Search Results (112,959)

Background: Depression and Metabolic Dysfunction-Associated Steatotic Fatty Liver Disease (MASLD) are common chronic diseases, respectively. However, the causal and molecular links between them remain unclear. In order to explore whether depression contributes to an increased risk of MASLD and whether inflammation mediates this effect, we integrated multi-level evidence from the epidemiology of the National Health and Nutrition Examination Survey (NHANES), the genetics of GWAS, the transcriptomes of GEO, and single-cell RNA sequencing datasets. Methods: A multi-level integrative analysis strategy was used to validate this pathway. First, a cross-sectional epidemiological analysis based on NHANES data was used to reveal the association between depression and MASLD, and to explore the mediating role of inflammation and liver injury markers. Secondly, a two-sample Mendelian randomization analysis was used to infer the causal direction of depression and MASLD, and to verify the mediating effect of systemic inflammation and liver injury indicators at the genetic level. Then, the transcriptome co-expression network analysis and machine learning were used to screen the common hub genes connecting the two diseases. Finally, single-cell transcriptome data were used to characterize the dynamic expression of potential key genes during disease progression at cellular resolution. Results: Depression significantly increased the risk of MASLD, especially in women (OR = 1.39, 95%CI [1.17–1.65]). Parallel mediation analysis showed that high-sensitivity C-reactive protein (hs-CRP) (p < 0.001), γ-glutamyltransferase (GGT) (p < 0.001), and alkaline phosphatase (ALP) (p < 0.001) mediated this relationship. Mendelian randomization analysis confirmed the unidirectional causal effect of depression on MASLD, and there was no reverse association (β = 0.483, SE = 0.146, p = 0.001). Weighted gene co-expression network analysis and machine learning identified CD40LG as a potential molecular bridge between depression-associated immune modules and MASLD. In addition, single-cell data analysis revealed a stage-specific trend of CD40LG expression in CD4⁺ T cells during MASLD progression, while its receptor CD40 was also activated in B cells. In the female sample, CD40LG maintained an upward trend. However, the stability of this result is limited by the limited sample size. Conclusions: This study provides converging multi-omics evidence that depression plays a causal role in MASLD through inflammation-mediated immune signaling. The CD40LG-CD40 axis has emerged as an immune mechanism that transposes depression into the pathogenesis of MASLD, providing a potential target for the intervention of gender-specific metabolic liver disease. Full article

Chimeric antigen receptor (CAR)-T-cell therapy is a revolutionary approach in immunotherapy that has shown remarkable success in the treatment of blood cancers. Many preclinical studies are currently underway worldwide to extend the CAR-T-cell therapy benefits to a broad spectrum of cancers, using rodent models. Alternative in vivo platforms are essential for overcoming the drawbacks associated with rodent models, including immunodeficiency in humanized models, ethical concerns, extended time requirements, and cost. In this work, we used the chicken chorioallantoic membrane (CAM) assay to evaluate the in vivo efficacy of cluster-of-differentiation 19 (CD19)-targeting CAR-T cells expressing a second-generation CAR construct against human lymphoma derived from the Raji cell line. Our results confirm the efficacy of selected CAR-T cells on tumor growth, metastasis, and angiogenesis. Further, the chicken embryo has an intrinsic active immune system. Therefore, the dialog between CAR-T cells and endogenous immune cells, as well as their participation in the tumor challenge, has also been studied. In conclusion, our study demonstrates that the chicken CAM assay provides a relevant in vivo, 3Rs (Replacement, Reduction and Refinement)-compliant new approach methodology (NAM), which is well-suited for the current needs of preclinical research on CAR-T-cell therapy. Full article

