ObjectiveTo explore the molecular mechanism of aerobic exercise to improve hippocampal neuronal degeneration by regulating tryptophan metabolic pathway. Methods60 SPF-grade C57BL/6J male mice were divided into a young group (2 months old, n=30) and a senile group (12 months old, n=30), and each group was further divided into a control group (C/A group, n=15) and an exercise group (CE/AE group, n=15). An aerobic exercise program was used for 8 weeks. Learning memory ability was assessed by Y-maze, and anxiety-depression-like behavior was detected by absent field experiment. Hippocampal Trp levels were measured by GC-MS. Nissl staining was used to observe the number and morphology of hippocampal neurons, and electron microscopy was used to detect synaptic ultrastructure. ELISA was used to detect the levels of hippocampal Trp,5-HT, Kyn, KATs, KYNA, KMO, and QUIN; Western blot was used to analyze the activities of TPH2, IDO1, and TDO enzymes. ResultsGroup A mice showed significant decrease in learning and memory ability (P<0.05) and increase in anxiety and depressive behaviors (P<0.05); all of AE group showed significant improvement (P<0.05). Hippocampal Trp levels decreased in group A (P<0.05) and increased in AE group (P<0.05). Nidus vesicles were reduced and synaptic structures were degraded in group A (P<0.05), and both were significantly improved in group AE (P<0.05). The levels of Trp, 5-HT, KATs, and KYNA were decreased (P<0.05) and the levels of Kyn, KMO, and QUIN were increased (P<0.05) in group A. The activity of TPH2 was decreased (P<0.05), and the activities of IDO1 and TDO were increased (P<0.05). The AE group showed the opposite trend. ConclusionThe aging process significantly reduces the learning memory ability and increases the anxiety-depression-like behavior of mice, and leads to the reduction of the number of nidus vesicles and degenerative changes of synaptic structure in the hippocampus, whereas aerobic exercise not only effectively enhances the spatial learning memory ability and alleviates the anxiety-depression-like behavior of aging mice, but also improves the morphology and structure of neurons in hippocampal area, which may be achieved by the mechanism of regulating the tryptophan metabolic pathway.

ObjectiveThe study explores the influence of voluntary wheel running on the behavioral abnormalities and the activation state of the hypothalamic-pituitary-adrenal (HPA) axis in autism spectrum disorder (ASD) rats through gut microbiota. MethodsSD female rats were selected and administered either400 mg/kg of valproic acid (VPA) solution or an equivalent volume of saline via intraperitoneal injection on day 12.5 of pregnancy. The resulting offspring were divided into 2 groups: the ASD model group (PASD, n=35) and the normal control group (PCON, n=16). Behavioral assessments, including the three-chamber social test, open field test, and Morris water maze, were conducted on postnatal day 23. After behavioral testing, 8 rats from each group (PCON, PASD) were randomly selected for serum analysis using enzyme-linked immunosorbent assay (ELISA) to measure corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) concentration, to evaluate the functional state of the HPA axis in rats. On postnatal day 28, the remaining 8 rats in the PCON group were designated as the control group (CON, n=8), and the remaining 27 rats in the PASD group were randomly divided into 4 groups: ASD non-intervention group (ASD, n=6), ASD exercise group (ASDE, n=8), ASD fecal microbiota transplantation group (FMT, n=8), and ASD sham fecal microbiota transplantation group (sFMT, n=5). The rats in the ASD group and the CON group were kept under standard conditions, while the rats in the ASDE group performed 6 weeks of voluntary wheel running intervention starting on postnatal day 28. The rats in the FMT group were gavaged daily from postnatal day 42 with 1 ml/100 g fresh fecal suspension from ASDE rats which had undergone exercise for 2 weeks, 5 d per week, continuing for 4 weeks. The sFMT group received an equivalent volume of saline. After the interventions were completed, behavioral assessments and HPA axis markers were measured for all groups. ResultsBefore the intervention, the ASD model group exhibited significantly reduced social ability, social novelty preference, spontaneous activity, and exploratory interest, as well as impaired spatial learning, memory, and navigation abilities compared to the normal control group (P<0.05). Serum concentration of corticotropin-releasing hormone (CRH), adrenocorticotropic hormone (ACTH), and corticosterone (CORT) in the PASD group were significantly higher than those in the PCON group (P<0.05). Following 6 weeks of voluntary wheel running, the ASDE group showed significant improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, spatial learning, memory, and navigation skills compared to the ASD group (P<0.05), with a significant decrease in serum CORT concentration (P<0.05), and a downward trend in CRH and ACTH concentration. After 4 weeks of fecal microbiota transplantation in the exercise group, the FMT group showed marked improvements in social ability, social novelty preference, spontaneous activity, exploratory interest, as well as spatial learning, memory, and navigation abilities compared to both the ASD and sFMT groups (P<0.05). In addition, serum ACTH and CORT concentration were significantly reduced (P<0.05), and CRH concentration also showed a decreasing trend. ConclusionExercise may improve ASD-related behaviors by suppressing the activation of the HPA axis, with the gut microbiota likely playing a crucial role in this process.

