Search

Abstract Background Residual symptoms of depressive disorders are serious health problems. However, the progression process is hardly predictable due to high heterogeneity of the disease. This study aims to: (1) classify the patterns of changes in residual symptoms based on homogeneous data, and (2) identify potential predictors for these patterns. Methods In this study, we conducted a data-driven Latent Class Growth Analysis (LCGA) to identify distinct tendencies of changes in residual symptoms, which were longitudinally quantified using the QIDS-SR16 at baseline and 1/3/6 months post-baseline for depressed patients. The association between baseline characteristics (e.g. clinical features and cognitive functions) and different progression tendencies were also identified. Results The tendency of changes in residual symptoms was categorized into four classes: “light residual symptom decline (15.4%)”, “residual symptom disappears (39.3%)”, “steady residual symptom (6.3%)” and “severe residual symptom decline (39.0%)”. We observed that the second class displayed more favorable recuperation outcomes than the rest of patients. The severity, recurrence, polypharmacy, and medication adherence of symptoms are intricately linked to the duration of residual symptoms’ persistence. Additionally, clinical characteristics including sleep disturbances, depressive moods, alterations in appetite or weight, and difficulties with concentration have been identified as significant factors in the recovery process. Conclusions Our research findings indicate that certain clinical characteristics in patients with depressive disorders are associated with poor recovery from residual symptoms following acute treatment. This revelation holds significant value in the targeted attention to specific patients and the development of early intervention strategies for residual symptoms accordingly.

Accurately determining if the sample parameters from a natural gas pipeline’s sampling system reflect the fluid characteristics of the main pipe has been a significant industry concern for many years. In this paper, samples of natural gas in a horizontal pipeline are investigated. CFD is used in this work and the turbulence is considered in the simulation. Firstly, the critical diameter for particles affected by gravity within such pipeline is determined. And then, the effects of the operation pressure and velocity of sampling branches on sample parameters, and the influence of particle density on these sample parameters, are analyzed. Finally, four different structures of sample branches for natural gas in a horizontal pipeline are compared. It is found that 100 μm is the critical diameter at which particles are affected by gravity; the operating pressure of the sampling branch has a significant impact on the particle mass concentration. The particle density has little impact on the sampling system. Overall, the design of the sampling branches does not cause significant sampling errors. This study provides guidance for optimal sampling in existing natural gas pipelines and enables effective monitoring of particle impurity content and properties in natural gas.

Background: Endoplasmic reticulum (ER) stress plays a pro-apoptotic role in colorectal adenocarcinoma (COAD). This study aimed to develop a novel ER-stress-related prognostic risk model for COAD and provide support for COAD cohorts with different risk score responses to immune checkpoint inhibitor therapies. Methods: TCGA-COAD and GSE39582 were included in this prospective study. Univariate and multivariate Cox analyses were performed to identify prognostic ER stress-related genes (ERSGs). Accordingly, the immune infiltration landscape and immunotherapy response in different risk groups were assessed. Finally, the expression of prognostic genes in 10 normal and 10 COAD tissue samples was verified using reverse transcription-quantitative polymerase chain reaction. Results: Eight prognostic genes were selected to establish an ERSG-based signature in the training set of the TCGA-COAD cohort. The accuracy of this was confirmed using a testing set of TCGA-COAD and GSE39582 cohorts. Gene set variation analysis indicated that differential functionality in high–low-risk groups was related to immune-related pathways. Corresponding to this, CD36 , TIMP1 , and PTGIS were significantly associated with 19 immune cells with distinct proportions between the different risk groups, such as central memory CD4T cells and central memory CD8T cells. Moreover, the risk score was considered effective for predicting the clinical response to immunotherapy, and the immunotherapy response was significantly and negatively correlated with the risk score of individuals with COAD. Furthermore, the immune checkpoint inhibitor treatment was less effective in the high-risk group, where the expression levels of PD-L1 and tumor immune dysfunction and exclusion scores in the high-risk group were significantly increased. Finally, the experimental results demonstrated that the expression trends of prognostic genes in clinical samples were consistent with the results from public databases. Conclusion: Our study established a novel risk signature to predict the COAD prognosis of patients and provide theoretical support for the clinical treatment of COAD.

Major depressive disorder (MDD) is a prevalent mental health condition and has become a pressing societal challenge. Early prediction of treatment response may aid in the rehabilitation engineering of depression, which is of great practical significance for the relief of suffering and burden of MDD. In this paper, we present a sequence modeling approach that uses data collected by passive sensing techniques to predict patients with an outcome of treatment responded defined by the reduction in clinical administrated scales. Hundreds of patients with MDD have been recruited from outpatient clinics at 4 psychiatric sites. Each has been delivered with a self-developed app to passively record their daily phone usage and physical data with minimal human action. An unavoidable dilemma in passive sensing is missing values. To overcome that, the proposed approach combined feature extraction and sequence modeling methods to fully utilize the pattern of missing values from longitudinal data. With no treatment constraints, it enables us to predict the treatment response of MDD 8–10 weeks before the completion of the treatment course, leaving time for preventative measures. Our work explored the feasibility of treatment response prediction using longitudinal passive sensing data and sparse ground truth, and also has the potential for preventing depression by forecasting treatment outcomes weeks in advance.

