Objective: To examine the associations of pet ownership and daily outdoor activity duration with depressive symptoms in middle school students, so as to provide evidence for targeted prevention strategies of depressive symptoms in middle school students. Methods: Using a stratified cluster random sampling method, 83 601 middle and high school students from 103 districts and counties in Inner Mongolia Autonomous Region were selected in 2024. A questionnaire survey was conducted to collect demographic data, household pet ownership, outdoor activity duration, and depressive symptoms of the research subjects. The comparison of reporting rates of depressive symptoms among different groups of middle school students was conducted using a χ 2 test. The association between pet ownership and outdoor activity duration and depressive symptoms among middle school students was evaluated using a Logistic regression model, and stratified analysis was conducted among different genders and regions to control for potential confounding factors and evaluate the stability of the association. Results: The prevalence of depressive symptoms among middleschool students in Inner Mongolia was 17.2%. Significant differences in depressive symptom reporting rates were observed across sex, grade, ethnicity, surveillance site, parental education, menarche/spermarche status, boarding status, smoking and alcohol use, daily breakfast consumption, school bullying, continuous 30 minute headphone use in a noisy environment, and often use the Internet ( χ 2=8.07-2 672.57, all P <0.01). Both pet ownership ( OR =0.78, 95% CI =0.75-0.81) and ≥2 h/d of outdoor activity( OR =0.81, 95% CI =0.78-0.84) were inversely associated with depressive symptoms;compared to the without owning pets and < 2 h of outdoor activity daily group, students who both owned pets and engaged in ≥2 h of outdoor activity daily had an even lower risk ( OR =0.83, 95% CI =0.78-0.87)(all P <0.05). Conclusion Pet ownership and increased daily outdoor activity duration may help mitigate depressive symptoms among middle school students.

Objective To observe the effect of comorbidity for patients with non-small cell lung cancer (NSCLC) on exercise tolerance and cardiopulmonary function. Methods NSCLC patients who underwent cardiopulmonary exercise testing (CPET) before surgery were retrospectively included. According to the Charlson comorbidity index (CCI) score, patients were divided into two groups: a CCI≥3 group and a CCI＜3 group. The patients were matched with a ratio of 1 : 1 by propensity score matching according to the age, body mass index, sex, smoking history, exercise habits, pathological stage and type of surgery. After matching, CPET indexes were compared between the two groups to explore the differences in exercise tolerance and cardiopulmonary function. Results A total of 276 patients were included before matching. After matching, 56 patients were enrolled with 28 patients in each group, including 38 (67.9%) males and 18 (32.1%) females with an average age of (70.7±6.8) years. Compared with the CCI＜3 group, work rate at peak (WR peak), WR peak/predicted value (WR peak%), kilogram oxygen uptake at anaerobic threshold (VO2/kg AT), VO2/kg peak, VO2/kg peak%, peak carbon dioxide output, the minute ventilation to carbon dioxide production slope, O2 pulse peak and O2 pulse peak% of CCI≥3 group were statistically different (P＜0.05). Among them, the rate of postoperative pulmonary complication in the CCI≥3 group was higher than that in the CCI＜3 group (60.7% vs. 32.1%, P=0.032). Conclusion In the NSCLC patients, exercise tolerance and cardiopulmonary function decreased in patients with CCI≥3 compared with those with CCI<3. CPET can provide an objective basis for risk assessment in patients with comorbidity scored by CCI for pulmonary resection.