Objective: To explore the key factors affecting the selection and effectiveness of bladder management modalities in patients with spinal cord injury，so as to provide reference for the optimization of individualized bladder management strategies. Methods: The clinical and follow-up data of 78 patients with spinal cord injury treated in our hospital during Jan.1,2013 and Dec.31,2022 were retrospectively analyzed.The distribution of bladder management modalities among different grades of injuries was analyzed. Bowker symmetry test was used to evaluate the difference between bladder management modalities at discharge and at the end of follow-up. Multiple linear regression was used to explore the influencing factors of bladder management effects. Plotting Kaplan-Meier survival curves were adopted to calculate the median time of changes in bladder management. Results: At discharge，there were 9 cases of self-catheterization，19 cases of intermittent catheterization，22 cases of reflexive voiding，26 cases of long-term catheterization，and 2 cases using urinary collector.At the end of follow-up，there were 15 cases of self-catheterization，8 cases of intermittent catheterization，34 cases of reflexive voiding，14 cases of long-term catheterization，and 7 cases using urinary collector.There was a significant difference between the modalities of bladder management at discharge and at the end of follow-up (χ =21.43，P=0.018).Multiple linear regression showed a significant decrease of 8.60 in the total neurogenic bladder symptom score (NBSS) for grade D injuries compared with grade A injuries (P=0.026). The median time to bladder management change was 7.93 months (95%CI:5.44-9.44), with approximately 50% of patients experiencing a change in bladder management within 8 months after discharge. Conclusion: The modalities of bladder management changed significantly after discharge.The grade of injury was a key factor affecting the effectiveness of bladder management.Higher grade was associated with worse effectiveness of bladder management.

Objective: Previous observational studies have confirmed the correlation between gut microbiota and bladder cancer，but the causal relationship is still unclear.This study aimed to explore the causal relationship between them with Mendelian randomization. Methods: Genetic variation summary data of 211 gut microbiota and bladder cancer genome-wide association studies (GWAS) were obtained from the MiBioGen Consortium and Finngen database.Single nucleotide polymorphisms (SNPs) closely related to these studies were screened as instrumental variables.The causal relationship between gut microbiota and bladder cancer were analyzed with inverse variance weighting (IVW)，MR-Egger，weighted median，maximum likelihood，robust adjustment feature score and MR-PRESSO，with IVW as the primary analysis method.Additionally，sensitivity analysis was used to test the heterogeneity (Cochran Q) and horizontal pleiotropy (MR-Egger intercept term and global test from MR-PRESSO estimator) to ensure the robustness of the results. Results: The IVW results indicated that Lachnospiraceae UCG004 (OR：1.42)，Desulfovibrionales (Order) (OR：1.48)，Eubacterium ruminantium group (OR：1.33)，Olsenella (OR：1.24)，Ruminococcaceae UCG002 (OR：1.39)，Ruminococcaceae UCG005 (OR：1.42) and Ruminococcaceae UCG013 (OR：1.64) significantly increased the risk of bladder cancer.Conversely，Bacteroidetes (Phylum) (OR：0.61)，Eubacterium brachy group (OR：0.80)，Ruminococcaceae UCG004 (OR：0.73)，Rikenellaceae (Family) (OR：0.67)，Lachnospiraceae ND3007 group (OR：0.47), Adlercreutzia (OR：0.73) and an unknow genus (OR：0.75) were associated with a reduced risk of bladder cancer.Sensitivity analyses did not reveal any heterogeneity or horizontal pleiotropy. Conclusion: This study reveals the causal role of 14 gut microbiota in the pathogenesis of bladder cancer，among which Lachnospiraceae UCG004，Desulfovibrionales (Order)，Eubacterium ruminantium group，Olsenella，Ruminococcaceae UCG002，Ruminococcaceae UCG005 and Ruminococcaceae UCG013 are risk factors for bladder cancer，while Bacteroidetes (Phylum)，Eubacterium brachy group，Ruminococcaceae UCG004，Rikenellaceae (Family)，Lachnospiraceae ND3007 group，Adlercreutzia and an unknown genus are the protective factors.

