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 assess the relationship between blood pressure reactivity index (BPRI) and in-hospital mortality risk in patients with sepsis-associated acute kidney injury (SA-AKI). METHODS: A retrospective cohort study was conducted to collect data from patients admitted to the intensive care unit (ICU) and clinically diagnosed with SA-AKI between 2008 and 2019 in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database in the United States. The collected data included demographic characteristics, comorbidities, vital signs, laboratory parameters, sequential organ failure assessment (SOFA) and simplified acute physiology scoreII(SAPSII) within 48 hours of SA-AKI diagnosis, stages of AKI, treatment regimens, mean BPRI during the first and second 24 hours (BPRI_0_24, BPRI_24_48), and outcome measures including primary outcome (in-hospital mortality) and secondary outcomes (ICU length of stay and total hospital length of stay). Variables with statistical significance in univariate analysis were included in LASSO regression analysis for variable selection, and the selected variables were subsequently incorporated into multivariate Logistic regression analysis to identify independent predictors associated with in-hospital mortality in SA-AKI patients. Restricted cubic spline (RCS) analysis was employed to examine whether there was a linear relationship between BPRI within 48 hours and in-hospital mortality in SA-AKI patients. Basic prediction models were constructed based on the independent predictors identified through multivariate Logistic regression analysis, and receiver operator characteristic curve (ROC curve) was plotted to evaluate the predictive performance of each basic prediction model before and after incorporating BPRI. RESULTS: A total of 3 517 SA-AKI patients admitted to the ICU were included, of whom 826 died during hospitalization and 2 691 survived. The BPRI values within 48 hours of SA-AKI diagnosis were significantly lower in the death group compared with the survival group [BPRI_0_24: 4.53 (1.81, 8.11) vs. 17.39 (5.16, 52.43); BPRI_24_48: 4.76 (2.42, 12.44) vs. 32.23 (8.85, 85.52), all P < 0.05]. LASSO regression analysis identified 20 variables with non-zero coefficients that were included in the multivariate Logistic regression analysis. The results showed that respiratory rate, temperature, pulse oxygen saturation (SpO₂), white blood cell count (WBC), hematocrit (HCT), activated partial thromboplastin time (APTT), lactate, oxygenation index, SOFA score, fluid balance (FB), BPRI_0_24, and BPRI_24_48 were all independent predictors for in-hospital mortality in SA-AKI patients (all P < 0.05). RCS analysis revealed that both BPRI showed "L"-shaped non-linear relationships with the risk of in-hospital mortality in SA-AKI patients. When BPRI_0_24 ≤ 14.47 or BPRI_24_48 ≤ 24.21, the risk of in-hospital mortality in SA-AKI increased as BPRI values decreased. Three basic prediction models were constructed based on the identified independent predictors: Model 1 (physiological indicator model) included respiratory rate, temperature, SpO₂, and oxygenation index; Model 2 (laboratory indicator model) included WBC, HCT, APTT, and lactate; Model 3 (scoring indicator model) included SOFA score and FB. ROC curve analysis showed that the predictive performance of the basic models ranked from high to low as follows: Model 3, Model 2, and Model 1, with area under the curve (AUC) values of 0.755, 0.661, and 0.655, respectively. The incorporation of BPRI indicators resulted in significant improvement in the discriminative ability of each model (all P < 0.05), with AUC values increasing to 0.832 for Model 3+BPRI, 0.805 for Model 2+BPRI, and 0.808 for Model 1+BPRI. CONCLUSIONS BPRI is an independent predictor factor for in-hospital mortality in SA-AKI patients. Incorporating BPRI into the prediction model for in-hospital mortality risk in SA-AKI can significantly improve its predictive capability.
Humans ; Acute Kidney Injury/mortality* ; Sepsis/complications* ; Retrospective Studies ; Hospital Mortality ; Prognosis ; Blood Pressure ; Intensive Care Units ; Male ; Female ; Length of Stay ; Middle Aged ; Aged ; Adult ; Logistic Models

