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Native α1‐antitrypsin (AAT) is a 52‐kDa glycoprotein that acts as an antiprotease and is the physiological inhibitor of neutrophil serine proteases. The main function of AAT is to protect the lung from proteolytic damage induced by inflammation. AAT deficiency (AATD) is a codominant autosomal disorder caused by pathogenic mutations in SERPINA1 gene, leading to reduced levels of serum AAT. The deficiency is known to increase the risk of pulmonary emphysema and chronic obstructive pulmonary disease as a consequence of proteolytic imbalance induced by inflammation, associated in many instances with cigarette smoking and other environmental hazards. Currently, the available therapy for lung disease associated with AATD is serum purified human AAT injected into patients on a weekly basis. It would be advantageous to replace serum‐derived AAT with a recombinant version which is stable and resistant to oxidation. We have expressed AAT in Escherichia coli as inclusion bodies and developed a highly efficient refolding and purification process. We engineered a series of mutant forms of AAT to achieve enhance thermostability and oxidation resistance. Moreover, we synthesized an active form of AAT via cysteine‐pegylation to achieve a markedly extended half‐life in vivo. The resulting molecule, which retains comparable activity to the wild‐type form, is expected to be an improved therapeutic agent for treating hereditary emphysema. In addition, the molecule may also be used to treat other types of emphysema caused by smoking, cystic fibrosis, pulmonary hypertension, pulmonary fibrosis, and chronic obstructive pulmonary disease.

Abstract Analyzing of the energy consumption (EC) in bus operation is important for reducing operating costs, improving sustainable solutions and creating environmentally friendly cities. The purpose of this paper is to identify the factors, such as passenger load, speed and acceleration, that affect significantly EC in bus operation. This paper builds a simulation framework for describing the level of energy based on the vehicle-engine combined (VEC) EC model. On the basis of the relationship between engine torque, power, traction and EC, the simulation framework mainly includes the road model, vehicle model, engine model and driver strategy. Furthermore, the correlations between energy consumption, passenger load, vehicle speed and acceleration are analyzed in different station spacing. The results show that the passenger load has a significant impact on EC of buses, and is related to the vehicle’s speed and acceleration. Generally, the higher the maximum driving speed, the higher the EC of the bus. Acceleration strategies and maximum speed limits are critical factors determining the EC of bus for a certain passenger load and station spacing. For the same station spacing and maximum driving speed, the acceleration phase is under a greater contribution to the increase of EC. In addition, the greater the maximum speed limit or the acceleration, the greater the contribution percentage of EC increase in the acceleration phase. The simulation framework based on vehicle-engine combined EC model and specific fuel consumption maps can obtain the operating EC of buses for situations with different station spacing and maximum speed, which is conducive to vehicle operation EC analysis. Acceleration strategies and maximum speed limits are critical factors determining the EC of bus for a certain passenger load and station spacing. Therefore, energy savings can be obtained by optimizing the driving strategy.

Abstract Background Recent studies have shown that the excitatory amino acid transporters (EAATs) are associated with schizophrenia. The aim of this study was to investigate the relationship between the polymorphism of EAAT1 and EAAT2 genes and schizophrenia in Chinese Han population. Methods A total of 233 patients with schizophrenia and 342 healthy controls were enrolled. Two SNPs in EAAT1 gene (rs2269272, rs2731880) and four SNPs in EAAT2 gene (rs12360706, rs3088168, rs12294045, rs10836387) were genotyped by SNaPshot. Clinical features were collected using a self-made questionnaire. Psychotic symptoms of patients were measured by the Positive and Negative Syndrome Scale (PANSS), and patients’ cognitive function was assessed by Matrics Consensus Cognitive Battery (MCCB). Results Significant difference in allelic distributions between cases and controls was confirmed at locus rs12294045 (Ρ = 0.004) of EAAT2 gene. Different genotypes of rs12294045 were associated with family history (P = 0.046), in which patients with CT genotype had higher proportion of family history of psychosis. The polymorphism of rs12294045 was related to working operational memory (LNS: P = 0.016) and verbal learning function (HVLT-R: P = 0.042) in patients in which CT genotype had lower scores. However, these differences were no longer significant after Bonferroni correction. Conclusions Our study showed that the polymorphism of rs12294045 in EAAT2 gene may be associated with schizophrenia in Chinese Han population. CT genotype may be one of the risk factors for family history and cognitive deficits of patients.

