Objective To investigate the association between physical activity levels and blood lipids among college freshmen, and to provide scientific evidence for the health management of college freshmen. Methods An electronic questionnaire survey on physical activity was conducted on freshmen of a university, and fasting blood biochemical indicators were detected. The International Physical Activity Questionnaire (IPAQ) short form was used to evaluate the physical activity levels of the participants. Dyslipidemia was defined as an abnormality in any one of the following serum lipid parameters: total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), or non-high-density lipoprotein cholesterol. Binary logistic regression and stratified analyses were employed to explore the relationship between physical activity and blood lipids. Results A total of 3 401 participants were included, with an average age of 18.45 ± 0.92 years, and 60.5% were female. The prevalence of dyslipidemia was 17.7%, with a higher rate among males (22.1%) than females (14.8%). After adjusting for confounding factors related to blood lipids, high-intensity physical activity was negatively associated with the risk of elevated LDL-C among males (OR = 0.36, 95% CI: 0.13–0.99, P = 0.049). Conclusion Among freshmen at a medical college in Hubei Province, high-intensity physical activity is negatively associated with the risk of elevated LDL-C in males, but this association needs to be further confirmed by larger prospective cohort studies.

ObjectiveTo investigate the effects of Huanglian Jiedutang （HLJDT） on cognitive function in mice with ischemic stroke （IS） and to elucidate whether its neuroprotective effects are mediated by inhibition of the nuclear factor-κB （NF-κB） signaling pathway and subsequent suppression of NF-κB-regulated neuronal apoptosis. MethodsAn IS model was established using middle cerebral artery occlusion （MCAO）. Sixty C57BL/6J mice were randomly assigned to five groups （n =12 per group）， i.e.， sham operation， model， HLJDT low-dose （3.9 g·kg^-1·d^-1）， HLJDT high-dose （7.8 g·kg^-1·d^-1）， and Ginkgo biloba extract （GBE， 31.2 mg·kg^-1·d^-1）. Post-operatively， neurological deficit scores （Longa score）， cerebral infarct volume assessed by 2，3，5-triphenyltetrazolium chloride （TTC） staining， and brain water content were evaluated. Learning and memory were assessed using new object recognition （NOR） and fear conditioning （FC） tests. Hippocampal pathology was examined via hematoxylin and eosin （HE） staining. Immunofluorescence detected expression of glial fibrillary acidic protein （GFAP， astrocyte marker）， cellular oncogene Fos （c-Fos， neuronal activation marker）， and glutamate decarboxylase 65 （GAD65）. Western blot measured nuclear factor-κB inhibitor protein α （IκBα）， phosphorylated IκBα （p-IκBα）， NF-κB p65， phosphorylated NF-κB p65 （p-NF-κB p65）， ionic calcium binding adapter molecule 1 （Iba-1）， tumor necrosis factor （TNF）-α， interleukin （IL）-1β， and apoptosis-related proteins， such as cleaved cysteinyl aspartate-specific protease 3 （Caspase-3）， B-cell lymphoma 2 （Bcl-2）， and Bcl-2-associated X protein （Bax）. Real-time quantitative PCR （Real-time PCR） was used to assess mRNA levels of Iba-1， TNF-α， IL-1β， NF-κB p65， cleaved Caspase-3， Bax， and Bcl-2. ResultsCompared with the sham group， the model group exhibited significantly increased neurological deficit scores， brain water content， and cerebral infarct volume （P<0.01）. Hippocampal CA1 neurons were disorganized， showing nuclear pyknosis and karyolysis. NOR exploration time and FC freezing time were significantly reduced （P<0.01）. GFAP and c-Fos expression were increased， while GAD65 expression was decreased （P<0.01）. Cleaved Caspase-3 and Bax were upregulated， Bcl-2 was downregulated， and the Bax/Bcl-2 ratio was elevated （P<0.01）. Expression levels of p-IκBα， p-NF-κB p65， IL-1β， TNF-α， and Iba-1 were significantly increased （P<0.01）. Compared with the model group， HLJDT high-dose， low-dose， and GBE groups showed significant improvements in all parameters （P<0.01）. Among them， the HLJDT high-dose group showed the most pronounced neuronal structural recovery and superior performance in NOR and FC tests （P<0.01）. In this group， GFAP and c-Fos decreased， GAD65 increased （P<0.01）， apoptosis-related protein expression was reversed， and NF-κB signaling and related inflammatory factor expression were suppressed （P<0.01）. ConclusionHLJDT ameliorates cognitive dysfunction in mice after IS， potentially by inhibiting the NF-κB signaling pathway， thereby reducing neuroinflammation and hippocampal neuronal apoptosis.

