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BackgroundPrevious studies have classified major depression and healthy control groups based on vocal acoustic features, but the classification accuracy needs to be improved. Therefore, this study utilized deep learning methods to construct classification and prediction models for major depression and healthy control groups.Methods120 participants aged 16–25 participated in this study, included 64 MDD group and 56 HC group. We used the Covarep open-source algorithm to extract a total of 1200 high-level statistical functions for each sample. In addition, we used Python for correlation analysis, and neural network to establish the model to distinguish whether participants experienced depression, predict the total depression score, and evaluate the effectiveness of the classification and prediction model.ResultsThe classification modelling of the major depression and the healthy control groups by relevant and significant vocal acoustic features was 0.90, and the Receiver Operating Characteristic (ROC) curves analysis results showed that the classification accuracy was 84.16%, the sensitivity was 95.38%, and the specificity was 70.9%. The depression prediction model of speech characteristics showed that the predicted score was closely related to the total score of 17 items of the Hamilton Depression Scale(HAMD-17) (r=0.687, P

Abstract Background Depression and anxiety are common and disabling mental health problems in children and young adults. Group cognitive behavioral therapy (GCBT) is considered that an efficient and effective treatment for these significant public health concerns, but not all participants respond equally well. The aim of this study was to examine the predictive ability of heart rate variability (HRV), based on sensor data from consumer-grade wearable devices to detect GCBT effectiveness in early intervention. Methods In a study of 33 college students with depression and anxiety, participants were randomly assigned to either GCBT group or a wait-list control (WLC) group. They wore smart wearable devices to measure their physiological activities and signals in daily life. The HRV parameters were calculated and compared between the groups. The study also assessed correlations between participants’ symptoms, HRV, and GCBT outcomes. Results The study showed that participants in GCBT had significant improvement in depression and anxiety symptoms after four weeks. Higher HRV was associated with greater improvement in depressive and anxious symptoms following GCBT. Additionally, HRV played a noteworthy role in determining how effective GCBT was in improve anxiety(P = 0.002) and depression(P = 0.020), and its predictive power remained significant even when considering other factors. Conclusion HRV may be a useful predictor of GCBT treatment efficacy. Identifying predictors of treatment response can help personalize treatment and improve outcomes for individuals with depression and anxiety. Trial registration The trial has been retrospectively registered on [22/06/2023] with the registration number [NCT05913349] in the ClinicalTrials.gov. Condensed abstract Variations in heart rate variability (HRV) have been associated with depression and anxiety, but the relationship of baseline HRV to treatment outcome in depression and anxiety is unclear. This study predicted GCBT effectiveness using HRV measured by wearable devices. 33 students with depression and anxiety participated in a trial comparing GCBT and wait-list control. HRV parameters from wearables correlated with symptoms (PHQ, PSS) and GCBT effectiveness. Baseline HRV levels are strongly associated with GCBT treatment outcomes. HRV may serve as a useful predictor of efficacy of GCBT treatment,facilitating personalized treatment approaches for individuals with depression and anxiety.

Underwater sensor networks (UWSNs) play a vital role in marine exploration and environmental monitoring. However, due to the characteristics of underwater acoustic channels such as high delay, low bandwidth, and energy limitation, the design of an underwater media access control (MAC) protocol has brought great challenges, and existing MAC protocol designs rarely consider the influence of channel interference factors in networking. Therefore, this paper proposes a collision avoidance MAC protocol for clustering underwater sensor networks. The protocol first classifies users by combining the channel characteristics of underwater nodes and the distance measurement between nodes. Then, based on the clustering network, according to the channel correlation distance measurement between nodes and the communication range of the cluster head (CH), the transmit power in clusters is controlled to reduce the lifetime of the network based on the cumulative reduction in node power consumption. Finally, the cluster structure in each cluster is used to schedule the transmission of member nodes in the cluster, and at the same time, the energy consumption of nodes is reduced while multi-node collision-free transmission is realized. The simulation results show that the throughput of the proposed adaptive power control clustering MAC protocol (APCC-MAC) is 26.5% and 19.5% higher than that of packet-level slot scheduling (PLSS) algorithm and Cluster-Based Spatial–Temporal Scheduling (CSS) algorithm, respectively, providing better communication performance and stability for clustered underwater acoustic networks.

