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Open-pit mining causes serious damage to the ground surface and vegetation. Remote sensing technology helps assess and monitor the effectiveness of ecological restoration in abandoned open-pit mines, offering vital information for exploring the ecosystem recovery process. However, previous studies have mostly focused on changes in characteristics and lack quantitative characterization of restoration stability and its dynamics. In this study, we applied the vegetation resilience indicator based on the LandTrendr algorithm, which can combine vegetation disturbance and recovery characteristics for abandoned open-pit mines. Eighteen abandoned open-pit mines around Dongting Lake in China were taken as the research areas. Google Earth Engine and all available Landsat remote sensing images for June-September 1984–2022 were utilized. The effectiveness of mine restoration was determined according to the vegetation resilience indicator and recovery magnitude. The results showed that eighteen abandoned open-pit mines had a range of vegetation resilience between 5.06 and 9.45, indicating that the overall open-pit mines around Dongting Lake were well-restored. The recovery curves of the abandoned open-pit mines had an “inverted S-shape” and “S-shape,” indicating that the overall recovery characteristics of the different types of abandoned open-pit mines before and after the implementation of ecological restoration measures differed. Mine type and restoration measures affect the restoration stability of open-pit mines. The average values of vegetation resilience of the three types of mines were 7.73, 6.91, and 5.79, respectively, indicating that the resilience of the abandoned open-pit mines with serious damage was lower, and at the same time, the restoration was more dependent on the degree of input of artificial restoration. This study provides a reference for the selection of measures and the determination of restoration costs in the future ecological restoration of abandoned open-pit mines.

Abstract Background Previously identified phenotypes of acute respiratory distress syndrome (ARDS) could not reveal the dynamic change of phenotypes over time. We aimed to identify novel clinical phenotypes in ARDS using trajectories of fluid balance, to test whether phenotypes respond differently to different treatment, and to develop a simplified model for phenotype identification. Methods FACTT (conservative vs liberal fluid management) trial was classified as a development cohort, joint latent class mixed models (JLCMMs) were employed to identify trajectories of fluid balance. Heterogeneity of treatment effect (HTE) for fluid management strategy across phenotypes was investigated. We also constructed a parsimonious probabilistic model using baseline data to predict the fluid trajectories in the development cohort. The trajectory groups and the probabilistic model were externally validated in EDEN (initial trophic vs full enteral feeding) trial. Results Using JLCMM, we identified two trajectory groups in the development cohort: Class 1 (n = 758, 76.4% of the cohort) had an early positive fluid balance, but achieved negative fluid balance rapidly, and Class 2 (n = 234, 24.6% of the cohort) was characterized by persistent positive fluid balance. Compared to Class 1 patients, patients in Class 2 had significantly higher 60-day mortality (53.5% vs. 17.8%, p

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a “building–floor–room” hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments.

Date palm (Phoenix dactylifera L.) has a significant role in our daily life due to its diverse nutritional composition and potential health benefits. This study was designed to evaluate the phytochemicals, antioxidant, and antidiarrheal potential of various extracts of Ajwa whole fruit, date palm pit, and flesh. The extracts with the highest phytochemical quantities were further subjected to bio-assay assessment against castor oil-induced diarrhea using different animal models. The results indicated that Ajwa whole fruit extract (AWFE) exhibited higher amount of total phenolic contents (TPC: 36 ± 16 mg/g GAE), total flavonoid contents (TFC: 360 ± 135 mg/g RE), and 2, 2-diphenyl-1-picrylhydrazyl (DPPH: 60 ± 7%) in hydro-ethanolic extracts. In vivo results revealed that Ajwa whole fruit 60% hydro-ethanolic extract at high dose (2000 mg/kg) showed significantly (p