ObjectiveTo elucidate the potential mechanisms by which the classic prescription Anmeidan alleviates cognitive impairment in insomnia model rats through metabolic profiling. MethodsA total of 60 SD rats were randomly divided into six groups： blank group， model group， low-， medium-， and high-dose Anmeidan groups， and the Suvorexant group， with 10 rats in each group. Except for the blank group， the insomnia model was established in all other groups via intraperitoneal injection of para-chlorophenylalanine. The Suvorexant group was administered Suvorexant solution （30 mg·kg^-1·d^-1） by gavage， while the low-， medium-， and high-dose Anmeidan groups received Anmeidan decoction （4.55， 9.09， 18.18 g·kg^-1·d^-1） by gavage. The blank group received an equivalent volume of normal saline. The open field test was used to assess spatial exploration and anxiety/depressive-like behaviors in rats. Serum levels of epidermal growth factor （EGF）， brain-derived neurotrophic factor （BDNF）， and vasoactive intestinal peptide （VIP） were measured using enzyme-linked immunosorbent assay （ELISA）. Untargeted metabolomics was employed to identify differential metabolites in rat serum， and systematic biological methods were applied to analyze the potential targets and pathways of Anmeidan. ResultsCompared to the blank group， the model group exhibited significant reductions in total distance traveled， average speed， number of entries into the central area， time spent in the central area， and frequency of upright events （P<0.01）， along with significant decreases in VIP， EGF， and BDNF levels （P<0.05，P<0.01）. A total of 100 differential metabolites were identified between the model and blank groups. Compared to the model group， the low-， medium-， and high-dose Anmeidan groups showed significant increases in total distance traveled， average speed， number of entries into the central area， time spent in the central area， and frequency of upright events （P<0.05，P<0.01）， as well as a significant increase in VIP levels （P<0.05，P<0.01）. Anmeidan significantly reversed abnormal changes in 67 metabolites compared to the model group. A combined analysis identified 134 potential targets of Anmeidan， with network topology analysis suggesting that Caspase-3， B-cell lymphoma 2 （Bcl-2）， nuclear transcription factor-κB （NF-κB）， interleukin-1β （IL-1β）， interleukin-2 （IL-2）， matrix metalloproteinase-9 （MMP-9）， and Toll-like receptor 4 （TLR4）， among others， may serve as key targets of Anmeidan. Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway analysis revealed major enriched pathways， including the cyclic adenosine monophosphate （cAMP） signaling pathway， hypoxia inducible factor-1 （HIF-1） signaling pathway， and IL-17 signaling pathway. ConclusionThis study demonstrates that Anmeidan can recalibrate abnormal metabolic profiles in insomnia model rats to mitigate cognitive impairment， with its mechanisms of action potentially involving the regulation of immune-inflammatory responses， energy metabolism， and apoptosis-related pathways.

Background/Aims: The long noncoding RNA DUXAP8 is a pivotal regulator in cancer pathogenesis, but the molecular mechanism underlying the role of DUXAP8 in colon cancer progression is underexplored. Methods: In addition to bioinformatic analyses, quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed to assess DUXAP8, microRNA-378a-3p, FOXQ1 expression in colon cancer tissues, and clinical data were analyzed to determine the correlation between DUXAP8 expression and colon cancer patient outcomes. Nuclear/cytoplasmic RNA fractionation was utilized to analyze the subcellular distribution of DUXAP8. Dual-luciferase and RNA immunoprecipitation assays were performed to confirm the binding of DUXAP8/FOXQ1 and microRNA-378a-3p. After cell transfection, qRT-PCR was performed to evaluate the modulatory relationship of DUXAP8/microRNA-378a-3p/FOXQ1. Cell Counting Kit-8, MTT, scratch healing, and Transwell assays were performed to evaluate the impact of DUXAP8/microRNA-378a-3p/ FOXQ1 expression on colon cancer cell functions. Results: The results revealed that the expression of DUXAP8 and FOXQ1 was upregulated in colon cancer tissues, while the expression of microRNA-378a-3p was down-regulated. The increased DUXAP8 expression was positively correlated with lymph node metastasis and TNM stage. Dual-luciferase and RNA immunoprecipitation assays demonstrated that DUXAP8 was a sponge for microRNA-378a-3p and targeted the ability of microRNA-378a-3p to regulate FOXQ1.In addition, functional experiment results revealed that overexpressed DUXAP8 facilitated the growth and migratory ability of colon cancer cells. DUXAP8 also reversed the tumor-suppressive effect of microRNA-378a-3p. However, silencing FOXQ1 could reverse the cancer-promoting effects of high DUXAP8 expression. Conclusions DUXAP8 expression was significantly increased in colon cancer, which was associated with lymph node metastasis and unfavorable outcomes in colon cancer patients. DUXAP8may hasten malignant progression of colon cancer cells through its effects on microRNA-378a-3p/FOXQ1.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

