Search Results (26,411)

Glioblastoma multiforme (GBM) remains one of the most aggressive and treatment-resistant brain tumors, with poor prognosis and limited therapeutic options. Background/Objectives: Integrin α_vβ₃, a cell surface receptor overexpressed in GBM, specifically binds to cyclic arginine-glycine-aspartate-D-phenylalanine-lysine (c(RGDfK)) motif, making it a valuable target for tumor-specific delivery and PET imaging. This study explores a novel radiotheranostic agent, [⁶⁴Cu]Cu-NOTA-TP-c(RGDfK), which combines the imaging and therapeutic capabilities of copper-64 (⁶⁴Cu) and the cytotoxic activity of a terpyridine-platinum (TP) complex, conjugated to c(RGDfK). Methods: A robust protocol was developed for the small-scale preparation of NOTA-TP-c(RGDfK). Comparative cellular studies were conducted using U87 MG glioblastoma (GBM) cells and SVG p12 human astrocytes to evaluate the performance of [⁶⁴Cu]Cu-NOTA-TP-c(RGDfK) relative to [⁶⁴Cu]Cu-NOTA-c(RGDfK), [⁶⁴Cu]Cu-NOTA-TP, ^natCu-NOTA-TP-c(RGDfK), cisplatin, and temozolomide. Results: ⁶⁴Cu-radiolabeling of NOTA-TP-c(RGDfK) was achieved with >99% radiochemical purity, and competition assays confirmed high binding affinity to integrin α_vβ₃ (IC₅₀ = 16 ± 8 nM). Cellular uptake, internalization, and retention studies demonstrated significantly higher accumulation of [⁶⁴Cu]Cu-NOTA-TP-c(RGDfK) in U87 MG cells compared to control compounds, with 38.8 ± 1.8% uptake and 28.0 ± 1.0% internalization at 24 h. Nuclear localization (6.0 ± 0.5%) and stable intracellular retention further support its therapeutic potential for inducing localized DNA damage. Importantly, [⁶⁴Cu]Cu-NOTA-TP-c(RGDfK) exhibited the highest cytotoxicity in U87 MG cells (IC₅₀ = 10 ± 2 nM at 48 h), while maintaining minimal toxicity in normal SVG p12 astrocytes. Conclusions: These results highlight [⁶⁴Cu]Cu-NOTA-TP-c(RGDfK) as a promising targeted radiotheranostic agent for GBM, warranting further preclinical development Full article

Tooth development (odontogenesis) is regulated by interactions between epithelial and mesenchymal tissues through signaling pathways such as Bone Morphogenetic Protein (BMP), Wingless-related integration site (Wnt), Sonic Hedgehog (SHH), and Fibroblast Growth Factor (FGF). Mesenchymal stem cells (MSCs) derived from dental tissues—including dental pulp stem cells (DPSCs), periodontal ligament stem cells (PDLSCs), and dental follicle progenitor cells (DFPCs)—show promise for regenerative dentistry due to their multilineage differentiation potential. Epigenetic regulation, particularly DNA methylation, is hypothesized to underpin their distinct regenerative capacities. This study reanalyzed publicly available DNA methylation data generated with Illumina Infinium HumanMethylation450 BeadChip arrays (450K arrays) from DPSCs, PDLSCs, and DFPCs. High-confidence CpG sites were selected based on detection p-values, probe variance, and genomic annotation. Principal Component Analysis (PCA) and hierarchical clustering identified distinct methylation profiles. Functional enrichment analyses highlighted biological processes and pathways associated with specific methylation clusters. Noncoding RNA analysis was integrated to construct regulatory networks linking DNA methylation patterns with key developmental genes. Distinct epigenetic signatures were identified for DPSCs, PDLSCs, and DFPCs, characterized by differential methylation across specific genomic contexts. Functional enrichment revealed pathways involved in odontogenesis, osteogenesis, and neurodevelopment. Network analysis identified central regulatory nodes—including genes, such as PAX6, FOXC2, NR2F2, SALL1, BMP7, and JAG1—highlighting their roles in tooth development. Several noncoding RNAs were also identified, sharing promoter methylation patterns with developmental genes and being implicated in regulatory networks associated with stem cell differentiation and tissue-specific function. Altogether, DNA methylation profiling revealed that distinct epigenetic landscapes underlie the developmental identity and differentiation potential of dental-derived mesenchymal stem cells. This integrative analysis highlights the relevance of noncoding RNAs and regulatory networks, suggesting novel biomarkers and potential therapeutic targets in regenerative dentistry and orthodontics. Full article

