Affiliated Hospital of Chengde Medical College

Dendritic cell-derived exosomes (DEXs) are emerging as effective platforms for cancer vaccines due to their capacity to present tumor antigens and regulate immune responses. Here, we developed a multifunctional DEX vaccine (DEXAGNP) that integrates liver cancer epitope presentation, innate immune activation, and checkpoint modulation. Human DEXAGNP was generated by loading HLA-A*02:01-restricted hAFP158-166 and hGPC3144-152 peptides, and murine DEXAGNP by loading mAFP212-219 and mGPC3127-136 peptides. The N-terminal functional domain of high-mobility group nucleosome-binding protein 1 (N1ND) was membrane-anchored via CHOL-PEG2000, and programmed death ligand 1 (PD-L1) of DEXs surface was blocked with anti-PD-L1 antibodies to mitigate immunosuppression. DEXAGNP vaccine enhanced antigen-specific CD8⁺ T-cell responses, as supported by peptide-MHC tetramer staining, and promoted cytokine-associated effector activity. In vitro, DEXAGNP-primed T cells mediated cytotoxicity against both human and murine liver cancer cell lines. In vivo, DEXAGNP suppressed tumor growth in an immunocompetent subcutaneous H22 model and a humanized HepG2 xenograft model, and demonstrated antitumor activity in an orthotopic HCC model. In addition, DEXAGNP induced measurable killing in ex vivo assays using clinical HCC specimens and, in the prophylactic setting, was associated with enhanced memory-like T-cell responses. DEXAGNP is a modular exosome vaccine that combines epitope-specific antigen presentation with immune activation and PD-L1 blockade, enabling robust antitumor T-cell responses across complementary liver cancer models. This engineered DEX platform provides a practical blueprint for developing next-generation exosome-based cancer vaccines.

Abstract Background This study aimed to identify risk factors for diastolic dysfunction in septic patients, establish a predictive model, enable early intervention, reduce the incidence of septic cardiomyopathy (SCM), and improve patient prognosis. Methods Ninety-eight sepsis patients admitted to the Affiliated Hospital of Chengde Medical College were divided into two groups: the diastolic dysfunction group and the normal cardiac function group. Baseline clinical data, echocardiographic parameters related to diastolic function, and serum biomarkers of myocardial injury were collected. Least Absolute Shrinkage and Selection Operator (LASSO) regression combined with binary logistic regression was used for variable selection, followed by model construction, evaluation, visualization, and internal validation. Results Key predictive variables included Sequential Organ Failure Assessment (SOFA) score, 28-day mortality, Cardiac Troponin I (cTnI), A (Atrial systole peak), E/A (Early diastolic peak/Atrial systole peak), e' (Early diastolic myocardial velocity peak), E/e' (Early diastolic peak/Early diastolic myocardial velocity peak), and Relative Wall Thickness (RWT). Nomogram analysis confirmed these as risk factors for SCM. The model showed high predictive value (Receiver Operating Characteristic(ROC) curve area under the curve = 0.983, 95% Confidence Interval ( CI ): 0.951-1.000) and good calibration (Hosmer-Lemeshow test: χ² = 1.784, df = 8, P = 0.987). Its clinical utility was validated by Decision Curve Analysis (DCA). Conclusion SOFA score, 28-day mortality, cTnI, A, e', E/e', E/A, and RWT are independent risk factors for diastolic dysfunction in septic patients. The constructed predictive model exhibits excellent performance and clinical applicability.

