Cancer stem cells (CSCs) represent a distinct subpopulation of cells characterized by self-renewal capacity, differentiation potential, and critical roles in driving tumor progression, therapeutic resistance, recurrence, and maintenance of the tumor microenvironment. Targeting CSCs has emerged as a pivotal direction in cancer research, offering novel strategies to overcome drug resistance and prevent metastasis and relapse. Lysosomes, traditionally recognized as central organelles for intracellular degradation and recycling, are indispensable for cellular homeostasis. Dysregulation of lysosomal function is intimately linked to various diseases, including cancer. In tumors, aberrant lysosomal activity can promote malignant progression through mechanisms such as altering metabolic pathways, enhancing lysosomal exocytosis, modulating drug resistance, and interfering with autophagy-lysosomal pathways. Recent studies have underscored the involvement of lysosomes in regulating CSC properties. This review synthesizes findings on lysosomal regulation of CSCs through the following aspects. (1) Lysosomes exert complex and critical bidirectional control over CSC stemness maintenance through three degradation pathways that are dependent on their degradative function. (i) The lysophagy pathway. This pathway exhibits dual roles. Activation can sustain CSC functions; for instance, in glioblastoma, hypoxia upregulates Gal-8 via the STAT3/HIF1α signaling axis to induce autophagy, supporting stem cell survival. In head and neck squamous cell carcinoma, degradation of GSK3β activates the Wnt pathway, enhancing stemness. Conversely, this pathway can suppress stemness by degrading stemness-related proteins such as BMI-1 and OCT4A, thereby impairing CSC self-renewal capacity. (ii) Mitophagy pathway. In non-small cell lung cancer stem cells, mitophagy-related mechanisms, such as the accumulation of mitochondrial DNA (mtDNA) activating the TLR9-Notch1-AMPK signaling axis, have been shown to promote CSC proliferation. (iii) Autophagosome-dependent lysosomal degradation pathway. This pathway directly regulates stemness-related proteins in a bidirectional manner. Enhanced degradative function can promote CSC properties, exemplified by the degradation of NUMB to activate Notch signaling. Conversely, attenuated degradative function can also enhance stemness by stabilizing oncoproteins (e.g., protecting Frizzled-1 from degradation to sustain Wnt signaling) or preventing the degradation of tumor suppressors (e.g., inhibiting Notch degradation). (2) Constituent proteins of lysosomes, including membrane proteins and luminal acid hydrolases, participate in regulating CSC stemness. Regarding membrane proteins, LAMP2A facilitates chaperone-mediated autophagy to maintain stemness in glioblastoma and ovarian cancer. V-ATPase, by maintaining an acidic luminal environment, promotes proliferation and drug resistance in glioma stem cells. Among hydrolases, cathepsins B and L are highly expressed in pancreatic and ovarian cancers and correlate with poor prognosis. Furthermore, targeting lysosomes to induce lysosomal membrane permeabilization (LMP) triggers lysosome-mediated cell death, presenting a potential therapeutic strategy for eradicating CSCs.(3) The acidic luminal environment, single-membrane structure, and the presence of transmembrane transporters (e.g., ABCA3) enable lysosomes to passively trap or actively uptake and sequester chemotherapeutic drugs. Subsequent drug extrusion via exocytosis confers drug resistance. In CSCs, this lysosome-mediated drug sequestration, often cooperating with autophagy, establishes multimodal drug resistance. Therefore, targeting lysosomal function represents a potential strategy to overcome therapy resistance. The central role of lysosomes in regulating CSC stemness and resistance positions them as highly promising therapeutic targets. Strategies aimed at disrupting lysosomal function to selectively eliminate CSCs include: inhibiting the lysosome-autophagy system using agents like IITZ or lovastatin; inducing lysosomal membrane permeabilization (LMP) with compounds such as hexamethylene amiloride to compromise membrane stability; and disrupting the acidic luminal environment using drugs like siramesine or the K/H transport compound 2. In conclusion, lysosomes critically regulate CSC stemness maintenance and drug resistance through degradative pathways, membrane protein functions, luminal hydrolase activities, and drug sequestration mechanisms. This redefines the lysosome from a traditional “waste disposal unit” to a “signal integration center” in CSCs. The duality and context-dependency of lysosomal function in CSCs offer novel insights into the heterogeneity observed across different tumors. Targeting lysosomal vulnerabilities—such as inducing LMP, disrupting acidity, or blocking autophagic flux—provides a strategy to bypass canonical CSC resistance mechanisms and directly trigger cell death. This establishes the lysosome as a key target to overcome CSC-mediated therapy resistance, paving the way for developing diverse candidate drugs and innovative combination therapies in oncology.

