ObjectiveThe controllability changes of structural brain network were explored based on the control and brain network theory in young smokers, this may reveal that the controllability indicators can serve as a powerful factor to predict the sleep status in young smokers. MethodsFifty young smokers and 51 healthy controls from Inner Mongolia University of Science and Technology were enrolled. Diffusion tensor imaging (DTI) was used to construct structural brain network based on fractional anisotropy (FA) weight matrix. According to the control and brain network theory, the average controllability and the modal controllability were calculated. Two-sample t-test was used to compare the differences between the groups and Pearson correlation analysis to examine the correlation between significant average controllability and modal controllability with Fagerström Test of Nicotine Dependence (FTND) in young smokers. The nodes with the controllability score in the top 10% were selected as the super-controllers. Finally, we used BP neural network to predict the Pittsburgh Sleep Quality Index (PSQI) in young smokers. ResultsThe average controllability of dorsolateral superior frontal gyrus, supplementary motor area, lenticular nucleus putamen, and lenticular nucleus pallidum, and the modal controllability of orbital inferior frontal gyrus, supplementary motor area, gyrus rectus, and posterior cingulate gyrus in the young smokers’ group, were all significantly different from those of the healthy controls group (P<0.05). The average controllability of the right supplementary motor area (SMA.R) in the young smokers group was positively correlated with FTND (r=0.393 0, P=0.004 8), while modal controllability was negatively correlated with FTND (r=-0.330 1, P=0.019 2). ConclusionThe controllability of structural brain network in young smokers is abnormal. which may serve as an indicator to predict sleep condition. It may provide the imaging evidence for evaluating the cognitive function impairment in young smokers.

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.

Objective: This study aims to explore the association between oral lichen planus (OLP) and Hashimoto’s thyroiditis (HT) and its anti-thyroid antibodies to provide clinical evidence for thyroid disease screening in patients with OLP. Methods: This study was approved by the institutional ethics committee. A total of 125 clinically and histopathologically confirmed patients with OLP were enrolled as the case group, and they were matched with 125 non-OLP controls based on sex and age. Demographic data (gender, age, lesion type, and disease duration) were collected from both groups. Serum levels of thyroid peroxidase antibodies (TPOAb) and thyroglobulin antibodies (TgAb) were measured to analyze their associations with sex, age, lesion type, and disease duration in patients with OLP. Result: The prevalence of HT in patients with OLP was 31.20%, significantly higher than that in the control group (9.60%) (χ2=18.504, P<0.001). The prevalence of HT in female patients with OLP (39.13%) was significantly higher than that in male patients (9.09%)（χ2=10.93，P<0.001）. The positivity rate of thyroid peroxidase antibodies (TPOAb) in patients with OLP (17.6%) was significantly higher than in the control group (4.0%) (χ2=10.989, P<0.001). The TPOAb positivity rate was significantly higher in female patients (22.83%) than in male patients (3.03%) (χ2=5.210, P=0.014). There was no statistically significant difference in the positivity rate of TgAb between patients with OLP (7.2%) and the control group (3.2%) (P>0.05). Patients with erosive lesions had a significantly higher TPOAb positivity rate (25.0%, 17/68) compared to those with non-erosive lesions (8.77%, 5/57), and the difference was statistically significant (χ2=4.831, P=0.028). Logistic regression analysis revealed that female patients with OLP had an 8.935-fold higher risk of being TPOAb positive compared to males (OR=8.935, 95%CI: 1.134-70.388, P=0.038). Patients with erosive OLP lesions had a 3.199-fold higher risk of TPOAb positivity compared to those with non-erosive lesions (OR=3.199, 95%CI: 1.064-9.618, P=0.038). Conclusion The prevalence of HT is higher in patients with OLP, with higher positivity rates of anti-thyroid antibodies observed in female patients and those with erosive OLP lesions. This suggests that thyroid disease screening should be incorporated into the clinical management of patients with OLP, especially for women and patients who present with erosive lesions.

