INTRODUCTION:Stroke remains a leading cause of disability and mortality worldwide, with an urgent need for novel therapeutic strategies to improve recovery. Bupropion hydrochloride, a norepinephrine-dopamine reuptake inhibitor, may promote motor and cognitive recovery due to its unique mechanism of action. To evaluate the efficacy and safety of early adjunctive treatment with bupropion hydrochloride versus placebo on functional recovery in patients with acute ischaemic stroke. METHODS AND ANALYSIS:BASE is an investigator-initiated, multicentre, randomised, double-blind, placebo-controlled trial. We plan to enrol 1054 eligible patients with acute ischaemic stroke (National Institutes of Health Stroke Scale (NIHSS) score 8-15, within 2-7 days after onset) from approximately 40 stroke centres across China. Participants will be randomly assigned (1:1) to receive either oral bupropion hydrochloride (75 mg two times per day) or matched placebo for 30 days, in addition to standard guideline-based care. The primary efficacy endpoint is the proportion of patients achieving a favourable functional outcome, defined as a modified Rankin Scale (mRS) score of 0-3 at 90 days. Key secondary endpoints include shifts in mRS scores, changes in NIHSS, Hamilton Depression Rating Scale (HAMD-17), Fugl-Meyer Motor Scale scores and quality of life (EQ-5D). Safety endpoints include mortality, vascular events and incidence of adverse events. Analyses will be performed on both the intention-to-treat and per-protocol populations ETHICS AND DISSEMINATION: The study protocol was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (ID ZZ2025-747-01), and all participants will provide written informed consent. Results will be disseminated through peer-reviewed publications and conference presentations. TRIAL REGISTRY NUMBER:ChiCTR2500105746 (www.chictr.org.cn).

Major depressive disorder (MDD) is common and disabling, yet reported brain structural differences vary across studies. Here we performed a large vertex-wise (point-by-point) meta-analysis of cortical thickness and surface area using harmonized magnetic resonance imaging processing across 64 cohorts from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) MDD and Depression Imaging Research Consortium (DIRECT) consortia (5,736 patients; 6,538 controls). We show significantly lower cortical thickness in patients with MDD in multiple brain regions, including the inferior parietal, lateral occipital, superior parietal, medial and lateral orbitofrontal, anterior and posterior cingulate, and precentral gyri, with cortical surface area showing no significant differences. Effects were most pronounced in adults with acute depression, whereas adolescents showed no significant case-control differences. Antidepressant medication use at scanning was associated with more extensive thinning, although effect sizes remained modest (mostly |Cohen's d| < 0.20). This high-resolution, globally generalizable map can support studies of mechanisms and help evaluate structural markers of the clinical course and treatment response.

Major depressive disorder (MDD) is associated with widespread alterations in functional brain networks across the lifespan. However, heterogeneity in atypical brain development among patients with MDD remains largely uncharacterized. Using a multisite resting-state functional MRI dataset consisting of 1,105 MDD patients and 1,065 healthy controls, we constructed a harmonized multicenter brain age prediction model based on individualized functional topography and identified two patient subgroups with positive or negative brain age gaps (BAGs). In patients with a positive BAG (BAG+), expansion of the salience network (SAL) into the dorsolateral prefrontal and ventrolateral prefrontal cortices, in addition to contraction of the sensorimotor and dorsal attention networks (DAN), contributes to accelerated brain aging. Conversely, in the negative BAG (BAG-) group, SAL expansion into the orbitofrontal cortex (OFC) and contraction of the visual and sensorimotor networks (SMN) were linked to delayed brain development. These subgroups also exhibited distinct neurodevelopmental trajectories. Clinically, BAG+ patients showed stronger associations between higher-order network topography and mood symptoms, whereas BAG- patients exhibited links between visual/default mode network topography and insomnia. At the molecular level, both groups showed enrichment of genes related to synaptic signaling but displayed distinct expression patterns and divergent expression trajectories in key neurodevelopmental gene sets. Notably, antidepressant treatment modulated the brain in ways that were specific to each subgroup. These findings reveal heterogeneous neurodevelopmental profiles in MDD with distinct biological and clinical signatures, offering insights into personalized precision medicine for this disorder.

