A Label-Free Fluorescent Amplification Strategy for High-Sensitive Detection of Pseudomonas aeruginosa based on Protective-EXPAR (p-EXPAR) and Catalytic Hairpin Assembly.

This study presents a fluorescent mechanism for two-step amplification by combining two widely used techniques, exponential amplification reaction (EXPAR) and catalytic hairpin assembly (CHA). Pseudomonas aeruginosa (P. aeruginosa) engaged in competition with the complementary DNA in order to attach to the aptamer that had been fixed on the magnetic beads. The unbound complementary strand in the liquid above was utilized as a trigger sequence to initiate the protective-EXPAR (p-EXPAR) process, resulting in the generation of a substantial quantity of short single-stranded DNA (ssDNA). The amplified ssDNA can initiate the second CHA amplification process, resulting in the generation of many double-stranded DNA (dsDNA) products. The CHA reaction was initiated by the target/trigger DNA, resulting in the release of G-quadruplex sequences. These sequences have the ability to bond with the fluorescent amyloid dye thioflavin T (ThT), generating fluorescence signals. The method employed in this study demonstrated a detection limit of 16 cfu/mL and exhibited a strong linear correlation within the concentration range of 50 cfu/mL to 105 cfu/mL. This method of signal amplification has been effectively utilized to create a fluorescent sensing platform without the need for labels, enabling the detection of P. aeruginosa with high sensitivity.

A multi-feature spatial-temporal fusion network for traffic flow prediction.

The traffic flow prediction is the key to alleviate traffic congestion, yet very challenging due to the complex influence factors. Currently, the most of deep learning models are designed to dig out the intricate dependency in continuous standardized sequences, which are dependent to high requirements for data continuity and regularized distribution. However, the data discontinuity and irregular distribution are inevitable in the real-world practical application, then we need find a way to utilize the powerful effect of the multi-feature fusion rather than continuous relation in standardized sequences. To this end, we conduct the prediction based on the multiple traffic features reflecting the complex influence factors. Firstly, we propose the ATFEM, an adaptive traffic features extraction mechanism, which can select important influence factors to construct joint temporal features matrix and global spatial features matrix according to the traffic condition. In this way, the feature's representation ability can be improved. Secondly, we propose the MFSTN, a multi-feature spatial-temporal fusion network, which include the temporal transformer encoder and graph attention network to obtain the latent representation of spatial-temporal features. Especially, we design the scaled spatial-temporal fusion module, which can automatically learn optimal fusion weights, further adapt to inconsistent spatial-temporal dimensions. Finally, the multi-layer perceptron gets the mapping function between these comprehensive features and traffic flow. This method helps to improve the interpretability of the prediction. Experimental results show that the proposed model outperforms a variety of baselines, and it can accurately predict the traffic flow when the data missing rate is high.

Cross-modal credibility modelling for EEG-based multimodal emotion recognition.

Objective.The study of emotion recognition through electroencephalography (EEG) has garnered significant attention recently. Integrating EEG with other peripheral physiological signals may greatly enhance performance in emotion recognition. Nonetheless, existing approaches still suffer from two predominant challenges: modality heterogeneity, stemming from the diverse mechanisms across modalities, and fusion credibility, which arises when one or multiple modalities fail to provide highly credible signals.Approach.In this paper, we introduce a novel multimodal physiological signal fusion model that incorporates both intra-inter modality reconstruction and sequential pattern consistency, thereby ensuring a computable and credible EEG-based multimodal emotion recognition. For the modality heterogeneity issue, we first implement a local self-attention transformer to obtain intra-modal features for each respective modality. Subsequently, we devise a pairwise cross-attention transformer to reveal the inter-modal correlations among different modalities, thereby rendering different modalities compatible and diminishing the heterogeneity concern. For the fusion credibility issue, we introduce the concept of sequential pattern consistency to measure whether different modalities evolve in a consistent way. Specifically, we propose to measure the varying trends of different modalities, and compute the inter-modality consistency scores to ascertain fusion credibility.Main results.We conduct extensive experiments on two benchmarked datasets (DEAP and MAHNOB-HCI) with the subject-dependent paradigm. For the DEAP dataset, our method improves the accuracy by 4.58%, and the F1 score by 0.63%, compared to the state-of-the-art baseline. Similarly, for the MAHNOB-HCI dataset, our method improves the accuracy by 3.97%, and the F1 score by 4.21%. In addition, we gain much insight into the proposed framework through significance test, ablation experiments, confusion matrices and hyperparameter analysis. Consequently, we demonstrate the effectiveness of the proposed credibility modelling through statistical analysis and carefully designed experiments.Significance.All experimental results demonstrate the effectiveness of our proposed architecture and indicate that credibility modelling is essential for multimodal emotion recognition.

