OBJECTIVE To investigate the apoptosis-inducing effect of esculetin （Esc） on acute myeloid leukemia （AML） HL-60 cells by regulating the protein kinase B （AKT）/S-phase kinase-associated protein 2 （SKP2）/MutT homolog 1 （MTH1） pathway. METHODS AML HL-60 cells were randomly divided into control group （routine culture）， Esc low-concentration group （L-Esc group， 25 μmol/L Esc）， Esc medium-concentration group （M-Esc group， 50 μmol/L Esc）， Esc high-concentration group （H-Esc group， 100 μmol/L Esc）， and high-concentration of Esc+ SC79 （AKT agonist） group （100 μmol/L Esc+5 μmol/L SC79）. Cell proliferation in each group was detected by MTT assay and colony formation assay. The level of reactive oxygen species （ROS） in cells was measured by using the CM-H2DCFDA fluorescent probe. Cell apoptosis was analyzed by flow cytometry. Western blot assay was performed to detect the expression levels of apoptosis-related proteins [B-cell lymphoma 2 （Bcl-2）， Bcl-2-associated X protein （Bax）， cleaved caspase-3]， AKT/SKP2/MTH1 pathway-related proteins （p-AKT， AKT， SKP2， MTH1）， along with the upstream and downstream proteins of AKT phosphatidylinositol 3-kinase （PI3K）， cyclin-dependent kinase inhibitor 1 （P21） and cyclin-dependent kinase inhibitor 1B （P27）. RESULTS Compared with control group， the cell viability， colony number， and the phosphorylation levels of AKT and PI3K proteins as well as protein expressions of SKP2， MTH1 and Bcl-2 were significantly decreased （P＜0.05）， while ROS level， apoptosis rate， and the expression levels of Bax， cleaved caspase-3， P21 and P27 proteins were significantly increased （P＜0.05）. Moreover， the effects of Esc exhibited concentration-dependence （P＜0.05）. Compared with H-Esc group， above indexes of high-concentration of Esc+ SC79 group were reversed significantly （P＜0.05）. CONCLUSIONS Esc may promote massive ROS production and induce activation of apoptosis in HL-60 cells by inhibiting the AKT/SKP2/MTH1 pathway， thus inhibiting the proliferation of HL-60 cells.

AIM To establish an HPLC-MS/MS method for the simultaneous content determination of gallic acid,protocatechuic acid,oxypaeoniflorin,catechin,epicatechin,albiflorin,paeoniflorin,rutin,calycosin-7-glucoside,syringaldehyde,ferulic acid,coumarin,ononin,calycosin,cinnamic alcohol,cinnamic acid,benzoylpaeoniflorin,cinnamaldehyde,astragaloside,astragaloside Ⅲ,6-gingerol in Huangqi Guizhi Wuwu Decoction.METHODS The analysis was performed on a 30 ℃ thermostatic Thermo Scientific Hypersil GOLD column(150 mmx4.6 mm,3 μm),with the mobile phase comprising of 0.015％formic acid-acetonitrile flowing at 0.4 mL/min in a gradient elution manner,and electrospray ionization source was adopted in positive and negative ion modes with multiple reaction monitoring.RESULTS Twenty-one constituents showed good linear relationships within their own ranges(r＞0.990 5),whose average recoveries were 93.99％-108.52％with the RSDs of 1.04％-5.97％.CONCLUSION This simple,feasible,stable and reliable method can be used for the quality control of Huangqi Guizhi Wuwu Decoction.

AIM To establish an HPLC-MS/MS method for the simultaneous content determination of gallic acid,protocatechuic acid,oxypaeoniflorin,catechin,epicatechin,albiflorin,paeoniflorin,rutin,calycosin-7-glucoside,syringaldehyde,ferulic acid,coumarin,ononin,calycosin,cinnamic alcohol,cinnamic acid,benzoylpaeoniflorin,cinnamaldehyde,astragaloside,astragaloside Ⅲ,6-gingerol in Huangqi Guizhi Wuwu Decoction.METHODS The analysis was performed on a 30 ℃ thermostatic Thermo Scientific Hypersil GOLD column(150 mmx4.6 mm,3 μm),with the mobile phase comprising of 0.015％formic acid-acetonitrile flowing at 0.4 mL/min in a gradient elution manner,and electrospray ionization source was adopted in positive and negative ion modes with multiple reaction monitoring.RESULTS Twenty-one constituents showed good linear relationships within their own ranges(r＞0.990 5),whose average recoveries were 93.99％-108.52％with the RSDs of 1.04％-5.97％.CONCLUSION This simple,feasible,stable and reliable method can be used for the quality control of Huangqi Guizhi Wuwu Decoction.