Abstract Background To effectively detect and investigate various cell-related diseases, it is essential to understand cell behaviour. The ability to detection mitotic cells is a fundamental step in diagnosing cell-related diseases. Convolutional neural networks (CNNs) have been successfully applied to object detection tasks, however, when applied to mitotic cell detection, most existing methods generate high false-positive rates due to the complex characteristics that differentiate normal cells from mitotic cells. Cell size and orientation variations in each stage make detecting mitotic cells difficult in 2D approaches. Therefore, effective extraction of the spatial and temporal features from mitotic data is an important and challenging task. The computational time required for detection is another major concern for mitotic detection in 4D microscopic images. Results In this paper, we propose a backbone feature extraction network named full scale connected recurrent deep layer aggregation (RDLA++) for anchor-free mitotic detection. We utilize a 2.5D method that includes 3D spatial information extracted from several 2D images from neighbouring slices that form a multi-stream input. Conclusions Our proposed technique addresses the scale variation problem and can efficiently extract spatial and temporal features from 4D microscopic images, resulting in improved detection accuracy and reduced computation time compared with those of other state-of-the-art methods.

Hepatocellular carcinoma (HCC) is a primary liver cancer that produces a high mortality rate. It is one of the most common malignancies worldwide, especially in Asia, Africa, and southern Europe. Although surgical resection is an effective treatment, patients with HCC are at risk of recurrence after surgery. Preoperative early recurrence prediction for patients with liver cancer can help physicians develop treatment plans and will enable physicians to guide patients in postoperative follow-up. However, the conventional clinical data based methods ignore the imaging information of patients. Certain studies have used radiomic models for early recurrence prediction in HCC patients with good results, and the medical images of patients have been shown to be effective in predicting the recurrence of HCC. In recent years, deep learning models have demonstrated the potential to outperform the radiomics-based models. In this paper, we propose a prediction model based on deep learning that contains intra-phase attention and inter-phase attention. Intra-phase attention focuses on important information of different channels and space in the same phase, whereas inter-phase attention focuses on important information between different phases. We also propose a fusion model to combine the image features with clinical data. Our experiment results prove that our fusion model has superior performance over the models that use clinical data only or the CT image only. Our model achieved a prediction accuracy of 81.2%, and the area under the curve was 0.869.

Non-small cell lung cancer (NSCLC) is a serious disease and has a high recurrence rate after surgery. Recently, many machine learning methods have been proposed for recurrence prediction. The methods using gene expression data achieve high accuracy rates but expensive. While, the radiomics features using computer tomography (CT) image is a cost-effective method, but their accuracy is not competitive. In this paper, we propose a genotype-guided radiomics method (GGR) for obtaining high prediction accuracy at a low cost. We used a public radiogenomics dataset of NSCLC, which includes CT images and gene expression data. Our proposed method is two steps method that uses two models. The first model is a gene estimation model, which is used to estimate the gene expression from radiomics features and deep features extracted from CT images. The second model is used to predict the recurrence using the estimated gene. The proposed GGR method is designed based on hybrid features which is the fusion of handcrafted- and deep learning-based features. The experiments demonstrated that the prediction accuracy can be improved significantly from 78.61% (existing radiomics method) and 79.09% (ResNet50) to 83.28% by the proposed GGR.

