Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state.

Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.

Correlation between inflammatory markers over time and disease severity in status epilepticus: a preliminary study.

Objectives: Convulsive status epilepticus (CSE) is a major subtype of status epilepticus that is known to be closely associated with systemic inflammation. Some important inflammatory biomarkers of this disorder include the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune inflammation index (SII), and pan-immune inflammation value (PIV). This study aimed to determine the NLR, PLR, MLR, SII, and PIV levels before and after treatment in adult patients with CSE and investigated the relationship of these parameters with disease severity. Methods: This retrospective study analyzed data from 103 adult patients with CSE and 103 healthy controls. The neutrophil, monocyte, platelet, and lymphocyte counts, as well as the NLR, PLR, MLR, SII, and PIV, were compared in adult patients with CSE during acute seizures (within 2 h of admission) and after treatment relief (1-2 weeks of complete seizure control). Furthermore, multivariate linear regression analysis investigated the relationship between NLR, PLR, MLR, SII, and PIV with the Status Epilepticus Severity Score (STESS). Results: The data revealed significant differences (p < 0.05) in neutrophils, monocytes, lymphocytes, NLR, PLR, MLR, SII, and PIV between adult patients with CSE during acute seizures and after treatment relief. The average neutrophil count was high during acute seizures in the patient group and decreased after remission. In contrast, the average lymphocyte count was lower after remission (p < 0.05). Furthermore, significant differences (p < 0.05) were observed in monocytes, lymphocytes, platelets, NLR, PLR, MLR, and PIV levels between adult patients with CSE after remission and the healthy control group. Multivariate linear regression analysis showed no significant correlation between NLR, PLR, MLR, SII, and PIV with STESS. Conclusion: The results of this study indicated that adult patients with CSE experienced a transient systemic inflammatory response during acute seizures, which gradually returned to baseline levels after remission. However, there was a lack of robust clinical evidence correlating the severity of adult CSE and systemic inflammatory response.

MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier.

In biological organisms, metal ion-binding proteins participate in numerous metabolic activities and are closely associated with various diseases. To accurately predict whether a protein binds to metal ions and the type of metal ion-binding protein, this study proposed a classifier named MIBPred. The classifier incorporated advanced Word2Vec technology from the field of natural language processing to extract semantic features of the protein sequence language and combined them with position-specific score matrix (PSSM) features. Furthermore, an ensemble learning model was employed for the metal ion-binding protein classification task. In the model, we independently trained XGBoost, LightGBM, and CatBoost algorithms and integrated the output results through an SVM voting mechanism. This innovative combination has led to a significant breakthrough in the predictive performance of our model. As a result, we achieved accuracies of 95.13% and 85.19%, respectively, in predicting metal ion-binding proteins and their types. Our research not only confirms the effectiveness of Word2Vec technology in extracting semantic information from protein sequences but also highlights the outstanding performance of the MIBPred classifier in the problem of metal ion-binding protein types. This study provides a reliable tool and method for the in-depth exploration of the structure and function of metal ion-binding proteins.

Altered brain functional networks in schizophrenia with persistent negative symptoms: an activation likelihood estimation meta-analysis.

Objective: To investigate brain structural and functional characteristics of three brain functional networks including default mode network (DMN), central executive network (CEN), and salience network (SN) in persistent negative symptoms (PNS) patients. Methods: We performed an activation likelihood estimation (ALE) meta-analysis of functional connectivity (FC) studies and voxel-based morphometry (VBM) studies to detect specific structural and functional alterations of brain networks between PNS patients and healthy controls. Results: Seventeen VBM studies and twenty FC studies were included. In the DMN, PNS patients showed decreased gray matter in the bilateral medial frontal gyrus and left anterior cingulate gyrus and a significant reduction of FC in the right precuneus. Also, PNS patients had a decrease of gray matter in the left inferior parietal lobules and medial frontal gyrus, and a significant reduction of FC in the bilateral superior frontal gyrus in the CEN. In comparison with healthy controls, PNS patients exhibited reduced gray matter in the bilateral insula, anterior cingulate gyrus, left precentral gyrus and right claustrum and lower FC in these brain areas in the SN, including the left insula, claustrum, inferior frontal gyrus and extra-nuclear. Conclusion: This meta-analysis reveals brain structural and functional imaging alterations in the three networks and the interaction among these networks in PNS patients, which provides neuroscientific evidence for more personalized treatment.Systematic Review RegistrationThe PROSPERO (https://www.crd.york.ac.uk/PROSPERO/, registration number: CRD42022335962).

