Search Results (22,516)

Plant immunity research is being reshaped by integrative multi-omics approaches that connect transcriptomic, proteomic, and interactomic data to build systems-level views of plant–pathogen interactions. This review outlines the scope and methodological landscape of these approaches, with particular emphasis on how transcriptomic and proteomic insights converge through network-based analyses to elucidate defense regulation. Transcriptomics captures infection-induced transcriptional reprogramming, while proteomics reveals protein abundance changes, post-translational modifications, and signaling dynamics essential for immune activation. Network-driven computational frameworks including iOmicsPASS, WGCNA, and DIABLO enable the identification of regulatory modules, hub genes, and concordant or discordant molecular patterns that structure plant defense responses. Interactomic techniques such as yeast two-hybrid screening and affinity purification–mass spectrometry further map host–pathogen protein–protein interactions, highlighting key immune nodes such as receptor-like kinases, R proteins, and effector-targeted complexes. Recent advances in machine learning and gene regulatory network modeling enhance the predictive interpretation of transcription–translation relationships, especially under combined or fluctuating stress conditions. By synthesizing these developments, this review clarifies how integrative multi-omics and network-based frameworks deepen understanding of the architecture and coordination of plant immune networks and support the identification of molecular targets for engineering durable pathogen resistance. Full article

Dual-target drug design addresses complex diseases and drug resistance, yet existing computational approaches struggle with simultaneous multi-protein optimization. This study presents SFG-Drug, a novel dual-target molecular generation model combining Monte Carlo tree search with gated recurrent unit neural networks for simultaneous MEK1 and mTOR targeting. The methodology employed DigFrag digital fragmentation on ZINC-250k dataset, integrated low-frequency masking techniques for enhanced diversity, and utilized molecular docking scores as reward functions. Comprehensive evaluation on MOSES benchmark demonstrated superior performance compared to state-of-the-art methods, achieving perfect validity (1.000), uniqueness (1.000), and novelty (1.000) scores with highest internal diversity indices (0.878 for IntDiv1, 0.860 for IntDiv2). Over 90% of generated molecules exhibited favorable binding affinity with both targets, showing optimal drug-like properties including QED values in [0.2, 0.7] range and high synthetic accessibility scores. Generated compounds demonstrated structural novelty with Tanimoto coefficients below 0.25 compared to known inhibitors while maintaining dual-target binding capability. The SFG-Drug model successfully bridges the gap between computational prediction and practical drug discovery, offering significant potential for developing new dual-target therapeutic agents and advancing AI-driven pharmaceutical research methodologies. Full article

Spermatogenesis is a sophisticated process coordinated by germ cells and the somatic microenvironment. Circular RNAs (circRNAs), key components of competitive endogenous RNA (ceRNA) networks, form intricate post-transcriptional regulatory systems by sequestering microRNAs (miRNAs). However, the specific functions of these networks in spermatogenesis, particularly regarding the cell-intrinsic regulatory programs of germ cells, remain poorly understood. To address this, we utilized a unique foxl3 mutant model in medaka (Oryzias latipes), in which XX female germ cells spontaneously transdifferentiate into functional sperm within the ovarian somatic environment. This model enables the functional enrichment of core spermatogenic programs largely independent of male-specific somatic cues. Through whole-transcriptome sequencing and bioinformatic analysis, we identified 58 key circRNAs, 27 core miRNAs, and 2965 mRNAs, and constructed a candidate ceRNA regulatory network mediated by six circRNAs. Under genetically consistent conditions, this study elucidated a putative ceRNA network directly involved in the germ cell-dominant initiation of spermatogenesis, suggesting an essential role of these networks in germ cell fate determination. These findings provide new insights into the regulatory mechanisms of teleost spermatogenesis and offer valuable molecular targets for advancing reproductive medicine and improving breeding efficiency in aquaculture. Full article