ObjectiveTo identify the active constituents of Kaixuan Jiedu core prescription （KXJD） and investigate its effective components and therapeutic targets in the treatment of common psoriasis. MethodsUltra-performance liquid chromatography-high resolution mass spectrometry （UPLC-Q-TOF MS） was used to identify and analyze the chemical components of KXJD and its blood-entering chemical components. The chemical components of the single decoction were further identified to clarify the source of the chemical components in KXJD. Then， all identified blood-entering compounds were screened for effective active ingredients through the Swiss ADME database. The active ingredient targets of KXJD were searched on the Swiss Target Prediction platform. The targets of common psoriasis were searched in OMIM， GEO， GeneCards， and DISGNET databases to obtain drug-disease intersection targets. The protein-protein interaction network of intersection targets was constructed using STRING， and then the core blood-entering component-intersection target network of KXJD was constructed. The core target proteins in the network were selected for molecular docking with the core components of KXJD. Small molecules and proteins were pretreated， and appropriate docking sites were selected. AutoDock Vina software was used for continuous local search and repeated iterations to find molecular docking conformations. PyMOL software and LigPlot+ software were used for analysis and visualization. Subsequently， the verification was carried out through animal experiments. SPF-grade male C57 mice were randomly divided into a blank group， a model group， a positive drug methotrexate group， and a KXJD group. In the model group， the methotrexate group， and the KXJD group， imiquimod was applied to the backs of the mice for modeling. The blank group and the model group were administered normal saline by gavage， while the methotrexate group and the KXJD group were respectively administered 1 mg·kg^-1 suspension and 30.42 g·kg^-1 decoction of traditional Chinese medicine by gavage. Five days after administration， the mice were sacrificed， and samples were collected. Hematoxylin-eosin （HE） staining was used to observe the skin tissue samples of mice， and the effect of KXJD on the pathological manifestations of psoriasis mice was analyzed. The expression areas of tumor necrosis factor-α （TNF-α）， cyclooxygenase 2 （PTGS2）， interleukin （IL）-1β， and IL-6 in the skin tissue of mice were detected by immunohistochemistry （IHC）. The mRNA expressions of TNF-α， IL-1β， IL-6， and PTGS2 in the skin tissue of mice were detected by real-time fluorescent quantitative polymerase chain reaction （Real-time PCR）. ResultsIn this study， 208 compounds in KXJD were identified by UPLC-Q-TOF MS， and 44 compounds were detected in serum， including 28 prototype components and 16 metabolites. 12 blood-entering active components were screened， and 1 153 active component targets and 1 076 psoriasis-related targets were identified. Eighty-five targets in the intersection set were drug-disease common targets. PPI network analysis showed that TNF-α， IL-1β， IL-6， PTGS2， and ALB were the key target proteins. The results of molecular docking showed that the binding ability of （+）-hexylphenylglycolic acid to key target proteins such as PTGS2 was stable， and PTGS2 showed high stability with a variety of core compounds. Compared with those in the blank group， the stratum corneum and epidermis in the model group mice were significantly thickened， and the pathological Baker score of the mice's skin in the model group was significantly higher than that of the blank group （P<0.01）. The expression areas of PTGS2， TNF-α， IL-1β， and IL-6 proteins in the skin tissue of the model group mice were significantly increased （P<0.01）， and the expression levels of PTGS2， TNF-α， IL-1β， and IL-6 mRNA in the skin tissue of the model group mice were significantly elevated （P<0.01）. Compared with the model group， the pathological Baker scores of the methotrexate group and the KXJD group were both decreased （P<0.01）， and the expression areas in the skin tissue of the methotrexate group and the KXJD group were significantly reduced （P<0.05， P<0.01）. The expression levels of PTGS2， TNF-α， IL-1β， and IL-6 mRNA in the skin tissue of the methotrexate group and the KXJD group were significantly decreased （P<0.01）. ConclusionKXJD can effectively improve the skin lesions of psoriasis model mice and reduce the expression of TNF-α， IL-1β， IL-6， and PTGS2 in the skin tissue of psoriasis model mice. PTGS2 has a stable binding ability with a variety of compounds in the decoction， which may be a key target for the treatment of psoriasis. The compound （+）-hexylphenylglycolic acid may play a key role.

