Altered N-linked glycosylation in depression: A pre-clinical study.

BACKGROUND: Neuroimmune plays an important role in major depressive disorders (MDD). N-linked protein glycosylation (NLG) might contribute to depression by regulating the neuroinflammatory response. As microglia is the main executor of neuroimmune function in the central neural system (CNS), targeting the process of N-linked protein glycosylation of microglia in the mice used for studying depression might potentially offer new avenues for the strategy for MDD. METHODS: The chronic unpredictable mild stress (CUMS) mouse model was established for the whole brain microglia isolating. Then, RNA samples of microglia were extracted for transcriptome sequencing and mRNA analysis. Immunofluorescence (IF) was used to identify the expression level of NLG-related enzyme, B4galt1, in microglia. RESULTS: The data showed that NLG was positively related to depression. Moreover, the NLG-related gene, B4galt1 increased expression in the microglia of CUMS mice. Then, the inhibition of NLG reversed the depressive behavior in CUMS mice. The expression level of B4galt1 in CUMS mice was upregulating following the NLG-inhibitor treatment. Similar results haven't been observed in neurons. Information obtained from these experiments showed increasing expression of B4galt1 in microglia following depressive-like behaviors. CONCLUSIONS: These findings indicate that NLG in microglia is associated with MDD, and suggest that therapeutically targeting NLG might be an effective strategy for depression. LIMITATIONS: How to modulate the B4galt1 or NLG pathways in microglia efficiently and economically request new technologies.

An efficient polynomial-based verifiable computation scheme on multi-source outsourced data.

With the development of cloud computing, users are more inclined to outsource complex computing tasks to cloud servers with strong computing capacity, and the cloud returns the final calculation results. However, the cloud is not completely trustworthy, which may leak the data of user and even return incorrect calculations on purpose. Therefore, it is important to verify the results of computing tasks without revealing the privacy of the users. Among all the computing tasks, the polynomial calculation is widely used in information security, linear algebra, signal processing and other fields. Most existing polynomial-based verifiable computation schemes require that the input of the polynomial function must come from a single data source, which means that the data must be signed by a single user. However, the input of the polynomial may come from multiple users in the practical application. In order to solve this problem, the researchers have proposed some schemes for multi-source outsourced data, but these schemes have the common problem of low efficiency. To improve the efficiency, this paper proposes an efficient polynomial-based verifiable computation scheme on multi-source outsourced data. We optimize the polynomials using Horner's method to increase the speed of verification, in which the addition gate and the multiplication gate can be interleaved to represent the polynomial function. In order to adapt to this structure, we design the corresponding homomorphic verification tag, so that the input of the polynomial can come from multiple data sources. We prove the correctness and rationality of the scheme, and carry out numerical analysis and evaluation research to verify the efficiency of the scheme. The experimental indicate that data contributors can sign 1000 new data in merely 2 s, while the verification of a delegated polynomial function with a power of 100 requires only 18 ms. These results confirm that the proposed scheme is better than the existing scheme.

Anti-Toxoplasma gondii effect of tylosin in vitro and in vivo.

BACKGROUND: Toxoplasma gondii is an important protozoan pathogen with medical and veterinary importance worldwide. Drugs currently used for treatment of toxoplasmosis are less effective and sometimes cause serious side effects. There is an urgent need for the development of more effective drugs with relatively low toxicity. METHODS: The effect of tylosin on the viability of host cells was measured using CCK8 assays. To assess the inhibition of tylosin on T. gondii proliferation, a real-time PCR targeting the B1 gene was developed for T. gondii detection and quantification. Total RNA was extracted from parasites treated with tylosin and then subjected to transcriptome analysis by RNA sequencing (RNA-seq). Finally, murine infection models of toxoplasmosis were used to evaluate the protective efficacy of tylosin against T. gondii virulent RH strain or avirulent ME49 strain. RESULTS: We found that tylosin displayed low host toxicity, and its 50% inhibitory concentration was 175.3 µM. Tylsoin also inhibited intracellular T. gondii tachyzoite proliferation, with a 50% effective concentration of 9.759 µM. Transcriptome analysis showed that tylosin remarkably perturbed the gene expression of T. gondii, and genes involved in "ribosome biogenesis (GO:0042254)" and "ribosome (GO:0005840)" were significantly dys-regulated. In a murine model, tylosin treatment alone (100 mg/kg, i.p.) or in combination with sulfadiazine sodium (200 mg/kg, i.g.) significantly prolonged the survival time and raised the survival rate of animals infected with T. gondii virulent RH or avirulent ME49 strain. Meanwhile, treatment with tylosin significantly decreased the parasite burdens in multiple organs and decreased the spleen index of mice with acute toxoplasmosis. CONCLUSIONS: Our findings suggest that tylosin exhibited potency against T. gondii both in vitro and in vivo, which offers promise for treatment of human toxoplasmosis.