BackgroundAkkermansia muciniphila (AKK) is a potential probiotic. Our previous studies have shown that it could alleviate depressive-like behaviors (DLBs) in mice by inhibiting neuroinflammation in brain. To further explore its antidepressant effect, this study focused on the effects of AKK on the metabolic activities in gut-brain axis.MethodsAfter chronic restraint stress (CRS) depression model was successfully built, AKK was used as intervention method for 3 weeks. The gut microbiome in feces and two intestinal permeability proteins in colon (Claudin-1, Occludin) were measured, and the metabolites in feces, colon, liver, and prefrontal cortex were also measured. In addition, two inflammation-related factors in hippocampus (Free fatty acid receptors 3 (FFAR3), phosphorylated NF-κB p65 (p-p65)) were measured.ResultsAKK was successfully colonized in gut of chronic restraint stress (CRS) mice. The DLBs in CRS mice receiving AKK (CRS + AKK) were significantly improved, along with the improved gut microbiome. Both Claudin-1 and Occludin in colon were significantly increased in CRS + AKK mice compared to CRS mice receiving phosphate buffer saline (PBS) (CRS + p). Metabolomics analysis indicated that AKK could significantly improve the changed lipids and lipid-like molecules in gut-brain axis of CRS mice; and function analysis using differential metabolites showed that AKK could significantly improve the disordered glycerophospholipid metabolism in feces, colon, liver, and prefrontal cortex of CRS mice. Additionally, we found that FFAR3 and phosphorylated NF-κB p65 were increased and decreased, respectively, in hippocampus of CRS + AKK mice compared to CRS + p mice.ConclusionOur results suggested that AKK might improve the disturbances of gut microbiome, intestinal permeability, host’s lipid metabolism and inflammation levels in hippocampus. Glycerophospholipid metabolism in gut-brain axis might be the important mediator in the process of AKK producing antidepressants effects.

Problem: Intracerebral hemorrhage (ICH) is a severe form of stroke characterized by high mortality and disability rates. Accurate early prognosis is crucial for guiding clinical treatment strategies, yet predicting ICH outcomes remains challenging due to the disease's complexity and the diverse information modalities involved. Aim: This study aims to enhance the accuracy of early prognostic prediction for ICH by proposing a Hybrid Hypergraph Convolutional and Graph Convolutional Network (HHGCN). The network integrates imaging features, radiomic features, and clinical features, modeling them as graph and hypergraph structures to capture the intricate relationships within and between modalities. Methods: Utilizes a pre-trained 3D ResNet34 model to extract deep learning image features, radiomic techniques to process medical images, along with structured clinical scales. These features are structured into graph and hypergraph frameworks, allowing for intra-modal and inter-modal feature extraction through graph convolution and hypergraph convolution. A Hybrid Modal feature Fusion (HMFF) module is designed to synthesize these features, enhancing the model's predictive capabilities. Results: Through cross-validation on a multimodal ICH prognosis dataset, achieved an accuracy of 81.47%, an F1 score of 0.8158, and an AUC value of 0.8433, outperforming other advanced methods. Conclusion: Proposes a graph and hypergraph-based model for ICH prognosis, which integrates multimodal data to enhance prediction accuracy, offering a robust framework for early prognostic prediction of ICH. Its integration of multimodal data through advanced graph and hypergraph convolutional techniques provides a comprehensive and accurate predictive tool.

INTRODUCTION:Depression is a complex and common mental disease, but the pathogenesis of depression is still unclear. This study was conducted to explore its pathogenesis via metabolic analysis in peripheral and central tissues. MATERIALS AND METHODS:The Chronic restraint stress (CRS) model of depression was established, and gut microbiota in feces and metabolites in the microbiota-gut-brain (MGB) axis (feces, colon, blood, and prefrontal cortex) were detected. At baseline, control group and CRS group were matched on age, body wight and sucrose preference to avoid the possible effects of confounding factors on metabolite levels in various tissues. RESULTS:There were six differential species in CRS mice, and the alanine, aspartate, and glutamate metabolism in which these species were involved was significantly suppressed in CRS mice. Metabolic analyses showed that there were 277, 155, 219, and 113 differential metabolites in feces, colon, blood, and prefrontal cortex, respectively. Pathway analyses showed that alanine, aspartate, and glutamate metabolism was found to be significantly affected in the MGB axis. The built metabolite interaction network using differential metabolites in this pathway showed that ammonia-related pathways occupied the main role in this network. NH4Cl (the chemical donor of ammonia) could improve the CRS-induced depressive-like behavior (immobility time) in mice after intraperitoneal administration for 12 hours, but not after 24 hours. DISCUSSION:Evidence showed that the disturbances of gut microbiota were closely related to the pathogenesis of depression, and gut microbiota-related metabolites played an important role in this process. Here, using the CRS-induced depression model, we found that the disturbances of gut microbial- related alanine, aspartate and glutamate metabolism in the MGB axis might be involved in the onset of CRS-induced depression. Our findings would be helpful for further exploring the pathogenesis of depression. CONCLUSION:These results indicated that alanine, aspartate, and glutamate metabolism might be a mediator in the crosstalk of gut and brain in depression, and future studies should further explore whether ammonia could be a potentially therapeutic target for depression.

