Search Results (30,650)

Background: Obesity is a highly prevalent chronic disease characterized by excessive weight gain and fat accumulation. There is growing evidence that Lactiplantibacillus plantarum strains with bile salt hydrolase (BSH) activity are effective in preventing and alleviating obesity. Methods: Initially, we screened bacterial strains with high hydrolytic activity against glycochenodeoxycholic acid (GDCA), and constructed an isogenic bsh1 knockout mutant. Subsequently, male C57BL/6J mice fed a high-fat diet (HFD) were randomly assigned to receive daily gavage of either the wild-type Lp20 (Lp20-WT) or the bsh1-deficient mutant (Lp20-Δbsh1) for 8 weeks. Serum cholesterol levels and histopathological changes in liver sections were monitored. Hepatic gene expression was quantified by RT-qPCR, and fecal bacterial communities were analyzed via 16S rRNA gene sequencing. These comprehensive assessments aimed to evaluate metabolic improvements and uncover the potential mechanisms behind the observed effects. Results:L. plantarum Lp20 hydrolyzed 91.62% of GDCA, exhibiting the highest bile-salt hydrolase (BSH) activity among tested isolates. Whole-genome sequencing and in-silico analyses mapped this activity to bsh1; gene deletion of bsh1 confirmed the role of bsh1 in GDCA hydrolysis. Daily gavage of the wild-type strain (Lp20-WT) to diet-induced obese mice markedly attenuated weight gain, reduced inguinal white adipose tissue and mesenteric fat mass, and lowered serum TC and LDL-C by 20.8% and 33.3%, respectively, while decreasing ALT and AST levels and reversing hepatic steatosis. In contrast, the bsh1-null mutant (Lp20-Δbsh1) failed to elicit any measurable metabolic benefit. Mechanistically, Lp20-WT upregulated rate-limiting bile-acid synthetic enzymes CYP7A1 and CYP27A1, thereby accelerating the catabolism of cholesterol into bile acids. Concurrently, it activated hepatic TGR5 and FXR signaling axes to modulate hepatic metabolism. Moreover, Lp20-WT restructured the gut microbiota by notably enhancing the abundance of beneficial bacteria such as norank_f__Muribaculaceae, Akkermansia, and Alistipes, while reducing the abundance of potentially harmful taxa, including norank_f__Desulfovibrionaceae, Dubosiella, and Mucispirillum. Conclusions: This study provides direct evidence of BSH’s anti-obesity effects through gene deletion. Specifically, BSH lowers cholesterol by modulating hepatic bile-acid metabolism-related gene expression and altering the gut microbiota composition. However, the study is limited by a small sample size (n = 6), the use of male mice only, and its preclinical stage, indicating a need for further validation across diverse strains and human populations. Full article

Background: Type 2 diabetes mellitus (T2DM) is a complex metabolic disease requiring multi-targeted therapeutic strategies. Gunnera perpensa and Erythrina zeyheri are traditionally used in diabetes management, but their mechanisms remain poorly understood. Methods: This study used in vitro, metabolomics, and network pharmacology approaches to elucidate their antidiabetic potential. Leaf extracts were screened for glucose utilization in C2C12 cells, and cytotoxicity in Vero cells. Metabolites profiled via GC×GC-TOF-MS and those retrieved from Phytochemical Interaction Database were evaluated for drug-likeness and target prediction using SwissADME and SwissTargetPrediction. Diabetes-related targets were obtained from databases, and overlapping targets were used to construct interaction networks using Cytoscape and STRING. Functional enrichment analyses were conducted via DAVID for GO and KEGG pathways. Results: G. perpensa acetone and methanol extracts enhanced superior glucose utilization (IC₅₀ = 78.5 and 94.8 µg/mL, respectively), with low cytotoxicity (LC₅₀ > 600 µg/mL). Key compounds including arabinose, identified from both plants, showed multi-target binding potential against STAT3, PIK3RI and JAK2. Enrichment analyses revealed pathways related to insulin signaling, inflammation, and glucose metabolism. Conclusions: This study supports the therapeutic relevance of phytochemical synergy in the traditional use of both plants and demonstrated systems-level approaches for elucidating complex drug–target interactions in T2DM. Full article