Objective:To analyze the predictive role of WT1 expression levels pre-and early post-transplantation on relapse and overall survival(OS)in patients with acute myeloid leukemia(AML)undergoing allogeneic hematopoietic stem cell transplantation(allo-HSCT)during their first complete remission(CR1).Methods:A retrospective analysis was conducted on the clinical data of 107 adult AML patients who underwent allo-HSCT during their CR1 at our center between May 2012 and December 2021.The predictive role of bone marrow WT1 expression levels before transplantation and at 3 and 6 months post-transplantation on relapse and OS was explored in combination with relevant clinical factors.Results:The median follow-up time for the 107 patients was 70(range:11-117)months.Among the patients,15 cases died.Kaplan-Meier survial analysis showed that the 3-year overall survival(OS)rate was 85.0％.20 patients experienced relapse,with a median time to relapse of 8(range:0.5-44)months and a l-year cumulative relapse rate of 13.1％.The overall median value of WT1 before transplantation,3 months after transplantation,and 6 months after transplantation was 0.26％(range:0％-23.64％),with an upper quartile value of 0.74％.No statistically significant differences in WT1 expression levels were observed among the pre-transplantation,3-month post-transplantation,and 6-month post-transplantation time points(P=0.227).Univariate analysis showed that patients with WT1 levels＞0.74％at 3 months post-transplantation had a higher 1-year relapse rate(P=0.029)and lower 3-year OS rate(P＜0.001)compared to patients with WT1 levels ≤0.74％.Other significant factors affecting 1-year relapse included stem cell source(P=0.041)and chronic graft-versus-host disease(cGVHD)(P=0.013).For 3-year OS,additional influencing factors were genetic high risk(P=0.048)and stem cell source(P=0.016).Multivariate analysis revealed that WT1 level＞0.74％at 3 months post-transplantation had a trend to affect 1-year relapse rate(HR=3.309,95％CI:0.958-11.431,P=0.058),while the absence of cGVHD was an independent risk factor for 1-year relapse(HR=3.473,95％CI:0.749-16.100,P=0.037).Only WT1 level＞0.74％at 3 months post-transplantation was an independent risk factor for 3-year OS(HR=6.886,95％CI:2.402-19.738,P＜0.001).Conclusion:High WT1 expression level at 3 months post-transplantation in AML patients undergoing allo-HSCT during CR1 affects the 1-year relapse rate and 3-year OS,and is an independent risk factor affecting 3-year OS.These findings suggest that dynamic monitoring of WT1 expression levels has certain value in prognostic assessment of AML patients who received allo-HSCT during CR1.

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 analyze the association between lifestyle and the risk of type 2 diabetes(T2D)among adult residents.Methods The data was sourced from the Shanghai Suburban Adult Cohort and Biobank.A total of 42 096 adult residents who had not developed T2D were recruited from four districts of Shanghai(Songjiang,Jiading,Minhang,and Xuhui)between 2016 and 2019.The follow-up ended on Feb 28,2023.A structured questionnaire was used to collect information on six lifestyle-related items,including smoking,alcohol consumption,BMI,waist circumference(WC),physical activity,and diet.The unhealthy lifestyle scores(UHLS)were calculated by counting the number of all the unhealthy lifestyle items,with a range of 0-6.New-onset T2D events diagnosed by physicians were obtained through the medical information system.Cox proportional hazards regression model and restricted cubic spline model were utilized to evaluate the association between unhealthy lifestyles and the risk of T2D incidence.Results About 28.1%of the participants led 4-6 unhealthy lifestyles.A total of 1 752 new T2D cases were identified during 218 513.4 person-years of follow-up.Analysis of single unhealthy lifestyle showed that abnormal WC(HR=1.5,95%CI:1.4-1.7)and abnormal BMI(HR=1.3,95%CI:1.2-1.5)were associated with an increased risk of T2D.Compared with individuals with a UHLS of 0-1,those with a UHLS of 3 and 4-6 had 30%(95%CI:1.1-1.6)and 50%(95%CI:1.2-1.8)higher risks of T2D,respectively.Each additional unhealthy lifestyle was associated with a 10%increase in T2D incidence risk(HR=1.1,95%CI:1.1-1.2).Conclusion The risk of T2D in adult residents increases with the cumulative number of unhealthy lifestyles.Adult residents with abnormal WC or BMI,or have three or more unhealthy lifestyles accumulated,will increase the risk of new-onset T2D.