BackgroundTo explore the relationship between TyG index, TyG–BMI and MACE events in patients after percutaneous coronary intervention, and to evaluate their potential as prognostic indicators compared to traditional risk assessment tools.MethodsFrom October 2022 to September of the following year, a total of 105 inpatients who underwent percutaneous coronary intervention (PCI) were carefully selected as research subjects within the scope of the Department of Cardiovascular Medicine at Chengde Central Hospital. MACE events such as recurrent angina pectoris, acute myocardial infarction, severe arrhythmia, in-stent restenosis, and cardiac death were recorded within 12 months after discharge. According to the occurrence of MACE events, the patients were divided into two groups: the MACE group and the non-MACE group. Subgroup analysis was performed for diabetic and non-diabetic patients. Internal validation was performed using the bootstrap resampling method with 1000 iterations to assess model stability.ResultsA total of 105 patients were followed up, and 23 MACE events occurred. The proportion of hypertension history in the MACE group was higher than that in the non-MACE group (82.6% vs. 69.5%). The age, LDL, creatinine, the TyG index and TyG–BMI values observed in the MACE group surpassed those of the non-MACE group, with these disparities reaching statistical significance (P < 0.05). TyG index and TyG–BMI were associated with MACE events in patients with coronary heart disease after PCI (P < 0.05). Bootstrap internal validation yielded optimism-corrected C-statistics of 0.742 for TyG index and 0.768 for TyG–BMI, with correction factors of 0.023 and 0.028, respectively, indicating moderate overfitting. Similar associations were observed in both diabetic and non-diabetic subgroups.ConclusionThis study suggests that TyG index and TyG–BMI may be associated with MACE events in patients with coronary heart disease after PCI. However, the small sample size, evidence of model overfitting, and lack of external validation limit the generalizability of these findings. Independent validation in larger, multicenter cohorts is essential before these indices can be recommended for clinical use. These preliminary findings provide a foundation for future adequately powered validation studies.

目的探索转录因子EB（TFEB）及其靶基因在硼替佐米治疗多发性骨髓瘤（MM）中的作用及分子机制。方法通过GTRD网站（http://gtrd.biouml.org/）预测TFEB靶基因，筛选出Ptch1基因。实时荧光定量PCR和Western blot法检测硼替佐米作用于MM细胞系RPMI8226和U266后Ptch1的表达变化。siRNA-TFEB分别转染RPMI8226和U266细胞系，实时荧光定量PCR和Western blot法检测Ptch1/Hedgehog信号通路关键因子（Ptch1、Gli1）的mRNA和蛋白相对表达情况。在MM细胞系中用慢病毒转染过表达Ptch1，吖啶橙染色观察自噬现象，Western blot法检测各组细胞自噬相关因子（LC3B、Beclin-1、Lamp-1）蛋白相对表达量，溶酶体荧光探针观察溶酶体数量变化。结果硼替佐米（6.0×10−6 mmol/L）处理RPMI8226和U266细胞系24 h，硼替佐米组Ptch1的mRNA和蛋白的相对表达水平均较空白对照组降低（P值均<0.05），转染siRNA-TFEB后逆转了硼替佐米对Hedgehog通路中关键因子Ptch1、Gli的抑制作用。Ptch1过表达显著减少了经硼替佐米处理的RPMI8226和U266细胞系中自噬相关因子LC3B、Beclin1、Lamp1蛋白的相对表达量（P值均为0.001）。吖啶橙染色显示两种细胞系酸性囊泡数量均减少（P值均为0.001），反映溶酶体数量的溶酶体探针相对荧光表达亦减少（RPMI8226和U266细胞系的P值分别为0.001、0.007）。结论敲低TFEB可靶向促进Ptch1/Hedgehog信号通路表达而减少硼替佐米诱导的MM细胞自噬，并逆转硼替佐米对MM细胞系的增殖抑制作用。