Heparan sulfate proteoglycans (HSPGs) are essential constituents of the extracellular matrix (ECM) and cell surface, orchestrating a wide range of biological processes, such as cell adhesion, migration, proliferation, and intercellular communication. Through their highly sulfated glycosaminoglycan chains, HSPGs serve as crucial modulators of bioavailability and signaling of growth factors, cytokines, and chemokines, thereby influencing tissue homeostasis. Their dynamic remodeling is mediated by numerous enzymes, with heparanase (HPSE) playing a predominant role as the only known human endo-β-D-glucuronidase that specifically cleaves heparan sulfate chains. Beyond its well-documented enzymatic activity in ECM degradation and the release of HS-bound molecules, HPSE also exerts non-enzymatic functions that regulate intracellular signaling cascades, transcriptional programs, and immune cell behavior. Dysregulated HPSE expression or activity has been implicated in various pathological conditions, including fibrosis, chronic inflammation, cancer progression, angiogenesis, metastasis, and immune evasion, positioning this enzyme as a pivotal driver of ECM plasticity in both health and disease. This review provides an updated overview of HSPG biosynthesis, structure, localization, and functional roles, emphasizing the activity of HPSE and its impact on tissue remodeling and disease pathogenesis. We further explored its involvement in the hallmark processes of cancer, the inflammatory tumor microenvironment, and its contribution to fibrosis. Finally, we summarize current therapeutic strategies targeting HPSE, outlining their potential to restore ECM homeostasis and counteract HPSE-driven pathological mechanisms. A deeper understanding of the HSPG/HPSE axis may pave the way for innovative therapeutic interventions in cancer, inflammatory disorders, and fibrotic diseases. Full article

Background/Objectives: Pancreatic cancer (PC) remains one of the deadliest malignancies, with challenges that hinder early detection and few actionable molecular targets. In this study, we aimed to identify biomarkers predictive of PC to support its diagnosis and treatment. Methods: Proteins from formalin-fixed, paraffin-embedded pooled samples of PC (n = 15; 5 pools) and chronic pancreatitis (n = 10; 5 pools) tissues were analyzed via label-free quantitative proteomics using liquid chromatography-tandem mass spectrometry. Immunohistochemistry (IHC) was performed on PC tissue microarrays to assess S100 calcium-binding protein P (S100P) and cathepsin E (CTSE) expression (IHC evaluable pairs: n = 78 for S100P; n = 82 for CTSE). Transwell invasion assays were conducted to evaluate the effects of these proteins on PC cell invasiveness, and Western blotting was used to validate protein expression and elucidate associated molecular mechanisms. Results: Both S100P and CTSE were overexpressed in PC tissues compared with those in adjacent normal tissues. Elevated S100P expression correlated with poor prognosis, whereas higher CTSE expression predicted favorable outcomes; both served as independent prognostic factors in PC. Functionally, S100P promoted PC cell invasion, whereas CTSE suppressed it. Mechanistically, both proteins appeared to regulate epithelial–mesenchymal transition (EMT) and invasive capacity through activation or inhibition of the phosphoinositide 3-kinase (PI3K)–protein kinase B (AKT) signaling pathway. Conclusions: Elevated expression of S100P and CTSE in PC tissues serves as independent indicators in our model of patient survival. Both proteins regulate EMT and invasion, potentially via the PI3K–AKT pathway, and hold significant promise as prognostic biomarkers and therapeutic targets in PC. Full article