Objective To investigate the differences in demographic characteristics,reproductive health status,and the distribution of pregnancy-related diseases between couples conceived via assisted reproductive technology(ART)and naturally conceived couples,and to analyze the impact of ART treatment on the incidence of preterm birth(PTB)in singleton and twin and multiple pregnancies.Methods We conducted a retrospective analysis of the maternal and infant cohort data of Jing'an District from 2013 to 2020.Based on the conception method,the subjects were categorized into two groups:the ART group and the natural conception group.Chi-square test was applied to compare baseline characteristics and disease distributions differences between the two groups,and logistic regression models were used to evaluate the association between ART and the PTB risks.A causal mediation model was used to evaluate the mediating effect of twin and multiple pregnancy in the relationship between ART and PTB.Results A total of 117 717 parturients were included,6 265 in the ART group and 111 452 in the natural conception group.Compared with the natural conception group,couples in the ART group were significantly older and had a higher prevalence of reproductive system diseases.The incidences of diabetes and hypertensive disorders during pregnancy in ART parturient were 13.76%and 9.99%,respectively,which were significantly higher than 7.88%and 4.75%in the natural conception group(both P<0.001).The overall PTB rate in the ART group was 14.81%,higher than 5.35%in the natural conceptions group(P<0.001).The PTB rate in ART for singleton pregnancies in the ART group was 6.40%,higher than 4.83%in the natural conception group(P<0.001),while the PTB rate in ART for twin and multiple pregnancies in the ART group was 53.97%,lower than 60.42%in the natural conception group(P<0.05).Mediation analysis showed that 97.99%of the effect of ART on PTB was mediated by twin and multiple pregnancy,with ART increasing the PTB risk by 3.44 times through multiple pregnancy.Conclusion The overall PTB rate of ART recipients is higher than that of natural recipients,but ART does not increase the PTB risk in singleton and twin and multiple pregnancies.Twin and multiple pregnancy is the key mediating factor contributing to PTB in ART-conceived recipients.Compared with naturally conceived couples,ART conception couples own more advanced maternal age,and have higher risks of suffering gestational diabetes,gestational hypertension,and PTB.

Objective:To investigate the preferences and influencing factors of Traditional Chinese Medicine(TCM)health management services among community healthcare workers,providing a reference for promoting and spreading TCM health management services in communities.Methods:Based on a discrete choice experiment,a questionnaire survey was conducted among 596 community healthcare workers across 9 administrative districts in Beijing.A mixed logit model was used to analyze their preferences.Results:Changes in post-tax monthly income,expected efficacy,single-service duration,technical difficulty,patient compliance,and insurance coverage significantly influenced community doctors'service preferences.Changes in post-tax monthly income had the highest relative importance(35.11%),followed by insurance coverage(34.87%).Preferences varied among healthcare workers with different professional titles,backgrounds,weekly working hours,and whether the service was included in performance evaluations.Conclusion:Both economic and non-economic factors influence community healthcare workers'service preferences to varying degrees.Optimizing the combination of TCM health management service attributes can enhance community healthcare workers'willingness to provide such services.It is recommended to use a combination of economic and non-economic incentive measures and develop differentiated strategies for healthcare workers with different characteristics to meet their service preferences and promote the development of TCM health management services in communities.