Background: Cervical cancer remains a major global health concern, with existing chemotherapy facing limited effectiveness owing to resistance. Polo-like kinase 1 (PLK1) overexpression in cervical cancer cells is a promising target for developing novel therapies to overcome chemoresistance and improve treatment efficacy. Methods: In this study, we developed a novel PROTAC, NC1, targeting PLK1 PBD via the N-end rule pathway. Results: This PROTAC effectively depleted the PLK1 protein in HeLa cells by inducing protein degradation. The crystal structure of the PBD-NC1 complex identified key PLK1 PBD binding interactions and isothermal titration calorimetry (ITC) confirmed a binding affinity of 6.06 µM between NC1 and PLK1 PBD. NC1 significantly decreased cell viability with an IC₅₀ of 5.23 µM, induced G2/M phase arrest, and triggered apoptosis in HeLa cells. In vivo, NC1 suppressed tumor growth in a HeLa xenograft mouse model. Conclusions: This research highlights the potential of N-degron-based PROTACs targeting the PLK1 protein in cancer therapies, highlighting their potential in future cervical anticancer treatment strategies. Full article

As people with cystic fibrosis (PwCF) live longer, kidney disease is emerging as a significant comorbidity that is increasingly linked to cardiovascular complications and progression to end-stage kidney disease. In our recent review, we proposed the unifying term CF-related kidney disease (CFKD) to encompass the spectrum of kidney dysfunction observed in this population. Early detection of kidney injury is critical for improving long-term outcomes, yet remains challenging due to the limited sensitivity of conventional laboratory tests, particularly in individuals with altered muscle mass and unique CF pathophysiology. Emerging approaches, including novel blood and urinary biomarkers, urinary extracellular vesicles, and genetic risk profiling, offer promising avenues for identifying subclinical kidney damage. When integrated with machine learning algorithms, these tools may enable the development of personalized risk stratification models and targeted therapeutic strategies. This precision medicine approach has the potential to transform kidney disease management in PwCF, shifting care from reactive treatment of late-stage disease to proactive monitoring and early intervention. Full article

The rugose surface trait in pepper (Capsicum annuum L.), marked by ridges and depressions on the fruit epidermis, is linked to improved fruit texture. To investigate its regulatory basis, histological, textural, and transcriptomic differences, contrasting genotypes were analyzed. Histological analysis revealed that disorganized epidermal cell layers contribute to rugosity, with morphological differences emerging around 10 days post-anthesis (DPA). A computer-aided design (CAD)-based rugosity index (RI) was developed and showed strong correlation with sensory rugosity scores (R² = 0.659, p < 0.001). Texture analysis demonstrated that increasing surface rugosity was associated with reduced rupture force and hardness, as well as elevated pectinase activity. Comparative transcriptome profiling identified 10 differentially expressed genes (DEGs) related to microtubule dynamics (e.g., CA03g18310 and CA09g13510) and phytohormone signaling (e.g., CA03g35180 and CA08g12070), which exhibited distinct spatial and temporal expression patterns. These findings suggest that coordinated cytoskeletal remodeling and hormonal regulation drive epidermal disorganization, leading to surface rugosity and altered fruit texture. The study provides novel insights into the molecular basis of fruit surface morphology and identifies promising targets for breeding high-quality pepper cultivars. Full article