Underwater Wireless Sensor Networks (UWSNs) are pivotal for advancing maritime capabilities. These networks predominantly utilize acoustic communication, characterized by an open and shared acoustic channel and energy-limited underwater nodes, which underscores the critical importance of node authentication and management. Blockchain technology, recognized for its security, confidentiality, and traceability, is particularly suitable for scenarios requiring secure data exchange. This paper proposes a blockchain-based collaborative node authentication model tailored for clustered networks in UWSNs to tackle the challenges posed by the open nature of acoustic channels and the constrained energy resources of underwater nodes. Autonomous Underwater Vehicles (AUVs) are deployed as blockchain nodes to aid cluster heads in identity verification, while all underwater acoustic nodes are integrated as lightweight blockchain nodes, thus ensuring uniform management and authentication. Furthermore, this study enhances existing clustering algorithms to prolong the operational lifespan of the network and introduces a group signature and authentication mechanism tailored to the unique conditions of underwater blockchain edge computing. This mechanism includes a robust two-round block verification scheme designed to secure the blockchain against potential consensus algorithm attacks. Comprehensive simulations are presented, validating the effectiveness of the proposed group signature solution in enhancing the security and sustainability of underwater clustered networks.

BackgroundPrior observational research has indicated a potential link between pediatric asthma and united airways disease (UAD). However, these findings could be subject to confounding factors and reverse causation. Therefore, our study utilizes Mendelian randomization (MR) method to further investigate the causal relationship between pediatric asthma and UAD.MethodsWe conducted a comprehensive two-sample Mendelian randomization (MR) analysis to investigate the association between pediatric asthma and seven groups of UAD, including chronic sinusitis, chronic rhinitis, nasopharyngitis and pharyngitis, chronic diseases of tonsils and adenoids, chronic laryngitis and laryngotracheitis, chronic bronchitis, bronchiectasis, chronic obstructive pulmonary disease (COPD). The present study employed a range of methods for two-sample MR analysis, including inverse variance weighted (IVW), MR-Egger regression, Simple mode, weighted median, and weighted models. The conclusion of the MR analysis primarily relies on the IVW results, while other analytical methods are utilized as supplementary evidence to ensure result robustness in this MR analysis. And sensitivity analyses were conducted, including heterogeneity test, horizontal pleiotropy test, MR-PRESSO test, and leave-one-out analysis to validate the results.ResultsThe results of the MR analysis indicate significant causal effects of pediatric asthma on chronic rhinitis, nasopharyngitis and pharyngitis (IVW: OR = 1.15, 95%CI: 1.05–1.26, p-value = 0.003), chronic diseases of tonsils and adenoids (IVW: OR = 1.07, 95%CI: 1.00–1.15, p-value = 0.038), chronic bronchitis (IVW: OR = 1.51, 95%CI: 1.42–1.62, p-value

In recent years, underwater environmental monitoring has primarily relied on monitoring systems based on underwater sensor networks (UWSNs). The underwater sensor node using a self-powered monitoring system has not been widely used because of the complicated design and high cost of its energy-harvesting device. Thus, the mobile monitoring nodes within UWSNs are typically powered by batteries with limited energy, and replacement on the seabed is challenging. As a result, optimizing the energy consumption of the mobile monitoring network is of significant importance. The clustering algorithm for UWSNs is acknowledged as a vital approach to balancing and reducing network energy consumption. Nevertheless, most existing clustering algorithms employ fixed schemes to balance the energy consumption among nodes, which are unable to dynamically adapt to changes in network topology and do not account for the complexities of the underwater channel environment, thus not aligning with the actual scenarios of marine environment monitoring. Consequently, this paper introduces an adaptive clustering algorithm for marine environment monitoring (MEMAC). The algorithm incorporates the multipath channel information of the underwater environment and the traffic weight between nodes into the probability model to calculate the probability of the node being elected as the cluster head (CH). The final calculated expected revenues are the user’s revenues after participating in the game under the influence of the multipath effect, and the revenues of all users jointly determine the performance of the clustering algorithm proposed in this paper. When the energy consumption of the CH node is too much and needs to be rotated, MEMAC, through a CH rotation mechanism and a comprehensive analysis of the overall remaining energy of the network, further optimizes the CH selection strategy while ensuring network stability. Simulation results indicate that the network lifetime of the proposed MEMAC method is extended by 58.9% and 19.17% compared to the two latest clustering algorithms, the Game Theory-Based Clustering Scheme (GTC) and the Centralized Control-Based Clustering Scheme (CCCS), respectively. This demonstrates that the algorithm can achieve efficient energy utilization and notably enhance network performance.

