Search Results (10,615)

Metabolic dysfunction-associated steatohepatitis (MASH) is the progressive inflammatory and fibrotic subtype of metabolic dysfunction-associated steatotic liver disease (MASLD), and its treatment landscape is rapidly moving from nonspecific liver fat reduction towards mechanism-based drug positioning. Since 2024, resmetirom and semaglutide have received U.S. Food and Drug Administration accelerated approval for non-cirrhotic MASH with moderate-to-advanced fibrosis, while tirzepatide, survodutide, and fibroblast growth factor 21 analogues have shown biopsy-based phase 2 or 2b efficacy signals. This narrative review organises approved and emerging pharmacotherapies within a pathogenesis-informed three-axis framework: the weight-insulin resistance-substrate load axis, the intrahepatic lipid reprogramming axis, and the inflammation-fibrosis transition and multi-axis integration axis. We further grade evidence maturity from regulatory or phase 3 histological evidence to phase 2 biopsy-based evidence and earlier imaging- or biomarker-based signals. This framework is intended to support phenotype-sensitive treatment positioning rather than a fixed therapeutic sequence. In particular, weight-centred treatment should not be assumed to apply to all patients, including normal-weight or lean MASH. Overall, future MASH therapy will likely depend on matching drug mechanisms, fibrosis stage, cardiometabolic phenotype, and treatment goals. Full article

Background/Objectives: Beam-hardening artifacts from the skull base reduce image quality in routine non-contrast head CT, particularly in the posterior fossa. Virtual monoenergetic images (VMIs) reconstructed from dual-layer spectral CT have been shown to reduce these artifacts, but previous studies have generally been limited by small cohorts or evaluation of only a limited range of monoenergetic reconstructions. The aim of this study was to comprehensively evaluate image quality and posterior fossa artifact reduction across the monoenergetic spectrum in a large cohort of normal head CT examinations. Methods: Consecutive adult patients undergoing non-contrast head CT on a dual-layer spectral CT system were retrospectively included if no intracranial pathology was identified on clinical interpretation. Regions of interest were placed in predefined posterior fossa and supratentorial locations in conventional images and automatically propagated to VMIs reconstructed from 40 to 200 keV. Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), attenuation, and a posterior fossa artifact index were quantified. Two neuroradiologists independently performed qualitative image quality assessment. Results: A total of 188 examinations were included in the quantitative analysis and 40 in the qualitative assessment. Compared with conventional images, VMIs reconstructed at ≥50 keV demonstrated significantly reduced image noise in the posterior fossa together with a higher SNR, while the CNR was improved between 40 and 80 keV. Qualitative assessment likewise demonstrated superior overall image quality and reduced artifact severity, with the highest ratings generally obtained for reconstructions around 60 keV. Improvements became marginal above approximately 80 keV. Conclusions: Virtual monoenergetic reconstructions from dual-layer spectral CT improve posterior fossa image quality by reducing beam-hardening artifacts while maintaining favorable image noise and tissue contrast. Intermediate-energy reconstructions (approximately 50–70 keV) provide the most favorable balance between beam-hardening artifact reduction, image noise, and tissue contrast, supporting their use as an image optimization strategy for non-contrast head CT. Whether these technical improvements translate into improved diagnostic performance should be evaluated in future studies including patients with posterior fossa pathology. Full article

Drosophila melanogaster shares approximately 75% of human disease-related genes and possesses sophisticated genetic toolkits, including GAL4/UAS, CRISPR-Cas9, and RNA interference (RNAi), making it a rapid, cost-effective, and genetically tractable in vivo platform for natural products (NPs) discovery. This review systematically summarizes the modeling strategies, pathological mechanisms, and therapeutic applications of Drosophila models for six major human diseases, including type 2 diabetes, nephrolithiasis, inflammatory bowel disease, cancer, Alzheimer’s disease, and Parkinson’s disease. Cross-disease analysis identifies five evolutionarily conserved signaling networks—IIS/PI3K/Akt/FOXO, JNK/JAK/STAT, Nrf2/Keap1, mTOR/TORC1, and IMD/Toll—as common molecular targets of bioactive NPs, providing a unified mechanistic framework for understanding their multi-target pharmacological activities and broad therapeutic potential. Critically, we propose a generalizable integrated stepwise pipeline: high-throughput fly screening of crude extracts, bioassay-guided isolation of active monomers, genetic mechanistic dissection via RNAi and mutant rescue, and layered validation in human cells and selective mammalian models. This pipeline addresses key challenges in NPs research, including the identification of bioactive constituents and mechanistic validation, while improving screening efficiency and translational potential. Overall, this review establishes a multi-disease-applicable framework linking disease modeling, conserved signaling mechanisms, and translational pharmacology, providing practical guidance for future mechanism-driven NP discovery and preclinical development using Drosophila. By leveraging Drosophila genetics to bridge evolutionary conservation and human pathology, this framework offers a powerful, paradigm-shifting strategy to accelerate mechanism-driven NP discovery and preclinical development. Full article