Accurately identifying hemagglutinin using sequence information and machine learning methods.

Introduction: Hemagglutinin (HA) is responsible for facilitating viral entry and infection by promoting the fusion between the host membrane and the virus. Given its significance in the process of influenza virus infestation, HA has garnered attention as a target for influenza drug and vaccine development. Thus, accurately identifying HA is crucial for the development of targeted vaccine drugs. However, the identification of HA using in-silico methods is still lacking. This study aims to design a computational model to identify HA. Methods: In this study, a benchmark dataset comprising 106 HA and 106 non-HA sequences were obtained from UniProt. Various sequence-based features were used to formulate samples. By perform feature optimization and inputting them four kinds of machine learning methods, we constructed an integrated classifier model using the stacking algorithm. Results and discussion: The model achieved an accuracy of 95.85% and with an area under the receiver operating characteristic (ROC) curve of 0.9863 in the 5-fold cross-validation. In the independent test, the model exhibited an accuracy of 93.18% and with an area under the ROC curve of 0.9793. The code can be found from https://github.com/Zouxidan/HA_predict.git. The proposed model has excellent prediction performance. The model will provide convenience for biochemical scholars for the study of HA.

A First Computational Frame for Recognizing Heparin-Binding Protein.

Heparin-binding protein (HBP) is a cationic antibacterial protein derived from multinuclear neutrophils and an important biomarker of infectious diseases. The correct identification of HBP is of great significance to the study of infectious diseases. This work provides the first HBP recognition framework based on machine learning to accurately identify HBP. By using four sequence descriptors, HBP and non-HBP samples were represented by discrete numbers. By inputting these features into a support vector machine (SVM) and random forest (RF) algorithm and comparing the prediction performances of these methods on training data and independent test data, it is found that the SVM-based classifier has the greatest potential to identify HBP. The model could produce an auROC of 0.981 ± 0.028 on training data using 10-fold cross-validation and an overall accuracy of 95.0% on independent test data. As the first model for HBP recognition, it will provide some help for infectious diseases and stimulate further research in related fields.

A neuromarker for deficit syndrome in schizophrenia from a combination of structural and functional magnetic resonance imaging.

AIM: Deficit schizophrenia (DS), defined by primary and enduring negative symptoms, has been proposed as a promising homogeneous subtype of schizophrenia. It has been demonstrated that unimodal neuroimaging characteristics of DS were different from non-deficit schizophrenia (NDS), however, whether multimodal-based neuroimaging features could identify deficit syndrome remains to be determined. METHODS: Functional and structural multimodal magnetic resonance imaging of DS, NDS and healthy controls were scanned. Voxel-based features of gray matter volume, fractional amplitude of low-frequency fluctuations, and regional homogeneity were extracted. The support vector machine classification models were constructed using these features separately and jointly. The most discriminative features were defined as the first 10% of features with the greatest weights. Moreover, relevance vector regression was applied to explore the predictive values of these top-weighted features in predicting negative symptoms. RESULTS: The multimodal classifier achieved a higher accuracy (75.48%) compared with the single modal model in distinguishing DS from NDS. The most predictive brain regions were mainly located in the default mode and visual networks, exhibiting differences between functional and structural features. Further, the identified discriminative features significantly predicted scores of diminished expressivity factor in DS but not NDS. CONCLUSIONS: The present study demonstrated that local properties of brain regions extracted from multimodal imaging data could distinguish DS from NDS with a machine learning-based approach and confirmed the relationship between distinctive features and the negative symptoms subdomain. These findings may improve the identification of potential neuroimaging signatures and improve the clinical assessment of the deficit syndrome.

Recognition of outer membrane proteins using multiple feature fusion.

