Search Results (862)

Calcific aortic valve disease (CAVD) is characterized by progressive valve remodeling and calcification. Moreover, microRNAs (miRNAs) are emerging as key regulators of cardiovascular pathology and potential circulating biomarkers. We performed high-throughput miRNA profiling in calcified aortic valve tissue and matched patient serum samples using an array that included 98 human miRNAs. Expression data were log10-transformed and filtered to identify biologically relevant miRNAs. Shared miRNAs between tissue and serum were further validated by quantitative real-time polymerase chain reaction (qRT-PCR) in patients and healthy controls. Of the 49 actively expressed miRNAs, 18 were shared between valve tissue and serum. Thus, qRT-PCR validation revealed significant downregulation of miR-17-5p and miR-29a-3p in CAVD patient serum compared to controls. These results indicate that disease-associated miRNA alterations in calcified valves are mirrored in circulation. miR-17-5p and miR-29a-3p represent promising circulating biomarkers for CAVD, reflecting underlying pathological remodeling and extracellular matrix dysregulation. Our findings provide a framework for non-invasive monitoring of valve calcification and highlight miRNA-mediated pathways as potential therapeutic targets. Full article

Subarachnoid hemorrhage (SAH), often resulting from aneurysmal rupture, remains a life-threatening cerebrovascular disorder with high morbidity and mortality. While previous research has focused primarily on cerebral damage and neurological outcomes, growing evidence suggests that SAH also causes systemic complications, including pulmonary dysfunction. The underlying mechanisms linking SAH to lung injury, however, are not fully understood. The glymphatic system, a perivascular network that facilitates the clearance of cerebrospinal fluid (CSF) and interstitial waste from the brain, plays a critical role in maintaining central nervous system (CNS) homeostasis. Aquaporin-4 (AQP4) water channels, predominantly expressed in astrocytic end feet, are essential for efficient glymphatic flow. Emerging studies have shown that SAH impairs glymphatic function by disrupting AQP4 polarity and CSF circulation, resulting in the accumulation of neurotoxic substances and neuroinflammation. Recent findings further suggest that glymphatic dysfunction may exert systemic effects beyond the CNS, contributing to a breakdown of the brain–lung axis. The release of pro-inflammatory cytokines, blood degradation products, and damage-associated molecular patterns (DAMPs) into systemic circulation can promote pulmonary endothelial injury and trigger immune responses in the lungs. This phenomenon is exacerbated by impaired clearance via the glymphatic system, amplifying systemic inflammation and increasing the risk of acute lung injury (ALI) or neurogenic pulmonary edema (NPE). This review proposes a novel perspective linking glymphatic impairment with pulmonary complications after SAH. Understanding this connection could open new therapeutic avenues—such as targeting AQP4 function, enhancing CSF circulation, or modulating the inflammatory response—to mitigate both neurological and respiratory sequelae in SAH patients. Full article

Adenomyosis (AM) is a hormonally responsive uterine disorder defined by ectopic endometrial tissue within the myometrium, causing pain, abnormal bleeding, and subfertility. Non-coding RNAs (ncRNAs)—including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs)—are post-transcriptional regulators implicated also in uterine remodeling. We systematically reviewed original studies evaluating ncRNAs in AM using human samples, in vitro and animal models, or bioinformatic approaches. Data sources included PubMed and Google Scholar (inception up to 10 August 2025). Forty-one studies were included and synthesized across mechanistic, diagnostic, and translational domains. miRNAs (n = 31) were the most studied subclass, followed by lncRNAs (n = 10) and circRNAs (n = 5). Recurrent miRNAs such as miR-10b and miR-30c-5p (downregulated, inhibitory) and miR-145 (upregulated, promotive) regulate epithelial invasion, epithelial–mesenchymal transition, and cytoskeletal remodeling via PI3K–AKT/MAPK and Talin1 signaling. The let-7a/LIN28B axis governed estrogen-sensitive proliferation in the junctional zone, while miR-21 exhibited compartment-specific roles in decidualization and ectopic cell survival. Extracellular-vesicle (EV)-bornemiRNAs (e.g., miR-92a-3p, miR-25-3p, miR-4669) contributed to immune polarization and show early diagnostic potential. lncRNAs and circRNAs acted via chromatin modifiers and ceRNA networks. Most findings remain at the discovery stage. Convergent dysregulation was observed in key signaling pathways, including JAK–STAT, Wnt/β-catenin, and Hippo–YAP. ncRNAs regulate critical axes of invasion, proliferation, immune modulation, and hormonal response in AM. Targets with preliminary causal support—miR-10b/ZEB1, let-7a/LIN28B, and miR-145/Talin1—warrant further validation. Circulating miRNAs—especially in EVs—offer promise for non-invasive diagnosis. Full article

