AIM To evaluate the quality of benchmark sample of Zexie Decoction.METHODS HPLC fingerprints were established,after which the content determination of epoxy alisma ene,23-acetyl alisol B,23-acetyl alisol C,alisol A,alisol B,atractylenolide Ⅰ,atractylenolide Ⅱ and atractylenolide Ⅲ was performed,and the transfer rate and paste yield were calculated.RESULTS There were 20 common peaks in the fingerprints for 15 batches of benchmark samples with the similarities of more than 0.95.The average contents of various effective constituents were 180.86 μg/g for alisol B 23-acetate,18.65 μg/g for alisol C 23-acetate,34.74 μg/g for alismoxide,17.65 μg/g for alisol A,238.19 μg/g for alisol B,2.85 μg/g for atractylenolide Ⅰ,6.38 μg/g for atractylenolide Ⅱ,and 15.42 μg/g for atractylenolide Ⅲ,respectively.In the decoction piece-benchmark sample,alisol B 23-acetate,alisol C 23-acetate,atractylenolide Ⅰ,atractylenolide Ⅱ and atractylenolide Ⅲ demonstrated the average transfer rates of 12.09％,16.45％,3.93％,12.17％and 34.37％respectively.The paste yields in various batches of benchmark samples were 15.2％-20.2％.CONCLUSION HPLC fingerprints combined with content determination can be used for the quality control of benchmark sample of Zexie Decoction,thus provides a reference for the development of its compound preparations.

Objective To investigate the current status of family resilience of children with cancer and analyze its influencing factors,to provide a basis for medical staff to formulate intervention plans.Methods Using a convenient sampling method,children with cancer who were hospitalized in 2 tertiary hospitals in Henan Province from January to April 2024 were selected for the survey.A general information questionnaire,family resilience assessment scale,quality of life family version,ZBI caregiver burden interview,and social support rating scale were used to understand the current status of family resilience of children with cancer and to explore the related influencing factors by univariate analysis and multiple stepwise linear regression analysis.Results A total of 280 questionnaires were distributed and 265 valid questionnaires were recovered,with a valid questionnaire recovery rate of 94.64％.The total score of family resilience for primary caregivers of children with cancer was(185.63±30.66).The multiple stepwise linear regression analysis results showed that the children's self-care ability,caregiver's work status,family care burden,and social support level were the influencing factors for family resilience of children with cancer(P＜0.05),and the explanatory variance was 51.3％.Conclusion The family resilience of children with cancer is at a medium level.The worse the children's self-care ability and the heavier the family care burden,the worse the family resilience;the caregiver's work status and good social support are helpful for the family resilience of children with cancer.Healthcare workers should develop intervention programs to address these factors to enhance the family resilience of children with cancer.

The application of sensors to the monitoring of spinal morphology and motion was reviewed in terms of the research object and monitoring index.The present situation of the application of sensors was introduced,such as inertial sensor,stretchable strain sensor and electromagnetic sensor.The deficiencies of sensors applied to the monitoring of spinal morphology and motion were analyzed,and the future directions of the application were pointed out.[Chinese Medical Equipment Journal,2025,46(6):105-110]

Central nervous system(CNS)degenerative disorders refer to a spectrum of pathological alterations triggered by struc-tural damage to cerebral neural tissues,clinically manifested as diverse neurological dysfunction syndromes,including multiple sclerosis(MS),neurodegenerative diseases(NDs),and ische-mic stroke.The hallmark pathological features of these disorders involve irreversible neuronal damage and decompensation of functional neural networks,ultimately leading to progressive neurological deficits.Notably,with the accelerating global popu-lation aging,the incidence of these diseases has surged signifi-cantly.According to WHO statistics,they now rank among the top three global causes of disability and mortality.Current re-search has confirmed that the pathogenesis of CNS degenerative disorders exhibits high heterogeneity,encompassing multifaceted pathophysiological processes such as genetic predisposition,oxi-dative stress,protein misfolding,and metabolic dysregulation.This intricate pathogenic network not only complicates clinical differential diagnosis but also poses substantial challenges to the development of precision therapeutic strategies.Importantly,re-cent studies have revealed that mitochondrial homeostasis disrup-tion-induced inflammatory cascades(termed mitochondrial in-flammation)play a pivotal regulatory role in neurodegenerative progression.Key molecular mechanisms include impaired mito-phagy,aberrant mitochondrial DNA(mtDNA)release and NL-RP3 inflammasome activation.This review systematically deci-phers the molecular regulatory network of mitochondrial inflam-mation,with a focus on its biological effects in critical pathologi-cal events such as blood-brain barrier disruption,microglial hy-peractivation and neuronal apoptosis.The overarching aim is to provide a theoretical foundation for developing innovative thera-peutic strategies targeting mitochondrial homeostasis restoration.