Objective:To investigate the preferences and influencing factors of Traditional Chinese Medicine(TCM)health management services among community healthcare workers,providing a reference for promoting and spreading TCM health management services in communities.Methods:Based on a discrete choice experiment,a questionnaire survey was conducted among 596 community healthcare workers across 9 administrative districts in Beijing.A mixed logit model was used to analyze their preferences.Results:Changes in post-tax monthly income,expected efficacy,single-service duration,technical difficulty,patient compliance,and insurance coverage significantly influenced community doctors'service preferences.Changes in post-tax monthly income had the highest relative importance(35.11%),followed by insurance coverage(34.87%).Preferences varied among healthcare workers with different professional titles,backgrounds,weekly working hours,and whether the service was included in performance evaluations.Conclusion:Both economic and non-economic factors influence community healthcare workers'service preferences to varying degrees.Optimizing the combination of TCM health management service attributes can enhance community healthcare workers'willingness to provide such services.It is recommended to use a combination of economic and non-economic incentive measures and develop differentiated strategies for healthcare workers with different characteristics to meet their service preferences and promote the development of TCM health management services in communities.

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 explore the mechanism of Bushen Tongfu prescription(BSTF)regulating gut microbiota and improving learning and memory ability of Alzhei-mer's disease(AD)model mice.Methods APP/PS1 mice were administered by BSTF for eight weeks.The spatial learning and memory ability of mice were detected by Morris water maze.The changes in the gut microbiota abundance and diversity of mice were detec-ted by 16S rDNA technology.The morphological chan-ges of hippocampus were observed by HE staining.The expression of the activation of microglia in hippocampus was detected by immunofluorescence.The expressions of pro-inflammatory factors of TLR4,NF-κB and IL-6 in brain tissue were detected by Western blot.Results Compared with the model group,the escape latency and swimming path were shortened,the times of target crossings after removing the platform increased in BSTF groups(P＜0.05),the α-diversity increased and the abundance of gut microbiota was regulated,the mor-phological structure and pathological damage of hipp-ocampal cells were improved,the co-expression of iN-OS/Iba1 decreased and the co-expression of Arg1/Iba1 increased significantly(P＜0.01),the transformation of M1 to M2 of microglial cells was promoted,and the expression of TLR4,NF-κB and IL-6 protein decreased significantly.Conclusions BSTF can improve the learning and memory ability of APP/PS1 mice,and its mechanism may be related to regulating the gut micro-biota and the LPS/TLR4/NF-κB pathway,inhibiting the activation of pro-inflammatory microglia,reducing inflammation in hippocampus and improving the patho-logical damage of hippocampal cells.

Bovine rotavirus(BRV)is an important pathogen causing diarrhea in calves.To understand the genomic charac-teristics and genetic variations in bovine rotavirus,and to further enrich data on the biological characteristics of rotavirus,we aimed to amplify 11 gene segments of the isolated and cultured G6P[1]bovine rotavirus BLL strain,perform whole genome se-quencing,and analyze the molecular characteristics.MEGA7.0 and DNAMAN software were used for homology and typing a-nalysis,and the whole genome phylogenetic tree was constructed to analyze genetic evolution relationships.The complete geno-type of the BLL strain was G6-P[1]-I2-R2-C2-M2-A3-N2-T6-E2-H3.Phylogenetic analysis of the VP7 and VP4 genes of the BLL strain showed that the VP7 gene had the highest homology with RVA/Cow-wt/HB01/China/2021,and the VP4 gene of the BLL strain was in the same branch as RVA/Human-tc/ISR/Ro8059/1995.From the sequence alignment of VP8*amino acids,the sialic acid domain of the BLL strain was found to be similar to that in other P[1]strains,but different from those in other types of strains,except for residue 189,which was the same as that in Ro8059 but different from that in other strains.The results suggested that the BLL strain might potentially infect humans.Therefore,continued monitoring and study of the biological characteristics of this strain are necessary to provide more information and evidence supporting further research on the cross-species transmission of group A rotavirus in China.