Randomized controlled trial(RCT) represents the "gold standard" for estimating the causal effects of interventions; however, their implementation in the field of health policy evaluation is frequently hindered by logistical feasibility and ethical constraints. Target Trial Emulation (TTE), a framework originating in clinical epidemiology, facilitates rigorous causal inference from observational data by explicitly emulating the design of an idealized "target trial". Recently, the application of TTE has transitioned from individual-level clinical interventions—such as pharmacological or surgical treatments—to population-level health policy evaluations. This interdisciplinary translation is not a localized conceptual shift but necessitates a series of comprehen-sive methodological adaptations. This paper systematically delineates the core logic of extending the TTEframework into the realm of health policy, providing a profound analysis of the transformation and reconstruction of critical elements, including study units, intervention definitions, time zero, causal estimands, and analytical strategies. Furthermore, it examines the unique challenges inherent in policy contexts, such as policy heterogeneity, staggered adoption, concurrent policy interference, and data granularity limitations. The paper also evaluates the integration of analytical methods, such as instrumental variables (IV) and difference-in-differences (DID), within the TTE framework. This synthesis aims to provide methodological guidance and prospective insights for conducting high-quality policy evaluations using real-world data (RWD).

Developing and identifying effective medications and targets for treating hepatic fibrosis is an urgent priority. Our previous research demonstrated the efficacy of artesunate (ART) in alleviating liver fibrosis by eliminating activated hepatic stellate cells (HSCs). However, the underlying mechanism remains unclear despite these findings. Notably, endocytic adaptor protein (NUMB) has significant implications for treating hepatic diseases, but current research primarily focuses on liver regeneration and hepatocellular carcinoma. The precise function of NUMB in liver fibrosis, particularly its ability to regulate HSCs, requires further investigation. This study aims to elucidate the role of NUMB in the anti-hepatic fibrosis action of ART in HSCs. We observed that the expression level of NUMB significantly decreased in activated HSCs compared to quiescent HSCs, exhibiting a negative correlation with the progression of liver fibrosis. Additionally, ART induced senescence in activated HSCs through the NUMB/P53 tumor suppressor (P53) axis. We identified NUMB as a crucial regulator of senescence in activated HSCs and as a mediator of ART in determining cell fate. This research examines the specific target of ART in eliminating activated HSCs, providing both theoretical and experimental evidence for the treatment of liver fibrosis.
Hepatic Stellate Cells/cytology* ; Liver Cirrhosis/genetics* ; Artesunate/pharmacology* ; Cellular Senescence/drug effects* ; Membrane Proteins/genetics* ; Animals ; Humans ; Nerve Tissue Proteins/genetics* ; Tumor Suppressor Protein p53/genetics* ; Male ; Mice

Objective:To screen the Alzheimer's disease(AD)-related genes and construct its diagnostic model using bioinformatics technology and machine learning(ML)algorithms,to discuss the immunological characteristics of AD patients,and to provide novel biomarkers for AD diagnosis.Methods:The AD-related gene expression dataset GSE125583 was downloaded from the Gene Expression Omnibus(GEO)database.Differentially expressed genes(DEGs)were identified through differential analysis.Gene Ontology(GO)functional enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analyses were performed to explore the biological functions and signaling pathways of DEGs.A protein-protein interaction(PPI)network was constructed,and hub genes were screened using Cytoscape software combined with three ML algorithms:Least Absolute Shrinkage and Selection Operator(LASSO),eXtreme Gradient Boosting(XGBoost),and Random Forest(RF).The screened hub genes were utilized to build an AD diagnostic model via RF,followed by feature importance ranking.The model's efficacy and key genes were evaluated using a test set.Single-sample gene set enrichment analysis(ssGSEA)was used for immune cell infiltration analysis between AD group and control group.Results:Differential analysis identified 1 287 DEGs.The GO functional enrichment analysis results revealed that DEGs were primarily involved in biological functions related to neural signaling,synapses,and vesicles.KEGG signaling pathway enrichment analysis indicated significant enrichment of DEGs in ion transport,neurotransmitter,and ligand-gated channel pathways.Nine overlapping hub genes were screened by the three ML algorithms.In the AD diagnostic model,the top four key genes with highest diagnostic performance were adenylate cyclase-activating polypeptide 1(ADCYAP1),brain-derived neurotrophic factor(BDNF),platelet-derived growth factor receptor β(PDGFRB),and C-X-C motif chemokine receptor 4(CXCR4),with corresponding area under the curve(AUC)values of 0.852,0.795,0.820,and 0.756,respectively.The model achieved an AUC of 0.828,accuracy of 81.25%,sensitivity of 84.40%,and specificity of 71.43%.The immune cell infiltration analysis results demonstrated higher infiltration of macrophages,monocytes,natural killer(NK)cells,and lymphocytes in AD tissue.Among these,NK/natural killer T(NKT)cells and plasmacytoid dendritic cells showed significant correlations with the four key genes(P<0.05).Conclusion:The feature genes screened based on bioinformatics and ML exhibit diagnostic potential for AD.Genes such as ADCYAP1 may serve as potential biomarkers for AD diagnosis,offering significant implications for early prevention and treatment.

