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.

Background Per- and polyfluoroalkyl substances (PFASs) are a class of persistent organic pollutants that pose potential health risks to humans. Infants and young children have higher requirements for food safety due to the underdeveloped detoxification and immune systems. Therefore, developing a comprehensive method for determination of PFASs and their novel alternatives in infant complementary food is of great significance. Objective To develop an analytical method using liquid chromatography high-resolution mass spectrometry technology for determination of 55 PFASs in plant- and animal-derived infant complementary fruit purees. Methods Oasis WAX (200 mg, 6 CC) solid-phase extraction columns were used for sample enrichment and purification. The pH of the acetonitrile extract was adjusted using 0%, 1%, 1.5%, and 2% formic acid aqueous solutions to evaluate its impact on the recovery rate of target compounds. Additionally, the impact of a 2 mL methanol wash during the purification process on the recovery of target compounds was assessed to determine the optimal pretreatment conditions. Three types of chromatographic columns—Agilent Poroshell 120 EC-C₁₈, Thermo InfinityLab Poroshell 120 Aq-C₁₈, Acquity Waters BEH-C₁₈, and changes in mobile phase, were compared for their effects on retention time, peak shape, and response of target compounds. The method was validated in terms of selectivity, linear range, detection limit, and precision. The established method was applied to 49 commercial samples of infant complementary fruit purees. Results Adjusting the sample pH using 1.5% formic acid water and incorporating a 2 mL methanol wash during purification achieved satisfactory recovery rates. The target compounds were chromatographically separated using an Agilent Poroshell 120 EC-C₁₈ column with a gradient elution system. The mobile phase consisted of methanol-water (methanol/water: 2/98, v/v) containing 5 mmol·L⁻¹ ammonium formate as mobile phase A, and methanol as mobile phase B. Good separation was achieved within 15 min, resulting in optimal chromatographic peak shapes. The 55 target compounds exhibited good linearity across the standard curve range, with correlation coefficients (R²) greater than 0.99. The method detection limits ranged from 0.02 to 0.05 µg·L⁻¹. In the plant- and animal-based fruit puree samples, the spiked recovery rates ranged from 60% to 112% and 57% to 119%, respectively, with relative standard deviations (RSD) ≤ 30%. A total of 9 traditional PFASs and 5 novel PFASs were positive in 49 samples of infant complementary fruit purees. Conclusion This method enables comprehensive detection of 55 traditional and emerging PFASs, offering wide coverage, high accuracy, and excellent sensitivity. It provides technical support for characterizing contamination by traditional and emerging PFASs in food matrices.

