ObjectiveTo investigate the heterogeneity and transcriptomic characteristics of T-cell subsets in the liver of mice with nonalcoholic steatohepatitis （NASH） at the single-cell level using single-cell RNA sequencing （scRNA-seq）， and to provide a reference for studying the mechanism of action of T cells in NASH. MethodsSix male C57BL/6 mice were randomly divided into control group fed with regular diet and NASH group fed with methionine-choline-deficient （MCD） diet， with three mice in each group， and liver tissue was collected for scRNA-seq after 6 weeks of modeling. Specific differentially expressed genes were analyzed between T-cell subsets， and related analyses were performed， including dimension clustering， cell type annotation， t-distributed stochastic neighbor embedding （t-SNE）， violin plot， gene ontology （GO） functional enrichment analysis， and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analysis. Immunofluorescent staining was used to observe the expression of the T cell marker Tcrα and the specific marker genes Tcf7 and Cxcr6 in the liver of mice in the two groups. The independent-samples t test was used for comparison of continuous data between two groups. ResultsTwo T cell subsets were identified in the liver of mice， and the percentage of cluster 6 decreased from 58.5% in the control group to 48.7% in the NASH group. The top four specific genes were Nsg2， Cd8b1， Cd8a， and Tcf7. Tcf7， a characteristic marker gene for cluster 6， was expressed in 65% of cells in cluster 6， and therefore， cluster 6 was defined as Tcf7⁺ T cells. The GO and KEGG enrichment analyses showed that the differentially expressed genes of cluster 6 were involved in T cell activation， leukocyte adhesion， binding ubiquitin-like protein ligase， and the signaling pathways for Th17， Th1， and Th2 cell differentiation. The percentage of cluster 7 increased from 41.5% in the control group to 51.3% in the NASH group. The top four specific genes of cluster 7 were Cd40lg， Tcrg-C1， Il2rα， and Cxcr6. Cxcr6 was expressed in 90% of cells in cluster 7， and therefore， cluster 7 was defined as Cxcr6⁺ T cells. The GO and KEGG enrichment analyses showed that cluster 7 was involved in T cell activation， cytokine production， the T cell receptor signaling pathway， and the Th17 cell differentiation and MAPK signaling pathway. Immunofluorescence assay showed that compared with the control group， the NASH group showed a significant reduction in the area with positive co-expression of Tcf7 protein and Tcrα protein （1.80%±0.67% vs 0.33%±0.13%， P<0.05） and a significant increase in the area with positive co-expression of Cxcr6 protein and Tcrα protein （0.50%±0.09% vs 2.66%± 0.33%， P<0.001）. ConclusionThere is a reduction in the percentage of Tcf7⁺ T cells and an increase in the percentage of Cxcr6⁺ T cells in NASH mice， revealing the characteristics and differences of T cells in the liver of NASH mice.

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

Sonodynamic therapy (SDT) can potentially induce immunogenic cell death in tumor cells, leading to the release of ATP, and facilitating the initiation of an immune response. Nevertheless, the enzymes CD39 and CD73 can swiftly convert ATP into immunosuppressive adenosine (ADO), resulting in an immunosuppressive tumor microenvironment (TME). This study introduced a nanomedicine (QD/POM1@NP@M) engineered to reprogram TME by modulating the CD39/CD73/ADO pathway. The nanomedicine encapsulated sonosensitizers silver sulfide quantum dots, and the CD39 inhibitor POM1, while also incorporating homologous tumor cell membranes to enhance targeting capabilities. This integrated approach, on the one hand, stimulates the release of ATP via SDT, thereby initiating the immune response. In addition, it reduced the accumulation of ADO by inhibiting CD39 activity, which ameliorated the immunosuppressive TME. Upon administration, the nanomedicine demonstrated substantial anti-tumor efficacy by facilitating the infiltration of anti-tumor immune cells, while reducing the immunosuppressive cells. This modulation effectively transformed the TME from an immunologically "cold" state to a "hot" state. Furthermore, combined with the checkpoint inhibitor α-PDL1, the nanomedicine augmented systemic anti-tumor immunity and promoted the establishment of long-term immune memory. This study provides an innovative strategy for combining non-invasive SDT and ATP-driven immunotherapy, offering new ideas for future cancer treatment.