Reducing energy consumption and promoting sustainable mobility solutions, including public transport (PT), are increasingly becoming key objectives for policymakers worldwide. Energy saving dispatching optimization for bus rapid transit (BRT) is one of the most efficient strategies for reducing traffic congestion and energy conservation. The purpose of this paper is to address the BRT dispatching problem while taking into account the association between the vehicle type, the waiting time of passengers and the energy consumption of vehicles. This paper presents a mechanical model to describe the level of energy used in different vehicles based on engine universal characteristics considering the characteristics of the vehicle, engine, road, and driving type. The load factor and the passenger average waiting time are used to estimate the quality of service. Furthermore, in order to determine the vehicle scheduling scheme, a multi-objective energy saving dispatching optimization model of BRT is developed aiming to minimize the waiting time of passengers and energy consumption of vehicles. Moreover, a two-phase algorithm is employed in order to solve this multi-objective model. The results show that the designed algorithm is valid for solving the dispatching optimization model of BRT, and the energy consumption and passenger waiting time can be reduced by using an appropriate dispatching scheme.

Post-operative early recurrence (ER) of hepatocellular carcinoma (HCC) is one of the leading causes of death. The prediction of the ER of HCC before treatment contributes to guiding treatment and follow-up protocols. In recent years, CT radiomics signatures have been proven effective in several studies in predicting early recurrence of HCC, there are still two major challenges. First, the radiomics features extracted were low or mid-level features, which may not fully characterize HCC heterogeneity. Second, the fusion approach of clinical textual data and image information is in little consensus. In this paper, we proposed a deep-learning based prediction model to extract high-level features from the triple-phase CT images and compare its performance with traditional radiomics model and clinical model. The accuracy and area under the curve (AUC) of receiver operating characteristics of three models was 69.52%/0.723, 67.04%/0.64, 76.03%/0.75, respectively. In addition, we proposed four fusion models to combine clinical data and high-level features. Among them, Fusion model D performed best, achieving a higher prediction accuracy of 78.66% and AUC of 0.8248. Moreover, fusion models with a joint loss function can further improve the prediction performance to 80.49% and 0.8331.

Abstract Background Schizophrenia is a complex psychiatric disorder with unknown etiology. A number of recent studies have shown that the polymorphism of the neural precursor cell expressed developmentally down-regulated 4 (NEDD4) gene is associated with a variety of neuropsychiatric disorders, such as schizophrenia, and may also be associated with cognitive dysfunction in these diseases. Methods A case-control study was carried out, the alleles and genotypes distributions of five loci (rs3088077, rs2303579, rs7162435, rs11550869, rs62043855) of the NEDD4 gene from 296 schizophrenia patients and 320 healthy controls were detected by using Taqman single-nucleotide polymorphism (SNP) genotyping technology. The clinical data of case and control group members were collected by self-made questionnaire and the psychotic symptoms of case group members were assessed by the Positive and Negative Syndrome Scale (PANSS). The Matrics Consensus Cognitive Battery (MCCB) was used to test the cognitive function of case group members. Results The alleles and genotypes frequency of two loci (rs3088077, rs2303579) between case and control group showed significant differences (P

The infant brain undergoes a remarkable period of neural development that is crucial for the development of cognitive and behavioral capacities (Hasegawa et al., 2018). Longitudinal magnetic resonance imaging (MRI) is able to characterize the developmental trajectories and is critical in neuroimaging studies of early brain development. However, missing data at different time points is an unavoidable occurrence in longitudinal studies owing to participant attrition and scan failure. Compared to dropping incomplete data, data imputation is considered a better solution to address such missing data in order to preserve all available samples. In this paper, we adapt generative adversarial networks (GAN) to a new application: longitudinal image prediction of structural MRI in the first year of life. In contrast to existing medical image-to-image translation applications of GANs, where inputs and outputs share a very close anatomical structure, our task is more challenging as brain size, shape and tissue contrast vary significantly between the input data and the predicted data. Several improvements over existing GAN approaches are proposed to address these challenges in our task. To enhance the realism, crispness, and accuracy of the predicted images, we incorporate both a traditional voxel-wise reconstruction loss as well as a perceptual loss term into the adversarial learning scheme. As the differing contrast changes in T1w and T2w MR images in the first year of life, we incorporate multi-contrast images leading to our proposed 3D multi-contrast perceptual adversarial network (MPGAN). Extensive evaluations are performed to assess the qualityand fidelity of the predicted images, including qualitative and quantitative assessments of the image appearance, as well as quantitative assessment on two segmentation tasks. Our experimental results show that our MPGAN is an effective solution for longitudinal MR image data imputation in the infant brain. We further apply our predicted/imputed images to two practical tasks, a regression task and a classification task, in order to highlight the enhanced task-related performance following image imputation. The results show that the model performance in both tasks is improved by including the additional imputed data, demonstrating the usability of the predicted images generated from our approach.