Objective:To evaluate the survival benefit of surgical treatment for intrahepatic cholangiocarcinoma.Methods:This study is conducted based on the hepatobiliary tumor registry database. From May 2009 to December 2022,a total of 704 patients who were initially diagnosed with intrahepatic cholangiocarcinoma and underwent liver resection were consecutively enrolled at the Hepatobiliary Center of the First Affiliated Hospital of Nanjing Medical University. Among them,there were 380 males and 324 females,aged ( M(IQR)) 61(15) years(range:27 to 88 years). Twenty-six (3.7%) patients received neoadjuvant therapy before surgery. The overall survival(OS) and disease-free survival(DFS) rates were estimated by life table method, and Kaplan-Meier survival curves were plotted. Log-rank test was used to compare the survival difference among tumor-node-metastasis(TNM) staging or three periods. The OS and DFS differences among lymph node groups or adjuvant treatment groups were quantified as HR with 95% CI estimated using Cox proportional-hazards model with adjustment for prognostic factors. Results:Among the 704 patients,349 cases(49.6%) underwent major hepatectomy (≥3 segments),331(47.0%) had lymph node resection during surgery,and 524 cases(74.4%) achieved R0 resection. The morbidity of Clavien-Dindo grade Ⅲ or higher complications was 16.5%(116/704),with a mortality rate of 3.0%(21/704) within 30 days post-surgery. The median OS time was 27.1 months, and the OS rates at 1-,3-,5- and 10-year were 69.1%, 42.4%,34.1% and 24.5%,respectively. The median DFS time was 10.5 months,and the corresponding DFS rates were 46.0%,25.4%,21.9% and 16.9%,respectively. According to the 8 th edition of AJCC staging system, the 5-year survival rates for ⅠA,ⅠB,Ⅱ,ⅢA,ⅢB and Ⅳ were 68.4%, 43.2%, 30.3%,32.2%,14.0% and 0,respectively. The corresponding DFS rates were 55.8%, 28.1%,13.8%,21.2%,3.3% and 0,respectively. There were no statistically significant differences of OS or DFS between stage ⅠB and Ⅱ, stage ⅠB and ⅢA, or between stage Ⅱ and ⅢA(Log-rank test:all P>0.05),while there were significant differences of OS and DFS among other stages(Log-rank test:all P<0.05). Using Cox model with adjustment for prognostic factors, there were no statistically significant differences of OS and DFS between non-lymphadenectomy group or the biopsy-N0 group and dissection-N0 group(both P>0.05). However,the overall and disease-free survival of the biopsy-N1 group or dissection-N1 group were worse than those of dissection-N0 group(both P<0.05),with overall survival being better in dissection-N1 group than biopsy-N1 group( P=0.017). Overall survival in the period from 2019 to 2022 were significantly superior to that during the periods from 2009 to 2013 and 2014 to 2018(both P<0.01). Adjusting for prognostic factors, the disease-free and overall survival of the postoperative adjuvant therapy group were significantly better than those of the observation group in the period 2019 to 2022(both P<0.01). Conclusions:Surgery remains a milestone for achieving long-term survival for patients with intrahepatic cholangiocarcinoma. Regional lymph node dissection is required for patients with lymph node metastasis. Adjuvant therapy can significantly reduce tumor recurrence and prolong overall survival.

Objective:To investigate the influence of 17β-estradiol(17β-E2)in the proliferation and differentiation capabilities of primary cultured hippocampal neural stem cells(NSCs),and to clarify its potential mechanism.Methods:The NSCs were isolated from the hippocampal tissue of SD rats within 24 h of birth,and divided into control group and 17β-E2 group.Immunofluorescence method was used to detect the expressions of Nestin and 5-ethynyl-2'-deoxyuridine(EdU)in NSCs in two groups,and the proliferation rates of NSCs were calculated.Western blotting method was used to detect the expression level of Nestin protein.Flow cytometry was used to analyze the percentages of NSCs at different cell cycles.Immunofluorescence method was used to identify the expressions of markers for neurons β Ⅲ-tubulin and astrocyte marker glial fibrillary acidic protein(GFAP)in the NSCs after differentiation,and the relative ratio of neurons to astrocytes was calculated.Western blotting method was used to detect the expression levels of β Ⅲ-tubulin and GFAP proteins as well as phosphatidylinositol 3-kinase(PI3K),protein kinase B(Akt),phosphorylated Akt(p-Akt),glycogen synthase kinase-3 beta(GSK-3β),phosphorylated GSK-3β(p-GSK-3β),and beta-catenin(β-catenin)proteins related to PI3K/Akt/GSK-3β pathway.Results:Immunofluorescence assay revealed positive Nestin expression in NSCs in two groups;compared with control group,the proliferation rate of NSCs in 17β-E2 group was increased(P＜0.01).Compared with control group,the expression level of Nestin protein in the NSCs in 17β-E2 group was increased(P＜0.05),and the percentage of NSCs in S phase was increased(P＜0.01).Compared with control group,the relative ratio of neurons to astrocytes in 17β-E2 group was significantly increased(P＜0.05).After differentiation,compared with control group,the expression level of β Ⅲ-tubulin protein in the NSCs in 17β-E2 group was increased(P＜0.05),and the expression level of GFAP protein was decreased(P＜0.01),while the expression levels of Akt,p-Akt,β-catenin,and p-GSK-3β proteins were increased(P＜0.05 or P＜0.01),and the expression level of GSK-3β protein was decreased(P＜0.05).Conclusion:17β-E2 can promote the proliferation of NSCs and facilitate their differentiation towards neurons,and its mechanism may be related to the PI3K/Akt/GSK-3β signaling pathway.

