Objective To develop a predictive model for postoperative pulmonary complications (PPC) following video-assisted thoracic surgery (VATS) in lung cancer patients by integrating cardiopulmonary exercise testing (CPET) parameters and machine learning techniques. Methods A retrospective analysis was conducted on patients with early-stage non-small cell lung cancer who underwent CPET and VATS at Guangdong Provincial People’s Hospital between October 2021 and July 2023. Patients were divided into a PPC group and a non-PPC group. The least absolute shrinkage and selection operator (LASSO) regression was used to select important features associated with PPC. Six machine learning algorithms were utilized to construct prediction models, including logistic regression, support vector machine, k-nearest neighbors, random forest, gradient boosting machine, and extreme gradient boosting. The optimal model was interpreted using SHapley Additive exPlanations (SHAP). Results A total of 325 patients were included, with an average age of 60.36 years, and 55.1% were male. Significant differences were observed between the PPC and non-PPC groups in age, diabetes, coronary heart disease, surgical approach, forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FVC% predicted, peak oxygen uptake (peak VO2), anaerobic threshold (AT), and ventilatory equivalent for carbon dioxide slope (VE/VCO2 slope) (P<0.05). In the predictive model constructed by selecting 7 key features using LASSO regression, the random forest model demonstrated the best overall performance across various metrics, with an area under the receiver operating curve of 0.930, an F1 score of 0.836, and a Brier score of 0.133 in the training set. It also exhibited good predictive ability and calibration in the test set. SHAP analysis ranked feature importance as follows: peak VO2, VE/VCO2 slope, age, FEV1, smoking history, diabetes, and surgical approach. Conclusion Integrating CPET parameters, the random forest model can effectively identify high-risk patients for PPC and has the potential for clinical application.

Objective To construct a model combining with traditional Chinese medicine(TCM)syndrome for predicting the risk of disease progression in patients with idiopathic membranous nephropathy(IMN)by machine learning methods,thus to quantitatively evaluating the value of TCM syndrome in the prediction of the risk of disease progression in IMN.Methods Monofactor analysis,recursive feature elimination(RFE)and multivariate binary Logistic regression analysis were used to screen the independent related factors affecting the risk of disease progression of IMN,and then a risk prediction model was constructed.A total of 102 patients with IMN were randomly assigned to the training set and the test set in a ratio of 65∶35,and then the comparison was conducted in the performance indicators of accuracy,sensitivity,specificity,F1 value,and area under the receiver operating characteristic(ROC)area under the curve(AUC)of the risk prediction model with or without the inclusion of the TCM syndrome information.Results Before the inclusion of TCM syndrome information,12 clinical characteristic variables for patients with MN were obtained after monofactor analysis combined with RFE screening,and they were age,hemoglobin quantification,urinary occult blood,24-hour urine protein quantification,urine protein-creatinine ratio,estimated glomerular filtration rate(eGFR),creatinine,uric acid,alanine transaminase,anti-phospholipase A2 receptor antibody(PLA2R-Ab),total cholesterol,and low-density lipoprotein cholesterd.A risk cholesterol prediction model containing the above variables was constructed.The multivariate binary Logistic regression analysis showed that the differences of the clinical variables mentioned above between the training-set group and test-set group were statistically significant,and the risk prediction model presented good sensitivity and predictability.Monofactor analysis combined with RFE screening was performed again after the inclusion of TCM syndrome information,and then 14 variables were obtained,which included blood stasis syndrome and dampness obstruction syndrome.The sensitivity and specificity of the model with the inclusion of the TCM syndrome information were significantly improved when compared with those without the inclusion of TCM syndrome information.Conclusion The results of the study initially indicate that TCM syndrome can be used as an important supplementary variable for predicting the risk of disease progression in IMN,and will provide a reference for intelligent diagnosis through the integration of traditional Chinese and western medicine information,and will supply the guidance for the treatment of IMN with TCM.