RNA-binding proteins (RBPs), particularly IGF2BP3, play critical but underexplored roles in lung adenocarcinoma (LUAD). This study investigated IGF2BP3′s clinical and functional significance using single-cell/RNA sequencing, validated by qPCR, Western blot, and immunohistochemistry. The results show IGF2BP3 was significantly upregulated in LUAD tissues and associated with advanced-stage, larger tumors, lymph node metastasis, and poor prognosis. A prognostic nomogram confirmed its independent predictive value. Functionally, IGF2BP3 knockdown suppressed proliferation, and induced G2/M arrest and apoptosis. GSEA linked high IGF2BP3 to cell cycle activation and low expression to metabolic pathways. Notably, high IGF2BP3 correlated with immune evasion markers (downregulated CD4+ effector T cells, upregulated Th2 cells), while TIDE analysis suggested a better immunotherapy response in low-expressing patients. Drug screening identified BI-2536 as a potential therapy for low-IGF2BP3 cases, supported by strong molecular docking affinity (−7.55 kcal/mol). These findings establish IGF2BP3 as a key driver of LUAD progression and a promising target for immunotherapy and precision medicine. Full article

Understanding the spatial dynamics and drivers of armed conflict is crucial for anticipating risk and informing targeted interventions. However, current research rarely considers the spatio-temporal clustering characteristics of armed conflicts. Here, we assess the distribution dynamics and driving factors of armed conflict from the perspective of armed conflict clusters, employing complex network dynamic community detection methods and interpretable machine learning approaches. The results show that conflict clusters vary in terms of regional distribution. Sub-Saharan Africa boasts the highest number of conflict clusters, accounting for 37.9% of the global total and covering 40.4% of the total cluster area. In contrast, South Asia and Afghanistan, despite having a smaller proportion of clusters at 12.1%, hold the second-largest cluster area, which is 18.1% of the total. The characteristics of different conflict networks are influenced by different factors. Historical exposure, socio-economic deprivation, and spatial structure are the primary determinants of conflict patterns, while climatic variables contribute less prominently as part of a broader system of environmental vulnerability. Moreover, the influence of driving factors shows spatial heterogeneity. By integrating cluster-level analysis with interpretable machine learning, this study offers a novel perspective for understanding the multidimensional characteristics of armed conflicts. Full article

B-cell lymphomas, including Burkitt lymphoma (BL), diffuse large B-cell lymphoma (DLBCL), and follicular lymphoma (FL), evade CD4+ T-cell immunity through novel HLA class II-associated immunosuppressive mechanisms. Despite expressing surface HLA-DR, these tumors fail to activate antigen-specific CD4+ T cells, independent of co-stimulation or PD-L1 checkpoint inhibition. We identified lymphoma-secreted factors that broadly disrupt HLA class II-mediated antigen presentation in both malignant B cells and dendritic cells (DCs), silencing T-cell responses. This inhibition is allele-independent (affecting DR1, DR4, DR7) but spares HLA class I-mediated CD8+ T-cell recognition, indicating a targeted immune evasion strategy. Biochemical and mass spectrometry (MALDI-MS) analyses revealed unique low-molecular-weight peptides (693–790 Da) in BL cells, absent in normal B cells, which may mediate this suppression. Functional fractionation confirmed bioactive inhibitory fractions in lymphoma lysates, further implicating tumor-intrinsic molecules in immune escape. These findings highlight a previously unrecognized axis of B-cell lymphoma immune evasion, where secreted factors disable HLA class II function across antigen-presenting cells. Therapeutically, neutralizing these immunosuppressive molecules could restore CD4+ T-cell surveillance and enhance immunotherapies in B-cell malignancies. This work underscores the importance of HLA class II dysfunction in lymphoma progression and identifies candidate targets for reversing immune suppression. Full article

