is an early marker for inflammation, and a relationship between serum CRP levels and survival in oral cancer has been demonstrated previously. In this study, we investigated the roles of CRP in different oral cancer subsites. METHODS: Three hundred and forty-three oral squamous cell carcinoma patients between June 1999 and March 2015 were retrospectively reviewed. Serum CRP levels were measured preoperatively. RESULTS: The elevation of CRP levels (≥5.0 mg/L) was significantly correlated with pathologic tumor status, pathologic nodal status, nodal extracapsular spread, tumor stage, skin invasion, tumor depth (≥10 mm), and bone invasion. The correlation between elevation of CRP and clinicopathologic factors was more evident in the buccal cancer compared to other tumor subsites. The disease-free survival and overall survival correlation was significant in buccal cancer (p = 0.003 and p < 0.001) but not in tongue cancer (p = 0.119 and p = 0.341) or other oral cancer subsites (p = 0.246 and p = 0.696). CONCLUSIONS: Preoperative serum CRP level was a prognosticator in oral squamous cell carcinoma, and its effect was more prominent in buccal cancer that occurs more frequently in areca-quid (AQ) endemic regions.

Derlin-1 regulates mutant VCP-linked pathogenesis and endoplasmic reticulum stress-induced apoptosis.

Mutations in VCP (Valosin-containing protein), an AAA ATPase critical for ER-associated degradation, are linked to IBMPFD (Inclusion body myopathy with Paget disease and frontotemporal dementia). Using a Drosophila IBMPFD model, we have identified the ER protein Derlin-1 as a modifier of pathogenic TER94 (the fly VCP homolog) mutants. Derlin-1 binds to TER94 directly, and this interaction is essential for Derlin-1 overexpression to suppress the pathogenic TER94-induced neurodegeneration. Derlin-1 overexpression reduces the elevated ATPase activity of pathogenic TER94, implying that IBMPFD is caused by ATPase hyper-activation. Under physiological condition, Derlin-1 expression is increased upon ER stress to recruit TER94 to the ER. However, in response to severe ER stress, Derlin-1 is required for activating apoptosis to eliminate damaged cells. This pro-apoptotic response is mimicked by Derlin-1 overexpression, which elicits acute ER stress and triggers apoptosis via a novel C-terminal motif (α). As this Derlin-1-dependent cell death is negated by TER94 overexpression, we propose that while Derlin-1 and VCP work cooperatively in ER stress response, their imbalance has a role in removing cells suffering prolonged ER stress.

On Holo-Hilbert spectral analysis: a full informational spectral representation for nonlinear and non-stationary data.

The Holo-Hilbert spectral analysis (HHSA) method is introduced to cure the deficiencies of traditional spectral analysis and to give a full informational representation of nonlinear and non-stationary data. It uses a nested empirical mode decomposition and Hilbert-Huang transform (HHT) approach to identify intrinsic amplitude and frequency modulations often present in nonlinear systems. Comparisons are first made with traditional spectrum analysis, which usually achieved its results through convolutional integral transforms based on additive expansions of an a priori determined basis, mostly under linear and stationary assumptions. Thus, for non-stationary processes, the best one could do historically was to use the time-frequency representations, in which the amplitude (or energy density) variation is still represented in terms of time. For nonlinear processes, the data can have both amplitude and frequency modulations (intra-mode and inter-mode) generated by two different mechanisms: linear additive or nonlinear multiplicative processes. As all existing spectral analysis methods are based on additive expansions, either a priori or adaptive, none of them could possibly represent the multiplicative processes. While the earlier adaptive HHT spectral analysis approach could accommodate the intra-wave nonlinearity quite remarkably, it remained that any inter-wave nonlinear multiplicative mechanisms that include cross-scale coupling and phase-lock modulations were left untreated. To resolve the multiplicative processes issue, additional dimensions in the spectrum result are needed to account for the variations in both the amplitude and frequency modulations simultaneously. HHSA accommodates all the processes: additive and multiplicative, intra-mode and inter-mode, stationary and non-stationary, linear and nonlinear interactions. The Holo prefix in HHSA denotes a multiple dimensional representation with both additive and multiplicative capabilities.