Ergopeptines or their derivatives are widely used for treating neurodegenerative and cerebrovascular diseases. The nonribosomal peptide synthetase-d-lysergyl peptide synthetase A (LPSA) determines ergopeptine formation but the detailed mechanism remains to be elucidated. Here, we characterized two LPSAs from Claviceps purpurea Cp-1 strain through heterologous expression in Aspergillus nidulans feeding with d-lysergic acid. We proved that Cp-LPSA1 catalyzed the formation of ergocornine, α-ergocryptine, and β-ergocryptine, precisely controlled by the substrate specificity of its three modules. Cp-LPSA2 was initially inactive but could be restored to catalyze α-ergosine formation. Using this platform, we validated that P1-LPSA1 and P1-LPSA2 from the reported C. purpurea P1 strain catalyzed ergotamine and α-ergocryptine formation, respectively. Typically, the non-ribosomal peptide codes implicated in every module of the LPSAs were defined and elucidated, in which certain key residues could play a switched role for substrate specificity and product interconversion. By constructing chimeric LPSAs through module assembly, the production of the desired ergopeptines was achieved. Notably, 1.46 mg/L of α-ergocryptine and 1.09 mg/L of ergotamine were produced respectively by mixed-culture of C. paspali No. 24 (fermentation supernatant) and the recombinants of A. nidulans. Our findings provide insights into the biosynthetic mechanism of ergopeptines and lay a foundation for directed ergopeptine biosynthesis.

Myocardial fibrosis is a serious cause of heart failure and even sudden cardiac death. However, the mechanisms underlying myocardial ischemia-induced cardiac fibrosis remain unclear. Here, we identified that the expression of sterile alpha and TIR motif containing 1 (SARM1), was increased significantly in the ischemic cardiomyopathy patients, dilated cardiomyopathy patients (GSE116250) and fibrotic heart tissues of mice. Additionally, inhibition or knockdown of SARM1 can improve myocardial fibrosis and cardiac function of myocardial infarction (MI) mice. Moreover, SARM1 fibroblasts-specific knock-in mice had increased deposition of extracellular matrix and impaired cardiac function. Mechanically, elevated expression of SARM1 promotes the deposition of extracellular matrix by directly modulating P4HA1. Notably, by using the Click-iT reaction, we identified that the increased expression of ZDHHC17 promotes the palmitoylation levels of SARM1, thereby accelerating the fibrosis process. Based on the fibrosis-promoting effect of SARM1, we screened several drugs with anti-myocardial fibrosis activity. In conclusion, we have unveiled that palmitoylated SARM1 targeting P4HA1 promotes collagen deposition and myocardial fibrosis. Inhibition of SARM1 is a potential strategy for the treatment of myocardial fibrosis. The sites where SARM1 interacts with P4HA1 and the palmitoylation modification sites of SARM1 may be the active targets for anti-fibrosis drugs.

ObjectiveTo evaluate the application of prostate-specific membrane antigen （PSMA）PET/CT in prostate biopsy screening， and propose effective strategies for prostate biopsy decision making based on PSMA PET/CT detection. MethodsA retrospective analysis was conducted on PSMA PET/CT imaging and clinical pathological data from 155 patients with suspected prostate cancer between January 2020 and December 2023. PRIMARY score was used as the standardized evaluation method for PSMA PET/CT in the diagnosis of prostate cancer. And compared the positive prostate biopsy rates， missed diagnosis rates and biopsy reduction rates were compared regarding different PRIMARY scores. Receiver operating characteristic （ROC） curves were used to analyze prostate-specific antigen （PSA） and its derived parameters and identify the most suitable supplementary screening indicators for combined use with the PRIMARY score. ResultsAmong patients with PRIMARY scores of 1 to 5， the proportions of patients diagnosed with prostate cancer were 15.8% （3/19）， 17.1% （7/41）， 50% （12/24）， 95.2% （20/21） and 98% （49/50）， respectively. Using PRIMARY score of 3-5 as the biopsy screening strategy resulted in a positive prostate biopsy rate of 85.3% and biopsy reduction rate of 38.7%， but a missed diagnosis rate of 11%. PSA density > 0.15 ng/（mL·cm³） was selected as a supplementary screening criterion to detect prostate cancer from patients with PRIMARY scores of 1-2. The combined application of the above two screening criteria reduced the missed diagnosis rate to 2.2%. ConclusionThis study proposes a novel biopsy screening strategy for suspected prostate cancer patients using PSMA PET/CT， that is， a PRIMARY score of 3-5 or a PRIMARY score of 1-2 but PSA density>0.15 ng/（mL·cm³）， which can effectively avoid unnecessary biopsies and significantly reduce the missed diagnosis rate.