ObjectiveFor prepubertal and urgently treated malignant tumor patients, ovarian tissue cryopreservation and transplantation represent more appropriate fertility preservation methods. Current clinical practices often involve freezing ovarian tissue with high concentrations of cryoprotectants (CPAs) and thawing with water baths. These processes lead to varying degrees of toxicity and devitrification damage to ovarian tissue. Therefore, this paper proposes optimized methods for vitrification of ovarian tissues based on sodium alginate hydrogel encapsulation and magnetic induction nanowarming technology. MethodsFirstly, the study investigated the effects of sodium alginate concentration, the sequence of hydrogel encapsulation and CPAs loading on vitrification efficiency of encapsulated ovarian tissue. Additionally, the capability of sodium alginate hydrogel encapsulation to reduce the required concentration of CPAs was validated. Secondly, a platform combining water bath and magnetic induction nanowarming was established to rewarm ovarian tissue under various concentrations of magnetic nanoparticles and magnetic field strengths. The post-warming follicle survival rate, antioxidant capacity, and ovarian tissue integrity were evaluated to assess the efficacy of the method. ResultsThe study found that ovarian tissue encapsulated with 2% sodium alginate hydrogel exhibited the highest follicle survival rate after vitrification. The method of loading CPAs prior to encapsulation proved more suitable for ovarian tissue cryopreservation, effectively reducing the required concentration of CPAs by 50%. A combination of 8 g/L Fe₃O₄ nanoparticles and an alternating magnetic field of 300 Gs showed optimal warming effectiveness for ovarian tissue. Combining water bath rewarming with magnetic induction nanowarming yielded the highest follicle survival rate, enhanced antioxidant capacity, and preserved tissue morphology. ConclusionSodium alginate hydrogel encapsulation of ovarian tissue reduces the concentration of CPAs required during the freezing process. The combination of magnetic induction nanowarming with water bath provides an efficient method ovarian tissue rewarming. This study offers novel approaches to optimize ovarian tissues vitrification.

With the growing emphasis on maternal and child oral health, the significance of managing oral health across preconception, pregnancy, and infancy stages has become increasingly apparent. Oral health challenges extend beyond affecting maternal well-being, exerting profound influences on fetal and neonatal oral development as well as immune system maturation. This expert consensus paper, developed using a modified Delphi method, reviews current research and provides recommendations on maternal and child oral health management. It underscores the critical role of comprehensive oral assessments prior to conception, diligent oral health management throughout pregnancy, and meticulous oral hygiene practices during infancy. Effective strategies should be seamlessly integrated across the life course, encompassing preconception oral assessments, systematic dental care during pregnancy, and routine infant oral hygiene. Collaborative efforts among pediatric dentists, maternal and child health workers, and obstetricians are crucial to improving outcomes and fostering clinical research, contributing to evidence-based health management strategies.
Humans ; Pregnancy ; Female ; Infant ; Consensus ; Mouth Diseases/therapy* ; Pregnancy Complications/therapy* ; Oral Health ; Infant, Newborn ; Delphi Technique ; Oral Hygiene