Novel application of multi dynamic trend analysis as a sensitive tool for detecting the effects of aging and congestive heart failure on heart rate variability.

The complex fluctuations in heart rate variability (HRV) reflect cardiac autonomic modulation and are an indicator of congestive heart failure (CHF). This paper proposes a novel nonlinear approach to HRV investigation, the multi dynamic trend analysis (MDTA) method, based on the empirical mode decomposition algorithm of the Hilbert-Huang transform combined with a variable-sized sliding-window method. Electrocardiographic signal data obtained from the PhysioNet database were used. These data were from subjects with CHF (mean age = 59.4 ± 8.4), an age-matched elderly healthy control group (59.3 ± 10.6), and a healthy young group (30.3 ± 4.8); the HRVs of these subjects were processed using the MDTA method, time domain analysis, and frequency domain analysis. Among all HRV parameters, the MDTA absolute value slope (MDTS) and MDTA deviation (MDTD) exhibited the greatest area under the curve (AUC) of the receiver operating characteristics in distinguishing between the CHF group and the healthy controls (AUC = 1.000) and between the healthy elderly subject group and the young subject group (AUC = 0.834 ± 0.067 for MDTS; 0.837 ± 0.066 for MDTD). The CHF subjects presented with lower MDTA indices than those of the healthy elderly subject group. Furthermore, the healthy elderly subjects exhibited lower MDTA indices than those of the young controls. The MDTA method can adaptively and automatically identify the intrinsic fluctuation on variable temporal and spatial scales when investigating complex fluctuations in the cardiac autonomic regulation effects of aging and CHF.

Investigation of the strontium (Sr(II)) adsorption of an alginate microsphere as a low-cost adsorbent for removal and recovery from seawater.

In this paper, we investigated alginate microspheres as a low-cost adsorbent for strontium (Sr(II)) removal and recovery from seawater. Alginate microspheres have demonstrated a superior adsorption capacity for Sr(II) ions (≈110 mg/g). A Freundlich isotherm model fits well with the Sr(II) adsorption of an alginate microsphere. The mechanism of Sr(II) adsorption is inferred as an ion exchange reaction with Ca(II) ions. The effects of the solution pH and co-existing ions in seawater are also investigated. Except for a pH of 1-2, Sr(II) adsorption capacity is not affected by pH. However, increasing the seawater concentration of metal cations seriously decreases Sr(II) uptake. In particular, highly concentrated (15,000 mg/L) Na(I) ions significantly interfere with Sr(II) adsorption. Sr(II) desorption was performed using 0.1 M HCl and CaCl2. Both regenerants show an excellent desorption efficiency, but the FTIR spectrum reveals that the chemical structure of the microsphere is destroyed after repeated use of HCl. Conversely, CaCl2 successfully desorbed Sr(II) without damage, and the Sr(II) adsorption capacity does not decrease after three repeated uses. The alginate microsphere was also applied to the adsorption of Sr(II) in a real seawater medium. Because of inhibition by co-existing ions, the Sr(II) adsorption capacity was decreased and the adsorption rate was retarded compared with D.I. water. Although the Sr(II) adsorption capacity was decreased, the alginate microsphere still exhibited 17.8 mg/g of Sr(II) uptake in the seawater medium. Considering its excellent Sr(II) uptake in seawater and its reusability, an alginate microsphere is an appropriate cost-effective adsorbent for the removal and recovery of Sr(II) from seawater.

Decreased resting-state brain activity complexity in schizophrenia characterized by both increased regularity and randomness.