ObjectiveTobacco-related diseases remain one of the leading preventable public health challenges worldwide and are among the primary causes of premature death. In recent years, accumulating evidence has supported the classification of nicotine addiction as a chronic brain disease, profoundly affecting both brain structure and function. Despite the urgency, effective diagnostic methods for smoking addiction remain lacking, posing significant challenges for early intervention and treatment. To address this issue and gain deeper insights into the neural mechanisms underlying nicotine dependence, this study proposes a novel graph neural network framework, termed TI-GNN. This model leverages functional magnetic resonance imaging (fMRI) data to identify complex and subtle abnormalities in brain connectivity patterns associated with smoking addiction. MethodsThe study utilizes fMRI data to construct functional connectivity matrices that represent interaction patterns among brain regions. These matrices are interpreted as graphs, where brain regions are nodes and the strength of functional connectivity between them serves as edges. The proposed TI-GNN model integrates a Transformer module to effectively capture global interactions across the entire brain network, enabling a comprehensive understanding of high-level connectivity patterns. Additionally, a spatial attention mechanism is employed to selectively focus on informative inter-regional connections while filtering out irrelevant or noisy features. This design enhances the model’s ability to learn meaningful neural representations crucial for classification tasks. A key innovation of TI-GNN lies in its built-in causal interpretation module, which aims to infer directional and potentially causal relationships among brain regions. This not only improves predictive performance but also enhances model interpretability—an essential attribute for clinical applications. The identification of causal links provides valuable insights into the neuropathological basis of addiction and contributes to the development of biologically plausible and trustworthy diagnostic tools. ResultsExperimental results demonstrate that the TI-GNN model achieves superior classification performance on the smoking addiction dataset, outperforming several state-of-the-art baseline models. Specifically, TI-GNN attains an accuracy of 0.91, an F1-score of 0.91, and a Matthews correlation coefficient (MCC) of 0.83, indicating strong robustness and reliability. Beyond performance metrics, TI-GNN identifies critical abnormal connectivity patterns in several brain regions implicated in addiction. Notably, it highlights dysregulations in the amygdala and the anterior cingulate cortex, consistent with prior clinical and neuroimaging findings. These regions are well known for their roles in emotional regulation, reward processing, and impulse control—functions that are frequently disrupted in nicotine dependence. ConclusionThe TI-GNN framework offers a powerful and interpretable tool for the objective diagnosis of smoking addiction. By integrating advanced graph learning techniques with causal inference capabilities, the model not only achieves high diagnostic accuracy but also elucidates the neurobiological underpinnings of addiction. The identification of specific abnormal brain networks and their causal interactions deepens our understanding of addiction pathophysiology and lays the groundwork for developing targeted intervention strategies and personalized treatment approaches in the future.

Objective To identify the independent predictors of programmed death-1 (PD-1) inhibitor-related endocrine adverse events and construct a clinically usable risk prediction model. Methods A total of 302 patients with solid tumors treated with PD-1 inhibitors were retrospectively enrolled. According to the presence or absence of endocrine immune-related adverse events (irAEs), the patients were divided into case group and control group. The clinical and laboratory indexes were compared between the two groups. Multivariable logistic regression was used to confirm independent predictors of endocrine irAEs. The nomogram was constructed, while the receiver operating characteristic (ROC) curve was used to test the prediction performance of the model. Results The overall incidence of endocrine irAEs was 21.9% (66/302), and the incidence of hypothyroidism was 19.5% (59/302). The age, PD-1 inhibitors, free thyroxine, thyroid peroxidase antibody (TPOAb), thyroglobulin, amylase, lymphocyte subset CD3 expression were statistically different between the two groups (P＜0.05). Multivariable logistic regression showed that higher expression of lymphocyte subset CD3 was a protective factor to prevent endocrine irAEs occurrence (P＝0.004), while age＜60 years, higher TPOAb and use of pembrolizumab were independent risk factors of endocrine irAEs (P＜0.05). The nomogram model thus constructed, and when the threshold probability of the model exceeded 0.1, its net benefit was higher. ROC curve showed that the AUC of the model to predict endocrine irAEs was 0.760. The prediction result of the model was highly consistent with the actual result. Conclusions The age, type of PD-1 inhibitor, baseline TPOAb level, and baseline CD3 expression can independently predict endocrine irAEs occurrence or not. The nomogram model based on this model has good predictive efficiency, which can provide reference for early identification of high-risk patients and immunotherapy management.