The corner reflector is an effective means of interference for radar seekers due to its high jamming intensity, wide frequency band, and combat effectiveness ratio. Properly arranging multiple corner reflectors in an array can form dilution jamming that resembles ships, substantially enhancing the interference effect. This results in a significant decline in the precision attack efficiency of radar seekers. Hence, it is critical to accurately identify corner reflector array. The common recognition methods involve extracting features on the high-resolution range profile (HRRP) and polarization domain. However, the former is constrained by the number of corner reflectors, while the latter is affected by the accuracy of polarization measurement, both of which have limited performance on the identification of corner reflector array. In terms of the evident variations in physical structures, there must be differences in their scattering characteristics. To highlight the differences, this paper proposes a new method based on the concept of mismatched filtering, which involves changing the frequency modulation slope of the chirp signal in the filter. Then, the variance of width and intervals within a specific scope are extracted as features to characterize these differences, and an identification process is designed in combination with the support vector machine. The simulation experiments demonstrate that the proposed method exhibits stable discriminative performance and can effectively combat dilution jamming. Its accuracy rate exceeds 0.86 when the signal-to-noise ratio is greater than 0 dB. Compared to the HRRP methods, the recognition accuracy of the proposed algorithm improves 15% in relation to variations in the quantity of corner reflectors.

PurposeRadiotherapy is the most important primary treatment for patients with nasopharyngeal carcinoma. Generally, the treatment duration of radiotherapy takes six or six and half weeks with 30 to 33 fractions. The current study was conducted to evaluate the association between prognosis and the duration of radiotherapy in nasopharyngeal carcinoma patients.MethodsPatients with primary nasopharyngeal carcinoma who were treated with intensity-modulated radiotherapy and concurrent cisplatin-based chemotherapy, with or without induction chemotherapy between January, 2008 and December, 2013 at a single institution were retrospectively reviewed.ResultsIn total, 1292 patients were included. At a median follow-up of 71.0 months (range 2.0–126.0 months), locoregional recurrence, distant failure and death were observed in 8.8%, 12.2% and 15.6% of all patients, respectively. Estimated 5-year locoregional relapse–free survival, distant metastasis–free survival, progression-free survival and overall survival in patients with radiation ≤ 7 weeks versus patients with radiation >7 weeks were: 93.2% versus 87.0% (P < 0.001), 89.4% versus 84.4% (P = 0.016), 79.8% versus 70.6% (P < 0.001) and 87.2% versus 78.4% (P < 0.001), respectively.ConclusionsProlonged duration of radiotherapy with a significantly higher risk of distant metastasis and death in nasopharyngeal carcinoma patients. Understanding this point, healthcare providers should make efforts to avoid prolonged duration of radiotherapy to minimize the risk of treatment failure.

Abstract Background The National Comprehensive Cancer Network guidelines recommend intensity-modulated radiotherapy (IMRT) as the primary curative treatment for newly diagnosed nasopharyngeal carcinoma (NPC), but the radiation-related complications and relatively high medical costs remain a consequential burden for the patients. Endoscopic nasopharyngectomy (ENPG) was successfully applied in recurrent NPC with radiation free and relatively low medical costs. In this study, we examined whether ENPG could be an effective treatment for localized stage I NPC. Methods Ten newly diagnosed localized stage I NPC patients voluntarily received ENPG alone from June 2007 to September 2017 in Sun Yat-sen University Cancer Center. Simultaneously, the data of 329 stage I NPC patients treated with IMRT were collected and used as a reference cohort. The survival outcomes, quality of life (QOL), and medical costs between two groups were compared. Results After a median follow-up of 59.0 months (95% CI 53.4–64.6), no death, locoregional recurrence, or distant metastasis was observed in the 10 patients treated with ENPG. The 5-year overall survival, local relapse-free survival, regional relapse-free survival, and distant metastasis-free survival among the ENPG-treated patients was similar to that among the IMRT-treated patients (100% vs. 99.1%, 100% vs. 97.7%, 100% vs. 99.0%, 100% vs. 97.4%, respectively, P > 0.05). In addition, compared with IMRT, ENPG was associated with decreased total medical costs ($ 4090.42 ± 1502.65 vs. $ 12620.88 ± 4242.65, P

