Search Results (5,002)

Estrogen (E2, 17β estradiol) is recognized for its regulatory role in numerous genes associated with energy metabolism and for its ability to disrupt mitochondrial function in various cancer types. However, the influence of E2 on the metabolism of colorectal cancer (CRC) cells remains largely unexplored. In this study, we examined how E2 affects mitochondrial function and energy production in CRC cells, utilizing two distinct CRC cell lines, HCT-116 and SW480. Cell viability, mitochondrial function, and the expression of several genes involved in oxidative phosphorylation (OXPHOS) were assessed in estrogen receptor α (ERα)-expressing and ERα-silenced cells treated with increasing concentrations of E2 for 48 h. Our results indicated that the cytotoxicity of E2 against CRC cells is mediated by the E2/ERα complex, which induces disturbances in mitochondrial function and the OXPHOS pathway. Furthermore, we identified two novel targets, RPS2 and TMEM177, which displayed overexpression, hypomethylation, and a negative association with ERα expression in CRC tissue. E2 treatment in CRC cells reduced the expression of both targets through promoter hypermethylation. Treatment with 5-Aza-2-deoxycytidine increased the expression of RPS2 and TMEM177. This epigenetic effect disrupts the mitochondrial membrane potential (MMP), resulting in decreased activity of the OXPHOS pathway and inhibition of CRC cell growth. Knockdown of RPS2 or TMEM177 in CRC cells resulted in anti-cancer effects and disruption of MMP and OXPHOS. These findings suggest that E2 exerts ERα-dependent epigenetic reprogramming that leads to significant mitochondria-related anti-growth effects in CRC. Full article

In this study, a strain of green microalga adapted to the extreme environmental conditions of the Tibetan Plateau was isolated from the Lalu Wetland. The isolate was identified and tentatively designated as Micractinium sp. LL-1. Following the inoculation of strain LL-1 into tannery wastewater, the ammonia nitrogen concentration was rapidly reduced, achieving a removal efficiency of 98.7%. The maximum accumulated biomass reached 1641.68 mg/L and 1461.28 mg/L. Integrated transcriptomic and label-free quantitative proteomic approaches were employed to systematically investigate the molecular response mechanisms of LL-1 under tannery wastewater stress. Transcriptomic analysis revealed that differentially expressed genes were enriched in pathways related to cell proliferation, morphogenesis, intracellular transport, protein synthesis, photosynthesis, and redox processes. Proteomic analysis indicated that LL-1 enhances cellular and enzymatic activities, strengthens regulatory capacity, modulates key metabolic pathways, and upregulates stress-responsive proteins. Under tannery wastewater stress, LL-1 exhibits dynamic adaptation involving signal perception and metabolic reconfiguration through the coordinated regulation of multiple pathways. Specifically, ribosomal translation and nucleic acid binding regulate biosynthetic capacity; the redistribution of energy metabolism boosts photosynthetic carbon fixation and ATP generation; and membrane transport coupled with antioxidant mechanisms mitigates stress-induced damage. Collectively, this study provides theoretical insights into microalgal adaptation to complex wastewater environments and offers potential targets for strain improvement and wastewater valorization. Full article

Silicosis is an irreversible and progressive pulmonary fibrotic disease caused by the long-term inhalation of silica dust. The precise molecular mechanisms underlying the disease remain incompletely understood, and effective early diagnostic biomarkers are still lacking. In this study, we used a silicosis mouse model and transcriptomic sequencing to identify 2950 mRNAs, 461 lncRNAs, 81 miRNAs, and 44 circRNAs that were differentially expressed in lung tissue. Enrichment analysis revealed that these differentially expressed genes were significantly enriched in the phosphatidylinositol 3-kinase (PI3K)–protein kinase B (Akt) signaling pathway, nuclear factor kappa-light-chain-enhancer of activated B cell (NF-κB) signaling pathway, and tumor necrosis factor (TNF) signaling pathway. The constructed competing endogenous RNA (ceRNA) network highlighted extensive regulatory interactions among lncRNAs/circRNAs, miRNAs, and mRNAs. Human validation showed that the expression levels of hsa-miR-215-5p and hsa-miR-146b-5p were significantly upregulated in the peripheral blood of early-stage pneumoconiosis patients, while hsa-miR-485-5p was downregulated. Logistic regression analysis revealed that hsa-miR-215-5p (OR = 1.966, 95% CI: 1.6938–2.2796, p < 0.001) and hsa-miR-146b-5p (OR = 1.9367, 95% CI: 1.697–2.201, p < 0.001) were independent risk factors for pneumoconiosis (p < 0.001). ROC curve analysis showed that both miRNAs demonstrated good diagnostic efficacy for pneumoconiosis, with AUC values of 0.9563 and 0.8876, respectively. These results provide novel insights into the complex ceRNA regulatory network involved in silicosis pathogenesis and suggest potential early, non-invasive diagnostic biomarkers. Full article