Due to the influence of insufficient production capacity or shortage of production materials, production enterprises may produce products in advance or be backordered. In order to improve the adaptability of enterprises and reduce production costs, the impacts of early delivery and delayed delivery are analyzed, and the method to determine the loss threshold is put forward. Moreover, the maximum allowable shortage of customers with different tardiness is calculated, and the cost of delayed delivery and loss of sales is determined. Considering the production cost, raw material cost, inventory cost, staff cost, stockout, and lost sales cost, an early/delay multi-objective optimization model is developed for an aggregate production planning (APP) problem to minimize total production costs and instability in the workforce. Three algorithms and three different hybrid strategies are designed to solve the model. Finally, some test experiments are employed in order to validate the performance of the proposed evaluation of the three algorithms. The results show that: The method of determining the loss threshold can effectively reflect the double influence of customer satisfaction with waiting time and shortage quantity. The definition of unit tardiness cost reflects the law that it increases gradually with waiting time. The determination of the feasible range of product output and the number of workers in the workforce can reduce the search scope of the algorithm and improve the efficiency of the algorithm.

Well-defined NaGd(MoO4)2 octahedral microcrystals have been synthesized via a simple hydrothermal route. The whole process was carried out under aqueous conditions without the use of any organic surfactant, solvent, or catalyst. During the synthesis process, the pH value of the initial solution plays a crucial role in determining the morphology and particle size of the final products. The as-obtained NaGd(MoO4)2:Ln3+ (Ln=Eu, Tb, Dy, and Sm) phosphors show intense red, green, yellow, and orange-red emissions under ultraviolet excitation, which can be confirmed by the CIE coordinates. The as-synthesized luminescent materials may find potential applications in fields of fluorescent lamps, light-emitting diodes (LEDs), and optoelectronic devices. [ABSTRACT FROM AUTHOR]

The characteristics of railway dangerous goods accidents are very complex. The rescue of railway dangerous goods accidents should consider the timeliness of rescue, the uncertainty of traffic environment and the diversity of rescue resources. Thus, the purpose of this paper is to confront the rescue resources scheduling problem of railway dangerous goods accident by considering factors such as rescue capacity, rescue demand and response time. Based on the analysis of travel time and reliability for rescue route, a multi-objective scheduling model of rescue resources based on travel time reliability is constructed in order to minimize the total arrival time of rescue resources and to maximize total reliability. The proposed model is more reliable than the traditional model due to the consideration of travel time reliability of rescue routes. Moreover, a two-stage algorithm is designed to solve this problem. A multi-path algorithm with bound constraints is used to obtain the set of feasible rescue routes in the first stage, and the NSGA-II algorithm is used to determine the scheduling of rescue resources for each rescue center. Finally, the two-stage algorithm is tested on a regional road network, and the results show that the designed two-stage algorithm is valid for solving the rescue resource scheduling problem of dangerous goods accidents and is able to obtain the rescue resource scheduling scheme in a short period of time.