Integrated multi-omics analysis reveals gut microbiota dysbiosis and systemic disturbance in major depressive disorder.

Major depressive disorder (MDD) involves systemic changes in peripheral blood and gut microbiota, but the current understanding is incomplete. Herein, we conducted a multi-omics analysis of fecal and blood samples obtained from an observational cohort including MDD patients (n = 99) and healthy control (HC, n = 50). 16S rRNA sequencing of gut microbiota showed structural alterations in MDD, as characterized by increased Enterococcus. Metagenomics sequencing of gut microbiota showed substantial functional alterations including upregulation in the superpathway of the glyoxylate cycle and fatty acid degradation and downregulation in various metabolic pathways in MDD. Plasma metabolomics revealed decreased amino acids and bile acids, together with increased sphingolipids and cholesterol esters in MDD. Notably, metabolites involved in arginine and proline metabolism were decreased while sphingolipid metabolic pathway were increased. Mass cytometry analysis of blood immune cell subtypes showed rises in proinflammatory immune subsets and declines in anti-inflammatory immune subsets in MDD. Furthermore, our findings revealed disease severity-related factors of MDD. Interestingly, we classified MDD into two immune subtypes that were highly correlated with disease relapse. Moreover, we established discriminative signatures that differentiate MDD from HC. These findings contribute to a comprehensive understanding of the MDD pathogenesis and provide valuable resources for the discovery of biomarkers.

Relationship between biological rhythm dysregulation and suicidal ideation in patients with major depressive disorder.

BACKGROUND: Although the disturbance of circadian rhythms represents a significant clinical feature of major depressive disorder (MDD), the relationship between biological rhythm disturbances and the severity of suicidal ideation in individuals with MDD remains unclear. We aimed to explore the characteristics of different biological rhythm dimensions in MDD and their association with the severity of depressive symptoms and suicidal ideation. METHODS: A total of 50 MDD patients and 50 healthy controls were recruited and their general information was collected. The severity of depressive symptoms was assessed with the 17-item Hamilton Depression Rating Scale (HDRS17). The intensity of suicidal ideation was evaluated with the Beck Scale for Suicide Ideation (BSS). The Chinese version of the Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN) scale was utilized to assess the participants' biological rhythm dysregulation. Multiple logistic regression analysis was conducted to explore the relationship between biological rhythm and the risk of MDD. Multiple linear regression analysis was performed in the MDD group to investigate the relationship between different biological rhythm dimensions and suicide ideation. RESULTS: Significant differences were observed between the MDD group and the control group in total BRIAN score (Z=-5.41, P < 0.001) as well as scores for each dimension. After adjusting for confounding factors, multiple logistic regression analysis revealed a significant association between total BRIAN score and the presence of MDD (OR = 1.20, 95% CI = 1.10-1.29, P < 0.001), as well as between scores in different BRIAN dimensions and the presence of MDD (activity: OR = 1.47, 95% CI = 1.24-1.74, P < 0.001; sleep: OR = 1.52, 95% CI = 1.28-1.79, P < 0.001; social: OR = 1.80, 95% CI = 1.32-2.46, P < 0.001; eating pattern: OR = 1.34, 95% CI = 1.12-1.60, P = 0.001). In patients with MDD, linear regression analysis demonstrated a positive relationship between BSS scores and BRIAN eating pattern scores (ß = 0.34, P = 0.022), even after adjusting for demographic factors and the severity of depression. CONCLUSIONS: Patients with MDD exhibited significantly higher levels of dysregulation in all four biological rhythm dimensions compared to healthy controls and the degree of dysregulation was associated with the severity of depression. More importantly, dysregulation of eating pattern may increase the intensity of suicidal ideation in MDD, thus elevating the risk of suicide.