Tumor immunotherapy has emerged as the fourth major therapeutic modality, following surgery, radiotherapy, and chemotherapy. Unlike traditional treatments that primarily target tumor cells directly, immunotherapy harnesses the body’s immune system to recognize and eliminate cancer cells. Over the past decade, various immunotherapeutic strategies have been developed, including immune checkpoint inhibitors (ICIs), chimeric antigen receptor (CAR) T cell therapy, cancer vaccines, and cytokine-based therapies. However, the immunosuppressive tumor microenvironment (TME) poses a significant obstacle to the effectiveness of these treatments. Polyamines—including putrescine, spermidine, and spermine—are polycationic metabolites that often accumulate abnormally in the TME and act as critical immunoregulatory molecules. T cells play a central role in antitumor immunity, yet their function is frequently influenced by immunoregulatory factors within the TME. Elevated polyamine levels in the TME have been implicated in dampening antitumor T cell responses, thereby facilitating tumor immune evasion. Polyamines in the TME originate from both tumor cells and tumor-associated immune cells. Tumor cells often overexpress the oncogene Myc, which drives the upregulation of polyamine biosynthetic enzymes, resulting in excessive intracellular polyamine production. Additionally, M2-polarized tumor-associated macrophages (M2-TAMs) contribute to polyamine accumulation by upregulating arginase-I (Arg-I), an enzyme that catalyzes the conversion of arginine into ornithine—a key precursor in the polyamine biosynthetic pathway. These combined sources lead to sustained polyamine enrichment in the TME, contributing to immune dysfunction and supporting tumor progression. Moreover, polyamines indirectly affect T cell activity by modulating macrophage polarization and directly suppress tumor cell apoptosis, further promoting an immunosuppressive environment. This review highlights the multifaceted roles of polyamines in modulating tumor-infiltrating T cell function, with a particular focus on their influence on CD4+ T cell differentiation,CD8+ T cell cytotoxicity, and immune checkpoint molecule expression. Recent studies suggest that polyamines suppress CD4+ T cell activation and differentiation by modulating the MAPK/ERK signaling pathway. Additionally, polyamines can impair T cell receptor (TCR) signaling and promote immune evasion through the upregulation of PD-L1 expression on tumor cells. These effects collectively contribute to weakened antitumor T cell responses. Polyamine blocking therapy (PBT), which primarily targets polyamine biosynthesis and transport, has emerged as a novel adjunctive immunotherapeutic strategy in cancer treatment. By reducing polyamine levels in the TME, PBT restores T cell effector functions and alleviates immunosuppression. Notably, studies have demonstrated that combining PBT with ICIs produces synergistic antitumor effects and may overcome resistance to ICI monotherapy. Although research has revealed the inhibitory effects of polyamines on T cell immune function, the underlying regulatory mechanisms remain to be fully elucidated. Moreover, due to compensatory mechanisms employed by tumor cells to maintain polyamine homeostasis, multi-targeted approaches may be necessary to achieve safe and effective therapeutic outcomes. Future PBT strategies may benefit from the integration of multi-omics technologies and the development of nanocarrier-based drug delivery systems, which could collectively enhance their specificity, efficacy, and applicability in cancer immunotherapy. This review systematically elucidates the immunomodulatory effects of polyamines on T cell function within the TME and provides theoretical support and novel insights for the advancement of tumor immunotherapeutic strategies.

AIM:To compare the changes in diopters and axial length after 1 a of wearing defocus incorporated multiple segments(DIMS)lenses or single vision(SV)spectacle lenses in children with monocular myopia.METHODS:In this retrospective case group study, monocular myopia children aged from 6 to 14 years old in Hankou Aier Eye Hospital from October 2020 to October 2022, who were fitted with DIMS lens(n=52)or single-vision(SV)spectacle lenses(n=49)were collected. The spherical degree of myopia eyes ranged from -4.00 D to -0.50 D and the nonmyopic eyes ranged from 0 to +1.00 D, astigmatism in all eyes ranged from 0 to -2.00 D. The DIMS lens group was classified into DIMS-myopia group(the myopic eyes)and DIMS-nonmyopia group(the nonmyopic eyes). The SV lens group was also divided into SV-myopia group and SV-nonmyopia group. The changes in spherical equivalent refraction(SER)and axial length(AL)of each group were compare before and after wearing lenses for 1 a, and variations in SER and AL of both eye among groups were analzed.RESULTS: After wearing lenses for 1 a, the changes of SER in the DIMS-myopic group and the DIMS-nonmyopic group were -0.41±0.44 and -0.26±0.54 D, respectively, and the changes of AL were 0.18±0.20 and 0.15±0.15 mm, respectively. SER changes were -0.74±0.63 and -0.70±0.68 D in SV-myopic group and SV-nonmyopic group, and AL changes were 0.30±0.28 and 0.31±0.28 mm. The changes of SER and AL in the DMS-myopic and non-myopic groups were slower than those in SV group(all P<0.05). Compared with SV lenses, wearing DIMS lenses delayed and 44.6% in myopia eyes, and 62.9% in non-myopia eyes, AL delayed by 40.0% in myopia eyes and 51.6% in non-myopia eyes. The percentage of 1-year AL change ≤0.2 mm in the DIMS-myopic group and non-myopic group was 53.9% and 65.4%, respectively, which was higher than that in the SV myopic group(34.7% and 42.9%, all P<0.05). The percentage of AL change >0.4 mm in the DIMS-myopic group and nonmyopic group was 17.3% and 7.7%, respectively, which was lower than that in the SV myopic group(32.7% and 28.6%, all P<0.05). There was no significant correlation between the change of AL and age and baseline AL in the DIMS-myopic and non-myopic groups after wearing lens for 1 a(all P>0.05); the change of AL in SV-myopic group and non-myopic group was negatively correlated with age(r=-0.446, P=0.001; r=-0.312, P=0.029), and there was no significant correlation with baseline AL(all P>0.05).CONCLUSION: DIMS lens has a good effect on myopia control and prevention in both myopia and non-myopia children with monocular myopia. Children with early pre-myopia can wear DIMS to prevent myopia.