Background and Aims:Extrahepatic cholangiocarcinoma(ECCA)is a malignancy with insidious onset,strong invasiveness,and poor prognosis,characterized by a high postoperative recurrence rate and a 5-year overall survival of less than 20％.Most existing prognostic models are based on the Cox proportional hazards model,which is limited by the proportional hazards assumption and linearity constraints.The random survival forest(RSF)model,a novel machine learning algorithm,can capture complex interactions and nonlinear effects among variables;however,its application in ECCA remains scarce.Therefore,this study developed a prognostic model for ECCA patients after radical resection using the RSF algorithm,aiming to provide precise and individualized prognostic assessments and support clinical decision-making.Methods:A total of 515 postoperative ECCA patients from the SEER database(2016-2021)were retrospectively enrolled and randomly divided into a training set(n=361)and a test set(n=154).Demographic and clinical variables were collected.Cox models were developed using univariate and multivariate regression,while RSF models were constructed using variable importance(VIMP)and minimal depth methods.Model performance was evaluated using the concordance index(C-index),time-dependent area under the curve(AUC),Brier scores,calibration plots,and decision curve analysis.Survival differences were assessed using Kaplan-Meier analysis,and interpretability was enhanced through the use of SurvSHAP and SurvLIME.Results:Multivariate Cox regression identified seven independent prognostic factors:age,race,income,T stage,N stage,tumor size,and chemotherapy.The RSF model selected four key predictors:age,tumor size,lymph node positive rate,and chemotherapy.In the test cohort,the RSF model achieved a C-index of 0.751,outperforming the Cox model(0.711).The RSF model yielded AUCs of 0.843,0.749,and 0.814 at 1,2,and 3 years,respectively,with superior calibration,overall performance,and net clinical benefit.Nonlinear associations were observed for lymph node positive rate,age,and tumor size,while chemotherapy was associated with reduced mortality risk.Stratified survival curves indicated poorer prognosis in patients without chemotherapy,lymph node positive rate＞0.1,age＞70 years,or tumor size＞20 mm.Conclusion:The RSF model,based on only four readily available clinical variables,demonstrated superior predictive performance compared with the Cox model.It provides a reliable tool for individualized prognosis and postoperative management in ECCA patients.The integration of interpretability frameworks further enhances its clinical applicability,offering potential to improve survival outcomes and quality of life.

Background and Aims:Extrahepatic cholangiocarcinoma(ECCA)is a malignancy with insidious onset,strong invasiveness,and poor prognosis,characterized by a high postoperative recurrence rate and a 5-year overall survival of less than 20％.Most existing prognostic models are based on the Cox proportional hazards model,which is limited by the proportional hazards assumption and linearity constraints.The random survival forest(RSF)model,a novel machine learning algorithm,can capture complex interactions and nonlinear effects among variables;however,its application in ECCA remains scarce.Therefore,this study developed a prognostic model for ECCA patients after radical resection using the RSF algorithm,aiming to provide precise and individualized prognostic assessments and support clinical decision-making.Methods:A total of 515 postoperative ECCA patients from the SEER database(2016-2021)were retrospectively enrolled and randomly divided into a training set(n=361)and a test set(n=154).Demographic and clinical variables were collected.Cox models were developed using univariate and multivariate regression,while RSF models were constructed using variable importance(VIMP)and minimal depth methods.Model performance was evaluated using the concordance index(C-index),time-dependent area under the curve(AUC),Brier scores,calibration plots,and decision curve analysis.Survival differences were assessed using Kaplan-Meier analysis,and interpretability was enhanced through the use of SurvSHAP and SurvLIME.Results:Multivariate Cox regression identified seven independent prognostic factors:age,race,income,T stage,N stage,tumor size,and chemotherapy.The RSF model selected four key predictors:age,tumor size,lymph node positive rate,and chemotherapy.In the test cohort,the RSF model achieved a C-index of 0.751,outperforming the Cox model(0.711).The RSF model yielded AUCs of 0.843,0.749,and 0.814 at 1,2,and 3 years,respectively,with superior calibration,overall performance,and net clinical benefit.Nonlinear associations were observed for lymph node positive rate,age,and tumor size,while chemotherapy was associated with reduced mortality risk.Stratified survival curves indicated poorer prognosis in patients without chemotherapy,lymph node positive rate＞0.1,age＞70 years,or tumor size＞20 mm.Conclusion:The RSF model,based on only four readily available clinical variables,demonstrated superior predictive performance compared with the Cox model.It provides a reliable tool for individualized prognosis and postoperative management in ECCA patients.The integration of interpretability frameworks further enhances its clinical applicability,offering potential to improve survival outcomes and quality of life.

