This paper aimed to explore the effects of Duzhong Decoction(DZD)-containing serum on the proliferation and osteoblast differentiation of MC3T3-E1 cells through L-type voltage-gated calcium channels(L-VGCCs). L-VGCCs inhibitors, nifedipine and verapamil, were used to block L-VGCCs in osteoblasts. MC3T3-E1 cells were divided into a control group, a low-dose DZD-containing serum(L-DZD) group, a medium-dose DZD-containing serum(M-DZD) group, a high-dose DZD-containing serum(H-DZD) group, a nifedipine group, a H-DZD + nifedipine group, verapamil group, and a H-DZD + verapamil group. The CCK-8 method was used for cell proliferation analysis, alkaline phosphatase(ALP) assay kits for intracellular ALP activity measurement, Western blot for protein expression level in cells, real-time fluorescence quantitative PCR technology for intracellular mRNA expression level determination, fluorescence spectrophotometer for free Ca~(2+) concentration determination in osteoblasts, and alizarin red staining(ARS) for mineralized nodule formation in osteoblasts. The experimental results show that compared to the control group, DZD groups can promote MC3T3-E1 cell proliferation, ALP activity, and mineralized nodule formation, increase intracellular Ca~(2+) concentrations, and upregulate the protein expression of bone morphogenetic protein 2(BMP2), collagen Ⅰ(COL1), α2 subunit protein of L-VGCCs(L-VGCCα2), and the mRNA expression of Runt-related transcription factor 2(RUNX2), and BMP2. After blocking L-VGCCs with nifedipine and verapamil, the intervention effects of DZD-containing serum were inhibited to varying degrees. Both nifedipine and verapamil could inhibit ALP activity, reduce mineralized nodule areas, and downregulate the expression of bone formation-related proteins. Moreover, the effects of DZD-containing serum on increasing MC3T3-E1 cell proliferation, osteoblast differentiation, and Ca~(2+) concentrations, upregulating the mRNA expression of osteoprotegerin(OPG) and protein expression of phosphorylated protein kinase B(p-Akt) and phosphorylated forkhead box protein O1(p-FOXO1), and upregulating phosphatase and tensin homolog(PTEN) expression were reversed by nifedipine. The results indicate that DZD-containing serum can increase the Ca~(2+) concentration in MC3T3-E1 cells to promote bone formation, which may be mediated by L-VGCCs and the PTEN/Akt/FoxO1 signaling pathway, providing a new perspective on the mechanism of DZD in treating osteoporosis.
Animals ; Osteoblasts/metabolism* ; Cell Proliferation/drug effects* ; Cell Differentiation/drug effects* ; Mice ; Drugs, Chinese Herbal/pharmacology* ; Calcium Channels, L-Type/genetics* ; Alkaline Phosphatase/genetics* ; Serum/chemistry* ; Cell Line ; Osteogenesis/drug effects* ; Bone Morphogenetic Protein 2/genetics*