The smu.1420 gene from the cariogenic pathogen Streptococcus mutans encodes a putative protein which has sequence homology to NQO [NAD(P)H:quinone oxidoreductase] family members, including mammalian NQO and bacterial MdaB (modulator of drug activity B). NQO can detoxify quinones by converting them to hydroquinones and prevent the generation of reactive oxygen species. Thus, comprehensive studies on Smu.1420 will be important for uncovering the antioxidation and antidrug mechanisms of S. mutans. Here, the catalytic properties of Smu.1420 have been characterized, and its structure was determined in complexes with NADP+ and menadione, respectively. Smu.1420 binds menadione directly and exhibits a pronounced preference for NADPH over NADH as a substrate, demonstrating that it is an NADPH- specific quinone oxidoreductase. The structure of Smu.1420 shows a compact homodimer with two substrate pockets located in the cleft of the dimer interface. The nicotinamide moiety of NADP+ is bound on top of the isoalloxazine moiety of the FAD cofactor and overlaps with the binding site of menadione, suggesting a hydride-transfer process from NADPH to FAD and then to menadione. Two strongly basic patches near the substrate pocket are expected to confer the preference for NADPH over NADH. These studies shed light on future drug development against the cariogenic pathogen S. mutans. [ABSTRACT FROM AUTHOR]

Depression is a severe psychological condition that affects millions of people worldwide. As depression has received more attention in recent years, it has become imperative to develop automatic methods for detecting depression. Although numerous machine learning methods have been proposed for estimating the levels of depression via audio, visual, and audiovisual emotion sensing, several challenges still exist. For example, it is difficult to extract long-term temporal context information from long sequences of audio and visual data, and it is also difficult to select and fuse useful multi-modal information or features effectively. In addition, how to include other information or tasks to enhance the estimation accuracy is also one of the challenges. In this study, we propose a multi-modal adaptive fusion transformer network for estimating the levels of depression. Transformer-based models have achieved state-of-the-art performance in language understanding and sequence modeling. Thus, the proposed transformer-based network is utilized to extract long-term temporal context information from uni-modal audio and visual data in our work. This is the first transformer-based approach for depression detection. We also propose an adaptive fusion method for adaptively fusing useful multi-modal features. Furthermore, inspired by current multi-task learning work, we also incorporate an auxiliary task (depression classification) to enhance the main task of depression level regression (estimation). The effectiveness of the proposed method has been validated on a public dataset (AVEC 2019 Detecting Depression with AI Sub-challenge) in terms of the PHQ-8 scores. Experimental results indicate that the proposed method achieves better performance compared with currently state-of-the-art methods. Our proposed method achieves a concordance correlation coefficient (CCC) of 0.733 on AVEC 2019 which is 6.2% higher than the accuracy (CCC = 0.696) of the state-of-the-art method.

Background: Hepatitis B virus (HBV) infection is a serious health problem worldwide. Treatment recommendation and response are mainly indicated by viral load, e antigen (HBeAg) seroconversion, and ALT levels. The S antigen (HBsAg) seroconversion is much less frequent. Since HBeAg can be negative in the presence of high viral replication, preS antigen (HBpreSAg) might be a useful indicator in management of chronic HBV infection. Results: A new assay of double antibody sandwich ELISA was established to detect preS antigens. Sera of 104 HBeAg-negative and 50 HBeAg-positive chronic hepatitis B patients have been studied and 23 HBeAg-positive patients were enrolled in a treatment follow-up study. 70% of the HbeAg-positive patients and 47% of the HBeAg-negative patients showed HBpreSAg positive. Particularly, in the HBeAg-negative patients, 30 out of 47 HBpreSAg positive patients showed no evidence of viral replication based on HBV DNA copies. A comparison with HBV DNA copies demonstrated that the overall accuracy of the HBpreSAg test could reach 72% for active HBV replication. HBpreSAg changes were well correlated with changes of HBsAg, HBV DNA and ALT levels during the course of IFN-α treatment and follow-up. HBeAg positive patients responded well to treatment when reduction of HBpreSAg levels was more pronounced. Conclusion: Our results suggested that HBpreSAg could be detected effectively, and well correlated with HBsAg and HBV DNA copies. The reduction of HBpreSAg levels in conjunction with the HBV DNA copies appears to be an improved predictor of treatment outcome. [ABSTRACT FROM AUTHOR]