Hybrid terahertz (THz)/free-space optical (FSO) systems offer a promising paradigm for high-capacity, all-weather wireless communication, yet their deployment is often hindered by the bulky size and high complexity of discrete transceivers. This paper experimentally demonstrates a low-complexity hybrid THz/FSO transmission architecture featuring a unified photonic transmitter. By leveraging a polymethyl methacrylate (PMMA) plate serving as a dichroic beam combiner—which reflects the 1550 nm optical signal while transmitting the 300 GHz THz signal—we realize simultaneous signal propagation over a shared aperture and link. Photonics-aided techniques are employed to generate both carriers, ensuring system integration and coherence. The experimental results verify that both the THz and FSO links independently support 30-GBaud quadrature phase-shift keying (QPSK) transmission over a 10-m wireless distance, achieving a net data rate of 60 Gbps per link while satisfying the 7% hard-decision forward error correction (HD-FEC) threshold of 3.8 × 10⁻³. This work validates the feasibility of shared-transmitter designs and provides a compact, cost-effective solution for future high-speed fronthaul/backhaul networks. Full article

In large-scale multiple-input multiple-output (MIMO) systems, inadequate pilots can necessitate pilot reuse, reducing channel-estimation accuracy, while too few received pilot observations can also lead to inaccurate interference-plus-noise covariance estimates. These estimation errors can further degrade the performance of downstream interference suppression and data detection. Learning-based methods have been developed for pilot assignment, channel estimation, and receiver processing, but these methods are often studied separately. This survey organizes recent studies according to where learning-based methods are applied in the signal-processing chain: pilot-domain mitigation, intelligent channel estimation with contaminated or limited pilots, and intelligent receiver processing with contaminated or limited pilots. We also classify the studies by learning method and compare them using the same set of evaluation criteria. Across the surveyed papers, performance is evaluated using different metrics. Many studies also lack evaluations under changing channel or system conditions and do not fully report implementation costs such as computational complexity, memory usage, and latency. Among the studies that satisfy our selection criteria, none directly investigates learning-based estimation of the interference-plus-noise covariance matrix for interference rejection combining (IRC) receivers when only limited pilot observations are available. Based on these findings, we propose a minimum set of benchmarking requirements and identify lightweight online adaptation, joint processing, learning-based covariance estimation for IRC receivers, and robust processing for large-array architectures as future research directions for emerging sixth-generation (6G) systems. Full article

Background: Cell migration is a biological process influenced by biochemical signals and mechanical stimuli from the microenvironment. In this context, the accurate characterization of the mechanical microenvironment generated within microfluidic platforms represents an essential step for the design and interpretation of cell migration studies. Understanding how hydrodynamic forces influence the mechanical microenvironment experienced by cells remains a challenge, especially in confined and biomimetic systems. Methods: In this study, a three-dimensional microfluidic device was developed in silico to characterize the effects of flow variation on mechanofluidic parameters and to provide a quantitative basis for designing future cell-migration experiments. Computational fluid dynamics simulations were performed to characterize the velocity, pressure, and wall shear stress (WSS) distributions under different inlet flow rates (0.5, 1, and 5 µL/min) and three distinct inlet/outlet configurations within the same three-dimensional geometry. Rigid hemispherical probe structures were incorporated into the model to quantify the local shear stress acting on cell-sized surfaces. Results: The results demonstrated a direct and linear relationship between the applied flow rate and the WSS, modulated by the channel geometry and the inlet and outlet configuration. Regions near micropores and lateral channels showed high WSS values, while central regions experienced less mechanical stimulation, depending on flow conditions. Comparison with WSS values and ranges associated with cellular responses reported in the literature indicated that certain operational configurations generated mechanical conditions comparable to those previously investigated in cell-based studies, including cell migration applications. Conclusions: Overall, the study highlights the importance of controlling flow conditions in microfluidic platforms and provides a quantitative basis for the development and optimization of three-dimensional microfluidic devices intended for designing future cell-migration experiments. The systematic comparison of three inlet/outlet configurations across three flow rates within the same three-dimensional geometry provides a comparative framework for identifying configuration-dependent changes in the local mechanofluidic environment, supporting the selection of operational conditions for future mechanobiological and cell-migration studies. Full article