Introduction: Outer membrane proteins are crucial in maintaining the structural stability and permeability of the outer membrane. Outer membrane proteins exhibit several functions such as antigenicity and strong immunogenicity, which have potential applications in clinical diagnosis and disease prevention. However, wet experiments for studying OMPs are time and capital-intensive, thereby necessitating the use of computational methods for their identification. Methods: In this study, we developed a computational model to predict outer membrane proteins. The non-redundant dataset consists of a positive set of 208 outer membrane proteins and a negative set of 876 non-outer membrane proteins. In this study, we employed the pseudo amino acid composition method to extract feature vectors and subsequently utilized the support vector machine for prediction. Results and Discussion: In the Jackknife cross-validation, the overall accuracy and the area under receiver operating characteristic curve were observed to be 93.19% and 0.966, respectively. These results demonstrate that our model can produce accurate predictions, and could serve as a valuable guide for experimental research on outer membrane proteins.

The motivation and pleasure deficits but not expressivity affects social functioning through cognitive function in male patients with schizophrenia: A structural equation model.

OBJECTIVE: This study aims to compare cognitive function and social functioning in male schizophrenia patients with deficit syndrome (DS) and non-DS, and to explore the associations among two different dimensions of negative symptoms (motivation and pleasure (MAP) and expressivity (EXP) deficits), cognitive function and social functioning base on a Structural Equation Model (SEM). METHODS: The current study enrolled 161 male schizophrenia patients and 120 age- and education- matched healthy controls. The DS and non-DS group were categorized by the Chinese version of Schedule for the Deficit Syndrome (SDS). The psychotic and negative symptoms were evaluated by the Brief Psychiatric Rating Scale (BPRS) and the Brief Negative Symptoms Scale (BNSS). The Social functioning was measured by Scale of Social function in Psychosis Inpatients (SSPI). A battery of classical neurocognitive tests was used for assessing cognition including sustained vigilance/attention, cognitive flexibility, ideation fluency and visuospatial memory. RESULTS: Our study indicated that DS patients performed worser in cognitive function and social functioning than non-DS patients. The SEM model demonstrated that MAP significantly affected social functioning through direct influence and mediation of cognitive function. However, our results found that EXP had little influence on cognitive function and social function. CONCLUSION: Our findings provided evidence supporting that DS may represent as a subtype of schizophrenia, and the MAP factor play a pivotal role to influence the cognitive and social functioning in schizophrenia patients.

The relevant research of adverse childhood experiences and "risky drinking" in children of alcoholics in China.

OBJECTIVE: To determine whether adverse childhood experiences (ACEs) of children of alcoholics (COA) in male were associated with their current "risky drinking". METHODS: This case-control study used the Alcohol Use Disorder Identification Test (AUDIT, cutoff is 7) to divide the participants into two groups, a "risky drinking" group (N = 53) and a "non-risky drinking" group (N = 97). Demographic data, Adverse Childhood Experiences-International Questionnaire (ACE-IQ), the Hamilton Anxiety Rating Scale (HAMA), the Hamilton Depression Rating Scale (HAMD) and the Mini-International Neuropsychiatric Interview (MINI) were used for assessment. The specific relationships between ACEs and "risky drinking" were explored. RESULTS: Respondents ranged in age from 29.70 ± 6.72 years; 74.5% were females; 94.7% were of Han nationality; 56.7% had a level of education above high school; 12% had no formal or stable job. There was difference in attitude to self-drinking between two groups (P < 0.001). The "risky drinking" group was more likely to have experienced a major depressive episode (P < 0.05), nonalcohol psychoactive substance use disorder (P < 0.01) and bulimia nervosa (P < 0.05), and they also experienced more physical abuse (P < 0.05), community violence (P < 0.001) and collective violence (P < 0.01). In a single factor logistic regression, physical abuse, community violence and collective violence were associated with a two to 11- fold increase in "risky drinking" in the adult COA, and in multiple factor logistic regression, community violence showed a graded relationship with "risky drinking". CONCLUSION: The childhood adverse experiences contribute to "risky drinking" in COA. This finding in the Chinese context have significant implications for prevention not only in China but in other cultures. There must be greater awareness of the role of ACEs in the perpetuation of alcoholism.

Attention is all you need: utilizing attention in AI-enabled drug discovery.

Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.

Early predictors of new-onset immune-related seizures: a preliminary study.

