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.

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.

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.

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.

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.

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.

OBJECTIVE: To evaluate the effectiveness and safety of Chinese medicine (CM) in the treatment of coronavirus disease 2019 (COVID-19) in China. METHODS: A multi-center retrospective cohort study was carried out, with cumulative CM treatment period of ⩾3 days during hospitalization as exposure. Data came from consecutive inpatients from December 19, 2019 to May 16, 2020 in 4 medical centers in Wuhan, China. After data extraction, verification and cleaning, confounding factors were adjusted by inverse probability of treatment weighting (IPTW), and the Cox proportional hazards regression model was used for statistical analysis. RESULTS: A total of 2,272 COVID-19 patients were included. There were 1,684 patients in the CM group and 588 patients in the control group. Compared with the control group, the hazard ratio (HR) for the deterioration rate in the CM group was 0.52 [95% confidence interval (CI): 0.41 to 0.64, P<0.001]. The results were consistent across patients of varying severity at admission, and the robustness of the results were confirmed by 3 sensitivity analyses. In addition, the HR for all-cause mortality in the CM group was 0.29 (95% CI: 0.19 to 0.44, P<0.001). Regarding of safety, the proportion of patients with abnormal liver function or renal function in the CM group was smaller. CONCLUSION This real-world study indicates that the combination of a full-course CM therapy on the basic conventional treatment, may safely reduce the deterioration rate and all-cause mortality of COVID-19 patients. This result can provide the new evidence to support the current treatment of COVID-19. Additional prospective clinical trial is needed to evaluate the efficacy and safety of specific CM interventions. (Registration No. ChiCTR2200062917).
Humans ; Retrospective Studies ; Male ; Female ; Middle Aged ; COVID-19/epidemiology* ; COVID-19 Drug Treatment ; Aged ; Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal/adverse effects* ; SARS-CoV-2 ; Treatment Outcome ; China/epidemiology* ; Adult

Objective To investigate the clinical characteristics and prognosis of pneumococcal meningitis(PM),and drug sensitivity of Streptococcus pneumoniae(SP)isolates in Chinese children.Methods A retrospective analysis was conducted on clinical information,laboratory data,and microbiological data of 160 hospitalized children under 15 years old with PM from January 2019 to December 2020 in 33 tertiary hospitals across the country.Results Among the 160 children with PM,there were 103 males and 57 females.The age ranged from 15 days to 15 years,with 109 cases(68.1% )aged 3 months to under 3 years.SP strains were isolated from 95 cases(59.4% )in cerebrospinal fluid cultures and from 57 cases(35.6% )in blood cultures.The positive rates of SP detection by cerebrospinal fluid metagenomic next-generation sequencing and cerebrospinal fluid SP antigen testing were 40% (35/87)and 27% (21/78),respectively.Fifty-five cases(34.4% )had one or more risk factors for purulent meningitis,113 cases(70.6% )had one or more extra-cranial infectious foci,and 18 cases(11.3% )had underlying diseases.The most common clinical symptoms were fever(147 cases,91.9% ),followed by lethargy(98 cases,61.3% )and vomiting(61 cases,38.1% ).Sixty-nine cases(43.1% )experienced intracranial complications during hospitalization,with subdural effusion and/or empyema being the most common complication[43 cases(26.9% )],followed by hydrocephalus in 24 cases(15.0% ),brain abscess in 23 cases(14.4% ),and cerebral hemorrhage in 8 cases(5.0% ).Subdural effusion and/or empyema and hydrocephalus mainly occurred in children under 1 year old,with rates of 91% (39/43)and 83% (20/24),respectively.SP strains exhibited complete sensitivity to vancomycin(100% ,75/75),linezolid(100% ,56/56),and meropenem(100% ,6/6).High sensitivity rates were also observed for levofloxacin(81% ,22/27),moxifloxacin(82% ,14/17),rifampicin(96% ,25/26),and chloramphenicol(91% ,21/23).However,low sensitivity rates were found for penicillin(16% ,11/68)and clindamycin(6% ,1/17),and SP strains were completely resistant to erythromycin(100% ,31/31).The rates of discharge with cure and improvement were 22.5% (36/160)and 66.2% (106/160),respectively,while 18 cases(11.3% )had adverse outcomes.Conclusions Pediatric PM is more common in children aged 3 months to under 3 years.Intracranial complications are more frequently observed in children under 1 year old.Fever is the most common clinical manifestation of PM,and subdural effusion/emphysema and hydrocephalus are the most frequent complications.Non-culture detection methods for cerebrospinal fluid can improve pathogen detection rates.Adverse outcomes can be noted in more than 10% of PM cases.SP strains are high sensitivity to vancomycin,linezolid,meropenem,levofloxacin,moxifloxacin,rifampicin,and chloramphenicol.[Chinese Journal of Contemporary Pediatrics,2024,26(2):131-138]

Endodontic diseases are a kind of chronic infectious oral disease.Common endodontic treatment concepts are based on the removal of inflamed or necrotic pulp tissue and the replacement by gutta-percha.However,it is very essential for endodontic treatment to debride the root canal system and prevent the root canal system from bacterial reinfection after root canal therapy(RCT).Recent research,encompassing bacterial etiology and advanced imaging techniques,contributes to our understanding of the root canal system's anatomy intricacies and the technique sensitivity of RCT.Success in RCT hinges on factors like patients,infection severity,root canal anatomy,and treatment techniques.Therefore,improving disease management is a key issue to combat endodontic diseases and cure periapical lesions.The clinical difficulty assessment system of RCT is established based on patient conditions,tooth conditions,root canal configuration,and root canal needing retreatment,and emphasizes pre-treatment risk assessment for optimal outcomes.The findings suggest that the presence of risk factors may correlate with the challenge of achieving the high standard required for RCT.These insights contribute not only to improve education but also aid practitioners in treatment planning and referral decision-making within the field of endodontics.