AimThis study aims to establish a nomogram model to predict the relevance of SA in Chinese female patients with mood disorder (MD).MethodThe study included 396 female participants who were diagnosed with MD Diagnostic Group (F30–F39) according to the 10th Edition of Disease and Related Health Problems (ICD-10). Assessing the differences of demographic information and clinical characteristics between the two groups. LASSO Logistic Regression Analyses was used to identify the risk factors of SA. A nomogram was further used to construct a prediction model. Bootstrap re-sampling was used to internally validate the final model. The Receiver Operating Characteristic (ROC) curve and C-index was also used to evaluate the accuracy of the prediction model.ResultLASSO regression analysis showed that five factors led to the occurrence of suicidality, including BMI (β = −0.02, SE = 0.02), social dysfunction (β = 1.72, SE = 0.24), time interval between first onset and first dose (β = 0.03, SE = 0.01), polarity at onset (β = −1.13, SE = 0.25), and times of hospitalization (β = −0.11, SE = 0.06). We assessed the ability of the nomogram model to recognize suicidality, with good results (AUC = 0.76, 95% CI: 0.71–0.80). Indicating that the nomogram had a good consistency (C-index: 0.756, 95% CI: 0.750–0.758). The C-index of bootstrap resampling with 100 replicates for internal validation was 0.740, which further demonstrated the excellent calibration of predicted and observed risks.ConclusionFive factors, namely BMI, social dysfunction, time interval between first onset and first dose, polarity at onset, and times of hospitalization, were found to be significantly associated with the development of suicidality in patients with MD. By incorporating these factors into a nomogram model, we can accurately predict the risk of suicide in MD patients. It is crucial to closely monitor clinical factors from the beginning and throughout the course of MD in order to prevent suicide attempts.

Prior research has indicated that animal models of abdominal aortic aneurysm (AAA) utilizing porcine pancreatic elastase (PPE) exhibit a perfusion duration of 30 min, and extended perfusion durations are associated with elevated mortality rates. Similarly, the AAA model, which relies solely on balloon dilation (BD), is limited by the occurrence of self-healing aneurysms. Consequently, we constructed a novel AAA model by PPE combined with balloon expansion to shorten the modeling time and improve the modeling success rate. The findings indicated that 5 min was the optimal BD time for rabbits, 3 min BD was ineffective for aneurysm formation, and 10 min BD had a high mortality rate. The model, constructed in combination with PPE and 5 min BD, exhibited a 100% model formation rate and a 244.7% ± 9.83% dilation rate. HE staining exhibited that severe disruption of the inner, middle, and outer membranes of the abdominal aorta, with a marked decrease in smooth muscle cells and elastase, and a marked increase in fibroblasts of the middle membrane, and many infiltrating inflammatory cells were seen in all three layers, especially in the middle membrane. EVG staining displayed that the elastic fibers of the abdominal aortic wall were fractured and degraded, and lost their normal wavy appearance. The protein expression of inflammatory factor (IL-1β, IL-6 and TNF-α) as well as extracellular matrix components (MMP-2 and MMP-9) were significantly increased compared to PPE and 5 min BD alone. In conclusion, PPE combined with BD allows the establishment of a novel AAA model that closely mimics human AAA in terms of histomorphology, inflammatory cell infiltration, and vascular stromal destruction. This model provides an ideal animal model for understanding the pathogenesis of AAA.

BackgroundThis study aims to investigate the factors associated with sexual dysfunction (SD), with a particular focus on the influence of sex on the occurrence and severity of this condition in patients with major depressive disorder (MDD).MethodSociodemographic and clinical assessments were conducted on 273 patients with MDD (female = 174, male = 99), including the ASEX, QIDS-SR16, GAD-7, and PHQ-15. Univariate analyses, independent samples t-test, Chi-square test, and Fisher’s exact test were used as appropriate, and logistic regression analysis was used to identify correlation factors for SD. Statistical analyses were performed using the Statistical Analysis System (SAS 9.4).ResultSD was reported in 61.9% of the participants (ASEX score = 19.6 ± 5.5), and the prevalence of it in females (75.3%, ASEX score = 21.1 ± 5.4) was significantly higher than that in males (38.4%, ASEX score = 17.1 ± 4.6). Factors associated with SD included being female, being aged 45 years or above, having a low monthly income (≤750 USD), feeling more sluggish than usual (a QIDS-SR16 Item 15 score of 1 or above), and having somatic symptoms (evaluated with the total score of PHQ15).LimitationThe use of antidepressants and antipsychotics might be a confounding factor affecting sexual function. Also, the lack of information in the clinical data regarding the number, duration, and time of onset of the episodes limits the richness of the results.ConclusionOur findings reveal the sex differences in the prevalence and severity of SD in patients with MDD. Evaluated with the ASEX score, female patients showed significantly worse sexual function than male patients. Being female, having a low monthly income, being aged 45 years or above, feeling sluggish, and having somatic symptoms may increase the risk of SD in patients with MDD.

