ObjectiveThis paper aims to investigate the efficacy and related actions of Guizhi Fulingwan in intervening in the mice with colitis-associated colon cancer （CAC） based on the immunosuppressive microenvironment associated with myeloid-derived suppressor cells （MDSCs）. MethodsSixty male C57BL/6 mice were randomly assigned to a blank group， a model group， an aspirin group （0.04 g·kg^-1）， and low-， medium-， and high-dose Guizhi Fulingwan groups （4.87， 9.75， and 19.50 g·kg^-1）， with ten mice per group. The CAC mouse model was established via combined induction of azoxymethane （AOM）/dextran sulphate sodium （DSS）. Drug intervention commenced in week five， with continuous intragastric administration for nine weeks. The food intake， body weight， fecal characteristics， and haematochezia were observed and recorded， and disease activity index （DAI） scores were calculated according to scoring criteria. Hematoxylin and eosin （HE） staining was used to observe the histopathological changes in the colon tissues of the mice. Immunohistochemistry was used to determine proliferating cell nuclear antigen-67 （Ki67） expression in the colon tissues， and enzyme-linked immunosorbent assay （ELISA） was used to detect the contents of interleukin-6 （IL-6）， IL-1β， and tumor necrosis factor-α （TNF-α） in the serum of the mice. Flow cytometry was employed to determine the proportion levels of MDSCs， CD4⁺ T cells， and CD8⁺ T cells in the spleen tissues of the mice. The mRNA expressions of MDSC-associated effector molecules， including arginase 1 （Arg1） and inducible nitric oxide synthase （iNOS）， were detected by real-time quantitative polymerase chain reaction （Real-time PCR）. After that， an in vitro co-culture model of MDSCs and CD8⁺ T cells was established， and drug-containing serum of Guizhi Fulingwan was used for intervention. The Flow cytometry was employed to assess the effects of drug-containing serum of Guizhi Fulingwan with different concentrations on the levels of reactive oxygen species （ROS） and iNOS in MDSCs and the proliferation of CD8⁺ T cells. The levels of granzyme B （GZMB） and interferon-γ （IFN-γ） in cell supernatant were detected by ELISA. ResultsCompared with those in the control group， the mice in the model group exhibited significantly reduced body weight， elevated DAI scores， shortened colon length （P<0.01）， increased number of tumors and Ki67 expression （P<0.01）， and significantly elevated contents of IL-6， IL-1β， and TNF-α in the serum （P<0.01）. Significant increases in the number of MDSCs were observed in mouse spleens， alongside marked reductions in the levels of CD4⁺ T and CD8⁺ T cells （P<0.01）. Furthermore， the mRNA expressions of MDSC function-associated effector molecules Arg1 and iNOS were significantly upregulated （P<0.01）. Compared with those in the model group， the mice in the middle-dose Guizhi Fulingwan group exhibited increased body weight and significantly decreased DAI scores （P<0.05， P<0.01）. The mice in the middle- and high-dose Guizhi Fulingwan groups exhibited significantly improved colon shortening， significantly decreased number of tumors and Ki67 expression （P<0.05， P<0.01）， and significantly decreased contents of IL-6， IL-1β， and TNF-α in the serum （P<0.05， P<0.01）. Furthermore， administration of Guizhi Fulingwan markedly reduced MDSC infiltration in the spleen of the mice， with different degrees of increase in the levels of both CD4⁺ T and CD8⁺ T cells （P<0.05， P<0.01）， alongside significant decreases in the mRNA expressions of Arg1 and iNOS （P<0.05， P<0.01）. In vitro cell co-culture shows that administration of drug-containing serum of Guizhi Fulingwan significantly decreases the activity levels of ROS and iNOS in MDSCs and promotes the proliferation of CD8⁺ T cells and the secretion of GZMB and IFN-γ （P<0.05， P<0.01）. ConclusionGuizhi Fulingwan can reduce pro-inflammatory cytokine secretion and inhibit tumor proliferation in the colon tissues of CAC mice. Its potential mechanism may involve reducing MDSC infiltration， enhancing effector T cells， particularly CD8⁺ T cell response， and improving the tumor immunosuppressive microenvironment.