Potassium (K) deficiency significantly decreases photosynthesis due to leaf chlorosis induced by accumulation of reactive oxygen species (ROS). But, the physiological mechanism for adjusting antioxidative defense system to protect leaf function in maize (Zea mays L.) is unknown. In the present study, four maize inbred lines (K-tolerant, 90-21-3 and 099; K-sensitive, D937 and 835) were used to analyze leaf photosynthesis, anatomical structure, chloroplast ultrastructure, ROS, and antioxidant activities. The results showed that the chlorophyll content, net photosynthetic rate (Pn), stomatal conductance (Gs), photochemical quenching (qP), and electron transport rate of PSII (ETR) in 90-21-3 and 099 were higher than those in D937 and 835 under K deficiency treatment. Parameters of leaf anatomical structure in D937 that were significantly changed under K deficiency treatment include smaller thickness of leaf, lower epidermis cells, and vascular bundle area, whereas the vascular bundle area, xylem vessel number, and area in 90-21-3 were significantly larger or higher. D937 also had seriously damaged chloroplasts and PSII reaction centers along with increased superoxide anion (O2−) and hydrogen peroxide (H2O2). Activities of antioxidants, like superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX), were significantly stimulated in 90-21-3 resulting in lower levels of O2− and H2O2. These results indicated that the K-tolerant maize promoted antioxidant enzyme activities to maintain ROS homeostasis and suffered less oxidative damage on the photosynthetic apparatus, thereby maintaining regular photosynthesis under K deficiency stress.

Abstract Background To evaluate the prognostic significance of pretreatment quality of life for patients with nasopharyngeal carcinoma treated with intensity-modulated radiotherapy. Methods We performed a prospective, longitudinal study on 554 newly diagnosed patients with NPC from April 2011 to January 2015. A total of 501 consecutive NPC patients were included. Patients were asked to complete the EORTC QLQ-C30 (version 3.0) and QLQ-H&N35 questionnaires before treatment. Results Global health status among QLQ-C30 correlates with EBV DNA(P = 0.019). In addition, pretreatment appetite loss was significantly correlated with EBV DNA(P = 0.02). Pretreatment teeth, opening mouth, feeding tube was significantly correlated with EBV DNA, with P value of 0.003,

Abstract Background It is important to timely capture the fluctuation of the symptoms related to major depressive disorder (MDD). However, most conventionally used assessment tools for MDD symptoms are not designed for real-time assessment. The Immediate Mood Scaler (IMS) is suitable for the real-time evaluation of the mood of patients with MDD. Methods The original IMS was translated into Chinese and back-translated. At baseline, data from 368 patients with MDD, including demographic information and scores on the Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and IMS, were collected. In total, 185 participants completed the retest at Week 2 which included the PHQ-9, GAD-7, and IMS. Internal structural validity, construct validity, and internal consistency were evaluated with the confirmatory factor analysis (CFA), the Pearson correlation, and Cronbach’s α, respectively. Responsiveness was anchored by the change of the PHQ-9 total scores from baseline to Week 2 and predictability was tested using the multivariate linear mixed model for repeated measures (MMRM). Results Two factors with an eigenvalue greater than 1, corresponding to IMS-Depression and IMS-Anxiety subscales, were identified using CFA. The Cronbach’s α that evaluated internal consistency was 0.96, 0.95, and 0.92 for the scores of the IMS, IMS-Depression subscale, and IMS-Anxiety subscale at baseline, respectively. The depression and anxiety subscales at baseline showed high subscale-total correlations (r = 0.96 for the depression subscale; r = 0.89 for the anxiety subscale). The test-retest ICC (0.65, 95%CI: 0.53–0.73) of the IMS at baseline and Week 2 show high reliability. The total score of IMS had significant correlations with that of the PHQ-9 (r = 0.52, P