Abstract Background Despite the recent findings presenting the benefits of measurement-based care (MBC) compared to treatment as usual (TAU), MBC is still not the standard of care used in clinical settings. The aim of the present study was to achieve the optimization of MBC (OMBC) for major depressive disorder (MDD) by establishing a comprehensive MBC framework based on all-round, continuous assessment for depression. Methods The target recruitment size is 900 patients, and the study is conducted at 8 centers in China. The patients are randomly assigned to the MBC and TAU groups at a 2:1 ratio. The subjects are scheduled to remain for 12 weeks in the acute phase and for 12 months in the maintenance phase. The primary outcomes are the complete remission rate and the proportion of patients with a 16-item Quick Inventory of Depressive Symptomatology–Self-Report (QIDS-SR 16) total score ≤ 5 of the MBC and TAU groups at the acute phase, and the recurrence rate/time between the two groups is measured at the maintenance phase. Secondary outcomes included the changes in the parameters QIDS-SR 16, Patient Health Questionnaire-9 (PHQ-9), and 17-item Hamilton Rating Scale for Depression (HAMD-17) from baseline and the response rate between the two groups at the acute phase as well as the comparison of recurrence rate between the two groups at the end of the study. Trial registration Chinese Clinical Trial Registry, ChiCTR-OOC-17012566 . The registration was performed retrospectively on 4 September 2017.

Abstract Background The combination of antipsychotics is not well studied among non-psychotic major depressive disorder (MDD). This study aims to explore the antipsychotics use in this population and its associated factors. Methods This cross-sectional and multi-site study was conducted in 11 sites of China. one Thousand five hundred three eligible MDD patients after 8–12 weeks of antidepressant treatment were included consecutively. A structured questionnaire was used to obtain socio-demographic data and medical histories. The Chinese version of the Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR), the Patient Health Questionnaire-15 (PHQ-15) and the Sheehan Disability Scale (SDS) were used for patient self-rating. Logistic regression model was used to explore the associated factors that could potentially be influential for the use antipsychotic augmentation. Results Overall, quetiapine (43.4%) was the most commonly used as an adjunct to antidepressants, followed by olanzapine (38.8%). And antipsychotics were commonly combined with escitalopram (23.1%), venlafaxine (21.7%), sertraline (14.8%). The factors influencing the combination of antipsychotics in non-psychotic depressed patients included service setting (OR = 0.444; p

Abstract Several care models have been developed to improve treatment for depression, all of which provide “enhanced” evidence-based care (EEC). The essential component of these approaches is Measurement-Based Care (MBC). Specifically, Collaborative Care (CC), and Algorithm-guided Treatment (AGT), and Integrated Care (IC) all use varying forms of rigorous MBC assessment, care management, and/or treatment algorithms as key instruments to optimize treatment delivery and outcomes for depression. This meta-analysis systematically examined the effectiveness of EEC versus usual care for depressive disorders based on cluster-randomized studies or randomized controlled trials (RCTs). PubMed, the Cochrane Library, and PsycInfo, EMBASE, up to January 6th, 2020 were searched for this meta-analysis. The electronic search was supplemented by a manual search. Standardized mean difference (SMD), risk ratio (RR), and their 95% confidence intervals (CIs) were calculated and analyzed. A total of 29 studies with 15,255 participants were analyzed. EEC showed better effectiveness with the pooled RR for response of 1.30 (95%CI: 1.13–1.50, I 2 = 81.9%, P

Aircraft maintenance technology is related to flight safety. It is not only a significant guarantee for the safety of passengers’ lives and property but also an important means for airlines to reduce costs and improve profitability. This paper focuses on aircraft maintenance decision-making, analyzes the current situation and defects of maintenance decision-making, applies knowledge graph technology to this field, and constructs aircraft maintenance knowledge graph; through the character analysis of sample data and experimental comparison, the key factors affecting the accuracy of aircraft maintenance decisions are found; few-shot knowledge reasoning technology is introduced to improve the maintenance strategy reasoning algorithm. In the 737NG aircraft maintenance decision-making system of an airline, the feasibility and effectiveness of the method are verified, which provides strong support for flight safety.

Smart dental materials are designed to intelligently respond to physiological changes and local environmental stimuli to protect the teeth and promote oral health. Dental plaque, or biofilms, can substantially reduce the local pH, causing demineralization that can then progress to tooth caries. Progress has been made recently in developing smart dental materials that possess antibacterial and remineralizing capabilities in response to local oral pH in order to suppress caries, promote mineralization, and protect tooth structures. This article reviews cutting-edge research on smart dental materials, their novel microstructural and chemical designs, physical and biological properties, antibiofilm and remineralizing capabilities, and mechanisms of being smart to respond to pH. In addition, this article discusses exciting and new developments, methods to further improve the smart materials, and potential clinical applications.

Abstract Background In the population of postmenopausal patients with major depressive disorder (MDD), the superiority of serotonin-norepinephrine reuptake inhibitors (SNRIs) over selective serotonin reuptake inhibitors (SSRIs) has not yet been definitively proven. Consequently, a direct comparison of the efficacy of SSRIs and SNRIs in the treatment of postmenopausal depression could provide relevant data. The aim of this study was to compare the efficacy and safety of venlafaxine vs. fluoxetine in the treatment of postmenopausal MDD. Methods This was an 8-week, multicenter, randomized, single-blind, active-controlled trial conducted at a psychiatric hospital (Beijing Anding Hospital) and a general hospital (Beijing Chaoyang Hospital) between April 2013 and September 2017. The primary outcome measure was improving depressive symptoms (Hamilton Depression Rating Scale (HAMD-24) score). The secondary outcomes included the change of HAMD-24 anxiety/somatization factor score and Clinical Global Impressions-Improvement (CGI-I) response rate. Safety was assessed by treatment-emergent adverse events (TEAEs) and laboratory tests. Efficacy was analyzed by using the full analysis set (FAS) following the modified intention-to-treat (mITT) principle. The primary endpoint measurements were analyzed using a mixed-effect model for repeated measures (MMRM) model with patients as a random-effect factor, treatment group as the independent variable, time as a repeated measure, and baseline covariates, using a first-order ante dependence covariance matrix. Results A total of 184 women were randomized. The full analysis set (FAS) included 172 patients (venlafaxine, n = 82; fluoxetine, n = 90). Over the 8-week study period, the reduction in HAMD-24 scores was significant (P

