AIM To optimize the extraction process for total polyphenols from Conioselinum vaginatu(Spreng.)Thell.,make component analysis,and evaluate their anti-oxidant,hypoglycemic activities.METHODS The effects of ultrasound,enzymatic hydrolysis,acid hydrolysis,alcohol extraction and hydrolysis processes on the extraction quantity of total polyphenols were investigated,respectively.With extraction temperature,extraction time,ethanol concetration and liquid-solid ratio as influencing factors,extraction quantity of total polyphenols as an evaluation index,the extraction process was optimized by response surface method.HPLC was adopted in the identification of polyphenolic composition and determination of their contents.Subsequently,total polyphenols' scavenging capacities on DPPH,ABTS,OH free radicals,total reducing power and inhibitory capacity on α-glucosidase were determined.RESULTS The highest extraction quantity of total polyphenols was observable when extraction process was employed.The optimal conditions were determined to be 62 ℃ for extraction temperature,54 min for extraction time,69％for ethanol concentration,and 50∶1 for liquid-solid ratio,the extraction quantity of total polyphenols was(9.51±0.2)mg GAE/g.Seven constituents existed in C.vaginatu,among which ferulic acid demonstrated the highest content,followed by that of myricetin,while D-tryptophan content was the lowest.At the concentration of 7.61 mg/L,total polyphenols displayed the scavenging rates on DPPH,ABTS,OH free radicals of 80.70％,85.97％,28.60％,total reducing power of 0.22,and inhibition rate on α-glucosidase of 77.23％,respectively.CONCLUSION This stable and reliable method can be used for the extraction of total polyphenols from C.vaginatum with strong anti-oxidant,hypoglycemic activities.

Human Respiratory Stem Cells (RSCs) play a crucial role in the maintenance, repair and regeneration of the respiratory system. As a novel therapeutic method, stem cell therapy is a popular research direction in the medical field. And with the in-depth research on the mechanism of pneumonia caused by respiratory infections in recent years, the use of RSCs to explore pneumonia caused by respiratory infections and its therapeutic strategies has become a hot topic. In this paper, we firstly outlined the types of RSCs, summarized the mechanism of pneumonia caused by respiratory tract infections, discussed the advantages of RSCs application and the progress of culture differentiation, and elaborated the therapeutic exploration of RSCs in pneumonia caused by respiratory tract infections.

Objective:Analysis of the composition of pathogen spectrum and prevalence characteristics in throat swabs of patients with acute respiratory infections (ARI) in Yucheng city, Henan province, from March to June 2023.Methods:After 1 153 throat swabs were collected from ARI patients in Yucheng, 18 respiratory pathogens were tested using a real-time fluorescence quantitative polymerase chain reaction (qPCR) method. The characterization of pathogens spectrum was analyzed.Results:A total of 1 153 throat swabs from ARI patients were collected from March to June 2023 in Yucheng, including 171 outpatients and 982 hospitalized patients. A total of 244 positive samples for common respiratory pathogens were detected (at least one pathogen per sample was detected). The total detection rate of respiratory pathogens was 21.16%, and the top three detection rates were, in descending order, human bocavirus (HBoV), enterovirus (EV), and human parainfluenza virus (HPIV). The main detection month for pathogens was May, with a detection rate of 42.3% (60/142). The main respiratory pathogens detected are HBoV, EV, and HPIV. The detection rate of the age group under 1 year old was the highest, at 25.1% (49/195), mainly consisting of HBoV, respiratory syncytial virus (RSV), and HPIV. The main clinical manifestations of respiratory pathogen-positive patients were fever and cough, and the clinical diagnosis was mainly lower respiratory tract infection, all of which were hospitalized patients.Conclusions:The respiratory pathogens in ARI patients were mainly HBoV, EV, and HPIV from March to June, 2023 in Yucheng. The peak of the epidemic was in May, mainly infecting children under 5 years of age.

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]

AIM To optimize the extraction process for total polyphenols from Conioselinum vaginatu(Spreng.)Thell.,make component analysis,and evaluate their anti-oxidant,hypoglycemic activities.METHODS The effects of ultrasound,enzymatic hydrolysis,acid hydrolysis,alcohol extraction and hydrolysis processes on the extraction quantity of total polyphenols were investigated,respectively.With extraction temperature,extraction time,ethanol concetration and liquid-solid ratio as influencing factors,extraction quantity of total polyphenols as an evaluation index,the extraction process was optimized by response surface method.HPLC was adopted in the identification of polyphenolic composition and determination of their contents.Subsequently,total polyphenols' scavenging capacities on DPPH,ABTS,OH free radicals,total reducing power and inhibitory capacity on α-glucosidase were determined.RESULTS The highest extraction quantity of total polyphenols was observable when extraction process was employed.The optimal conditions were determined to be 62 ℃ for extraction temperature,54 min for extraction time,69％for ethanol concentration,and 50∶1 for liquid-solid ratio,the extraction quantity of total polyphenols was(9.51±0.2)mg GAE/g.Seven constituents existed in C.vaginatu,among which ferulic acid demonstrated the highest content,followed by that of myricetin,while D-tryptophan content was the lowest.At the concentration of 7.61 mg/L,total polyphenols displayed the scavenging rates on DPPH,ABTS,OH free radicals of 80.70％,85.97％,28.60％,total reducing power of 0.22,and inhibition rate on α-glucosidase of 77.23％,respectively.CONCLUSION This stable and reliable method can be used for the extraction of total polyphenols from C.vaginatum with strong anti-oxidant,hypoglycemic activities.