Schizophrenia is characterized by heterogeneous pathophysiology. Using multiscale entropy (MSE) analysis, which enables capturing complex dynamics of time series, we characterized MSE patterns of blood-oxygen-level-dependent (BOLD) signals across different time scales and determined whether BOLD activity in patients with schizophrenia exhibits increased complexity (increased entropy in all time scales), decreased complexity toward regularity (decreased entropy in all time scales), or decreased complexity toward uncorrelated randomness (high entropy in short time scales followed by decayed entropy as the time scale increases). We recruited 105 patients with schizophrenia with an age of onset between 18 and 35 years and 210 age- and sex-matched healthy volunteers. Results showed that MSE of BOLD signals in patients with schizophrenia exhibited two routes of decreased BOLD complexity toward either regular or random patterns. Reduced BOLD complexity toward regular patterns was observed in the cerebellum and temporal, middle, and superior frontal regions, and reduced BOLD complexity toward randomness was observed extensively in the inferior frontal, occipital, and postcentral cortices as well as in the insula and middle cingulum. Furthermore, we determined that the two types of complexity change were associated differently with psychopathology; specifically, the regular type of BOLD complexity change was associated with positive symptoms of schizophrenia, whereas the randomness type of BOLD complexity was associated with negative symptoms of the illness. These results collectively suggested that resting-state dynamics in schizophrenia exhibit two routes of pathologic change toward regular or random patterns, which contribute to the differences in syndrome domains of psychosis in patients with schizophrenia.

Advances in heart rate variability signal analysis: joint position statement by the e-Cardiology ESC Working Group and the European Heart Rhythm Association co-endorsed by the Asia Pacific Heart Rhythm Society.

Following the publication of the Task Force document on heart rate variability (HRV) in 1996, a number of articles have been published to describe new HRV methodologies and their application in different physiological and clinical studies. This document presents a critical review of the new methods. A particular attention has been paid to methodologies that have not been reported in the 1996 standardization document but have been more recently tested in sufficiently sized populations. The following methods were considered: Long-range correlation and fractal analysis; Short-term complexity; Entropy and regularity; and Nonlinear dynamical systems and chaotic behaviour. For each of these methods, technical aspects, clinical achievements, and suggestions for clinical application were reviewed. While the novel approaches have contributed in the technical understanding of the signal character of HRV, their success in developing new clinical tools, such as those for the identification of high-risk patients, has been rather limited. Available results obtained in selected populations of patients by specialized laboratories are nevertheless of interest but new prospective studies are needed. The investigation of new parameters, descriptive of the complex regulation mechanisms of heart rate, has to be encouraged because not all information in the HRV signal is captured by traditional methods. The new technologies thus could provide after proper validation, additional physiological, and clinical meaning. Multidisciplinary dialogue and specialized courses in the combination of clinical cardiology and complex signal processing methods seem warranted for further advances in studies of cardiac oscillations and in the understanding normal and abnormal cardiac control processes.

Revealing the brain's adaptability and the transcranial direct current stimulation facilitating effect in inhibitory control by multiscale entropy.

The abilities to inhibit impulses and withdraw certain responses are critical for human's survival in a fast-changing environment. These processes happen fast, in a complex manner, and sometimes are difficult to capture with fMRI or mean electrophysiological brain signal alone. Therefore, an alternative measure that can reveal the efficiency of the neural mechanism across multiple timescales is needed for the investigation of these brain functions. The present study employs a new approach to analyzing electroencephalography (EEG) signal: the multiscale entropy (MSE), which groups data points with different timescales to reveal any occurrence of repeated patterns, in order to theoretically quantify the complexity (indicating adaptability and efficiency) of neural systems during the process of inhibitory control. From this MSE perspective, EEG signals of successful stop trials are more complex and information rich than that of unsuccessful stop trials. We further applied transcranial direct current stimulation (tDCS), with anodal electrode over presupplementary motor area (preSMA), to test the relationship between behavioral modification with the complexity of EEG signals. We found that tDCS can further increase the EEG complexity of the frontal lobe. Furthermore, the MSE pattern was found to be different between high and low performers (divided by their stop-signal reaction time), where the high-performing group had higher complexity in smaller scales and less complexity in larger scales in comparison to the low-performing group. In addition, this between-group MSE difference was found to interact with the anodal tDCS, where the increase of MSE in low performers benefitted more from the anodal tDCS. Together, the current study demonstrates that participants who suffer from poor inhibitory control can efficiently improve their performance with 10min of electrical stimulation, and such cognitive improvement can be effectively traced back to the complexity within the EEG signals via MSE analysis, thereby offering a theoretical basis for clinical intervention via tDCS for deficits in inhibitory control.