The chemical constituents in dried roots of Atractylodes macrocephala were investigated in this study. Through utilizing normal-phase silica gel and ODS column chromatography, TLC, and semi-preparative HPLC, 4 new sesquiterpenoids were purified from the ethyl acetate extract of A. macrocephala. By various spectroscopic techniques, such as 1D and 2D NMR, HR-ESI-MS, IR, UV, and CD, their structures were identified as atractylmacron A (1), 9β-hydroxyasterolide (2), atractylenolide H (3), atractylenolide J (4). Compound 1 possesses a rare 6/7 bicyclic skeleton.

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

Objective: To investigate the potential protective effect of Shexiang Tongxin dropping pills (STDP) on ischemia-reperfusion injury and its underlying mechanisms in improving endothelial cell function in coronary microvascular disease (CMVD). Methods: A rat model of myocardial ischemia-reperfusion injury with CMVD was established using ligation and reperfusion of the left anterior descending artery. The effect of STDP (21.6 mg/kg) on cardiac function was evaluated using echocardiography, hematoxylin-eosin staining, and Evans blue staining. The effects of STDP on the microvascular endothelial barrier were assessed based on nitric oxide production, endothelial nitric oxide synthase expression, structural variety of tight junctions (TJs), and the expression of zonula occludens-1 (ZO-1), claudin-5, occludin, and vascular endothelial (VE)-cadherin proteins. The mechanisms of STDP (50 and 100 ng/mL) were evaluated by examining the expression of sphingosine 1-phosphate receptor 2 (S1PR2), Ras Homolog family member A (RhoA), and Rho-associated coiled-coil-containing protein kinase (ROCK) proteins and the distribution of ZO-1, VE-cadherin, and F-actin proteins in an oxygen and glucose deprivation/reoxygenation model. Results: The administration of STDP on CMVD rat model significantly improved cardiac and microvascular endothelial cell barrier functions (all P < .05). STDP enhanced the structural integrity of coronary microvascular positioning and distribution by clarifying and completing TJs and increasing the expression of ZO-1, occludin, claudin-5, and VE-cadherin in vivo (all P < .05). The S1PR2/RhoA/ROCK pathway was inhibited by STDP in vitro, leading to the regulation of endothelial cell TJs, adhesion junctions, and cytoskeletal morphology. Conclusion STDP showed protective effects on cardiac impairment and microvascular endothelial barrier injury in CMVD model rats induced by myocardial ischemia-reperfusion injury through the modulation of the S1PR2/RhoA/ROCK pathway.

OBJECTIVE: To screen anti-tumor drugs that improve antigen processing and presentation in acute myeloid leukemia (AML) cells. METHODS: A TCR-like or TCR mimic antibody that can specifically recognize HLA-A*0201:WT1_126-134 ( RMFPNAPYL) complex (hereafter referred to as HLA-A2:WT1) was synthesized to evaluate the function of antigen processing and presentation machinery (APM) in AML cells. AML cell line THP1 was incubated with increasing concentrations of IFN-γ, hypomethylating agents (HMA), immunomodulatory drugs (IMiD), proteasome inhibitors (PI) and γ-secretase inhibitors (GSI), followed by measuring of HLA-ABC, HLA-A2 and HLA-A2:WT1 levels by flow cytometry at consecutive time points. RESULTS: The TCR-like antibody we generated only binds to HLA-A*0201⁺WT1⁺ cells, indicating the specificity of the antibody. HLA-A2:WT1 level of THP-1 cells detected with the TCR-like antibody was increased significantly after co-incubation with IFN-γ, showing that the HLA-A2:WT1 TCR like antibody could evaluate the function of APM. Among the anti-tumor agents screened in this study, GSI (LY-411575) and HMA (decitabine and azacitidine) could significantly increase the HLA-A2:WT1 level. The IMiD lenalidomide and pomalidomide could aslo upregulate the expression of HLA-A2:WT1 complex under certain concentrations of the drugs and incubation time. As proteasome inhibitors, carfilzomib could significantly decreased the expression of HLA-A2:WT1, while bortezomib had no significant effect on HLA-A2:WT1 expression. CONCLUSION HLA-A2:WT1 TCR-like antibody can effectively reflect the APM function. Some of the anti-tumor drugs can affect the APM function and immunogenicity of tumor cells.
Humans ; Leukemia, Myeloid, Acute/immunology* ; Antineoplastic Agents/pharmacology* ; Antigen Presentation/drug effects* ; HLA-A2 Antigen/immunology* ; Receptors, Antigen, T-Cell/immunology* ; Cell Line, Tumor ; Interferon-gamma