Time delays are intrinsic to mitotic regulation, particularly within the spindle assembly checkpoint (SAC) and the spindle position checkpoint (SPOC). These delays emerge from multi-step protein activation, molecular transport, force-dependent conformational transitions, and spatial redistribution of regulatory complexes. They span seconds to minutes and strongly influence checkpoint activation, maintenance, and silencing. Increasing evidence shows that such delayed processes shape mitotic timing, checkpoint robustness, and cell-fate decisions. While classical ordinary differential equation (ODE) models assume instantaneous biochemical responses, delay differential equations (DDEs) provide a natural framework for representing these finite timescales by explicitly incorporating system history. Recent DDE-based studies have revealed how delayed signaling contributes to bistability, oscillatory responses, prolonged mitotic arrest, and variability in checkpoint outputs. This review summarizes the biological origins of delays in SAC and SPOC, including Mad2 activation, MCC assembly and turnover, APC/C reactivation, tension maturation at kinetochores, and Bfa1–Bub2 regulation of Tem1. The article further discusses how mechanistic models with explicit delays improve our understanding of SAC–SPOC ordering, error-correction dynamics, and mitotic exit control. Finally, open challenges and future directions are outlined for integrative delay-aware modeling that unifies biochemical, mechanical, and spatial processes to better explain checkpoint function and chromosomal stability. Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

Lithium-ion battery (LIB) recycling has become a key area where sustainability goals and circular economy ambitions meet practical challenges. While research often focuses on regulatory or technological solutions, real progress depends on stakeholder action and alignment. This paper combines a literature review and a stakeholder survey (n = 26) to map risks, opportunities, barriers, and interventions, formulating a roadmap for sustainable LIB recycling from the stakeholder perspective. The literature identified 27 opportunities, 21 risks, 32 barriers, and 23 enablers across strategic, operational, institutional, cultural, and technical domains. The study confirms that an implementation gap persists between ambition and practice. Stakeholders know the opportunities, but structural barriers, limited resources, and insufficient attention to cultural enablers dampen progress. The barrier–intervention mapping and the derived roadmap show that interventions must be sequenced strategically: securing resources first, then building data infrastructures and strengthening know-how to finally reduce complexity. The findings show that sustainability progress depends less on technical capability than on sound resource management, reliable data, and institutional support offering a transferable framework to close implementation gaps, as presented in this study, and supports future research on how stakeholder alignment can accelerate sustainable transitions across industries. Full article

Background: Metabolic-dysfunction-associated steatotic liver disease (MASLD) is a multifactorial liver disease in which mitochondrial dysfunction, oxidative stress, and inflammation play key roles in driving the progression toward metabolic dysfunction-associated steatohepatitis (MASH) and hepatocellular carcinoma (HCC). Dysfunctional mitochondria generate excess reactive oxygen species (ROS), impair antioxidant defenses, activate pro-inflammatory pathways and hepatic stellate cells, and perpetuate liver injury. Mitochondrial Complex I is a major ROS source, particularly under conditions of dysregulated energy metabolism. Since Complex I inhibition by metformin was shown to reduce ROS and activate the adenosine monophosphate-activated protein kinase (AMPK), this study aimed to evaluate whether a novel Complex I Modulator (CIM, BI4500) could attenuate oxidative stress, inflammation, and consequently reduce lipid accumulation and fibrosis in a methionine- and choline-deficient diet (MCD)-fed rat model of MASH. Methods: Rats were fed an MCD or an isocaloric control diet for six weeks. From week four, animals received daily oral treatment with CIM (10 mg/kg) or vehicle (Natrosol). At the endpoint, liver tissue was collected for histological, biochemical, and molecular analyses. Lipid droplet area, inflammatory infiltration, and collagen deposition were evaluated on tissue sections; total lipid content and oxidative stress markers were assessed in homogenates and isolated mitochondria. Molecular pathways related to oxidative stress, lipid metabolism, and fibrosis were assessed at protein and mRNA levels. Results: CIM treatment significantly reduced oxidative stress (ROS, lipid peroxidation, nitrogen species), promoting AMPK activation and metabolic reprogramming. This included increased expression of peroxisome proliferator-activated receptor alpha (PPAR-α) and its target genes, and decreased sterol regulatory element binding protein-1c (SREBP-1c)-driven lipogenesis. These changes halted fibrosis progression, as confirmed by Picro-Sirius Red staining and fibrosis markers. Conclusions: these findings indicate that Complex I modulation may represent a promising strategy to counteract MASLD progression toward MASH. Full article

