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While periodontitis is increasingly linked to systemic disorders through the oral–gut axis, the molecular mediators driving gut microbiota dysbiosis and barrier disruption remain elusive. Hepatocyte growth factor (HGF), a novel regulator of inflammatory bone loss in periodontitis, may serve as a critical communicator between oral infection and distal intestinal pathology. This study investigates how HGF overexpression modulates the gut microbial ecosystem and intestinal barrier integrity in a transgenic periodontitis model. In this study, we combined 16S rRNA sequencing of fecal microbiota with comprehensive gut barrier assessments, including systemic markers (D-lactate, LPS, and DAO ELISA), structural integrity (villous morphology), and molecular analysis (ZO-1, occludin, and NOD2 immunohistochemistry), using HGF-overexpressing transgenic (HGF-Tg) mice with periodontitis. The results demonstrated that HGF increased gut permeability in the context of periodontitis, as evidenced by elevated serum levels of D-lactate and LPS compared to wild type (WT) mice. In addition, gut villous morphology disorder was observed in HGF-Tg mice with periodontitis. HGF also diminished the protein level of occludin and upregulated NOD2 expression in mice with periodontitis. Moreover, HGF-Tg mice with periodontitis exhibited significant dysbiosis of gut microbiota, with reduced levels of probiotics (e.g., Faecalibaculum). Notably, HGF also increased the enrichment of the periodontitis-associated pathogens (e.g., Desulfovibrio and Streptococcus) in the gut. Microbial functions, particularly metabolic pathways, were significantly altered by HGF when periodontitis occurred. Some microorganisms like g_Desulfovibrio may play a role in gut barrier disorder in HGF-Tg mice with periodontitis. Overall, our findings position HGF as a novel orchestrator of oral–gut crosstalk, where its overexpression reshapes gut microbial ecology toward a “leaky gut” phenotype to compromise intestinal barrier integrity, further deepening our understanding of the oral–gut axis.

Terpenoids are critical components of plant environmental adaptation mechanisms. They also exhibit significant therapeutic potential in herbal medicine. 3-Hydroxy-3-methylglutaryl coenzyme A reductase (HMGR), a pivotal rate-limiting enzyme governing the initial stage of the mevalonate (MVA) pathway in triterpene saponin biosynthesis, remains uncharacterized in Ziziphus jujuba var. spinosa . Through genome-wide and molecular analysis, we systematically identified ZjHMGR isoforms and revealed differential tissue-specific expression patterns and significant salt stress-responsive regulation across identified isoforms. Our findings reveal three evolutionarily conserved ZjHMGR isoforms with a complete HMG-CoA reductase domain and closely related to Populus trichocarpa . Collinearity analysis revealed two collinear gene pairs, and purifying selection was identified as the primary evolutionary force acting on the ZjHMGR gene family. Cis-acting element analysis revealed that ZjHMGR gene family enriched MYB-related, TC-rich repeats, light- and hormone-responsive elements, suggesting transcriptional regulation by environmental stimuli and phytohormones. Spatiotemporal expression analysis via qRT-PCR revealed differential transcriptional patterns of ZjHMGR members, with pronounced upregulation under ABA, MeJA, and light induction. Saline stress disrupted the growth of wild jujube seedlings while activating ZjHMGR expression alongside other MVA pathway genes. Overexpression of ZjHMGR enhances salt stress resistance in Arabidopsis thaliana . This study lays the foundation for further investigations into the molecular mechanisms of the ZjHMGR gene family concerning saponin biosynthesis, phytohormone interactions, and salt tolerance in wild jujube.

With the increasing demand for quality control in the traditional Chinese medicine industry, there is a need for the development of quality markers and a quick, non-destructive technique for the discrimination of related species. In our previous study, ultra-performance liquid chromatography (UPLC) was used for the simultaneous determination of five compounds, including three alkaloids (nitidine chloride, chelerythrine, and magnoflorine), one flavonoid (aurantiamarin), and one lignan (sesamin). In this study, the simultaneous quantification of the above-mentioned compounds could be used to discriminate the powders of roots from those of stems. To further test the reliability of the five compounds, seventy-two batches of wild and seventy-five batches of cultivated Zanthoxylum nitidum samples collected from Guangdong, Guangxi, and Fujian provinces in China were analyzed by UPLC and near-infrared spectroscopy (NIRS). In general, the quantitative results of UPLC were consistent with those of NIRS, and cultivated Z . nitidum has similar major bioactive compounds as the wild one, as supported by principal component analysis. Consequently, these five major bioactive compounds are suggested as potential quality markers. In addition, the NIRS method with discriminant analysis successfully differentiated Z . nitidum from three related species ( Z . avicennae , Z . scandens and Toddalia asiatica ) of the Rutaceae family. In summary, this study provides a method for the rapid identification of Z . nitidum and discrimination of root and stem powders, and suggests five compounds as quality markers for the evaluation of Z . nitidum .