This paper investigates the feasibility of diagnosing abdominal aortic aneurysm (AAA) via deep learning (DL)-enabled analysis of non-invasive arterial pulse waveform signals. We generated arterial blood pressure (BP) and pulse volume recording (PVR) waveform signals across a diverse synthetic patient cohort using a systemic arterial circulation model coupled with a viscoelastic model relating arterial BP to PVR while simulating a range of AAA severity levels. We confirmed the plausibility of the synthetic data by comparing the alterations in the simulated waveform signals due to AAA against previously reported in vivo findings. Then, we developed a convolutional neural network (CNN) with continuous property-adversarial regularization that can estimate AAA severity from brachial and tibial PVR signals. We evaluated the algorithm’s performance in comparison with an identical CNN trained on invasive arterial BP waveform signals. The DL-enabled PVR-based algorithm achieved robust AAA detection across different severity thresholds with area under the ROC curve values >0.89, and showed reasonable accuracy in severity estimation, though slightly lower than its invasive BP counterpart (MAE: 12.6% vs. 10.3%). These findings suggest that DL-enabled analysis of PVR waveform signals offers a non-invasive and cost-effective approach for AAA diagnosis, potentially enabling accessible screening through operator-agnostic and point-of-care technologies. Full article

Background: Cardiorenal syndrome (CRS) reflects bidirectional heart–kidney injury whose mechanisms extend far beyond hemodynamics. High-throughput genomics and multi-omics now illuminate the molecular circuits that couple cardiac and renal dysfunction. Methods: We narratively synthesize animal and human studies leveraging transcriptomics, proteomics, peptidomics, metabolomics, and non-coding RNA profiling to map convergent pathways in CRS and to highlight biomarker and therapeutic implications. Results: Across acute and chronic CRS models, omics consistently converge on extracellular matrix (ECM) remodeling and fibrosis (e.g., FN1, POSTN, collagens), immune–inflammatory activation (IL-6 axis, macrophage/complement signatures), renin–angiotensin–aldosterone system hyperactivity, oxidative stress, and metabolic/mitochondrial derangements in both organs. Single-nucleus and bulk transcriptomes reveal tubular dedifferentiation after cardiac arrest-induced AKI and myocardial reprogramming with early CKD, while quantitative renal proteomics in heart failure demonstrates marked upregulation of ACE/Ang II and pro-fibrotic matricellular proteins despite near-normal filtration. Human translational data corroborate these signals: urinary peptidomics detects CRS-specific collagen fragments and protease activity, and circulating FN1/POSTN and selected microRNAs (notably miR-21) show diagnostic potential. Epigenetic and microRNA networks appear to integrate these axes, nominating targets such as anti-miR-21 and anti-fibrotic strategies; pathway-directed repurposing exemplifies dual-organ benefit. Conclusions: Genomics and multi-omics recast CRS as a systems disease driven by intertwined fibrosis, inflammation, neurohormonal and metabolic programs. We propose a translational framework that advances (i) composite biomarker panels combining injury, fibrosis, and regulatory RNAs; (ii) precision, pathway-guided therapies; and (iii) integrated, longitudinal multi-omics of well-phenotyped CRS cohorts to enable prediction and personalized intervention. Full article