Objective:To investigate the mosquito-borne viruses carried by mosquito specimens collected from Huachuan county and Huanan county in Heilongjiang province.Methods:Mosquito samples were collected locally in 2023 and processed in the laboratory. Homogenates of the mosquitoes were inoculated into cells for virus isolation, followed by molecular and bioinformatics analyses of the viral isolates.Results:In 2023, ten viral isolates were obtained from Anopheles sinensis specimens collected in Heilongjiang province, China. Among these isolates, one was identified as Culex flavivirus (CxFV), one as Menghai rhabdovirus (MRV), and eight as Nam Dinh virus (NDiV). The phylogenetic analysis showed that CxFV belongs to genotype I and is clustered with the strains isolated from Liaoning province in 2011 and Ningxia Hui autonomous Region in 2019 in the same evolutionary branch, with amino acid similarity ranging from 98.2% to 99.2% and nucleotide similarity ranging from 98.8% to 99.2%. The MRV strain belongs to the same evolutionary subclade as the strain detected in Guangdong, with both nucleotide and amino acid similarity of 98.0%. Eight NDiV isolates clustered with the South Korean isolates on the same evolutionary branch, forming an independent evolutionary sub-branch. The nucleotide similarity among these eight isolates ranged from 98.5% to 99.7%, while the amino acid similarity ranged from 98.1% to 99.7%. In comparison, when matched with other NDiV isolates from China, the nucleotide similarity of these eight isolates ranged from 94.1% to 97.8%, and the amino acid similarity ranged from 93.5% to 97.7%.Conclusions:This study represents the first isolation of CxFV, MRV, and NDiV in Heilongjiang province, China, and the findings provide fundamental data for the prevention and control of mosquito-borne viral diseases in this region.

Objective:To characterize the etiological and genetic features of pediatric influenza outbreaks in Changzhou between 2021 and 2024，with the goal of informing evidence-based prevention strategies and guiding effective management of influenza outbreaks in school settings.Methods:During the period of 2021 to 2024，throat swabs of influenza-like cases from school outbreaks in Changzhou were collected. These samples underwent real-time reverse transcription-polymerase chain reaction（RT-PCR）testing and virus isolation. Epidemiological data were integrated to conduct pathogenetic analysis. The HA genes of isolated strains were amplified and sequenced to perform genetic characterization.Results:Between 2021 and 2024，a total of 256 influenza outbreaks were reported in schools in Changzhou. A total of 3 201 specimens were collected，of which 2 245 were tested positive for influenza viruses，resulting in a positivity rate of 70.13%. The outbreak season was primarily concentrated from December to February each year，with settings predominantly distributed in primary schools（accounting for 73.83%）. The predominant epidemic strains were influenza A viruses，including 118 outbreaks caused by H1N1 and 104 by H3N2. A total of 74 influenza virus strains were successfully isolated from positive specimens，and sequencing of the hemagglutinin（HA）gene was completed. Phylogenetic analysis revealed that certain B/Victoria lineage strains（e.g.，B/Changzhou/01/2021）clustered closely with the vaccine strain B/Austria/3594/17（bootstrap support：99%）. Among influenza H1N1 strains，multiple isolates from 2023—2024 clustered within the same major branch as A/Victoria/4897/2022（bootstrap support：100%）. In contrast，the H3N2 strains exhibited a complex evolutionary pattern，showing variable genetic distances to vaccine strains from different years（e.g.，A/Massachusetts/18/2022，A/Darwin/6/2021）；some isolates were closely related to vaccine strains，while others were more distantly related and scattered across the phylogenetic tree.Conclusions:The influenza outbreak situation in schools was severe and has significant public health implications. Continuous surveillance is essential，and preventive strategies should be promptly adjusted based on the epidemiological and genetic characteristics of circulating strains.

