OBJECTIVE From the perspective of China’s health system， to evaluate the economic efficiency of Irinotecan liposome+fluorouracil+calcium folinate+oxaliplatin（NALIRIFOX regimen） versus paclitaxel （albumin-bound） combined with gemcitabine （AG regimen） in the first-line treatment of metastatic pancreatic cancer. METHODS A dynamic Markov model was constructed based on the data from the NAPOLI 3 clinical trial， with a cycle period of 28 days and a simulation time limit of 5 years. Incremental cost-effectiveness ratio （ICER） and quality-adjusted life year （QALY） were used as the model output indicators. The willingness-to-pay （WTP） threshold was set at three times China’s 2024 per capita gross domestic product （GDP）， and a discount rate of 5% was adopted. A cost-utility analysis was conducted to analyze the economic efficiency of the NALIRIFOX regimen compared to the AG regimen. Univariate sensitivity analysis and probabilistic sensitivity analysis were used to evaluate the robustness of the model results， and scenario analysis was conducted by reducing the cost of irinotecan liposome by 60% and 70%. RESULTS The base-case analysis showed that the ICER of the NALIRIFOX regimen was 854 669.96 yuan/QALY compared to the AG regimen， which was greater than the WTP threshold （287 247 yuan/QALY）， indicating that the NALIRIFOX regimen was not economically efficient. The univariate sensitivity analysis results indicated that the discount rate， the cost of irinotecan liposome， the utility value of the progression-free survival state， and the utility value of the disease progression state had a significant impact on the ICER. The probabilistic sensitivity analysis results showed that under the WTP threshold of this study， the NALIRIFOX regimen was not economically efficient compared with the AG regimen. The scenario analysis results indicated that when the price was reduced by 70%， the probability of the NALIRIFOX regimen being economically efficient compared with the AG regimen was 9.60%. CONCLUSIONS From the perspective of China’s health system， when the WTP threshold is set at three times China’s 2024 per capita GDP， the NALIRIFOX regimen is not economically efficient in the first-line treatment of metastatic pancreatic cancer， compared with the AG regimen.

OBJECTIVE To estimate the cost-effectiveness of penpulimab combined with chemotherapy versus chemotherapy alone in first-line treatment of advanced squamous non-small-cell lung cancer （sq-NSCLC）. METHODS From the perspective of Chinese health system， cost-utility analysis was used to evaluate the cost-effectiveness of penpulimab combined with chemotherapy （paclitaxel + carboplatin） versus chemotherapy （paclitaxel + carboplatin） in first-line treatment of sq-NSCLC. A three-health states Markov model was constructed with R packages， and clinical data used in the model were derived from the AK105-302 clinical trial. Costs and utilities were collected from the open-access database and published literature. The quality-adjusted life-years （QALY） was used as the utility index， and the willingness-to-pay （WTP） threshold was set at three times China’s per capita GDP in 2024， equivalent to 287 247 yuan/QALY. The cost-effectiveness of the schemes was evaluated by comparing the incremental cost- utility ratios （ICER） of the two schemes with the WTP threshold. One-way sensitivity analysis and probabilistic sensitivity analysis （PSA） were used to verify the stability of the basic analysis results. RESULTS Compared with chemotherapy， penpulimab combined with chemotherapy increased 0.73 QALY with an incremental cost of 150 681.93 yuan， and the ICER was 206 413.60 yuan/QALY. One-way sensitivity analysis showed that the utility of progression-free survival was the most sensitive factor on ICERs. At the WTP threshold of 3 times China’s per capita GDP， the economic probability of this scheme was 98.80%. At the WTP threshold of 1 times China’s per capita GDP， the probability of ICER being cost-effective was less than 0.01%. CONCLUSIONS For patients with advanced sq-NSCLC， penpulimab combined with chemotherapy is a cost-effective first-line treatment option when WTP threshold is 3 times China’s per capita GDP.

Cognitive dysfunction often occurs in Alzheimer's disease, Parkinson's disease, cerebrovascular disease, or other neurodegenerative diseases, and can significantly impact the life quality of patients and create serious social, psychological, and economic burdens for individuals and their families. Numerous studies have confirmed that exercise can slow the decline in cognitive function through multiple pathways, in which fibronectin type III domain-containing protein 5 (FNDC5) plays an important role. However, the current research on the modulation of FNDC5 by exercise and its ability to improve hippocampal cognitive function lacks a systematic and comprehensive understanding. Therefore, this review focuses on the latest research progress regarding the role of exercise-induced FNDC5 in cognitive function, systematically reviews the positive effects of FNDC5 on cognitive function impairment caused by various factors, and clarifies the specific mechanisms by which exercise-induced FNDC5 improves cognitive function by inhibiting neuroinflammation and improving hippocampal neurogenesis and hippocampal synaptic plasticity. Based on the existing literature, we also identify the areas that require further research in this field. Overall, this review provides a theoretical basis for exercise-based prevention and improvement of cognitive function impairment.
Humans ; Cognition/physiology* ; Fibronectins/physiology* ; Exercise/physiology* ; Hippocampus/physiology* ; Cognitive Dysfunction/prevention & control* ; Neuronal Plasticity ; Animals ; Neurogenesis