ObjectiveTo explore the correlation between word frequency and 17 items of the Hamilton Depression Scale (HAMD-17) in assessing the severity of depression in clinical interviews.MethodsThis study included 70 patients with major depressive disorder (MDD) who were hospitalized in the Beijing Anding Hospital. Clinicians interviewed eligible patients, collected general information, disease symptoms, duration, and scored patients with HAMD-17. The words used by the patients during the interview were classified and extracted according to the HowNet sentiment dictionary, including positive evaluation words, positive emotional words, negative evaluation words, negative emotional words. Symptom severity was grouped according to the HAMD-17 score: mild depressive symptoms is 8–17 points, moderate depressive symptoms is 18–24 points and severe depressive symptoms is >24 points. Analysis of Variance (ANOVA) was used to analyze the four categories of words among the groups, and partial correlation analysis was used to analyze the correlation between the four categories of word frequencies based on HowNet sentiment dictionary and the HAMD-17 scale to evaluate the total score. Receiver operating characteristic (ROC) curves were used to determine meaningful cut-off values.ResultsThere was a significant difference in negative evaluation words between groups (p = 0.032). After controlling for gender, age and years of education, the HAMD-17 total score was correlated with negative evaluation words (p = 0.009, r = 0.319) and negative emotional words (p = 0.027, r = 0.272), as the severity of depressive symptoms increased, the number of negative evaluation and negative emotional words in clinical interviews increased. Negative evaluation words distinguished patients with mild and moderate-severe depression. The area under the curve is 0.693 (p = 0.006) when the cut-off value is 8.48, the Youden index was 0.41, the sensitivity was 55.2%, and the specificity was 85.4%.ConclusionIn the clinical interview with MDD patients, the number of word frequencies based on HowNet sentiment dictionary may be beneficial in evaluating the severity of depressive symptoms.

The issue of sentiment classification in short-term and small-scale data scenarios is considered in this paper. It is a hot topic because the text sentiment classification task in the public opinion analysis scene has two characteristics: short time and small data scale. Existing work focused on improving the accuracy at the cost of data and training time, without considering scenarios where time and data are lacked. The most commonly used method to solve the problem of small data scale is to use multi-modal information such as pictures, sounds and videos, which will lead to unbearable training time. The shorter training time determines that the classification model is generally selected as a deep neural network with fewer layers, such as TextCNN, TextRNN, and so on. However, such models are limited by the structure and have a low classification accuracy. In order to solve both short-term and small-scale data problems, a common information user attribute on social media is added to the model as multimodal information, which includes twelve attributes such as user age, location, and posting time. This paper proposed a sentiment classification algorithm based on multi-modal social media text information. The algorithm makes use of parallel convolutional neural networks (CNN) and recurrent neural network (RNN) to process text information and user attributes respectively, and combines the feature vectors of the two models for classification, which is called User attributes Convolutional and Recurrent Neural Network (UCRNN). The addition of user attributes can improve accuracy, and the CNN network used to extract user attributes features has fewer parameters, which proves that the algorithm can achieve high accuracy under short-term and small-scale data. Experiments verify that the training time of this model is slightly less than TextRNN. The classification accuracy can reach 90.2%, which is the state-of-the-art in the field of short-term and small-scale data sentiment classification.

OBJECTIVES: The objective of the study is to describe the association of high-sensitivity C-reactive protein (hsCRP) in patients with severe coronavirus disease-19 (COVID-19) pneumonia and determine the relationship between hsCRP, prognosis, and other factors. METHODS: The hsCRP level and other clinical data of patients with severe COVID-19 pneumonia in Tongji Hospital were retrospectively reviewed. Unpaired t-test and Mann–Whitney test were used to compare difference in data between groups. Spearman analysis was used to analyze the correlation. RESULTS: Fifty-seven patients (25 male [43.9%] and 32 female [56.1%]) were included, and their condition was monitored for 28 days. The baseline hsCRP level was 35.5 ± 81.9 mg/L, and the hsCRP level of 49 patients increased (86.0%). Moreover, 45 (78.9%) patients recovered to ordinary/mild COVID-19 pneumonia and 12 (21.1%) progressed to critical COVID-19 pneumonia or continued having severe pneumonia. In the recovered group, the hsCRP level decreased significantly (P = 0.0023). The hsCRP level in the deteriorated group was significantly higher than that of the recovered group (P = 0.0019 and P = 0.0004). ΔhsCRP was significantly correlated with time from onset (P = 0.0151), prognosis (P = 0.0151), ΔD-dimer level (P = 0.0320), Δwhite blood cell count (P = 0.0018), and Δneutrophil count (P = 0.0061). CONCLUSIONS: Severe COVID-19 pneumonia is crucial to patient prognosis and manifests high hsCRP levels; decreased hsCRP level is associated with improved patient prognosis, but hsCRP level may not increase in patients with deteriorating conditions. Simultaneous increase in hsCRP and D-dimer levels may indicate risk for venous thromboembolism in COVID-19 patients.