Ferroptosis is being increasingly implicated in the pathophysiology of major depressive disorder (MDD). Endoplasmic reticulum (ER) stress is closely linked to ferroptosis, yet the endogenous regulators connecting ER dysfunction to ferroptosis during chronic stress remain unclear. Here, we report that the expression of mesencephalic astrocyte-derived neurotrophic factor (MANF), an ER stress-inducible protein, was upregulated in the hippocampus (Hip) of male mice subjected to chronic social defeat stress (CSDS). Hippocampal MANF overexpression alleviated depressive-like behaviors and suppressed ferroptosis, whereas MANF knockdown promoted stress susceptibility and facilitated ferroptosis. Ferrostatin-1 (Fer-1) mimicked the protective effects of MANF overexpression and rescued MANF deficiency-induced phenotypes. Mechanistically, MANF inhibited PERK/ATF4 signaling, and overexpression of PERK or ATF4 partially counteracted MANF-mediated protection, whereas their knockdown rescued MANF deficiency-induced phenotypes. Furthermore, ATF4 increased the promoter activity of glutathione-specific γ-glutamylcyclotransferase 1 (CHAC1) in an ATF4-binding-site-dependent manner, thereby impairing the SLC7A11/GPX4 anti-ferroptotic defense axis and driving ferroptosis. These findings identify hippocampal MANF as an endogenous protective factor that suppresses stress-induced ferroptosis through the PERK/ATF4 pathway and position MANF as a potential therapeutic target for MDD.

Depression leads to complex changes in protein regulation in the brain and other tissues. Reproducibility and data integration remain challenges in this field. We systematically integrated proteomic data from our previous established database Pro-MENDA, encompassing brain, cerebrospinal fluid (CSF), blood, and urine samples from patients with depression. Using a vote-counting statistics to assess consistency of protein expression changes across studies, we identified 2094 different expression proteins from 1804 samples. Functional characterization included Gene Ontology, KEGG pathway enrichment, protein-protein interaction analysis, and post-translational modification. In brain, we observed changes in proteins related to synaptic function and energy metabolism, such as Glial fibrillary acidic protein (GFAP) and Histidine triad nucleotide-binding protein 1 (HINT1). These changes suggest issues with oxidative phosphorylation and synaptic activity. The CSF and blood revealed immune-inflammatory markers like Afamin (AFM) and Serpin Family F Member 1 (SERPINF1), while urine analysis showed signs of neutrophil activation. We also identified 13 shared proteins across brain, CSF, and blood, including Clusterin (CLU), that link complement and coagulation, and reactive oxygen pathways. In this protein-protein interaction network of brain, proteins related to cell adhesion, respiration, neuron and synapse are significantly enriched. Post-translational modifications, particularly phosphorylation, were common. Our findings highlight systemic protein dysregulation in depression. This connects brain and peripheral mechanisms, offering insights for identifying multi-tissue biomarkers and developing targeted therapies.

Gut microbiota-derived metabolites play a crucial role in depression. This study aimed to elucidate the role of tryptophan metabolites herein. In a CSDS mouse model, we identified eight differential species, twelve altered neurotransmitters, and two up-regulated inflammatory factors (IL-6 and IL-1β). Notably, 3-indolepropionic acid (IPA) levels were consistently reduced in feces, colon, blood, and hippocampus of CSDS mice. The decreased abundance of Lactobacillus johnsonii (L. johnsonii) was correlated closely with depression-like behaviors (DLBs), reduced fecal IPA, and elevated IL-6 and IL-1β. Both L. johnsonii and IPA supplementation alleviated DLBs, along with up-regulated AhR and down-regulated NF-κB, NLRP3, IL-6, and IL-1β in hippocampus. Moreover, both treatments significantly elevated IPA levels in peripheral and central samples, and improved the mRNA levels of AhR and NF-κB p65 in hippocampus. Critically, the antidepressant effects of L. johnsonii and IPA were counteracted by AhR antagonist CH223191. Independent experimental results showed that CH223191 had no significant effects on behaviors of CSDS mice. To our knowledge, this was the first study to report reduced IPA levels in both peripheral and central samples of CSDS mice. We also provided the first demonstration that the antidepressant effects of L. johnsonii and IPA were mediated, at least in part, through the inhibition of neuroinflammation via AhR pathway, accompanied by the restoration of IPA levels in gut-brain axis. These findings positioned L. johnsonii and IPA as promising therapeutic candidates for depression.