To address the challenges of tomato maturity detection in natural environments—such as interference from complex backgrounds and the difficulty in distinguishing adjacent fruits with similar maturity levels—this study proposes a lightweight tomato maturity detection model, YOLOv8n-FDE. Four maturity stages are defined: mature, turning-mature, color-changing, and immature. The model incorporates a newly designed C3-FNet feature extraction and fusion module to enhance target feature representation, and integrates the DySample operator to improve adaptability under complex conditions. Furthermore, the detection head is optimized as the parameter-sharing lightweight detection head (PSLD), which boosts the accuracy of multi-scale tomato fruit feature prediction and precisely focuses on tomato color characteristics. A novel PIoUv2 loss function is also introduced to further improve localization performance and accelerate convergence. Experimental results demonstrate that the improved YOLOv8n-FDE model achieves a parameter count of 1.56 × 10⁶, computational complexity of 4.5 GFLOPs, and a model size of 3.20 MB. The model attains an mAP@0.5 of 97.6%, representing reductions of 46%, 21%, and 60% in parameter count, computation, and size, respectively, compared to YOLOv8n, with a 1.8 percentage point increase in mAP@0.5. This study significantly reduces model complexity and improves the accuracy of tomato maturity detection, providing a more robust data foundation for subsequent orchard yield prediction. Full article

One of the aggressive and lethal cancers, pancreatic ductal adenocarcinoma (PDAC) is characterized by poor prognosis and resistance to conventional treatments. Moreover, the tumor immune microenvironment (TIME) plays a crucial role in the progression and therapeutic resistance of PDAC. It is associated with T-cell exhaustion, leading to the progressive loss of T-cell functions with an impaired ability to kill tumor cells. Therefore, this study employed single-cell RNA sequencing (scRNA-seq) analysis of a publicly available human PDAC dataset, with cells isolated from the primary tumor and adjacent normal tissues, identifying upregulated genes of T-cells and cancer cells in two groups (“cancer cells_vs_all-PDAC” and “cancer-PDAC_vs_all-normal”). Common and unique markers of cancer cells from both groups were identified. The Reactome pathways of cancer and T-cells were selected, while the genes implicated in those pathways were used to perform PPI analysis, revealing the hub genes of cancer and T-cells. The gene expression validation of cancer and T-cells hub-genes was performed using GEPIA2 and TISCH2, while the overall survival analysis of cancer cells hub-genes was performed using GEPIA2. Conclusively, this study unraveled 16 novel markers of cancer and T-cells, providing the groundwork for future research into the immune landscape of PDAC, particularly T-cell exhaustion. However, further clinical studies are needed to validate these novel markers as potential therapeutic targets in PDAC patients. Full article

With the rapid adoption of automated driving systems, ensuring safe and efficient driver takeover has become a crucial challenge for road safety. This study introduces a novel psychological framework for understanding and predicting takeover behavior in conditionally automated vehicles, leveraging an extended Theory of Planned Behavior (TPB) model enriched by real-world driver experience. Drawing on survey data from 385 automated driving system users recruited in Shaoguan City, China, through face-to-face questionnaire administration covering various ADS types (ACC, lane-keeping, automatic parking), we demonstrate that driver attitudes, perceived behavioral control, and subjective norms are significant determinants of takeover intention, collectively explaining nearly half of its variance (R² = 48.7%). Importantly, our analysis uncovers that both intention and perceived behavioral control have robust, direct effects on actual takeover behavior. Crucially, this work is among the first to reveal that individual user characteristics—such as driving experience and ADS (automated driving system) usage frequency—substantially moderate these psychological pathways: experienced or frequent users rely more on perceived control and attitude, while less experienced drivers are more susceptible to social influences. By advancing a multi-dimensional psychological model that integrates personal, social, and experiential moderators, our findings deliver actionable insights for the design of adaptive human–machine interfaces, tailored driver training, and targeted safety interventions in the context of automated driving. Using structural equation modeling with maximum likelihood estimation (χ²/df = 2.25, CFI = 0.941, RMSEA = 0.057), this psychological approach complements traditional engineering models by revealing that takeover behavior variance is explained at 58.3%. Full article