PHB3 interacts with BRI1 and BAK1 to mediate brassinosteroid signal transduction in Arabidopsis and tomato.

Brassinosteroids (BRs) are plant hormones that are essential in plant growth and development. BRASSINOSTEROID-INSENSITIVE 1 (BRI1) and BRI1 ASSOCIATED RECEPTOR KINASE 1 (BAK1), which are located on the plasma membrane, function as co-receptors that accept and transmit BR signals. PROHIBITIN 3 (PHB3) was identified in both BRI1 and BAK1 complexes by affinity purification and LC-MS/MS analysis. Biochemical data showed that BRI1/BAK1 interacted with PHB3 in vitro and in vivo. BRI1/BAK1 phosphorylated PHB3 in vitro. When the Thr-80 amino acid in PHB3 was mutated to Ala, the mutant protein was not phosphorylated by BRI1 and the mutant protein interaction with BRI1 was abolished in the yeast two-hybrid assay. BAK1 did not phosphorylate the mutant protein PHB3T54A . The loss-of-function phb3 mutant showed a weaker BR signal than the wild-type. Genetic analyses revealed that PHB3 is a BRI1/BAK1 downstream substrate that participates in BR signalling. PHB3 has five homozygous in tomato, and we named the closest to AtPHB3 as SlPHB3.1. Biochemical data showed that SlBRI1/SlSERK3A/SlSERK3B interacted with SlPHB3.1 and SlPHB3.3. The CRISPR-Cas9 method generated slphb3.1 mutant led to a BR signal stunted relatively in tomatoes. PHB3 is a new component of the BR signal pathway in both Arabidopsis and tomato.

Does decreased autophagy and dysregulation of LC3A in astrocytes play a role in major depressive disorder?

Astrocytic dysfunction contributes to the molecular pathogenesis of major depressive disorder (MDD). However, the astrocytic subtype that mainly contributes to MDD etiology and whether dysregulated autophagy in astrocytes is associated with MDD remain unknown. Using a single-nucleus RNA sequencing (snRNA-seq) atlas, three astrocyte subtypes were identified in MDD, while C2 State-1Q astrocytes showed aberrant changes in both cell proportion and most differentially expressed genes compared with other subtypes. Moreover, autophagy pathways were commonly inhibited in astrocytes in the prefrontal cortices (PFCs) of patients with MDD, especially in C2 State-1Q astrocytes. Furthermore, by integrating snRNA-seq and bulk transcriptomic data, we found significant reductions in LC3A expression levels in the PFC region of CUMS-induced depressed mice, as well as in postmortem PFC tissues and peripheral blood samples from patients with MDD. These results were further validated by qPCR using whole-blood samples from patients with MDD and healthy controls. Finally, LC3A expression in the whole blood of patients with MDD was negatively associated with the severity of depressive symptoms. Overall, our results underscore autophagy inhibition in PFC astrocytes as a common molecular characteristic in MDD and might reveal a novel potential diagnostic marker LC3A.

A 6-week, phase IIb, randomized, double-blind, placebo-controlled trial of Anyu Peibo capsules for the treatment of major depressive disorder in adults.