ABSTRACT The intricate interplay between chronic psychological stress and periodontitis, mediated by oral microbiota and macrophage polarization, remains largely enigmatic. Here, we demonstrate that chronic restraint stress (CRS) exacerbates periodontitis by inducing oral microbial dysbiosis and a consequential shift in host metabolism. Clinical observations reveal a significant correlation between depressive symptoms and the severity of periodontitis, which is underpinned by a distinct oral microbiome. Crucially, fecal microbiota transplantation from CRS‐exposed mice into germ‐free mice was sufficient to transmit the heightened periodontitis phenotype, establishing a causal role for the stress‐altered microbiota. Metabolomic profiling identified a depletion of eicosapentaenoic acid (EPA) in stressed, ligature‐induced periodontitis mice. Mechanistically, supplementation with EPA ameliorates periodontitis by suppressing the NF‐κB signaling pathway, thereby inhibiting the pro‐inflammatory M1 polarization of macrophages. Our findings unveil a novel gut‐oral axis mediated by microbiota and metabolites under stress, and position the omega‐3 fatty acid EPA as a promising therapeutic agent for mitigating stress‐aggravated inflammatory disorders.

Strong heterogeneity limits precision medicine in youth with mood disorders during major depressive episodes (MDE). Here we propose and evaluate a neurobiomarker-guided repetitive transcranial magnetic stimulation (rTMS) strategy in a double-blind randomized controlled trial. Using multicenter resting-state functional magnetic resonance imaging data (N = 2,328), we validated functional imbalance along the sensorimotor-association axis as a robust neurobiomarker distinguishing two neurosubtypes-archetypal and atypical. We then conducted a double-blind randomized controlled trial (2 August 2022-20 January 2024) in 219 youth with MDE. Participants were classified into subtypes and randomly assigned to subtype-specific active rTMS or sham stimulation. The primary outcome was depressive symptom improvement measured by the 17-item Hamilton Depression Rating Scale; secondary outcomes assessed neurobiological effects using functional magnetic resonance imaging. The trial met its prespecified primary endpoint. The active rTMS showed greater symptom improvement than sham (adjusted mean difference of -2.34, 95% confidence interval -3.80 to -0.87, P = 0.002), with significant effects observed in both subtypes. However, the clinical efficacy was moderate, with only the anxiety/somatization factor showing significantly higher rates of partial response and response. The archetypal subtype exhibited increased fluctuation amplitude in the visual cortex following active rTMS compared with sham, meeting the prespecified secondary endpoint. By contrast, no significant neurobiological effects were observed in the atypical subtype. Symptom improvement was associated with functional activity alterations along the sensorimotor-association axis. These findings provide preliminary support for biologically driven, neurosubtype-specific treatments in youth with MDE (ClinicalTrials.gov, NCT05465928).

Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral measurements prediction, and mental disorder diagnosis. However, the application of deep learning techniques to brain fMRI analysis is hindered by the small sample size of fMRI datasets. Transfer learning offers a solution to this problem, but most existing approaches are designed for large-scale 2D natural images. The heterogeneity between 4D fMRI data and 2D natural images makes direct model transfer infeasible. This study proposes a novel geometric mapping-based fMRI transfer learning method that enables transfer learning from 2D natural images to 4D fMRI brain images, bridging the transfer learning gap between fMRI data and natural images. The proposed Multi-scale Multi-domain Feature Aggregation (MMFA) module extracts effective aggregated features and reduces the dimensionality of fMRI data to 3D space. By treating the cerebral cortex as a folded Riemannian manifold in 3D space and mapping it into 2D space using surface geometric mapping, we make the transfer learning from 2D natural images to 4D brain images possible. Moreover, the topological relationships of the cerebral cortex are maintained with our method, and calculations are performed along the Riemannian manifold of the brain, effectively addressing signal interference problems. The experimental results based on the Human Connectome Project (HCP) dataset demonstrate the effectiveness of the proposed method. Our method achieved state-of-the-art performance in sex classification, age estimation, and behavioral measurement prediction tasks. Moreover, we propose a cascaded transfer learning approach for depression diagnosis, and proved its effectiveness on 23 depression datasets. In summary, the proposed fMRI transfer learning method, which accounts for the structural characteristics of the brain, is promising for applying transfer learning from natural images to brain fMRI images, significantly enhancing the performance in various fMRI analysis tasks.

