Search Results (8,601)

Studies have shown that the iron concentration in the peritoneal fluid of women is associated with the severity of endometriosis. Therefore, investigation of iron metabolism-related genes (IM-RGs) in endometriosis holds significant implications for both prevention and therapeutic strategies in affected patients. Differentially expressed IM-RGs (DEIM-RGs) were identified by intersecting IM-RGs with differentially expressed genes derived from GSE86534. Mendelian randomization analysis was employed to determine DEIM-RGs causally associated with endometriosis, with subsequent verification through sensitivity analyses and the Steiger test. Biomarkers associated with IM-RGs in endometriosis were validated using expression data from GSE86534 and GSE105764. Functional annotation, regulatory network construction, and immunological profiling were conducted for these biomarkers. Single-cell RNA sequencing (scRNA-seq) (GSE213216) was utilized to identify distinctively expressed cellular subsets between endometriosis and controls. Experimental validation of biomarker expression was performed via reverse transcription–quantitative polymerase chain reaction (RT-qPCR). BMP6 and SLC48A1, biomarkers indicative of cellular BMP response, were influenced by a medicus variant mutation that inactivated PINK1 in complex I, concurrently enriched by both biomarkers. The lncRNA NEAT1 regulated BMP6 through hsa-mir-22-3p and hsa-mir-124-3p, while SLC48A1 was modulated by hsa-mir-423-5p, hsa-mir-19a-3p, and hsa-mir-19b-3p. Immune profiling revealed a negative correlation between BMP6 and monocytes, whereas SLC48A1 displayed a positive correlation with activated natural killer cells. scRNA-seq analysis identified macrophages and stromal stem cells as pivotal cellular components in endometriosis, exhibiting altered self-communication networks. RT-qPCR confirmed elevated expression of BMP6 and SLC48A1 in endometriosis samples relative to controls. Both BMP6 and SLC48A1 were consistently overexpressed in endometriosis, reinforcing their potential as biomarkers. Moreover, macrophages and stromal stem cells were delineated as key contributors. These findings provide novel insights into therapeutic and preventive approaches for patients with endometriosis. Full article

Background/Objectives: Accurate blood glucose forecasting is critical for closed-loop insulin delivery systems to support effective disease management in people with type 1 diabetes (T1D). While long short-term memory (LSTM) neural networks have shown strong performance in glucose prediction tasks, the relative performance of individualized versus aggregated training remains underexplored. Methods: In this study, we compared LSTM models trained on individual-specific data to those trained on aggregated data from 25 T1D subjects using the HUPA UCM dataset. Results: Despite having access to substantially less training data, individualized models achieved comparable prediction accuracy to aggregated models, with mean root mean squared error across 25 subjects of 22.52 ± 6.38 mg/dL for the individualized models, 20.50 ± 5.66 mg/dL for the aggregated models, and Clarke error grid Zone A accuracy of 84.07 ± 6.66% vs. 85.09 ± 5.34%, respectively. Subject-level analyses revealed only modest differences between the two approaches, with some individuals benefiting more from personalized training. Conclusions: These findings suggest that accurate and clinically reliable glucose prediction is achievable using personalized models trained on limited individual data, with important implications for adaptive, on-device training, and privacy-preserving applications. Full article