Objective To evaluate the efficacy and safety of transcatheter aortic valve implantation(TAVI)for the treatment of primary aortic valve regurgitation(NAVR)and to compare the difference in the choice of prosthetic valve size and the difference in complications with aortic stenosis(AS).Methods According to the definition of Valve Academic Research Consortium(VARC-3),143 patients with NAVR/AS treated with TAVI and patients with NAVR treated with surgical aortic valve replacement(SAVR)at Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,China,from March 2019 to September 2024 were selected,and clinical data on baseline,perioperative,and primary endpoint events were were retrospectively collected and compared.Results Forty-three patients with NAVR were treated with TAVI,with a device success rate of 86.0％and a surgical success rate of 95.3％.Subgroup comparisons:(1)NAVR-TAVI group than NAVR-SAVR group:patients in the TAVI group had a significantly shorter operative time than those in the SAVR group(P＜0.001);complete left bundle branch block was more likely to occur after TAVI(P=0.042),and complete right bundle branch block was more likely to occur after SAVR(P=0.044).SAVR postoperatively The incidence of congestive heart failure was higher(P=0.013),and the mortality rate was significantly higher in the SAVR group than in the TAVI group(P=0.019).(2)NAVR-TAVI group than AS-TAVI group:the differences in access selection,THV size[28(22,34)mm vs.24(22,32)mm,P=0.044]and proportion of THV overdiameter[14％(7％,20％)vs.7％(3％,11％),P＜0.001]were statistically significant.patients in AS and NAVR groups had 1 case of permanent pacing after TAVI treatment.In the AS and NAVR groups,there was 1 case of permanent pacemaker implantation after TAVI.2 patients in the AS group were converted to surgical treatment,and 6 patients died.Conclusions The use of"off-label"(transfemoral)and"on-label"(transapical)TAVI devices(both from domestic sources)is safer than SAVR for the treatment of NAVR,especially in elderly and high-risk patients.Compared with patients with AS treated with TAVI,larger diameter annulas are usually selected for NAVR,with higher rates of valve migration,but overall safety and efficacy are comparable to AS.

Objective To explore the relationship between insulin resistance and idiopathic central precocious puberty(ICPP)in girls and the diagnostic value of insulin resistance.Methods Clinical data of 245 girls aged 4 to 7.5 years with low luteinizing hormone(LH)levels(0.2-0.83 IU/L),normal body weight(body mass index standard deviation score between-2 and+2),and early breast development who visited the Department of Pediatric Endocrinology,Henan Provincial Maternal and Child Health Hospital from January 2022 to March 2025 were retrospectively analyzed.According to the Expert Consensus on the Diagnosis and Treatment of Central Precocious Puberty(2022),patients were assigned to an ICPP group(n=123)or a control group(n=122).Correlations between the homeostasis model assessment of insulin resistance(HOMA-IR)and selected indices were assessed.Multivariable logistic regression was used to evaluate the association between HOMA-IR and ICPP,and the diagnostic performance of various indices for ICPP was evaluated.Results HOMA-IR was higher in the ICPP group than in the control group(P<0.001)and was positively correlated with LH peak(rs=0.467,P<0.05)and the LH peak/FSH peak ratio(rs=0.444,P<0.05).The multivariable logistic regression model including age,BMI,and basal LH showed that HOMA-IR was closely associated with ICPP(OR=2.756,95%CI:1.940-3.913).Receiver operating characteristic curve analysis showed that the areas under the curve for basal LH,HOMA-IR,and their combination in diagnosing ICPP were 0.735,0.735,and 0.805,respectively(P<0.05),and the combined model had a greater area under the curve than either basal LH or HOMA-IR alone(both P<0.05).Conclusions HOMA-IR is closely associated with ICPP in girls with low LH and normal body weight,and combining HOMA-IR with basal LH improves early identification and diagnostic efficiency in this population.