The research on the expression of emotion in human-computer dialogue can greatly improve the user experience. Existing research has paid a lot of attention to how to generate specific emotional content and how to improve the extraction rate of emotions, while ignoring the reduction of emotion expression caused by factors such as topics and emotions added to the encoder. This paper proposes a novel Topic-extended Emotional Conversation Generation Model Based on Joint Decoding (TECM-JD). The model embeds the specified emotion category as an additional input into the emotional independent unit of the decoder, in order to reduce the expression of the content affected by adding emotion into the model. The joint attention mechanism is used to obtain the input sequence content and the input sequence topic word content obtained by the Twitter LDA model, which ensures that the output topic and the input are under the same topic. The experimental results show that the proposed model can generate richer emotional content related to the topic and have good performance and are superior to traditional dialogue models.

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing algorithms which are not fully understood and utilized. In this paper, we propose a novel method based on deep learning for timing analysis of modularized detectors without explicit needs of labeling event data. By taking advantage of the intrinsic time correlations, a label-free loss function with a specially designed regularizer is formed to supervise the training of neural networks (NNs) towards a meaningful and accurate mapping function. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model. The proposed method is validated on two experimental datasets based on silicon photomultipliers as main transducers. In the toy experiment, the NN model achieves the single-channel time resolution of 8.8 ps and exhibits robustness against concept drift in the dataset. In the electromagnetic calorimeter experiment, several NN models (fully-connected, convolutional neural network and long short-term memory) are tested to show their conformance to the underlying physical constraint and to judge their performance against traditional methods. In total, the proposed method works well in either ideal or noisy experimental condition and recovers the time information from waveform samples successfully and precisely.

Background: Insomnia is a common clinical manifestation in patients with depression. Insomnia is not only a depression symptom but also an independent risk factor for recurrence. Cordyceps militaris (C. militaris) is thought to have the potential to treat insomnia. This study aimed to examine the efficacy and safety of duloxetine with C. militaris in improving sleep symptoms in patients with depression.Methods: This study was a single-center, randomized, double-blind, placebo-controlled study that recruited outpatients admitted to Beijing Anding hospital from January 2018 to January 2019. Major depressive disorder (MDD) with insomnia was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria and Mini-International Neuropsychiatric Interview (M.I.N.I.). Eligible subjects will be randomly assigned to two treatment groups in a 1:1 ratio, and receive treatment and follow-up of about 6 weeks of duloxetine plus Cordyceps militaris or placebo, respectively. The severity of depression and insomnia was evaluated at baseline and at 1, 2, 4, and 6 weeks using the 17-item Hamilton Depression Scale (HAMD-17) and Athens Insomnia Scale (AIS).Results: A total of 59 subjects were included in the study (31 in the placebo group and 28 in the C. militaris group). 11 (18.6%) participants withdrew during the study period, 5 (17.9%) in the C. militaris group, and 6 (19.3%) in the placebo group. Depressive and sleep symptoms in all patients reduced over time. We found that the total scores of AIS and its subscales decreased more in the placebo group compared to the C. militaris group (p < 0.05). Secondary outcome revealed that there were no significant differences between the two groups in total HAMD-17 and its sleep factor scores (p > 0.05) at 1, 2, 4, and 6 weeks after treatment initiation. The incidences of adverse events were not significantly different between the two groups (all p > 0.05).Conclusion:C. militaris at the current dose and duration did not improve sleep symptoms in patients with depression, but it is safe with rare side effects.

Bone tissue engineering is a promising approach that uses seed-cell-scaffold drug delivery systems to reconstruct bone defects caused by trauma, tumors, or other diseases (e.g., periodontitis). Metformin, a widely used medication for type II diabetes, has the ability to enhance osteogenesis and angiogenesis by promoting cell migration and differentiation. Metformin promotes osteogenic differentiation, mineralization, and bone defect regeneration via activation of the AMP-activated kinase (AMPK) signaling pathway. Bone tissue engineering depends highly on vascular networks for adequate oxygen and nutrition supply. Metformin also enhances vascular differentiation via the AMPK/mechanistic target of the rapamycin kinase (mTOR)/NLR family pyrin domain containing the 3 (NLRP3) inflammasome signaling axis. This is the first review article on the effects of metformin on stem cells and bone tissue engineering. In this paper, we review the cutting-edge research on the effects of metformin on bone tissue engineering. This includes metformin delivery via tissue engineering scaffolds, metformin-induced enhancement of various types of stem cells, and metformin-induced promotion of osteogenesis, angiogenesis, and its regulatory pathways. In addition, the dental, craniofacial, and orthopedic applications of metformin in bone repair and regeneration are also discussed.