Objective To investigate the efficacy of an interpretable integrated learning model combining clinical indicators,CT image features and radiomics features for the differential diagnosis of lung squamous cell carcinoma and adenocarcinoma,so as to provide references for clincal treatment decisions.Methods A retrospective analysis was conducted on clinical and imaging data from 220 patients(231 lesions)with primary non-small cell lung cancer at Jiangjin Central Hospital of Chongqing(Center 1)and 83 patients(84 lesions)at Chongqing General Hospital(Center 2).In Center 1,the squamous cell carcinoma group consisted of 60 patients(60 lesions),while the adenocarcinoma group included 160 patients(171 lesions).In Center 2,the squamous cell carcinoma group comprised 18 patients(18 lesions),and the adenocarcinoma group involved 65 patients(66 lesions).The patients were categorized into squamous cell carcinoma and adenocarcinoma groups based on pathological findings.Center 1 was randomly partitioned into a training set and a validation set at a 7∶3 ratio,while Center 2 served as the independent test set.Firstly,a deep learning model,VB-Net,was used to automatically segment the tumor region on the lung window image;secondly,the SMOTE(synthetic minority oversampling technique)method was used to balance the categories in the training set and standardize the extracted features with Z-scores;thirdly,the least absolute shrinkage and selection operator(LASSO)were used to select the optimal radiomics features and calculate the radiomics score(Radscore),and univariate and multivariate logistic regression was used to screen clinical indicators and independent clinical factors for differentiating lung squamous cell carcinoma and adenocarcinoma in CT image features;finally,three ensemble learning algorithms(AdaBoost,Bagging decision tree and XGBoost)were used to combine independent clinical factors and Radscore to construct the model.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic performance of the models.SHAP technique was used to analyze the feature contribution and model decision-making process.Results Among the evaluated ensemble models,AdaBoost and Bagging decision trees demonstrated overfitting tendencies.In contrast,the XGBoost model showed the best performance,achieving AUC values of 0.939,0.887 and 0.853 in the training,validation and independent test sets,respectively.SHAP indicated that Radscore was the most important feature affecting the performance of the model.The decision diagram enabled the visualization of the diagnostic process of the model.Conclusion The interpretable integrated learning model based on clinical indicators,CT image and radiomics features is expected to non-invasively diagnose lung squamous cell carcinoma and adenocarcinoma before treatment and assist clinicians make treatment decisions as early as possible.[Chinese Medical Equipment Journal,2025,46(7):12-20]

Objective To investigate the efficacy of an interpretable integrated learning model combining clinical indicators,CT image features and radiomics features for the differential diagnosis of lung squamous cell carcinoma and adenocarcinoma,so as to provide references for clincal treatment decisions.Methods A retrospective analysis was conducted on clinical and imaging data from 220 patients(231 lesions)with primary non-small cell lung cancer at Jiangjin Central Hospital of Chongqing(Center 1)and 83 patients(84 lesions)at Chongqing General Hospital(Center 2).In Center 1,the squamous cell carcinoma group consisted of 60 patients(60 lesions),while the adenocarcinoma group included 160 patients(171 lesions).In Center 2,the squamous cell carcinoma group comprised 18 patients(18 lesions),and the adenocarcinoma group involved 65 patients(66 lesions).The patients were categorized into squamous cell carcinoma and adenocarcinoma groups based on pathological findings.Center 1 was randomly partitioned into a training set and a validation set at a 7∶3 ratio,while Center 2 served as the independent test set.Firstly,a deep learning model,VB-Net,was used to automatically segment the tumor region on the lung window image;secondly,the SMOTE(synthetic minority oversampling technique)method was used to balance the categories in the training set and standardize the extracted features with Z-scores;thirdly,the least absolute shrinkage and selection operator(LASSO)were used to select the optimal radiomics features and calculate the radiomics score(Radscore),and univariate and multivariate logistic regression was used to screen clinical indicators and independent clinical factors for differentiating lung squamous cell carcinoma and adenocarcinoma in CT image features;finally,three ensemble learning algorithms(AdaBoost,Bagging decision tree and XGBoost)were used to combine independent clinical factors and Radscore to construct the model.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic performance of the models.SHAP technique was used to analyze the feature contribution and model decision-making process.Results Among the evaluated ensemble models,AdaBoost and Bagging decision trees demonstrated overfitting tendencies.In contrast,the XGBoost model showed the best performance,achieving AUC values of 0.939,0.887 and 0.853 in the training,validation and independent test sets,respectively.SHAP indicated that Radscore was the most important feature affecting the performance of the model.The decision diagram enabled the visualization of the diagnostic process of the model.Conclusion The interpretable integrated learning model based on clinical indicators,CT image and radiomics features is expected to non-invasively diagnose lung squamous cell carcinoma and adenocarcinoma before treatment and assist clinicians make treatment decisions as early as possible.[Chinese Medical Equipment Journal,2025,46(7):12-20]