Objective To investigate the anti-inflammatory and antioxidant effects of berberine for sepsis rats based on kelch-like ECH-associated protein 1(Keap1)/nuclear factor erythroid 2-relat-ed factor 2(Nrf2)/antioxidant response element(ARE)signaling pathway.Methods Adult health-y rats were randomly divided into normal group(20 rats)and sepsis group(80 rats).The sepsis model in rats of the sepsis group was established by cecal ligation and puncture.According to differ-ent doses of berberine administered by gavage,the sepsis group was further divided into model group(0 mg/kg,without berberine administration),low-dose group(25 mg/kg),medium-dose group(50 mg/kg)and high-dose group(100 mg/kg),with 20 rats in each group.Both the model group and the normal group were given an equal volume of pure water by gavage.Ten rats were randomly selected from each group for a survival test,and they were continuously observed for 5 days to com-pare the survival rates among the groups.Lung,kidney,and liver tissues of rats in each group were collected for hematoxylin-eosin(HE)staining,enzyme-linked immunosorbent assay(ELISA),Western blot,and real-time fluorescence quantitative polymerase chain reaction(qRT-PCR)detection.The pathological tissue changes,serum inflammatory cytokine levels,serum biochemical indicator levels,and the relative expression levels of pathway-related proteins and their mRNAs in each group were observed and compared.Results The survival rates of rats in the medium-dose and high-dose groupsat various time points were all higher than those in the model group,and the differences were statistically significant(P＜0.05).Compared with the normal group,the model group showed in-creased levels of serum alanine aminotransferase(ALT),aspartate aminotransferase(AST),serum creatinine(SCr),blood urea nitrogen(BUN),interleukin-6(IL-6),interleukin-1 β(IL-β)and tumor necrosis factor-α(TNF-α).There were obvious pathological injuries in lung,liver,and kid-ney tissues.The relative expression levels of Nrf2 and heme oxygenase-1(HO-1)proteins and their mRNAs in lung tissues were decreased,while the relative expression levels of Keap1 mRNA and Keap1 protein were increased,and the differences were statistically significant(P＜0.05).Com-pared with the model group,the serum SCr and BUN levels in the medium-dose group,and the ser-um ALT,AST,SCr and BUN levels in the high-dose group were all decreased,with statistically sig-nificant differences(P＜0.05).Compared with the model group,the pathological injuries in lung,liver,and kidney tissues of rats in the low-dose,medium-dose,and high-dose groups were all alle-viated.The relative expression levels of Nrf2 mRNA and HO-1 mRNA as well as Nrf2 and HO-1 proteins in the lung tissues showed a dose-dependent increase,while the relative expression levels of Keap1 mRNA and Keap1 protein exhibited a dose-dependent decrease.The serum levels of IL-6 and TNF-α in the low-dose group,as well as the serum levels of IL-6,IL-1 β and TNF-α in the medium-dose and high-dose groups were all reduced(P＜0.05).Conclusion Berberine can alleviate the inflammatory response and activate the antioxidant response in sepsis rats by regulating the Keap1/Nrf2/ARE signaling pathway.

The discipline of allied health sciences provides vital support to clinical diagnosis and treatment while promoting the translation of medical research into practice.As the integration of medicine and engineering deepens,talent cultivation in allied health faces new opportunities and challenges.Herein,we reviewed representative cases from domestic and international universities that have implemented interdisciplinary training in fields related to allied health,including exploration and practices concerning the development of cross-disciplinary platform,dual-degree programs,and collaborative education mechanisms involving the academia,industry,and research.We highlight the efforts of the School of Allied Health,West China School of Medicine,Sichuan University,which has continuously improved its disciplinary system and training programs through top-level planning and platform construction.The School of Allied Health Sciences has accelerated platform construction and established a new model for multidisciplinary collaborative education.At the practical level,the school has promoted curriculum reform through initiatives in ideological and political theories education,instructional system design,and the development of textbook systems.The school has created courses focused on the integration of medicine and engineering,interdisciplinary project-based modules,and specialized project-based curricula.These initiatives aim to strengthen the foundation for training high-level interdisciplinary professionals in allied health sciences under the evolving landscape of medicine-engineering integration.This articile also highlights other prospects,such as establishing a collaborative education model integrating medicine and engineering through industry-university-research collaboration,optimizing academic program structures,and enhancing professional certification systems,to inform policy-making and optimize practice.