Glass fiber-reinforced polymer composites are important structural materials and are widely used in structure engineering. In this study, a new V-notch non-standard tensile specimen is proposed. All the in-plane stiffness coefficients of glass fiber-reinforced polymer composites could be obtained by the virtual fields method with only one uniaxial tensile test. First, the special virtual fields method for inversion of elastic constitutive parameters of orthotropic materials in the uniaxial tensile test was introduced. The optimization of the geometrical design of the specimen was conducted using finite element simulation experiments. Batch modeling calculation was conducted within the designed range of the geometric parameters of the specimen. The generated ideal strain field data were substituted into the virtual fields method to invert and identify the stiffness coefficients. The optimized geometry of the specimen was determined according to the objective function of minimum error. Through a tensile experiment on glass fiber composites, the influence of specimen deformation on the identification results was assessed, and the load level suitable for parameter identification was determined. Based on the results, it can be concluded that the inversion identification accuracy meets the requirements of engineering measurement.

As an outstanding visual system for insects and crustaceans to cope with the challenges of survival, compound eye has many unique advantages, such as wide field of view, rapid response, infinite depth of field, low aberration and fast motion capture. However, the complex composition of their optical systems also presents significant challenges for manufacturing. With the continuous development of advanced materials, complex 3D manufacturing technologies and flexible electronic detectors, various ingenious and sophisticated compound eye imaging systems have been developed. This paper provides a comprehensive review on the microfabrication technologies, photoelectric detection and functional applications of miniature artificial compound eyes. Firstly, a brief introduction to the types and structural composition of compound eyes in the natural world is provided. Secondly, the 3D forming manufacturing techniques for miniature compound eyes are discussed. Subsequently, some photodetection technologies for miniature curved compound eye imaging are introduced. Lastly, with reference to the existing prototypes of functional applications for miniature compound eyes, the future development of compound eyes is prospected.

To leverage the exceptional biomedical properties of Prussian blue (PB), we synthesized Mn2+-doped hollow mesoporous Prussian blue nanocubes (Mn-HMPB) via a straightforward ion-exchange method to enhance the photothermal/chemo synergistic therapeutic efficiency. The precise modulate of nanostructures of Mn-HMPB with notable photothermal conversion efficiencies and quercetin (QUE) loading capacities. Introducing Mn2+ doping in Mn-HMPB enables pH-responsive drug release under different physiological conditions. The systematic in vitro experiments have verified that the drug release and NIR irradiation mediated by Mn-HMPB@QUE nanocubes significantly inhibit the proliferation of Hela cells, resulting in an exceptional synergistic therapeutic outcome compared to single Mn-HMPB nanocubes or free QUE. Moreover, Mn-HMPB nanocubes exhibits outstanding biocompatibility and stability in psychological environment, positioning it as a reliable nanomedicine candidate. In conclusion, this study findings demonstrate the effective inhibition of growth and metastasis of Hela cells by Mn-HMPB@QUE nanocubes, exhibiting their potential as a versatile nanoplatform for tumor therapy.