Major depressive disorder (MDD) imposes significant global health burdens, yet its underlying neural mechanisms remain elusive. Traditional static functional metrics inadequately capture the brain’s dynamic nature, motivating the exploration of dynamic functional metrics to understand both the temporal and spatial reconfigurations of brain networks in MDD. Leveraging the Depression Imaging Research Consortium (DIRECT) dataset, this study conducted vertex-wise dynamic analyses in a large cohort of MDD patients (n = 1660) and healthy controls (n = 1341). We identified significant alterations in temporal stability across the brain, with MDD patients exhibiting increased stability in higher-order association areas (e.g., frontoparietal and default mode networks) and decreased stability in primary sensory-motor regions. Among the regions showing altered temporal stability, brain-symptom relationships were further explored. We identified a set of brain regions including the superior frontal gyrus, postcentral gyrus and superior insular sulcus, which were potentially involved in the common abnormal dFC network and associated with insomnia, feelings of guilt, and insight symptoms in MDD. By incorporating advanced vertex-wise dynamic functional analyses and a large sample size, this study provides insights into the neural mechanisms of MDD, emphasizing the value of dynamic approaches for identifying biomarkers. Future longitudinal and task-based studies are promising to elucidate causal relationships and refine personalized therapeutic interventions targeting specific dynamic dysfunctions in MDD.

ObjectivePatients with hypertensive intracerebral hemorrhage (ICH) are at risk for hematoma expansion (HE) in the early stages. Although the “spot sign” on computed tomography angiography (CTA) is a useful predictor of HE, the method has limitations, including low sensitivity and difficulty in identification. This study introduces a more recognizable and accessible “brush sign” on plain CT images following CTA examination and compares its effectiveness with the “spot sign” in predicting HE.MethodsHypertensive ICH patients admitted to the Advanced Stroke Center of the hospital from January 2023 to December 2024 were retrospectively analyzed. This study evaluated sequential plain CT neuroimaging after CTA in these patients, defined the “brush sign,” and identified two morphological types, namely “isolated” and “continuous.” This study analyzed the correlations between the CTA “spot sign,” “brush sign,” and other clinical data with HE. Finally, the HE prediction efficiency of the “brush sign” and “spot sign” was compared using the receiver operating characteristic (ROC) analysis.ResultsA total of 162 hypertensive ICH patients were enrolled, with 45 patients (27.8%) exhibiting HE. The spot, isolated brush, and continuous brush signs were observed in 48 (29.6%), 12 (7.4%), and 43 (26.5%) cases, respectively. The spot sign (p = 0.017, OR = 3.943, 95% CI [1.289–12.617]) and continuous brush sign (p = 0.016, OR = 3.997, 95% CI [1.302–12.787]) were independent HE predictors. The ROC analysis showed that the continuous brush sign (AUC = 0.895, specificity = 0.949, sensitivity = 0.862) and its combination with the spot sign (AUC = 0.900, specificity = 0.932, sensitivity = 0.852) predicted HE with high accuracy.ConclusionThe “brush sign” can independently predict early HE in hypertensive ICH patients. The use of the continuous “brush sign” either alone or in combination with the “spot sign” demonstrates high accuracy for predicting HE. These markers can be used to stratify HE risk and suggest early intervention strategies for hypertensive ICH patients.

Subthreshold depression (StD) confers a high risk for major depression and is characterized by substantial individual clinical heterogeneity. However, the neurobiological substrates underlying this heterogeneity remain largely unknown. Using a large multisite resting-state functional MRI dataset including 1203 healthy participants and 197 individuals with StD, we constructed connectome-based normative models to identify individual brain deviations and biotypes in StD. We highlighted remarkable individual variability in the connectome deviations in StD, leading to the identification of two distinct biotypes. Subtype 1 exhibits severe positive deviations primarily in the default mode regions and negative deviations in the sensorimotor and ventral attention areas, while subtype 2 shows a moderate but opposite deviation pattern. The two subtypes differ significantly in depressive symptoms, gene expression profiles, and treatment responses to bright light therapy. These findings highlight the neurobiological underpinnings of the clinical diversity in StD, emphasizing the necessity for developing personalized interventions for this condition.