Electromyography (EMG)-based sensing is an important tool for assessing neuromuscular activity and monitoring athlete development; its reliability depends on electrode placement, signal quality, and accurate identification of muscle activation periods. This study proposes an intelligent EMG sensing framework integrating preliminary electrode placement assessment, muscle activity detection, feature extraction, and regression-based progression prediction. A placement assessment indicated that positioning the electrode adjacent to the innervation zone produced the highest RMS under the tested conditions. A two-stage activity detection method based on clustering and probabilistic modeling achieved an average error of 1.5% and a temporal deviation of 19 ms. Nine time-domain EMG features extracted from the detected activity segments were used to characterize athlete progression and estimate the time required to reach a reference neuromuscular profile. Among the methods, Linear Regression provided the best fit to the data, obtaining R² = 0.987 and RMSE = 4.21 and suggesting a predominantly linear relationship between the EMG-derived features and training duration within the dataset. However, these results were obtained from only six longitudinal observation periods for a single representative athlete, with each period represented by a 90-dimensional EMG feature vector derived from the ten movement classes. Therefore, the results should be interpreted as preliminary, athlete-specific goodness-of-fit findings rather than evidence of generalizable predictive performance. Validation using larger longitudinal cohorts and independent datasets is required. The proposed framework is compatible with future IoT-enabled wearable and edge-computing architectures; however, hardware-level implementation was beyond the scope of this study. Full article

Regular exercise mediates inter-tissue communication between skeletal muscle and the immune system through skeletal muscle-derived secretory factors, providing an important molecular basis for the beneficial effects of exercise on chronic inflammation, metabolic dysregulation, and impaired tissue repair. As key effector cells of the innate immune system, macrophages do not simply conform to a dichotomous classification of classically activated M1 macrophages and alternatively activated M2 macrophages; rather, their functional states constitute a dynamic spectrum shaped by exercise load, recovery time window, tissue microenvironment, and disease context. This review focuses on recent advances in exercise-induced skeletal muscle secretory factors involved in macrophage functional remodeling. Representative signals, including interleukin-6 (IL-6), irisin, meteorin-like protein (METRNL), fibroblast growth factor 21 (FGF21), oncostatin M (OSM), decorin, myostatin, chemokines, and extracellular vesicles, are systematically summarized in terms of their exercise responsiveness, evidence for skeletal muscle origin, and evidence supporting macrophage regulation. Based on these dimensions, an evidence-strength grading framework is further proposed. Moreover, this review integrates key signaling axes, including glycoprotein 130 (gp130)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT), signal transducer and activator of transcription 6 (STAT6)/peroxisome proliferator-activated receptor gamma (PPARγ), AMP-activated protein kinase (AMPK)/nuclear factor erythroid 2-related factor 2 (Nrf2)/nuclear factor kappa B (NF-κB), transforming growth factor beta (TGF-β)/Smad, and chemokine receptor pathways, to explain how exercise-induced secretory networks participate in the dynamic regulation of the macrophage functional spectrum through immune cell recruitment, inflammatory clearance, immunometabolic reprogramming, matrix remodeling, and repair-niche formation. Current evidence indicates the translational potential of exercise-induced skeletal muscle secretory factors in skeletal muscle repair, metabolic inflammation, aging-related functional decline, and cancer rehabilitation. However, this field still faces several major challenges, including insufficient tracing of skeletal muscle-derived signals, limited direct causal validation, a lack of human tissue-level evidence, and unclear exercise dose–response relationships. Future studies should combine tissue-specific genetic interventions, receptor blockade, single-cell and spatial omics, metabolic flux analysis, and standardized human exercise interventions to further clarify the mechanistic basis and application boundaries of exercise-induced skeletal muscle–macrophage communication, thereby providing a theoretical foundation for precision exercise prescription and chronic inflammation intervention. Full article

