The accumulation of uremic toxins such as urea nitrogen, blood creatinine, and uric acid of patients with renal failure in vivo would lead to aggravated kidney damage. In this study, coated aldehyde oxy-starch (CAO) was used as an adsorbent to investigate its in vitro adsorption performance on renal failure indexes for urea, indoxyl sulfate (INS), monomethylamine (MMA), dimethylamine (DMA), uric acid (UA), and creatinine (Cr). The effects of variables such as pH, temperature, concentration, dosage, and time on the adsorption capacity of CAO were systematically investigated, employing analytical techniques of high-performance liquid chromatography (HPLC) and gas chromatography (GC). The results revealed that CAO exhibited a strong adsorption capacity for urea, INS, and MMA, alongside a moderate adsorption capacity for DMA, UA, and Cr. The adsorption kinetics and thermodynamic studies indicated that the adsorption of urea and UA by CAO were fitted in pseudo-first-order kinetics, and the adsorption isotherm aligned with the Freundlich adsorption model. The enthalpy change ΔH of urea in adsorption was in the range of 40 to 60 kJ·mol^-1, which demonstrated the presence of strong adsorption force due to the interactions of coordinating groups. The ΔH of UA was greater than 80 kJ·mol^-1, indicating the generation of chemical bonds during the adsorption. Both of them, Gibbs free energy ΔG was less than 0, within the range of -20 to 0 kJ·mol^-1, suggested that the adsorption of urea and UA by CAO occurred spontaneously as physical adsorption process. The adsorption entropy ΔS of urea and UA was > 0, which indicated an increase in entropy throughout the adsorption. The infrared spectroscopygram showed the formation of a chemical bond, specifically the imine bond, following the adsorption of urea by CAO, thereby indicating a chemical reaction during the adsorption. This study elucidates the adsorption mechanism of CAO on various indexes of renal failure, providing a scientific basis for its clinical usage.

A method for simultaneous determination of 21 kinds of Aconitum alkaloids(ATS)in biological specimens and infused liquor using ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)was developed.The biological samples were pretreated with methanol-acetonitrile(1∶2,V/V)for protein precipitation,while infused liquors were diluted 100-fold with acetonitrile,followed by centrifugation,and filtration by a 0.22-μm membrane.Chromatographic separation was carried out on an EC-C18 column using gradient elution with the mixture of 10 mmol/L ammonium acetate and 0.2%formic acid as mobile phase A and acetonitrile as mobile phase B.With this method,all the analytes were separated within 9.5 min.The samples were detected in positive ESI mode with dynamic multiple reaction monitoring(MRM)and quantified via external standard calibration.The results showed that the concentrations of the analytes in the range of 2-1000 ng/mL had excellent linearity(R2>0.9992)with the peak area.The developed method was successfully used for detection of 21 kinds of aconitum alkaloids,with limits of detection of 0.5-2 ng/mL,quantification limits of 2-6 ng/mL,intra/inter-day precision≤6.0%,spiked recoveries of 89.4%-100.9%,extraction recoveries of 74.2%-104.4%,and matrix effects ranging from-11.1%to 9.2%in blood/urine.The method was applied to detection of 12 samples from 4 fatal aconite poisoning cases,and all 21 kinds of ATS with total alkaloid concentrations of 0.04-4.18 μg/mL in blood and 154.96-422.83 μg/mL in medicinal liquors were detected.Tissue distribution revealed that the order of concentrations from highest to lowest is as follows:urine(157.22 μg/mL)>gastric contents(51.37 μg/mL)>kidney(21.6 μg/g)>whole blood(4.18 μg/mL)>liver(0.03 μg/g).This method showed many advantages such as simple pretreatment,low detection limits,accurate quantification,broad analyte coverage,and superior anti-interference capability in complex matrices,proving ideal for forensic and toxicological analysis of aconitum alkaloids.