Leptospirosis is a globally distributed zoonosis of major public health and veterinary relevance, caused by pathogenic species of the genus Leptospira. Brazil is a hotspot for transmission due to its ecological diversity and complex host–environment interfaces. This study explored the genetic diversity and structure of circulating pathogenic Leptospira spp. in Brazil through a single-locus sequence typing (SLST) analysis based on the secY gene. A total of 531 sequences were retrieved from GenBank and subjected to phylogenetic and haplotype diversity analyses. Maximum likelihood reconstruction revealed strongly supported clades for seven species, with L. interrogans being the most prevalent and broadly distributed across hosts and regions. This species showed evidence of clonal expansion, with a dominant haplotype (n = 242) shared by humans, domestic animals, and wildlife. In contrast, L. santarosai and L. noguchii exhibited high haplotypic diversity and reticulated network structures, reflecting greater evolutionary variability. The species L. kirschneri and L. borgpetersenii displayed reduced haplotypic variation, the latter mainly associated with cattle, consistent with its host-adapted profile. Host- and biome-based haplotype networks revealed both the broad ecological adaptability of certain lineages and the exclusive presence of haplotypes restricted to specific environments, such as those found in marine mammals from the Atlantic Ocean. Genetic distance analyses confirmed the strong taxonomic resolution of the gene secY, which effectively distinguished closely related species while capturing intraspecific diversity. These findings provide a comprehensive molecular overview of pathogenic Leptospira in Brazil, highlighting ecological connectivity across hosts and biomes, as well as the contrasting evolutionary dynamics among species. Beyond describing genetic patterns, our analyses emphasize evolutionary processes, host–environment connectivity, and the implications for One Health. This integrative framework strengthens the basis for surveillance and control strategies in other endemic regions in the world. Full article

In the booming digital economy, data circulation—particularly for massive multimodal data generated by IoT sensor networks—faces critical challenges: ambiguous ownership and broken cross-domain traceability. Traditional property rights theory, ill-suited to data’s non-rivalrous nature, leads to ownership fuzziness after multi-source fusion and traceability gaps in cross-organizational flows, hindering marketization. This study aims to establish native ownership confirmation capabilities in trusted IoT-driven data ecosystems. The approach involves a dual-factor system: the collaborative extraction of text (from sensor-generated inspection reports), numerical (from industrial sensor measurements), visual (from 3D scanning sensors), and spatio-temporal features (from GPS and IoT device logs) generates unique SHA-256 fingerprints (first factor), while RSA/ECDSA private key signatures (linked to sensor node identities) bind ownership (second factor). An intermediate state integrates these with metadata, supported by blockchain (consortium chain + IPFS) and cross-domain protocols optimized for IoT environments to ensure full-link traceability. This scheme, tailored to the characteristics of IoT sensor networks, breaks traditional ownership confirmation bottlenecks in multi-source fusion, demonstrating strong performance in ownership recognition, anti-tampering robustness, cross-domain traceability and encryption performance. It offers technical and theoretical support for standardized data components and the marketization of data elements within IoT ecosystems. Full article

Based on five years of data (2017–2021) from the China National Lightning Detection Network (CNLDN), this study compares and analyzes the temporal and spatial distribution characteristics of cloud-to-ground (CG) lightning activities in the Hengduan Mountain region and its surroundings. It explores the relationship between CG lightning occurrences and altitude, topography, and various meteorological elements. Our findings reveal a stark east–west divide: high lightning density in the Sichuan Basin and the central Yungui Plateau contrasts sharply with lower densities over the eastern Tibetan Plateau and Hengduan Mountains. This geographical dichotomy extends to the diurnal cycle, where positive cloud-to-ground (PCG) lightning activities are more prevalent in the western part of the study area, while significant nocturnal activity defines the eastern basin and plateau. The study also finds that the relationship between CG lightning activities in the four sub-regions and 2 m temperature, precipitation, convective available potential energy, and Bowen ratio (the ratio of sensible heat flux to latent heat flux) exhibits similarities. Furthermore, we show that the relationship between lightning frequency and altitude is highly region-specific, with each area displaying a unique signature reflecting its underlying topography: a normal distribution over the eastern Tibetan Plateau, a bimodal pattern in the Hengduan Mountains, a sharp low-altitude peak in the Sichuan Basin, and a complex trimodal structure on the Yungui Plateau. These distinct regional patterns highlight the intricate interplay between large-scale circulation, complex terrain, and local meteorology in modulating lightning activity. Full article