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 investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective To explore the diagnostic value of deep learning image reconstruction(DLIR)com-bined with low-dose chest computed tomography(CT)with axial scan in the diagnosis of mycoplasma pneumoniae pneumonia(MPP)in children,and to provide reference for clinical practice.Methods 160 cases MPP children from February 2024 to June 2025 were selected as study subjects,and low-dose chest CT with axial scan was performed on all patients.DLIR and conventional adaptive iterative reconstruction-V(ASIR-V)were used for image reconstruction.The objective image quality[background noise(SD),signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)],subjective image quality,and CT sign detection rate were compared,and the consistency of DLIR and ASIR-V in the diagnosis of MPP severity and clinical diagnosis was compared.Results As the intensity of DLIR and the weight of ASIR increasd,SD gradually decreased,while SNR and CNR gradually increased.The high-strength DLIR(DLIR-H)SD was lower than that of ASIR with a blending level of 80%(ASIR-V80%).The SNR and CNR were higher than those of ASIR-V80%,showing statistical significance(P<0.05).Ridit test showed that DLIR-H had the best subjective image quality score under different DLIR intensities,and ASIR-V80%had the best subjective image quality score under different ASIR weights.Furthermore,the subjective image quality score of DLIR-H was higher that of ASIR-V80%,and the differences were statistically significant(P<0.05).Using DLIR-H,the detection rates of air bronchogram,pulmonary consolidation,and interstitial infiltration(69.38%,86.88%,20.63%,respectively)were higher than those using ASIR-V80%(50.00%,71.88%,7.50%,respectively),and the differences were statistically significant(P<0.05).Consistency analysis showed that the Kappa value between the diagnostic results of MPP severity using DLIR-H and clinical diagnosis was 0.856(95%CI:0.771～0.996),while that between the diagnostic results of MPP severity using ASIR-V80%and clinical diagnosis was 0.498(95%CI:0.346～0.650).ROC analysis showed that the area under the curve(AUC)for diagnosing MPP severity was 0.925(95%CI:0.872～0.960)for DLIR-H and 0.729(95%CI:0.653～0.796)for ASIR-V80%,and the diagnostic value of DLIR-H was superior to that of ASIR-V80%(Z=3.952,P<0.001).Conclusion DLIR can effectively improve image quality.DLIR-H combined with low-dose chest CT with axial scan has high diagnostic value for the severity of MPP,and can serve as a feasible solution for clinical diagnosis of MPP severity and reducing radiation dose.

Objective To explore the diagnostic value of deep learning image reconstruction(DLIR)com-bined with low-dose chest computed tomography(CT)with axial scan in the diagnosis of mycoplasma pneumoniae pneumonia(MPP)in children,and to provide reference for clinical practice.Methods 160 cases MPP children from February 2024 to June 2025 were selected as study subjects,and low-dose chest CT with axial scan was performed on all patients.DLIR and conventional adaptive iterative reconstruction-V(ASIR-V)were used for image reconstruction.The objective image quality[background noise(SD),signal-to-noise ratio(SNR),and contrast-to-noise ratio(CNR)],subjective image quality,and CT sign detection rate were compared,and the consistency of DLIR and ASIR-V in the diagnosis of MPP severity and clinical diagnosis was compared.Results As the intensity of DLIR and the weight of ASIR increasd,SD gradually decreased,while SNR and CNR gradually increased.The high-strength DLIR(DLIR-H)SD was lower than that of ASIR with a blending level of 80%(ASIR-V80%).The SNR and CNR were higher than those of ASIR-V80%,showing statistical significance(P<0.05).Ridit test showed that DLIR-H had the best subjective image quality score under different DLIR intensities,and ASIR-V80%had the best subjective image quality score under different ASIR weights.Furthermore,the subjective image quality score of DLIR-H was higher that of ASIR-V80%,and the differences were statistically significant(P<0.05).Using DLIR-H,the detection rates of air bronchogram,pulmonary consolidation,and interstitial infiltration(69.38%,86.88%,20.63%,respectively)were higher than those using ASIR-V80%(50.00%,71.88%,7.50%,respectively),and the differences were statistically significant(P<0.05).Consistency analysis showed that the Kappa value between the diagnostic results of MPP severity using DLIR-H and clinical diagnosis was 0.856(95%CI:0.771～0.996),while that between the diagnostic results of MPP severity using ASIR-V80%and clinical diagnosis was 0.498(95%CI:0.346～0.650).ROC analysis showed that the area under the curve(AUC)for diagnosing MPP severity was 0.925(95%CI:0.872～0.960)for DLIR-H and 0.729(95%CI:0.653～0.796)for ASIR-V80%,and the diagnostic value of DLIR-H was superior to that of ASIR-V80%(Z=3.952,P<0.001).Conclusion DLIR can effectively improve image quality.DLIR-H combined with low-dose chest CT with axial scan has high diagnostic value for the severity of MPP,and can serve as a feasible solution for clinical diagnosis of MPP severity and reducing radiation dose.