Objective: Retinal capillary dropout, characterized by acellular capillaries or “ghost vessels,” is an early pathological sign of diabetic retinopathy (DR) that remains undetectable through standard clinical imaging techniques until visible morphological changes, such as microaneurysms or hemorrhages, occur. This study aims to develop a non-destructive artificial intelligence (AI)-based method using fluorescein angiography (FA) images to detect early-stage, silent retinal capillary dropout. Methods: We utilized 94 FA images and corresponding destructive retinal capillary density measurements obtained through retinal trypsin digestion from 51 Nile rats. Early capillary dropout was defined as having an acellular capillary density of ≥18 counts per mm². A DenseNet based deep learning model was trained to classify images into early capillary dropout or normal. A Bayesian framework incorporating diabetes duration was used to enhance model predictions. RNA sequencing was conducted on retinal vasculature to identify molecular markers associated with capillary early dropout. Results: The AI-based FA imaging model demonstrated an accuracy of 80.85%, sensitivity of 84.21%, specificity of 75.68%, and an AUC of 0.86. Integration of diabetes duration into a Bayesian predictive framework further improved the model’s performance (AUC = 0.90). Transcriptomic analysis identified 43 genes significantly upregulated in retinal tissues preceding capillary dropout. Notably, inflammatory markers such as Bcl2a1, Birc5, and Il20rb were among these genes, indicating that inflammation might play a critical role in early DR pathogenesis. Conclusions: This study demonstrates that AI-enhanced FA imaging can predict silent retinal capillary dropout before conventional clinical signs of DR emerge. Combining AI predictions with diabetes duration data significantly improves diagnostic performance. The identified gene markers further highlight inflammation as a potential driver in early DR, offering novel insights and potential therapeutic targets for preventing DR progression. Full article

Traffic safety is a significant global concern, and traffic sign recognition (TSR) is essential for the advancement of intelligent transportation systems. Traditional YOLO11s-based methods often struggle to balance detection accuracy and processing speed, particularly in the context of small traffic signs within complex environments. To address these challenges, this study presents Ghost-YOLO-GBH, an innovative lightweight model that incorporates three key enhancements: (1) the integration of a GhostNet backbone, which substitutes the conventional YOLO11s architecture and utilizes Ghost modules to exploit feature redundancy, resulting in a 40.6% reduction in computational load while ensuring effective feature extraction for small targets; (2) the development of a HybridFocus module that combines large separable kernel attention with multi-scale pooling, effectively minimizing background interference and improving contextual feature aggregation by 4.3% in isolated tests; and (3) the implementation of a Bidirectional Dynamic Multi-Scale Feature Pyramid Network (BiDMS-FPN) that allows for bidirectional cross-stage feature fusion, significantly enhancing the accuracy of small target detection. Experimental results on the TT100K dataset indicate that Ghost-YOLO-GBH achieves an impressive 81.10% mean Average Precision (mAP) at a threshold of 0.5, along with an 11.7% increase in processing speed (45 FPS) and an 18.2% reduction in model parameters (7.74 M) compared to the baseline YOLO11s. Overall, Ghost-YOLO-GBH effectively balances accuracy, efficiency, and lightweight deployment, demonstrating superior performance in real-world applications characterized by small signs and cluttered backgrounds. This research provides a novel framework for resource-constrained TSR applications, contributing to the evolution of intelligent transportation systems. Full article