Embodied energy flows link production systems with the energy sector, reflecting dependencies and structural risks under globalization and regional coordination. Guangdong, China’s most manufacturing-intensive, open, and energy-consuming province, is a central hub in both global value chains and domestic production networks, playing a pivotal role in national energy security. Understanding Guangdong’s embodied energy flows is essential for revealing the transmission of energy across multi-level spatial systems and the resilience of China’s energy infrastructure. This study integrates international (EXIOBASE) and Chinese inter-provincial input–output data to build a province-level nested global MRIO model, combined with Structural Path Analysis (SPA), to characterize Guangdong’s manufacturing embodied energy flows in domestic and international dual circulation from 2002 to 2017. Our findings confirm Guangdong’s pivotal bridging role in embodied energy transfers. First, flows are dual-directional and dominated by international transfers. Second, energy efficiency has improved, narrowing the intensity gap between export- and domestic-oriented industries. Third, flows have diversified spatially from concentration in developed regions toward developing regions, with domestic inter-provincial flows more dispersed. Finally, embodied energy remains highly concentrated across sectors, with leading industries shifting from labor- and capital-intensive to capital- and technology-intensive sectors. This research offers vital empirical evidence and policy reference for enhancing national energy security and optimizing spatial energy allocation. Full article

The increasing need for high-resolution, real-time radiative transfer (RT) modeling in climate science, remote sensing, and planetary exploration has exposed limitations of traditional solvers such as the Discrete Ordinate Radiative Transfer (DISORT) and Rapid Radiative Transfer Model for General Circulation Models (RRTMG), particularly in handling spectral complexity, non-local thermodynamic equilibrium (non-LTE) conditions, and computational scalability. Quantum-Inspired Neural Radiative Transfer (QINRT) frameworks, combining tensor-network parameterizations and quantum neural operators (QNOs), offer efficient approximation of high-dimensional radiative fields while preserving key physical correlations. This review highlights the advances of QINRT in enhancing spectral fidelity and computational efficiency, enabling energy-efficient, real-time RT inference suitable for satellite constellations and unmanned aerial vehicle (UAV) platforms. By integrating physics-informed modeling with scalable neural architectures, QINRT represents a transformative approach for next-generation Earth-system digital twins and autonomous climate intelligence. Full article

Climate change exacerbates challenges for old urban parks in high-density cores, intensifying urban heat islands and overcrowding hazards and causing limited extreme weather resilience. These parks face climate maladaptation, urban health risks, and reduced adaptive capacity. This study applies performance-based urban design through an “environmental analysis Overlay method,” integrating space syntax, CFD-Phoenics wind simulation, and solar analysis to translate climate adaptation, urban health, and urban resilience dimensions into measurable indicators including ventilation efficiency, crowd dispersion comfort, and flexible space capacity. Using Chengzhong Park in Wuxi as a case study, the method employs a diagnosis–optimization–validation process. Results demonstrate substantial improvements: (1) Climate: Problematic wind areas (>4 m/s or <0.5 m/s (stagnant)) decreased from 30% to 11%, while comfortable wind zones (0.5–1 m/s) increased to over 30%, achieving optimal microclimate conditions 89% of the park; (2) Health: Pedestrian circulation capacity increased by 25%, and activity areas with under 3 h of winter sunlight reduced from 26% to 19%; (3) Resilience: Spatial units consolidated from 155 to 115, with global-local integration improving from R² = 0.39 to 0.64, significantly enhancing network coherence and adaptive functionality. The findings confirm that this method provides a scientifically rigorous, replicable pathway for climate-adaptive renewal of old urban parks, supporting urban resilience agendas. Full article

As the penetration rate of distributed power sources increases and distribution network structures grow increasingly complex, the uncertainty in switch action control during load transfer has become a critical issue affecting grid safety and reliability. Traditional control methods based on precise probability-based predictive control are susceptible to bias introduced by prior settings under small-sample conditions, making it difficult to meet the stringent requirements of time-synchronized control. To address this, this study proposes an imprecise probability-based synchronous load transfer control method for distribution networks. By integrating the Imprecise Dirichlet model (IDM) with a Naive Credal Classifier (NCC), it constructs an interval predictive control model for switching action timing. This approach effectively mitigates the prior dependency issue and enhances estimation robustness under small-sample conditions. Combined with a dynamic delay strategy, this approach strictly controls the interval between disconnection and reconnection actions within 20 ms, preventing circulating current risks and ensuring transfer reliability. The simulation and experimental results demonstrate that the proposed method outperforms traditional Bayesian classifiers in both time prediction control accuracy and model robustness, providing a theoretical foundation and a reference for engineering applications for secure action control in distribution networks. Full article