This study aims to evaluate the effect of rhythm training in the early coordination development and to find out its role in the transfer of specific performance in young swimmers. Eighty young swimmers aged eight to twelve were divided randomly into a control group (CON, n = 40, M/F=17/23) or an experimental group (EXP, n = 40, M/F=19/21). A total of 72 swimmers completed all the training programs and tests included in this study (CON, n = 35, M/F=15/20; EXP, n = 37, M/F=16/21). The training programme lasted 12 weeks. During the study period, all groups participated in the same swimming training program with the control group participating six times a week, and the experimental group participating four times a week with an additional two rhythm trainings each week. The height, weight, walking backwards ability, jumping sideways ability, and 25 m freestyle were measured for each participant and an index of coordination was analyzed from recordings of the participant’s 25 m freestyle. The results indicated that participation in rhythm training resulted in improved general motor and swimming-specific coordination among the swimmers, but the results varied by number of years of sport-specific training experience. The swimmers with more training experience improved more in swimming-specific coordination but less in general motor coordination. This study strengthens the evidence for the effectiveness of early rhythm training in swimmers, indicating that it is feasible to design programs to address general and sport-specific coordination in young athletes.

BackgroundDepression is a widespread mental disorder that affects a significant portion of the population. However, the assessment of depression is often subjective, relying on standard questions or interviews. Acoustic features have been suggested as a reliable and objective alternative for depression assessment. Therefore, in this study, we aim to identify and explore voice acoustic features that can effectively and rapidly predict the severity of depression, as well as investigate the potential correlation between specific treatment options and voice acoustic features.MethodsWe utilized voice acoustic features correlated with depression scores to train a prediction model based on artificial neural network. Leave-one-out cross-validation was performed to evaluate the performance of the model. We also conducted a longitudinal study to analyze the correlation between the improvement of depression and changes in voice acoustic features after an Internet-based cognitive-behavioral therapy (ICBT) program consisting of 12 sessions.ResultsOur study showed that the neural network model trained based on the 30 voice acoustic features significantly correlated with HAMD scores can accurately predict the severity of depression with an absolute mean error of 3.137 and a correlation coefficient of 0.684. Furthermore, four out of the 30 features significantly decreased after ICBT, indicating their potential correlation with specific treatment options and significant improvement in depression (p

Background Anthropometric characteristics are important factors that affect swimming performance. The aim of this study is to build a discriminant model using anthropometric factors to identify elite short-to-medium-distance freestyle swimmers through an adaptive Lasso approach. Methods The study recruited 254 swimmers (145 males and 109 females) who were divided them into elite (aged 17.9 ± 2.2 years, FINA points 793.8 ± 73.8) and non-elite (aged 17.1 ± 1.3 years, FINA points 560.6 ± 78.7) groups. Data for 73 variables were obtained, including basic information, anthropometric and derivative indicators. After filtering out highly correlated variables, 24 candidate variables were retained to be used in adaptive Lasso to select variables for prediction of elite swimmers. Deviance and area under the curve (AUC) were applied to assess the goodness of fit and prediction accuracy of the model, respectively. Results The adaptive Lasso selected 12 variables using the whole sample, with an AUC being 0.926 (95% CI [0.895–0.956]; P = 2.42 × 10−29). In stratified analysis by gender, nine variables were selected for male swimmers with an AUC of 0.921 (95% CI [0.880–0.963]; P = 8.82 × 10−17), and eight variables were for female swimmers with an AUC of 0.941 (95% CI [0.898–0.984]; P = 7.67 × 10−15). Conclusion The adaptive Lasso showed satisfactory performance in selecting anthropometric characteristics to identify elite swimmers. Additional studies with longitudinal data or data from other ethnicities are needed to validate our findings.

Abstract Objective The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. This study aims to identify subgroups of medical students based on depression and anxiety and explore the influencing factors during the COVID-19 epidemic in China. Methods A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Depression and anxiety symptoms were assessed using Patient Health Questionnaire 9 (PHQ9) and Generalized Anxiety Disorder 7 (GAD7) respectively. Latent class analysis was performed based on depression and anxiety symptoms in medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. Results In this study, three distinct subgroups were identified, namely, the poor mental health group, the mild mental health group and the low symptoms group. The number of medical students in each class is 4325, 9321 and 16,017 respectively. The multinomial logistic regression results showed that compared with the low symptoms group, the factors influencing depression and anxiety in the poor mental health group and mild mental health group were sex, educational level, drinking, individual psychiatric disorders, family psychiatric disorders, knowledge of COVID-19, fear of being infected, and participate in mental health education on COVID-19. Conclusions Our findings suggested that latent class analysis can be used to categorize different medical students according to their depression and anxiety symptoms during the outbreak of COVID-19. The main factors influencing the poor mental health group and the mild mental health group are basic demographic characteristics, disease history, COVID-19 related factors and behavioural lifestyle. School administrative departments can carry out targeted psychological counseling according to different subgroups to promote the physical and mental health of medical students.