In the digital economy era, corporate digital transformation has become a key strategy for enhancing competitiveness and sustainable development. This study investigates the impact of top management team (TMT) heterogeneity on corporate digital transformation and its underlying mechanisms. The findings reveal that TMT heterogeneity significantly enhances the degree of corporate digital transformation, driven by the mechanisms of internal control quality and dynamic capabilities. Specifically, TMT heterogeneity increases decision-making diversity and risk identification capabilities, thereby strengthening internal control quality. Meanwhile, through diverse perspectives and innovative thinking, it enhances the dynamic capabilities of firms, jointly promoting digital transformation. The shareholder relationship network positively moderates the effect of TMT heterogeneity on corporate digital transformation. These effects are particularly pronounced in high-tech firms, state-owned enterprises, heavily polluting companies, firms with higher financing constraints, and those in regions with lower levels of marketization. This study not only enriches the theoretical understanding of the relationship between TMT heterogeneity and corporate digital transformation but also provides valuable guidance for practice.

Acellular epicardial patches that treat myocardial infarction by increasing the mechanical integrity of damaged left ventricular tissues exhibit widely scattered therapeutic efficacy. Here, we introduce a viscoelastic adhesive patch, made of an ionically crosslinked transparent hydrogel, that accommodates the cyclic deformation of the myocardium and outperforms most existing acellular epicardial patches in reversing left ventricular remodelling and restoring heart function after both acute and subacute myocardial infarction in rats. The superior performance of the patch results from its relatively low dynamic modulus, designed at the so-called ‘gel point’ via finite-element simulations of left ventricular remodelling so as to balance the fluid and solid properties of the material. A viscoelastic adhesive patch that accommodates the myocardium’s cyclic deformation outperforms most existing acellular epicardial patches in reversing left ventricular remodelling and in restoring heart function after myocardial infarction in rats.

Secretory phosphoprotein 1 (SPP1) has been linked to tumor progression and immune regulation, but its prognostic value, impact on the tumor immune microenvironment (TIME), and drug sensitivity in HCC remain unclear. We performed a pan-cancer analysis using TIMER and validated SPP1 upregulation in six GEO datasets (GSE45436, GSE54236, GSE121248, GSE76427, GSE64041, and GSE60502) and HPA protein data. In TCGA-LIHC, we assessed overall survival (OS) and progression-free survival (PFS) using univariate/multivariate Cox analyses, ROC analysis, and a calibrated nomogram. We identified differentially expressed genes (DEGs) and performed GO/KEGG and GSEA analyses. Immune infiltration was estimated with CIBERSORT and TIMER, and relationships with immune checkpoints were explored. Drug sensitivity was predicted with pRRophetic using GDSC data. In vitro, SPP1 was knocked down or overexpressed in HCC cell lines to evaluate effects on proliferation, migration, invasion, and apoptosis via qRT-PCR, Western blot, CCK-8, colony formation, wound healing, Transwell invasion, and TUNEL assays. SPP1 was significantly upregulated in HCC at mRNA and protein levels. High SPP1 predicted poorer OS and PFS and was associated with higher histological grade, advanced stage, and greater T stage. The nomogram showed good calibration and discrimination. DEGs and enrichment analyses implicated cytokine receptor interaction, fatty acid metabolism, and PI3K-Akt signaling; GSEA confirmed immune- and metabolism-related pathways. High SPP1 correlated with higher immune/ESTIMATE scores, increased M0/M2 macrophages and dendritic cells, reduced CD8 + T cells, and upregulation of multiple immune checkpoints. Drug-sensitivity predictions showed high-SPP1 tumors were more sensitive to several anti-cancer drugs (e.g., sorafenib), while resistance to others was suggested. Functionally, SPP1 knockdown inhibited, while overexpression promoted, proliferation, migration, and invasion; knockdown increased apoptosis. SPP1 acts as an oncogenic driver in HCC, associated with poor prognosis, an immunosuppressive TIME, and distinct drug-response patterns.

Deep learning has been applied to structural damage detection and achieved great success in recent years, such as the popular structural damage detection methods based on structural vibration response and convolutional neural networks (CNN). However, due to the limited number of vibration response samples that can be acquired in practice for damage detection, the CNN-based models may not be fully trained; thus, their performance for identifying different damage severity as well as the damage locations may be reduced. To solve this issue, in this paper, we follow the strategy of \"divide-and-conquer\" and propose a two-stage structural damage detection method. Specifically, in the first stage, a 1D-CNN model is constructed to extract the damage features automatically and identify the damage locations. In the second stage, a support vector machine (SVM) model and wavelet packet decomposition technique are combined to further quantify the damage. Experiments are conducted on an eight-level steel frame structure, and the accuracy of the experimental results is greater than 99%, which demonstrates the superiority of the proposed method compared to the state-of-the-art approaches.

