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.

OBJECTIVE To optimize the extraction process of flavonoids from Potentilla discolor .METHODS Deep eutectic solvents （DESs） were used as the extraction solvent to prepare test sample solutions. The contents of rutin， quercitrin， and kaempferol were determined by ultra-performance liquid chromatography. The analytic hierarchy process combined with the criteria importance through intercriteria correlation weighting method was employed to assign comprehensive weights to the contents of the three flavonoids， and calculate the comprehensive score. The optimal DESs were screened based on the comprehensive score.Mean while， using the comprehensive score as the response variable and the liquid-to-solid ratio， ultrasonic time， and urltrasonic temperature as the influencing factors， the extraction technology of flavonoids from P.discolor was optimized by Box-Behnken response surface methodology and subsequently validated.RESULTS The comprehensive weight coefficients of rutin， quercitrin， and kaempferol were 0.646 7， 0.207 5， and 0.145 8， respectively. The optimal DESs were choline chloride-lactic acid （molar ratio 1∶4） with a water content of 40%. The optimal extraction conditions were determined as follows： liquid-to-solid ratio of 25∶1， ultrasonic time of 40 min， and ultrasonic temperature of 50 ℃. The validation results showed that the average comprehensive score of three batches of samples was 99.28， with an RSD of 0.54% （ n ＝3）.CONCLUSIONS The optimal extraction technology of flavonoids from P. discolor based on DESs obtained in this study was stable and feasible.

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.

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 understand the epidemic characteristics and trends of gonorrhea in Shiyan City in recent years, and to provide a scientific basis for the formulation of prevention and control strategies and measures. Methods The network data of gonorrhea reported in Shiyan City from 2012 to 2023 were collected, and the epidemiological characteristics of reported gonorrhea cases in Shiyan City from 2012 to 2023 were analyzed by descriptive epidemiological statistical analysis methods. Results A total of 2 224 cases of gonorrhea were reported in Shiyan City from 2012 to 2023, with an average annual incidence of 5.55/100 000. The annual incidence ranged from 3.46/100 000 to 7.58/100 000, with statistical significance (χ² trend =95.193, df=1, P<0.001). The incidence of gonorrhea was generally on the rise. The cases of gonorrhea were mainly concentrated in the two main urban areas of Maojian District and Zhangwan District, with the incidence rates of 13.64/100 000 and 7.01/100 000, respectively. The average incidence rate of gonorrhea in the main urban area was higher than that in other counties and urban areas (χ²=634.017, df=1, P<0.001). The incidence in males was much higher than that in females (χ²=1171.064, df=1, P<0.001), with a male to female ratio of 7.46:1. The age of the cases was mainly concentrated in the age group of 20-49 years, accounting for 82.01%. In terms of occupational distribution, the number of cases reported by household and unemployed, unknown and farmers ranked the top three, accounting for 41.55%, 22.98%, and 18.12%, respectively. Conclusion The incidence of gonorrhea in Shiyan City from 2012 to 2023 is generally on the rise. It is necessary to carry out timely intervention measures according to the epidemic characteristics of gonorrhea, monitor and educate key groups, and strengthen the diagnosis, treatment and prevention of venereal diseases in order to effectively prevent the transmission of gonorrhea.

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.

Objective:To investigate key genes and cellular communication involved in spinal cord injury (SCI) repair using transcriptome sequencing and single-cell sequencing analysis.Methods:Two transcriptome sequencing datasets related to SCI and one single-cell sequencing dataset were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in the two transcriptome sequencing datasets were analyzed using the limma package in R, followed by cross-analysis to identify common DEGs. Functional enrichment analysis of DEGs was performed using the DAVID database for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Hub genes in the protein-protein interaction (PPI) network were identified using the cytoHubba plugin in Cytoscape. Single-cell sequencing data were analyzed using the Seurat package in R. Principal Component Analysis (PCA) was applied for dimensionality reduction, and t-distributed Stochastic Neighbor Embedding (t-SNE) was used for visualization. Cellular communication between different cell groups was analyzed using the CellChat package.Results:Differential analysis of transcriptome sequencing data identified 98 common DEGs. GO and KEGG enrichment analysis highlighted biological processes such as wound healing, macrophage activation, and immune response-related cell activation. The PPI network predicted 10 hub genes: ITGAM, TGFB1, CCL2, ICAM1, CD44, FN1, TIMP1, TLR2, ITGB2, and LGALS3. Single-cell sequencing analysis identified six distinct cell populations, including astrocytes, neurons, and fibroblasts. Cellular communication analysis revealed key signaling pathways between cell subpopulations, including SPP1- (ITGAV+ITGB1), SPP1- (ITGA5+ITGB1), TNF-TNFRSF1A transmission pathway, etc.Conclusions:During SCI repair, astrocytes receive signals from fibroblasts, monocytes, and neurons via the SPP1 pathway, while astrocytes and neurons receive signals from monocytes through the TNF pathway. These findings provide critical insights into the molecular mechanisms underlying SCI repair and offer a foundation for future research.

Objective:To investigate whether beta-sitosterol(BS)can affect the polarization of alveolar macrophages and the nuclear factor kappa B(NF-κB)pathway in a rat model of acute lung injury(ALI)induced by lipopolysaccharides(LPS).Methods:Thirty spe-cific pathogen-free Sprague-Dawley rats were randomly divided into normal saline group(NS group),ALI model group(LPS group),low-dose BS intervention group(BS-L group),high-dose BS intervention group(BS-H group),and dexamethasone group(DX group),with six rats in each group.After successful modeling,the rats were sacrificed for the following examinations:the pathological changes and wet-to-dry(W/D)weight ratio of lung tissue;the levels of tumor necrosis factor α(TNF-α)and interleukin 1β(IL-1β)in bron-choalveolar lavage fluid(BALF);the relative mRNA expression levels of NF-κB p65,IL-1β,and TNF-α in lung tissue;the protein expression levels of NF-κB p65 and TNF-α and the percentage of CD80-positive M1 alveolar macrophages in lung tissue.Results:Ab-normal behaviors and cyanosis were observed in all the groups except the NS group,with milder symptoms in the BS groups and DX group than in the LPS group.Compared with the NS group,the LPS group showed significant increases in the lung tissue pathological injury score,the W/D ratio,the relative mRNA expression levels of NF-κB p65,IL-1β,and TNF-α in lung tissue,the protein expres-sion levels of TNF-α and NF-κB p65 in lung tissue,the percentage of CD80-positive M1 alveolar macrophages,and the levels of the in-flammatory factors TNF-α and IL-1β in BALF(all P<0.01).Com-pared with the LPS group,the BS-L group,BS-H group and DX group showed significant decreases in the lung tissue pathological injury score,the W/D ratio,the relative expression levels of NF-κB p65,IL-1β,and TNF-α mRNAs in lung tissue,the protein expression levels of NF-κB p65 and TNF-α in lung tissue,the percentage of CD80-positive M1 alveolar macrophages,and the levels of TNF-α and IL-1β in BALF(all P<0.001).The above indicators were sig-nificantly lower in the BS-H group than in the BS-L group(all P<0.001).Compared with those in the BS-L and BS-H groups,the above indicators in the DX group were significantly decreased(all P<0.01).Conclusion:BS may alleviate the severity of ALI through inhibiting the activation of the NF-κB pathway and suppressing the polarization of alveolar macrophages towards M1-type macro-phages in ALI rats.