With the rapid development of artificial intelligence and machine learning algorithms in the med-ical field,as well as the high-quality development requirements put forward by the state for hospitals and vari-ous departments,the development of laboratory medicine shows a new trend.The development of clinical labo-ratory has evolved from the initial test suite to the test pathway based on clinical pathway and diagnosis-relat-ed groups payment,and then to clinical laboratory omics.Clinical laboratory omics refers to the use of high-throughput methods to obtain a large number of laboratory project results data.Combined with the clinical characteristics of patients,statistical methods and machine learning algorithms are commonly used to reveal the information behind a large number of medical data to assist clinicians in diagnosis and treatment.Clinical laboratory omics may be a new trend in the future development of laboratory medicine,and it is likely to play an important role in the field of laboratory medicine.

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

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Background: s/Aims: Non-invasive models stratifying clinically significant portal hypertension (CSPH) are limited. Herein, we developed a new non-invasive model for predicting CSPH in patients with compensated cirrhosis and investigated whether carvedilol can prevent hepatic decompensation in patients with high-risk CSPH stratified using the new model. Methods: Non-invasive risk factors of CSPH were identified via systematic review and meta-analysis of studies involving patients with hepatic venous pressure gradient (HVPG). A new non-invasive model was validated for various performance aspects in three cohorts, i.e., a multicenter HVPG cohort, a follow-up cohort, and a carvediloltreating cohort. Results: In the meta-analysis with six studies (n=819), liver stiffness measurement and platelet count were identified as independent risk factors for CSPH and were used to develop the new “CSPH risk” model. In the HVPG cohort (n=151), the new model accurately predicted CSPH with cutoff values of 0 and –0.68 for ruling in and out CSPH, respectively. In the follow-up cohort (n=1,102), the cumulative incidences of decompensation events significantly differed using the cutoff values of <–0.68 (low-risk), –0.68 to 0 (medium-risk), and >0 (high-risk). In the carvediloltreated cohort, patients with high-risk CSPH treated with carvedilol (n=81) had lower rates of decompensation events than non-selective beta-blockers untreated patients with high-risk CSPH (n=613 before propensity score matching [PSM], n=162 after PSM). Conclusions Treatment with carvedilol significantly reduces the risk of hepatic decompensation in patients with high-risk CSPH stratified by the new model.

Coronavirus disease 2019 (COVID-19) is an acute infectious respiratory disease that has been prevalent since December 2019. Chinese medicine (CM) has demonstrated its unique advantages in the fight against COVID-19 in the areas of disease prevention, improvement of clinical symptoms, and control of disease progression. This review summarized the relevant material components of CM in the treatment of COVID-19 by searching the relevant literature and reports on CM in the treatment of COVID-19 and combining with the physiological and pathological characteristics of the novel coronavirus. On the basis of sorting out experimental methods in vivo and in vitro, the mechanism of herb action was further clarified in terms of inhibiting virus invasion and replication and improving related complications. The aim of the article is to explore the strengths and characteristics of CM in the treatment of COVID-19, and to provide a basis for the research and scientific, standardized treatment of COVID-19 with CM.
Humans ; Drugs, Chinese Herbal/pharmacology* ; COVID-19 Drug Treatment ; SARS-CoV-2/drug effects* ; COVID-19/therapy* ; Medicine, Chinese Traditional/methods* ; Antiviral Agents/pharmacology* ; Animals