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]

Human Respiratory Stem Cells (RSCs) play a crucial role in the maintenance, repair and regeneration of the respiratory system. As a novel therapeutic method, stem cell therapy is a popular research direction in the medical field. And with the in-depth research on the mechanism of pneumonia caused by respiratory infections in recent years, the use of RSCs to explore pneumonia caused by respiratory infections and its therapeutic strategies has become a hot topic. In this paper, we firstly outlined the types of RSCs, summarized the mechanism of pneumonia caused by respiratory tract infections, discussed the advantages of RSCs application and the progress of culture differentiation, and elaborated the therapeutic exploration of RSCs in pneumonia caused by respiratory tract infections.

Objective:Analysis of the composition of pathogen spectrum and prevalence characteristics in throat swabs of patients with acute respiratory infections (ARI) in Yucheng city, Henan province, from March to June 2023.Methods:After 1 153 throat swabs were collected from ARI patients in Yucheng, 18 respiratory pathogens were tested using a real-time fluorescence quantitative polymerase chain reaction (qPCR) method. The characterization of pathogens spectrum was analyzed.Results:A total of 1 153 throat swabs from ARI patients were collected from March to June 2023 in Yucheng, including 171 outpatients and 982 hospitalized patients. A total of 244 positive samples for common respiratory pathogens were detected (at least one pathogen per sample was detected). The total detection rate of respiratory pathogens was 21.16%, and the top three detection rates were, in descending order, human bocavirus (HBoV), enterovirus (EV), and human parainfluenza virus (HPIV). The main detection month for pathogens was May, with a detection rate of 42.3% (60/142). The main respiratory pathogens detected are HBoV, EV, and HPIV. The detection rate of the age group under 1 year old was the highest, at 25.1% (49/195), mainly consisting of HBoV, respiratory syncytial virus (RSV), and HPIV. The main clinical manifestations of respiratory pathogen-positive patients were fever and cough, and the clinical diagnosis was mainly lower respiratory tract infection, all of which were hospitalized patients.Conclusions:The respiratory pathogens in ARI patients were mainly HBoV, EV, and HPIV from March to June, 2023 in Yucheng. The peak of the epidemic was in May, mainly infecting children under 5 years of age.

Objective To discuss the value of the apparent diffusion coefficient(ADC)in the prediction of therapeutic effect of neoadjuvant concurrent chemoradiotherapy(nCRT)in rectal cancer.Methods The clinical data of 52 patients with pathologi-cally diagnosed rectal cancer who underwent neoadjuvant chemoradiotherapy(nCRT)and received pelvic MR1 examinations before and 8 weeks after nCRT at the 900th Hospital of Joint Logistics Support Force from January 2019 to February 2023 were analyzed retrospectively.The ADC values before and after nCRT were obtained,and ΔADC and ΔADC％were calculated.The 52 patients were divided into a sensitive group(n=40)and a non-sensitive group(n=12)based on the tumor regression grade(TRG).The ADC values before nCRT,ΔADC and ΔADC％of the above two groups were compared by Mann-Whitney U test.The ADC values after nCRT of the two groups were compared by independent samples t-test.Spearman's rank correlation coefficient was used to evaluate the correlation of ΔADC and ΔADC％with TRG.The baseline data of the two groups were compared by independent samples t-test,Mann-Whitney U test and chi-square test.Binary Logistic regression was used to analyze the factors predicting efficacy.ROC was drawn to assess the ability of ΔADC in predicting nCRT efficacy,and determine optimal cut-off value.Results There was no statistical difference between the sensitive group and the non-sensitive group with regard to ADCs before and after nCRT(P＞0.05).There were statistical differences between the above two groups with regard to ΔADC and ΔADC％(P＜0.05).ΔADC and ΔADC％showed negative correlation with TRG(r=-0.378,-0.368,P＜0.05).Univariate analysis showed that there were statistically significant differences in tumor diameter between the two groups(P＜0.05).Multivariate analysis showed that ΔADC was an independent predictor of the efficacy.ROC curve analysis showed the optimal cut-off value of ΔADC was 236.600×10-6 mm2/s.Conclusion The ADC values before and after nCRT have limited predictive significance for assessing the efficacy of nCRT,whereas ΔADC can predict nCRT efficacy effectively and provide references for subsequent clinical treatment.[Chinese Medical Equipment Journal,2024,45(12):61-66]

Objective:To provide reference for hospital drug selection and clinical rational drug selection,through evaluating the nutritional value of six commonly used balanced compound amino acid injection (BCAA) in clinical practice,including 18AA (250 mL:12.5 g),18AA-I (250 mL:17.5 g),18AA-Ⅱ(250 mL:21.25 g),18AA-IV (250 mL:8.7 g),18AA-V (250 mL:8.06 g),and 18AA-V-SF (250 mL:8.06 g). Methods:Based on the whole egg protein model,the nutritional value of six varieties of BCAA from two aspects were evaluated,including the first limiting amino acid chemical score (CS),value of essential amino acid (EAA) and the comprehensive quality of total EAA (both essential amino acid index and closeness to standard protein). Results:The first limiting amino acid CS value from high to low was 18AA-Ⅱ>18AA>18AA-V=18AA-V-SF>18AA-I=18AA-Ⅳ. Total EAA comprehensive quality:the essential amino acid index from high to low was 18AA-Ⅱ>18AA>18AA-I>18AA-Ⅳ>18AA-V=18AA-V-SF. The closeness to whole egg protein from high to low was 18AA-Ⅱ=18AA=18AA-I>18AA-Ⅳ>18AA-V=18AA-V-SF. Ultimately,the nutritional value of the 6 varieties of BCAA decreased from high to low:18AA-Ⅱ>18AA>18AA-I>18AA-Ⅳ>18AA-V=18AA-V-SF. Conclusions:Among the six varieties of BCAA,18AA-Ⅱ has the highest nutritional value and the highest amino acid content in the same liquid volume,making it the preferred drug for patients with normal liver and kidney function.