Financial media risk is jointly driven by multisource content, including news reports, corporate announcements, social media posts, short videos, and livestreams, while content authenticity, propagation velocity, asset relevance, and trading infrastructure conditions can simultaneously influence short-term market fluctuations. To address the limitations of existing methods, including their reliance on either textual information or market sequences, insufficient source verification, and inadequate alignment of asynchronous multimodal signals, FMRP-Net is proposed for artificial intelligence-driven sensing. Event semantics, risk categories, and asset association information are first extracted through a pretrained financial language model and cross-modal consistency analysis. A dual-layer hardware reliability perception module is then employed to integrate sensing evidence from cameras, microphones, terminal inertial signals, server temperature, power consumption, network traffic, and transmission latency. Heterogeneous temporal propagation graphs, cross-frequency alignment, and bidirectional propagation–market coupling are further incorporated to jointly predict market direction, volatility, risk level, and propagation trends. Experimental results demonstrate that FMRP-Net achieved an Accuracy of $0.832$, a Macro-F1 of $0.824$, a ROC-AUC of $0.891$, an MCC of $0.665$, and a PR-AUC of $0.883$ for market direction prediction over future horizons of 5, 15, 30, and 60 min, indicating a balanced performance in terms of Precision and Recall. For volatility prediction, MAE, RMSE, and MAPE values of $0.0178$, $0.0271$, and $12.46\%$ were obtained, respectively, together with an $R^2$ of $0.812$. In the ablation study, the media risk Macro-F1 and source reliability AUC reached $0.842$ and $0.929$, respectively, while the propagation-scale prediction error was reduced to $0.109$ and the average early-warning lead time reached $10.6$ min. These results demonstrate that the integration of multimedia semantics, hardware sensing evidence, and propagation structures can effectively improve the accuracy, stability, and interpretability of financial market prediction and risk early warning. Full article

Recursive evidence fusion gives an intrusion detection system temporal memory, but it also gives unreliable windows and erroneous review outcomes a path to influence later diagnoses. Existing drift-handling, open-set, conformal, continual-learning, and human-in-the-loop methods provide useful signals or update classifiers and memories; they do not, by themselves, specify when a post-classification evidential state may be written or replaced. We present RTEF-IDS, a protocol that separates current action, model-evidence admission, reviewed-feedback admission, and history replacement. The protocol retains the history-relative reliability principle from our previous work, instantiates it for singleton-plus-ignorance IDS evidence, and assigns operation-specific credentials. Reviewed windows make no base-state change, mapped-known feedback may be appended, and replacement requires persistent confirmation. On 33,384 frozen windows, 30% retrospective admission excludes 26.3% of held-out-or-misclassified mass while retaining 94.8% of known-correct evidence. Under paired imperfect feedback, retrospective replacement increases one-window future history-state agreement by 0.107 in the primary block and 0.129 in IoT-23 leave-scenario-out replay. Under a past-only rolling-budget gate within externally supplied frozen partitions, the corresponding increments are 0.001 and 0.000, indicating that the tested gate exposes few qualifying replacement opportunities; bounded external short streams show the same opportunity constraint. Independent second review reduces false authorization from 5.66 to 0.124 per 1000 first-stage reviewed windows under independent errors and from 34.27 to 0.181 under five-window correlated errors, with a corresponding increase in review demand and a reduction in admitted corrective feedback. A shared systematic label alias remains unresolved by the tested review arms. These results support explicit, auditable state-mutation control while identifying the causal-opportunity and feedback-provenance conditions under which it operates. Full article