Lung cancer in never-smokers (LCINS) represents a distinct clinical entity driven by dominant oncogenic alterations and characterized by a low tumor mutational burden. Although tyrosine kinase inhibitors (TKIs) achieve high initial response rates, their long-term efficacy is limited by suboptimal pharmacokinetics, restricted central nervous system (CNS) penetration, tumor microenvironment barriers, and acquired resistance. In this review, we critically assess the current state of nanotechnology-assisted drug delivery systems for LCINS, with a primary focus on how rationally designed nanocarriers can overcome biological barriers, enable molecular subtype-specific therapeutic strategies, and address mechanisms that limit clinical efficacy and durability of response. We conducted a structured literature search using PubMed and Web of Science (January 2022 to November 2025), focusing on primary studies reporting the preparation, physicochemical properties, and therapeutic performance of nanocarriers in in vitro and in vivo models, as well as available pharmacokinetic and clinical data. LCINS is characterized by inefficient vasculature, high extracellular matrix density, active efflux transporters, and immunosuppressive niches, and is frequently complicated by brain metastases. Nanocarrier-based platforms can enhance aqueous solubility, prolong systemic circulation, and improve tumor or CNS targeting. Co-delivery systems combining TKIs with nucleic acid-based therapeutics, together with stimuli-responsive platforms, offer the potential for simultaneous modulation of multiple oncogenic pathways and partial mitigation of resistance mechanisms. In summary, nanotechnology provides a promising strategy to improve both the efficacy and specificity of targeted therapies in LCINS. Successful clinical translation will depend on biologically aligned carrier–payload combinations, scalable and reproducible manufacturing processes, and biomarker-guided patient selection. Full article

Background/Objectives: Neuroblastoma cells are phenotypically plastic, transitioning between mesenchymal and adrenergic states. Core functional genes (e.g., YAP1) mark the mesenchymal state, which is linked to unfavorable prognosis. We and others previously demonstrated relapse-specific Hippo-YAP pathway activation in matched primary/relapsed neuroblastomas. Here we explored the role of YAP1 in neuroblastoma aggressiveness and response to therapy. Methods: RT-qPCR and immunoblotting assessed YAP1 expression in neuroblastoma cell lines. RNA-sequencing detected YAP1-dependent gene signatures in Tet-ON SK-N-AS and SH-EP neuroblastoma cell models expressing wildtype YAP1 or constitutively activated YAP1^S127A. Data from cell models were compared with our published YAP1 expression data from neuroblastomas. Efficacy of commonly used chemotherapeutics was comparatively analyzed in the cell models. Results: YAP1 expression showed marked variability across a panel of neuroblastoma cell lines, assessed by mRNA analysis in 10 cell lines and protein analysis in a subset of 9 cell lines. RNA sequencing in constitutively activated YAP1^S127A mutant and wildtype YAP1 models detected 2162 and 1837 significantly differentially expressed genes in the SK-N-AS and SH-EP backgrounds, respectively. Continuously activating YAP1 in SK-N-AS cells upregulated mesenchymal signature genes and mesenchymal-associated transcription factors. Gene expression influenced by YAP1 activity in the cell models significantly overlapped with YAP1-associated genes (e.g., CYR61 and SPRY4) in published tumor data. Functionally, YAP1^S127A expression rendered neuroblastoma cells resistant to chemotherapy. Conclusions: Findings corroborate the idea of a mechanistic role for YAP1 in neuroblastoma adrenergic to mesenchymal reprogramming and therapy resistance. The YAP1-mediated plastic switch towards a mesenchymal expression state in neuroblastoma cells may provide the molecular basis for novel therapeutic avenues. Full article

Tree pollen allergies are influenced by regional atmospheric pollen concentrations and flora distribution. Climate change and urban landscaping have altered airborne pollen profiles in Ukraine, potentially affecting sensitization patterns. We examined 7518 patients (57.63% children) sensitized to at least one of 26 molecular components from 19 tree species using ALEX testing (2020–2022). Atmospheric pollen data from Ukrainian aerobiology stations were integrated with clinical data. Regional sensitization was mapped using the Geographic Information System, and Bayesian network modeling determined hierarchical relationships. Sensitization to Cry j 1 (46.01%), Bet v 1 (41.67%), and Fag s 1 (34.38%) dominated across age groups. High Fagales sensitization correlated with elevated atmospheric Betula, Alnus, and Corylus pollen concentrations, confirming environmental exposure-sensitization relationships. Bayesian modeling identified Bet v 1 as the root allergen (89.43% accuracy) driving cascading sensitization to other Fagales and non-Fagales allergens. Unexpectedly high Cry j 1 sensitization despite minimal atmospheric Cryptomeria presence suggests Thuja and Ambrosia cross-reactivity. Fagales sensitization dominated 10 of 17 regions, correlating with forest geography and urban landscaping. This study validates aerobiological monitoring’s clinical relevance. Diagnostic protocols should prioritize Bet v 1 while interpreting Cry j 1 positivity as potential cross-reactivity. Climate-driven shifts in atmospheric pollen patterns require ongoing coordinated aerobiological and clinical surveillance. Full article