The increasing energy crisis demands sustainable functional materials. Wood, with its natural three-dimensional porous structure, offers an ideal renewable template. This study demonstrates that microstructural engineering of wood is a decisive strategy for enhancing magnetothermal conversion. Using eucalyptus wood, we precisely tailored its pore architecture via delignification and synthesized Fe₃O₄ nanoparticles in situ through coprecipitation. We systematically investigated the effects of delignification and precursor immersion time (24, 48, 72 h) on the loading, distribution, and magnetothermal performance of the composites. Delignification drastically increased wood porosity, raising the Fe₃O₄ loading capacity from ~5–6% (in non-delignified wood) to over 14%. Immersion time critically influenced nanoparticle distribution: 48 h achieved optimal deep penetration and uniformity, whereas extended time (72 h) induced minor local agglomeration. The optimized composite (MDW-48) achieved an equilibrium temperature of 51.2 °C under a low alternating magnetic field (0.06 mT, 35 kHz), corresponding to a temperature rise (ΔT) > 24 °C and a Specific Loss Power (SLP) of 1.31W·g⁻¹. This performance surpasses that of the 24 h sample (47 °C, SLP = 1.16 W·g⁻¹) and rivals other bio-based magnetic systems. This work establishes a clear microstructure–property relationship: delignification enables high loading, while controlled impregnation tunes distribution uniformity, both directly governing magnetothermal efficiency. Our findings highlight delignified magnetic wood as a robust, sustainable platform for efficient low-field magnetothermal conversion, with promising potential in low-carbon thermal management. Full article

Metastatic uveal melanoma (MUM) remains largely refractory to immune-checkpoint inhibition, so recent research has turned to bispecific T-cell engagers (BTCEs), adoptive-cell therapies (ACTs), and oncolytic viruses (OVs). To summarize the available clinical evidence, we performed a structured literature search across PubMed, Scopus, and Europe PMC for primary studies published between 1 January 2010 and 31 May 2025 that enrolled at least three adults with MUM, treated with one of these modalities, and that reported efficacy or grade-3+ safety outcomes; two reviewers independently performed screening, data extraction, and risk-of-bias assessment, and because of notable heterogeneity, we synthesized the findings narratively. Twenty-two studies met the criteria—thirteen phase I–III trials, eight observational cohorts, and one case series—covering fifteen BTCE cohorts, four ACT cohorts, and three OV cohorts. Tebentafusp, the dominant BTCE evaluated in roughly 1150 HLA-A*02:01-positive patients, extended median overall survival from 16.0 to 21.7 months (hazard ratio 0.51, with three-year follow-up HR 0.68) in its pivotal phase-III trial despite objective response rates of only 5–12%, with early skin rash and week-12 circulating-tumor-DNA clearance emerging as consistent markers of benefit. Tumor-infiltrating lymphocyte therapy, administered to about thirty patients, produced objective responses in 11–35% and occasional durable complete remissions, although median progression-free survival remained 2–6 months and severe cytopenias were universal. Three early-phase OV studies, totaling twenty-nine patients, yielded no radiographic responses but showed tumor-specific T-cell expansion and transient disease stabilization. Safety profiles reflected the mechanism of action: tebentafusp most often caused rash, pyrexia, and usually manageable cytokine-release syndrome with grade-3+ events in 40–70% yet discontinuation in roughly 2%; TIL therapy toxicity was driven by lymphodepleting chemotherapy and high-dose interleukin-2 with one treatment-related death; and OVs were generally well tolerated with no more than 20% grade-3 events. Full article

With the widespread diffusion of personal Internet of Things (IoT) devices, Electromagnetic Side-Channel Attacks (EM-SCAs), which exploit electromagnetic emissions to uncover critical data such as cryptographic keys, are becoming extremely common. Existing shielding approaches typically rely on bulky or opaque materials, which limit integration in modern IoT environments; this motivates the need for a transparent, lightweight, and easily integrable solution. Thus, to address this threat, we propose the use of electromagnetic metasurfaces with shielding capabilities, fabricated with an optically transparent conductive film. This film can be easily integrated into glass substrates, offering a novel and discrete shielding solution to traditional methods, which are typically based on opaque dielectric media. The paper presents two proof-of-concept case studies for shielding against EM-SCAs. The first one investigates the design and fabrication of a passive metasurface aimed at shielding emissions from chip processors in IoT devices. The metasurface is conceived to attenuate a specific frequency range, characteristic of the considered IoT processor, with a target attenuation of 30 dB. At the same time, the metasurface ensures that signals from 4G and 5G services are not affected, thus preserving normal wireless communication functioning. Conversely, the second case study introduces an active metasurface for dynamic shielding/transmission behavior, which can be modulated through diodes according to user requirements. This active metasurface is designed to block undesired electromagnetic emissions within the 150–465 MHz frequency range, which is a common band for screen gleaning security threats. The experimental results demonstrate an attenuation of approximately 10 dB across the frequency band when the shielding mode is activated, indicating a substantial reduction in signal transmission. Both the case studies highlight the potential of transparent metasurfaces for secure and dynamic electromagnetic shielding, suggesting their discrete integration in building windows or other environmental structural elements. Full article