Inflammatory bowel disease (IBD) is a chronic and recurrent intestinal inflammatory disease, which not only affects the digestive tract, but also involves extraintestinal organs and tissues such as joints and eyes, and ultimately endangers human health and affects the quality of life of patients. With the development of information technology, there is an increasing application of digital therapeutics in the management of IBD. This article aims to comprehensively analyze the characteristics, use, and challenges of digital therapeutics in the management of IBD, and explore its potential to improve treatment adherence, disease surveillance, and improve patients' quality of life.

Pyroptosis,a form of programmed cell death primarily mediated by gasdermin D(GSD-MD),plays a crucial role in infections and inflammatory responses.Although the OTU deubiquiti-nase 4(OTUD4)has shown its importance in various cellular processes,its specific function in py-roptosis remains unclear,which necessitates a deeper understanding of its role.This study aims to explore the role of OTUD4 in regulating GSDMD-mediated pyroptosis.Through experiments inclu-ding cell transfection,lactate dehydrogenase(LDH)release assays,co-immunoprecipitation(Co-IP),and Western blot(WB)analysis,we investigated the interaction between OTUD4 and GSD-MD and their influence on GSDMD-mediated pyroptosis.Our results indicated that OTUD4 could bind to GSDMD,significantly reduce Caspase-1-mediated cleavage of GSDMD,and lower protein levels of GSDMD-p30,thereby inhibiting pyroptosis.Conversely,the enzymatically inactive mutant of OTUD4(C45A)exhibited a markedly diminished inhibitory effect on pyroptosis.Furthermore,OTUD4 also demonstrated a significant inhibitory effect on pyroptosis in porcine cells.In conclu-sion,this study reveals that OTUD4 can inhibit GSDMD-p30-mediated pyroptosis through its deu-biquitinating activity,highlighting the critical function of OTUD4 in the regulation of pyroptosis and suggesting a potential target for the development of new therapeutic strategies.