Background: Clear cell renal cell carcinoma (ccRCC) is a prevalent urological malignancy, accounting for approximately 1.6% of all cancer-related deaths in 2022. While endocrine-disrupting chemicals (EDCs) have been implicated as risk factors for ccRCC, the toxicological profiles and immune mechanisms underlying Bisphenol A (BPA) exposure in ccRCC progression remain inadequately understood. Materials and Methods: Protein–protein interaction (PPI) analysis and visualization were performed on overlapping genes between ccRCC and BPA exposure. This was followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to elucidate potential underlying mechanisms. Subsequently, 108 distinct machine learning algorithm combinations were evaluated to identify the optimal predictive model. An integrated CoxBoost and Ridge regression model was constructed to develop a prognostic signature, the performance of which was rigorously validated across two independent external datasets. Finally, molecular docking analyses were employed to investigate interactions between key genes and BPA. Results: A total of 114 overlapping targets associated with both ccRCC and BPA were identified. GO and KEGG analyses revealed enrichment in cancer-related pathways, including pathways in cancer, endocrine resistance, PD-L1 expression and PD-1 checkpoint signaling, T-cell receptor signaling, endocrine function, and immune responses. Machine learning algorithm selection identified the combined CoxBoost-Ridge approach as the optimal predictive model (achieving a training set concordance index (C-index) of 0.77). This model identified eight key genes (CHRM3, GABBR1, CCR4, KCNN4, PRKCE, CYP2C9, HPGD, FASN), which were the top-ranked by coefficient magnitude in the prognostic model. The prognostic signature demonstrated robust predictive performance in two independent external validation cohorts (C-index = 0.74 in cBioPortal; C-index = 0.81 in E-MTAB-1980). Furthermore, molecular docking analyses predicted strong binding affinities between BPA and these key targets (Vina scores all <−6.5 kcal/mol), suggesting a potential mechanism through which BPA may modulate their activity to promote renal carcinogenesis. Collectively, These findings suggested potential molecular mechanisms that may underpin BPA-induced ccRCC progression, generating hypotheses for future experimental validation. Conclusions: These findings enhance our understanding of the molecular mechanisms by which BPA induces ccRCC and highlight potential targets for therapeutic intervention, particularly in endocrine and immune-related pathways. This underscores the need for collaborative efforts to mitigate the impact of environmental toxins like BPA on public health. Full article

Toll-like receptor 9 (TLR9) and Toll-like receptor 3 (TLR3), which are widely expressed in dendritic cells (DCs), function as key pattern recognition receptors (PRRs) in the immune system. Their primary roles involve specifically detecting pathogen-associated molecular patterns (PAMPs): TLR9 recognizes unmethylated CpG motifs predominantly found in bacterial and viral DNA, while TLR3 identifies viral double-stranded RNA (dsRNA), a molecular signature associated with viral replication. Their specific agonists [CpG ODN (a TLR9 agonist) and poly(I:C) (a TLR3 agonist)] can effectively activate DCs and enhance the expression of immune activation-related molecules. In this study, by establishing a mouse primary dendritic cell model and a glioma-bearing mouse model, and employing techniques such as transcriptome sequencing, we found that combined stimulation with CpG ODN and poly(I:C) significantly enhanced the anti-tumor function of DCs: in vitro, DCs subjected to combined stimulation showed upregulation of anti-tumor-related surface markers, enhanced migratory capacity, and a more effective activation of CD8⁺ T cells; in vivo, a DC vaccine loaded with tumor lysate antigen and stimulated with this combined regimen significantly delayed the progression of glioma in tumor-bearing mice. Further investigation revealed that the underlying mechanism for this enhanced effect may involve TLR9 activation promoting TLR3 upregulation through the p38 MAPK-ATF3 signaling axis. Consequently, we designed a sequential stimulation protocol (first CpG ODN then poly(I:C)), which demonstrated a stronger anti-glioma effect compared to simple combined stimulation. This study provides a new strategy for enhancing the immune efficacy of DC vaccines and has potential significance for promoting the clinical translation of DC vaccines. Full article

To address the low efficiency and high subjectivity of manual interpretation in fluorescence in situ hybridization (FISH) tissue and cell images, this study proposes an intelligent FISH image classification model based on an improved ResNet50 architecture. By analyzing the characteristics of multi-channel fluorescence signals and the bottlenecks of clinical interpretation, a Convolutional Block Attention Module (CBAM) is introduced to enhance the representation of salient fluorescence features through dual channel–spatial attention mechanisms. A Pyramid Pooling Module (PPM) is integrated to fuse multi-scale contextual information, improving the detection accuracy of small targets such as microdeletions. Furthermore, the shortcut connections in residual blocks are optimized to reduce feature loss. To mitigate the limitation of insufficient annotated samples, transfer learning is employed, combined with a focal loss function to enhance classification performance under class-imbalanced conditions. Experiments conducted on a clinical dataset of 12,000 FISH images demonstrate that the proposed model achieves an overall classification accuracy of 92.4%, representing a 9.9% improvement over the original ResNet50. The recall rate for complex categories (e.g., translocation and fusion) exceeds 90.7%, with an inference time of 22.3 ms per sample, meeting the real-time requirements of clinical diagnosis. These results provide an efficient and practical solution for the automated intelligent interpretation of FISH images, offering significant potential for precision-assisted diagnosis of tumors and genetic disorders. Full article