Background: The apicomplexan parasite Toxoplasma gondii causes serious diseases in animals and humans. The in vitro efficacy of the antimicrobial peptide mixture tyrothricin, composed of tyrocidines and gramicidins, against T. gondii tachyzoites was investigated. Methods: Effects against T. gondii were determined by monitoring inhibition of tachyzoite proliferation and electron microscopy, host cell and splenocyte toxicity was measured by Alamar blue assay, and early embryo toxicity was assessed using zebrafish embryos. Differential affinity chromatography coupled to mass spectrometry and proteomics (DAC-MS-proteomics) was employed to identify potential molecular targets in T. gondii cell-free extracts. Results: Tyrothricin inhibited T. gondii proliferation at IC₅₀s < 100 nM, with tyrocidine A being the active and gramicidin A the inactive component. Tyrothricin also impaired fibroblast, T cell and zebrafish embryo viability at 1 µM. Electron microscopy carried out after 6 h of treatment revealed cytoplasmic vacuolization and structural alterations in the parasite mitochondrion, but these changes appeared only transiently, and tachyzoites recovered after 96 h. Tyrothricin also induced a reduction in the mitochondrial membrane potential. DAC-MS-proteomics identified 521 proteins binding only to tyrocidine A. No specific binding to gramicidin A was noted, and four proteins were common to both peptides. Among the proteins binding specifically to tyrocidine A were several SRS surface antigens and secretory proteins, mitochondrial inner and outer membrane proteins associated with the electron transfer chain and porin, and several calcium-binding proteins putatively involved in signaling. Discussion: These results suggest that tyrocidine A potentially affected multiple pathways important for parasite survival and development. Full article

Lentiviral transduction remains the gold standard in adoptive modified cellular therapy, such as CAR-T; however, genome integration is not always desirable, such as when treating non-fatal autoimmune disease or for additional editing steps using CRISPR to produce allogeneic CAR-modified cells. Delivering in vitro-transcribed (IVT) mRNA represents an alternative solution but the labile nature of mRNA has led to efforts to improve half-life and translation efficiencies using a range of approaches including chemical and structural modifications. In this study, we explore the role of N⁶–methyladenosine (m6A) in a CD19-CAR sequence when delivered to T cells as an IVT mRNA. In silico analysis predicted the presence of four m6A consensus (DRACH) motifs in the CAR coding sequence and treating T cells with an inhibitor of the m6A methyltransferase (METTL3) resulted in a significant reduction in CAR protein expression. RNA analysis confirmed m6A bases at three of the predicted sites, indicating that the modification occurs independently of nuclear transcription. Synonymous mutation of the DRACH sites reduced the levels of CAR protein from 15 to >50% depending on the T cell donor. We also tested a panel of CAR transcripts with different UTRs, some containing m6A consensus motifs, and identified those which further improved protein expression. Furthermore, we found that the methylation of consensus m6A sites seems to be somewhat sequence-context-dependent. These findings demonstrate the importance of the m6A modification in stabilising and enhancing expression from IVT-derived mRNA and that this occurs within the cell, meaning targeted in vitro chemical modification during mRNA manufacturing may not be necessary. Full article