OBJECTIVE: To elucidate how spinal manipulative therapy (SMT) exerts its analgesic effects through regulating brain function in lumbar disc herniation (LDH) patients by utilizing resting-state functional magnetic resonance imaging (rs-fMRI). METHODS: From September 2021 to September 2023, we enrolled LDH patients (LDH group, n=31) and age- and sex-matched healthy controls (HCs, n=28). LDH group underwent rs-fMRI at 2 distinct time points (TPs): prior to the initiation of SMT (TP1) and subsequent to the completion of the SMT sessions (TP2). SMT was administered once every other day for 30 min per session, totally 14 treatment sessions over a span of 4 weeks. HCs did not receive SMT treatment and underwent only one fMRI scan. Additionally, participants in LDH group completed clinical questionnaires on pain using the Visual Analog Scale (VAS) and the Japanese Orthopedic Association (JOA) score, whereas HCs did not undergo clinical scale assessments. The effects on the brain were jointly characterized using the amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo). Correlation analyses were conducted between specific brain regions and clinical scales. RESULTS: Following SMT treatment, pain symptoms in LDH patients were notably alleviated and accompanied by evident activation of effects in the brain. In comparison to TP1, TP2 exhibited the most significant increase in ALFF values for Temporal_Sup_R and the most notable decrease in ALFF values for Paracentral_Lobule_L (voxelwise P<0.005; clusters >30; FDR correction). Additionally, the most substantial enhancement in ReHo values was observed for the Cuneus_R, while the most prominent reduction was noted for the Olfactory_R (voxelwise P<0.005; clusters >30; FDR correction). Moreover, a comparative analysis revealed that, in contrast to HCs, LDH patients at TP1 exhibited the most significant increase in ALFF values for Temporal_Pole_Sup_L and the most notable decrease in ALFF values for Frontal_Mid_L (voxelwise P<0.005; clusters >30; FDR correction). Furthermore, the most significant enhancement in ReHo values was observed for Postcentral_L, while the most prominent reduction was identified for ParaHippocampal_L (voxelwise P<0.005; clusters >30; FDR correction). Notably, correlation analysis with clinical scales revealed a robust positive correlation between the Cuneus_R score and the rate of change in the VAS score (r=0.9333, P<0.0001). CONCLUSIONS Long-term chronic lower back pain in patients with LDH manifests significant activation of the "AUN-DMN-S1-SAN" neural circuitry. The visual network, represented by the Cuneus_R, is highly likely to be a key brain network in which the analgesic efficacy of SMT becomes effective in treating LDH patients. (Trial registration No. NCT06277739).
Humans ; Magnetic Resonance Imaging ; Intervertebral Disc Displacement/diagnostic imaging* ; Male ; Female ; Brain/diagnostic imaging* ; Adult ; Manipulation, Spinal/methods* ; Middle Aged ; Lumbar Vertebrae/physiopathology* ; Pain Management ; Rest ; Case-Control Studies

Objective:Malnutrition is prevalent among patients with chronic obstructive pulmonary disease(COPD)and closely associ-ated with adverse outcomes.This study aimed to evaluate the effectiveness of three nutritional indices in predicting all-cause mortality among COPD patients.Methods:Based on the National Health and Nutrition Examination Survey(NHANES),this study included 1640 patients with COPD surveyed from 1999 to 2018.The optimal cutoff values for controlling nutritional status(CONUT)score,geri-atric nutritional risk index(GNRI),and prognostic nutritional index(PNI)were determined using receiver operating characteristic curves.The predictive value of these nutritional indices was assessed using the area under the receiver operating characteristic curve and C-index.Their predictive abilities were compared using the net reclassification improvement and integrated discrimination improvement.A Cox regression analysis was conducted to explore the association of the three nutritional indices with all-cause mortality.Results:Log-rank tests revealed lower overall survival rates in patients with higher nutritional risks(P<0.001).In multivariate Cox regression adjusting for all covariates,CONUT score(hazard ratio[HR]=1.31,95%CI=1.03-1.67,P=0.030),GNRI(HR=2.02,95%CI=1.26-3.24,P=0.004),and PNI(HR=2.05,95%CI=1.53-2.75,P<0.001)were independently associated with all-cause mortality.Conclusion:This study confirms that the three nutritional indices are effective predictors of all-cause mortality in COPD patients.Compared with PNI,CONUT score and GNRI demonstrate im-proved predictive abilities,and they are recommended for routine screening for high-risk malnutrition in COPD patients.