Low-density Fe–Mn–Al–C steels are widely used in the automotive and aerospace industries as advanced lightweight materials. In this study, highly spherical Fe–30Mn–9Al–C–3Ni powder with a narrow particle-size distribution, low oxygen content, and good flowability was prepared using the plasma rotating electrode process (PREP). Furthermore, Fe–30Mn–9Al–C–3Ni low-density alloy steel was manufactured for the first time using the selective laser melting (SLM) molding technique. Laser power was found to significantly impact the microstructure and mechanical properties of SLM-printed Fe–30Mn–9Al–C–3Ni alloys. The Fe–30Mn–9Al–C–3Ni alloy prepared at a laser power of 110 W (denoted as P-110) exhibited the best mechanical properties, including a yield strength of 887.1 MPa, tensile strength of 1076.8 MPa, elongation of 40.80%, and a maximum hardness value of 294 HV. This study highlights the potential of SLM molding for fabricating low-density Fe–Mn–Al–C steels with complex geometrical shapes and outstanding comprehensive mechanical properties.

The Fe–Mn–Al–C lightweight steels have attracted great attention in the military, aerospace, and automotive industries due to their high strength and lightweight design. Nevertheless, the excessive Mn and Al inclusions have led to poor formability and processability, which brings challenges to the manufacture of large size complex structural parts. In this study, the lightweight steel with a composition of Fe–Mn–Al–C was prepared by laser powder bed fusion for the first time, aimed at investigating the effects of laser scanning speed. The results show that different laser scanning speeds have significant effects on the process sensitivity of lightweight steel, and too low or too high laser scanning speeds may lead to suboptimal sample quality, increased porosity and surface roughness. At a scanning speed of 800 mm/s, the optimal conditions for the lowest porosity (0.03%) and surface roughness (Sa = 3.34 μm) are achieved, resulting in the highest quality of the formed products. Furthermore, an increase in scanning speed results in a reduction in grain size and an enhancement of dislocation strength. When the scanning speed is 800 mm/s, the significant enrichment of Ni–Mn phase helps to enhance the pinning ability of dislocations, while the precipitation of nanoscale small and dense κ-carbide optimizes the Orowan strengthening effect. As a result, dislocation movement is effectively impeded, leading to superior comprehensive mechanical properties of exceptional strength-plasticity matched with a tensile strength, yield strength, and elongation after fracture of 1125.2 MPa, 1019.8 MPa, and 41.9%, respectively.

Abstract Suicidal ideation (SI) is a significant precursor and risk marker for suicide behaviors in major depressive disorder (MDD). Exploration of SI trajectory from a longitudinal framework are essential for treatment guidelines and clinical management of suicide risk. This study sought to explore SI trajectories and its associated clinical, sociodemographic characteristics, and initial treatment among patients with MDD. We used data from a non-interventional, national multi-centered prospective cohort study. 1 461 patients with MDD were included in the growth mixture modeling using SI at baseline, 2 weeks, 4 weeks, 8 weeks, 12 weeks, and 6 months, 9 months, and 12 months as the indicator. A multinomial regression was employed with SI trajectory as the outcome and anhedonia, depressive symptoms, atypical depressive symptoms, pharmacological treatments, and other covariates as the predictors. Four distinct SI trajectories were identified: a consistently low SI trajectory(50.7%), a persistently mild SI trajectory(20.6%), a fast declined SI trajectory(8.9%), and a slowly declined trajectory(19.8%). Compared to those with a consistently low SI trajectory, a higher score of anhedonia was associated with an increased risk of experiencing persistently mild (RRR = 1.20, 95%CI: 1.05, 1.38) and slowly declined SI (1.54, 95%CI: 1.32, 1.80). Severity of depressive symptom was also positively associated with the risk of experiencing persistently mild (1.15, 95%CI: 1.13, 1.18) and slowly declined SI (1.17, 95%CI: 1.14, 1.21). And the risk of experiencing slowly declined SI was higher for those use SSRI(1.49, 95%CI: 1.02, 2.31), and for those use antidepressant and antipsychotic/mood stabilizer combined therapy (3.78, 95%CI: 1.48, 9.61). The findings of this study are potentially useful for clinical practice as critical indicators of profiles and interventions for prognosis among patients with MDD. Further research is warranted to explore potential modifiable factors and the association between SI trajectories and suicide behavior.