Postpartum depression (PPD) is a common and serious mental health problem that is associated with maternal suffering and numerous negative consequences for offspring. The benefit of breastfeeding for the infant and mother is well documented; therefore, the information about the risk–benefit of antidepressants, if used while mothers are breastfeeding, is necessary for the clinician’s decision. The case series and systematic data on antidepressants in breastfeeding consist mainly of selective serotonin reuptake inhibitors (SSRIs), serotonin–norepinephrine reuptake inhibitors (SNRIs), and mirtazapine, whereas information on newer antidepressants such as agomelatine in pregnant or lactating women is rare, especially the adverse effects on the infant of the mother with PPD treated with agomelatine. To add to the limited available data, we report the case of agomelatine treatment in a breastfeeding woman with PPD. In this case report, we took advantage of the short half-life of agomelatine to reduce the potential effect on infant in the treatment of a nursing woman with PPD. The results confirm the effectiveness of agomelatine in the treatment of PPD and demonstrate the safety in breastfeeding.

Neurodevelopmental disorders are psychiatric diseases that are usually first diagnosed in infancy, childhood and adolescence. Autism spectrum disorder (ASD) is a neurodevelopmental disorder, characterized by core symptoms including impaired social communication, cognitive rigidity and repetitive behavior, accompanied by a wide range of comorbidities such as intellectual disability (ID) and dysmorphisms. While the cause remains largely unknown, genetic, epigenetic, and environmental factors are believed to contribute toward the onset of the disease. Autism Susceptibility Candidate 2 (Auts2) is a gene highly associated with ID and ASD. Therefore, understanding the function of Auts2 gene can provide a unique entry point to untangle the complex neuronal phenotypes of neurodevelpmental disorders. In this review, we discuss the recent discoveries regarding the molecular and cellular functions of Auts2. Auts2 was shown to be a key-regulator of transcriptional network and a mediator of epigenetic regulation in neurodevelopment, the latter potentially providing a link for the neuronal changes of ASD upon environmental risk-factor exposure. In addition, Auts2 could synchronize the balance between excitation and inhibition through regulating the number of excitatory synapses. Cytoplasmic Auts2 could join the fine-tuning of actin dynamics during neuronal migration and neuritogenesis. Furthermore, Auts2 was expressed in developing mouse and human brain regions such as the frontal cortex, dorsal thalamus, and hippocampus, which have been implicated in the impaired cognitive and social function of ASD. Taken together, a comprehensive understanding of Auts2 functions can give deep insights into the cause of the heterogenous manifestation of neurodevelopmental disorders such as ASD.

Abstract Background Major depressive disorder (MDD) is a prevalent and disabling disorder that can lead to heavy individual, familial, and societal burdens. Although pharmaceutical interventions still play an essential role in therapeutic measures, limitations, including effects that are delayed for weeks, are noteworthy. Antidepressants with rapid efficacy and acceptable tolerance have been investigated for many years; rapid antidepressant effects and promising clinical applications have been obtained with intravenous and oral scopolamine. This study aims to evaluate the efficacy of repeated intramuscular scopolamine as an add-on treatment to escitalopram. Methods This is a single-center, saline-controlled, double-blind, three-armed, randomized trial. Sixty-six participants diagnosed with MDD will be recruited at Beijing Anding Hospital and randomly assigned to one of three groups: a high-dose intramuscular scopolamine augmentation group; a low-dose intramuscular scopolamine augmentation group; and a placebo control group. Our primary endpoint is improvement in the 17-Item Hamilton Rating Scale for Depression (HRSD17) score from the baseline (at least a 20% reduction). Prespecified secondary endpoints include response rates and remission rates as well as changes in the total or subscale scores between the baseline and week 4. Discussion This study will provide the first insight regarding the rapid antidepressant efficacy and tolerability of an intramuscular scopolamine add-on to the usual treatment in Chinese MDD patients. The first discussion concerns whether augmentation can accelerate early antidepressant efficacy. A pilot study of intramuscular scopolamine is performed. The limitations of this study include its small sample size and it being a single-center study, suggesting the need for further confirmation with trials enrolling larger populations. Ethics and dissemination The study protocol and all related materials have been approved by the Institutional Ethics Committee of the Beijing Anding Hospital (No. 2016–106, Beijing, China). The findings will be disseminated through peer-reviewed journals and at national and international conferences. Trial registration ClinicalTrials.gov, NCT03131050. Registered on 18 April 2017.

Abstract Objective The incidence of diabetic atherosclerosis (DA) is increasing worldwide. The study aim was to identify differentially expressed microRNAs (DE-miRs) potentially associated with the initiation and/or progression of DA, thereby yielding new insights into this disease. Methods Matched iliac artery tissue samples were isolated from 6 male rats with or without DA. The Affymetrix GeneChip microRNA 4.0 Array was used to detect miRs. Differential expression between atherosclerotic group and non-atherosclerotic group samples was analyzed using the Gene-Cloud of Biotechnology Information platform. Targetscan and miRanda were then used to predict targets of DE-miRs. Functions and pathways were identified for significantly enriched candidate target genes and a DE-miR functional regulatory network was assembled to identify DA-associated core target genes. Results A total of nine DE-miRs (rno-miR-206-3p, rno-miR-133a-5p, rno-miR-133b-3p, rno-miR-133a-3p, rno-miR-325-5p, rno-miR-675-3p, rno-miR-411-5p, rno-miR-329-3p, and rno-miR-126a-3p) were identified, all of which were up-regulated and together predicted to target 3349 genes. The target genes were enriched in known functions and pathways related to lipid and glucose metabolism. The functional regulatory network indicated a modulatory pattern of these metabolic functions with DE-miRs. The miR-gene network suggested arpp19 and MDM4 as possible DA-related core target genes. Conclusion The present study identified DE-miRs and miRNA-gene networks enriched for lipid and glucose metabolic functions and pathways, and arpp19 and MDM4 as potential DA-related core target genes, suggesting DE-miRs and/or arpp19 and MDM4 could act as potential diagnostic markers or therapeutic targets for DA.