Amidrapid advances in intelligent medicine, decoding brain activity from electroencephalogram (EEG) signals has emerged as a critical technical frontier for brain–computer interfaces and medical AI systems. Given the inherent spatial resolution limitations of an EEG, researchers frequently integrate functional magnetic resonance imaging (fMRI) to enhance neural activity representation. However, fMRI acquisition is inherently complex. Consequently, efforts increasingly focus on cross-modal transformation methods that map EEG signals to fMRI data, thereby extending EEG applications in neural mechanism studies. The central challenge remains generating high-fidelity fMRI images from EEG signals. To address this, we propose a diffusion model-based framework for cross-modal EEG-to-fMRI generation. To address pronounced noise contamination in electroencephalographic (EEG) signals acquired via simultaneous recording systems and temporal misalignments between EEGs and functional magnetic resonance imaging (fMRI), we first apply Fourier transforms to EEG signals and perform dimensionality expansion. This constructs a spatiotemporally aligned EEG–fMRI paired dataset. Building on this foundation, we design an EEG encoder integrating a multi-layer recursive spectral attention mechanism with a residual architecture.In response to the limited dynamic mapping capabilities and suboptimal image quality prevalent in existing cross-modal generation research, we propose a diffusion-model-driven EEG-to-fMRI generation algorithm. This framework unifies the EEG feature encoder and a cross-modal interaction module within an end-to-end denoising U-Net architecture. By leveraging the diffusion process, EEG-derived features serve as conditional priors to guide fMRI reconstruction, enabling high-fidelity cross-modal image generation. Empirical evaluations on the resting-state NODDI dataset and the task-based XP-2 dataset demonstrate that our EEG encoder significantly enhances cross-modal representational congruence, providing robust semantic features for fMRI synthesis. Furthermore, the proposed cross-modal generative model achieves marked improvements in structural similarity, the root mean square error, and the peak signal-to-noise ratio in generated fMRI images, effectively resolving the nonlinear mapping challenge inherent in EEG–fMRI data. Full article

The accurate detection of high-impedance faults (HIFs) in distribution systems is fundamentally dependent on the extraction of weak fault signatures. However, these features are often obscured by complex and high-level noise present in current transformer (CT) measurement data. To address this challenge, an energy-proportion-guided channel-wise attention stacked denoising autoencoder (EPGCA-SDAE) model is proposed. In this model, wavelet decomposition is employed to transform the signal into informative frequency band components. A channel attention mechanism is utilized to adaptively assign weights to each component, thereby enhancing model interpretability. Furthermore, a physics-informed prior, based on energy distribution, is introduced to guide the loss function and regulate the attention learning process. Extensive simulations using both synthetic and real-world $10\mathrm{kV}$ distribution network data are conducted. The superiority of the EPGCA-SDAE over traditional wavelet-based methods, stacked denoising autoencoders (SDAE), denoising convolutional neural network (DnCNN), and Transformer-based networks across various noise conditions is demonstrated. The lowest average mean squared error (MSE) is achieved by the proposed model (simulated: $50.60\times {10}^{-5}\mathrm{p}.\mathrm{u}.$; real: $76.45\times {10}^{-5}\mathrm{p}.\mathrm{u}.$), along with enhanced noise robustness, generalization capability, and physical interpretability. These results verify the method’s feasibility within the tested 10 kV distribution system, providing a reliable data recovery framework for fault diagnosis in noise-contaminated distribution network environments. Full article