The retina is highly sensitive to oxygen and blood supply, and hypoxia plays a key role in retinal diseases such as diabetic retinopathy (DR) and age-related macular degeneration (AMD). Müller glial cells, which are essential for retinal homeostasis, respond to injury and hypoxia with reactive gliosis, characterized by the upregulation of the glial fibrillary acidic protein (GFAP) and vimentin, cellular hypertrophy, and extracellular matrix changes, which can impair retinal function and repair. The retinal pigment epithelium (RPE) supports photoreceptors, forms part of the blood–retinal barrier, and protects against oxidative stress; its dysfunction contributes to retinal degenerative diseases such as AMD, retinitis pigmentosa (RP), and Stargardt disease (SD). Extracellular vesicles (EVs) play a crucial role in intercellular communication, protein homeostasis, and immune modulation, and have emerged as promising diagnostic and therapeutic tools. Understanding the role of extracellular vesicles’ (EVs’) signaling machinery of glial cells and the retinal pigment epithelium (RPE) is critical for developing effective treatments for retinal degeneration. In this study, we investigated the bidirectional EV-mediated crosstalk between RPE and Müller cells under hypoxic conditions and its impact on cellular metabolism and retinal cell integrity. Our findings demonstrate that RPE-derived extracellular vesicles (RPE EVs) induce time-dependent metabolic reprogramming in Müller cells. Short-term exposure (24 h) promotes pathways supporting neurotransmitter cycling, calcium and mineral absorption, and glutamate metabolism, while prolonged exposure (72 h) shifts Müller cell metabolism toward enhanced mitochondrial function and ATP production. Conversely, Müller cell-derived EVs under hypoxia influenced RPE metabolic pathways, enhancing fatty acid metabolism, intracellular vesicular trafficking, and the biosynthesis of mitochondrial co-factors such as ubiquinone. Proteomic analysis revealed significant modulation of key regulatory proteins. In Müller cells, hypoxic RPE-EV exposure led to reduced expression of Dyskerin Pseudouridine Synthase 1 (DKc1), Eukaryotic Translation Termination Factor 1 (ETF1), and Protein Ser/Thr phosphatases (PPP2R1B), suggesting alterations in RNA processing, translational fidelity, and signaling. RPE cells exposed to hypoxic Müller cell EVs exhibited elevated Ribosome-binding protein 1 (RRBP1), RAC1/2, and Guanine Nucleotide-Binding Protein G(i) Subunit Alpha-1 (GNAI1), supporting enhanced endoplasmic reticulum (ER) function and cytoskeletal remodeling. Functional assays also revealed the compromised barrier integrity of the outer blood–retinal barrier (oBRB) under hypoxic co-culture conditions. These results underscore the adaptive but time-sensitive nature of retinal cell communication via EVs in response to hypoxia. Targeting this crosstalk may offer novel therapeutic strategies to preserve retinal structure and function in ischemic retinopathies. Full article

Chronic infection with the hepatitis B virus (HBV) results in over 1 million deaths annually. Although currently licensed treatments, including pegylated interferon-α and nucleoside/nucleotide analogs, can inhibit viral replication, they rarely eradicate covalently closed circular DNA (cccDNA) reservoirs. Moreover, vaccination does not offer therapeutic benefit to already infected individuals or non-responders. Consequently, chronic infection is maintained by the persistence of cccDNA in infected hepatocytes. For this reason, novel therapeutic strategies that permanently inactivate cccDNA are a priority. Obligate heterodimeric transcription activator-like effector nucleases (TALENs) provide the precise gene-editing needed to disable cccDNA. To develop this strategy using a therapeutically relevant approach, TALEN-encoding mRNA targeting viral core and surface genes was synthesized using in vitro transcription with co-transcriptional capping. TALENs reduced hepatitis B surface antigen (HBsAg) by 80% in a liver-derived mammalian cell culture model of infection. In a stringent HBV transgenic murine model, a single dose of hepatotropic lipid nanoparticle-encapsulated TALEN mRNA lowered HBsAg by 63% and reduced viral particle equivalents by more than 99%, without evidence of toxicity. A surveyor assay demonstrated mean in vivo HBV DNA mutation rates of approximately 16% and 15% for Core and Surface TALENs, respectively. This study presents the first evidence of the therapeutic potential of TALEN-encoding mRNA to inactivate HBV replication permanently. Full article