Background: The nucleus accumbens (NAc) is an important region in reward circuit that has been linked with anhedonia, which is a characteristic symptom of major depressive disorder (MDD). However, the relationship between the functional connectivity of the NAc subregions and anhedonia in MDD patients remains unclear. Methods: We acquired resting-state functional magnetic resonance imaging (fMRI) scans from fifty-one subjects (23 MDD patients and 28 healthy controls). We assessed subjects’ trait anhedonia with the Temporal Experience of Pleasure Scale (TEPS). Seed-based resting-state functional connectivity (rsFC) was conducted for each of the NAc subregions (bilateral core-like and shell-like subdivisions) separately to identify regions whose rsFCs with the NAc subregions were altered in the MDD patients and regions whose rsFCs with the NAc subregions showed different correlates with anhedonia between the MDD patients and the healthy controls. Results: Compared with the health controls, the MDD patients showed decreased rsFCs of the right NAc core-like subdivision with the left mid-anterior orbital prefrontal cortex and the right inferior parietal lobe as well as decreased rsFC of the left NAc core-like subdivision with the right middle frontal gyrus. Moreover, the severity of anhedonia by the group interaction was significant for the rsFC of the right NAc shell-like subdivision with the subgenual/pregenual anterior cingulate cortex and the rsFC of the right NAc core-like subdivision with the precuneus. Conclusions: We found that the neural correlates of anhedonia indicated by the rsFCs of the NAc subregions were modulated by depression. The modulation effect was regionally-dependent. These findings enrich our understanding of the neural basis of anhedonia in MDD.

ObjectiveThis meta-analysis examined the prevalence of poor sleep quality and its associated factors in patients with hypertension in China.MethodsBoth English (PubMed, PsycINFO, EMBASE) and Chinese (Wan Fang Database and Chinese National Knowledge Infrastructure) databases were systematically and independently searched. The random-effects model was used to estimate the prevalence of poor sleep quality in Chinese patients with hypertension. The funnel plot and Egger’s tests were used to assess publication bias.ResultsThe prevalence of poor sleep quality in 24 studies with 13,920 hypertensive patients was 52.5% (95% confidence interval [CI]: 46.1–58.9%). In contrast, the prevalence of poor sleep quality in six studies with 5,610 healthy control subjects was 32.5% (95% CI: 19.0–49.7%). In these studies, compared to healthy controls, the pooled odds ratio (OR) of poor sleep quality was 2.66 (95% CI: 1.80–3.93) for hypertensive patients. Subgroup and meta-regression analyses revealed that patients in hospitals were more likely to have poor sleep quality than patients in the community. Studies with smaller sample size, studies using convenience or consecutive sampling and those published in Chinese reported higher prevalence of poor sleep quality. Furthermore, poor sleep quality was more common in older and male hypertensive patients, while the proportion of poor sleep quality was negatively associated with survey year.ConclusionAppropriate strategies for screening, prevention, and treatment of poor sleep quality in this population should be developed.

Cerebellar outputs contribute to motor as well as cognitive behaviors. Here, the authors elucidate the connectivity between deep cerebellar nuclei and specific cell types in the striatum via the intralaminar thalamic nucleus and the participation of this circuit in striatum-dependent behavior.

The fossil dinosaur embryo ‘Baby Louie’ and associated clutch of eggs were first discovered in the early 1990s, but were not formally described. Here, the authors identify the specimen as an embryo and eggs of the new large caenagnathid oviraptorosaur,Beibeilong sinensis, from the Late Cretaceous of China.

BackgroundExposure to childhood abuse has been identified as a salient risk factor for the development of depression. However, the mediating factors between childhood abuse and depressive symptoms have not been sufficiently elucidated. This study aims to investigate the mediating effects of neuroticism, social support, and coping style between childhood abuse and depressive symptoms in population covering general adults, depressed patients, bipolar disorder patients, and high risk population for depression.MethodsThis is a cross-sectional study. Five validated questionnaires were used to measure the psychological outcomes (Childhood Trauma Questionnaire CTQ-SF, Eysenck Personality Questionnaire EPQR-S, Social Support Rating Scale SSRS, Simplified Coping Style Questionnaire SCSQ, and Patient Health Questionnaire-9 PHQ-9) of 312 participants. Multiple regressions and structural equation modeling (SEM) were used to conduct data analysis.ResultsMultiple regression analysis and SEM showed a significant association between childhood emotional abuse and depression symptoms. Neuroticism, use of social support, and active coping style were important mediating variables of this association. The R2 for our model was 0.456, indicating that 45.6% of the variability in depressive symptoms can be explained by the model.Conclusion:This study suggested that neuroticism, active coping, and use of social support play important role in mediating the effects of childhood abuse on adult depressive symptoms.