The scarcity of fault samples significantly impedes the generalization of data-driven diagnosis models for local thermal imbalances in integrated energy systems. To overcome this limitation, this paper proposes a novel knowledge graph-guided conditional generative adversarial network (KG-GAN) framework. The approach begins by constructing a dynamically updatable fault knowledge graph for district heating systems, which explicitly encapsulates pipeline topology, thermodynamic principles, and fault propagation mechanisms. The derived knowledge embeddings are then fused with physics-based constraints into the adversarial learning process, effectively alleviating the issue of physically implausible sample generation that plagues conventional data-centric models. Experimental validation on a district heating platform, involving four common fault types, demonstrates the superiority of our method. With only 100 samples per fault category, a diagnostic model trained on KG-GAN-generated data achieves a classification accuracy of 91.7%, outperforming a GAN-based baseline by 8.3%. Furthermore, t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization reveals a 92.3% feature distribution consistency between generated and real samples, confirming the method’s capability to enhance diagnostic robustness and physical interpretability under small-sample conditions. Full article

Early and accurate date palm disease detection is the key to successful smart farming ecosystem sustainability. In this paper, we introduce DoST-DPD, a new Dual-Stream Transformer architecture for multimodal disease diagnosis utilizing RGB, thermal and NIR imaging. In contrast with standard deep learning approaches, our model receives ontology-based semantic supervision (via per-dataset OWL ontologies), enabling knowledge injection via SPARQL-driven reasoning during training. This structured knowledge layer not only improves multimodal feature correspondence but also restricts label consistency for improving generalization performance, particularly in early disease diagnosis. We tested our proposed method on a comprehensive set of five benchmarks (PlantVillage, PlantDoc, Figshare, Mendeley, and Kaggle Date Palm) together with domain-specific ontologies. An ablation study validates the effectiveness of incorporating ontology supervision, consistently improving the performance across Accuracy, Precision, Recall, F1-Score and AUC. We achieve state-of-the-art performance over five widely recognized baselines (PlantXViT, Multi-ViT, ERCP-Net, andResNet), with our model DoST-DPD achieving the highest Accuracy of 99.3% and AUC of 98.2% on the PlantVillage dataset. In addition, ontology-driven attention maps and semantic consistency contributed to high interpretability and robustness in multiple crop and imaging modalities. Results: This work presents a scalable roadmap for ontology-integrated AI systems in agriculture and illustrates how structured semantic reasoning can directly benefit multimodal plant disease detection systems. The proposed model demonstrates competitive performance across multiple datasets and highlights the unique advantage of integrating ontology-guided supervision in multimodal crop disease detection. Full article

Background: Nipah virus (NiV), a zoonotic paramyxovirus with high case fatality and pandemic potential, remains without a licensed vaccine for humans to date. Although there has been progress in vaccine development, it remains limited, and peptide vaccines have rarely been validated in vivo. Methods: Here, we report the rational antigen selection, synthesis, and preliminary immunogenicity evaluation of NiV fusion glycoprotein (NiV-F)-derived linear peptides as vaccine candidates. Candidate epitopes were identified by in silico, and a total of 18 B- and T-cell epitope-derived peptides were shortlisted for synthesis and antigenicity validation by ELISA. Results: Antigenicity evaluation showed that 9 of the synthesized peptides have A_450nm of over 1 (8 from the F11 group, A_450nm: 1.13–3.6; 1 from the F18 group, A_450nm: 1.51), with the peptide constructs F11-3 (A_450nm: 3.5) and F11-4 (A_450nm: 3.6) showing the highest antigenicity. Interestingly, peptides from F11 with amidation increased antibody binding (F11-4-NH2, A_450nm: 3.05; F11-4-9mer-1-NH2, A_450nm: 0.87). The lead peptide candidates, F11-3 and F11-4, were subsequently used for the immunization experiment, and mouse sera were assessed against their homologous peptide antigens or recombinant NiV-F protein. ELISA result showed detectable antibody reactivity against their homologous antigen for the intramuscular (IM) F11-3 vaccinated group (A_450nm: 0.30 ± 0.35), whereas increased binding was observed for both IM-administered F11-3 (A_450nm: 1.62 ± 0.97) and F11-4 (A_450nm: 2.0 ± 0.77) against NiV-F protein, albeit without statistical significance compared to the negative control (NC, p > 0.05), and were markedly lower compared to mice immunized with NiV-F recombinant protein (PC, p < 0.01), underscoring the need for further optimization procedures. Conclusions: Collectively, these results support an exploratory antigen discovery and prioritization framework for NiV-F-derived peptide candidates and provide a foundation for future studies aimed at optimizing immunogenicity and evaluating protective relevance in appropriate preclinical models. Full article