Background/Objectives: Glioblastoma (GBM), the most aggressive primary malignant brain tumor, has a dismal prognosis and limited treatment options. The dried rhizome of Ligusticum chuanxiong Hort. (Chuanxiong, CX) is a traditional Chinese medicinal herb frequently prescribed in formulas intended to invigorate blood circulation. CX also exhibits anti-glioma activity, but its molecular mechanisms remain incompletely understood. Methods: In this study, we combined transcriptomics and Raman spectroscopy to investigate the effects of reconstituted CX-dispensing granules (hereafter referred to as CXG solution) on U87MG cells, suggesting their dual role in promoting cell death and modulating collagen deposition and lipid metabolism. Results: Mechanistically, we demonstrated that the CXG solution downregulates hsa-miR-10a-5p, which directly targets BCL2L11, known to induce pro-apoptotic effects, as validated by qPCR and dual-luciferase reporter assays. Furthermore, the CXG solution and hsa-miR-10a-5p suppress lipid metabolism through a coherent feed-forward loop via targeting transcription factors SREBF1 and E2F1. An electrophoretic mobility shift assay (EMSA) confirmed E2F1 binds to the hsa-miR-29a promoter, leading to the synergistic repression of hsa-miR-29a-3p by SREBF1 and E2F1. Network pharmacology analysis combined with molecular docking suggested that the ferulic acid and adenosine in CX potentially modulate EGFR-the E2F1-hsa-miR-10a-5p axis. Conclusions: These findings elucidate CX’s multi-target anti-GBM mechanisms and propose a novel therapeutic strategy combining metabolic intervention with miRNA-targeted therapy, providing novel insights into feed-forward loop regulation in miRNA networks. Full article

To ensure the safe operation of the external heat exchanger (EHE) in a circulating fluidized bed (CFB) boiler, it is essential to obtain real-time information on the flow conditions within the bed. This paper establishes a predictive model for the external heat exchanger of the high-temperature reheater in an ultra-supercritical CFB boiler by combining computational fluid dynamics (CFD) with model order reduction and artificial neural networks. The model enables rapid prediction of the solid volume fraction, solid temperature, and gas temperature within the external heat exchanger. The results show that the three predictive models can accurately forecast flow field information under unknown operating conditions. For inlet velocities of 0.225 m/s and 0.325 m/s, the calculation errors are 2.89%, 1.04%, 1.03% and 2.99%, 1.08%, 1.09%, respectively. The predictive models significantly save computational resources, reducing the computation time from 6000 min for the full-order model to approximately 1 s. This lays the foundation for real-time monitoring of the external heat exchanger. Full article

Background: Isorhamnetin (ISO), a dietary O-methylated flavonol, was evaluated for hypoglycemic activity and mechanism in a streptozotocin (STZ) model of type 1 diabetes. Methods: We conducted untargeted plasma metabolomics (ESI±), network integration and docking, and measured pancreatic PI3K, phosphorylated AKT, and COX-2; INS-1 β cells challenged with the PI3K inhibitor LY294002 were used to assess viability, intracellular ROS, and PI3K phosphorylation. Results: ISO lowered fasting glycemia, increased circulating insulin, improved dyslipidemia by reducing low-density lipoprotein cholesterol (LDL-C), and preserved islet architecture. Untargeted plasma metabolomics (ESI±) indicated broad remodeling with enrichment of arachidonic-, linoleic-, starch/sucrose- and glycerophospholipid pathways. Network integration and docking prioritized targets converging on PI3K/AKT and COX-2/eicosanoid signaling. Consistently, in pancreatic tissue, ISO increased PI3K, phosphorylated AKT, and reduced COX-2. In INS-1 beta cells challenged with the PI3K inhibitor LY294002, ISO improved viability, decreased intracellular ROS, and partially restored PI3K phosphorylation at 4 µM. Conclusions: Together, these data indicate that ISO exerts hypoglycemic effects while supporting β-cell integrity through activation of PI3K/AKT and tempering of COX-2–linked lipid-mediator pathways. ISO therefore emerges as a food-derived adjunct candidate for autoimmune diabetes, and the work motivates targeted lipidomics and in vivo pathway interrogation in future studies. Full article