This paper empirically examines the moderating effect of executive team heterogeneity on the influence of equity incentive on overinvestment behavior by using the data of Chinese A-share listed companies in Shanghai and Shenzhen from 2014 to 2018. The results show that equity incentive can restrain the overinvestment behavior of enterprises; The greater the age heterogeneity of the senior management team, the weaker the influence of equity incentive on overinvestment will be; The greater the gender heterogeneity of the senior management team, the greater the influence of equity incentive on the overinvestment of enterprises will be. The heterogeneity of executive team education and tenure has no effect on the relationship between equity incentive and overinvestment. The conclusion of this study has some enlightenment on how to construct and optimize the senior management team of listed companies, so that the senior management team can play its role fully and make reasonable and effective investment decisions.

The unique anatomical and functional features of principal and interneuron populations are critical for the appropriate function of neuronal circuits. Cell type-specific properties are encoded by selective gene expression programs that shape molecular repertoires and synaptic protein complexes. However, the nature of such programs, particularly for post-transcriptional regulation at the level of alternative splicing is only beginning to emerge. We here demonstrate that transcripts encoding the synaptic adhesion molecules neurexin-1,2,3 are commonly expressed in principal cells and interneurons of the mouse hippocampus but undergo highly differential, cell type-specific alternative splicing. Principal cell-specific neurexin splice isoforms depend on the RNA-binding protein Slm2. By contrast, most parvalbumin-positive (PV+) interneurons lack Slm2, express a different neurexin splice isoform and co-express the corresponding splice isoform-specific neurexin ligand Cbln4. Conditional ablation of Nrxn alternative splice insertions selectively in PV+ cells results in elevated hippocampal network activity and impairment in a learning task. Thus, PV-cell-specific alternative splicing of neurexins is critical for neuronal circuit function

MoS2 nanomaterial has unique properties, including innate affinity with ss-DNA and quenching ability for fluorescence dyes. Here, we present the development of a simple fluorescence biosensor based on water-soluble MoS2 nanosheets and restriction endonuclease BstUI for methylation analysis of p16 promoter. The biosensing platform exhibited excellent sensitivity in detecting DNA with a linear range of 100 pM~20 nM and a detection limit of 140 pM. More importantly, our method could distinguish as low as 1% difference in methylation level. Compared with previous methylation analysis, our design is both time saving and simple to operate, avoiding the limitations of PCR-based assays without compromising performance.

Abstract Background FBI-1 (factor that binds to the inducer of short transcripts of human immunodeficiency virus-1) is a member of the POK (POZ and Kruppel) family of transcription factors and play important roles in cellular differentiation and oncogenesis. Recent evidence suggests that FBI-1 is expressed at high levels in a subset of human lymphomas and some epithelial solid tumors. However, the function of FBI-1 in human ovarian cancers remains elusive. Results In this study, we investigated the role of FBI-1 in human ovarian cancers, in particularly, its function in cancer cell invasion via modulating membrane type 1-matrix metalloproteinase (MT1-MMP). Significantly higher FBI-1 protein and mRNA expression levels were demonstrated in ovarian cancers samples and cell lines compared with borderline tumors and benign cystadenomas. Increased FBI-1 mRNA expression was correlated significantly with gene amplification (P = 0.037). Moreover, higher FBI-1 expression was found in metastatic foci (P = 0.036) and malignant ascites (P = 0.021), and was significantly associated with advanced stage (P = 0.012), shorter overall survival (P = 0.032) and disease-free survival (P = 0.016). In vitro, overexpressed FBI-1 significantly enhanced cell migration and invasion both in OVCA 420 and SKOV-3 ovarian carcinoma cells, irrespective of p53 status, accompanied with elevated expression of MT1-MMP, but not MMP-2 or TIMP-2. Moreover, knockdown of MT1-MMP abolished FBI-1-mediated cell migration and invasion. Conversely, stable knockdown of FBI-1 remarkably reduced the motility of these cells with decreased expression of MT1-MMP. Promoter assay and chromatin immunoprecipitation study indicated that FBI-1 could directly interact with the promoter spanning ~600bp of the 5'-flanking sequence of MT1-MMP and enhanced its expression in a dose-dependent manner. Furthermore, stable knockdown and ectopic expression of FBI-1 decreased and increased cell proliferation respectively in OVCA 420, but not in the p53 null SKOV-3 cells. Conclusions Our results suggested an important role of FBI-1 in ovarian cancer cell proliferation, cell mobility, and invasiveness, and that FBI-1 can be a potential target of chemotherapy.

Zn3Mo2O9 with various morphologies were constructed using hydrothermal, solvothermal, and high-temperature solid-phase strategies and used as negative electrodes of lithium-ion batteries firstly. The Zn3Mo2O9 obtained by high-temperature solid-state method (ZMO-t) is constructed by lots of small nanosheets, which has relatively uniform size and small particles among three samples. The unique morphology is beneficial for electrolyte permeation and further promotes the migration rate of lithium ions. At various current densities, test results can illustrate that the ZMO-t exhibited the best electrochemical performance than those of Zn3Mo2O9 obtained by solvothermal method (ZMO-s) and hydrothermal way (ZMO-h) materials at the same rates. Based on these results, the Zn3Mo2O9 synthesized via high-temperature solid-state strategy has the potential to become a novel negative electrode for Li-ion batteries going forward. [ABSTRACT FROM AUTHOR]