ObjectiveTo characterize the systemically absorbed components of Zhengqi prescription （ZQP） and investigate its therapeutic effects and mechanisms against cancer-related fatigue （CRF） through an integrated approach of network pharmacology and animal experiments. MethodsThe systemically absorbed components of ZQP were identified by ultra-performance liquid chromatography coupled with quadrupole-Orbitrap mass spectrometry （UPLC-Q-Exactive Orbitrap MS）. Network pharmacology was employed to predict the potential targets and signaling pathways involved in the anti-CRF effects of ZQP. A mouse model of paclitaxel-induced CRF， mimicking breast cancer-related fatigue， was established. Mice were administered different doses of ZQP extract. The therapeutic efficacy was evaluated based on fatigue-related indicators， behavioral test results， and energy metabolism parameters. Western blot analysis was conducted to validate the regulation of core signaling pathways. ResultsA total of 48 systemically absorbed components were identified. Network pharmacology analysis revealed 124 intersecting targets （including Akt1） and implicated signaling pathways such as phosphatidylinositol 3-kinase/protein kinase （PI3K/Akt）. Pharmacodynamic studies with the established CRF model demonstrated that ZQP alleviated fatigue symptoms. Specifically， ZQP-treated mice exhibited increased exhaustive swimming time （P<0.05）， prolonged struggling time in the tail suspension test， and enhanced movement duration and distance in the open field test （P<0.05， P<0.01）. Furthermore， ZQP treatment decreased serum lactate and blood urea nitrogen levels （P<0.05， P<0.01）， increased liver and muscle glycogen content （P<0.05， P<0.01）， and upregulated the expression of phosphorylated （p）-Akt and p-PI3K in the gastrocnemius muscle （P<0.05， P<0.01）. ConclusionZQP exerts significant therapeutic effects on paclitaxel-induced breast cancer-related fatigue by regulating the PI3K/Akt signaling pathway. Furthermore， this study provides a preliminary foundation for subsequent quality control of the formula and further investigation into its mechanism against CRF.

Objective To interpret the major updates in the "2026 ACC/AHA Guideline on the Management of Dyslipidemia" and provide guidance for perioperative and long-term lipid management in patients undergoing cardiovascular surgery. Methods Based on the 2026 guideline and relevant cardiovascular outcome studies, this article summarized key aspects of lipid management across the entire life span, PREVENT-ASCVD risk assessment, low-density lipoprotein cholesterol (LDL-C) and non-high-density lipoprotein cholesterol (non-HDL-C) targets, lipoprotein(a) and apolipoprotein B testing, management of hypertriglyceridemia, and the use of non-statin therapies, while analyzing their clinical implications from the perspective of cardiovascular surgery. Results The 2026 guideline expanded the focus of lipid management from simple cholesterol control to comprehensive, lifelong lipid protein risk management across the entire spectrum. It recommended the PREVENT-ASCVD model, integrated risk-enhancing factors and coronary artery calcium scores to refine risk stratification, re-established LDL-C and non-HDL-C targets, strengthened management of lipoprotein(a), apolipoprotein B, and hypertriglyceridemia, and emphasized early combination lipid-lowering therapy. These updates helped identify residual cardiovascular risks in surgical patients, promoted postoperative lipid target achievement, and supported long-term secondary prevention. Conclusion The 2026 guideline advances lipid management toward precise risk stratification, goal-directed therapy, and longitudinal intervention. Standardized lipid assessment and lipid-lowering therapy should be integrated into perioperative and long-term postoperative management in cardiovascular surgery to improve long-term patient outcomes.

Objective To explore latent classes of behavioural and psychological symptoms in the patients with Alzheimer's disease(AD)and to identify the factors influencing the latent classes and provide a basis for fomulating personalized nursing measures.Methods Convenience sampling was employed to recruit 361 AD inpatients from our hospital between November 2023 and May 2024 for this cross-sectional study.A general data questionnaire,the neuropsychiatric inventory questionnaire,Monteria cognitive assessment scale,activity of daily life scale,and mini-nutritional assessment scale were used in the survey.Latent class analysis was conducted to analyse the data acquired from the survey.Univariate analysis and multiple Logistic regression analysis were used to identify the factors influencing latent classes.Results Toally 346 patients finished the study.It was found that a 72.5%of AD patients developed behavioural and psychological symptoms.The symptoms were categorised into three classes:low symptom-apathy,middle symptom-emotional disturbance and high symptom-behaviour disorder.The course of disease,cognitive function,daily living ability and nutritional status were identified as the factors that influenced the latent classes(all P<0.05).Conclusion AD patients with low cognitive function,poor daily living ability,malnutrition and a course of disease over 5 years are at high risks of behavioural and psychological symptoms which are heterogeneous.Care providers are advised to propose personalised care strategies to improve the behavioural and psychological symptoms.