A large-scale, high-efficiency and low-cost platform based on a Beckman Coulter Biomek FX and custom-made automation systems for structural genomics has been set up at Peking University, Beijing, People's Republic of China. This platform has the capacity to process up to 2000 genes per year for structural and functional analyses. Bacillus subtilis, a model organism for Gram-positive bacteria, and Streptococcus mutans, a major pathogen of dental caries, were selected as the main targets. To date, more than 470 B. subtilis and 1200 S. mutans proteins and hundreds of proteins from other sources, including human liver proteins, have been selected as targets for this platform. The selected genes are mainly related to important metabolism pathways and/or have potential relevance for drug design. To date, 40 independent structures have been determined; of these 11 are in the category of novel structures by the criterion of having less than 30% sequence identity to known structures. More than 13 structures were determined by SAD/MAD phasing. The macromolecular crystallography beamline at the Beijing Synchrotron Radiation Facility and modern phasing programs have been crucial components of the operation of the platform. The idea and practice of the genomic approach have been successfully adopted in a moderately funded structural biology program and it is believed this adaptation will greatly improve the production of protein structures. The goal is to be able to solve a protein structure of moderate difficulty at a cost about US $10 000. [ABSTRACT FROM AUTHOR]

Characterization and individual trait analysis of the focal liver lesions (FLL) is a challenging task in medical image processing and clinical site. The character analysis of a unconfirmed FLL case would be expected to benefit greatly from the accumulated FLL cases with experts’ analysis, which can be achieved by content-based medical image retrieval (CBMIR). CBMIR mainly includes discriminated feature extraction and similarity calculation procedures. Bag-of-Visual-Words (BoVW) (codebook-based model) has been proven to be effective for different classification and retrieval tasks. This study investigates an improved codebook model for the fined-grained medical image representation with the following three advantages: (1) instead of SIFT, we exploit the local patch (structure) as the local descriptor, which can retain all detailed information and is more suitable for the fine-grained medical image applications; (2) in order to more accurately approximate any local descriptor in coding procedure, the sparse coding method, instead of K-means algorithm, is employed for codebook learning and coded vector calculation; (3) we evaluate retrieval performance of focal liver lesions (FLL) using multiphase computed tomography (CT) scans, in which the proposed codebook model is separately learned for each phase. The effectiveness of the proposed method is confirmed by our experiments on FLL retrieval.

As a substrate of extracellular signal-related kinase (ERK), the p90 ribosome S6 kinase 1 (RSK1) is at the terminus of the Ras/ERK pathway. Residues 411–735 of human RSK1, covering the C-terminal serine/threonine kinase catalytic domain and the functionally important tail, were cloned into an Escherichia coli expression vector. The protein was expressed, purified and crystallized. The crystals diffracted to 2.7 Å and belonged to space group P21, with unit-cell parameters a = 39.8, b = 143.8, c = 59.9 Å, β = 95.7°. [ABSTRACT FROM AUTHOR]

Bacillus subtilis YjcG is a functionally uncharacterized protein with 171 residues that has no structural homologue in the Protein Data Bank. However, it shows sequence homology to bacterial and archaeal 2'-5' RNA ligases. In order to identify its exact function via structural studies, the yjcG gene was amplified from B. subtilis genomic DNA and cloned into the expression vector pET21- DEST. The protein was expressed in a soluble form in Escherichia coli and was purified to homogeneity. Crystals suitable for X-ray analysis were obtained that diffracted to 2.3 Å and belonged to space group C2, with unit-cell parameters a = 99.66, b = 73.93, c = 61.77 Å, β = 113.56°. [ABSTRACT FROM AUTHOR]

The adrenal gland protein AD-004 was identified in the human adrenal gland. Full-length AD-004 contains 172 amino acids, with a predicted molecular weight of about 20 kDa. In attempts to crystallize human AD-004, the gene was subcloned into a modified pET vector, pET21-DEST, with an N-terminal His5 tag using the Gateway cloning system, followed by protein expression in Escherichia coli strain BL21(DE3). The protein was purified in two steps to near-homogeneity and was crystallized. The crystals belong to space group P61 or P65, with unit-cell parameters a = b = 99.56, c = 57.19 Å. A complete 2.0 Å data set has been collected at a rotating-anode X-ray source and structure determination is under way. [ABSTRACT FROM AUTHOR]