Artificial intelligence (AI), as a smart and connected technology, has significantly expanded the educational landscape.As more educators and learners begin to rely on GAI to assist with tasks such as instructional design and information generation, its potential to support problem-solving has gained increasing attention. However, the extent to which GAI contributes to the development of higher-order thinking skills-such as creative thinking, critical thinking, and metacognitive awareness-and how these thinking processes interact to influence problem-solving ability remains underexplored. A more comprehensive understanding of this relationship is needed to guide the effective integration of GAI in educational practice. The study encompassed 473 pre-service teachers from three distinct higher education institutions, with specialties in science, computer science, and mathematics, and included a four-week generative AI-supported instructional design training program. And a semistructured interview comprising open-ended questions was administered to 50 pre-service teachers within the experimental group to present their views on generative AI-assisted teaching. Assessments were conducted before and after this program using a higher-order thinking skills survey. The relationship between thinking and problem-solving ability development of pre-service teachers using generative artificial intelligence was analyzed with a moderated model. The study encompassed 473 pre-service teachers from three distinct higher education institutions, with specialties in science, computer science, and mathematics, and included a four-week generative AI-supported instructional design training program. And a semistructured interview comprising open-ended questions was administered to 50 pre-service teachers within the experimental group to present their views on generative AI-assisted teaching. Assessments were conducted before and after this program using a higher-order thinking skills survey. The relationship between thinking and problem-solving ability development of pre-service teachers using generative artificial intelligence was analyzed with a moderated model. This study confirms that training pre-service teachers using generative AI to foster creative thinking can elevate their critical thinking, thereby impacting their problem-solving abilities. Moreover, metacognitive thinking amplifies the impact of creative thinking on critical thinking, resulting in a moderated mediation effect.

Usnic acid, a compound from Usneae Filum, has shown notable antitumor effects. Nevertheless, the mechanism of its anti-NSCLC action remains incompletely elucidated. This study used metabolomics, network pharmacology, molecular docking, and dynamics simulation to investigate usnic acid's potential mechanism on NSCLC utilizing A549 cell samples. The integration of metabolomics and network pharmacology was confirmed through molecular docking and molecular dynamics simulation. Combining data-dependent acquisition (DDA) and data-independent acquisition (DIA) enables maximal MS/MS coverage of endogenous substances in complex biological matrices. Metabolomics based on DDA and DIA revealed 47 potential metabolites linked to usnic acid's therapeutic effects on NSCLC. Network pharmacology identified 24 targets, with key pathways including cancer, human cytomegalovirus infection, and p53 signaling. A network analysis highlighted myeloperoxidase (MPO) as a shared target, with molecular docking and dynamics simulations confirming strong binding and stability between usnic acid and MPO. This study uncovered usnic acid's molecular mechanisms in NSCLC, primarily through MPO targeting and modulation of purine metabolism. MPO inhibition attenuates oxidative stress-driven purine catabolism, reduces uric acid-induced inflammation, and restores metabolic homeostasis. These findings illuminate novel mechanisms of usnic acid's anticancer potential and advance mechanistic insights into traditional Chinese medicine (TCM) for clinical oncology applications.

This study aims to elucidate the mechanisms underlying Dementia using bioinformatics analysis and machine learning algorithms, to identify novel therapeutic targets for its clinical management. Gene expression datasets related to dementia were sourced from the GEO database. Differentially expressed genes (DEGs) were identified using R, and key module genes were determined through the Weighted Gene Co-expression Network Analysis (WGCNA) method. Oligodendrocyte (OL) related targets were retrieved from the GeneCards database. The intersecting genes from DEGs, WGCNA, and OL were analyzed using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Subsequently, three machine learning algorithms were employed to pinpoint core genes associated with OL in dementia. The CIBERSORT algorithm was used to evaluate the abundance of 22 immune cell types and their correlation with Dementia-related immune infiltration. Validation was carried out via quantitative reverse transcription polymerase chain reaction (RT-qPCR). Through bioinformatics and machine learning techniques, six core OL genes associated with Dementia were identified, notably C1QA, CD163, and TGFB2, which showed elevated expression in Dementia. Immune cell infiltration analysis indicated that several immune cell types may contribute to Dementia's pathogenesis, and RT-qPCR results corroborated the bioinformatics findings. The discovered genes may contribute to dementia pathogenesis through oligodendrocyte dysfunction and neuroimmune interactions. Notably, TGFB2 and complement-related genes (C1QA, CD163) suggest involvement in both myelination defects and neuroinflammation, highlighting their therapeutic potential. The six feature genes: TGFB2, C1QA, CD163, ACTG1, WIF1, and OPALIN are significantly linked to Dementia.