Short-term financial risk in intelligent trading systems is reflected not only in prices, trading volumes, and textual sentiment but also in infrastructure operating states, including server workload, device power consumption, network latency, and packet loss rate. We propose HSF-LLMNet, a hardware sensor and large language model semantic fusion network for jointly modeling external information shocks and infrastructure responses. A large language model extracts event category, sentiment polarity, risk intensity, and semantic uncertainty from financial texts. Reliability-aware temporal modeling handles sensor missingness, drift, and abnormal noise, while asynchronous soft alignment, bidirectional cross-attention, and reliability-aware gated fusion integrate irregular textual events with continuous hardware signals. The model jointly predicts market direction, realized volatility, and three-level risk over the subsequent 30 min. Experiments were conducted on eight Chinese A-share indices: the SSE Composite Index (000001.SH), SSE 50 Index (000016.SH), CSI 300 Index (000300.SH), STAR 50 Index (000688.SH), CSI 500 Index (000905.SH), CSI 1000 Index (000852.SH), Shenzhen Component Index (399001.SZ), and ChiNext Index (399006.SZ). The common observation period for market, textual, and hardware data extended from 1 March 2024 to 30 June 2025. After data cleaning, timestamp matching, and multimodal temporal alignment, 169,208 aligned asset–time prediction windows were retained for the 30 min forecasting task. Realized volatility was defined as the square root of the sum of squared one-minute log returns over the future 30 min interval. The three-level risk label was constructed from future realized volatility, absolute 30 min return, and liquidity stress, with all thresholds estimated exclusively from the training portion of each fold. A sample was labeled high risk when at least two of the three indicators exceeded their 85th-percentile thresholds or when any indicator exceeded its 95th-percentile threshold. It was labeled medium risk when, after excluding high-risk samples, at least two indicators exceeded their 60th-percentile thresholds or any indicator exceeded its 85th-percentile threshold; all remaining samples were labeled low risk. Results showed that HSF-LLMNet achieved an accuracy of 78.62%, a precision of 78.14%, a recall of 77.83%, an F1-score of 77.98%, an area under the receiver operating characteristic curve of 84.91%, and a Matthews correlation coefficient of 57.36% for directional prediction. For realized-volatility regression, the MAE, RMSE, MAPE, and $R^2$ were 0.0089, 0.0135, 9.21%, and 0.812, respectively. For high-risk-event warning, the mean effective warning time, defined as the interval between the first valid alarm and the corresponding event, was 15.37 min; the false-alarm rate and missed-alarm rate were 6.82% and 8.14%, respectively. Ablation experiments showed performance reductions after removing semantic encoding, sensor-reliability estimation, asynchronous alignment, bidirectional cross-attention, gated fusion, or multi-task learning. These results indicate that textual events and infrastructure operating states provide complementary information for quantitative risk analytics and fintech applications. Full article

Diabetic cardiomyopathy (DCM) is characterized by myocardial remodeling that may already be evident during prediabetes, yet the molecular alterations accompanying these early changes remain poorly understood. The present study examined mouse models of Nucleoside diphosphate kinase (NDPK-B)-deficient prediabetes and streptozotocin-induced diabetes using O-GlcNAc-associated proteomic profiling to define stage-specific molecular alterations during the progression from prediabetic to diabetic cardiomyopathy. Both models exhibited increased left ventricular extracellular matrix deposition and impaired diastolic function, together with activation of the hexosamine biosynthesis pathway. Profiling of O-GlcNAc-associated proteins uncovered extensive remodeling of the mitochondrial proteome already at the prediabetic stage, with respiratory complex I among the most prominently altered targets, alongside changes in substrate metabolism and inflammatory signaling. In overt DCM, the putative O-GlcNAc proteomic profile was associated with a shift toward wider lipid-dependent metabolic reprogramming and remodeling of mitochondrial proteins. These findings identify early remodeling of the mitochondrial O-GlcNAc-associated proteome as a molecular signature of prediabetic cardiomyopathy and highlight respiratory complex I proteins as candidate targets for future mechanistic investigations. Full article