Background/Objectives: Breast cancer is one of the most common malignancies, but its heterogeneous molecular subtypes make treatment decision-making complex and patient-specific. Both the pathology reports and the electronic medical record (EMR) play a critical role for an appropriate treatment decision. This study aimed to develop an integrated clinical decision support system (CDSS) that combines a large language model (LLM)-based pathology analysis with deep learning-based treatment prediction to support standardized and reliable decision-making. Methods: Real-world data (RWD) obtained from a cohort of 5015 patients diagnosed with breast cancer were analyzed. Meta-Llama-3-8B-Instruct automatically extracted the TNM stage and tumor size from the pathology reports, which were then integrated with EMR variables. A multi-label classification of 16 treatment combinations was performed using six models, including Decision Tree, Random Forest, GBM, XGBoost, DNN, and Transformer. Performance was evaluated using accuracy, macro/micro-averaged precision, recall, F1 score, and AUC. Results: Using combined LLM-extracted pathology and EMR features, GBM and XGBoost achieved the highest and most stable predictive performance across all feature subset configurations (macro-F1 ≈ 0.88–0.89; AUC = 0.867–0.868). Both models demonstrated strong discrimination ability and consistent recall and precision, highlighting their robustness for multi-label classification in real-world settings. Decision Tree and Random Forest showed moderate but reliable performance (macro-F1 = 0.84–0.86; AUC = 0.849–0.821), indicating their applicability despite lower predictive capability. By contrast, the DNN and Transformer models produced comparatively lower scores (macro-F1 = 0.74–0.82; AUC = 0.780–0.757), especially when using the full feature set, suggesting limited suitability for structured clinical data without strong contextual dependencies. These findings indicate that gradient-boosting ensemble approaches are better optimized for tabular medical data and generate more clinically reliable treatment recommendations. Conclusions: The proposed artificial intelligence-based CDSS improves accuracy and consistency in breast cancer treatment decision support by integrating automated pathology interpretation with deep learning, demonstrating its potential utility in real-world cancer care. Full article

Per- and polyfluoroalkyl substances (PFAS) are a large group of synthetic chemicals used in daily life and industrial production. Due to their widespread use, these compounds are frequently detected in environmental samples. Many studies have shown that PFAS pose a significant threat to both ecological environments and human health, leading to widespread public concern. This study developed and optimized an analytical method for the detection of 32 common PFAS compounds in chemical additives and environmental samples, including oil displacement agents, groundwater and soil, utilizing High-Performance Liquid Chromatography–Quadrupole-Orbitrap High-Resolution Mass Spectrometry (HPLC–Q-Orbitrap HRMS) technology. Applications in an eastern Chinese oilfield revealed significant PFAS accumulation, with ∑PFAS concentrations in groundwater and soil at the well site ranging from 212.29 to 262.80 ng/L and from 23.70 to 71.65 ng/g, respectively, exceeding background levels by 10-fold. The oil displacement agents used in oilfields are one of the important sources of PFAS, particularly p-perfluorous nonenoxybenzenesulfonate (OBS), a perfluorooctanesulfonic acid (PFOS) substitute. Soil analysis indicated greater mobility of short-chain PFAS, while long-chain compounds adsorbed more readily to surface layers. Molecular docking and quantitative structure–property relationship (QSPR) modeling suggest that the bioaccumulation potential of OBS is high and comparable to that of PFOS. Zebrafish embryo assays demonstrated that OBS induced significant concentration-dependent cardiac developmental toxicity, including pericardial edema and apoptosis, showing 1.5–2.4 times greater toxicity than PFOS across multiple endpoints. These findings reveal OBS as a pervasive contaminant with elevated environmental and health risks, necessitating urgent re-evaluation of its use as a PFOS substitute. Full article