Objective:To evaluate the cost-effectiveness and impact on mortality of health management services for the elderly aged 65 years and older in national essential public health service project.Methods:Based on the data of county-level medical institutions in Henan Province from 2019 to 2024,the Random Forest Method was used to construct a counterfactual framework to predict the hospitalization expenses under the unmanaged scenario,and then the cost-benefit ratio(BCR)and net income were calculated.Time-dependent Cox proportional hazards model was used to evaluate the effect of health management on all-cause mortality and cardiovascular and cerebrovascular disease mortality in the elderly.Results:A total of 962 955 elderly patients were included,451 119(46.85%)were included in the management group.The average hospitalization cost of the management group was significantly lower than that of the non-management group(P<0.05).Except for 2020-2021,BCRS in 2019 and 2022-2024 were 6.34,2.05,4.45 and 6.60,respectively.The risk of all-cause death was reduced by 76.96%,and the risk of cardiovascular and cerebrovascular death was reduced by 75.57%in the elderly patients included in the management group compared with those not included in the management group.Suggestions:It is necessary to establish a health outcomes-based evaluation system and promote the transformation and upgrading of the service model from single chronic disease management to"integrated health services with multi-disease management".

Dormant tumor cells constitute a population of cancer cells that reside in a non-proliferative or low-proliferative state, typically arrested in the G0/G1 phase and exhibiting minimal mitotic activity. These cells are commonly observed across multiple cancer types, including breast, lung, and ovarian cancers, and represent a central cellular component of minimal residual disease (MRD) following surgical resection of the primary tumor. Dormant cells are closely associated with long-term clinical latency and late-stage relapse. Due to their quiescent nature, dormant cells are intrinsically resistant to conventional therapies—such as chemotherapy and radiotherapy—that preferentially target rapidly dividing cells. In addition, they display enhanced anti-apoptotic capacity and immune evasion, rendering them particularly difficult to eradicate. More critically, in response to microenvironmental changes or activation of specific signaling pathways, dormant cells can re-enter the cell cycle and initiate metastatic outgrowth or tumor recurrence. This ability to escape dormancy underscores their clinical threat and positions their effective detection and elimination as a major challenge in contemporary cancer treatment. Hypoxia, a hallmark of the solid tumor microenvironment, has been widely recognized as a potent inducer of tumor cell dormancy. However, the molecular mechanisms by which tumor cells sense and respond to hypoxic stress—initiating the transition into dormancy—remain poorly defined. In particular, the lack of a systems-level understanding of the dynamic and multifactorial regulatory landscape has impeded the identification of actionable targets and constrained the development of effective therapeutic strategies. Accumulating evidence indicates that hypoxia-induced dormancy tumor cells are accompanied by a suite of adaptive phenotypes, including cell cycle arrest, global suppression of protein synthesis, metabolic reprogramming, autophagy activation, resistance to apoptosis, immune evasion, and therapy tolerance. These changes are orchestrated by multiple converging signaling pathways—such as PI3K-AKT-mTOR, Ras-Raf-MEK-ERK, and AMPK—that together constitute a highly dynamic and interconnected regulatory network. While individual pathways have been studied in depth, most investigations remain reductionist and fail to capture the temporal progression and network-level coordination underlying dormancy transitions. Systems biology offers a powerful framework to address this complexity. By integrating high-throughput multi-omics data—such as transcriptomics and proteomics—researchers can reconstruct global regulatory networks encompassing the key signaling axes involved in dormancy regulation. These networks facilitate the identification of core regulatory modules and elucidate functional interactions among key effectors. When combined with dynamic modeling approaches—such as ordinary differential equations—these frameworks enable the simulation of temporal behaviors of critical signaling nodes, including phosphorylated AMPK (p-AMPK), phosphorylated S6 (p-S6), and the p38/ERK activity ratio, providing insights into how their dynamic changes govern transitions between proliferation and dormancy. Beyond mapping trajectories from proliferation to dormancy and from shallow to deep dormancy, such dynamic regulatory models support topological analyses to identify central hubs and molecular switches. Key factors—such as NR2F1, mTORC1, ULK1, HIF-1α, and DYRK1A—have emerged as pivotal nodes within these networks and represent promising therapeutic targets. Constructing an integrative, systems-level regulatory framework—anchored in multi-pathway coordination, omics-layer integration, and dynamic modeling—is thus essential for decoding the architecture and progression of tumor dormancy. Such a framework not only advances mechanistic understanding but also lays the foundation for precision therapies targeting dormant tumor cells during the MRD phase, addressing a critical unmet need in cancer management.