Background: Tumor-associated macrophages (TAMs) are essential regulators of the glioblastoma (GBM) microenvironment; their functional heterogeneity and interaction networks are not fully elucidated. We identify RNF135 as a novel TAM-enriched gene associated with immune activation and adverse prognosis in GBM. Methods: To evaluate RNF135’s expression profile, prognostic significance, and functional pathways, extensive transcriptome analyses from TCGA and CGGA cohorts were conducted. The immunological landscape and cellular origin of RNF135 were outlined using single-cell RNA-seq analyses and bulk RNA-seq immune deconvolution (MCP-counter, xCell and ssGSEA). Cell–cell communication networks between tumor cells and RNF135-positive and -negative tumor-associated macrophage subsets were mapped using CellChat. Results: RNF135 predicted a poor overall survival and was markedly upregulated in GBM tissues. Functional enrichment analyses showed that increased cytokine signaling, interferon response, and innate immune activation were characteristics of RNF135-high samples. Immune infiltration profiling showed a strong correlation between the abundance of T cells and macrophages and RNF135 expression. According to the single-cell analyses, RNF135 was primarily expressed in TAMs, specifically in proliferation, phagocytic, and transitional subtypes. RNF135-positive TAMs demonstrated significantly improved intercellular communication with aggressive tumor subtypes in comparison to RNF135-negative TAMs. This was facilitated by upregulated signaling pathways such as MHC-II, CD39, ApoE, and most notably, the CCL signaling axis. The CCL3/CCL3L3–CCR1 ligand–receptor pair was identified as a major mechanistic driver of TAM–TAM crosstalk. High RNF135 expression was also linked to greater sensitivity to Selumetinib, a selective MEK1/2 inhibitor that targets the MAPK/ERK pathway, according to drug sensitivity analysis. Conclusions: RNF135 defines a TAM phenotype in GBM that is both immunologically active and immunosuppressive. This phenotype promotes inflammatory signaling and communication between cells in the tumor microenvironment. Targeting the CCL–CCR1 axis or combining RNF135-guided immunomodulation with certain inhibitors could be a promising therapeutic strategies for GBM. Full article

This paper presents a rigid–flexible coupling dynamic modeling framework and a fixed-time control strategy for a flexible space-tethered satellite (STS) system. A high-fidelity rigid–flexible coupling dynamic model of STS is developed using the finite element method, accurately capturing the coupled attitude dynamics of the satellite platform and flexible tether. Leveraging a simplified representation of the STS model, a nonsingular terminal sliding-mode controller (NTSMC) is synthesized via fixed-time stability theory. Uncertainties and disturbances within the system are compensated for by a radial basis function neural network (RBFNN), ensuring strong robustness. The controller’s fixed-time convergence property—with convergence time independent of initial conditions—is established using Lyapunov stability theory, enabling reliable operation in complex space environments. Numerical simulations implemented on the STS rigid–flexible coupling model validate the controller’s efficacy. Comparative analyses demonstrate superior tracking performance and enhanced practicality over conventional sliding-mode controllers, especially in the aspect of chattering suppression for the satellite thrusters. Full article

Obstructive sleep apnea (OSA) is a chronic disorder associated with serious health consequences, yet many cases remain undiagnosed due to limited access to standard diagnostic tools such as polysomnography. Recent advances in artificial intelligence (AI) have enabled the development of deep convolutional neural networks that analyze craniofacial radiographs, particularly lateral cephalograms, to detect anatomical risk factors for OSA. The goal of this approach is not to replace polysomnography but to identify individuals with a high suspicion of OSA at the primary care or dental level and to guide them toward timely and appropriate diagnostic evaluation. Current studies have demonstrated that AI can recognize patterns of oropharyngeal crowding and anatomical imbalance of the upper airway with high accuracy, often exceeding manual assessment. Furthermore, interpretability analyses suggest that AI focuses on clinically meaningful regions, including the tongue, mandible, and upper airway. Unexpected findings such as predictive signals from outside the airway also suggest AI may detect subtle features associated with age or obesity. Ultimately, integrating AI with cephalometric imaging may support early screening and referral for polysomnography, improving care pathways and reducing delays in OSA treatment. Full article

Achieving decarbonization targets in the aviation sector requires transformative approaches to sustainable aviation fuel (SAF) production. In this pursuit, feedstock innovation has emerged as a critical challenge. This research uses the U.S. SAF Grand Challenge as a case study, focusing on its feedstock innovation workstream, to investigate how Industry 4.0 technologies can fulfill that workstream’s objectives. An integrative literature review, drawing on academic, industry, and policy sources, is used to evaluate the Technology Readiness Levels (TRLs) of Industry 4.0 technology applications across the SAF biomass supply chain. The analysis identifies several key technologies as essential for improving yield prediction, optimizing resource allocation, and linking stochastic models to techno-economic analyses (TEAs): IoT-enabled sensor networks, probabilistic/precision forecasting, and automated quality monitoring. Results reveal an uneven maturity landscape, with some applications demonstrating near-commercial readiness, while others remain in early research or pilot stages, particularly in areas such as logistics, interoperability, and forecasting. The study contributes a structured TRL-based assessment that not only maps maturity but also highlights critical gaps and corresponding policy implications, including data governance, standardization frameworks, and cross-sector collaboration. By aligning digital innovation pathways with SAF deployment priorities, the findings offer both theoretical insights and practical guidance for advancing sustainable aviation fuel adoption and accelerating progress toward net-zero aviation. Full article