Objective To investigate the expression changes of T-cell exhaustion-related genes in multiple myeloma(MM)and their potential causal relationships.Methods A bidirectional summary-level Mendelian randomization(MR)analysis was used to explore the causal relationship between T-cell exhaustion and MM.The eQTL data and genome-wide association study(GWAS)were used to summarize data,and corresponding single nucleotide polymorphisms(SNPs)were extracted as instru-mental variables.Four methods,namely inverse variance weighted(IVW)method,MR Egger,weighted median,and weighted mode were used to assess the reliability of the causal relationship.The robustness of the results was validated using Cochran's Q heterogeneity test and pleiotropy test.In cel-lular models,RNA interference was used to silence key target genes,and phenotypic changes such as myeloma cell viability,colony-forming ability,and apoptosis were observed to experimentally confirm the causal effects revealed by MR.Results The genes PRDM1,ENTPD1,PTPN11,and HLA-B were involved in the T-cell exhaustion process in MM.The presence of the PRDM1 gene(OR=0.998 5,95％CI,0.997 1 to 0.999 8,P=0.024 6)may reduce the risk of MM,whereas ENTPD1(OR=1.000 4,95％CI,1.000 1 to 1.000 7,P=0.015 8),HLA-B(OR=1.000 4,95％CI,1.000 1 to 1.000 8,P=0.012 4),and PTPN11(OR=1.002 5,95％CI,1.001 0 to 1.003 9,P=0.001 2)were associated with an increased risk of MM.Real-time quantitative polymerase chain reaction showed overexpression of PTPN11 in MM cell lines and patients' samples.By assessing cell viabili-ty,colony formation and detecting apoptosis,it was found that inhibiting PTPN11 promoted apopto-sis in MM cell lines.Conclusion A causal relationship exists between T-cell exhaustion and MM.Targeted interventions against specific T-cell exhaustion-related genes may help reduce the incidence of MM.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective To explore latent classes of behavioural and psychological symptoms in the patients with Alzheimer's disease(AD)and to identify the factors influencing the latent classes and provide a basis for fomulating personalized nursing measures.Methods Convenience sampling was employed to recruit 361 AD inpatients from our hospital between November 2023 and May 2024 for this cross-sectional study.A general data questionnaire,the neuropsychiatric inventory questionnaire,Monteria cognitive assessment scale,activity of daily life scale,and mini-nutritional assessment scale were used in the survey.Latent class analysis was conducted to analyse the data acquired from the survey.Univariate analysis and multiple Logistic regression analysis were used to identify the factors influencing latent classes.Results Toally 346 patients finished the study.It was found that a 72.5%of AD patients developed behavioural and psychological symptoms.The symptoms were categorised into three classes:low symptom-apathy,middle symptom-emotional disturbance and high symptom-behaviour disorder.The course of disease,cognitive function,daily living ability and nutritional status were identified as the factors that influenced the latent classes(all P<0.05).Conclusion AD patients with low cognitive function,poor daily living ability,malnutrition and a course of disease over 5 years are at high risks of behavioural and psychological symptoms which are heterogeneous.Care providers are advised to propose personalised care strategies to improve the behavioural and psychological symptoms.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective:To evaluate the effects of different types of psychological interventions on the fear of cancer recurrence through a network Meta-analysis.Methods:Randomized controlled trials on the effects of different types of psychological interventions on the fear of cancer recurrence were retrieved from PubMed, PsycINFO, Web of Science, The Cochrane Library, Embase, EBSCO, China Biomedical Literature Database, CNKI, Wanfang Database and Vip Database. The retrieval period was from the establishment of the database to December, 31 2022. Two researchers conducted literature screening, extraction and quality evaluation, and used Stata14.0 software to conduct network Meta-analysis.Results:A total of 29 pieces of research involving 3 068 cancer patients and 11 psychological intervention measures. The results of network Meta-analysis showed that narrative therapy, PERMA(Positive, Engagement, Relationship, Meaning, Accomplishment) happiness theory model, acceptance and commitment therapy and cognitive behavior therapy had statistically significant differences in the intervention effect on the fear of cancer recurrence compared with conventional nursing ( SMD values were -1.93--0.83, all P<0.05); there was no significant difference among narrative therapy, PERMA happiness model, acceptance and commitment therapy and gratitude-expansion behavior theory (all P>0.05). The results of the cumulative probability map showed the best intervention was narrative therapy. Conclusions:The results of this study suggest that narrative therapy, acceptance and commitment therapy, and cognitive behavior therapy may be effective psychological intervention measures to improve the fear of cancer recurrence. However, more studies are still needed for further verification.

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) technology has the characteristics of high specificity and high throughput, making it rapidly applied and developed in the field of clinical testing. Its application in the monitoring of therapeutic drugs can effectively improve the quantitative accuracy and sensitivity, and formulate a personalized and optimal dosing plan for patients. However, this technology still faces some challenges, and automation, quality control, and quantitative traceability will be the future development direction.

Objective:To understand the psychological experience of tumor radiotherapy patients after participating in acceptance and commitment therapy (ACT), and to provide reference and supplement for the development of ACT psychological intervention plans for tumor radiotherapy patients.Methods:This study was a qualitative study. Using the purposive sampling method, a total of 17 tumor radiotherapy patients treated at Shanghai Proton and Heavy Ion Center from January to March 2023 were selected as the research objects. Phenomenological research method was used to conduct semi-structured in-depth interviews with patients, and the interview data were analyzed by Colaizzi 7-step analysis method.Results:Three themes were extracted, namely, positive physical and mental experience (including improving physical symptoms, relieving negative emotions and cooperating with particle therapy), gaining personal growth (including learning flexible and varied psychological coping strategies, gaining mutual help and friendship, feeling valuable family affection and drawing a better future life) and recommendations for optimizing the ACT intervention program (including building teams according to the patients' conditions, increasing the frequency of activities, trying to experience relief exercises outdoors and increasing the continuity of the intervention program) .Conclusions:ACT can help cancer radiotherapy patients improve their physical and mental health, cope with psychological problems, so that patients can face the disease, treatment and life more positively.