Abstract Background High-fat diet (HFD)-induced obesity causes immune cells to infiltrate adipose tissue, leading to chronic inflammation and metabolic syndrome. Brown adipose tissue (BAT) can dissipate the energy produced by lipid oxidation as heat, thereby counteracting obesity. Aerobic exercise activates BAT, but the specific underlying mechanism is still unclear. Methods Male C57BL/6 J mice were divided into a normal diet control group (NC group) and HFD group (H group). After becoming obese, the animals in the H group were subdivided into a control group (HC group) and an exercise group (HE group, with treadmill training). After 4 weeks, the mRNA profile of BAT was determined, and then differentially expressed key genes and pathways were verified in vitro. Results Relative to the NC group, the genes upregulated in the HC group coded mainly for proteins involved in immune system progression and inflammatory and immune responses, while the downregulated genes regulated lipid metabolism and oxidation–reduction. Relative to the HC group, the genes upregulated in the HE group coded for glycolipid metabolism, while those that were downregulated were involved in cell death and apoptosis. VEGF and other signaling pathways were enhanced by aerobic exercise. Interaction analysis revealed that the gene encoding cyclooxygenase 2 (COX2) of the VEGF signaling pathway is central to this process, which was verified by a sympathetic activator (isoprenaline hydrochloride) and COX2 inhibitor (NS-398). Conclusions In mice with HFD-induced obesity, four weeks of aerobic exercise elevated BAT mass and increased the expression of genes related to glycolipid metabolism and anti-inflammatory processes. Several pathways are involved, with COX2 in the VEGF signaling pathway playing a key role.

The relationship between technology and society is an ever-changing dynamic, but one in which education is a key domain. In educational practice, the use of computer technology has increasingly become an inseparable part of teaching students in numerous ways across the world. The COVID-19 global pandemic accelerated this dramatically, with online teaching environments becoming the sole way for students to access education for extended periods of time. This shift to online teaching also required that teachers learn new skills and deal with new challenges. Based on mixed-methods research conducted with 20 teachers from an established content and language integrated learning school in mainland China, this research paper investigates the different challenges and problems that were faced by content and language integrated learning teachers in their experiences of online teaching and, in tandem with wider content and language integrated learning and technology-enhanced language learning literature, develops some potential solutions for future use.

With the advancements in wireless sensor networks and the Internet of Underwater Things (IoUT), underwater acoustic sensor networks (UASNs) have attracted much attention, which has also been widely used in marine engineering exploration and disaster prevention. However, UASNs still face many challenges, including high propagation latency, limited bandwidth, high energy consumption, and unreliable transmission, influencing the good quality of service (QoS). In this paper, we propose a routing protocol based on the on-site architecture (SROA) for UASNs to improve network scalability and energy efficiency. The on-site architecture adopted by SROA is different from most architectures in that the data center is deployed underwater, which makes the sink nodes closer to the data source. A clustering method is introduced in SROA, which makes the network adapt to the changes in the network scale and avoid single-point failure. Moreover, the Q-learning algorithm is applied to seek optimal routing policies, in which the characteristics of underwater acoustic communication such as residual energy, end-to-end delay, and link quality are considered jointly when constructing the reward function. Furthermore, the reduction of packet retransmissions and collisions is advocated using a waiting mechanism developed from opportunistic routing (OR). The SROA realizes opportunistic routing to choose candidate nodes and coordinate packet forwarding among candidate nodes. The scalability of the proposed routing protocols is also analyzed by varying the network size and transmission range. According to the evaluation results, with the network scale ranging from 100 to 500, the SROA outperforms the existing routing protocols, extensively decreasing energy consumption and end-to-end delay.