ObjectiveTo use bioinformatics technology to screen the molecular patterns and diagnostic biomarkers of ferroptosis closely related to psoriasis， observe the therapeutic effect of Kaixuan Jiedu core prescription on psoriasis and explore its potential mechanism through animal experiments. MethodsPsoriasis microarray data from GEO were analyzed to identify differentially expressed genes （DEGs）. Intersection with a ferroptosis gene set yielded psoriasis ferroptosis-related genes （FRGs）， which underwent correlation， consensus clustering， enrichment， and immune infiltration analyses. Core diagnostic FRGs （Hub-FRGs） were identified using random forest （RF）， support vector machine （SVM）， LASSO regression， Nomogram， and ROC analyses. In vivo， imiquimod （5% cream） induced psoriasis in mice （except controls）. Drug treatment groups received respective doses， while control and model groups received saline via daily gavage for 7 days. Back skin changes were recorded and PASI scored. Hematoxylin-eosin （HE） staining assessed histopathology. The levels of ferrous ion （Fe²⁺）， malondialdehyde （MDA）， 4-hydroxynonenal （4-HNE） and free fatty acid （FFA） in skin tissue were detected. The level of reactive oxygen species （ROS） in skin tissue was detected by immunofluorescence. Immunohistochemistry was used to detect the expression of ChaC glutathione-specific γ-glutamyl transferase 1 （CHAC1）， arachidonic acid 12-lipoxygenase β （ALOX12B）， trimotif protein 21 （TRIM21）， proliferation marker （Ki67） and nuclear transcription factor-κB （NF-κB） protein. ResultsAnalysis of GSE30999 identified 2 100 DEGs and 24 FRGs. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment revealed 1 000 biological functions and 75 pathways. After cluster analysis， combined with three machine learning algorithms， Nomogram and ROC curve analysis， the core Hub-FRGs （CHAC1， ALOX12 B， TRIM21） were obtained. Immunoinfiltration showed inactive memory CD4⁺T cells and activated dendritic cells abundance significantly correlated with Hub-FRGs. In vivo， model group vs. control showed significantly increased PASI/Baker scores （P<0.05）， epidermal hyperkeratosis， inflammatory infiltration， and elevated levels of Fe²⁺， MDA， 4-HNE， FFA， ROS， CHAC1， ALOX12B， TRIM21， Ki67， and NF-κB （P<0.05）. Drug groups vs. model group exhibited significantly reduced scores （P<0.05）， alleviated skin lesions， and decreased levels of Fe²⁺， MDA， 4-HNE， FFA， ROS， Hub-FRGs， Ki67， and NF-κB （P<0.05）. ConclusionKaixuan Jiedu core prescription can significantly improve the skin pathological injury of psoriasis mice， showing good therapeutic and repair effects， and its mechanism may be related to regulating the expression of ferroptosis genes CHAC1， ALOX12B and TRIM21， which are closely related to the pathogenesis of psoriasis.