Exploring the bidirectional relationship between metabolic syndrome and thyroid autoimmunity: a Mendelian randomization study.

Background: Observational studies have reported a possible association between metabolic syndrome (MetS) and thyroid autoimmunity. Nevertheless, the relationship between thyroid autoimmunity and MetS remains unclear. The objective of this research was to assess the causal impact of MetS on thyroid autoimmunity through the utilization of Mendelian randomization (MR) methodology. Methods: We performed bidirectional MR to elucidate the causal relationship between MetS and their components and thyroid autoimmunity (positivity of TPOAb). Single nucleotide polymorphisms (SNPs) of MetS and its components were obtained from the publicly available genetic variation summary database. The Thyroidomics Consortium conducted a genome-wide association analysis, which provided summary-level data pertaining to thyroid autoimmunity. The study included several statistical methods, including the inverse variance weighting method (IVW), weighted median, simple mode, weight mode, and MR-Egger methods, to assess the causal link. In addition, to ensure the stability of the results, a sensitivity analysis was conducted. Results: IVW showed that MetS reduced the risk of developing thyroid autoimmunity (OR = 0.717, 95% CI = 0.584 - 0.88, P = 1.48E-03). The investigation into the causative association between components of MetS and thyroid autoimmune revealed a statistically significant link between triglycerides levels and the presence of thyroid autoimmunity (IVW analysis, OR = 0.603, 95%CI = 0.45 -0.807, P = 6.82E-04). The reverse analysis did not reveal any causal relationship between thyroid autoimmunity and MetS, including its five components. Conclusions: We have presented new genetic evidence demonstrating that MetS and its triglyceride components may serve as potential protective factors against thyroid autoimmunity.

AZGP1 deficiency promotes angiogenesis in prostate cancer.

BACKGROUND: Loss of AZGP1 expression is a biomarker associated with progression to castration resistance, development of metastasis, and poor disease-specific survival in prostate cancer. However, high expression of AZGP1 cells in prostate cancer has been reported to increase proliferation and invasion. The exact role of AZGP1 in prostate cancer progression remains elusive. METHOD: AZGP1 knockout and overexpressing prostate cancer cells were generated using a lentiviral system. The effects of AZGP1 under- or over-expression in prostate cancer cells were evaluated by in vitro cell proliferation, migration, and invasion assays. Heterozygous AZGP1± mice were obtained from European Mouse Mutant Archive (EMMA), and prostate tissues from homozygous knockout male mice were collected at 2, 6 and 10 months for histological analysis. In vivo xenografts generated from AZGP1 under- or over-expressing prostate cancer cells were used to determine the role of AZGP1 in prostate cancer tumor growth, and subsequent proteomics analysis was conducted to elucidate the mechanisms of AZGP1 action in prostate cancer progression. AZGP1 expression and microvessel density were measured in human prostate cancer samples on a tissue microarray of 215 independent patient samples. RESULT: Neither the knockout nor overexpression of AZGP1 exhibited significant effects on prostate cancer cell proliferation, clonal growth, migration, or invasion in vitro. The prostates of AZGP1-/- mice initially appeared to have grossly normal morphology; however, we observed fibrosis in the periglandular stroma and higher blood vessel density in the mouse prostate by 6 months. In PC3 and DU145 mouse xenografts, over-expression of AZGP1 did not affect tumor growth. Instead, these tumors displayed decreased microvessel density compared to xenografts derived from PC3 and DU145 control cells, suggesting that AZGP1 functions to inhibit angiogenesis in prostate cancer. Proteomics profiling further indicated that, compared to control xenografts, AZGP1 overexpressing PC3 xenografts are enriched with angiogenesis pathway proteins, including YWHAZ, EPHA2, SERPINE1, and PDCD6, MMP9, GPX1, HSPB1, COL18A1, RNH1, and ANXA1. In vitro functional studies show that AZGP1 inhibits human umbilical vein endothelial cell proliferation, migration, tubular formation and branching. Additionally, tumor microarray analysis shows that AZGP1 expression is negatively correlated with blood vessel density in human prostate cancer tissues. CONCLUSION: AZGP1 is a negative regulator of angiogenesis, such that loss of AZGP1 promotes angiogenesis in prostate cancer. AZGP1 likely exerts heterotypical effects on cells in the tumor microenvironment, such as stromal and endothelial cells. This study sheds light on the anti-angiogenic characteristics of AZGP1 in the prostate and provides a rationale to target AZGP1 to inhibit prostate cancer progression.

Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters.

In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: (i) obtaining the forecast track of a target landfalling typhoon, (ii) constructing its generalized initial value (GIV), (iii) identifying its analogs based on the GIV, and (iv) assembling typhoon disasters of the analogs. Typhoon track, intensity, and landfall date are introduced in GIV at this early development stage. The pre-assessment results show that the mean threat scores of two important damage levels of LTDs reach 0.48 and 0.55, respectively. Of significance is that most of the damage occurs near the typhoon centers around the time of landfall. These results indicate the promising performance of the model in capturing the main damage characteristics of typhoon disasters, which would help coastal community mitigate damage from destructive typhoons.

AutoEER: automatic EEG-based emotion recognition with neural architecture search.

Objective.Emotion recognition based on electroencephalography (EEG) is garnering increasing attention among researchers due to its wide-ranging applications and the rise of portable devices. Deep learning-based models have demonstrated impressive progress in EEG-based emotion recognition, thanks to their exceptional feature extraction capabilities. However, the manual design of deep networks is time-consuming and labour-intensive. Moreover, the inherent variability of EEG signals necessitates extensive customization of models, exacerbating these challenges. Neural architecture search (NAS) methods can alleviate the need for excessive manual involvement by automatically discovering the optimal network structure for EEG-based emotion recognition.Approach.In this regard, we propose AutoEER (AutomaticEEG-basedEmotionRecognition), a framework that leverages tailored NAS to automatically discover the optimal network structure for EEG-based emotion recognition. We carefully design a customized search space specifically for EEG signals, incorporating operators that effectively capture both temporal and spatial properties of EEG. Additionally, we employ a novel parameterization strategy to derive the optimal network structure from the proposed search space.Main results.Extensive experimentation on emotion classification tasks using two benchmark datasets, DEAP and SEED, has demonstrated that AutoEER outperforms state-of-the-art manual deep and NAS models. Specifically, compared to the optimal model WangNAS on the accuracy (ACC) metric, AutoEER improves its average accuracy on all datasets by 0.93%. Similarly, compared to the optimal model LiNAS on the F1 Ssore (F1) metric, AutoEER improves its average F1 score on all datasets by 4.51%. Furthermore, the architectures generated by AutoEER exhibit superior transferability compared to alternative methods.Significance.AutoEER represents a novel approach to EEG analysis, utilizing a specialized search space to design models tailored to individual subjects. This approach significantly reduces the labour and time costs associated with manual model construction in EEG research, holding great promise for advancing the field and streamlining research practices.

Identification of age- and immune-related gene signatures for clinical outcome prediction in lung adenocarcinoma.

BACKGROUND: The understanding of the factors causing decreased overall survival (OS) in older patients compared to younger patients in lung adenocarcinoma (LUAD) remains. METHODS: Gene expression profiles of LUAD were obtained from publicly available databases by Kaplan-Meier analysis was performed to determine whether age was associated with patient OS. The immune cell composition in the tumor microenvironment (TME) was evaluated using CIBERSORT. The fraction of stromal and immune cells in tumor samples were also using assessed using multiple tools including ESTIMATE, EPIC, and TIMER. Differentially expressed genes (DEGs) from the RNA-Seq data that were associated with age and immune cell composition were identified using the R package DEGseq. A 22-gene signature composed of DEGs associated with age and immune cell composition that predicted OS were constructed using Least Absolute Shrinkage and Selection Operator (LASSO). RESULTS: In The Cancer Genome Atlas (TCGA)-LUAD dataset, we found that younger patients (≤70) had a significant better OS compared to older patients (>70). In addition, older patients had significantly higher expression of immune checkpoint proteins including inhibitory T cell receptors and their ligands. Moreover, analyses using multiple bioinformatics tools showed increased immune infiltration, including CD4+ T cells, in older patients compared to younger patients. We identified a panel of genes differentially expressed between patients >70 years compared to those ≤70 years, as well as between patients with high or low immune scores and selected 84 common genes to construct a prognostic gene signature. A risk score calculated based on 22 genes selected by LASSO predicted 1, 3, and 5-year OS, with an area under the curve (AUC) of 0.72, 0.72, 0.69, receptively, in TCGA-LUAD dataset and an independent validation dataset available from the European Genome-phenome Archive (EGA). CONCLUSION: Our results demonstrate that age contributes to OS of LUAD patients atleast in part through its association with immune infiltration in the TME.