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 predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the screening criteria, a total of 651 patients were included. The training set consisted of 468 patients (181 males, 287 females) with an average age of (58.39±11.23) years, ranging from 29 to 78 years, the testing set included 140 patients (56 males, 84 females) with an average age of (58.81±10.70) years, ranging from 34 to 82 years, and the internal validation set comprised 43 patients (14 males, 29 females) with an average age of (60.16±10.68) years, ranging from 29 to 78 years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 0.952 [95%CI (0.87, 1.00)], an F1 score of 84.62%, and a precision-recall AUC of 0.892, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

OBJECTIVE To optimize the salt-processing technology for Anemarrhena asphodeloides. METHODS Taking soaking time， stir-frying temperature， and stir-frying time as factors， Box-Behnken response surface methodology was employed to optimize the salt-processing technology of A. asphodeloides using the contents of mangiferin， neomangiferin， isomangiferin， timosaponin BⅡ， timosaponin AⅢ， timosaponin BⅢ， total flavonoids， and total saponins as evaluation indicators. The entropy weight method was applied to determine the weight of each indicator and calculate the comprehensive score. Based on the 17 sets of Box-Behnken response surface methodology results， a genetic algorithm （GA）-back propagation （BP） neural network was used to further optimize the salt-processing technology， with soaking time， stir-frying temperature， and stir-frying time as input layers and the comprehensive score as the output layer. The salt-processing parameters obtained from the two methods were validated and compared to determine the optimal salt-processing technology for A. asphodeloides. RESULTS The optimal salt-processing conditions obtained via the Box-Behnken response surface methodology were as follows： soaking time of 23 min， stir-frying temperature of 160 ℃ ， and stir-frying time of 12 min， yielding a comprehensive score of 63.370 2. The GA-BP neural network optimization resulted in the following conditions： soaking time of 24 min， stir-frying temperature of 163 ℃， and stir-frying time of 12 min， with a comprehensive score of 65.163 8. The GA-BP neural network optimization outperformed the results obtained by Box-Behnken response surface methodology. CONCLUSIONS This study successfully optimized the salt-processing technology for A. asphodeloides. Specifically， the technology involves adding 15 mL of 0.1 g/mL saline solution to 50 g of the herbal slices， allowing them to moisten for 24 minutes， and then stir-frying at 163 ℃ for 12 minutes.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Nanoparticle delivery systems have good application prospects in the field of precision therapy, but the preparation process of nanomaterial has problems such as short in vivo circulation time, easy recognition, and clearance by the immune system in the body. In recent years, biomimetic nanoparticle delivery systems mediated by natural cell membranes have become a research hotspot to address these issues. The natural membrane biomimetic nanoparticle delivery system cleverly integrates the advantages of natural biofilm "autologous" and "artificial" functional carriers by using endogenous cell membranes to modify the surface of nanocarriers, endowing them with characteristics such as tumor targeting, low immunogenicity, and long blood circulation. Currently, biomimetic nanoparticle delivery systems have been used in the treatment of malignant tumors, cardiovascular diseases, bacterial infections, and other diseases. This paper analyzes the development status and current research hotspots of natural cell membrane camouflaged biomimetic nanoparticle delivery systems mainly reviews the latest research progress of red blood cell membrane camouflaged biomimetic nanoparticle delivery systems in the field of disease treatment in recent years. It focuses on exploring the advantages, future development prospects, and limitations of biomimetic nanoparticle delivery systems based on red blood cell membrane camouflage in improving drug delivery, to provide a reference for the in-depth research and development of this system.