BACKGROUND: Approximately 60% of patients with autoimmune encephalitis (AE) exhibit secondary acute symptomatic seizures and showed highly sensitive to immunotherapy. However, it is difficult for many patients to receive early immunotherapy since the early identification of the cause in AE is more complex. This study aimed to investigate the early predictors of initial immune-related seizures and to guide the evaluation of treatment and prognosis. METHODS: One hundred and fifty-four patients with new-onset "unknown etiology" seizures with a course of disease less than 6 months were included. Serum and/or cerebrospinal fluid neuron-specific autoantibodies (NSAbs), including N-methyl-D-aspartate receptor (NMDAR), α-amino-3-hydroxy-5- Methyl-4-isoxazole propionic acid receptor 1 (AMPAR1), AMPAR2, anti-leucine rich glioma inactivated 1 antibody (LGI1), anti-gamma-aminobutyric acid type B receptor (GABABR), anti-contact protein-related protein-2 (CASPR2) were used to screen for immune etiology of the seizures. In addition, patients with epilepsy and encephalopathy were also examined via brain MRI, long-term video EEG, antibody prevalence in epilepsy and encephalopathy (APE2) score, and modified Rankin Scale (mRS). A logistic regression model was used to analyze the early predictors of immune etiology. RESULTS: Thirty-four cases (22.1%) were positive for NSAbs. Among all 154 patients, 23 cases of autoimmune encephalitis (AE) (21 cases of NSAbs positive), 1 case of ganglionic glioma (NSAbs positive), 130 cases of epilepsy or seizures (12 cases of NSAbs positive) were recorded. Also, there were 17 patients (11.0%) with APE2 ≥ 4 points, and all of them met the clinical diagnosis of AE. The sensitivity and specificity of APE2 ≥ 4 points for predicting AE were 73.9% and 100%. The results of multivariate analysis showed that the NSAbs and APE2 scores independently influenced the early prediction of initial immune-related seizures (P < 0.05). CONCLUSION: NSAbs and APE2 scores could act as early predictors of initial immune-related seizures.

Meta-analysis of structural and functional brain abnormalities in schizophrenia with persistent negative symptoms using activation likelihood estimation.

Background: Persistent negative symptoms (PNS) include both primary and secondary negative symptoms that persist after adequate treatment, and represent an unmet therapeutic need. Published magnetic resonance imaging (MRI) evidence of structural and resting-state functional brain abnormalities in schizophrenia with PNS has been inconsistent. Thus, the purpose of this meta-analysis is to identify abnormalities in structural and functional brain regions in patients with PNS compared to healthy controls. Methods: We systematically searched PubMed, Web of Science, and Embase for structural and functional imaging studies based on five research methods, including voxel-based morphometry (VBM), diffusion tensor imaging (DTI), functional connectivity (FC), the amplitude of low-frequency fluctuation or fractional amplitude of low-frequency fluctuation (ALFF/fALFF), and regional homogeneity (ReHo). Afterward, we conducted a coordinate-based meta-analysis by using the activation likelihood estimation algorithm. Results: Twenty-five structural MRI studies and thirty-two functional MRI studies were included in the meta-analyses. Our analysis revealed the presence of structural alterations in patients with PNS in some brain regions including the bilateral insula, medial frontal gyrus, anterior cingulate gyrus, left amygdala, superior temporal gyrus, inferior frontal gyrus, cingulate gyrus and middle temporal gyrus, as well as functional differences in some brain regions including the bilateral precuneus, thalamus, left lentiform nucleus, posterior cingulate gyrus, medial frontal gyrus, and superior frontal gyrus. Conclusion: Our study suggests that structural brain abnormalities are consistently located in the prefrontal, temporal, limbic and subcortical regions, and functional alterations are concentrated in the thalamo-cortical circuits and the default mode network (DMN). This study provides new insights for targeted treatment and intervention to delay further progression of negative symptoms. Systematic review registration: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42022338669].

Outcomes and safety of concomitant topiramate or metformin for antipsychotics-induced obesity: a randomized-controlled trial.

