AIM To establish a GC-MS/MS method for the analysis of volatile components in Yinhu Ganmao Powder,and to determine the contents of α-pinene,camphene,sabinene,β-pinene,α-terpinene,(+)-limonene,p-cymene,1,8-cineole,linalool,L-menthol,terpinen-4-ol,DL-menthol,α-terpineol,tridecane,pulegone,caryophyllene,humulene,n-hexadecane and patchouli alcohol.METHODS The analysis was performed on a DB-624 UI capillary column(30 m×0.25 mm×1.40 μm ),and electron ionization source was adopted with multiple reaction monitoring mode.RESULTS Fifty volatile components and twenty-five liposoluble components were identified in volatile oils and medicinal material powder,respectively.Nineteen constituents showed good linear relationships within their own ranges(r ≥ 0.999 0),whose average recoveries were 84.43％-113.31％with the RSDs of less than 9.15％.CONCLUSION This stable,accurate and reproducible method can provide a reference for the quality evaluation of Yinhu Ganmao Powder.

Removal of endogenic renal sinus tumors which are located at the renal hilum,is recognized as one of the most challenging surgeries in urology due to the complex anatomical structure.With the help of the high-definition field of vision and flexible robotic arms of the Da Vinci robot system,our team have carried out precision intra-sinus tumor enucleation based on preoperative interactive qualitative and Interactive Quantitative Quality Assurance for 3D Imaging(IQQA-3D)accurate reconstruction technology,as well as the clinical anatomical characteristics of patients with renal sinus tumors.During operation,the renal pedicle is fully dissected,the perihilar fat is cleared,and the tumor capsule is exposed.Subsequently,the blood supply to the kidney is blocked,and the tumor is precisely excised along the tumor capsule.The vascular stumps are sutured point-to-point,and the wound is closed and locked.After the blood supply restores,regional arterial occlusion is performed if necessary to ensure the safety of the surgery and maximize the preservation of renal function.This article discusses the definition of tumor boundaries during robot-assisted endogenic sinus tumor enucleation,the use of intraoperative cooling techniques,and the improvement of surgical safety.

Removal of endogenic renal sinus tumors which are located at the renal hilum,is recognized as one of the most challenging surgeries in urology due to the complex anatomical structure.With the help of the high-definition field of vision and flexible robotic arms of the Da Vinci robot system,our team have carried out precision intra-sinus tumor enucleation based on preoperative interactive qualitative and Interactive Quantitative Quality Assurance for 3D Imaging(IQQA-3D)accurate reconstruction technology,as well as the clinical anatomical characteristics of patients with renal sinus tumors.During operation,the renal pedicle is fully dissected,the perihilar fat is cleared,and the tumor capsule is exposed.Subsequently,the blood supply to the kidney is blocked,and the tumor is precisely excised along the tumor capsule.The vascular stumps are sutured point-to-point,and the wound is closed and locked.After the blood supply restores,regional arterial occlusion is performed if necessary to ensure the safety of the surgery and maximize the preservation of renal function.This article discusses the definition of tumor boundaries during robot-assisted endogenic sinus tumor enucleation,the use of intraoperative cooling techniques,and the improvement of surgical safety.

Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.

AIM To establish a GC-MS/MS method for the analysis of volatile components in Yinhu Ganmao Powder,and to determine the contents of α-pinene,camphene,sabinene,β-pinene,α-terpinene,(+)-limonene,p-cymene,1,8-cineole,linalool,L-menthol,terpinen-4-ol,DL-menthol,α-terpineol,tridecane,pulegone,caryophyllene,humulene,n-hexadecane and patchouli alcohol.METHODS The analysis was performed on a DB-624 UI capillary column(30 m×0.25 mm×1.40 μm ),and electron ionization source was adopted with multiple reaction monitoring mode.RESULTS Fifty volatile components and twenty-five liposoluble components were identified in volatile oils and medicinal material powder,respectively.Nineteen constituents showed good linear relationships within their own ranges(r ≥ 0.999 0),whose average recoveries were 84.43％-113.31％with the RSDs of less than 9.15％.CONCLUSION This stable,accurate and reproducible method can provide a reference for the quality evaluation of Yinhu Ganmao Powder.

