OBJECTIVE To investigate the efficacy and safety of omadacycline in the treatment of macrolide-unresponsive Mycoplasma pneumoniae pneumonia （MUMPP） in children. METHODS A retrospective study was conducted on children aged 1-18 years old with MUMPP who were hospitalized in the Department of Pediatrics， the First Affiliated Hospital of Xinjiang Medical University from January 2022 to June 2025. According to the selection of secondary antibiotics after 72 h of initial treatment with macrolides， they were divided into the omadacycline group and the doxycycline group. Based on conventional treatment， children in the omadacycline group were given intravenous infusion of 2.4 mg/kg （once daily） of omadacycline tosylate， while children in the doxycycline group were given oral doxycycline hydrochloride tablets at 2 mg/kg （twice daily）. The efficacy and safety were compared between the two groups of pediatric patients. Univariate analysis and multivariate Logistic regression analysis were performed on clinical efficacy， and subgroup analysis along with multiple sensitivity analyses were conducted to verify the robustness of the conclusions. RESULTS A total of 284 children with MUMPP were included in this study， with 142 in the omadacycline group and 142 in the doxycycline group. In terms of efficacy， although the hospitalization time of children in the omadacycline group was longer than that in the doxycycline group （ P ＜0.05）， the lung lesion absorption rate and clinical efficacy were significantly higher or better than those in the doxycycline group （ P ＜0.05）. The results of multivariate Logistic regression analysis showed that medication （OR＝5.300， 95%CI： 2.526-11.123）， length of hospital stay （OR＝1.348， 95%CI： 1.167-1.556）， and medication duration （OR＝1.422， 95%CI： 1.169-1.729） were influencing factors of clinical efficacy （ P ＜0.05）. The subgroup analysis results showed that the clinical efficacy of omadacycline was significantly better than that of doxycycline in all subgroups （ P ＜0.05）. The results of multiple sensitivity analysis showed that the regression coefficients B of the four models （gradually adjust variables） before and after inverse probability of treatment weighting were significantly greater than 1 （ P ＜0.05）. In terms of safety， there was no statistically significant difference in the inci dence of adverse drug reactions between the two groups of patients （ χ 2 ＝0.447， P ＝0.504）. CONCLUSIONS In the case of hospitalization and prolonged medication， the efficacy of omadacycline in treating childhood MUMPP is superior to that of doxycycline， and its safety is good.

ObjectiveTo evaluate the antitumor activity of Periplaneta americana extract（PAE） against human-derived lung adenocarcinoma organoids（LUAD-PDOs） and to elucidate its potential mechanism based on transcriptomics. MethodsFresh tumor and adjacent normal tissues from patients with LUAD were collected to construct LUAD-PDOs and normal lung organoid（Nor-PDOs） models using 3D organoid culture technology. The effective intervention concentration of PAE was determined using the cell counting kit-8（CCK-8） assay. Experimental groups included the model group（LUAD-PDOs）， normal group， model administration group（LUAD-PDOs+PAE）， and normal administration group（Nor-PDOs+PAE）. Hematoxylin-eosin（HE） staining was used to observe the pathological structures of PDOs， immunohistochemistry（IHC） was performed to detect the expressions of the proliferation marker Ki-67 and lung adenocarcinoma differentiation markers cytokeratin-7（CK-7） and Napsin A， TUNEL staining was applied to detect cell apoptosis. RNA sequencing（RNA-Seq） was conducted to identify differentially expressed genes（DEGs）， followed by Gene Ontology（GO）， Kyoto Encyclopedia of Genes and Genomes（KEGG）， and Gene Set Enrichment Analysis（GSEA）， alongside protein-protein interaction（PPI） network analysis to screen core mechanisms. Finally， key targets were validated by integrating external database analysis with immunofluorescence（IF）. ResultsNor-PDOs and LUAD-PDOs that highly recapitulated the pathological characteristics of the primary tissues were successfully established. The CCK-8 assay determined that the effective intervention concentration of PAE was 16 g·L^-1. Morphological observation showed that Nor-PDOs exhibited lumen-forming structures， whereas LUAD-PDOs displayed dense， solid structures. CCK-8 and TUNEL assays revealed that， compared with the model group， PAE intervention inhibited the proliferation of LUAD-PDOs and promoted apoptosis in LUAD cells， while showing no significant effect on the viability of Nor-PDOs. Transcriptomic analysis identified 719 DEGs that were significantly reversed after PAE intervention（347 up-regulated and 372 down-regulated）（P<0.05）. GO enrichment analysis indicated that DEGs in the model administration group were significantly enriched in biological processes related to cell cycle regulation compared to the model group. KEGG pathway analysis revealed that PAE affected pathways related to proliferation and metabolism， including pathways in cancer and the p53 signaling pathway. GSEA further confirmed that PAE significantly enhanced the activity of the p53 signaling pathway（P<0.05）. PPI network analysis indicated that breast cancer type 1 susceptibility protein（BRCA1） and checkpoint kinase 1（CHEK1） were the core down-regulated targets in the p53 pathway. IF verified the high expression of BRCA1 and CHEK1 in LUAD-PDOs and their significant downregulation after PAE intervention（P<0.05）. Furthermore， survival analysis based on The Cancer Genome Atlas（TCGA） database indicated that low expression of BRCA1 and CHEK1 was significantly associated with prolonged overall survival in patients with LUAD（P<0.05）. ConclusionPAE effectively inhibits proliferation of LUAD-PDOs and promotes their apoptosis， its anti-tumor mechanism is potentially associated with the activation of the p53 signaling pathway， with BRCA1 and CHEK1 genes likely serving as key downstream targets for the effects of PAE.