Objective:To investigate the effects of baicalin on ferroptosis of mouse fibroblasts (Fbs) under high glucose treatment and its mechanism, and to provide a basis for the treatment of diabetic wounds.Methods:The study was an experimental study. Mouse Fbs were collected and divided into control group with conventional culture, high glucose group treated with glucose at final molarity of 30.0 mmol/L, and low baicalin group and high baicalin group pretreated with baicalin at final molarties of 5 and 10 μmol/L respectively and then treated as that in high glucose group. After 48 h of culture, the cell survival rate was detected by the cell counting kit-8, the reactive oxygen species level in cells was detected by the fluorescent probe method, the levels of malondialdehyde, glutathione, and ferrous ion in cells were detected by colorimetry, and the protein expression levels of solute carrier family 7 member 11 (SLC7A11) and glutathione peroxidase 4 (GPX4) in cells and nuclear factor-erythroid 2-related factor 2 (Nrf2) in cytoplasm and nucleus were detected by Western blotting. Another batch of mouse Fbs were collected and divided into control group, high glucose group, high baicalin group, and high baicalin+ML385 group. The cells in the first three groups were treated as before, the cells in the last group were pretreated with baicalin and ML385 of Nrf2 inhibitor at final molarties of 10 μmol/L and then treated as that in high glucose group. After 48 h of culture, the protein expression levels of SLC7A11 and GPX4 in cells and the protein expression level of Nrf2 in cytoplasm and nucleus were detected as before. Except that the sample number in detecting SLC7A11 and GPX4 was 4, the sample number in detecting other indexes was 3.Results:After 48 h of culture, the cell survival rates in control group, high glucose group, low baicalin group, and high baicalin group were (100.0±10.7)%, (70.0±5.0)%, (80.9±3.2)%, and (91.4±1.9)%, respectively. Compared with those in control group, the cell survival rate, the glutathione level, and SLC7A11 and GPX4 protein expression levels in cells, and nuclear Nrf2 protein expression level were significantly decreased in high glucose group ( P<0.05), and the levels of reactive oxygen species, malondialdehyde, and ferrous ion in cells, and cytoplasmic Nrf2 protein expression level were significantly increased in high glucose group ( P<0.05). Compared with those in high glucose group, the cell survival rate, glutathione level, SLC7A11 and GPX4 protein expression levels in cells, and nuclear Nrf2 protein expression level in low baicalin group and high baicalin group were significantly increased ( P<0.05), the reactive oxygen species and ferrous ion levels in cells, and cytoplasmic Nrf2 protein expression level in low baicalin group and high baicalin group were significantly decreased ( P<0.05), and the malondialdehyde level in cells in high baicalin group was significantly decreased ( P<0.05). Compared with those in low baicalin group, the cell survival rate, glutathione level, SLC7A11 and GPX4 protein expression levels in cells, and nuclear Nrf2 protein expression level in high baicalin group were significantly increased ( P<0.05), and the reactive oxygen species, malondialdehyde, and ferrous ion levels in cells, and cytoplasmic Nrf2 protein expression level in high baicalin group were significantly decreased ( P<0.05). After 48 h of culture, compared with those in control group, the nuclear Nrf2 protein expression level and SLC7A11 and GPX4 protein expression levels in cells were significantly decreased ( P<0.05), and the cytoplasmic Nrf2 protein expression level was significantly increased in high glucose group ( P<0.05); compared with those in high glucose group, the cytoplasmic Nrf2 protein expression level was significantly decreased ( P<0.05), and the nuclear Nrf2 protein expression level and SLC7A11 and GPX4 protein expression levels in cells were significantly increased in high baicalin group ( P<0.05); compared with those in high baicalin group, the cytoplasmic Nrf2 protein expression level was significantly increased ( P<0.05), and the nuclear Nrf2 protein expression level and SLC7A11 and GPX4 protein expression levels in cells were significantly decreased in high baicalin+ML385 group ( P<0.05). Conclusions:Baicalin can inhibit the occurrence of ferroptosis in cells by activating the Nrf2 signaling pathway and up-regulating the expressions of proteins related to SLC7A11/GPX4 axis in Fbs in high glucose treatment, thus increasing the cell survival rate.

Ursodeoxycholic acid(UDCA)is a naturally occurring,low-toxicity,and hydrophilic bile acid(BA)in the human body that is converted by intestinal flora using primary BA.Solute carrier family 7 member 11(SLC7A11)functions to uptake extracellular cystine in exchange for glutamate,and is highly expressed in a variety of human cancers.Retroperitoneal liposarcoma(RLPS)refers to liposarcoma originating from the retroperitoneal area.Lipidomics analysis revealed that UDCA was one of the most significantly down-regulated metabolites in sera of RIPS patients compared with healthy subjects.The augmentation of UDCA concentration(≥25 μg/mL)demonstrated a suppressive effect on the proliferation of liposarcoma cells.[15N2]-cystine and[13Cs]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione(GSH)synthesis.Mechanistically,UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis,leading to reactive oxygen species(ROS)accumulation and mitochondrial oxidative damage.Furthermore,UDCA can promote the anti-cancer effects of ferroptosis inducers(Erastin,RSL3),the murine double minute 2(MDM2)inhibitors(Nutlin 3a,RG7112),cyclin dependent kinase 4(CDK4)inhibitor(Abemaciclib),and glutaminase inhibitor(CB839).Together,UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity,and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA.More importantly,in combination with other antitumor chemotherapy or physiotherapy treatments,UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

One of the basic questions in the aging field is whether there is a fundamental difference between the aging of lower invertebrates and mammals. A major difference between the lower invertebrates and mammals is the abundancy of noncoding RNAs, most of which are not conserved. We have previously identified a noncoding RNA Terc-53 that is derived from the RNA component of telomerase Terc. To study its physiological functions, we generated two transgenic mouse models overexpressing the RNA in wild-type and early-aging Terc-/- backgrounds. Terc-53 mice showed age-related cognition decline and shortened life span, even though no developmental defects or physiological abnormality at an early age was observed, indicating its involvement in normal aging of mammals. Subsequent mechanistic study identified hyaluronan-mediated motility receptor (Hmmr) as the main effector of Terc-53. Terc-53 mediates the degradation of Hmmr, leading to an increase of inflammation in the affected tissues, accelerating organismal aging. adeno-associated virus delivered supplementation of Hmmr in the hippocampus reversed the cognition decline in Terc-53 transgenic mice. Neither Terc-53 nor Hmmr has homologs in C. elegans. Neither do arthropods express hyaluronan. These findings demonstrate the complexity of aging in mammals and open new paths for exploring noncoding RNA and Hmmr as means of treating age-related physical debilities and improving healthspan.
Animals ; Mice ; RNA, Untranslated/metabolism* ; Aging/genetics* ; Mice, Transgenic ; Telomerase/metabolism* ; RNA/genetics* ; Hippocampus/metabolism* ; Humans ; Mice, Inbred C57BL