With the fast development of the 5G technology and IoV, a vehicle has become a smart device with communication, computing, and storage capabilities. However, the limited on-board storage and computing resources often cause large latency for task processing and result in degradation of system QoS as well as user QoE. In the meantime, to build the environmentally harmonious transportation system and green city, the energy consumption of data processing has become a new concern in vehicles. Moreover, due to the fast movement of IoV, traditional GSI-based methods face the dilemma of information uncertainty and are no longer applicable. To address these challenges, we propose a T2VC model. To deal with information uncertainty and dynamic offloading due to the mobility of vehicles, we propose a MAB-based QEVA-UCB solution to minimize the system cost expressed as the sum of weighted latency and power consumption. QEVA-UCB takes into account several related factors such as the task property, task arrival queue, offloading decision as well as the vehicle mobility, and selects the optimal location for offloading tasks to minimize the system cost with latency energy awareness and conflict awareness. Extensive simulations verify that, compared with other benchmark methods, our approach can learn and make the task offloading decision faster and more accurately for both latency-sensitive and energy-sensitive vehicle users. Moreover, it has superior performance in terms of system cost and learning regret. Full article

The deployment of Vision-Language Models (VLMs) in Satellite IoT scenarios is critical for real-time disaster assessment but is often hindered by the substantial memory and compute requirements of state-of-the-art models. While parameter-efficient fine-tuning (PEFT) enables adaptation, with minimal computational overhead, standard supervised methods often fail to ensure robustness and reliability on resource-constrained edge devices. To address this, we propose EdgeV-SE, a self-reflective fine-tuning framework that significantly enhances the performance of VLM without introducing any inference-time overhead. Our framework incorporates an uncertainty-aware self-reflection mechanism with asymmetric dual pathways: a generative linguistic pathway and an auxiliary discriminative visual pathway. By estimating uncertainty from the linguistic pathway using a log-likelihood margin between class verbalizers, EdgeV-SE identifies ambiguous samples and refines its decision boundaries via consistency regularization and cross-pathway mutual learning. Experimental results on hurricane damage assessment demonstrate that our approach improves image classification accuracy, enhances image–text semantic alignment, and achieves superior caption quality. Notably, our work achieves these gains while maintaining practical deployment on a commercial off-the-shelf edge device such as NVIDIA Jetson Orin Nano, preserving the inference latency and memory footprint. Overall, our work contributes a unified self-reflective fine-tuning framework that improves robustness, calibration, and deployability of VLMs on edge devices. Full article

In type 1 diabetes (T1D) in non-obese diabetic (NOD) mice, dendritic cells (DCs) exhibit a Stat5b mutation that impairs regulatory T cell (Tregs) numbers and suppressive function. To correct this defect, we generated transgenic NOD mice expressing constitutively active Stat5b (NOD.Stat5b-CA) in DCs, which conferred protection from diabetes that was associated with an expanded Treg population and a marked reduction in CD8⁺ T cell frequencies in secondary lymphoid organs. However, the phenotypic characteristics and underlying mechanisms to eliminate CD8⁺ T cells in NOD.Stat5b-CA mice are unknown. In this study, we found that the frequency of Tregs was significantly higher in the thymus and peripheral lymphoid organs of NOD.Stat5b-CA mice compared with NOD mice. Tregs in the peripheral lymphoid organs exhibited increased expression of activation markers CD69 and OX40, alongside reduced CD62L. We also found that CD8⁺ T cell frequencies were reduced in the peripheral organs but not in the thymus of NOD.Stat5b-CA mice, while CD4⁺ T cell frequencies remained unchanged across all organs. Furthermore, NOD.Stat5b-CA mice exhibited a reduced frequency of central Tregs (CD62L^high CD44^low) and increased frequency of effector Tregs (CD62L^low CD44^high) under steady-state conditions compared to NOD mice. Notably, Tregs from NOD.Stat5b-CA mice displayed enhanced cytotoxic activity, evidenced by increased expression of perforin, granzyme B, and Fas ligand, potentially mediating CD8⁺ T cell frequency reduction. Collectively, these findings highlight a novel role for Stat5b-CA.DC-educated Tregs in modulating immune responses by eliminating peripheral pathogenic CD8⁺ T cells via cytotoxic pathways, thereby contributing to immune regulation in NOD.Stat5b-CA mice. Full article