ObjectiveTo explore the construction of a machine learning model for the diagnosis of traditional Chinese medicine （TCM） syndromes in chronic cough and the optimization of this model using the Voting ensemble algorithm. MethodsA retrospective analysis was conducted using clinical data from 921 patients with chronic cough treated at the Respiratory Department of Dongfang Hospital， Beijing University of Chinese Medicine. After standardized processing， 84 clinical features were extracted to determine TCM syndrome types. A specialized dataset for TCM syndrome diagnosis in chronic cough was formed by selecting syndrome types with more than 50 cases. The synthetic minority over-sampling technique （SMOTE） was employed to balance the dataset. Four base models， logistic regression （LR）， decision tree （dt）， multilayer perceptron （MLP）， and Bagging， were constructed and integrated using a hard voting strategy to form a Voting ensemble model. Model performance was evaluated using accuracy， recall， precision， F1-score， receiver operating characteristic （ROC） curve， area under the curve （AUC）， and confusion matrix. ResultsAmong the 921 cases， six syndrome types had over 50 cases each， phlegm-heat obstructing the lung （294 cases）， wind pathogen latent in the lung （103 cases）， cold-phlegm obstructing the lung （102 cases）， damp-heat stagnating in the lung （64 cases）， lung yang deficiency （54 cases）， and phlegm-damp obstructing the lung （53 cases）， yielding a total of 670 cases in the specialized dataset. High-frequency symptoms among these patients included cough， expectoration， odor-induced cough， throat itchiness， itch-induced cough， and cough triggered by cold wind. Among the four base models， the MLP model showed the best diagnostic performance （test accuracy： 0.9104； AUC： 0.9828）. Compared with the base models， the Voting ensemble model achieved superior performance with an accuracy of 0.9289 on the training set and 0.9253 on the test set， showing a minimal overfitting gap of 0.0036. It also achieved the highest AUC （0.9836） in the test set， outperforming all base models. The model exhi-bited especially strong diagnostic performance for damp-heat stagnating in the lung （AUC： 0.9984） and wind pathogen latent in the lung （AUC： 0.9970）. ConclusionThe Voting ensemble algorithm effectively integrates the strengths of multiple machine learning models， resulting in an optimized diagnostic model for TCM syndromes in chronic cough with high accuracy and enhanced generalization ability.

OBJECTIVE: To assess the safety and topical efficacy of prim-O-glucosylcimifugin (POG) and investigate the molecular mechanisms of its therapeutic effects in atopic dermatitis (AD). METHODS: The effects of POG on human keratinocyte cell viability and its anti-inflammatory properties were evaluated using cell counting kit-8 assay and reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Subsequently, the impact of POG on the differentiation of cluster of differentiation (CD) 4⁺ T cell subsets, including T-helper type (Th) 1, Th2, Th17, and regulatory T (Treg), was examined through in vitro experiments. Network pharmacology analysis was used to elucidate POG's therapeutic mechanisms. Furthermore, the therapeutic potential of topically applied POG was further evaluated in a calcipotriol-induced mouse model of AD. The protein and transcript levels of inflammatory markers, including cytokines, lymphocyte-specific protein tyrosine kinase (Lck) mRNA, and LCK phosphorylation (p-LCK), were quantified using immunohistochemistry, RT-qPCR, and Western blot analysis. RESULTS: POG was able to suppress cell proliferation and downregulate the transcription of interleukin 4 (Il4) and Il13 mRNA. In vitro experiments indicated that POG significantly inhibited the differentiation of Th2 cells, whereas it exerted negligible influence on the differentiation of Th1, Th17 and Treg cells. Network pharmacology identified LCK as a key therapeutic target of POG. Moreover, the topical application of POG effectively alleviated skin lesions in the calcipotriol-induced AD mouse models without causing pathological changes in the liver, kidney or spleen tissues. POG significantly reduced the levels of Il4, Il5, Il13, and thymic stromal lymphopoietin (Tslp) mRNA in the AD mice. Concurrently, POG enhanced the expression of p-LCK protein and Lck mRNA. CONCLUSION Our research revealed that POG inhibits Th2 cell differentiation by promoting p-LCK protein expression and hence effectively alleviates AD-related skin inflammation. Please cite this article as: Zhao H, Ma X, Wang H, Ding XJ, Kuai L, Song JK, Zhang Z, Yang D, Gao CJ, Li B, Zhou M. Prim-O-glucosylcimifugin mitigates atopic dermatitis by inhibiting Th2 differentiation through LCK phosphorylation modulation. J Integr Med. 2025; 23(3): 309-319.
Dermatitis, Atopic/drug therapy* ; Animals ; Humans ; Cell Differentiation/drug effects* ; Phosphorylation/drug effects* ; Mice ; Th2 Cells/drug effects* ; Keratinocytes/drug effects* ; Disease Models, Animal ; Mice, Inbred BALB C ; Calcitriol/analogs & derivatives*