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 effect of Pirfenidone(PFN)on respiratory syncytial virus(RSV)infection-induced damage to bronchial epithelial cells by regulating the high mobility group protein B1(HMGB1)/receptor for advanced glycation end products(RAGE)signaling pathway.Methods Human bronchial epithelial cells(HBE)were divided into Control group(cultured for 24 h under normal conditions),RSV group(inoculated with 4.65×106/mL RSV at 33℃for 2 h);low PFN(L-PFN)group(treated with 0.05 mg/mL PFN for 24 h),moderate PFN(M-PFN)group(treated with 0.10 mg/mL PFN for 24 h),high PFN(H-PFN)group(treated with 0.20 mg/mL PFN for 24 h)and recombinant HMGB1(rHMGB1)group(treated with 1 μg/mL rHMGB1+0.20 mg/mL PFN for 24 h).EdU method was applied to detect the proliferation rate of cells in each group,Hochest33258 staining method was applied to detect apoptosis status of cells in each group,and the migration of cells in each group was evaluated by the scratch experiment.Enzyme linked immunosorbent assay(ELISA)was applied to measure the levels of interferon(IFN)-α,IFN-γ,tumor necrosis factor-α(TNF-α),interleukin(IL)-1α,IL-6 and IL-4 in each group of cells,and Western blot was applied to detect the protein expression of HMGB1,RAGE,B lymphoblastoma-2-associated X protein(Bax),cysteine aspartic protease-3(Caspase-3),and B lymphoblastoma-2(Bcl-2).Results Compared with the RSV group,the cell proliferation rate,scratch closure rate,IL-4 levels,and expression of Bcl-2 in L-,M-,and H-PFN groups increased,while the apoptosis rate,the levels of IFN-α,IFN-γ,TNF-α,IL-1α,IL-6,and the expression of HMGB1,RAGE,Bax,and Caspase-3 reduced(P<0.05);rHMGB1 weakened the effect of H-PFN on the above-mentioned indicators(P<0.05).Conclusion PFN may suppress the apoptosis and inflammatory damage of RSV-infected bronchial epithelial cells by inhibiting the HMGB1/RAGE pathway.Conclusion PFN may suppress the apoptosis and inflammatory damage of RSV-infected bronchial epithelial cells by inhibiting the HMGB1/RAGE pathway.

Objective To investigate the effect of Pirfenidone(PFN)on respiratory syncytial virus(RSV)infection-induced damage to bronchial epithelial cells by regulating the high mobility group protein B1(HMGB1)/receptor for advanced glycation end products(RAGE)signaling pathway.Methods Human bronchial epithelial cells(HBE)were divided into Control group(cultured for 24 h under normal conditions),RSV group(inoculated with 4.65×106/mL RSV at 33℃for 2 h);low PFN(L-PFN)group(treated with 0.05 mg/mL PFN for 24 h),moderate PFN(M-PFN)group(treated with 0.10 mg/mL PFN for 24 h),high PFN(H-PFN)group(treated with 0.20 mg/mL PFN for 24 h)and recombinant HMGB1(rHMGB1)group(treated with 1 μg/mL rHMGB1+0.20 mg/mL PFN for 24 h).EdU method was applied to detect the proliferation rate of cells in each group,Hochest33258 staining method was applied to detect apoptosis status of cells in each group,and the migration of cells in each group was evaluated by the scratch experiment.Enzyme linked immunosorbent assay(ELISA)was applied to measure the levels of interferon(IFN)-α,IFN-γ,tumor necrosis factor-α(TNF-α),interleukin(IL)-1α,IL-6 and IL-4 in each group of cells,and Western blot was applied to detect the protein expression of HMGB1,RAGE,B lymphoblastoma-2-associated X protein(Bax),cysteine aspartic protease-3(Caspase-3),and B lymphoblastoma-2(Bcl-2).Results Compared with the RSV group,the cell proliferation rate,scratch closure rate,IL-4 levels,and expression of Bcl-2 in L-,M-,and H-PFN groups increased,while the apoptosis rate,the levels of IFN-α,IFN-γ,TNF-α,IL-1α,IL-6,and the expression of HMGB1,RAGE,Bax,and Caspase-3 reduced(P<0.05);rHMGB1 weakened the effect of H-PFN on the above-mentioned indicators(P<0.05).Conclusion PFN may suppress the apoptosis and inflammatory damage of RSV-infected bronchial epithelial cells by inhibiting the HMGB1/RAGE pathway.Conclusion PFN may suppress the apoptosis and inflammatory damage of RSV-infected bronchial epithelial cells by inhibiting the HMGB1/RAGE pathway.

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 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]

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

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]