In microchannels, microstructure-induced acoustic streaming can be achieved at low frequencies, providing simple platforms for biomedicine and microfluidic manipulation. Nowadays, microstructures are generally fabricated by photolithography or soft photolithography. Existing studies mainly focused on the projection plane, while ignoring the side profile including microstructure’s sidewall and channel’s upper wall. Based on the perturbation theory, the article focuses on the effect of microstructure’s sidewall errors caused by machining and the viscous dissipation of upper wall on the streaming. We discovered that the side profile parameters, particularly the gap (gap g between the top of the structure and the upper wall of the channel), have a significant impact on the maximum velocity, mode, and effective area of the streaming.To broaden the applicability, we investigated boundary layer thickness parameters including frequency and viscosity. Under different thickness parameters, the effects of side profile parameters on the streaming are similar. But the maximum streaming velocity is proportional to the frequency squared and inversely proportional to the viscosity. Besides, the ratio factor θ of the maximum streaming velocity to the vibration velocity is affected by the side profile parameter gap g and sidewall profile angle α.

Disruption of circadian rhythms is related to disorders of glucose metabolism, and the molecular clock also exists in skeletal muscle. The ChIP-derived repressor of network oscillator (Chrono) and brain and muscle ARNT-like 1 (Bmal1) are core circadian components. Chrono is considered to be the repressor of Bmal1, and the Chrono–Bmal1 pathway is important in regulating the circadian rhythm; it has been speculated that this pathway could be a new mechanism for regulating glucose metabolism. The purpose of this study was to investigate the effects of Chrono on glucose metabolism in skeletal muscle and exercise capacity by using mice with skeletal-muscle-specific overexpression of Chrono (Chrono TG) and wild-type (WT) mice as the animal models. The results of this cross-sectional study indicated that the Chrono TG mice had an impaired glucose tolerance, lower exercise capacity, and higher levels of nonfasted blood glucose and glycogen content in skeletal muscle compared to WT mice. In addition, the Chrono TG mice also showed a significant increase in the amount of Chrono bound to Bmal1 according to a co-IP analysis; a remarkable decrease in mRNA expression of Tbc1d1, Glut4, Hk2, Pfkm, Pdp1, Gbe1, and Phka1, as well as in activity of Hk and protein expression of Ldhb; but higher mRNA expression of Pdk4 and protein expression of Ldha compared with those of WT mice. These data suggested the skeletal-muscle-specific overexpression of Chrono led to a greater amount of Chrono bound to Bmal1, which then could affect the glucose transporter, glucose oxidation, and glycogen utilization in skeletal muscle, as well as exercise capacity.

Objective: Alcohol use disorder (AUD) is a serious issue worldwide and frequently co-occurs with depression. However, the quality of life (QOL) of AUD patients with and without depression is not well studied in the Chinese Han population. The aim of this study was to investigate QOL and its correlates in AUD patients with and without depression in China.Methods: Five hundred and fifteen psychiatric patients diagnosed with AUD were recruited. All these patients completed the Beck Depression Inventory (BDI) to assess depression, the Medical Outcome Study 36-Item Short Form Health Survey (SF-36) to evaluate QOL and the Alcohol Use Disorders Identification Test (AUDIT) to measure the severity of drinking.Results: Compared with AUD patients without depression, those with depression had a lower QOL in all eight domains of the SF-36 (all P < 0.001), but were more willing to have alcohol-related treatment (P < 0.05). Negative correlations were noted between (i) the BDI total score and all eight domains of the SF-36 (all P < 0.001); and (ii) between the AUDIT total score and six domains of the SF-36 (all P < 0.05).Conclusions: Depression impairs QOL in patients with AUD in China. Early intervention in comorbid depression to improve QOL is needed.