PurposeThis study aimed to investigate the safety, feasibility, and possible advantages of the cystic plate approach during laparoscopic cholecystectomy in a retrospective cohort of surgical patients.MethodsWe summarized the key points of the technical approach, retrospectively analyzed the clinical outcomes of 156 patients in the cystic plate approach group from July 2018 to July 2023, and compared the findings with those of 173 cases in the routine approach group from the same period.ResultsWe observed no differences in the average stone size, operation time, postoperative hospital stay, conversion rate, complications, or Visual Analog Scale pain scores on the second day of surgery between the two groups (p = 0.076, 0.067, 0.278, 1.000, 0.633, and 0.131, respectively). However, intraoperative blood loss, number of clips used, volume of postoperative drainage fluid, and Visual Analog Scale pain scores on the day of surgery in the cystic plate approach group were significantly lower than those in the routine approach group (p = 0.000, 0.031, 0.027, and 0.021, respectively).ConclusionsThe cystic plate approach is a safe, feasible, and effective approach that has the advantages of minimal invasiveness with less bleeding and seepage, reduced use of biological clips, and less pain, potentially minimizing the risk of iatrogenic biliary injury. Trial registrationThis study was registered at the International Clinical Trial Registry (ChiCTR2100052860). Registration date: November 6, 2021.

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.

ObjectiveObservational studies have frequently shown a co-occurrence of psychiatric disorders and Obstructive sleep apnea (OSA), with major depressive disorder (MDD) being a prevalent psychiatric disorder. This study aims to investigate the genetic overlap between MDD and OSA to explore their underlying pathological mechanisms.MethodsLeveraging the extensive and recent GWAS for OSA and MDD, we conducted genetic correlation analyses utilizing Linkage disequilibrium score regression (LDSC), re-evaluated their pleiotropic Single-nucleotide polymorphisms (SNP) with Cross-Phenotype Association (CPASSOC) and Colocalization (COLOC), investigated the overlap at the gene level using physical annotations and Multi-marker Analysis of GenoMic Annotation (MAGMA), and finally employed Mendelian randomization (MR) to assess potential causal relationships between the two disorders.ResultsUpon our investigation, we established that MDD and OSA exhibit high heritability (h2MDD=0.02, h2OSA=0.04) alongside a significant genetic correlation (rg=0.31, P= 1.42E-23). Utilizing CPASSOC, we identified 397 pleiotropic SNPs, associable with 45 loci, two of which share common genetic fragments with a pleiotropic role. Furthermore, the MAGMA study uncovered a total of 154 pleiotropic genes capable of influencing multiple brain regions. Lastly, leveraging MR analysis, we concluded that MDD heightens the risk of developing OSA (P=3. 10E-04, OR (95%CI):1.28(1.12~ 1.47)).ConclusionIn summary, our study identified PCLO as a common gene between OSA and MDD and provided evidence that MDD causally contributes to the development of OSA. These insights enhance our understanding of the shared mechanisms underlying the comorbidity of these conditions.