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 analyze the causal relationship between immune cell phenotype and gastric cancer. Methods Bidirectional two-sample Mendelian randomization (MR) analysis was used to select 731 genetic variants involving immune cell phenotypes from the GWAS dataset as instrumental variables. Inverse-variance weighting method (IVW), weighted median method (WM), and MR-Egger regression were used for sensitivity analysis. Cochran Q test, MR-Egger regression, MR-PRESSO method, and remain-one method were also conducted. Results Changes in the absolute count of IgD+ B cells and CD14-CD16- cells were significantly associated with the risk of gastric cancer. A lower proportion of IgD+ B cells was associated with a lower risk of gastric cancer (OR=0.86, 95%CI: 0.79-0.94), while an increased number of CD4-CD8-T cells was associated with an increased risk of gastric cancer (OR=1.2, 95%CI: 1.1-1.3). Conclusion A causal relationship exists between immune cell phenotype and the risk of gastric cancer. Changes in specific immune markers may regulate the development of gastric cancer by affecting the tumor microenvironment.

ObjectiveBased on the conjoint analysis of transcriptome and metabolome， the key genes in the phenylpropanoid biosynthesis pathway of Lonicera macranthoides were explored， which provided a basis for further exploring the synthesis and regulation mechanism of phenylpropanoid compounds in "Xianglei" L. macranthoides. MethodsThe stem， leaves， and three flowering flowers of "Xianglei" L. macranthoides were selected as experimental materials to construct transcriptome and metabolome. The transcriptome and metabolomics were conjointly analyzed by the Kyoto Encyclopedia of Genes and Genomes （KEGG） database and weighted correlation network analysis （WGCNA）， and the key genes in the phenylpropanoid biosynthesis pathway of L. macranthoides were explored. ResultsIn this study， 77 differential phenylpropanoids and 315 differential genes were found. Through the joint analysis of transcription and metabolism， nine key differential metabolites and four key genes related to them were finally discovered. Among them， cinnamic acid， 5-O-caffeoylshikimic acid，sinapyl alcohol， and chlorogenic acid were higher in flowers， and the content of the iconic effective component， namely chlorogenic acid，decreased sharply during the withering period. Caffeic acid，ferulic acid， 5-hydroxyconiferaldehyde，p-coumaryl alcohol， and syringin were higher in leaves. These four key genes belong to the cinnamic alcohol dehydrogenase （CAD） family， 4-coumaric acid： Coenzyme A （4CL） family， hydroxycinnamyl transferase （HCT） family， and L-phenylalanine ammonlyase （PAL） family genes. ConclusionAmong the four key genes excavated from L. macranthoides， TRINITY_DN42767_c0_g6 is related to the synthesis of p-coumaryl alcohol and sinapyl alcohol. TRINITY_DN43525_c4_g1 uses caffeic acid，ferulic acid，and cinnamic acid as substrates to catalyze the next reaction. TRINITY_DN47958_c3_g4 correlates with the synthesis of 3-p-coumaroyl quinic acid and caffeoyl-CoA， and TRINITY_DN52595_c1_g2 correlates with cinnamic acid synthesis. These findings provide a basis for further exploring the synthesis and regulation mechanism of phenylpropanoids in "Xianglei" L. macranthoides.

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.

This study investigated the role of the nuclear factor of activated T cells c3 (NFATc3) in vascular smooth muscle cells (VSMCs) during aortic aneurysm and dissection (AAD) progression and the underlying molecular mechanisms. Cytoplasmic and nuclear NFATc3 levels were elevated in human and mouse AAD. VSMC-NFATc3 deletion reduced thoracic AAD (TAAD) and abdominal aortic aneurysm (AAA) progression in mice, contrary to VSMC-NFATc3 overexpression. VSMC-NFATc3 deletion reduced extracellular matrix (ECM) degradation and maintained the VSMC contractile phenotype. Nuclear NFATc3 targeted and transcriptionally upregulated matrix metalloproteinase 9 (MMP9) and MMP2, promoting ECM degradation and AAD development. NFATc3 promoted VSMC phenotypic switching by binding to eukaryotic elongation factor 2 (eEF2) and inhibiting its phosphorylation in the VSMC cytoplasm. Restoring eEF2 reversed the beneficial effects in VSMC-specific NFATc3-knockout mice. Cabamiquine-targets eEF2 and inhibits protein synthesis-inhibited AAD development and progression in VSMC-NFATc3-overexpressing mice. VSMC-NFATc3 promoted VSMC switch and ECM degradation while exacerbating AAD development, making it a novel potential therapeutic target for preventing and treating AAD.