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 construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Background: Epimedii Folium, first recorded in the Shennong’s Classic of Materia Medica (Shen Nong Ben Cao Jing), is a traditional Chinese medicine (TCM) known for its effects of “benefiting Qi and strengthening the heart.” Icariside II (ICS II) is one of the main active components of Epimedii Folium, possessing cardiovascular protective and anti-inflammatory properties. However, the potential mechanisms of ICS II on myocardial ischemia (MI) remain unclear. Objective: The aim of the study was to investigate the effects and preliminary molecular mechanisms of ICS II in treating isoproterenolinduced MI in rats. Methods: A rat model of MI was established by subcutaneous injection of isoproterenol. Electrocardiography, echocardiography, myocardial enzymes analysis, heart weight index, triphenyltetrazolium chloride staining, histopathology, TUNEL staining, RT-qPCR, and Western blot were employed to evaluate the effects and preliminary molecular mechanisms of ICS II on MI rats. Results: Pharmacodynamic studies suggested that ICS II inhibited ST-segment elevation in electrocardiograms, improved cardiac function, reduced heart weight index and myocardial enzyme levels, decreased myocardial infarct size, alleviated cardiac histological damage, and inhibited apoptosis, thereby exerting cardioprotective effects in MI rats. Further studies revealed that ICS II may partially inhibit the expression of NLRP3/Caspase-1 axis-related targets at both protein and mRNA levels. Conclusions: Our findings indicate that ICS II exerts anti-MI effects, and its preliminary molecular mechanisms may be related to inhibiting the activation of the NLRP3/Caspase-1 axis to alleviate inflammatory responses.

Background: Epimedii Folium, first recorded in the Shennong’s Classic of Materia Medica (Shen Nong Ben Cao Jing), is a traditional Chinese medicine (TCM) known for its effects of “benefiting Qi and strengthening the heart.” Icariside II (ICS II) is one of the main active components of Epimedii Folium, possessing cardiovascular protective and anti-inflammatory properties. However, the potential mechanisms of ICS II on myocardial ischemia (MI) remain unclear. Objective: The aim of the study was to investigate the effects and preliminary molecular mechanisms of ICS II in treating isoproterenolinduced MI in rats. Methods: A rat model of MI was established by subcutaneous injection of isoproterenol. Electrocardiography, echocardiography, myocardial enzymes analysis, heart weight index, triphenyltetrazolium chloride staining, histopathology, TUNEL staining, RT-qPCR, and Western blot were employed to evaluate the effects and preliminary molecular mechanisms of ICS II on MI rats. Results: Pharmacodynamic studies suggested that ICS II inhibited ST-segment elevation in electrocardiograms, improved cardiac function, reduced heart weight index and myocardial enzyme levels, decreased myocardial infarct size, alleviated cardiac histological damage, and inhibited apoptosis, thereby exerting cardioprotective effects in MI rats. Further studies revealed that ICS II may partially inhibit the expression of NLRP3/Caspase-1 axis-related targets at both protein and mRNA levels. Conclusions: Our findings indicate that ICS II exerts anti-MI effects, and its preliminary molecular mechanisms may be related to inhibiting the activation of the NLRP3/Caspase-1 axis to alleviate inflammatory responses.

Background: Epimedii Folium, first recorded in the Shennong’s Classic of Materia Medica (Shen Nong Ben Cao Jing), is a traditional Chinese medicine (TCM) known for its effects of “benefiting Qi and strengthening the heart.” Icariside II (ICS II) is one of the main active components of Epimedii Folium, possessing cardiovascular protective and anti-inflammatory properties. However, the potential mechanisms of ICS II on myocardial ischemia (MI) remain unclear. Objective: The aim of the study was to investigate the effects and preliminary molecular mechanisms of ICS II in treating isoproterenolinduced MI in rats. Methods: A rat model of MI was established by subcutaneous injection of isoproterenol. Electrocardiography, echocardiography, myocardial enzymes analysis, heart weight index, triphenyltetrazolium chloride staining, histopathology, TUNEL staining, RT-qPCR, and Western blot were employed to evaluate the effects and preliminary molecular mechanisms of ICS II on MI rats. Results: Pharmacodynamic studies suggested that ICS II inhibited ST-segment elevation in electrocardiograms, improved cardiac function, reduced heart weight index and myocardial enzyme levels, decreased myocardial infarct size, alleviated cardiac histological damage, and inhibited apoptosis, thereby exerting cardioprotective effects in MI rats. Further studies revealed that ICS II may partially inhibit the expression of NLRP3/Caspase-1 axis-related targets at both protein and mRNA levels. Conclusions: Our findings indicate that ICS II exerts anti-MI effects, and its preliminary molecular mechanisms may be related to inhibiting the activation of the NLRP3/Caspase-1 axis to alleviate inflammatory responses.