CRISPR/Cas9-based therapeutics face significant challenges in penetrating the dense microenvironment of solid tumors, resulting in insufficient gene editing and compromised treatment efficacy. Current nanostrategies, which mainly focus on the paracellular pathway attempted to improve gene editing performance, whereas their efficiency remains uneven in the heterogenous extracellular matrix. Here, the nanoCRISPR system is prepared with self-cascading mechanisms for gene editing-mediated robust apoptosis and transcellular penetration. NanoCRISPR unlocks its self-cascade capability within the matrix metallopeptidase 2-enriched tumor microenvironment, initiating the transcellular penetration. By facilitating cellular uptake, nanoCRISPR triggers robust apoptosis in edited malignancies, promoting further transcellular penetration and amplifying gene editing in neighboring tumor cells. Benefiting from self-cascade between robust apoptosis and transcellular penetration, nanoCRISPR demonstrates continuous gene transfection/tumor killing performance (transfection/apoptosis efficiency: 1st round: 85%/84.2%; 2nd round: 48%/27%) and homogeneous penetration. In xenograft tumor-bearing mice, nanoCRISPR treatment achieves remarkable anti-tumor efficacy (∼83%) and significant survival benefits with minimal toxicity. This strategy presents a promising paradigm emphasizing transcellular penetration to enhance the effectiveness of CRISPR-based antitumor therapeutics.

Mesangial cell proliferation is an early pathological indicator of diabetic nephropathy (DN). Growing evidence highlights the pivotal role of paired-related homeobox 1 (Prrx1), a key regulator of cellular proliferation and tissue differentiation, in various disease pathogenesis. Notably, Prrx1 is highly expressed in mesangial cells under DN conditions. Both in vitro and in vivo studies have demonstrated that Prrx1 overexpression promotes mesangial cell proliferation and contributes to renal fibrosis in db/m mice. Conversely, Prrx1 knockdown markedly suppresses hyperglycemia-induced mesangial cell proliferation and mitigates renal fibrosis in db/db mice. Mechanistically, Prrx1 directly interacts with the Yes-associated protein 1 (YAP) promoter, leading to the upregulation of YAP expression. This upregulation promotes mesangial cell proliferation and exacerbates renal fibrosis. These findings emphasize the crucial role of Prrx1 upregulation in high glucose-induced mesangial cell proliferation, ultimately leading to renal fibrosis in DN. Therefore, targeting Prrx1 to downregulate its expression presents a promising therapeutic strategy for treating renal fibrosis associated with DN.

Butyrylation(Kbu),as an important post-translational modification(PTM)of proteins,precisely regu-lates various biological behaviors of tumor cells by modulating the structure and function of proteins.Abnormal bu-tyrylation promotes tumor cell proliferation and migration by regulating protein stability,enzyme activity,cell signa-ling pathways,chromatin structure and gene expression,and further participates in tumor occurrence and develop-ment.This review focuses on the role of butyrylation modification in the occurrence and development of various hu-man tumors,aiming to summarize the research progress of butyrylation modification in the field of tumors,and to provide new directions for tumor treatment through the intervention strategies of butyrylation modification.

Objective:To construct machine learning-based delirium prediction models for ICU patients with multiple trauma and evaluate their prediction efficiency.Methods:A retrospective case-control study was conducted to analyze the clinical data of 417 ICU multiple trauma patients admitted to the First Affiliated Hospital of Zhengzhou University from July 2019 to June 2022, including 305 males and 112 females, aged 18-88 years [(47.8±15.7)years]. The score of acute physiology and chronic health status assessment II (APACHE II) was 0-50 points [(9.80±0.29)points]. The patients were randomly divided into training set ( n=291) and test set ( n=126) with a ratio of 7∶3. The demographic data, past history, treatment and laboratory results of the patients were collected. Lasso regression analysis was applied to screen variables that were significantly correlated to the incidence of delirium in the training set and the variables were then included into the machine learning models. Six machine learning methods including the random forest, gradient boosting tree, extreme gradient boosting, logistic regression, support vector machine and K nearest neighbor were used to construct the delirium prediction models for ICU multiple trauma patients. The accuracy, sensitivity, precision, F1 fraction and area under the curve (AUC) of the receiver′s operating characteristics (ROC) curve were calculated by using the data in the test set to evaluate the prediction efficiency of the models. Results:With regards to the six prediction models, namely random forests, gradient boosting tree, extreme gradient boosting, logistic regression, support vector machine and K nearest neighbor prediction models, the accuracy in the test set was 0.70, 0.68, 0.69, 0.73, 0.70 and 0.60 respectively; the sensitivity was 0.74, 0.80, 0.81, 0.86, 0.85 and 0.69 respectively; the precision was 0.72, 0.69, 0.70, 0.73, 0.71 and 0.65 respectively; the F1 fraction was 0.73, 0.74, 0.75, 0.79, 0.78 and 0.67 respectively; the AUC was 0.72, 0.73, 0.72, 0.80, 0.74 and 0.64 respectively. Among them, the logistic regression model had the best discriminability.Conclusion:Delirium prediction models for ICU patients with multiple trauma have been successfully constructed, among which the logistic regression model has the best prediction efficiency and can serve as an effective tool for early prediction and prevention of delirium in the clinical care of patients with multiple trauma.