Background: Almost one-third of patients with major depressive disorder (MDD) do not respond to conventional antidepressants, and new treatments for MDD are urgently needed. Objectives: This phase IIb clinical trial was designed to evaluate the efficacy and safety of Anyu Peibo capsules in the treatment of adults with MDD. Design: A multicenter, randomized, double-blind, placebo-controlled, parallel-group, fixed-dose study. Methods: A total of 172 patients with MDD from nine study centers were randomized (1:1) to receive placebo (n = 86) or oral Anyu Peibo capsules (0.8 g) twice per day (n = 86) for 6 weeks. The primary endpoint was the change in the Montgomery Åsberg Depression Rating Scale (MADRS) total score from baseline to week 6, analyzed using an analysis of covariance (ANCOVA) approach with the baseline MADRS score, center effect and center by group interaction as the covariates. Other efficacy endpoints and variables included clinical response and remission rates according to the MADRS and the 17-item Hamilton Depression Rating Scale (HAMD-17) scores, the change in the HAMD-17, Clinical Global Impression - Severity scale and Clinical Global Impression - Improvement scale scores and the reduction in the Hamilton Anxiety Scale from baseline to week 6. Results: The mean baseline MADRS total scores were 29.20 and 29.72 in the Anyu Peibo (n = 82) and placebo groups (n = 81), respectively. The least squares mean change in the MADRS score from baseline to week 6 was 16.59 points in the Anyu Peibo group and 14.51 points in the placebo group. Although there were greater reductions in the MADRS score from baseline to week 6 in the Anyu Peibo capsule group compared to the placebo group, the difference did not reach statistical significance (least-squares mean difference, 2.07 points; 95% confidence interval, -0.27 to 4.41; p = 0.0819). The results of sensitivity analyses by ANCOVA with the last observation carried forward method for missing data indicated that the administration of Anyu Peibo capsules may lead to a significant reduction in depressive symptoms compared to the placebo (least-squares mean difference: 3.29 points; 95% confidence interval: 0.64-5.93; p = 0.0152). Furthermore, Anyu Peibo capsules showed significant benefits over placebo when the change in the HAMD-17 score from baseline to week 6 was evaluated as the secondary analysis (t = 2.01; 95% confidence interval, 0.03-4.23; p = 0.0464). Conclusion: Anyu Peibo capsules may have an effective and safe antidepressant effect, which warrants further research.

[Robot-assisted PVP for the treatment of osteoporotic fractures of the upper thoracic vertebra].

OBJECTIVE: To investigate the clinical effect of "Tianji" orthopedic robot-assisted percutaneous vertebro plasty(PVP) surgery in the treatment of upper thoracic osteoporotic fracture. METHODS: A retrospective analysis was performed on 32 patients with upper thoracic osteoporotic fracture who underwent PVP surgery in Shenzhen Hospital of Traditional Chinese Medicine from August 2016 to June 2022. There were 8 males and 24 females, ranging in age from 58 to 90 years old, with a mean of (67.75±12.27) years old. Fifteen patients were treated with robot-assisted PVP surgery (robot group), including 3 males and 12 females, with an average age of (68.5±10.3) years. Fracture location:1 case of T2 fracture, 1 case of T3 fracture, 3 cases of T4 fracture, 3 cases of T5 fracture, and 7 cases of T6 fracture. The follow-up period ranged from 1.0 to 3.0 months, with a mean of (1.6±0.7) months. Seventeen patients underwent routine PVP surgery (conventional group), including 5 males and 12 females, with an average age of (66.8±11.6) years old. Fracture location:1 case of T1 fracture, 5 cases of T4 fracture, 2 cases of T5 fracture and 9 cases of T6 fracture. The follow-up period ranged from 0.5 to 4.0 months, with a mean of (1.5±0.6) months. Preoperative and postoperative visual analogue scale(VAS) and Oswestry disability index(ODI) scores were compared between the two groups, and the number of punctures, perspective times, operation time, intraoperative blood loss, bone cement distribution, bone cement leakage, and intraoperative radiation dose were compared between the two groups. RESULTS: Number of punctures times, perspective times, operation time, intraoperative blood loss, bone cement distribution, bone cement leakage and intraoperative radiation dose in the robot group were all significantly better than those in the conventional group(P<0.05). VAS of 2.03±0.05 and ODI of (22.16±4.03) % in the robot group were significantly better than those of the robot group before surgery, which were (8.67±0.25) score and (79.40±7.72)%(t=100.869, P<0.001;t=25.456, P<0.001). VAS of 2.17±0.13 and ODI of (23.88±6.15)% in the conventional group were significantly better than those before surgery, which were (8.73±0.18) score and (80.01±7.59)%(t=121.816, P<0.001;t=23.691, P<0.001). There was no significant difference in VAS and ODI between the two groups after operation (t=-3.917, P=0.476;t=-0.922, P=0.364). CONCLUSION: Robot-assisted PVP in the treatment of upper thoracic osteoporotic fractures can further improve surgical safety, reduce bone cement leakage, and achieve satisfactory clinical efficacy.

Identification and cross-validation of autophagy-related genes in cardioembolic stroke.