Objective To investigate the diagnostic value of 4 novel inflammatory markers related to routine blood tests,namely neutrophil-to-lymphocyte ratio(NLR),red blood cell distribution width(RDW),hemoglobin-to-RDW ratio(HRR)and systemic immune-inflammation index(SII),in elderly patients with chronic cardiovascular disease(CVD)complicated with frailty.Methods Retrospectively analyze 110 patients with chronic stable CVD who were hospitalized in the cadre ward of cardiovascular medicine at the Air Force Characteristic Medical Center from January 2022 to June 2023.According to the assessment results of the Fried scale,they were divided into three groups:non-frailty group(Fried score=0,n=30),the pre-frailty group(Fried score 1 or 2,n=40)and frailty group(Fried score≥3,n=40).The differences in general information,the impairment rate of daily living activities,miniature nutritional assessment-short form(MNA-SF)scores,mini-mental state examination(MMSE)scores,and the indicators such as NLR,RDW,HRR,and SII among the three groups were compared.Spearman rank correlation was used to analyze the correlation between NLR,RDW,HRR,SII and frailty scores as well as each frailty indicator.Multivariate logistic regression analysis was performed to identify the independent risk factors for frailty in elderly patients with chronic CVD,and the receiver operating characteristic(ROC)curve was used to assess the clinical diagnostic value of NLR and HRR in elderly patients with chronic CVD complicated with frailty.Results Compared with non-frailty group and pre-frailty group,patients in frailty group were older,with higher impaired rates of daily living activities,NLR,RDW,and SII,and lower MNA-SF scores,MMSE scores,and HRR,and differences were statistically significant(P<0.05).Spearman rank correlation analysis showed that the frailty score was positively correlated with NLR(rs=0.354,P<0.001),and RDW(rs=0.448,P<0.001),negatively correlated with HRR(rs=-0.232,P=0.024),and had no significant correlation with SII(rs=0.144,P=0.167).Further analysis of the correlation between the above novel inflammatory markers and the 5 components of frailty showed that NLR was positively correlated with fatigue(rs=0.228,P=0.017),slowed walking speed(rs=0.299,P<0.001),and low physical function(rs=0.319,P<0.001);RDW was positively correlated with decreased grip strength(rs=0.321,P<0.001),slowed walking speed(rs=0.422,P<0.001),and low physical function(rs=0.246,P=0.001);and HRR was negatively correlated with slowed walking speed(rs=-0.230,P=0.025),and low physical function(rs=-0.299,P=0.003).Multivariate logistic regression analysis showed that MNA-SF score(OR=0.577,95%CI 0.342-0.973)was an independent protective factor for pre-frailty in elderly patients with chronic CVD(P<0.05);NLR(OR=7.866,95%CI 1.101-56.185)was an independent risk factor for frailty,while HRR(OR=0.344,95%CI 0.120-0.983)and MNA-SF score(OR=0.292,95%CI 0.146-0.580)were independent protective factors for frailty in elderly CVD patients(P<0.05).The area under the ROC curve of NLR and HRR for diagnosing frailty in elderly patients with chronic CVD were 0.778 and 0.749,respectively.Conclusion NLR and HRR have high clinical diagnostic value for frailty in elderly patients with chronic CVD,and are expected to become effective inflammatory markers for screening elderly patients with chronic CVD complicated with frailty.