BackgroundOur prior study in Han Chinese women has shown that women with a history of childhood sexual abuse (CSA) are at increased risk for developing major depression (MD). Would this relationship be found in our whole data set?MethodThree levels of CSA (non-genital, genital, and intercourse) were assessed by self-report in two groups of Han Chinese women: 6017 clinically ascertained with recurrent MD and 5983 matched controls. Diagnostic and other risk factor information was assessed at personal interview. Odds ratios (ORs) were calculated by logistic regression.ResultsWe confirmed earlier results by replicating prior analyses in 3,950 new recurrent MD cases. There were no significant differences between the two data sets. Any form of CSA was significantly associated with recurrent MD (OR 4.06, 95% confidence interval (CI) [3.19-5.24]). This association strengthened with increasing CSA severity: non-genital (OR 2.21, 95% CI 1.58-3.15), genital (OR 5.24, 95% CI 3.52-8.15) and intercourse (OR 10.65, 95% CI 5.56-23.71). Among the depressed women, those with CSA had an earlier age of onset, longer depressive episodes. Recurrent MD patients those with CSA had an increased risk for dysthymia (OR 1.60, 95%CI 1.11-2.27) and phobia (OR 1.41, 95%CI 1.09-1.80). Any form of CSA was significantly associated with suicidal ideation or attempt (OR 1.50, 95% CI 1.20-1.89) and feelings of worthlessness or guilt (OR 1.41, 95% CI 1.02-2.02). Intercourse (OR 3.47, 95%CI 1.66-8.22), use of force and threats (OR 1.95, 95%CI 1.05-3.82) and how strongly the victims were affected at the time (OR 1.39, 95%CI 1.20-1.64) were significantly associated with recurrent MD.ConclusionsIn Chinese women CSA is strongly associated with recurrent MD and this association increases with greater severity of CSA. Depressed women with CSA have some specific clinical traits. Some features of CSA were associated with greater likelihood of developing recurrent MD.

To investigate the risk factors that contribute to smoking in female patients with major depressive disorder (MDD) and the clinical features in depressed smokers.We examined the smoking status and clinical features in 6120 Han Chinese women with MDD (DSM-IV) between 30 and 60 years of age across China. Logistic regression was used to determine the association between clinical features of MDD and smoking status and between risk factors for MDD and smoking status.Among the recurrent MDD patients there were 216(3.6%) current smokers, 117 (2.0%) former smokers and 333(5.6%) lifetime smokers. Lifetime smokers had a slightly more severe illness, characterized by more episodes, longer duration, more comorbid illness (panic and phobias), with more DSM-IV A criteria and reported more symptoms of fatigue and suicidal ideation or attempts than never smokers. Some known risk factors for MDD were also differentially represented among smokers compared to non-smokers. Smokers reported more stressful life events, were more likely to report childhood sexual abuse, had higher levels of neuroticism and an increased rate of familial MDD. Only neuroticism was significantly related to nicotine dependence.Although depressed women smokers experience more severe illness, smoking rates remain low in MDD patients. Family history of MDD and environmental factors contribute to lifetime smoking in Chinese women, consistent with the hypothesis that the association of smoking and depression may be caused by common underlying factors.