The deposition of amyloid beta (Aβ) in the human brain is a hallmark of Alzheimer’s disease (AD). Aβ has been shown to exert a wide range of effects on neurons in cell and animal models. Here, we take advantage of differentiated neurons from iPSC-derived neural stem cells of human donors to examine its effects on human neurons. Specifically, we employed two types of neurons from genetically distinct donors: one male carrying APO E2/E2 (M E2/E2) and one female carrying APO E3/E3 (F E3/E3). Genome-wide RNA-sequencing analysis identified 64 and 44 genes that were induced by Aβ in M E2/E2 and F E3/E3 neurons, respectively. GO and pathway analyses showed that Aβ-induced genes in F E3/E3 neurons do not constitute any statistically significant pathways whereas Aβ-induced genes in M E2/E2 neurons constitute a complex network of activated pathways. These pathways include those promoting inflammatory responses, such as IL1β, IL4, and TNF, and those promoting cell migration and movement, such as chemotaxis, migration of cells, and cell movement. These results strongly suggest that the effects of Aβ on neurons are highly dependent on their genetic background and that Aβ can promote strong responses in inflammation and cell migration in some, but not all, neurons. Full article

Earle’s balanced salt solution (EBSS) is a classical autophagy inducer that provides a special culture environment lacking amino acids and serum, causing cell starvation. However, the production of relevant omics data surrounding EBSS-induced autophagy is still in the early stage. The objective of this study was to identify new potential functional proteins in the autophagy process through omics analysis. We selected EBSS-induced autophagy as our research object and uncovered autophagy-regulatory proteins using RNA-seq analysis. Western blotting showed that EBSS increased LC3B-II protein levels in NRK cells, reaching the maximum amount at 2 h of culture. Then, we used next-generation sequencing to obtain quantified RNA-seq data from cells incubated with EBSS and the bowtie–tophat–cufflinks flow path to analyze the transcriptome data. Using significant differences in the FPKM values of genes in the treated group compared with those in the control group to indicate differential expression, 470 candidate genes were selected. Subsequently, GO and KEGG analyses of these genes were performed, revealing that most of these signaling pathways were closely associated with autophagy, and to better understand the potential functions and connections of these genes, protein–protein interaction networks were studied. Considering all the conclusions of the analysis, 27 candidate genes were selected for verification, where the knockdown of Txnrd1 decreased LC3B-II protein levels in NRK cells, consistent with the results of confocal experiments. In conclusion, we uncovered autophagy-regulatory proteins using RNA-seq analysis, with our results indicating that TXNRD1 may play a role in regulating EBSS-induced autophagy via an unknown pathway. We hope that our research can provide useful information for further autophagy omics research. Full article

The submandibular glands (SMGs), along with the parotid and sublingual glands, generate the majority of saliva and play critical roles in maintaining oral and systemic health. Despite their physiological importance, long-term therapeutic options for salivary gland dysfunction remain limited, highlighting the need for a deeper molecular understanding of SMG biology, particularly in humans. To address this knowledge gap, we have performed transcriptomic- and epigenomic-based analyses and molecular characterization of the human SMG. Our integrated analysis of multiorgan RNA-sequencing datasets has identified an SMG-enriched gene expression signature comprising 289 protein-coding and 75 long non-coding RNA (lncRNA) genes that include both known regulators of salivary gland function and several novel candidates ripe for future exploration. To complement these transcriptomic studies, we have generated chromatin immunoprecipitation sequencing (ChIP-seq) datasets of key histone modifications on human SMGs. Our epigenomic analyses have allowed us to identify genome-wide enhancers and super-enhancers that are likely to drive genes and regulatory pathways that are important in human SMG biology. Finally, comparative analysis with mouse and human SMG and other tissue datasets reveals evolutionary conserved gene and regulatory networks, underscoring fundamental mechanisms of salivary gland biology. Collectively, this study offers a valuable knowledge-based resource that can facilitate targeted research on salivary gland dysfunction in human patients. Full article