ObjectiveTo identify the active constituents of Kaixuan Jiedu core prescription （KXJD） and investigate its effective components and therapeutic targets in the treatment of common psoriasis. MethodsUltra-performance liquid chromatography-high resolution mass spectrometry （UPLC-Q-TOF MS） was used to identify and analyze the chemical components of KXJD and its blood-entering chemical components. The chemical components of the single decoction were further identified to clarify the source of the chemical components in KXJD. Then， all identified blood-entering compounds were screened for effective active ingredients through the Swiss ADME database. The active ingredient targets of KXJD were searched on the Swiss Target Prediction platform. The targets of common psoriasis were searched in OMIM， GEO， GeneCards， and DISGNET databases to obtain drug-disease intersection targets. The protein-protein interaction network of intersection targets was constructed using STRING， and then the core blood-entering component-intersection target network of KXJD was constructed. The core target proteins in the network were selected for molecular docking with the core components of KXJD. Small molecules and proteins were pretreated， and appropriate docking sites were selected. AutoDock Vina software was used for continuous local search and repeated iterations to find molecular docking conformations. PyMOL software and LigPlot+ software were used for analysis and visualization. Subsequently， the verification was carried out through animal experiments. SPF-grade male C57 mice were randomly divided into a blank group， a model group， a positive drug methotrexate group， and a KXJD group. In the model group， the methotrexate group， and the KXJD group， imiquimod was applied to the backs of the mice for modeling. The blank group and the model group were administered normal saline by gavage， while the methotrexate group and the KXJD group were respectively administered 1 mg·kg^-1 suspension and 30.42 g·kg^-1 decoction of traditional Chinese medicine by gavage. Five days after administration， the mice were sacrificed， and samples were collected. Hematoxylin-eosin （HE） staining was used to observe the skin tissue samples of mice， and the effect of KXJD on the pathological manifestations of psoriasis mice was analyzed. The expression areas of tumor necrosis factor-α （TNF-α）， cyclooxygenase 2 （PTGS2）， interleukin （IL）-1β， and IL-6 in the skin tissue of mice were detected by immunohistochemistry （IHC）. The mRNA expressions of TNF-α， IL-1β， IL-6， and PTGS2 in the skin tissue of mice were detected by real-time fluorescent quantitative polymerase chain reaction （Real-time PCR）. ResultsIn this study， 208 compounds in KXJD were identified by UPLC-Q-TOF MS， and 44 compounds were detected in serum， including 28 prototype components and 16 metabolites. 12 blood-entering active components were screened， and 1 153 active component targets and 1 076 psoriasis-related targets were identified. Eighty-five targets in the intersection set were drug-disease common targets. PPI network analysis showed that TNF-α， IL-1β， IL-6， PTGS2， and ALB were the key target proteins. The results of molecular docking showed that the binding ability of （+）-hexylphenylglycolic acid to key target proteins such as PTGS2 was stable， and PTGS2 showed high stability with a variety of core compounds. Compared with those in the blank group， the stratum corneum and epidermis in the model group mice were significantly thickened， and the pathological Baker score of the mice's skin in the model group was significantly higher than that of the blank group （P<0.01）. The expression areas of PTGS2， TNF-α， IL-1β， and IL-6 proteins in the skin tissue of the model group mice were significantly increased （P<0.01）， and the expression levels of PTGS2， TNF-α， IL-1β， and IL-6 mRNA in the skin tissue of the model group mice were significantly elevated （P<0.01）. Compared with the model group， the pathological Baker scores of the methotrexate group and the KXJD group were both decreased （P<0.01）， and the expression areas in the skin tissue of the methotrexate group and the KXJD group were significantly reduced （P<0.05， P<0.01）. The expression levels of PTGS2， TNF-α， IL-1β， and IL-6 mRNA in the skin tissue of the methotrexate group and the KXJD group were significantly decreased （P<0.01）. ConclusionKXJD can effectively improve the skin lesions of psoriasis model mice and reduce the expression of TNF-α， IL-1β， IL-6， and PTGS2 in the skin tissue of psoriasis model mice. PTGS2 has a stable binding ability with a variety of compounds in the decoction， which may be a key target for the treatment of psoriasis. The compound （+）-hexylphenylglycolic acid may play a key role.