Ursodeoxycholic acid (UDCA) is a naturally occurring, low-toxicity, and hydrophilic bile acid (BA) in the human body that is converted by intestinal flora using primary BA. Solute carrier family 7 member 11 (SLC7A11) functions to uptake extracellular cystine in exchange for glutamate, and is highly expressed in a variety of human cancers. Retroperitoneal liposarcoma (RLPS) refers to liposarcoma originating from the retroperitoneal area. Lipidomics analysis revealed that UDCA was one of the most significantly downregulated metabolites in sera of RLPS patients compared with healthy subjects. The augmentation of UDCA concentration (≥25 μg/mL) demonstrated a suppressive effect on the proliferation of liposarcoma cells. [¹⁵N₂]-cystine and [¹³C₅]-glutamine isotope tracing revealed that UDCA impairs cystine uptake and glutathione (GSH) synthesis. Mechanistically, UDCA binds to the cystine transporter SLC7A11 to inhibit cystine uptake and impair GSH de novo synthesis, leading to reactive oxygen species (ROS) accumulation and mitochondrial oxidative damage. Furthermore, UDCA can promote the anti-cancer effects of ferroptosis inducers (Erastin, RSL3), the murine double minute 2 (MDM2) inhibitors (Nutlin 3a, RG7112), cyclin dependent kinase 4 (CDK4) inhibitor (Abemaciclib), and glutaminase inhibitor (CB839). Together, UDCA functions as a cystine exchange factor that binds to SLC7A11 for antitumor activity, and SLC7A11 is not only a new transporter for BA but also a clinically applicable target for UDCA. More importantly, in combination with other antitumor chemotherapy or physiotherapy treatments, UDCA may provide effective and promising treatment strategies for RLPS or other types of tumors in a ROS-dependent manner.

OBJECTIVE To construct a classification prediction model for vancomycin blood concentration， and to optimize its precision dosing strategies. METHODS Patient records meeting inclusion criteria were extracted from the Medical Information Mart for Intensive Care database. Following data cleaning and preprocessing， a final cohort of 9 902 patient was analyzed. Feature selection was performed through correlation analysis and the Boruta feature selection algorithm. Vancomycin blood concentrations were discretized into three categories based on clinical therapeutic windows： low （＜10 μg/mL）， intermediate （10-20 μg/mL）， and high （≥20 μg/mL）. Six machine learning algorithms were employed to construct classification models： tabular prior-data fitted network （TabPFN）， logistic regression （LR）， random forest （RF）， extreme gradient boosting （XGBoost）， support vector machine （SVM）， K-nearest neighbors （KNN）. Model performance was evaluated using 10-fold cross-validation （10-CV）， with primary metrics including： accuracy， balanced accuracy， precision macro， recall macro， macro F1， area under the receiver operating characteristic curve （OvR-AUC）. Shapley Additive Explanations （SHAP） was adopted to analyze the direction and magnitude of the impact that different features had on the model’s predictive outcomes. RESULTS The results showed that the RF and TabPFN models performed the best （with accuracy of 0.741 4 and 0.737 7， and OvR-AUC of 0.907 0 and 0.895 8， respectively）. XGBoost model exhibited moderate performance， while LR， SVM， and KNN models demonstrated relatively poor performance. Confusion matrix heatmap analysis revealed that both RF and TabPFN achieved higher accuracy in predicting high- concentration cases but exhibited slightly lower performance in the low and medium concentration categories. Bootstrap with 10-CV revealed that the RF model demonstrated stable performance across various evaluation metrics （accuracy： 0.741 4； balanced accuracy： 0.740 3； precision macro： 0.732 1； recall macro： 0.736 0； macro F1： 0.736 0； OvR-AUC： 0.907 0）， indicating good classification performance and generalization ability. SHAP analysis revealed that creatinine， urea nitrogen， daily cumulative dose and administration frequency of vancomycin， which were key predictors， had a significant impact on the prediction results. CONCLUSIONS RF and TabPFN models demonstrate certain advantages in the classification prediction of vancomycin trough blood concentrations； however， their performance in the low to moderate concentration categories still requires improvement.