A transformable nanoplatform with multiple therapeutic and immunostimulatory properties for treatment of advanced cancers.

Cancer is a complex pathological phenomenon that needs to be treated from different aspects. Herein, we developed a size/charge dually transformable nanoplatform (PDR NP) with multiple therapeutic and immunostimulatory properties to effectively treat advanced cancers. The PDR NPs exhibit three different therapeutic modalities (chemotherapy, phototherapy and immunotherapy) that can be used to effectively treat primary and distant tumors, and reduce recurrent tumors; the immunotherapy is simultaneously activated by three major pathways, including toll-like receptor, stimulator of interferon genes and immunogenic cell death, effectively suppresses the tumor development in combination with an immune checkpoint inhibitor. In addition, PDR NPs show size and charge responsive transformability in the tumor microenvironment, which overcomes various biological barriers and efficiently delivers the payloads into tumor cells. Taking these unique characteristics together, PDR NPs effectively ablate primary tumors, activate strong anti-tumor immunity to suppress distant tumors and reduce tumor recurrence in bladder tumor-bearing mice. Our versatile nanoplatform shows great potential for multimodal treatments against metastatic cancers.

NUSAP1 Binds ILF2 to Modulate R-Loop Accumulation and DNA Damage in Prostate Cancer.

Increased expression of NUSAP1 has been identified as a robust prognostic biomarker in prostate cancer and other malignancies. We have previously shown that NUSAP1 is positively regulated by E2F1 and promotes cancer invasion and metastasis. To further understand the biological function of NUSAP1, we used affinity purification and mass spectrometry proteomic analysis to identify NUSAP1 interactors. We identified 85 unique proteins in the NUSAP1 interactome, including ILF2, DHX9, and other RNA-binding proteins. Using proteomic approaches, we uncovered a function for NUSAP1 in maintaining R-loops and in DNA damage response through its interaction with ILF2. Co-immunoprecipitation and colocalization using confocal microscopy verified the interactions of NUSAP1 with ILF2 and DHX9, and RNA/DNA hybrids. We showed that the microtubule and charged helical domains of NUSAP1 were necessary for the protein-protein interactions. Depletion of ILF2 alone further increased camptothecin-induced R-loop accumulation and DNA damage, and NUSAP1 depletion abolished this effect. In human prostate adenocarcinoma, NUSAP1 and ILF2 mRNA expression levels are positively correlated, elevated, and associated with poor clinical outcomes. Our study identifies a novel role for NUSAP1 in regulating R-loop formation and accumulation in response to DNA damage through its interactions with ILF2 and hence provides a potential therapeutic target.

Programmed-response cross-linked nanocarrier for multidrug-resistant ovarian cancer treatment.

The application of numerous chemotherapeutic drugs has been limited due to poor solubility, adverse side effects, and even multidrug resistance in patients. Polymeric micelles with reversibly cross-linked structures provide a promising solution to these issues. Herein, we optimized and synthesized programable-released disulfide cross-linked micelle (PDCM) based on our previous well-defined dendrimers to deliver the antitumor drug betulinic acid (BA) and paclitaxel (PDCM@PTX) and evaluated the therapeutic efficacy of multidrug-resistant (MDR) simulative orthotopic intraperitoneal ovarian cancer mice models. Comprehensive results demonstrated that PDCM@PTX formed stable nanoparticles able to improve the pharmacokinetic profile and circulation time of PTX, allowing for increased tumor penetration. Furthermore, in the tumor microenvironment, the programable-switches (ester bonds and disulfide cross-linking) of PDCM@PTX were cleaved by the high concentration of glutathione (tumor microenvironment) and esterase (intracellular) present in the tumor, allowing for in situ release of PTX and BA, resulting in intensive therapeutic efficacy in MDR ovarian cancer.

Identification of a Novel Cuproptosis-Related Gene Signature and Integrative Analyses in Thyroid Cancer.