Objective To investigate the predictive value of new simplified insulin resistance(IR)assessment indexes in identifying subclinical left ventricular systolic function impairment in patients with type 2 diabetes mellitus(T2DM).Methods A total of 150 T2DM patients with preserved left ventricular ejection fraction(LVEF≥50%)who were admitted to Department of Endocrinology of the First Affiliated Hospital of Air Force Medical University from June 2021 to December 2021 were retrospectively analyzed.All patients underwent two-dimensional speckle tracking echocardiography to measure left ventricular global longitudinal strain(GLS).According to GLS value,the subjects were divided into the normal group(GLS≥18%group,n=80)and the impaired group(GLS<18%group,n=70).Some new simplified IR assessment indicators were calculated and compared between the two groups,including body mass index(BMI),TG/HDL-C ratio,triglyceride-glucose(TyG)index,TyG-BMI index,TyG-WHR and metabolic score for IR(METS-IR).Correlation between the GLS and the new simplified IR assessment indexes was analyzed.The receiver operating characteristic(ROC)curve was used to analyze the diagnostic efficacy of different simplified IR assessment indexes,with the area under the curve(AUC)calculated.Furthermore,according to whether the subjects were complicated with hypertension,binary logistics regression analysis was performed to explore the independent correlation between the simplified IR assessment index and GLS<18%.Results Total 150 were included with aged(54.5±13.7)years with 96(64.0%)men and 54(36.0%)women.Compared with the GLS≥18%group,the TG/HDL-C ratio,TyG index,TyG-BMI,and METS-IR of subjects in the GLS<18%group were significantly increased(P<0.05).Pearson correlation analysis showed that TG/HDL-C ratio,TyG index,TyG-BMI,TyG-WHR,and METS-IR were negatively correlated with GLS(P<0.05).ROC analysis showed that TyG index had a certain predictive value for the evaluation of GLS<18%(AUC=0.678,95%CI 0.591-0.765,P<0.001).Stratification based on hypertension and further adjusting for confounding factors,TyG index remains significantly associated with GLS<18%(OR=3.249,95%CI 1.045-10.103,P=0.042).Conclusions The novel simplified insulin resistance evaluation indexes are closely associated with left ventricular subclinical systolic dysfunction in T2DM patients with preserved ejection fraction.TyG index is an effective index to identify left ventricular subclinical dysfunction in these populations.

Melatonin (Mel) has been shown to have cardioprotective effects, but its action on ion channels is unclear. In this experiment, we investigated the inhibitory effect of Mel on late sodium currents (I_Na.L) in mouse ventricular myocytes and the anti-arrhythmic effect at the organ level as well as its mechanism. The whole-cell patch clamp technique was applied to record the ionic currents and action potential (AP) in mouse ventricular myocytes while the electrocardiogram (ECG) and monophasic action potential (MAP) were recorded simultaneously in mouse hearts using a multichannel acquisition and analysis system. The results demonstrated that the half maximal inhibitory concentration (IC₅₀) values of Mel on transient sodium current (I_Na.T) and specific I_Na.L opener 2 nmol·L^-1 sea anemone toxins II (ATX II) increased I_Na.L were 686.615 and 7.37 μmol·L^-1, respectively. Mel did not affect L-type calcium current (I_Ca.L), transient outward current (I_to), and AP. In addition, 16 μmol·L^-1 Mel shortened ATX II-prolonged action potential duration (APD), suppressed ATX II-induced early afterdepolarizations (EADs), and significantly reduced the incidence of ventricular tachycardia (VT) and ventricular fibrillation (VF) in Langendorff-perfused mouse hearts. In conclusion, Mel exerted its antiarrhythmic effects principally by blocking I_Na.L, thus providing a significant theoretical basis for new clinical applications of Mel. Animal welfare and experimental process are in accordance with the regulations of the Experimental Animal Ethics Committee of Wuhan University of Science and Technology (2023130).