Intervertebral disc (IVD) herniation is a complex and multifactorial condition with a challenging diagnosis and limited therapeutic options, highlighting the need for reliable biomarkers to improve clinical decision-making. The aim of this study was to identify circulating prognostic biomarkers of IVD herniation regression. The plasma proteomic profile and the expression of circulating non-coding RNAs were analysed in a rat model of IVD herniation and were correlated with herniation size. Four candidate proteins (TNC, COPS3, JUP, and GNAI2) were significantly correlated with herniation size, with TNC further validated by ELISA. Additionally, miR-143-3p, miR-10b-5p, miR-27a-3p, miR-140-5p, miR-155-5p, miR-146a-5p, and miR-21-5p were positively correlated with herniation size. Moreover, TNC, COPS3, JUP, and GNAI2 were found to be potential targets of miR-155-5p. This study provides the first combined proteomic and miRNA account of preclinical plasma biomarkers of IVD herniation size, where TNC-miR-155-5p emerge as promising elements of a regulatory module with IVD herniation prognostic potential. Full article

Background/Objectives: Mobile health (mHealth) interventions can enhance chronic disease management, but their integration into public healthcare systems remains complex. DiabeText is the first SMS-based intervention in Spain delivering personalized diabetes self-management support using electronic health record data. This study explored perceived barriers and facilitators to the implementation of DiabeText in the Spanish public health context from the perspective of key stakeholders. Methods: A qualitative study was conducted using semi-structured interviews with 14 purposively selected stakeholders involved in digital health, diabetes care, data protection, and healthcare management across several Spanish regions. Interviews were thematically analyzed using Braun and Clarke’s approach and guided by the Implementation Research Logic Model. Results: Participants reported several barriers, including concerns regarding data protection, uncertainty about long-term sustainability, insufficient training and engagement of healthcare professionals and low digital literacy among certain patient groups. Facilitators included favorable institutional momentum for digital innovation, funding availability, perceived clinical utility and scalability of DiabeText, and growing patient familiarity with digital tools. Recommended strategies included integration into existing healthcare systems and workflows, professional training and use of familiar communication platforms. Conclusions: Effective implementation of DiabeText requires addressing regulatory, organizational, and equity-related barriers while leveraging institutional support and readiness for innovation. Early involvement of healthcare professionals, robust data governance, and investment in digital literacy are essential to ensure sustainable and equitable adoption. These findings provide actionable insights to support the integration of mHealth tools into chronic disease care in Spain and similar settings. Full article

Chronic ethanol exposure and genetic factors interact to drive neuroadaptations in alcohol use disorders (AUD). However, the system-level coordination of molecular responses across brain regions remains unclear. The 5-HT system and BDNF are key regulators of neuroplasticity in alcoholism. The 5-HT₇ receptor modulates both behavior and serotonin signaling. We investigated midbrain 5-HT₇ overexpression in C57BL/6 mice given 5-week ethanol access. Our results showed complex, region-specific changes in 5-HT and BDNF signaling, as well as selective behavioral alterations. Ethanol abolished the antidepressant-like effect of 5-HT₇ overexpression and increased anxiety-like behavior, without affecting baseline locomotion or novel object recognition. At the molecular level, ethanol suppressed 5-HT₇-mediated CREB/BDNF signaling and differentially regulated 5-HT_1A and 5-HT_2A expression across regions. To extract general principles, we used integrative systems analysis based on population-averaged generalized estimating equations (GEE), and mapped effects in the (t₁, t₂) plane. We identified two regularities: first, regional specificity of responses, and second, divergence across regulatory levels, with opposing effects more frequent at the mRNA level and concordant effects more common at the protein level. These findings suggest that neuroadaptation to combined 5-HT₇ and ethanol factors follows region- and level-specific rules, rather than a single global program, underscoring the value of integrative analysis. Full article