Objective: Cardioembolic stroke (CE stroke, also known as cardiogenic cerebral embolism, CCE) has the highest recurrence rate and fatality rate among all subtypes of ischemic stroke, the pathogenesis of which was unclear. Autophagy plays an essential role in the development of CE stroke. We aim to identify the potential autophagy-related molecular markers of CE stroke and uncover the potential therapeutic targets through bioinformatics analysis. Methods: The mRNA expression profile dataset GSE58294 was obtained from the GEO database. The potential autophagy-related differentially expressed (DE) genes of CE stroke were screened by R software. Protein-protein interactions (PPIs), correlation analysis, and gene ontology (GO) enrichment analysis were applied to the autophagy-related DE genes. GSE66724, GSE41177, and GSE22255 were introduced for the verification of the autophagy-related DE genes in CE stroke, and the differences in values were re-calculated by Student's t-test. Results: A total of 41 autophagy-related DE genes (37 upregulated genes and four downregulated genes) were identified between 23 cardioembolic stroke patients (≤3 h, prior to treatment) and 23 healthy controls. The KEGG and GO enrichment analysis of autophagy-related DE genes indicated several enriched terms related to autophagy, apoptosis, and ER stress. The PPI results demonstrated the interactions between these autophagy-related genes. Moreover, several hub genes, especially for CE stroke, were identified and re-calculated by Student's t-test. Conclusion: We identified 41 potential autophagy-related genes associated with CE stroke through bioinformatics analysis. SERPINA1, WDFY3, ERN1, RHEB, and BCL2L1 were identified as the most significant DE genes that may affect the development of CE stroke by regulating autophagy. CXCR4 was identified as a hub gene of all types of strokes. ARNT, MAPK1, ATG12, ATG16L2, ATG2B, and BECN1 were identified as particular hub genes for CE stroke. These results may provide insight into the role of autophagy in CE stroke and contribute to the discovery of potential therapeutic targets for CE stroke treatment.

Associations between sleep disturbance, inflammatory markers and depressive symptoms: Mediation analyses in a large NHANES community sample.

Both depression and sleep disturbance have been linked to inflammation. However, the role that inflammation plays in the relationship between sleep disturbance and depression remains unclear. We examined pairwise associations between inflammatory markers (neutrophil-to-lymphocyte ratio [NLR] and C-reactive protein level [CRP]), sleep disturbance, and depressive symptoms in a robust, ethnically diverse sample (n = 32,749) from the National Health and Nutrition Examination Survey (NHANES). We found higher levels of inflammatory markers in participants with depression and/or sleep disturbance compared to those without depression or sleep disturbance. Sleep disturbance was positively associated with inflammatory markers and depressive symptoms even after considering a wide range of potential confounders (e.g., age, sex, body mass index). Inflammatory marker levels were nonlinearly associated with depressive symptoms and were positively associated with depressive symptoms after reaching the inflection point (NLR, 1.67; CRP, 0.22 mg/dL). Inflammatory markers mediated a marginal portion (NLR, 0.0362%, p = 0.026; CRP, 0.0678%; p = 0.018) of the potential effects of sleep disturbance on depressive symptoms. Our research showed that inflammatory markers, sleep disturbance, and depression are pairwise correlated. Increased inflammatory markers levels slightly mediate the association between sleep disturbance and depression.

A Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD): study protocol for a randomized clinical trial and prognosis study.