The medicinally and ornamentally valuable genus Thunbergia faces taxonomic uncertainty, while certain Acanthaceae species are threatened by habitat loss, underscoring the need for chloroplast genome studies to support conservation efforts. The chloroplast genome of Thunbergia grandiflora was sequenced and assembled. Additionally, 28 Acanthaceae species with significant medicinal value were selected for comparative genomic analysis. Based on the chloroplast genome data of Acanthaceae species, this study conducted phylogenetic and comparative evolutionary analyses. The results preliminarily support a systematic framework that divides Acanthaceae into eight tribes within five subfamilies. Concurrently, the study revealed significant inverted repeat (IR) region structural variations. A clear correspondence was observed between the contraction of IR length and the topological structure of the phylogenetic tree. In particular, species within the genus Strobilanthes exhibited significant contraction in their IR regions, which corresponded consistently with their tendency to cluster into an independent clade in the phylogenetic tree. This suggests that structural variation in the IR regions may be closely associated with the evolutionary divergence of this group. SSR analysis revealed a prevalent mononucleotide A/T repeat dominant pattern across Acanthaceae species. Furthermore, selection pressure analysis detected positive selection acting on multiple key genes, including rbcL, rps3, rps12, cemA, and ycf4, suggesting that these genes may play important roles in the adaptive evolution of Acanthaceae. This study reveals that the chloroplast genomes of Acanthaceae exhibit distinctive characteristics in phylogenetic architecture, dynamic variations in IR regions, and adaptive evolution of key genes, providing important molecular insights for understanding the mechanisms underlying species diversity and for the conservation of medicinal resources within this family. Full article

The increasing penetration of variable renewable energy sources intensifies grid imbalance and challenges the reliability of small-scale power systems. This study addresses these challenges by developing and analyzing a fully integrated hybrid energy system that combines biogas upgrading to biomethane, photovoltaic (PV) generation, hydrogen production via alkaline electrolysis, hydrogen storage, and a gas-steam combined cycle (CCGT). The system is designed to supply uninterrupted electricity to a small municipality of approximately 4500 inhabitants under predominantly self-sufficient operating conditions. The methodology integrates high-resolution, full-year electricity demand and solar resource data with detailed process-based simulations performed using Aspen Plus, Aspen HYSYS, and PVGIS-SARAH3 meteorological inputs. Surplus PV electricity is converted into hydrogen and stored, while upgraded biomethane provides dispatchable backup during periods of low solar availability. The gas-steam combined cycle enables flexible and efficient electricity generation, with hydrogen blending supporting dynamic turbine operation and further reducing fossil fuel dependency. The results indicate that a 10 MW PV installation coupled with a 2.9 MW CCGT unit and a hydrogen storage capacity of 550 kg is sufficient to ensure year-round power balance. During winter months, system operation is sustained entirely by biomethane, while in high-solar periods hydrogen production and storage enhance operational flexibility. Compared to a conventional grid-based electricity supply, the proposed system enables near-complete elimination of operational CO₂ emissions, achieving an annual reduction of approximately 8800 tCO₂, corresponding to a reduction of about 93%. The key novelty of this work lies in the simultaneous and process-level integration of biogas, hydrogen, photovoltaic generation, energy storage, and a gas-steam combined cycle within a single operational framework, an approach that has not been comprehensively addressed in the recent literature. The findings demonstrate that such integrated hybrid systems can provide dispatchable, low-carbon electricity for small communities, offering a scalable pathway toward resilient and decentralized energy systems. Full article