Objective To explore the effect of a R language-based autoregressive integrated moving average(ARIMA)model for predicting the consumption of medical consumables.Methods The monthly consumption data of a certain type of pre-filled flush syringe from July 2018 to June 2023 was selected as the sample data,which underwent smoothness test and difference operation with R language.An ARIMA model was established and the optimal model was determined according to the Akaike and Bayesian information criteria.The corresponding data of the third quarter of 2023 was used as the validation set to predict the consumption,and the prediction result was compared with the actual values to evaluate the prediction effect of the ARIMA model.Results The ARIMA model with the best fitting was ARIMA(0,1,1)(1,0,0)12,all the predicted data were within 95％confidence interval,and its mean absolute percentage error MAPE was 9.92％.P-value proved to be higher than 0.05 when the residual series were tested using the Ljung-Box statistics,which meant the prediction result was satisfactory.Conclusion The R language-based ARIMA model behaves well in predicting the consumption of medical consumables,and provides references for demand planning,budgeting,purchasing and management of medical consumables.[Chinese Medical Equipment Journal,2024,45(10):84-87]

Objective:? To develop a portable and easy-to-operate Cystatin C (CYS-C) transmission turbidimeter based on smartphones.Methods:? Leveraging the sensitivity of smartphone ambient light sensors to optical signals and the principle of immunoturbidimetry, a smartphone-based turbidimeter was designed and fabricated for point-of-care testing (POCT) of CYS-C in serum. The main structure of the smartphone turbidimeter was created using 3D printing technology and assembled according to the designed circuits and optical paths. At the same time, the stability of the light source and the feasibility of the device were verified. Finally, the sensitivity, specificity, and concordance rate of the smartphone turbidimeter were assessed by comparing its results with those from 127 serum samples of automated biochemical analyzer used in clinic.Results:? During device performance testing, the smartphone turbidimeter exhibited a coefficient of variation ( CV) of 0.9% for light intensity over a continuous 60-minute operation. Additionally, the CV values were below 5% in batch repeat tests of CYS-C samples. The detection results of CYS-C in 127 serum samples showed a sensitivity of 84.4%, specificity of 95.8%, and concordance rate of 92.9%, with a correlation coefficient of r=0.902 9 compared to the automatic biochemistry analyzer. Conclusion:The developed smartphone turbidimeter demonstrated good stability, consistency, and accuracy, with a high correlation to results from fully automated biochemical analyzers, confirming its practicality and effectiveness in clinical detection of CYS-C.

Objective:? To develop a portable and easy-to-operate Cystatin C (CYS-C) transmission turbidimeter based on smartphones.Methods:? Leveraging the sensitivity of smartphone ambient light sensors to optical signals and the principle of immunoturbidimetry, a smartphone-based turbidimeter was designed and fabricated for point-of-care testing (POCT) of CYS-C in serum. The main structure of the smartphone turbidimeter was created using 3D printing technology and assembled according to the designed circuits and optical paths. At the same time, the stability of the light source and the feasibility of the device were verified. Finally, the sensitivity, specificity, and concordance rate of the smartphone turbidimeter were assessed by comparing its results with those from 127 serum samples of automated biochemical analyzer used in clinic.Results:? During device performance testing, the smartphone turbidimeter exhibited a coefficient of variation ( CV) of 0.9% for light intensity over a continuous 60-minute operation. Additionally, the CV values were below 5% in batch repeat tests of CYS-C samples. The detection results of CYS-C in 127 serum samples showed a sensitivity of 84.4%, specificity of 95.8%, and concordance rate of 92.9%, with a correlation coefficient of r=0.902 9 compared to the automatic biochemistry analyzer. Conclusion:The developed smartphone turbidimeter demonstrated good stability, consistency, and accuracy, with a high correlation to results from fully automated biochemical analyzers, confirming its practicality and effectiveness in clinical detection of CYS-C.

Drug discrimination is a behavioral pharmacological technique to study the discriminative stimulus effects of drug. Currently drug discrimination has been widely used in preclinical drug development of CNS drugs, the most extensive of which is psychodependent research in the field of drug abuse. This review describes in general the basic principles of drug discrimination, preliminarily elaborates on the relevant characteristics and applications of the subjective effects, time-course effect, stereo specificity, individual differences, and receptor mechanisms, and its development prospects for hallucinogens and cannabis drugs are also presented.