Abstract Objective A prospective, multicenter, randomized study evaluated the efficacy of major depressive disorder (MDD) patients after 2–3 months of acute treatment based on the dual factors of education and age. Methods This study classified the included patients into four groups using two classification parameters: age (≤45 years, vs. >45 years) and education years (≤12 vs. >12). We analyzed age, gender, marital status, personal income, depression onset history, medication use, and follow‐up across various groups. We evaluated residual somatic symptoms and social functioning in depression patients was conducted using the 16‐item Quick Inventory of Depressive Symptomatology Self‐report (QIDS‐SR16), the Patient Health Questionnaire‐15 (PHQ15), and the Sheehan Disability Scale (SDS). Results In China, 16 hospitals, 553 depression patients, and 428 fulfilled the inclusion criteria. Baseline patient data revealed significant differences among the different age groups in gender, marital status, income, first onset age, physical illness, combination of antipsychotics, and benzodiazepines use (all p .05) were found in residual somatic symptoms and social functioning parameters between different education levels (≤12 years vs. >12 years) at baseline, 3 months, and 6 months, based on total scores on the scale. Repeated measures mixed model indicates that the QIDS‐SR16 assessment indicates statistical differences among various marital statuses, income levels, medical histories, and antipsychotic medication use (p 45 years old, those ≤45 years old often exhibit more residual depression, somatic symptoms, and severe social functional impairment; patients' education levels less influence this trend.

Piezocatalytic materials have attracted widespread attention in the fields of clean energy and water treatment because of their ability to convert mechanical energy directly into chemical energy. In this study, γ-AlON particles synthesised using carbothermal reduction and nitridation (CRN) were used for the first time as a novel piezocatalytic material to degrade dye solutions under ultrasonic vibration. The γ-AlON particles exhibited good performance as a piezocatalytic material for the degradation of organic pollutants. After 120 min under ultrasonic vibration, 40 mg portions of γ-AlON particles in 50 mL dye solutions (10 mg/L) achieved 78.06%, 67.74%, 74.29% and 64.62% decomposition rates for rhodamine B (RhB), methyl orange (MO), methylene blue (MB) and crystal violet (CV) solutions, respectively; the fitted k values were 13.35 × 10−3, 10.79 × 10−3, 12.09 × 10−3 and 8.00 × 10−3 min−1, respectively. The piezocatalytic mechanism of γ-AlON particles in the selective degradation of MO was further analysed in free-radical scavenging activity experiments. Hydroxyl radicals (•OH), superoxide radicals (•O2−), holes (h+) and electrons (e−) were found to be the main active substances in the degradation process. Therefore, γ-AlON particles are an efficient and promising piezocatalytic material for the treatment of dye pollutants.

All-solid-state sodium (Na)-metal batteries (ASSSMBs) are considered promising candidates for large-scale energy storage systems due to their abundant sodium resources, unparalleled safety performance, and impressive energy density. Na superionic conductors (NASICONs) are among the best enablers of ASSSMBs in view of their high ionic conductivity, ease of synthesis, and excellent thermal stability and good electrochemical/chemical compatibility with common electrodes. However, challenges surrounding the NASICON/electrode interface, such as high interfacial resistance and dendrite formation, have hindered the development of practical ASSSMBs based on NASICONs. This review starts with an explicit summary of the interface problems between the metallic Na anode and NASICON arising from mechanical, chemical, and electrochemical aspects (i.e., poor interface contact, insulating side-reaction products, and irregular dendrite growth). Subsequently, we systematically analyze and logically categorize modification strategies for addressing anode interface problems and provide a comprehensive discussion on the underlying enhancement mechanisms. As such, we identify underlying and universal interface enhancement mechanisms by comparatively studying various modification strategies. Furthermore, we briefly summarize the challenges in the cathode/electrolyte interface and early-stage research efforts in constructing stable cathode/electrolyte interface and fabricating high-performance composite cathodes. Finally, key suggestions and future prospectives for the advancement of NASICON-based ASSSMBs are outlined.

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.

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.

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.

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.

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.

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.

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

By evaluating the results of treating quinoa with different microwave conditions,this paper compares the effects of microwave heating and traditional conduction heating (water bath and baking) on lipase and flavor components of quinoa.Experiments results show that with the increase of microwave power and the extension of treatment time,the activity of lipase decreased significantly.After heating at 600 W power for 4 min,the lipase activity tended to be stable.Through analyzing flavor components in quinoa,we found microwave treatment dramatically reduced the levels of Methyl Ketone,1-octen-3-ol and trans-2-Nonenal.Moreover,with the extension of storage time,the oxidation of oil in quinoa was inhibited by microwave,with the peroxide value and acid value of quinoa decreased.Compared with the traditional heating methods,microwave heating can significantly reduce the activity of lipase in quinoa,and improve the flavor and organoleptic attribute of quinoa milk.