Dysregulated lipid metabolism constitutes the fundamental etiology underlying the global burden of obesity and its associated metabolic disorders. N⁶-methyladenosine (m⁶A) is the most abundant reversible chemical modification on messenger RNA and influences virtually every aspect of RNA metabolism. Recent studies demonstrate that m⁶A mediates regulatory networks governing lipid metabolism and contributes to the pathogenesis of multiple metabolic diseases. However, the precise roles of m⁶A in lipid metabolism and related metabolic disorders remain incompletely understood. This review positions m⁶A modification as a central epigenetic switch that governs lipid homeostasis. We first summarize the molecular components of the dynamic m⁶A regulatory machinery and delineate the mechanisms by which it controls key lipid metabolic processes, with an emphasis on adipogenesis, thermogenesis and lipolysis. Building on this, we further discuss how dysregulated m⁶A acts as a shared upstream driver linking obesity, type 2 diabetes (T2D), metabolic dysfunction-associated steatotic liver disease (MASLD), and insulin resistance through tissue-specific and inter-organ communication mechanisms. We also evaluate the potential of targeting m⁶A regulators as therapeutic strategies for precision intervention in metabolic diseases. Ultimately, deciphering the complex interplay between m⁶A modification and lipid homeostasis offers a promising frontier for the development of epitranscriptome-targeted precision medicine against obesity and its associated metabolic disorders. Full article

Background/Objectives: The rapid development of biopharmaceuticals has heightened attention from both industry and regulatory agencies toward product quality, particularly regarding subvisible particles as a critical quality attribute. Existing pharmacopoeial methods, Light Obscuration (LO) and Microscopic Particle Count (MC), exhibit limitations in meeting increasingly refined analytical requirements. Flow Imaging Microscopy (FIM) technology shows promise as an alternative, yet its standardized methodologies are still under development. Methods: This study employed polystyrene microsphere standard beads and intravenous immunoglobulin to perform instrument standardization and consistency evaluations on FIM instruments sharing the same operating principles but from different manufacturers. The consistency and transferability of particle counting across platforms were assessed. Additionally, particle images obtained from parallel testing on two platforms were classified using confusion matrices based on convolutional neural networks and the Unified Manifold Approximation and Projection (UMAP) dimensionality reduction method. Results: This study investigated the consistency and developed a transfer strategy for particle counting results across different FIM platforms. Analysis of particle image classification confirmed the consistency of image-based categorization while also revealing the complexity associated with cross-platform image recognition. Conclusions: The findings provide valuable insights for the further standardization of Flow Imaging Microscopy, supporting its potential as a reliable analytical tool for subvisible particle analysis in biopharmaceutical quality control. Full article

Metformin, a 60-year-old biguanide and cornerstone of type 2 diabetes therapy, continues to challenge and inspire modern pharmaceutical science. Despite its chemical simplicity, metformin displays highly complex pharmacokinetic and pharmacodynamic behavior driven by transporter dependence, luminal activity, and formulation-sensitive exposure. Originally regarded as limited by low permeability and incomplete absorption, metformin has emerged as a paradigm for gut-targeted therapy, controlled- and delayed-release systems, and personalized pharmaceutics. Growing evidence has repositioned the intestine, rather than systemic plasma exposure, as a major site of action, highlighting the central role of organic cation transporters and multidrug efflux systems in determining efficacy, variability, and gastrointestinal tolerability. Beyond metabolic control, insights into transporter regulation, pharmacogenetics, microbiome interactions, and manufacturing quality have expanded metformin’s relevance as a model compound for contemporary drug development. Advances in formulation design, quality-by-design manufacturing, and regulatory control have further reinforced its clinical robustness, while repurposing efforts in oncology, immunometabolism, and regenerative medicine underscore its translational potential. This review integrates mechanistic pharmacology, formulation science, and clinical translation to position metformin not merely as an antidiabetic agent, but as a didactic model illustrating the evolution of pharmaceutics from molecule-centered design to system-oriented, precision-driven therapy. Full article

The advent of sixth-generation (6G) imposes stringent demands on wireless networks, while traditional 2D rigid reconfigurable intelligent surfaces (RISs) face bottlenecks in regulatory freedom and scenario adaptability. To address this, stacked intelligent metasurfaces (SIMs) have emerged. This paper presents a systematic review of SIM technology. It first elaborates on the SIM multi-layer stacked architecture and wave-domain signal-processing principles, which overcome the spatial constraints of conventional RISs. Then, it analyzes challenges, including beamforming and channel estimation for SIM, and explores its application prospects in key 6G scenarios such as integrated sensing and communication (ISAC), low earth orbit (LEO) satellite communication, semantic communication, and UAV communication, as well as future trends like integration with machine learning and nonlinear devices. Finally, it summarizes the open challenges in low-complexity design, modeling and optimization, and performance evaluation, aiming to provide insights to promote the large-scale adoption of SIM in next-generation wireless communications. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article