In the context of large-scale renewable energy integration driven by China’s dual-carbon goals, and under distribution network scenarios with continuously increasing shares of wind and photovoltaic generation, this paper proposes a source–grid–load–storage coordinated planning method embedded with a multi-agent game mechanism. First, the interest transmission pathways among distributed generation operators (DGOs), distribution network operators (DNOs), energy storage operators (ESOs), and electricity users are mapped, based on which a profit model is established for each stakeholder. Building on this, a coordinated planning framework for active distribution networks (DN) is developed under the assumption of bounded rationality. Through an evolutionary-game process among DGOs, DNOs, and ESOs, and in combination with user-side demand response, the model jointly determines the optimal network reinforcement scheme as well as the optimal allocation of distributed generation (DG) and energy storage system (ESS) resources. Case studies are then conducted to verify the feasibility and effectiveness of the proposed method. The results demonstrate that the approach enables coordinated planning of DN, DG, and ESS, effectively guides users to participate in demand response, and improves both planning economy and renewable energy accommodation. Moreover, by explicitly capturing the trade-offs among multiple stakeholders through evolutionary-game interactions, the planning outcomes align better with real-world operational characteristics.

Background The triglyceride glucose-body mass index (TyG-BMI) is recognized as a reliable surrogate for evaluating insulin resistance and an effective predictor of cardiovascular disease. However, the link between TyG-BMI index and adverse outcomes in heart failure (HF) patients remains unclear. This study examines the correlation of the TyG-BMI index with long-term adverse outcomes in HF patients with coronary heart disease (CHD). Methods This single-center, prospective cohort study included 823 HF patients with CHD. The TyG-BMI index was calculated as follows: ln [fasting triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2] × BMI. To explore the association between the TyG-BMI index and the occurrences of all-cause mortality and HF rehospitalization, we utilized multivariate Cox regression models and restricted cubic splines with threshold analysis. Results Over a follow-up period of 9.4 years, 425 patients died, and 484 were rehospitalized due to HF. Threshold analysis revealed a significant reverse “J”-shaped relationship between the TyG-BMI index and all-cause mortality, indicating a decreased risk of all-cause mortality with higher TyG-BMI index values below 240.0 (adjusted model: HR 0.90, 95% CI 0.86–0.93; Log-likelihood ratio p  = 0.003). A distinct “U”-shaped nonlinear relationship was observed with HF rehospitalization, with the inflection point at 228.56 (adjusted model: below: HR 0.95, 95% CI 0.91–0.98; above: HR 1.08, 95% CI 1.03–1.13; Log-likelihood ratio p  < 0.001). Conclusions This study reveals a nonlinear association between the TyG-BMI index and both all-cause mortality and HF rehospitalization in HF patients with CHD, positioning the TyG-BMI index as a significant prognostic marker in this population.

Hepatic insulin resistance is central to the metabolic syndrome. Here we investigate the role of BTB and CNC homology 1 (BACH1) in hepatic insulin signaling. BACH1 is elevated in the hepatocytes of individuals with obesity and patients with non-alcoholic fatty liver disease (NAFLD). Hepatocyte-specific Bach1 deletion in male mice on a high-fat diet (HFD) ameliorates hyperglycemia and insulin resistance, improves glucose homeostasis, and protects against steatosis, whereas hepatic overexpression of Bach1 in male mice leads to the opposite phenotype. BACH1 directly interacts with the protein-tyrosine phosphatase 1B (PTP1B) and the insulin receptor β (IR-β), and loss of BACH1 reduces the interaction between PTP1B and IR-β upon insulin stimulation and enhances insulin signaling in hepatocytes. Inhibition of PTP1B significantly attenuates BACH1-mediated suppression of insulin signaling in HFD-fed male mice. Hepatic BACH1 knockdown ameliorates hyperglycemia and improves insulin sensitivity in diabetic male mice. These results demonstrate a critical function for hepatic BACH1 in the regulation of insulin signaling and glucose homeostasis. Hepatic insulin resistance plays a central role in metabolic syndrome. Here, the authors show that transcription factor BACH1 acts as a negative regulator of insulin signaling and that hepatic BACH1 deficiency ameliorates hyperglycemia and insulin resistance in high-fat diet-fed or diabetic mice