Bipolar II disorder (BD-II) major depression episode is highly associated with suicidality, and objective neural biomarkers could be key elements to assist in early prevention and intervention. This study aimed to integrate altered brain functionality in the frontolimbic system and machine learning techniques to classify suicidal BD-II patients and predict suicidality risk at the individual level. A cohort of 169 participants were enrolled, including 43 BD-II depression patients with at least one suicide attempt during a current depressive episode (SA), 62 BD-II depression patients without a history of attempted suicide (NSA), and 64 demographically matched healthy controls (HCs). We compared resting-state functional connectivity (rsFC) in the frontolimbic system among the three groups and explored the correlation between abnormal rsFCs and the level of suicide risk (assessed using the Nurses' Global Assessment of Suicide Risk, NGASR) in SA patients. Then, we applied support vector machines (SVMs) to classify SA vs. NSA in BD-II patients and predicted the risk of suicidality. SA patients showed significantly decreased frontolimbic rsFCs compared to NSA patients. The left amygdala-right middle frontal gyrus (orbital part) rsFC was negatively correlated with NGASR in the SA group, but not the severity of depressive or anxiety symptoms. Using frontolimbic rsFCs as features, the SVMs obtained an overall 84% classification accuracy in distinguishing SA and NSA. A significant correlation was observed between the SVMs-predicted NGASR and clinical assessed NGASR (r = 0.51, p = 0.001). Our results demonstrated that decreased rsFCs in the frontolimbic system might be critical objective features of suicidality in BD-II patients, and could be useful for objective prediction of suicidality risk in individuals.

BackgroundDepressive symptoms are common among psychiatric patients with alcohol dependence (AD). However, the prevalence and clinical correlates of comorbid depressive symptoms are less well studied in Chinese Han patients.MethodsIn this hospital-based survey, we recruited 378 psychiatric patients diagnosed with AD according to the Diagnostic and Statistical Manual of Mental Disorders-Fourth Edition (DSM-IV). All patients completed the Beck Depression Inventory (BDI) to evaluate depressive symptoms and the Alcohol Use Disorders Identification Test (AUDIT) to assess the severity of drinking.ResultsCompared to patients without depressive symptoms, 48.9% (185/378) of the patients with comorbid depressive symptoms were younger, had a more unstable marital status, had a higher AUDIT total score, and had a higher adverse consequences subscore (all P < 0.05). Further logistic regression analysis showed that unstable marital status (Odds ratios [OR] = 2.20, 95% confidence interval [CI] 1.21–3.99) and AUDIT total score (OR=1.07, 95% CI 1.03–1.11) were significantly associated with depressive symptoms.ConclusionsOur findings indicate high comorbidity between AD and depressive symptoms in Chinese psychiatric patients. Moreover, some variables are correlates of comorbid depressive symptoms. Particular attention should be paid to the early detection and intervention for this comorbid condition and its risk factors.

Background: Major depressive disorders often involve somatic symptoms and have been found to have fundamental differences from non-somatic depression (NSD). However, the neural basis of this type of somatic depression (SD) is unclear. The aim of this study is to use the amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) analyses to examine the abnormal, regional, spontaneous, neuronal activity and the corresponding brain circuits in SD patients.Methods: 35 SD patients, 25 NSD patients, and 27 matched healthy controls were selected to complete this study. The ALFF and seed-based FC analyses were employed, and the Pearson correlation was determined to observe possible clinical relevance.Results: Compared with NSD, the SD group showed a significant ALFF increase in the right inferior temporal gyrus; a significant ALFF decrease in left hippocampus, right inferior frontal orbital gyrus and left thalamus; and a significant decrease in the FC value between the right inferior frontal orbital gyrus and the left inferior parietal cortex (p < 0.05, corrected). Within the SD group, the mean ALFF value of the right inferior frontal orbital gyrus was associated with the anxiety factor scores (r = –0.431, p = 0.010, corrected).Conclusions: Our findings suggest that abnormal differences in the regional spontaneous neuronal activity of the right inferior frontal orbital gyrus were associated with dysfunction patterns of the corresponding brain circuits during rest in SD patients, including the limbic-cortical systems and the default mode network. This may be an important aspect of the underlying mechanisms for pathogenesis of SD at the neural level.