Cuproptosis is a novel programmed cell death that depends on copper. The role and potential mechanism of cuproptosis-related genes (CRGs) in thyroid cancer (THCA) are still unclear. In our study, we randomly divided THCA patients from the TCGA database into a training set and a testing set. A cuproptosis-related signature consisting of six genes (SLC31A1, LIAS, DLD, MTF1, CDKN2A, and GCSH) was constructed using the training set to predict the prognosis of THCA and was verified with the testing set. All patients were classified into low- and high-risk groups according to risk score. Patients in the high-risk group had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) values for 5 years, 8 years, and 10 years were 0.845, 0.885, and 0.898, respectively. The tumor immune cell infiltration and immune status were significantly higher in the low-risk group, which indicated a better response to immune checkpoint inhibitors (ICIs). The expression of six cuproptosis-related genes in our prognostic signature were verified by qRT-PCR in our THCA tissues, and the results were consistent with TCGA database. In summary, our cuproptosis-related risk signature has a good predictive ability regarding the prognosis of THCA patients. Targeting cuproptosis may be a better alternative for THCA patients.

A Practical Statin Recommendation System Based on Real-World Data to Improve LDL-C Management in Secondary Prevention.

ABSTRACT: Statins are considered the cornerstone of secondary prevention in patients with atherosclerotic cardiovascular disease (ASCVD). However, many patients fail to achieve the guide-recommended goal of low-density lipoprotein cholesterol (LDL-C) after statin monotherapy, leading to a high residual risk of cardiovascular events. Owing to individual differences in statin therapy, it is possible first to consider changing the type of statin before adding nonstatin medications in certain patients to improve LDL-C management. We developed and evaluated a statin recommendation system using real-world data. Ensemble learning was performed to develop the recommendation system that integrated the output results of support vector machines (SVM) and the similarity of patients. Model performance was assessed to investigate whether treatment according to the recommended model would increase the proportion of patients with the primary end point. Finally, a total of 3510 patients were enrolled in the development and validation of the recommender system. Of them, 1240 patients received atorvastatin (35.3%), 1714 patients received rosuvastatin (48.8%), and 556 patients received pitavastatin (15.8%). The statin recommendation system could significantly improve LDL-C target rate achievement in the recommended treatment group compared with the nonrecommended treatment group in the validation set (50.8% vs. 31.5%, P < 0.001). This study demonstrated that the statin recommendation system could significantly improve the achievement of LDL-C goals in ASCVD patients, providing a new approach to improve LDL-C management.

Logistics Service Selection Strategy of Green Manufacturers in Green Low-Carbon Supply Chain.

In order to analyze the logistics service mode and sales mode selection strategy, a green low-carbon supply chain consisting of a single manufacturer and a single e-commerce platform is considered. First, in the green low-carbon supply chain which consists of a direct selling channel and reselling channel, the selection strategy of the manufacturer's logistics service mode is analyzed. Second, in the green low-carbon supply chain which consists of a direct selling channel and an agency channel, the selection strategy of the manufacturer's logistics service mode is analyzed. Last, the manufacturer's sales mode is analyzed. We use the backward induction method to solve the theoretical model. This study contributes to the literature by considering the optimal decision of a green low-carbon supply chain. This study brings together the literature from streams on the selling channel selection strategy in green supply chains and the logistics service strategy in green supply chains. The impacts of the logistics service cost, the selling cost, and the green input cost coefficient on the optimal decision and the firms' profit are discussed. The result shows that in the direct selling channel and reselling channel, when the basic market demand and the logistics service level of the third-party logistics service provider are low, manufacturers will choose the e-commerce platform logistics service; in the opposite case, manufacturers will choose the third-party logistics service. In the direct selling channel and agency channels, when the logistics service level of the third-party logistics service provider is greater than or equal to a certain critical value and less than or equal to the logistics service level of the e-commerce platform, manufacturers will choose the e-commerce platform logistics service; in the opposite case, manufacturers will choose the third-party logistics service. No matter whether the manufacturer chooses the logistics service provided by the third-party logistics service provider or the logistics service provided by the e-commerce platform, the manufacturer should choose the direct selling channel and the agency channel.

The microbiota-gut-brain axis in pathogenesis of depression: A narrative review.

The microbiota-gut-brain axis is a bidirectional regulatory pathway between the brain and the gastrointestinal tract, which plays an important role in maintain homeostasis. Gut microbiota could influence the behavior, cognition, stress response and others via the axis. Depression is a complex psychiatric disease, giving rise to heavy social health and economic burden. In recent years, studies have shown that the gut microbiota are closely linked to the pathophysiological processes of depression. In this article, the interaction and its underlying mechanisms between depression and gut microbiota were summarized.