Objective: To discover molecular markers associated with lung adenocarcinoma diagnosis/prognosis and drug resistance through screening of differentially expressed genes based on published chip data in gene expression databases using bioinformatics methods. Methods: Comprehensive analysis was performed in available mRNA microarray datasets including lung adenocarcinoma tissues dataset GSE32863 and lung adenocarcinoma taxane-platin resistance dataset GSE77209 from the gene expression omnibus(GEO) database. Gene ontology enrichment analysis, gene pathway enrichment analysis and protein interaction network analysis were performed based on significantly correlated genes. The expression level of genes was validated in the cancer genome atlas(TCGA) dataset. Survival differences were assessed by the log-rank test in TCGA lung adenocarcinoma dataset. Based on the publications genomics of drug sensitivity in cancer(GDSC) database in CellMiner cross database(CellMiner CDB), Pearson correlation analysis was used to analyze the correlation between differentially expressed genes and the half-maximal inhibitory concentration(IC50) of anticancer drugs. Results : There were a total of 77 genes which had a different expression in resistance lung adenocarcinoma cells and lung adenocarcinoma cancer tissues. The functional enrichment analysis showed that these co-different expression genes were mainly enriched in microtubule, extracellular exosome, cell cycle and signaling by nuclear receptors. Protein-protein interactions(PPI) network screened 6 most connected genes as molecular complex(MCODE). Among the MCODE, overexpressed ubiquitin conjugating enzyme E2 T(UBE2T), kinesin family member 20A(KIF20A), PCNA clamp associated factor(KIAA0101), pituitary tumor-transforming gene 1(PTTG1) and NIMA related kinase 2(NEK2) were associated with poor outcomes. Survival analysis results showed that these five genes were upregulated in lung adenocarcinoma tissues and drug-resistant cells and were significantly associated with poor prognosis in lung adenocarcinoma patients. Drug sensitivity analysis results suggested that high expression of PTTG1 and UBE2T was significantly associated with sensitivity to multiple anticancer drugs, including paclitaxel and docetaxel. RT-PCR validation showed that PTTG1 andUBE2T were highly expressed in docetaxel-resistant cells A549-TXR and H358-TXR. Conclusion PTTG1 andUBE2T holds the potential to be chemoresistance markers in lung adenocarcinoma.

Objective: To construct a prediction model for preterm birth risk among pregnant women, so as to provide the reference for screening high-risk population and preventing preterm birth. Methods: Pregnant women who received antenatal examination and delivered at the Women's Hospital, School of Medicine, Zhejiang University from January 1 to December 31, 2019 were selected as the study subjects, among them, 80% were included in the modeling group, and 20% were included in the validation group. Demographic and clinical information were collected. A multivariable logistic regression model was used to analyze the predictive factors of preterm birth risk in the modeling group, and a preterm birth risk prediction model was established based on the OR values of predictive factors. The model was validated with the data from the validation group. The Youden index was used to determine the critical score for predicting preterm birth risk. The prediction performance of the model was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 15 197 pregnant women were surveyed, including 12 131 pregnant women in the observation group and 3 066 pregnant women in the validation group. There was no statistically significant difference in age, education level and gravidity between the two groups of pregnant women (all P<0.05). Multivariable logistic regression analysis identified the number of pregnancies, education level, place of residence, hypertension, diabetes, history of preterm birth, twin-pregnancy, placenta praevia, and gestational hypertension as risk prediction factors for preterm birth risk among pregnant women. The risk score system for preterm birth was established as follows: >2 pregnancies (2 points), high school education or below (4 points), college degree or above (-4 points), rural residence (5 points), hypertension (7 points), diabetes (11 points), history of preterm birth (11 points), twin-pregnancy (28 points), placenta previa (19 points), and gestational hypertension (12 points). The total score of the preterm birth risk scoring system ranged from -4 to 99 points. When the critical score was 8 points, the Youden index was the highest at 0.480, with an area under the ROC curve of 0.749 (95%CI: 0.732-0.767), a sensitivity of 0.610, and a specificity of 0.886, indicating good prediction performance of the model. Conclusion The preterm birth risk prediction model established in this study based on demographic and clinical characteristics of pregnant women can effectively predict the risk of preterm birth among pregnant women.