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, with tumor recurrence and metastasis posing significant challenges despite advances in targeted therapies and immunotherapy. Cellular dormancy, a reversible, quiescent state marked by cell cycle arrest, has emerged as a key driver of therapeutic resistance and disease relapse, particularly in small-cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). Multiple mechanisms, including autophagy, stress-adaptive signaling, microenvironmental cues, and epigenetic dysregulation, have been implicated in the regulation of dormancy and long-term cell survival. Among these, epigenetic modifications such as DNA methylation, histone modifications, and non-coding RNAs (ncRNAs) play pivotal roles in maintaining dormancy by repressing proliferative gene expression programs. Increasing evidence suggests that dormant tumor cells harbor distinct epigenomic signatures, which may serve as predictive biomarkers for minimal residual disease (MRD) and relapse risk. This review summarizes current advances in understanding the epigenetic regulation of cellular dormancy in lung cancer, with a particular emphasis on the interplay between epigenetic modifiers and oncogenic signaling pathways. Furthermore, emerging molecular targets and associated therapeutic agents currently under clinical evaluation are presented, emphasizing how a deeper understanding of the epigenetic landscape governing dormancy may inform the development of novel interventions to improve long-term clinical outcomes in lung cancer patients. Full article

This study systematically explored the regulatory mechanisms of uterine function across four reproductive stages: sexual maturity sow (SMS), low-yield sow (LYS), high-yield sow (HYS), and culled sow (CS) in Large White (LW) pigs through integrated transcriptomic, proteomic, and metabolomic analyses. Twelve healthy LW sows were selected, and uterine tissues were collected for multi-omics detection. Combined with bioinformatics analysis, molecular regulatory networks were constructed. Results showed that transcriptomics identified 12 types of alternative splicing and 1243 novel genes, which were enriched in energy metabolism and signal transduction pathways. Proteomics revealed 430 differentially co-expressed proteins, indicating high protein synthesis activity in the SMS stage and extracellular inflammatory characteristics in the CS stage. Metabolomics detected numerous differential metabolites, among which XTP and DHA ethyl ester were associated with high fecundity and aging, respectively. Integrated multi-omics analysis identified hub genes such as PLA2G4A, which influence reproductive performance by regulating inflammatory and metabolic balance, and clarified stage-specific “gene–protein–metabolite” modules. This study provides a molecular map for understanding dynamic changes in uterine function in Large White pigs and offers a theoretical basis for optimizing reproductive lifespan and breeding strategies. Full article

Silk fibroin (SF) has evolved from a traditional biopolymer to a leading regenerative medicine material. Its combination of mechanical strength, biocompatibility, tunable degradation, and molecular adaptability makes SF a unique matrix that is both bioactive and intelligent. Advances in hydrogel engineering have transformed SF from a passive scaffold into a smart, living hydrogel. These systems can instruct cell fate, sense microenvironmental signals, and deliver therapeutic signals as needed. By incorporating stem cells, progenitors, or engineered immune and microbial populations, SF hydrogels now serve as synthetic niches for organoid maturation and as adaptive implants for tissue regeneration. These platforms replicate extracellular matrix complexity and evolve with tissue, showing self-healing, shape-memory, and stimuli-responsive properties. Such features are redefining biomaterial–cell interactions. SF hydrogels are used for wound healing, musculoskeletal repair, neural and cardiac patches, and developing scalable organoid models for disease and drug research. Challenges remain in maintaining long-term cell viability, achieving clinical scalability, and meeting regulatory standards. This review explores how advances in SF engineering, synthetic biology, and organoid science are enabling SF-based smart living hydrogels in bridging the gap between research and clinical use. Full article