BACKGROUND: Major depressive disorder (MDD) with atypical features, namely depression with atypical features (AFD), is one of the most common clinical specifiers of MDD, closely associated with bipolar disorder (BD). However, there is still a lack of clinical guidelines for the diagnosis, treatment, and prognosis of AFD. Our study mainly focuses on three issues about how to identify AFD, what is the appropriate individualized treatment for AFD, and what are the predictive biomarkers of conversion to BD. METHODS: The Study of Individualized Diagnosis and Treatment for Depression with Atypical Features (iDoT-AFD) is a multicenter, prospective, open-label study consisting of a 12-week randomized controlled trial (RCT) and a continued follow-up until 4 years or reaching the study endpoint. It is enrolling 480 patients with AFD (120 per treatment arm), 100 patients with BD, and 100 healthy controls (HC). Multivariate dimension information is collected including clinical features, cognitive function, kynurenine pathway metabolomics, and multimodal magnetic resonance imaging (MRI) data. Firstly, multivariate informatics analyses are performed to recognize patients with AFD from participants including the first-episode and recurrent atypical depression, patients with BD, and patients with HC. Secondly, patients with atypical depression are randomly allocated to one of the four treatment groups including "single application of selective serotonin reuptake inhibitor (SSRI) or serotonin-noradrenaline reuptake inhibitor (SNRI)", "SSRI/SNRI combined with mood stabilizer," "SSRI/SNRI combined with quetiapine (≥ 150 mg/day)," or "treatment as usual (TAU)" and then followed up 12 weeks to find out the optimized treatment strategies. Thirdly, patients with atypical depression are followed up until 4 years or switching to BD, to explore the risk factors of conversion from atypical depression to BD and eventually build the risk warning model of conversion to BD. DISCUSSION: The first enrolment was in August 2019. The iDoT-AFD study explores the clinical and biological markers for the diagnosis, treatment, and prognosis of AFD and further provides evidence for clinical guidelines of AFD. TRIAL REGISTRATION: ClinicalTrials.gov NCT04209166. Registered on December 19, 2019.

Plasma biomarker panel for major depressive disorder by quantitative proteomics using ensemble learning algorithm: A preliminary study.

Major depressive disorder (MDD) is a major international public health issue; thus, investigating its underlying mechanisms and identifying suitable biomarkers to enable its early detection are imperative. Using data-independent acquisition-mass spectrometry-based proteomics, the plasma of 44 patients with MDD and 25 healthy controls was studied to detect differentially expressed proteins. Bioinformatics analyses, such as Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, Protein-Protein Interaction network, and weighted gene co-expression network analysis were employed. Moreover, an ensemble learning technique was used to build a prediction model. A panel of two biomarkers, L-selectin and an isoform of the Ras oncogene family was identified. With an area under the receiver operating characteristic curve of 0.925 and 0.901 for the training and test sets, respectively, the panel was able to distinguish MDD from the controls. Our investigation revealed numerous potential biomarkers and a diagnostic panel based on several algorithms, which may contribute to the future development of a plasma-based diagnostic approach and better understanding of the molecular mechanisms of MDD.

Recurrent metastatic retroperitoneal dedifferentiated liposarcoma: a case report and literature review.

BACKGROUND: Retroperitoneal liposarcoma (RPLPS), a rare tumor, is often treated using surgical procedures as the first choice for treatment. However, there is no consensus on the scope of surgical resection. In addition, the treatment outcomes of conventional radiotherapy and chemotherapy have not been satisfactory, particularly for specific types of LPS, such as dedifferentiated LPS. In this case study, we present a brief review of other cases of RPLPS, highlighting the selection of surgical scope for RPLPS and related adjuvant treatment for advanced RPLPS. CASE PRESENTATION: A case study is reported regarding an extremely rare recurrent and metastatic retroperitoneal dedifferentiated LPS. The primary RPLPS tumor, with a diameter of 20 cm and a weight of 2.5 kg, occupied the whole left abdomen and adhered to the left kidney. A surgical tumor resection combined with a left nephrectomy is performed. During the 6th -month postoperative follow-up examination, we observed the local recurrence of the tumor in the operation area, in addition to multiple metastatic tumors in both lungs. Further, the prescribed 3-month targeted treatment with anlotinib significantly reduced the size of the metastatic pulmonary tumors. However, the recurrent retroperitoneal tumors showed no significant change in size. Eventually, we observed no substantial evidence of tumor progression, with the patient's condition under control. CONCLUSION: The case demonstrated that the postoperative recurrence of widespread RPLPS required R0 resection to cure the disease, considering targeted therapy for advanced RPLPS control.

Multi-semantic feature fusion attention network for binary code similarity detection.