Purpose: Considering the high disease burden and unique features of Asian patients with breast cancer (BC), it is essential to have a comprehensive view of genetic characteristics in this population. An institutional targeted sequencing platform was developed through the Korea Research-Driven Hospitals project and was incorporated into clinical practice. This study explores the use of targeted next-generation sequencing (NGS) and its outcomes in patients with advanced/metastatic BC in the real world. Materials and Methods: We reviewed the results of NGS tests administered to BC patients using a customized sequencing platform—FiRST Cancer Panel (FCP)—over 7 years. We systematically described clinical translation of FCP for precise diagnostics, personalized therapeutic strategies, and unraveling disease pathogenesis. Results: NGS tests were conducted on 548 samples from 522 patients with BC. Ninety-seven point six percentage of tested samples harbored at least one pathogenic alteration. The common alterations included mutations in TP53 (56.2%), PIK3CA (31.2%), GATA3 (13.8%), BRCA2 (10.2%), and amplifications of CCND1 (10.8%), FGF19 (10.0%), and ERBB2 (9.5%). NGS analysis of ERBB2 amplification correlated well with human epidermal growth factor receptor 2 immunohistochemistry and in situ hybridization. RNA panel analyses found potentially actionable and prognostic fusion genes. FCP effectively screened for potentially germline pathogenic/likely pathogenic mutation. Ten point three percent of BC patients received matched therapy guided by NGS, resulting in a significant overall survival advantage (p=0.022), especially for metastatic BCs. Conclusion Clinical NGS provided multifaceted benefits, deepening our understanding of the disease, improving diagnostic precision, and paving the way for targeted therapies. The concrete advantages of FCP highlight the importance of multi-gene testing for BC, especially for metastatic conditions.

Hepatocellular carcinoma (HCC) is an aggressive primary liver malignancy often diagnosed at an advanced stage, resulting in a poor prognosis. Accurate risk stratification and early detection of HCC are critical unmet needs for improving outcomes. Several blood-based biomarkers and imaging tests are available for early detection, prediction, and monitoring of HCC. However, serum protein biomarkers such as alpha-fetoprotein have shown relatively low sensitivity, leading to inaccurate performance. Imaging studies also face limitations related to suboptimal accuracy, high cost, and limited implementation. Recently, liquid biopsy techniques have gained attention for addressing these unmet needs. Liquid biopsy is non-invasive and provides more objective readouts, requiring less reliance on healthcare professional’s skills compared to imaging. Circulating tumor cells, cell-free DNA, and extracellular vesicles are targeted in liquid biopsies as novel biomarkers for HCC. Despite their potential, there are debates regarding the role of these novel biomarkers in the HCC care continuum. This review article aims to discuss the technical challenges, recent technical advancements, advantages and disadvantages of these liquid biopsies, as well as their current clinical application and future directions of liquid biopsy in HCC.

Objective:To investigate the relationship between adiponectin (ADPN), the ratio of high-sensitivity C-reactive protein (hs-CRP) to albumin (ALB), and the progression of acute cerebral infarction (ACI).Methods:This case-control study involved 147 patients with ACI who were treated at Xingyuan Hospital of Yulin between January 2023 and March 2024. The National Institutes of Health Stroke Scale (NIHSS) was used to evaluate disease progression. Based on the progression of the disease, the patients were divided into two groups: the progressive group ( n = 38) and the non-progressive group ( n = 109). ADPN level and the hs-CRP/ALB ratio were compared between the two groups. Additionally, multivariate logistic regression analysis was conducted to identify risk factors for disease progression in ACI patients. The predictive value of ADPN and the hs-CRP/ALB ratio for ACI was assessed using receiver operating characteristic curve analysis. Results:In the progressive group, hs-CRP level and hs-CRP/ALB ratio were (14.38 ± 3.27) mg/L and (0.43 ± 0.13), respectively, both of which were significantly higher than those in the non-progressive group [(8.92 ± 2.73) mg/L, (0.20 ± 0.07), t = 9.22, 10.37, both P < 0.001]. In contrast, the levels of ADPN and albumin in the progressive group were (2.71 ± 0.59) mg/L and (33.54 ± 8.66) g/L, respectively, both of which were significantly lower than those in the non-progressive group [(5.36 ± 1.42) mg/L, (45.05 ± 10.42) g/L, t = -15.99, -6.68, both P < 0.001]. The progressive group had higher admission NIHSS scores, a higher proportion of patients with diabetes, higher white blood cell counts, higher hemoglobin A1c (HbA1c) and fibrinogen levels compared with the non-progressive group ( t = 8.43, 2.88, 5.79, 2.77, χ2 = 4.40, all P < 0.05). Multivariate logistic regression analysis results indicated that higher admission NIHSS scores, HbA1c levels, and hs-CRP/ALB ratios were independent risk factors for disease progression in ACI patients ( OR = 2.438, 1.600, 2.971, all P < 0.05), while higher levels of ADPN were identified as a protective factor against disease progression ( OR = 0.321, P < 0.05). The receiver operating characteristic curve analysis showed that the area under the curve for predicting disease progression after admission for ACI patients using serum ADPN and hs-CRP/ALB ratio alone were 0.730 (95% CI: 0.658-0.800) and 0.758 (95% CI: 0.685-0.826), respectively. The area under the curve for the combined prediction of serum ADPN and the hs-CRP/ALB ratio was 0.895 (95% CI: 0.845-0.940), demonstrating higher predictive efficacy ( Z = 1.80, 2.13, both P < 0.05). Conclusions:ADPN level and hs-CRP/ALB ratio are closely related to the progression of ACI and demonstrate good predictive efficacy for disease progression.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