Objectives: Kadsura coccinea fruit is a traditional medicinal plant rich in dibenzocyclooctadiene lignans, with established hepatoprotective effects. Binankadsurin A (BKA), a dibenzocyclooctadiene lignan isolated from the K. coccinea fruits. This study aims to evaluate its hepatoprotective efficacy in an acetaminophen (APAP)-induced mouse liver injury model. Methods: The structure of BKA was elucidated by HR-ESI-MS, NMR, single-crystal X-ray diffraction and comparison of their data with those of the literature. Mice were randomly divided into five groups: Control, APAP (400 mg/kg, single intraperitoneal injection), APAP + bicyclol (50 mg/kg), APAP + low-dose BKA (50 mg/kg), and APAP + high-dose BKA (100 mg/kg). Untargeted metabolomics, immunohistochemistry, Western blot analysis, and molecular docking were performed. Results: BKA was determined as a dibenzocyclooctadiene lignan, and the single-crystal structure is reported for the first time. The untargeted metabolomics revealed that metabolites and pathways are closely associated with oxidative stress. In vivo studies showed that pretreatment with BKA can mitigate liver injury. BKA reduced serum levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) and stored hepatic glutathione (GSH) levels. Immunohistochemical analysis results also showed that CYP2E1 expression in the mouse liver could be improved through BKA pretreatment. Furthermore, Western blot analysis presented that BKA could increase the protein expression of Nrf2, HO-1, and NQO-1. Additionally, molecular docking indicated that BKA directly blocks the binding site of Nrf2 with Keap1. Conclusions: BKA reduces APAP-induced acute liver damage by inhibiting oxidative stress by activating the Keap1/Nrf2/HO-1 signaling pathway, providing a theoretical basis for BKA as a potential therapeutic agent for APAP-induced liver injury. Full article

Tomato leaf diseases cause substantial yield and quality losses worldwide, yet reliable detection in real fields remains challenging. Two practical bottlenecks dominate current research: (i) limited data, including small samples for rare diseases, class imbalance, and noisy field images, and (ii) multimodal heterogeneity, where RGB images, textual symptom descriptions, spectral cues, and optional molecular assays provide complementary but hard-to-align evidence. This review summarizes recent advances in tomato leaf disease detection under these constraints. We first formalize the problem settings of limited and multimodal data and analyze their impacts on model generalization. We then survey representative solutions for limited data (transfer learning, data augmentation, few-/zero-shot learning, self-supervised learning, and knowledge distillation) and multimodal fusion (feature-, decision-, and hybrid-level strategies, with attention-based alignment). Typical model–dataset pairs are compared, with emphasis on cross-domain robustness and deployment cost. Finally, we outline open challenges—including weak generalization in complex field environments, limited interpretability of multimodal models, and the absence of unified multimodal benchmarks—and discuss future opportunities toward lightweight, edge-ready, and scalable multimodal systems for precision agriculture. Full article

Background/Objectives: To investigate the antagonistic effect of probiotic Lactiplantibacillus plantarum GUANKE against respiratory syncytial virus (RSV) and its underlying molecular mechanisms. Methods: in vitro cell models (A549 and HEp2 cells) and an in vivo mouse model (BALB/c mice) were employed. RT-qPCR, TCID50 assay, immunofluorescence, ELISA, Western blot, and histopathological analysis were used to investigate the effects of GUANKE on RSV replication, inflammatory responses, and the type I interferon pathway. Results: Oral administration of GUANKE effectively cleared RSV and alleviated RSV-induced pulmonary inflammatory responses. GUANKE inhibited viral replication. The GUANKE intervention group exhibited significantly reduced pathological damage to lung tissue and decreased the expression of inflammatory cytokines (IL-1β, IL-6, MCP-1, TNF-α). GUANKE augmented the early type I interferon response and activated the STING-TBK1-IRF3-IFN signaling pathway. Conclusions: GUANKE exerts anti-RSV effects by enhancing the early type I interferon response and activating the STING-TBK1-IRF3-IFN signaling pathway, thereby inhibiting RSV replication and alleviating pulmonary inflammatory responses. This suggests its potential value as an anti-RSV agent. Full article

Salicyluric acid (SUA), the main metabolite of aspirin and a natural product, is known for its ability to chelate iron and other metal ions. In particular, the chelation and increased excretion of iron by SUA may contribute to the aspirin-induced iron deficiency anemia observed in long-term aspirin users. The redox activity of iron and copper complexes of drugs and also drug metabolites, such as SUA, is an important parameter of their overall toxicity profile, including the induction of ferroptosis, which has been associated with many diseases. In this context, the effect of SUA on iron- and copper-induced lipid peroxidation and also its localization within a model lipid membrane have been investigated. A combination of physicochemical methods, including Nuclear Magnetic Resonance (¹H NMR), molecular dynamics (MD), and Nuclear Overhauser Effect Spectroscopy (¹H NOESY), has been used to demonstrate that SUA does not promote the peroxidation of linoleic acid micelles in the presence of Fe(II) or Cu(II) ions. NMR experiments revealed that SUA incorporates into the lipid bilayer, which stabilizes the ligands and inhibits its metal chelation ability in comparison to the control. NOESY experiments and MD simulations further showed that SUA localizes shallowly within the membrane, interacting primarily with the head group and upper acyl chain regions of lipids. These findings provide crucial insights into the membrane redox reactivity and other behavior of SUA, explaining its lack of pro-oxidant activity and also highlighting its complex role in the pharmacological and toxicological effects on iron metabolism in long-term aspirin users. Full article