ObjectiveTo use bioinformatics technology to screen the molecular patterns and diagnostic biomarkers of ferroptosis closely related to psoriasis， observe the therapeutic effect of Kaixuan Jiedu core prescription on psoriasis and explore its potential mechanism through animal experiments. MethodsPsoriasis microarray data from GEO were analyzed to identify differentially expressed genes （DEGs）. Intersection with a ferroptosis gene set yielded psoriasis ferroptosis-related genes （FRGs）， which underwent correlation， consensus clustering， enrichment， and immune infiltration analyses. Core diagnostic FRGs （Hub-FRGs） were identified using random forest （RF）， support vector machine （SVM）， LASSO regression， Nomogram， and ROC analyses. In vivo， imiquimod （5% cream） induced psoriasis in mice （except controls）. Drug treatment groups received respective doses， while control and model groups received saline via daily gavage for 7 days. Back skin changes were recorded and PASI scored. Hematoxylin-eosin （HE） staining assessed histopathology. The levels of ferrous ion （Fe²⁺）， malondialdehyde （MDA）， 4-hydroxynonenal （4-HNE） and free fatty acid （FFA） in skin tissue were detected. The level of reactive oxygen species （ROS） in skin tissue was detected by immunofluorescence. Immunohistochemistry was used to detect the expression of ChaC glutathione-specific γ-glutamyl transferase 1 （CHAC1）， arachidonic acid 12-lipoxygenase β （ALOX12B）， trimotif protein 21 （TRIM21）， proliferation marker （Ki67） and nuclear transcription factor-κB （NF-κB） protein. ResultsAnalysis of GSE30999 identified 2 100 DEGs and 24 FRGs. Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment revealed 1 000 biological functions and 75 pathways. After cluster analysis， combined with three machine learning algorithms， Nomogram and ROC curve analysis， the core Hub-FRGs （CHAC1， ALOX12 B， TRIM21） were obtained. Immunoinfiltration showed inactive memory CD4⁺T cells and activated dendritic cells abundance significantly correlated with Hub-FRGs. In vivo， model group vs. control showed significantly increased PASI/Baker scores （P<0.05）， epidermal hyperkeratosis， inflammatory infiltration， and elevated levels of Fe²⁺， MDA， 4-HNE， FFA， ROS， CHAC1， ALOX12B， TRIM21， Ki67， and NF-κB （P<0.05）. Drug groups vs. model group exhibited significantly reduced scores （P<0.05）， alleviated skin lesions， and decreased levels of Fe²⁺， MDA， 4-HNE， FFA， ROS， Hub-FRGs， Ki67， and NF-κB （P<0.05）. ConclusionKaixuan Jiedu core prescription can significantly improve the skin pathological injury of psoriasis mice， showing good therapeutic and repair effects， and its mechanism may be related to regulating the expression of ferroptosis genes CHAC1， ALOX12B and TRIM21， which are closely related to the pathogenesis of psoriasis.

Lung cancer is the most common malignant tumor worldwide, ranking first in both incidence and mortality rates. According to the latest statistics from the International Agency for Research on Cancer (IARC), approximately 2.5 million new cases and around 1.8 million deaths from lung cancer occurred in 2022, placing a tremendous burden on global healthcare systems. The high mortality rate of lung cancer is closely linked to its subtle early symptoms, which often lead to diagnosis at advanced stages. This not only complicates treatment but also results in substantial economic losses. Current treatment options for lung cancer include surgery, radiotherapy, chemotherapy, targeted drug therapy, and immunotherapy. Among these, immunotherapy has emerged as the most groundbreaking advancement in recent years, owing to its unique antitumor mechanisms and impressive clinical benefits. Unlike traditional therapies such as radiotherapy and chemotherapy, immunotherapy activates or enhances the patient’s immune system to recognize and eliminate tumor cells. It offers advantages such as more durable therapeutic effects and relatively fewer toxic side effects. The main approaches to lung cancer immunotherapy include immune checkpoint inhibitors, tumor-specific antigen-targeted therapies, adoptive cell therapies, cancer vaccines, and oncolytic virus therapies. Among these, immune checkpoint inhibitors and tumor-specific antigen-targeted therapies have received approval from the U.S. Food and Drug Administration (FDA) for clinical use in lung cancer, significantly improving outcomes for patients with advanced non-small cell lung cancer. Although other immunotherapy strategies are still in clinical trials, they show great potential in improving treatment precision and efficacy. This article systematically reviews the latest research progress in lung cancer immunotherapy, including the development of novel immune checkpoint molecules, optimization of treatment strategies, identification of predictive biomarkers, and findings from recent clinical trials. It also discusses the current challenges in the field and outlines future directions, such as the development of next-generation immunotherapeutic agents, exploration of more effective combination regimens, and the establishment of precise efficacy prediction systems. The aim is to provide a valuable reference for the continued advancement of lung cancer immunotherapy.

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.