To address the challenges of complex digital model management and difficulties in model interoperability during the development of sophisticated products,this paper proposes an intelligent cloud architecture-based method for MBSE(model-based systems engineering)model microservice aggregation and adaptive integration.By introducing ontology semantic annotation and hash encoding mechanisms,we achieve unified encapsulation and service-oriented management of heterogeneous modeling tool resources.Combined with model virtual execution modes and dynamic resource orchestration technology,we design a task-oriented of Model-Based Microservice Deployment Framework,significantly improving model service scheduling efficiency and resource utilization.Leveraging a service-cluster-based model aggregation approach,we employ Hamming distance to calculate model capability compatibility,optimizing model discovery and orchestration processes.The prototype implementation and multi-phase experimental validation conducted in a domestic private cloud environment demonstrate that the proposed method effectively supports digital development across the entire lifecycle of aerospace products,enhancing model resource sharing and system integration efficiency.

Gastrointestinal motility disorder is an important cause of digestive system diseases. Patients often suffer from nausea， vomiting， gastric retention， gastroparesis， constipation， and many other symptoms， and their quality of life is seriously reduced. Prokinetic agents are routinely used in clinical practice， but their long-term use is prone to problems such as reduced efficacy and increased adverse reactions. Since the incidence of gastrointestinal diseases has continued to rise globally in recent years， there is an urgent need for clinical development of safe and effective treatment strategies. Aurantii Fructus， a traditional Chinese medicine， has the effect of smoothing Qi and eliminating distention， and it has been used to treat gastrointestinal diseases for thousands of years. In modern clinical practice， it is mainly used for the treatment and auxiliary treatment of various gastrointestinal diseases such as functional dyspepsia， functional constipation， and irritable bowel syndrome. The efficacy is remarkable， and no adverse reactions have been reported at conventional doses. Therefore， it can greatly improve the symptoms of patients with gastrointestinal diseases and improve their quality of life. Modern research has revealed that there are many active components in Aurantii Fructus， among which flavonoids have the highest content and the most types. Flavonoids are the main active components in Aurantii Fructus to regulate gastrointestinal motility. Aurantii Fructus and its active components can affect gastrointestinal hormones， neural pathways， Cajal mesenchymal cells， and other multiple mechanisms. They can adjust gastrointestinal motility and correct gastrointestinal motility disorders， showing potential application value in the treatment of gastrointestinal motility disorders. However， a comprehensive analysis of Aurantii Fructus in this aspect is still lacking. This study summarized the pharmacological activities of active components of Aurantii Fructus extract and its flavonoids， volatile oils， alkaloids， and coumarin on the regulation of gastrointestinal motility and explored the latest research progress on its mechanism. Finally， the adverse reactions of Aurantii Fructus were summarized. It aims to provide a scientific basis for the research and clinical application of Aurantii Fructus and its active components in the regulation of gastrointestinal motility.

BackgroundChronic insomnia is characterized by a prolonged and recurrent course. The efficacy of repeated transcranial magnetic stimulation （rTMS） as a physical therapy method to improve sleep quality remains inadequately supported by evidence， particularly regarding its relationship with personality traits. ObjectiveTo explore the efficacy and influencing factors of rTMS in the treatment of chronic insomnia， and to provide insights into its therapeutic potential. MethodA total of 46 patients who met the diagnostic criteria for chronic insomnia according to the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5）， and were treated at the Third Hospital of Mianyang from September 2022 to September 2023 were selected. Prior to treatment， participants underwent assessments using the Eysenck Personality Questionnaire （EPQ）， Hamilton Depression Scale-17 item （HAMD-17） and Hamilton Anxiety Scale （HAMA）. The Pittsburgh Sleep Quality Index （PSQI） was used to assess sleep quality before treatment， at the end of the second week of treatment and one week post-treatment. ResultsAt the end of the second week of treatment， patients exhibited significantly improved total PSQI score and subscale scores related to subjective sleep quality， sleep latency， sleep duration， sleep efficiency， sleep disturbance and daytime dysfunction （t=4.755~13.361， P<0.01）， with 24 cases （54.35%） showing effective treatment outcomes. Multiple linear regression analysis showed that introverted and extroverted personality traits contributed significantly to the regression equation （B=0.317， P<0.01）， explaining 29.90% of the total variation （R²=0.299）. ConclusionrTMS treatment may effectively improve the sleep quality of patients with chronic insomnia， with its therapeutic effect appearing to associated with introverted and extroverted personality traits. ［Funded by National Natural Science Project of China （number， 82372080）］