177Lu-prostate specific membrane antigen(PSMA)radio-ligand therapy has been approved abroad for advanced prostate cancer and has been in several clinical trials in China.Based on domestic clinical practice and experimental data and referred to international experience and viewpoints,the expert group forms a consensus on the clinical application of 177Lu-PSMA radio-ligand therapy in prostate cancer to guide clinical practice.

Objective To explore the analgesic effect of different doses of dezocine combined with ropivacaine for thoracic paravertebral block(TPVB)on patients undergoing thoracoscopic radical resection of lung cancer and the influence on hemodynamics and immune function of patients.Methods Patients with lung cancer who underwent thoracoscopic radical resection were divided into low-dose group and high-dose group according to random number table method.Both groups of patients were given total intravenous anesthesia to complete the surgery.At 15 min before general anesthesia induction,the low-dose group was given TPBV with 0.1 mg·kg-1 dezocine+0.375％ropivacaine for a total of 20 mL,and the high-dose group was given TPBV with 0.15 mg·kg-1 dezocine+0.375％ropivacaine for a total of 20 mL.Comparisons were performed on both groups in terms of analgesic effect,hemodynamic parameters,immune function and occurrence of adverse drug reactions.Results There were 48 cases in low-dose group and 46 cases in high-dose group.In low-dose group,the heart rate values before TPVB,before skin incision,at 5 min after sectioning and at the end of surgery were(78.52±6.54),(70.79±7.07),(74.48±6.68)and(76.69±7.29)beat·min-1,the mean arterial pressure values were(93.16±5.72),(86.38±7.51),(92.15±6.36)and(91.14±6.13)mmHg.In high-dose group,the heart rate values at the above time points were(79.36±7.11),(71.68±6.49),(74.76±7.06)and(76.57±6.52)beat·min-1;the mean arterial pressure values were(93.89±7.18),(85.27±7.41),(90.34±6.52)and(92.43±6.34)mmHg,there were no statistical differences between the two groups(all P＞0.05).The resting state scores at 2,6 and 12 h after surgery were(1.38±0.19),(1.54±0.21)and(1.72±0.16)points,the pain scores at motion state were(1.88±0.15),(2.36±0.37)and(3.26±0.38)points in low-dose group;in high-dose group,the resting state scores were(1.32±0.17),(1.58±0.22)and(1.81±0.18)points,the pain scores at motion state were(1.81±0.13),(2.11±0.31)and(3.03±0.36)points,respectively,there were no statistical differences between the two groups(all P＞0.05).The number of analgesic pump compressions at 24 h after surgery and the number of cases with analgesic remedy were(5.12±1.26)times and 15 cases in low-dose group and were(4.74±1.03)times and 10 cases in high-dose group,with no statistical differences between the groups(all P＞0.05).The percentages of CD3+cells in low-dose group at the end of surgery and at 12 h and 24 h after surgery were(68.51±6.76)％,(54.22±5.43)％and(51.47±6.58)％,the percentages of CD4+cells were(40.29±5.02)％,(34.94±4.79)％and(30.48±5.11)％,CD4+/CD8+ratios were 1.54±0.34,1.36±0.28 and 1.16±0.23;the percentages of CD3+cells in high-dose group were(67.92±7.11)％,(56.58±6.36)％and(54.47±6.89)％,percentages of CD4+cells were(41.33±5.75)％,(35.86±5.21)％and(32.27±4.78)％,the CD4+/CD8+were 1.53±0.35,1.40±0.30 and 1.22±0.26,all with no significant difference(all P＞0.05).The incidence of postoperative adverse drug reactions in high-dose group and low-dose group were 32.61％and 14.58％,with significant difference(P＜0.05).Conclusion When TPVB regimen of dezocine combined with ropivacaine is used in thoracoscopic radical resection of lung cancer,the analgesic effect of low-dose dezocine is comparable to that of high-dose dezocine,with lower risk of adverse drug reactions.

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]