Ion channels are protein pores that regulate ionic flow across cell membranes, enabling vital processes such as nerve signaling. They often conduct multiple ionic species simultaneously, leading to complex nonlinear transport phenomena. Because experimental techniques provide only indirect measurements of ion channel currents, mathematical models—particularly Poisson–Nernst–Planck (PNP) equations—are indispensable for analyzing the underlying transport mechanisms. In this work, we examine ionic transport through a one-dimensional steady-state PNP model of a narrow membrane channel containing multiple cation species of different valences. The model incorporates a small fixed charge distribution along the channel and imposes relaxed electroneutrality boundary conditions, allowing for a slight charge imbalance in the baths. Using singular perturbation analysis, we first derive approximate solutions that capture the boundary-layer structure at the channel—reservoir interfaces. We then perform a regular perturbation expansion around the neutral reference state (zero fixed charge with electroneutral boundary conditions) to obtain explicit formulas for the steady-state ion fluxes in terms of the system parameters. Through this analytical approach, we identify several critical applied potential values—denoted $V_k^a$ (for each cation species k), $V^b$, and $V^c$—that delineate distinct transport regimes. These critical potentials govern the sign of the fixed charge’s influence on each ion’s flux: depending on whether the applied voltage lies below or above these thresholds, a small positive permanent charge will either enhance or reduce the flux of each ion species. Our findings thus characterize how a nominal fixed charge can nonlinearly modulate multi-ion currents. This insight deepens the theoretical understanding of nonlinear ion transport in channels and may inform the interpretation of current–voltage relations, rectification effects, and selective ionic conduction in multi-ion channel experiments. Full article

Background: Alzheimer’s disease (AD) is a progressive neurodegenerative disorder with unclear pathogenic mechanisms. Dysregulated zinc metabolism contributes to AD pathology. This study aimed to identify zinc metabolism-related hub genes to provide potential biomarkers and therapeutic targets for AD. Methods: We performed an integrative analysis of multiple transcriptomic datasets from AD patients and normal controls. Differentially expressed genes and weighted gene co-expression network analysis (WGCNA) were combined to identify hub genes. We then conducted Gene Set Enrichment Analysis (GSEA), immune cell infiltration analysis (CIBERSORT), and receiver operating characteristic (ROC) curve analysis to assess the hub gene’s biological function, immune context, and diagnostic performance. Drug-gene interactions were predicted using the DrugBank database. Results: We identified a single key zinc transporter–related hub gene, SLC30A3, which was significantly downregulated in AD and demonstrated potential diagnostic value (AUC 0.70–0.80). Lower SLC30A3 expression was strongly associated with impaired synaptic plasticity (long-term potentiation, long-term depression, calcium signaling pathway, and axon guidance), mitochondrial dysfunction (the citrate cycle and oxidative phosphorylation), and pathways common to major neurodegenerative diseases (Parkinson’s disease, AD, Huntington’s disease, and amyotrophic lateral sclerosis). Furthermore, SLC30A3 expression correlated with specific immune infiltrates, particularly the microglia-related chemokine CX3CL1. Zinc chloride and zinc sulfate were identified as potential pharmacological modulators. Conclusions: Our study systematically identifies SLC30A3 as a novel biomarker in AD, linking zinc dyshomeostasis to synaptic failure, metabolic impairment, and neuroimmune dysregulation. These findings offer a new basis for developing targeted diagnostic and therapeutic strategies for AD. Full article

Mechanical fault samples of disconnectors are scarce, the fault types vary, and the self-evidence is weak, which leads to a lack of perfect fault diagnosis methods, and hidden defects cannot be found in time. To solve this problem, a mechanical fault diagnosis method for disconnectors based on a parallel dual-channel feature fusion model is proposed. Firstly, the optimal parameters for variational mode decomposition (VMD) are obtained using the black-winged kite algorithm (BKA). After the signal decomposition, the kurtosis values of each intrinsic mode function (IMF) are calculated, screened, and reconstructed. The reconstructed signal is input into the gated recurrent unit (GRU) to capture its time-series characteristics. Then, the vibration signal is generated by the recurrence plot (RP) to generate the atlas set and input into the vision Transformer (ViT) to extract its spatial characteristics. Finally, the time-series and spatial characteristics are fused, the multi-head self-attention mechanism is used for training, and softmax is used for fault classification. The measured data results show that the diagnostic accuracy of the model for mechanical fault types reaches 97.9%, which is 3.2%, 4.3%, 1.0%, 2.4%, 2.9%, 1.8%, 2.1%, 9%, and 7.5% higher than the other nine models numbered #2–#10, respectively, verifying its effectiveness and adaptability. Full article