Previous studies have found that neural functional abnormalities detected by functional magnetic resonance imaging (fMRI) in brain regions implicated in reward processing during reward tasks show promise to distinguish bipolar from unipolar depression (UD), but little is known regarding resting-state functional connectivity (rsFC) within the reward circuit. In this study, we investigated neurobiomarkers for early recognition of bipolar disorder (BD) by retrospectively comparing rsFC within the reward circuit between UD and depressed BD. Sixty-six depressed patients were enrolled, none of whom had ever experienced any manic/hypomanic episodes before baseline. Simultaneously, 40 matched healthy controls (HC) were also recruited. Neuroimaging data of each participant were obtained from resting-state fMRI scans. Some patients began to manifest bipolar disorder (tBD) during the follow-up period. All patients were retrospectively divided into two groups (33 tBD and 33 UD) according to the presence or absence of mania/hypomania in the follow-up. rsFC between key regions of the reward circuit was calculated and compared among groups. Results showed decreased rsFC between the left ventral tegmental area (VTA) and left ventral striatum (VS) in the tBD group compared with the UD group, which showed good accuracy in predicting diagnosis (tBD vs. UD) according to receiver operating characteristic (ROC) analysis. No significant different rsFC was found within the reward circuit between any patient group and HC. Our preliminary findings indicated that bipolar disorder, in early depressive stages before onset of mania/hypomania attacks, already differs from UD in the reward circuit of VTA-VS functional synchronicity at the resting state.

Background: Sleep disturbances, such as early wakening, are frequently observed in patients with major depressive disorder (MDD). The suprachiasmatic nuclei (SCN), which controls circadian rhythm, is innervated by the raphe nucleus, a region where Tryptophan hydroxylase-2 (TPH-2) gene is primarily expressed. Although TPH-2 is often implicated in the pathophysiology of depression, few studies have applied a genetic and imaging technique to investigate the mechanism of early wakening symptom in MDD. We hypothesized that TPH-2 variants could influence the function of SCN in MDD patients with early wakening symptom.Methods: One hundred and eighty five MDD patients (62 patients without early wakening and 123 patients with early wakening) and 64 healthy controls participated in this study. Blood samples were collected and genotyping of rs4290270, rs4570625, rs11178998, rs7305115, rs41317118, and rs17110747 were performed by next-generation sequencing (NGS) technology. Logistic regression model was employed for genetic data analysis using the PLINK software. Based on the allele type, rs4290270, which was significant in the early wakening MDD group, participants were categorized into two groups (A allele and T carrier). All patients underwent whole brain resting-state functional magnetic resonance imaging (rs-fMRI) scanning and a voxel-wise functional connectivity comparison was performed between the groups.Results: rs4290270 was significantly linked to MDD patients who exhibited early wakening symptom. The functional connectivities of the right SCN with the right fusiform gyrus and right middle frontal gyrus were increased in the T carrier group compared to the A allele group. In addition, the functional connectivities of the left SCN with the right lingual gyrus and left calcarine sulcus were decreased in the T carrier group compared to the A allele group.Conclusion: These findings suggested that the TPH-2 gene variant, rs4290270, affected the circadian regulating function of SCN. The altered functional connectivities, observed between the SCN and right fusiform gyrus, right middle frontal gyrus, the right lingual gyrus and left calcarine sulcus, could highlight the neural mechanism by which SCN induces sleep-related circadian disruption in T carrier MDD patients. Hence, rs4290270 could potentially serve as a reliable biomarker to identify MDD patients with early wakening symptom.

A bioflocculant (MBFA18) was produced by Aspergillus niger (A18) using potato starch wastewater (PSW) as nutrients. The cultivation processes and flocculating treatment for PSW purification were systematically studied. The flocculating rate of the MBFA 18 achieved 90.06% (kaolin clay) under the optimal cultivation condition (PSW with 5950 mg/L COD, 20 g/L glucose, 0.2 g/L urea and without phosphorus source addition and pH adjustment). Furthermore, effects of flocculant dosage, initial pH, coagulant aid (CaCl2) addition and sedimentation time on the PSW treatment were discussed and studied in detail. The optimum flocculation treatment conditions were determined according to the treatment efficiency, cost and flocculation conditions. During the PSW treatment, 2 mL/L bioflocculant (1.89 g/L) dosage and 0.5 mol/L coagulant aid addition were applied without pH adjustment and 91.15% COD and 60.22% turbidity removal rate could be achieved within 20 min. The comparative study between the bioflocculant and conventional chemical flocculants showed excellent flocculating efficiency of MBFA 18 with lower cost (4.7 yuan/t), which indicated that the bioflocculant MBFA 18 produced in PSW substrate has a great potential to be an alternative flocculant in PSW treatment.