BACKGROUND: Although there are some existing data describing the usage of topiramate in patients with antipsychotic-induced obesity, study on its comparison with metformin is limited. This study aimed to explore the effectiveness and safety of concomitant topiramate on antipsychotic-induced obesity as well as its comparison with metformin. METHODS: 62 stabilized outpatients with antipsychotic-induced obesity were randomized into the topiramate group and the metformin group with 16-week treatment. The patients' weight, body mass index (BMI), waist-hip ratio, and their side effects were assessed and compared. Intention-to-treat and completer analyses were performed. Meanwhile, covariance analysis was conducted to control the impact of the significant difference in BMI between the two groups. RESULTS: The two groups had comparable characteristics, though their difference in baseline BMI was significant. (1) Intention-to-treat analyses: the random missing values were replaced using the last observation carried forward method when intention-to-treat analyses were conducted. Compared with the baseline, the weight, BMI, and waist-hip ratio in the topiramate group markedly decreased at each follow-up, whereas, in the metformin group, only waist-hip ratio significantly decreased at 4 weeks after treatment. Compared with the metformin, only weight and BMI in the topiramate group were significantly decreased at week 4 after treatment, and at week 8-16, weight, BMI and waist-hip ratio were remarkably declined. (2) Completer analyses: compared with the baseline, the weight, BMI, and waist-hip ratio in the topiramate group at week 4-16 were markedly decreased, whereas only waist-hip ratio with metformin was significantly decreased at week 4. Compared with the metformin, all BMI with topiramate were markedly decreased at week 4-16. Moreover, its weight and waist-hip ratio also were notably lowered at week 8. No significant differences in adverse events were found between the two groups. CONCLUSIONS: Topiramate, similar to metformin in reducing obesity as previously reported, also significantly reduced body weight, BMI, and waist-hip ratio in patients with antipsychotic-induced obesity and demonstrated well tolerance in psychiatric patients. Trial registration The trial was registered at http://www.chictr.org.cn , and the number was ChiCTR-IPR-17013122.

Association of adenylate cyclase-2 gene polymorphism with bipolar disorder.

The pathogenesis of the Bipolar Disorder(BPD) is still unclear. Some studies suggest that abnormal signal transduction in specific pathways may play an important role in the pathogenesis of BPD (Sui et al., 2015). Adenylate cyclase (ADCY) is an essential component of the adenylate signaling pathway. Previous studies have shown that some SNPs within the adenylate cyclase gene could affect the therapeutic response to mood stabilizers and antidepressants. Moreover, in 2014, one whole-genome study suggested that the ADCY-2 gene may be associated with BPD (Mühleisen et al., 2014). This study aims to investigate the association between ADCY-2 gene polymorphism and BPD in Chinese Han population.

The Application of Suctioning Flexible Ureteroscopy With Intelligent Pressure Control in Treating Upper Urinary Tract Calculi on Patients With a Solitary Kidney.

OBJECTIVE: To investigate the usage of intelligently pressure-controlled flexible ureteroscopy (URS) in managing upper urinary tract calculi in patients with a solitary kidney. METHODS: Forty patients with a solitary kidney and upper urinary tract calculus were included in this study. All the patients underwent suctioning URS with intelligent control of renal pelvic pressure by connecting pressure-measuring suctioning ureteral access sheath to an irrigation and suctioning platform. Treatment outcome and perioperative data were collected. RESULTS: The mean operative time was 25.2 ± 14.5 minutes. The mean hospital stay was 4.7 ± 1.4 days. The stone-free rate at 4 weeks after surgery was 87.5%, and it was 92.5% at 12 weeks after surgery. Two patients (5%) experienced complications of fever postoperatively. There were no complications of elevated serum creatinine, severe bleeding, sepsis, stone street, ureteral mucosa stripping, and ureteral stenosis. CONCLUSION: It is safe and efficient to use the intelligently pressure-controlled flexible URS in treating upper urinary tract calculi for patients with a solitary kidney with advantages of high lithotripsy efficacy and low complication rate.

The efficacy, acceptability, and safety of five atypical antipsychotics in patients with first-episode drug-naïve schizophrenia: a randomized comparative trial.