Objective To explore the potential categories of health risk behaviors among students aged 11 to 18 years in Minhang District,Shanghai in order to identify the influencing factors of health risk behaviors among different categories of students.Methods Using stratified cluster sampling techniques,a random selection of 1 690 middle school students from Minhang District,Shanghai,were chosen as the study sample.Data was collected using the"Shanghai Adolescent Health-Related Behavior Survey Questionnaire"to assess participants'scores on six categories of health risk behaviors.Latent profile analysis(LPA)was employed to cluster students'health risk behaviors,and a multifactorial Logistic regression model was used to analyze the related influencing factors.Results The health risk behaviors of students aged 11 to 18 in Minhang District,Shanghai,were categorized into 3 groups:Class 1,comprising 915 students(54.14%),with moderate to low scores in health risk behaviors,named"the group with moderately low levels of various health risk behaviors";Class 2,comprising 539 students(31.89%),with higher scores in psychological addiction,named"the group with psychological addiction as the prominent health risk behavior";Class 3,comprising 236 students(13.97%),with higher scores in substance addiction,named"the group with substance addiction as the prominent health risk behavior".The results of the univariate analysis for the three potential categories of student health risk behaviors show that there are statistically significant differences in the 3 potential categories of health risk behaviors among students of different schools,ages,ethnicities,and boarding situations(P<0.05).The analysis results of the multivariate Logistic regression indicate that compared to junior high school students,both senior high school students and vocational high school students have a higher risk of exhibiting unhealthy behaviors characterized by psychological addiction and substance addiction,with all differences being statistically significant(P<0.05).Conclusion The latent profile analysis reveals three distinct categories of health risk behaviors among students aged 11 to 18 years in Minhang District,Shanghai,with significant distribution differences among students from diverse backgrounds.Consequently,it is recommended that tailored health education and intervention measures be implemented for students with different school characteristics and at various educational stages.

Objective To explore the relationship between insulin resistance and idiopathic central precocious puberty(ICPP)in girls and the diagnostic value of insulin resistance.Methods Clinical data of 245 girls aged 4 to 7.5 years with low luteinizing hormone(LH)levels(0.2-0.83 IU/L),normal body weight(body mass index standard deviation score between-2 and+2),and early breast development who visited the Department of Pediatric Endocrinology,Henan Provincial Maternal and Child Health Hospital from January 2022 to March 2025 were retrospectively analyzed.According to the Expert Consensus on the Diagnosis and Treatment of Central Precocious Puberty(2022),patients were assigned to an ICPP group(n=123)or a control group(n=122).Correlations between the homeostasis model assessment of insulin resistance(HOMA-IR)and selected indices were assessed.Multivariable logistic regression was used to evaluate the association between HOMA-IR and ICPP,and the diagnostic performance of various indices for ICPP was evaluated.Results HOMA-IR was higher in the ICPP group than in the control group(P<0.001)and was positively correlated with LH peak(rs=0.467,P<0.05)and the LH peak/FSH peak ratio(rs=0.444,P<0.05).The multivariable logistic regression model including age,BMI,and basal LH showed that HOMA-IR was closely associated with ICPP(OR=2.756,95%CI:1.940-3.913).Receiver operating characteristic curve analysis showed that the areas under the curve for basal LH,HOMA-IR,and their combination in diagnosing ICPP were 0.735,0.735,and 0.805,respectively(P<0.05),and the combined model had a greater area under the curve than either basal LH or HOMA-IR alone(both P<0.05).Conclusions HOMA-IR is closely associated with ICPP in girls with low LH and normal body weight,and combining HOMA-IR with basal LH improves early identification and diagnostic efficiency in this population.