The development of high-speed computers and electronic memories, high-frequency communication networks, electroluminescent and photovoltaic devices, flexible displays, and more requires new materials with unique properties, such as a low dielectric constant, an adjustable refractive index, high hardness, thermal resistance, and processability. SiOC coatings possess a number of desirable properties required by modern technologies, including good heat and UV resistance, transparency, high electrical insulation, flexibility, and solubility in commonly used organic solvents. Chemical vapor deposition (CVD) is a very useful and convenient method to produce this type of layer. In this article we present the results of studies on SiOC coatings obtained from tetramethylcyclotetrasiloxane in a remote hydrogen plasma CVD process. The elemental composition (XPS, EDS) and chemical structure (FTIR and NMR spectroscopy-¹³C, ²⁹Si) of the obtained coatings were investigated. Photoluminescence analyses and ellipsometric and thermogravimetric measurements were also performed. The surface morphology was characterized using AFM and SEM. The obtained results allowed us to propose a mechanism for the initiation and growth of the SiOC layer. Full article

Alkali resistance is a critical factor for the long-term performance of glass fibers in cementitious composites. While zirconium oxide doping has proven effective in enhancing the durability of basalt fibers, its high cost and limited solubility motivate the search for viable alternatives. This study presents the first systematic investigation of titanium dioxide (TiO₂) doping in basalt-based glasses across a wide compositional range (0–8 mol%). X-ray fluorescence and diffraction analyses confirm complete dissolution of TiO₂ within the amorphous silicate network, with no phase segregation. At low concentrations (≤3 mol%), Ti⁴⁺ acts as a network modifier in octahedral coordination ([TiO₆]), reducing melt viscosity and lowering processing temperatures. As TiO₂ content increases, titanium in-corporates into tetrahedral sites ([TiO₄]), competing with Fe³⁺ for network-forming positions and displacing it into octahedral coordination, as revealed by Mössbauer spectroscopy. This structural redistribution promotes phase separation and triggers the crystallization of pseudobrukite (Fe₂TiO₅) at elevated temperatures. The formation of a protective Ti(OH)₄ surface layer upon alkali exposure enhances chemical resistance, with optimal performance observed at 4.6 mol% TiO₂—reducing mass loss in NaOH and seawater by 13.3% and 25%, respectively, and improving residual tensile strength. However, higher TiO₂ concentrations (≥5 mol%) lead to pseudobrukite crystallization and a narrowed fiber-forming temperature window, rendering continuous fiber drawing unfeasible. The results demonstrate that TiO₂ is a promising, cost-effective dopant for basalt fibers, but its benefits are constrained by a critical solubility threshold and structural trade-offs between durability and processability. Full article

Although bacterial and fungal communities play essential roles in organic matter degradation and humification during composting, their composition, interactions, abiotic compost properties, and succession patterns remain unclear. In this study, the succession of bacterial and fungal communities during algal sludge composting was explored using 16S and ITS rRNA amplicon sequencing. The compost rapidly entered the thermophilic phase (>50 °C) within the first phase. During the composting process, the diversity of bacterial and fungal communities did not show a significant response to the different composting phases. The physicochemical parameters and microbial community structures changed significantly during the thermophilic and cooling phases, particularly in the former, and gradually stabilized as the compost matured. Integrated random forest and network analyses suggested that the bacteria genera Geobacillus and Parapedobacter, along with the fungus genus Gilmaniella, could serve as potential biomarkers for different composting phases. The functional activity of the bacterial communities was obviously higher during the thermophilic phase than during the other phases, while fungal activity remained relatively high during both the thermophilic and cooling phases. Structural Equation Modeling (SEM) further indicated that bacterial communities primarily mediated nitrogen transformation and humification processes, while fungal communities mainly contributed to humification. These results cast a new light on understanding about microbial function during aerobic algal sludge composting. Full article