BACKGROUND: Differences in effectiveness and tolerability between different atypical antipsychotics may affect schizophrenic patients' treatment adherence or prognosis. However, which kind of antipsychotic was more effective and safe in the treatment of schizophrenia is still being debated. This study attempted to understand whether there are any differences in efficacy, acceptability, and safety between the five atypical antipsychotics in patients with first-episode schizophrenia. METHODS: Two hundred cases of inpatients with first-episode drug-naïve schizophrenia were randomly assigned to 6-8 weeks of treatment with either of aripiprazole, risperidone, quetiapine, olanzapine, or ziprasidone from October 2012 to November 2014. The efficacy, acceptability, and safety measurement after 6-8 weeks of treatment of the five kinds of antipsychotics were evaluated by the deduction rate of Brief Psychiatric Rating Scale (BPRS) total score, the proportion of treatment discontinuation, and adverse events, respectively. Whether the treatment discontinuation or combination therapy for baseline antipsychotics after titration mainly depended on ineffective or less effective on an initial-assigned antipsychotic during the study period. RESULTS: BPRS total scores in each antipsychotic group were significantly decreased at the end of the study (P < 0.01), and only the deduction rate of BPRS total scores in the risperidone group was markedly higher than those in the groups of aripiprazole (P < 0.01) and olanzapine (P < 0.05) after controlling the impact of the differences of age of onset. There were significant differences between quetiapine (χ2 = 5.46, P = 0.019), olanzapine (χ2 = 5.6, P = 0.018), and ziprasidone regarding the proportion of maintaining on initially allocated therapy. In addition, the difference in treatment discontinuation between male and female patients was also significant (χ2 = 9.897, P = 0.002), and odds ratio of treatment discontinuation in male and female patients was 0.37 (95% CI 0.198-0.693); however, no difference in treatment discontinuation was found between five antipsychotics. Extrapyramidal symptoms in the groups of quetiapine and olanzapine were notably less than the other three kinds of antipsychotics (P < 0.05), but there were no significant differences in other adverse events between the five antipsychotic groups. CONCLUSIONS: Risperidone was more effective than aripiprazole and olanzapine in treating first-episode schizophrenia. The present study revealed the superiority of quetiapine and olanzapine over ziprasidone with remarkably less severe extrapyramidal adverse effects, especially with lower drop-out and treatment discontinuation. There were no differences in terms of other adverse events although the risk of treatment discontinuation was higher in female patients. Trial registration 2012-3-88. Registered 20 July 2012.

[Ecological executive function profiles in depression patients before and after treatment].

OBJECTIVE: To explore the ecological executive function profile in depression patients before and after antidepressants treatment and analyze the relationship of ecological executive function and depression symptoms and effect. METHODS: A total of 33 inpatients diagnosed as depression disorder according to ICD-10 completed the Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A) and Hamilton Depression Scale (HAMD) before and after a 6-week antidepressant treatment. RESULTS: (1) After treatment, they yielded lower scores significantly on most subscales of BRIEF-A (t = 2.061 - 4.229, P < 0.05), including total score [(127 ± 27) vs (113 ± 28)], shifting [(11.7 ± 2.7) vs (10.3 ± 2.6)], emotion control [(18.8 ± 4.6) vs (15.8 ± 4.4)], monitoring [(9.6 ± 3.0) vs (8.8 ± 2.7)], initiation [(15.6 ± 3.7) vs (13.2 ± 3.6)], working memory [(14.9 ± 3.4) vs (13.3 ± 3.9)], planning [(18.3 ± 4.4) vs (16.6 ± 4.4)], organization [(12.4 ± 3.8) vs (11.4 ± 3.1)], behavioral regulation index (BRI), metacognition index (MI) and global executive composite (GEC). (2) Before treatment, Person's correlation test showed that the total score of HAMD and all subscales of BRIEF-A had no significant correlation respectively (r = -0.145 - 0.220, P > 0.05). (3) After treatment, the deduction of total score of HAMD and the deduction of the scores of inhibiting belonging to behavioral regulation index (BRI) and initiation, planning belonging to metacognition index (MI) had moderate correlations respectively(r = 0.450, 0.432, 0.403, P < 0.05). (4) Multiple regression analysis showed that the scores of working memory, organization and the deduction of total score of HAMD had significantly negative correlations respectively (t = -2.295, -2.488, P < 0.05). CONCLUSION: The antidepressant treatment can improve ecological executive function of depression patients. And the improvements of ecological executive function and depression symptoms are partially correlated.