Objective:To evaluate the cost-effectiveness and impact on mortality of health management services for the elderly aged 65 years and older in national essential public health service project.Methods:Based on the data of county-level medical institutions in Henan Province from 2019 to 2024,the Random Forest Method was used to construct a counterfactual framework to predict the hospitalization expenses under the unmanaged scenario,and then the cost-benefit ratio(BCR)and net income were calculated.Time-dependent Cox proportional hazards model was used to evaluate the effect of health management on all-cause mortality and cardiovascular and cerebrovascular disease mortality in the elderly.Results:A total of 962 955 elderly patients were included,451 119(46.85%)were included in the management group.The average hospitalization cost of the management group was significantly lower than that of the non-management group(P<0.05).Except for 2020-2021,BCRS in 2019 and 2022-2024 were 6.34,2.05,4.45 and 6.60,respectively.The risk of all-cause death was reduced by 76.96%,and the risk of cardiovascular and cerebrovascular death was reduced by 75.57%in the elderly patients included in the management group compared with those not included in the management group.Suggestions:It is necessary to establish a health outcomes-based evaluation system and promote the transformation and upgrading of the service model from single chronic disease management to"integrated health services with multi-disease management".

Objective To analyze the clinical characteristics of high energy metabolism in lung cancer patients and its correlation with body composition,nutritional status,and quality of life,and to develop a corresponding risk prediction model.Methods Retrospectively analyzed 132 primary lung cancer patients admitted to the First Hospital of Shanxi Medical University from January 2022 to May 2023,and categorized into high(n=94)and low energy metabolism group(n=38)based on their metabolic status.Differences in clinical data,body composition,Patient Generated Subjective Global Assessment(PG-SGA)scores,and European Organization for Research and treatment of Cancer(EORTC)Quality of Life Questionnaire-Core 30(QLQ-C30)scores were compared between the two groups.Logistic regression was used to identify the risk factors for high energy metabolism in lung cancer patients,and a risk prediction model was established accordingly;the Hosmer-Lemeshow test was used to assess the model fit,and the ROC curve was used to test the predictive efficacy of the model.Results Of the 132 patients with primary lung cancer,94(71.2%)exhibited high energy metabolism.Compared with low energy metabolism group,patients in high-energy metabolism group had a smoking index of 400 or higher,advanced disease staging of stage Ⅲ or Ⅳ,and higher levels of IL-6 level,low adiposity index,low skeletal muscle index,and malnutrition(P<0.05),and lower levels of total protein,albumin,hemoglobin level,and prognostic nutritional index(PNI)(P<0.05).There was no significant difference in age,gender,height,weight,BMI and disease type between the two groups(P>0.05).Logistic regression analysis showed that smoking index≥400,advanced disease stage,IL-6≥3.775 ng/L,and PNI<46.43 were independent risk factors for high energy metabolism in lung cancer patients.The AUC of the ROC curve for the established prediction model of high energy metabolism in lung cancer patients was 0.834(95%CI 0.763-0.904).Conclusion The high energy metabolic risk prediction model of lung cancer patients established in this study has good fit and prediction efficiency.