Binary code similarity detection (BCSD) plays a big role in the process of binary application security test. It can be applied in several fields, such as software plagiarism detection, malware analysis, vulnerability detection. Most research is based on recurrent neural networks, which is difficult to get the overall or long-distance semantic information of functions. Besides, exiting works simply extract high-level semantic features, lacking in-depth investigations on the potential mechanisms for fusing low-level and high-level semantic features. In this paper we propose a multi-semantic feature fusion attention network (MFFA-Net) for BCSD. MFFA-Net contains two critical modules: semantic feature fusion (SFF) and attention feature fusion (AFF). The SFF module concatenates multiple semantic features to represent the semantics of the function, which helps to obtain the overall semantic information of the function. The AFF module is designed to find useful information from various features, which assigns an attention matrix to research the relationship between features. In order to evaluate the proposed method, we made extensive experiments on two datasets. MFFA-Net can achieve a high degree of AUC at 99.6% and 98.3% respectively on the two datasets. The experimental results show that MFFA-Net has better performance for BCSD.

miR-124 Exacerbates depressive-like behavior by targeting Ezh2 to induce autophagy.

On the basis of our previous research, miR-124 and autophagy have been shown to be associated with depression and antidepressant treatment, respectively. However, whether miR-124 is involved in depressive-like behavior and antidepressant efficacy through regulating autophagy remains poorly understood. The chronic unpredictable mild stress (CUMS) depression model in mice was established, and then intraperitoneal fluoxetine injections (10 mg/kg) were administered for a duration of 4 weeks. The behavioral changes induced by CUMS were evaluated by the tail suspension test, open field test, sucrose preference test, and elevated plus maze test. Quantitative real-time PCR was used to detect expression levels of miR-124 and its three precursor genes in hippocampus of mice. Western blotting was used to detect the expressions of Ezh2 and autophagy proteins (P62, Atg3, Atg7, LC3-I, and LC3- II) in hippocampus of mice. Depression-like behaviors were successfully induced in CUMS models and reversed by SSRI treatments. The expression levels of miR-124 and its precursor gene ( miR-124-3 ) were significantly increased in the hippocampus of CUMS mice, while the expression levels were significantly decreased after 4 weeks of fluoxetine treatment. The mRNA and protein expressions of Ezh2, a validated target of miR-124, were decreased in the hippocampus of CUMS mice, and the fluoxetine treatment could reverse the expressions. A correlation analysis suggested that miR-124 had a significant negative correlation with Ezh2 mRNA expression. The protein levels of LC3-II/I, P62, and Atg7, which were found to be regulated by Ezh2, were increased in the hippocampus of CUMS mice and decreased after fluoxetine treatment. We speculated that autophagy was enhanced in the CUMS model of depression and might be mediated by miR-124 targeting Ezh2.

Differences in cognitive functions of atypical and non-atypical depression based on propensity score matching.

BACKGROUND: Clinical and etiological heterogeneity have hindered our understanding of depression, thus driving the studies of major depressive disorder (MDD) subtypes. Atypical depression (AD) is a subtype of MDD with atypical features. Cognitive impairment is one of the factors that contribute to the suffering of patients with MDD. Therefore, this study investigated the characteristics and differences in cognitive functioning of AD and non-atypical depression (non-AD) using the MATRICS Consensus Cognitive Battery (MCCB). METHODS: A total of 101 patients with AD and 252 patients with non-AD were assessed with the MCCB and clinical scales. Propensity score matching (PSM) was used to balance confounders between groups. After PSM, between-group differences were compared for cognitive and clinical variables. In addition, multiple linear regression analyses were performed to explore the effects of cognitive and clinical variables on the quality of life. RESULTS: The AD group scored significantly lower in attention/vigilance and social cognition in all cognitive domains than the non-AD group. Attention/vigilance and social cognition were significant positive predictors of quality of life, whereas atypical symptoms and depressive severity were significant negative predictors. CONCLUSIONS: This study suggests significant differences in cognitive functions between the AD and non-AD subtypes. Atypical symptoms and impaired cognition have a negative impact on patients' quality of life. Attention/vigilance and social cognition are worse in AD than non-AD, which the atypical features of patients with AD may explain. The pathological mechanisms and treatment strategies of AD should be further explored in the future to promote individualized treatment strategies.

Causal relationships between mood instability and autoimmune diseases: A mendelian randomization analysis.