Purpose To explore the predictive value of CT-enhanced for tumor mutation burden(TMB)in hepatocellular carcinoma(HCC).Materials and Methods A total of 22 patients with pathologically confirmed HCC after undergoing radical resection in the First Affiliated Hospital,Army Medical University(Third Military Medical University)from January 2020 to January 2023 were collected,all of whom were quantified for TMB.Clinical,laboratory tests,CT imaging characteristics and follow-up of patients were recorded.Variables with P<0.2 were screened by stepwise regression analysis for independent risk factors for TMB.The area under the curve of receiver operating characteristic was used to assess the diagnostic efficacy.Results High TMB level was a risk factor for disease-free survival after HCC surgery(HR=1.115,P<0.05).According to the optimal cut-off value,TMB was classified into a high-risk group(>9.25 mutation/Mb)and low-risk group(≤9.25 mutation/Mb).Univariate analysis of intratumor ischemia or necrosis was statistically different between the high-risk and low-risk groups(P=0.005),and only intratumor ischemia or necrosis was an independent risk factor for predicting high TMB level by stepwise regression analysis(P<0.05).The area under the curve for predicting disease-free survival was 0.833(95%CI 0.615-0.956,P<0.001),with a sensitivity of 100.0%and a specificity of 66.7%.Conclusion High TMB level is associated with poor prognosis after HCC resection.Intratumor ischemia or necrosis have certain clinical value in predicting high TMB level,and are expected to provide a reference basis for personalized diagnosis and treatment of HCC patients.

OBJECTIVE: This study investigates the efficacy and mechanisms of Qingjie Fuzheng Granules (QFG) in inhibiting colitis-associated colorectal cancer (CAC) development via RNA sequencing (RNA-seq) and 16S ribosomal RNA (rRNA) correlation analysis. METHODS: CAC was induced in BALB/c mice using azoxymethane (AOM) and dextran sulfate sodium (DSS), and QFG was administered orally to the treatment group. The effects of QFG on CAC were evaluated using disease index, histology, and serum T-cell ratios. RNA-seq and 16S rRNA analysis assessed the transcriptome and microbiome change. Key pharmacodynamic pathways were identified by integrating these data and confirmed via Western blotting and immunofluorescence. The link between microbiota and CAC-related markers was explored using linear discriminant analysis effect size and Spearman correlation analysis. RESULTS: Long-term treatment with QFG prevented AOM/DSS-induced CAC formation, reduced levels of interleukin (IL)-1β, tumor necrosis factor-alpha (TNF-α), IL-6, and interferon γ (IFN-γ), and increased CD3⁺ and CD4⁺/CD8⁺ T cells ratio, without causing hepatic or renal toxicity. A 16S rRNA analysis revealed that QFG rebalanced the Firmicutes/Bacteroidetes ratio and mitigated AOM/DSS-induced microbiota disturbances. Transcriptomics and Western blotting analysis identified the nucleotide-binding oligomerization domain-containing protein 2 (NOD2)/nuclear factor kappa-B (NF-κB) pathway as key for QFG's treatment against CAC. Furthermore, QFG decreased the abundance of Bacilli, Bacillales, Staphylococcaceae, Staphylococcus, Lactobacillales, Aerococcus, Alloprevotella, and Akkermansia, while increasing Clostridiales, Lachnospiraceae, Lachnospiraceae_NK4A136_group, Ruminococcaceae, and Muribaculaceae, which were highly correlated with CAC-related markers or NOD2/NF-κB pathway. CONCLUSION By mapping the relationships between CAC, immune responses, microbiota, and key pathways, this study clarifies the mechanism of QFG in inhibiting CAC, highlighting its potential for clinical use as preventive therapy.