Objective:To analyze the development trend of medical institution bed allocation in OECD countries and provide insights for optimizing bed resources allocation in China.Methods:Data on indicators of bed allocation and related factors from 1991 to 2022 were collected from the OECD online database.Bed allocation impact dimensions were identified based on health needs and demand theories.The random effect model and correlation analysis were used to explore influencing pathways of bed allocation.Results:The number of beds per 1 000 population and average life expectancy showed a significant positive correlation in countries below the average bed level,while an inverse trend was observed in countries above the average level.The number of long-term care beds per 1000 population showed positive correlation with average life expectancy.Multidimensional factors such as the disease spectrum and bed utilization efficiency were significant correlated with the bed allocation levels.The greater the gap in inpatient and outpatient reimbursement rations,the higher the demand for hospitalization among patients with chronic disease.Conclusions:An optimal number of beds is essential for achieving higher health outcomes.The bed classification planning system should be designed to adapt to demographic changes and strengthen the medical security support system.

Objective To investigate the value of ultrasound for differentiating pancreatoblastoma(PB)and solid pseudopapillary tumor of pancreas(SPT)in children.Methods Data of 7 children with PB(PB group)and 22 with SPT(SPT group)were retrospectively analyzed.The clinical data and lesion's ultrasonic manifestations were compared between groups.Receiver operating characteristic(ROC)curves of clinical and ultrasound related parameters being significantly different between groups showed by univariate analysis were draw,and the area under the curve(AUC)were calculated to evaluate their efficacy for differentiating PB and SPT.Ultrasound parameters with P＜0.05 in univariate analysis were incorporated into binary logistic analysis,and a ultrasound regression model was constructed to distinguish PB and SPT,and its diagnostic efficacy was evaluated.Results Significant differences of children'age,gender,serum alpha fetoprotein level,and the shape,maximum diameter,texture,calcification and local invasion of lesions were found between groups(all P＜0.05).AUC of single serum alpha fetoprotein level,the shape,maximum diameter,texture,calcification and local invasion of lesion for differentiating PB and SPT was 1.000,0.766,0.854,0.776,0.789 and 0.714,respectively(all P＜0.05).The shape(OR=8.704,P=0.075)and maximum diameter of lesions(OR=1.695,P=0.042)showed with ultrasound were both important differentiating factors for PB and SPT,and AUC of the ultrasound regression model constructed based on them was 0.886.Conclusion Ultrasound could effectively differentiate PB and SPT in children.

Aim To explore the possible regulatory effect of leptin on acyl-CoA synthetase long chain fami-ly member ACSL5 and their effect on migration and in-vasion of breast cancer cell,and to explore the underly-ing mechanism.Methods The expression of leptin receptor was detected by immunofluorescence assay.The migration and invasion ability of MCF-7 cells were detected by wound healing assay and Transwell assay respectively.The downstream target gene of leptin was analyzed by PCR microarray data.The expression of ACSL5 in breast cancer and its correlation with the staging and prognosis of breast cancer patients were as-sessed uing bioinformatics methods.The expression of ACSL5 in MCF-7 cells treated with different concentra-tions of leptin was detected using real time fluorescence quantitative polymerase chain reaction(RT-qPCR).Overexpressing ACSL5 was constructed by lentiviral transfection;the expressions of EMT related proteins,AMPK-α and p-AMPK-α were detected by Western blot.Results Leptin promoted breast cancer cell mi-gration and invasion and EMT.ACSL5 was significant-ly low expressed in breast cancer and related to progno-sis.Leptin downregulated the expression of ACSL5 through OBR.Leptin activated AMPK pathway to downregulate ACSL5 and promote migration,invasion and EMT of breast cancer cells.Conclusions Leptin may promote the migration,invasion and EMT of breast cancer by downregulating ACSL5 through activating AMPK pathway.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To compare the predictive performance of the deep forest ensemble model,deep forest,and random forest in medical classification.Methods This study proposed a deep forest ensemble model that integrated Sobol-MDA(Sobol-mean decrease accuracy)with the cascading structure of deep forest and feature extraction capabilities of random forest.The model was applied to both simulation and real medical data analysis.The simulation experiments covered scenarios such as unbalanced outcome variables,nonlinear relationships,noise variables,multicollinearity,and interactions.The real data analysis was conducted on parotid MRI data to compare the performance of the models in terms of area under curve(AUC)and other metrics.Results In both the simulation and real data analysis,the deep forest ensemble model demonstrated superiority,especially in complex interaction scenarios,where its predictive performance significantly outperformed deep forest and random forest models.Conclusion Deep forest ensemble model shows significant advantages in addressing complex medical classification tasks.Its predictive performance outperforms traditional models.