The human cortical functional hierarchy, spanning from primary sensorimotor to transmodal association regions, represents a fundamental principle of brain organisation. Here, we show lifespan changes in the sensorimotor-association (S-A) gradient in the cortical functional hierarchy using multimodal neuroimaging data from 33,247 participants aged 32 postmenstrual weeks to 80 years. We identify three critical neurodevelopmental milestones: initiation (third trimester to perinatal period), establishment (infancy to early childhood), and expansion-stabilisation (late childhood to adulthood). Pronounced gradient changes are predominantly observed during the first decade, with continued refinement extending into mid-adulthood. Spatiotemporally heterogeneous growth patterns in functional gradients align with evolutionary hierarchies, segregation-integration dynamics, structural maturation, and cognitive spectrum development, proceeding along a dominant S-A growth axis. These findings establish a unified neurodevelopmental framework that links connectome gradient dynamics to multifaceted functional and structural properties, advancing our understanding of cortical hierarchy maturation across the lifespan.

Biological sex fundamentally shapes human brain organization, but sex-specific normative neuroanatomical trajectories across the lifespan remain largely uncharted. Here, we constructed independent, sex-specific lifespan brain charts using structural neuroimaging data from 59,915 healthy individuals (29,760 males and 30,155 females) ranging in age from 266 postconception days to 100 years. By examining 296 structural phenotypes across global, cortical, and subcortical measures, these models revealed widespread sex differences in maturational timing, with males reaching peak milestones later than females. These trajectories demonstrate that sex differences evolve dynamically, with phenotype-specific windows of emergence and maximal separation. Compared with conventional sex-pooled references, sex-specific models achieved superior predictive accuracy and reduced misestimation of individual deviations in healthy populations. Across five neuropsychiatric conditions, sex-specific models improved the detection of extreme deviations and revealed both shared and sex-dependent patterns of disorder-related neuroanatomical abnormalities. These sex-specific charts establish tailored normative references for assessing brain development, ageing, and disease.

Fluoxetine is a widely used antidepressant, yet integrated analyses of its molecular mechanism remain limited. This study systematically investigated potential molecular mechanisms underlying the antidepressant effects of fluoxetine by integrating these scattered data. Using the ProMENDA database, we identified metabolites and proteins altered by fluoxetine in the brain of animal models of depression. We curated 273 differentially expressed metabolite entries and 791 differentially expressed protein entries from fluoxetine treatment and performed vote-counting, pathway enrichment, pathway crosstalk and drug-associated metabolite set enrichment analyses. Vote-counting analysis showed altered neurotransmitter levels, including increased levels of monoamines and decreased levels of neurotoxic quinolinic acid and glutamate. The results of pathway analyses based on both altered metabolites and proteins showed 121 significantly enriched pathways. Pathway crosstalk analysis identified four pathway-based modules, which were mainly involved in amino acid metabolism, neurotransmitters and multiple biological processes. Drug-associated metabolite set enrichment analysis revealed 76 significantly enriched drug-related pathways, which were mainly involved in antidepressants. This study provides a comprehensive understanding of the antidepressant effects of fluoxetine, which may provide insights for the development of novel antidepressants.

Background:Intracerebral hemorrhage (ICH) is a severe form of stroke lacking effective pharmacotherapy, in part because upstream regulators initiating secondary brain injury are not well understood. Pyroptosis mediated by activation of the NLRP3 inflammasome is a major contributor to neuronal death after ICH. However, the upstream mechanisms remain to be fully elucidated. Methods:We performed integrative transcriptomic-proteomic profiling of mouse ICH brain tissues with in vivo functional validation. Annexin A2 (ANXA2), identified as a hub protein, was silenced via genetic knockdown. Neurological function, brain pathology, and pyroptotic signaling were assessed by behavioral tests, histology, Western blotting, immunofluorescence, and co-immunoprecipitation. Results:Multi-omics and network analyses identified ANXA2 as a prominently upregulated hub protein after ICH. Co-immunoprecipitation demonstrated an association between ANXA2 and NLRP3, while ANXA2 silencing reduced NLRP3 inflammasome activation, decreased GSDMD cleavage and IL-1β/IL-18 secretion and significantly improved neurological function while alleviating brain injury. Conclusions:This study reveals a previously unrecognized ANXA2-NLRP3-pyroptosis pathway in ICH, revealing a neuronal-immune convergence mechanism in inflammasome regulation. These findings provide new insight into neuronal pyroptosis after ICH and underscore ANXA2 as a predominantly neuronal factor associated with inflammasome activation in hemorrhagic stroke.