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

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.

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.

The environmental and biological factors that affect the food security during the food storage,such as the food temperature,environment humidity,moisture,gas,mildew,pests and others pose a threat to the food storage security,among which the pest is an important factor threatening food storage security.Therefore,a fast and effective detection method is needed to detect stored grain pests.Some of the existing methods are time consuming,using expensive equipment,potentially harmful to health and inefficient.A non-contact,fast and low-cost detection method for stored grain pests based on the amplitude of the channel state information (CSI) was proposed,namely,wireless-pest (Wi-Pest).The feasibility of the pest detection in the stored grain was verified by using CSI amplitude data.On this basis,a Wi-Pest detection method was designed.Firstly,the amplitude data of CSI was preprocessed by outliers removal,data normalization and noise elimination.Then the principal component analysis (PCA) was used to compress the data and extract the main feature components.Finally,random forest (RF) classification method was used to detect stored grain pests.Experiments show that the abnormal density of live pests in grain heaps can be detected under the line of sight (LOS) scenario,and the detection accuracy of the proposed method can reach 97% on average.

The environmental and biological factors that affect the food security during the food storage,such as the food temperature,environment humidity,moisture,gas,mildew,pests and others pose a threat to the food storage security,among which the pest is an important factor threatening food storage security.Therefore,a fast and effective detection method is needed to detect stored grain pests.Some of the existing methods are time consuming,using expensive equipment,potentially harmful to health and inefficient.A non-contact,fast and low-cost detection method for stored grain pests based on the amplitude of the channel state information (CSI) was proposed,namely,wireless-pest (Wi-Pest).The feasibility of the pest detection in the stored grain was verified by using CSI amplitude data.On this basis,a Wi-Pest detection method was designed.Firstly,the amplitude data of CSI was preprocessed by outliers removal,data normalization and noise elimination.Then the principal component analysis (PCA) was used to compress the data and extract the main feature components.Finally,random forest (RF) classification method was used to detect stored grain pests.Experiments show that the abnormal density of live pests in grain heaps can be detected under the line of sight (LOS) scenario,and the detection accuracy of the proposed method can reach 97% on average.

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.

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.

To uncover the evaluation information on the academic contribution of research papers cited by peers based on the content cited by citing papers, and to provide an evidence-based tool for evaluating the academic value of cited papers.

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.

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.

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.

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.

HDL apoA-1-mediated cholesterol efflux pathway requires multiple cellular proteins and signal transduction processes, including adenylyl cyclase (AC)/cAMP signaling. Due to the existence of multiple transmembrane AC isoforms, it was not known how many AC isoforms are expressed and which ones are essential for cholesterol efflux in macrophage foam cells. These questions were investigated in THP-1 macrophages in this study. Quantitative RT-PCR detected mRNAs for all nine transmembrane AC isoforms, but only the mRNA and protein of the AC1 isoform were consistently upregulated by cholesterol loading and apoA-1. AC1 shRNA interference decreased AC1 mRNA and protein levels, resulting in reduction of apoA-1-mediated cAMP production and cholesterol efflux, while the intracellular cholesterol levels remained high. Confocal microscopy showed that apoA-1 promoted translocation of cholesterol and formation of cholesterol-apoA-1 complexes (protrusions) on the cholesterol-loaded macrophage surface. AC1 shRNA-interfered macrophages showed no translocation of cholesterol to the cell surface. AC1 shRNA interference also disrupted cellular localization of the intracellular cholesterol indicator protein adipophillin, and the expression as well as surface translocation of ABCA1. Together, our results show that AC1 is a major isoform for apoA-1-activated cAMP signaling to promote cholesterol transport and exocytosis to the surface of THP-1 macrophage foam cells.