Since its emergence in 2019, SARS-CoV-2 has resulted in more than 7.1 million deaths worldwide. It has been shown that co-infection with influenza A virus (IAV) can worsen clinical symptoms in COVID-19 patients and small animal models have been used to elucidate the molecular mechanisms of co-infection between SARS-CoV-2 and IAV. This study aimed to establish a co-infection model in pigs, a natural reservoir for IAV but resistant to SARS-CoV-2 infection, by determining whether pigs become susceptible to SARS-CoV-2 following a primary IAV infection. Here, pigs were primarily infected with swine influenza A virus (SIV) via the intratracheal route and, 3 days later, secondarily challenged with SARS-CoV-2. SIV was isolated from nasal swabs, and pigs seroconverted to SIV. In contrast, SARS-CoV-2 RNA was detected in nasal and oropharyngeal swabs only at day 1 post-secondary challenge, with no evidence of seroconversion against SARS-CoV-2. These data indicate that pigs are not susceptible to SARS-CoV-2 following SIV infection and therefore are not a suitable model for IAV/SARS-CoV-2 co-infection research. Full article

This study investigated the cytotoxic, antiproliferative, and molecular interaction profiles of the phenylpropanoids verbascoside and isoverbascoside from Pyrostegia venusta using in silico approaches. Computational predictions suggested differential cytotoxicity trends between tumor and non-tumor breast cell models compared with tamoxifen. QSAR analyses indicated antiproliferative potential, while docking studies revealed stable ligand–protein interactions with estrogen-related targets and PTEN. ADMET predictions suggested favorable metabolic characteristics, including limited CYP3A4 interaction. Overall, these results provide predictive insights that support further experimental investigation of these phenylpropanoids in ER⁺ breast cancer models. Full article

Intravenous thrombolysis with recombinant tissue-type plasminogen activator (tPA) remains a keystone of acute ischemic stroke treatment but in a subset of patients is complicated by angioedema, a potentially life-threatening adverse event largely mediated by bradykinin signaling. The unpredictable and idiosyncratic nature of this reaction has long suggested an underlying genetic contribution, yet its molecular architecture has remained poorly characterized. We hypothesized that alteplase-associated angioedema represents a multigenic susceptibility phenotype, arising from the convergence of rare genetic variants across multiple interacting physiological systems rather than from a single causal variant. To explore this hypothesis, we performed clinical exome sequencing in a cohort of 11 patients who developed angioedema following alteplase administration. Rather than identifying a shared pathogenic variant, we observed distinct yet convergent patterns of genetic vulnerability, allowing patients to be grouped according to dominant, but overlapping, biological axes. These included alterations affecting bradykinin regulation (e.g., ACE, SERPING1, XPNPEP2), endothelial structure and hemostasis (e.g., VWF, COL4A1), neurovascular and calcium signaling (e.g., SCN10A, RYR1), and vascular repair or remodeling pathways (e.g., PSEN2, BRCA2). Notably, many of the identified variants were classified as Variant of Uncertain Significance (VUS) or likely benign significance in isolation. However, when considered within an integrated, pathway-based framework, these variants can be interpreted as capable of contributing cumulatively to system level fragility, a phenomenon best described as “contextual pathogenicity”. Under the acute biochemical and proteolytic stress imposed by thrombolysis, this reduced physiological reserve may allow otherwise compensated vulnerabilities to become clinically manifest. Together, these findings support a model in which severe alteplase-associated angioedema appears as an emergent property of interacting genetic networks, rather than a monogenic disorder. This systems level perspective underscores the limitations of gene centric interpretation for adverse drug reactions and highlights the potential value of pathway informed, multi-genic approaches to risk stratification. Such frameworks may ultimately contribute to safer, more personalized thrombolytic decision, while providing a conceptual foundation for future functional and translational studies. Full article