Glutathione is an abundant intracellular low-molecular-weight thiol that contributes importantly to cellular redox homeostasis. Besides its well-established role in the antioxidant defense of the cell, glutathione regulates mitochondrial function, metabolism of toxicants, protein thiol oxidation/reduction, redox signaling, and immunity. Disturbances in glutathione metabolism have been shown to play a role in various diseases; however, it has become clear that changes in glutathione metabolism are a part of a complex, multifactorial process. In this review, we summarize current knowledge of the molecular mechanisms governing glutathione synthesis, recycling, compartmentalization, and biological functions, with particular emphasis on redox signaling, the nuclear factor erythroid 2-related factor 2/Kelch-like ECH-associated protein 1 (Nrf2/Keap1) pathway, and reversible protein S-glutathionylation. We further examine how disturbances in glutathione homeostasis interact with mitochondrial dysfunction, chronic inflammation, metabolic stress, and impaired cellular signaling in Parkinson’s disease, Alzheimer’s disease, Huntington’s disease, multiple sclerosis, Wilson’s disease, type 2 diabetes, and nonalcoholic fatty liver disease. We also evaluate current translational interventions targeting restoration of glutathione balance through glutathione supplementation, precursor supplementation, pharmacological modulation of endogenous antioxidant mechanisms, dietary interventions, and changes in lifestyle. Despite the fact that many interventions have been promising at the mechanistic and experimental level, there are still insufficient clinical data because of the problems associated with glutathione availability, tissue specificity, disease variability, and a lack of sufficiently powered clinical trials. The conclusion of this review is that glutathione should not be viewed as a universal therapeutic target; instead, glutathione should be perceived as an important factor contributing to cellular resilience and able to help other disease-specific interventions. Future progress in glutathione-based interventions will likely depend on integrating redox biomarkers, patient stratification, and precision medicine strategies to identify individuals most likely to benefit from targeted modulation of glutathione homeostasis. Full article

SWEET (Sugars Will Eventually be Exported Transporter) proteins constitute a conserved family of sugar transporters that play pivotal roles in carbohydrate allocation and stress responses. In this study, we systematically identified 14 SWEET homologs in the genome of sacred lotus (Nelumbo nucifera) and validated their transport activity for both hexoses and sucrose. Subsequent analysis revealed that stress-responsive elements are the most enriched promoter sequences of NnSWEET genes. Quantitative expression profiling of the members found that NnSWEET4a was strongly upregulated under salt stress. NnSWEET4a was localized at the plasma membrane; its expression conferred salt sensitivity in both yeast and Arabidopsis thaliana. Transgenic Arabidopsis lines overexpressing NnSWEET4a exhibited substantial downregulation of the SOS3–SOS2–SOS1 signaling module and concomitant alterations in cellular sugar homeostasis. Further analysis revealed that exogenous sugar application aggravated salt sensitivity and SOS pathway inhibition in NnSWEET4a transgenic plants, and NnSWEET15-overexpressing Arabidopsis recapitulated identical phenotypic and molecular responses, including salt sensitivity and repression of SOS genes. These findings indicate that NnSWEET4a impairs salt tolerance through disruption of sugar homeostasis. The results establish a mechanistic framework for future investigations into SWEET-dependent regulation of sugar homeostasis and salt stress adaptation. Full article

Mirabilis himalaica is a Tibetan medicinal plant whose tuberous root is its principal medicinal organ. To characterise molecular and metabolic differences associated with root enlargement, we integrated untargeted metabolomic and transcriptomic analyses of enlarged and non-enlarged roots. We identified 1465 metabolites, including 336 differentially accumulated metabolites, and 4058 differentially expressed genes. Flavonoids and phenolic acids were predominantly less abundant in enlarged roots, whereas several alkaloids showed higher relative abundance. Three gibberellin-related metabolites were lower in enlarged roots, while 1-Aminocyclopropanecarboxylic acid (ACC), L-tryptophan, tryptamine, and several cytokinin-related metabolites showed higher relative signals. Tryptophan metabolism was a shared enriched pathway in the integrated analysis. L-Tryptophan was positively correlated with Anthranilate synthase beta subunit 2 (ASB2) and negatively correlated with Tryptophan synthase alpha chain (TSA) and Probable indole-3-pyruvate monooxygenase (YUC) across the six samples; these exploratory correlations do not establish regulatory relationships. The observed patterns are consistent with coordinated changes in hormone-related metabolites, secondary metabolism, and gene expression during root enlargement. Because Indole-3-acetic acid (IAA) and lignin were not directly quantified, the study does not infer their concentrations or deposition. These findings provide a multi-omics resource for investigating tuberous-root development and for guiding future functional and targeted validation studies in this endangered medicinal species. Full article