Acute liver failure (ALF) is lack of broadly approved therapeutic strategy except liver transplantation. As a glycogen metabolic intermediate, UDP-glucose (UDP-G) has been considered to accelerate liver repairment. Nevertheless, the role of UDP-G and its receptor P2Y purinoceptor 14 (P2Y₁₄R) in ALF remains unknown. The present study aims to investigate the role and underlying mechanisms of UDP-G/P2Y₁₄R axis in ALF. In this study, hepatic P2Y₁₄R is significantly increased in TAA-induced and partial hepatectomy-induced ALF, while knockout of whole-body P2Y₁₄R aggravates liver failure, manifested by inhibiting β-Catenin-mediated liver regeneration. Consistently, P2Y₁₄R deficiency exhibits impaired liver regeneration in mice suffer partial hepatectomy. Importantly, only hepatocellular specific deletion of P2Y₁₄R (P2Y₁₄R ^flox/flox Alb ^cre/+ ) mice shows a similar phenomenon, rather than stellate cell specific deletion of P2Y₁₄R (P2Y₁₄R ^flox/flox Lrat ^cre/+ ) mice. Mechanistically, P2Y₁₄R induction regulates methylation of Dact-2 through CREB/DNMT3b signals in hepatocytes, subsequently inhibiting the expression of Dact-2 which is a stabilizer of β-Catenin degradation complex, leading to the activation of β-Catenin -mediated liver regeneration. Interestingly, the administration of exogenous UDP-G can accelerate liver regeneration and liver function recovery after partial hepatectomy in hepatocellular carcinoma mice. Together, the findings propose an unrecognized role of P2Y₁₄R in ALF and provide an effective adjuvant strategy for treatment of ALF.

Coronavirus-related diseases pose a significant challenge to the global health system. Given the diversity of coronaviruses and the unpredictable nature of disease outbreaks, the traditional "one bug, one drug" paradigm struggles to address the growing number of emerging crises. Therefore, there is an urgent need for therapeutic agents with broad-spectrum anti-coronavirus activity. Here, we provide evidence that ATV006, an anti-SARS-CoV-2 nucleoside analog targeting RNA-dependent RNA polymerase (RdRp), has broad antiviral activity against human and animal coronaviruses. Using mouse hepatitis virus (MHV) and human coronavirus NL63 (HCoV-NL63) as a model, we show that ATV006 has potent prophylactic and therapeutic activity against murine coronavirus infection in vivo. Remarkably, ATV006 successfully inhibits viral replication in mice even when administered 96 h after infection. Due to its oral bioavailability and potency against multiple coronaviruses, ATV006 has the potential to become a useful antiviral agent against SARS-CoV-2 and other circulating and emerging coronaviruses in humans and animals.

Chemotherapy is currently the mainstay of systemic management for triple-negative breast cancer (TNBC), but chemoresistance significantly impacts patient outcomes. Our research indicates that Doxorubicin (Dox)-resistant TNBC cells exhibit increased glycolysis and ATP generation compared to their parental cells, with this metabolic shift contributing to chemoresistance. We discovered that ALKBH3, an m¹A demethylase enzyme, is crucial in regulating the enhanced glycolysis in Dox-resistant TNBC cells. Knocking down ALKBH3 reduced ATP generation, glucose consumption, and lactate production, implicating its involvement in mediating glycolysis. Further investigation revealed that aldolase A (ALDOA), a key enzyme in glycolysis, is a downstream target of ALKBH3. ALKBH3 regulates ALDOA mRNA stability through m¹A demethylation at the 3'-untranslated region (3'UTR). This methylation negatively affects ALDOA mRNA stability by recruiting the YTHDF2/PAN2-PAN3 complex, leading to mRNA degradation. The ALKBH3/ALDOA axis promotes Dox resistance both in vitro and in vivo. Clinical analysis demonstrated that ALKBH3 and ALDOA are upregulated in breast cancer tissues, and higher expression of these proteins is associated with reduced overall survival in TNBC patients. Our study highlights the role of the ALKBH3/ALDOA axis in contributing to Dox resistance in TNBC cells through regulation of ALDOA mRNA stability and glycolysis.