The prevalence of major depressive disorder (MDD) is higher in those with low levels of educational attainment, the unemployed and those with low social status. However the extent to which these factors cause MDD is unclear. Most of the available data comes from studies in developed countries, and these findings may not extrapolate to developing countries. Examining the relationship between MDD and socio economic status in China is likely to add to the debate because of the radical economic and social changes occurring in China over the last 30 years.We report results from 3,639 Chinese women with recurrent MDD and 3,800 controls. Highly significant odds ratios (ORs) were observed between MDD and full time employment (OR = 0.36, 95% CI = 0.25-0.46, logP = 78), social status (OR = 0.83, 95% CI = 0.77-0.87, logP = 13.3) and education attainment (OR = 0.90, 95% CI = 0.86-0.90, logP = 6.8). We found a monotonic relationship between increasing age and increasing levels of educational attainment. Those with only primary school education have significantly more episodes of MDD (mean 6.5, P-value = 0.009) and have a clinically more severe disorder, while those with higher educational attainment are likely to manifest more comorbid anxiety disorders.In China lower socioeconomic position is associated with increased rates of MDD, as it is elsewhere in the world. Significantly more episodes of MDD occur among those with lower educational attainment (rather than longer episodes of disease), consistent with the hypothesis that the lower socioeconomic position increases the likelihood of developing MDD. The phenomenology of MDD varies according to the degree of educational attainment: higher educational attainment not only appears to protect against MDD but alters its presentation, to a more anxious phenotype.

The relationship between suicidality and major depression is complex. Socio- demography, clinical features, comorbidity, clinical symptoms, and stressful life events are important factors influencing suicide in major depression, but these are not well defined. Thus, the aim of the present study was to assess the associations between the above-mentioned factors and suicide ideation, suicide plan, and suicide attempt in 6008 Han Chinese women with recurrent major depression (MD). Patients with any suicidality had significantly more MD symptoms, a significantly greater number of stressful life events, a positive family history of MD, a greater number of episodes, a significant experience of melancholia, and earlier age of onset. Comorbidity with dysthymia, generalized anxiety disorder (GAD), social phobia, and animal phobia was seen in suicidal patients. The present findings indicate that specific factors act to increase the likelihood of suicide in MD. Our results may help improve the clinical assessment of suicide risk in depressed patients, especially for women.

Dysthymia is a form of chronic mild depression that has a complex relationship with major depressive disorder (MDD). Here we investigate the role of environmental risk factors, including stressful life events and parenting style, in patients with both MDD and dysthymia. We ask whether these risk factors act in the same way in MDD with and without dysthymia.We examined the clinical features in 5,950 Han Chinese women with MDD between 30-60 years of age across China. We confirmed earlier results by replicating prior analyses in 3,950 new MDD cases. There were no significant differences between the two data sets. We identified sixteen stressful life events that significantly increase the risk of dysthymia, given the presence of MDD. Low parental warmth, from either mother or father, increases the risk of dysthymia. Highly threatening but short-lived threats (such as rape) are more specific for MDD than dysthymia. While for MDD more severe life events show the largest odds ratio versus controls, this was not seen for cases of MDD with or without dysthymia.There are increased rates of stressful life events in MDD with dysthymia, but the impact of life events on susceptibility to dysthymia with MDD differs from that seen for MDD alone. The pattern does not fit a simple dose-response relationship, suggesting that there are moderating factors involved in the relationship between environmental precipitants and the onset of dysthymia. It is possible that severe life events in childhood events index a general susceptibility to chronic depression, rather than acting specifically as risk factors for dysthymia.

Diagnostic information for psychiatric research often depends on both clinical interviews and medical records. Although discrepancies between these two sources are well known, there have been few studies into the degree and origins of inconsistencies.We compared data from structured interviews and medical records on 1,970 Han Chinese women with recurrent DSM-IV major depression (MD). Correlations were high for age at onset of MD (0.93) and number of episodes (0.70), intermediate for family history (+0.62) and duration of longest episode (+0.43) and variable but generally more modest for individual depressive symptoms (mean kappa = 0.32). Four factors were identified for twelve symptoms from medical records and the same four factors emerged from analysis of structured interviews. Factor congruencies were high but the correlation of factors between interviews and records were modest (i.e. +0.2 to +0.4).Structured interviews and medical records are highly concordant for age of onset, and the number and length of episodes, but agree more modestly for individual symptoms and symptom factors. The modesty of these correlations probably arises from multiple factors including i) inconsistency in the definition of the worst episode, ii) inaccuracies in self-report and iii) difficulties in coding medical records where symptoms were recorded solely for clinical purposes.