Objective:To explore the composition of oral microbiome in patients of long-term survival after liver transplantation, and compare the diversity and distribution of oral microbiome in liver transplant patients with or without liver cancer.Methods:This is a cross-sectional study. Clinical data of 20 patients of long-term survival after liver transplantation from Beijing Chaoyang Hospital Affiliated to Capital Medical University from July 2023 to October 2023 were continuously collected, including 13 males and 7 females, aged 55 (48, 60) years. There were eight patients with hepatocellular carcinoma (HCC) undergoing liver transplantation and 12 non-HCC liver transplant patients. Oral microbiome was analyzed by 16S rDNA gene sequencing, and the diversity analysis and LEfSe analysis were performed.Results:The top ten groups of bacteria in the HCC and non-HCC liver transplant patients were both Bacteroidota, Firmicutes, Proteobacteria, Actinobacteria, Fusobacteriota, Patescibacteria, Campylobacterota, Cyanobacteria, spirochaetota, and Aenigmarchaeota. The top ten genus levels of bacteria in the HCC and non-HCC liver transplant patients were both Haemophilus, Porphyromonas, Gemella, Fusobacterium, Actinomyces, Prevotella, Rothia, Streptococcus, Neisseria. By LEfSe analysis, we found the enrichment of Flavobacteriales, Flavobacteriaceae, Capnocytophaga, Mogibacterium_timidum, Capnocytophaga_leadbetteri, Campylobacter_showae in the HCC group, while the enrichment of Veillonella_atypica, Prevotella_histicola in the non-HCC group.Conclusion:The main microbiome was similar between HCC and non-HCC liver transplant patients, but there were also some differences in microbiome communities.

Objective:To predict the mechanism of Panacis Quinquefolii Radix- Acori Tatarinowii Rhizoma (PQ-AT) in the treatment of diabetes encephalopathy (DE) using network pharmacology combined with molecular docking; To conduct experimental verification.Methods:The active components and targets of PQ and AT were screened by TCMSP database. The GeneCards and Disgenet were used to collect DE related target genes. String database and Cytoscape software were used to structure PPI network and perform visualization analysis. The common targets were imported into Metascape platform for GO annotation and KEGG enrichment analysis. Molecular docking was used to verify the binding ability of active components to core targets. Rats were randomly divided into a blank group, a model group, and a low-dose group of PQ-AT (1.08 g/kg), a high-dose group of PQ-AT (2.16 g/kg), and a metformin group (0.18 g/kg) using a random number table. To establish the rat model of diabetes encephalopathy, intraperitoneal injection of streptozotocin was used in addition to the blank group. After a 12-week drug intervention, TNF-α and Cyclooxygenase-2 (PTGS2) protein expression in the cerebral cortex of rats was detected using Western blot.Results:A total of 26 active components in PQ-AT and 107 related targets of DE were obtained, mainly including TNF, JUN, and PTSG2, which were mainly concentrated in TNF signaling pathway, cancer and other signal pathways. Molecular docking showed that the main active components of PQ-AT had relatively stable binding activity with TNF-α and PTGS2. Western blot results shows that compared with the model group, the expressions of PTGS2 and TNF-α significantly decreased in each administration group ( P<0.05 or P<0.01). Conclusion:PQ-AT can act on TNF, CASP3, JUN, STAT3, PTGS2 and other core targets to regulate signal pathways such as TNF, and inhibit inflammatory reaction to achieve the effect of treating DE.

Objective To evaluate the predictive value of CT-enhanced imaging-based radiomics nomogram for the status of microsatel-lite instability(MSI)in colon cancer.Methods A retrospective analysis was conducted on 129 postoperative colon cancer patients with confirmed MSI status.They were randomly divided into a training group(n=90)and a validation group(n=39)at a ratio of 7:3.Radiomics features were extracted from preoperative CT-enhanced images of the patients.The predictive performance of various machine learning algorithms was evaluated using the area under the curve(AUC).A nomogram model was developed by incorporating clinical independent risk factors,and the model's overall performance was assessed using decision curve analysis(DCA).Results Age and lesion site were identified as prominent independent risk factors and utilized in the construction of a clinical model.The light gradient boosting machine(LightGBM)algorithm was chosen for building a radiomics model.As a joint model,the AUC of the nomogram model of 0.917 in the training group and 0.822 in the validation group.The DCA confirmed the substantial clinical applicability of the nomogram model.Conclusion The CT-enhanced imaging-based radiomics nomogram offers a pioneering and individualized predic-tive approach for determining the MSI status in colon cancer.