Clinical and ultrasonic risk factors for high-volume central lymph node metastasis in cN0 papillary thyroid microcarcinoma: A retrospective study and meta-analysis.

OBJECTIVE: Papillary thyroid microcarcinoma (PTMC) comprises more than 50% of all newly detected cases of papillary thyroid carcinoma (PTC). High-volume lymph node metastasis (involving >5 lymph nodes) (hv-LNM) is associated with PTMC recurrence. In half of the clinically node-negative (cN0) PTMC patients, central lymph node metastasis (CLNM) is pathologically present. However, clinical risk factors for high-volume CLNM (hv-CLNM) in cN0 PTMC have not been defined well. Therefore, we aimed to obtain evidence for hv-CLNM risk factors in cN0 PTMC. DESIGN: Data on patients who visited our hospital between January 2020 and December 2021 were collected; a preoperative diagnosis of cN0 and a postoperative pathological confirmation of PTMC were obtained. After filtering by inclusion versus exclusion criteria, the obtained data (N = 2268) were included in the meta-analysis. Relevant studies published as of 10 April 2022, were identified from the Web of Science, PubMed, WANFANG, and CNKI databases. These eligible studies were included in the meta-analysis and the association between clinicopathological factors and hv-CLNM in cN0 PTMC was assessed. SPSS and MetaXL were used for statistical analyses. RESULTS: The meta-analysis included 10 previous studies (11,734 patients) and 2268 patients enroled in our hospital for a total of 14,002 subjects. The results of which suggested that younger age (<40, odds ratio [OR] = 3.28, 95% confidence interval [CI] = 2.75-3.92, p < .001 or <45 odds ratio [OR] = 2.93, 95% CI = 2.31-3.72, p < .001), male sex (OR = 2.81, 95% CI = 2.25-3.52, p < .001), tumour size >5 mm (OR = 1.85, 95% CI = 1.39-2.47, p < .001), multifocality (OR = 1.88, 95% CI = 1.56-2.26, p < .001), extrathyroidal extension (OR = 2.58, 95% CI = 2.02-3.30, p < .001), capsule invasion (OR = 2.02, 95% CI = 1.46-2.78, p < .001), microcalcification (OR = 3.25, 95% CI = 2.42-4.36, p < .001) and rich blood flow (OR = 1.65, 95% CI = 1.21-2.25, p = .002) were the significant factors related to an elevated hv-CLNM risk in cN0 PTMC patients. Hashimoto thyroiditis (OR = 0.76, 95% CI = 0.55-1.07, p = .114), irregular margin (versus regular margin, OR = 0.96, 95% CI = 0.68-1.33, p = .787) and hypoechoic (versus nonhypoechoic, OR = 1.27, 95% CI = 0.84-1.92, p = .261) showed no significant association with hv-CLNM. CONCLUSIONS: Younger age, tumour size >5 mm, males, extrathyroidal extension, multifocality, microcalcification, capsular invasion, and rich blood flow were the significant clinicopathological risk factors for hv-CLNM risk in cN0 PTMC patients. These predictors may compensate for the sensitivity of imaging diagnosis in the preoperative period, thus helping in the effective identification of PTMCs with an invasive phenotype.

Gut microbiota regulates chronic ethanol exposure-induced depressive-like behavior through hippocampal NLRP3-mediated neuroinflammation.

Chronic ethanol exposure (CEE), which can lead to neuroinflammation, is an increasing risk factor for depression disorder, but the underlying mechanism is not clear. Recent observations have revealed the associations among psychiatric disorders, ethanol exposure and alterations of the gut microbiota. Here, we found that CEE induced depressive-like behavior, which could be alleviated by probiotics and transferred from donor to recipient mice by fecal microbiota transplantation (FMT). Neuroinflammation and the activation of the NLRP3 inflammasome were also observed in recipient mice. The downregulation of NLRP3 in the hippocampus mitigated CEE-induced depressive-like behavior and neuroinflammation but had no significant effect on FMT recipient mice. Moreover, elevated serum inflammatory factors in recipient mice showed a significant mediation effect between the gut microbiota and depressive-like behavior. Together, our study findings indicate that the gut microbiota contributes to both hippocampal NLRP3-mediated neuroinflammation and depressive-like behavior induced by CEE, which may open avenues for potential interventions against CEE-associated psychiatric disorders.