Background: Unintended adolescent pregnancy and sexually transmitted infections (STIs) remain pressing public health concerns in Northeastern Thailand. Although school-based sexuality education is widespread, risk behaviors persist, underscoring the need for innovative approaches. This study evaluated the SESS (System–Empowerment–Support–Social Network) model, a multi-component framework designed to strengthen adolescent sexual health. Methods: A quasi-experimental, two-school study was conducted among 240 students aged 15–19 years in Nakhon Ratchasima Province. One school (n = 120) implemented a 16-week SESS program, while a comparison school (n = 120) continued with the standard curriculum. The SESS model combined system coordination, empowerment workshops, peer and institutional support, and digital platforms (Facebook, LINE). Data were collected with validated questionnaires and analyzed using ANCOVA, adjusting for baseline values. Exploratory analyses reported mean differences with 95% confidence intervals (CIs). Results: Groups were comparable at baseline. Post-intervention, the intervention school showed higher perception scores (mean difference = +13.0; 95% CI: 10.5–17.0) and preventive practice scores (mean difference = +14.0; 95% CI: 10.1–17.9). Attitudes showed minimal change. No pregnancies or self-reported STI cases were documented among intervention participants during the follow-up period. Conclusions: In this two-school quasi-experimental evaluation, the SESS model was associated with improvements in perceptions and practices, though attitudinal changes were limited. Findings suggest the feasibility of integrating empowerment, social support, and digital engagement into school-based programs while highlighting the need for multi-school trials to establish effectiveness. Full article

Traditional Hopfield neural networks (HNNs) suffer from limitations in generating controllable chaotic dynamics, which are essential for applications in neuromorphic computing and secure communications. Memristors, with their memory-dependent nonlinear characteristics, provide a promising approach to regulate neuronal activities, yet systematic studies on attractor offset behaviors remain scarce. In this study, we propose a fully memristive electromagnetic radiation neural network by incorporating three distinct memristors as external electromagnetic stimuli into an HNN. The parameters of the memristors were tuned to modulate chaotic oscillations, while variations in initial conditions were employed to explore multistability through bifurcation and basin stability analyses. The results demonstrate that the system enables large-scale amplitude control of chaotic signals via memristor parameter adjustments, allowing arbitrary scaling of attractor amplitudes. Various offset behaviors emerge, including parameter-driven symmetric double-scroll relocations in phase space and initial-condition-induced offset boosting that leads to extreme multistability. These dynamics were experimentally validated using an STM32-based electronic circuit, confirming precise amplitude and offset control. Furthermore, a multi-channel pseudo-random number generator (PRNG) was implemented, leveraging the initial-boosted offset to enhance security entropy. This offers a hardware-efficient chaotic solution for encrypted communication systems, demonstrating strong application potential. Full article

Walnuts (Juglans regia L.) are an economically important woody crop, but spring frost poses a serious threat to their growth and productivity. However, the molecular mechanisms underlying walnut responses to freezing stress remain largely unknown. In this study, transcriptome analyses were performed on cold-tolerant and cold-sensitive walnut varieties subjected to freezing stress. A total of 9611 differentially expressed genes (DEGs) responsive to freezing stress were obtained, of which 2853 were common up-regulated and 2880 were common down-regulated in both varieties. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed 15 significantly enriched pathways in both varieties, including flavonoid biosynthesis. A simplified walnut flavonoid biosynthesis pathway was constructed, encompassing 36 DEGs encoding 13 key enzymes, demonstrating that flavonoid biosynthesis in walnut is significantly activated under freezing stress. Furthermore, weighted gene co-expression network analysis (WGCNA) identified a regulatory network centered on the JrCBF genes and uncovered 34 potential interacting genes. Collectively, these findings provide novel insights into the molecular responses of walnut to freezing stress and establish a foundation for elucidating the mechanisms underlying walnut cold tolerance. Full article