Objective:To explore the correlation between serum uric acid/high-density lipoprotein cholesterol ratio (UHR) and the risk of hypertension in elderly physical examination populations.Methods:This study was a cross-sectional study. A total of 1 028 patients aged≥60 years who underwent physical examinations at the Health Management Center of Fuwai Central China Cardiovascular Hospital from September 2023 to February 2024 were included in this study. The general demographic data, past medical history, physical examination and laboratory examination indicators of the physical examiners were collected, and according to whether they had hypertension or not, they were divided into hypertension group (390 cases) and non-hypertension group (638 cases), and all UHR values were arranged from small to large, and the UHR was divided into three groups by tertiles of UHR, and the general data and blood biochemical indexes between the groups were compared. Spearman rank correlation was used to analyze the correlation between UHR level and body mass index, total cholesterol, triglyceride and other indexes in the elderly population. Logistic regression was used to explore the relationship between UHR level and hypertension in the elderly population, and the stratification analysis of the physical examination population was carried out according to diabetes, coronary heart disease and dyslipidemia, and the interaction test between groups was carried out.Results:Among the 1 028 geriatric physical examination cases, 580 (56.4%) were males and 448 (43.6%) were females, aged (66.7±5.8) years. UHR levels were higher in the hypertensive group compared to the non-hypertensive group [248.88 (191.19, 322.25) vs 213.52 (165.94, 275.29); Z=-5.445, P<0.05]. With the increase of UHR level, the detection rate of hypertension in the elderly population increased (accounted for 27.8%, 38.2% and 47.8%, respectively; χ2=29.211, P<0.05). Spearman rank correlation analysis showed that UHR was positively correlated with body mass index, triglycerides, serum uric acid, serum creatinine and fasting blood glucose ( r=0.318, 0.334, 0.774, 0.474, 0.080; all P<0.05), and negatively correlated with total cholesterol, glomerular filtration rate and low-density lipoprotein cholesterol ( r=-0.239, -0.303, -0.154; all P<0.05). When the confounding factors were not adjusted (model 1), the risk of hypertension in high UHR group was 2.382 times higher than that in low UHR group and 1.607 times higher than that in medium UHR group; after adjusting for all confounding factors such as age, gender, body mass index, systolic blood pressure, diastolic blood pressure, junior high school education or below, smoking, alcohol consumption, glomerular filtration rate, etc., the risk of hypertension in the high-level UHR group was 1.732 times higher than that in the low-level UHR group (95% CI: 1.139-2.635) ( P<0.05). The elderly physical examination population was further stratified according to whether there was diabetes, dyslipidemia and coronary heart disease, and it was found that there was no interaction between UHR and diabetes, dyslipidemia and coronary heart disease on the prevalence of hypertension (all P>0.05). Conclusions:Hypertension detection rate increases with higher UHR levels. UHR is closely related to the incidence of hypertension in the elderly population.

Objective To construct large medical model named by "Huaxi HongYi"and explore its application effectiveness in assisting medical record generation. Methods By the way of a full-chain medical large model construction paradigm of "data annotation - model training - scenario incubation", through strategies such as multimodal data fusion, domain adaptation training, and localization of hardware adaptation, "Huaxi HongYi" with 72 billion parameters was constructed. Combined with technologies such as speech recognition, knowledge graphs, and reinforcement learning, an application system for assisting in the generation of medical records was developed. Results Taking the assisted generation of discharge records as an example, in the pilot department, after using the application system, the average completion times of writing a medical records shortened (21 min vs. 5 min) with efficiency increased by 3.2 time, the accuracy rate of the model output reached 92.4%. Conclusion It is feasible for medical institutions to build independently controllable medical large models and incubate various applications based on these models, providing a reference pathway for artificial intelligence development in similar institutions.

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.