OBJECTIVES: The interrelationship between mental health and autoimmunity gains more and more attention in recent years. However, the causality between personality traits and autoimmune diseases remained largely unknown. METHODS: We first conducted two-sample mendelian randomization (MR) analysis on the relationships between mood instability, which is a common personality trait in the general population, and 10 autoimmune diseases. The results were further validated in secondary analyses of sensitivity where different MR methods, ethnicities, genders, and ascertainment methods were compared. RESULTS: In the primary analyses, three autoimmune diseases showed genetical predisposition to mood instability: asthma (OR [95%CI] = 3.45 [2.48, 4.78], P = 1.33E- 13), hypothyroidism (OR [95%CI] = 1.02 [1.00, 1.03], P = 1.71E-02), and systemic lupus erythematosus (OR [95%CI] = 5.25 [1.21, 22.76], P = 2.67E-02). The results were consistent in subsequent secondary analyses. Three diseases remained significantly correlated with mood instability by different MR methods with asthma remaining significant in Finnish and mixed populations, and in females from the UK biobank, while hypothyroidism remained significant in both genders from the UK biobank. CONCLUSION: Mood instability is a modifiable risk factor for three autoimmune diseases including asthma, hypothyroidism and systemic lupus erythematosus.

Reliability and validity of the Chinese version of the biological rhythms interview of assessment in neuropsychiatry in patients with major depressive disorder.

BACKGROUND: Although disturbances in biological rhythms are closely related to the onset of major depressive disorder (MDD), they are not commonly assessed in Chinese clinical practice. The Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN) has been used to evaluate disturbances in biological rhythms in MDD. We aimed to assess and confirm the reliability and validity of the Chinese version of the BRIAN (C-BRIAN) in patients with MDD. METHODS: A total of 120 patients with MDD and 40 age- and sex-matched controls were recruited consecutively. Reliability was estimated using Cronbach's alpha, the split-half coefficient, and the test-retest coefficient; test-retest reliability was assessed using Spearman's correlation coefficient. A confirmatory factor analysis was used to determine the construct validity of the scale. The Pittsburgh Sleep Quality Index (PSQI) and the Morningness-Eveningness Questionnaire (MEQ) were used to check concurrent validity by evaluating the correlation between the C-BRIAN, PSQI, and MEQ. RESULTS: The overall Cronbach's α value was 0.898, indicating good internal consistency. The Guttman split-half coefficient was 0.792, indicating good split-half reliability. Moreover, the test-retest reliability for both the total and individual item score was excellent. Confirmatory factor analysis revealed that construct validity was acceptable (χ2/df = 2.117, GFI = 0.80, AGFI = 0.87, CFI = 0.848, and RMSEA = 0.097). Furthermore, total BRIAN scores were found to be negatively correlated with MEQ (r = - 0.517, P < 0.001) and positively correlated with PSQI (r = 0.586, P < 0.001). In addition, patients with MDD had higher BRIAN scores than those in controls. CONCLUSIONS: This study revealed that the C-BRIAN scale has great validity and reliability in evaluating the disturbance of biological rhythms in patients with MDD.

Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) in patients with major depressive disorder: rationale and design of a prospective multicentre cohort study.

INTRODUCTION: Major depressive disorder (MDD) represents a worldwide burden on healthcare and the response to antidepressants remains limited. Systems biology approaches have been used to explore the precision therapy. However, no reliable biomarker clinically exists for prognostic prediction at present. The objectives of the Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) study are to predict the efficacy of antidepressants by integrating multidimensional omics and performing validation in a real-world setting. As secondary aims, a series of potential biomarkers are explored for biological subtypes. METHODS AND ANALYSIS: iMore is an observational cohort study in patients with MDD with a multistage design in China. The study is performed by three mental health centres comprising an observation phase and a validation phase. A total of 200 patients with MDD and 100 healthy controls were enrolled. The protocol-specified antidepressants are selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors. Clinical visits (baseline, 4 and 8 weeks) include psychiatric rating scales for symptom assessment and biospecimen collection for multiomics analysis. Participants are divided into responders and non-responders based on treatment response (>50% reduction in Montgomery-Asberg Depression Rating Scale). Antidepressants' responses are predicted and biomarkers are explored using supervised learning approach by integration of metabolites, cytokines, gut microbiomes and immunophenotypic cells. The accuracy of the prediction models constructed is verified in an independent validation phase. ETHICS AND DISSEMINATION: The study was approved by the ethics committee of Shanghai Mental Health Center (approval number 2020-87). All participants need to sign a written consent for the study entry. Study findings will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04518592.