Objective To investigate the risk factors for bronchopulmonary dysplasia(BPD)in twin preterm infants with a gestational age of<34 weeks,and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.Methods A retrospective analysis was performed for the twin preterm infants with a gestational age of<34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020.According to their conditions,they were divided into group A(both twins had BPD),group B(only one twin had BPD),and group C(neither twin had BPD).The risk factors for BPD in twin preterm infants were analyzed.Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.Results A total of 904 pairs of twins with a gestational age of<34 weeks were included in this study.The multivariate logistic regression analysis showed that compared with group C,birth weight discordance of>25%between the twins was an independent risk factor for BPD in one of the twins(OR=3.370,95%CI:1.500-7.568,P<0.05),and high gestational age at birth was a protective factor against BPD(P<0.05).The conditional logistic regression analysis of group B showed that small-for-gestational-age(SGA)birth was an independent risk factor for BPD in individual twins(OR=5.017,95%CI:1.040-24.190,P<0.05).Conclusions The development of BPD in twin preterm infants is associated with gestational age,birth weight discordance between the twins,and SGA birth.

Objective To explore the value of constructing a model to predict the number of positive cores in systematic biopsy in prostate cancer(PCa)using a combination of radiomics features based on magnetic resonance imaging and clinical indicators.Methods Retrospectively collected magnetic resonance imaging and clinical data from two medical institutions(Gansu Provincial Hospital from January 2018 to February 2023,Zhangye People's Hospital Affiliated to Hexi College from April 2020 to February 2023).The 155 patients from Gansu Provincial Hospital were randomly divided into a training set(n=109;80 cases with positive needle count≥6 and 29 cases with positive needle count<6)and an internal validation set(n=46;34 cases with positive needle count≥6 and 12 cases with positive needle count<6)in a 7:3 ratio.The 43 patients from Zhangye People's Hospital Affiliated to Hexi College were used as external validation set.Small field of view high-resolution T2-weighted imaging(sFOV HR-T2WI)and contrast-enhanced delayed-phase images were selected to extract radiomic features from the three-dimensional region of interest of the entire prostate,and radiomics model was constructed and Radscores calculated after dimensionality reduction and feature selection.Univariate and multivariate logistic regressions were used to screen for independent risk factors for positive cores in systematic biopsy.Nomogram was constructed using Radscore and clinical independent risk factors to predict the number of positive cores in systematic biopsy in PCa patients,which was then externally validated.Results Age,alkaline phosphatase(ALP),free prostate specific antigen(FPSA),total prostate specific antigen(TPSA),FPSA/TPSA ratio,and prostate specific antigen density(PSAD)were not statistically significantly different between the training,internal validation,and external validation sets(P>0.05).FPSA,TPSA,FPSA/TPSA ratio,and PSAD were significantly different between the positive cores<6 and positive cores≥6 groups(P<0.001).Univariate logistic regression analysis showed that FPSA(P<0.001),TPSA(P<0.001),FPSA/TPSA ratio(P=0.001),PSAD(P<0.001),and Radscore(P<0.001)were risk factors for positive cores in systematic biopsy in PCa.Multivariate logistic regression analysis showed that PSAD(OR=0.251,95%CI 0.063-0.996,P=0.049)and Radscore(OR=1.990,95%CI 1.409-2.812,P<0.001)were independent risk factors for positive cores in systematic biopsy in PCa.The clinical models achieved AUCs of 0.849(95%CI 0.774-0.924),0.817(95%CI 0.693-0.941),and 0.631(95%CI 0.439-0.822);the 12 features for radiomics models are derived solely from sFOV HR-T2WI,the radiomics models achieved AUCs of 0.868(95%CI 0.791-0.945),0.846(95%CI 0.695-0.996),and 0.815(95%CI 0.660-0.970);the nomogram achieved AUCs of 0.921(95%CI 0.869-0.973),0.868(95%CI 0.743-0.992),and 0.840(95%CI 0.702-0.978)in the training set,internal validation set,and external validation set,respectively.Conclusions The combination of radiomic features extracted from sFOV HR-T2WI and PSAD can preoperatively be used as a noninvasive manner to predict the number of positive cores of the PCa patients.This approach has a certain value in risk stratification of PCa patients and guiding personalized clinical management.