Objective:To develop and explore the clinical value of a nomogram model for the preoperative diagnosis of proliferative hepatocellular carcinoma (HCC) based on gadoxetate disodium (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI).Methods:The preoperative Gd-EOB-DTPA-enhanced MRI data and clinical pathological data of patients with pathologically confirmed proliferative (178 cases) and non-proliferative type HCC (378 cases) from September 2017 to November 2022 were retrospectively collected. The MRI features and clinicopathological features of proliferative and non-proliferative type HCC were evaluated. Multivariate logistic regression analysis was used to determine the independent predictive factors of proliferative-type HCC. The nomogram prediction model was constructed using R software. The receiver operating characteristic curve (ROC) was used to evaluate its diagnostic efficacy. The calibration curve and decision curve analysis (DCA) were drawn to evaluate the calibration performance and clinical application value of the nomogram model. The optimal threshold for distinguishing high-risk from low-risk was determined using the Youden index. The survival prognosis of proliferative and non-proliferative type HCC was analyzed and compared using the Kaplan-Meier survival curve and the log-rank test. The measurement data were analyzed using the independent sample t-test or the Mann-Whitney U test. The count data were compared using the χ2 test. Results:There were statistically significant differences in alpha-fetoprotein (AFP) levels ( χ2=17.244, P<0.001), tumor morphology ( χ2=13.669, P<0.001), intratumoral fatty degeneration ( χ2=10.495, P=0.001), abnormal enhancement of peritumoral abnormalities during arterial phase ( χ2=37.662, P<0.001), tumor capsule ( χ2=23.961, P<0.001), intratumoral necrosis ( χ2=77.184, P<0.001), intratumoral hemorrhage ( χ2=4.892, P=0.027), peritumoral hypointense in hepatobiliary phase ( χ2=47.675, P<0.001), rim arterial phase hyperenhancement ( χ2=115.976, P<0.001), intratumoral artery ( χ2=15.528, P<0.001) and intravenous tumor thrombus ( χ2=10.532, P=0.001) between proliferative and non-proliferative type HCC groups. Multivariate logistic regression analysis showed that AFP>200 μg/L ( OR=1.561, P=0.044), no intratumoral fatty degeneration ( OR=1.947, P=0.033), intratumoral necrosis ( OR=2.084, P=0.003), peritumoral hypointensity in the hepatobiliary phase ( OR=2.314, P=0.001), and annular hyperenhancement in the arterial phase ( OR=5.557, P<0.001) were independent predictors for preoperative diagnosis of proliferative-type HCC. A nomogram model for preoperative prediction of proliferative type HCC was constructed based on the independent predictors. The area under the ROC curve model for predicting proliferative-type HCC was 0.772 (95% CI: 0.735-0.807), with a sensitivity of 69.1% and a specificity of 75.4%. The calibration curve and DCA curve showed superior calibration performance and clinical applicability of the nomogram model. The Kaplan-Meier curve showed that the recurrence free survival rate after liver resection was significantly lower in patients with proliferative-type HCC than that of non-proliferative-type HCC ( P<0.001), and the high-risk group was significantly lower than the low-risk group ( P<0.001). Conclusions:The construction of a nomogram model based on Gd-EOB-DTPA-enhanced MRI features combined with AFP >200μg/L can accurately diagnose proliferative-type HCC and predict its preoperative prognosis.

Objective:To analyze the genetic characteristics of the prevalent strains of Varicella-Zoster virus (VZV) in the population of Dali, Yunnan, and to understand its evolutionary status in the population of Dali.Methods:Herpes fluid and 163 sera were collected from 249 patients clinically suspected to have varicella or herpes zoster in the Department of Dermatology of the Second People′s Hospital of Dali city, Yunnan province, China, from 2023 to 2024. The levels of VZV-specific IgG and IgM antibodies in serum were detected using enzyme-linked immunoassay. Viral DNA was extracted from the herpes fluid, and the cycle threshold ( Ct) of the samples was detected using quantitative real-time polymerase chain reaction (qPCR), and some samples with Ct ≦ 22 were selected for sequencing by next-generation sequencing technology (next-generation sequencing). Next-generation sequencing (NGS) was used to obtain 90 whole genome sequences of VZV, and the sequencing result were compared with the sequences of reference strains for multiple sequence comparison and evolutionary analysis. Snapgene was used to translate the nucleotides into amino acids, and the result were compared with the amino acid sequences of the reference strain. Results:Of the 90 VZV whole-genome sequences, one whole-genome sequence was from an adult varicella patient, and the remaining 89 whole-genome sequences were from herpes zoster patients. The serum-specific IgG antibody positivity rate was 99.4%, and the IgM antibody positivity rate was 52.8%. The result of both single nucleotide polymorphism (SNPs) site typing and genome-wide phylogenetic tree analysis showed that 83 of the 90 VZV whole-genome sequences in this study were on the same branch as Clade 2, and 7 VZV whole-genome sequences were on the branch of Clade 9.Conclusions:The main endemic branch in Dali region in 2023-2024 was Clade 2, with the emergence of Clade 9 branch; there were amino acid mutations in the proteins encoded by ORF22 and ORF68 in 83 VZV whole genome sequences of Clade 2 branch, and the mutations did not cause significant changes to the protein structure.