Search Results (12,648)

The global prevalence of autoimmune diseases ranges from 3% to 8%, with women at a significantly higher risk than men. The core mechanisms underlying these diseases include impaired T-cell and B-cell immune tolerance, abnormal cytokine production, and aberrant activation of related signaling pathways. Conventional treatments primarily focus on suppressing immune responses, but their efficacy remains limited and they are often associated with substantial side effects. Nanomedicine leverages nanoscale materials to enable precise diagnosis and targeted therapy. Nanocarriers can penetrate biological barriers, enhance cellular uptake, and prolong circulation time in vivo, demonstrating considerable potential for drug delivery. Common nanoscale drug delivery platforms include nanoparticles, polymeric micelles, liposomes, dendrimers, mesoporous materials, hydrogels, and exosomes. Each carrier type possesses distinct characteristics in terms of drug-loading capacity, stability, responsiveness, and biocompatibility, thereby enabling targeted delivery and controlled release. This review summarizes recent advances in nano-delivery technologies for three representative chronic autoimmune diseases: diabetes mellitus (DM), inflammatory bowel disease (IBD), and rheumatoid arthritis (RA). Nano-delivery systems can improve therapeutic outcomes by optimizing drug delivery, targeting complications, and modulating the pathological microenvironment. They enhance drug bioavailability, reduce off-target and systemic adverse effects, and provide novel strategies for the precise and efficient treatment of chronic autoimmune diseases. Full article

Objective: The objectives of this study were to characterise induction-phase regional cerebral oxygen saturation (rSO₂)–mean arterial pressure (MAP) dynamics during near-infrared spectroscopy (NIRS)-guided selective carotid endarterectomy (CEA) and to examine whether the Awake→Intubated pressure–oxygenation pattern may represent an early adjunctive physiological signal of subsequent cross-clamp-related ipsilateral cerebral desaturation. Methods: In this retrospective observational cohort study, 322 consecutive elective CEAs managed with an NIRS-guided selective shunting protocol between October 2019 and February 2025 were analysed, after excluding patients considered for routine pre-emptive shunting because of contralateral internal carotid artery occlusion or ≥70% stenosis. Standardised MAP and bilateral rSO₂ values were extracted at the Awake, Intubated, and Clamp stages, defined as 3 min after carotid cross-clamping. Awake→Intubated ipsilateral ΔrSO₂/ΔMAP was evaluated as a continuous, exploratory pressure–oxygenation index, with MAP–rSO₂ directional change classified as concordant or discordant. Clamp-related desaturation was defined as a ≥20% ipsilateral rSO₂ decrease from Awake to Clamp. Discrimination and adjusted associations were evaluated using receiver operating characteristic analysis and multivariable logistic regression, respectively. Results: Clamp-related ≥20% ipsilateral rSO₂ desaturation occurred in 43 patients (13.4%). The Awake→Intubated ipsilateral ΔrSO₂/ΔMAP ratio differed significantly between patients with and without ≥20% desaturation and showed significant discrimination on receiver operating characteristic analysis, with an area under the curve (AUC) of 0.799 (95% confidence interval [CI] 0.723–0.876; p < 0.001). Concordant pressure–oxygenation change was more frequent among patients with ≥20% desaturation (31/43, 72.1%), whereas discordant change predominated among those without desaturation (228/279, 81.7%; p < 0.001). In multivariable analysis, Awake→Intubated ipsilateral ΔrSO₂/ΔMAP remained associated with clamp-related ≥20% desaturation after adjustment (adjusted odds ratio [OR] 1.63, 95% CI 1.15–2.33; p = 0.006), along with symptomatic presentation and 50–69% contralateral stenosis. Postoperative stroke occurred in 4/322 patients (1.2%), and no 30-day mortality occurred. Conclusions: During NIRS-guided selective CEA, induction-phase rSO₂–MAP dynamics were associated with subsequent cross-clamp-related ipsilateral cerebral desaturation. As the outcome was a NIRS-defined desaturation rather than an independent clinical, neurological, or imaging endpoint, these findings indicate association with a surrogate marker rather than prediction of clinically relevant cerebral ischaemia. The Awake→Intubated ΔrSO₂/ΔMAP ratio and directional pressure–oxygenation pattern may represent early adjunctive physiological signals associated with clamp-related desaturation. These findings are hypothesis-generating and require prospective validation with systematic multimodal monitoring. Full article

Commutation failure is likely to occur when an AC fault occurs at the receiving end of an LCC-HVDC system. This threatens transient stability. Conventional commutation failure prevention (CFPREV) control mainly responds to commutation-voltage magnitude variation. However, commutation-voltage phase variation is not fully considered. Its fixed start-up threshold also makes it difficult to adapt to different fault severities. To address these problems, this paper establishes a transient nonlinear large-signal model of the inverter. The model incorporates power angle variation and describes the coupled effects of DC current rise, commutation-voltage drop, and power angle deviation on the extinction angle. Phase-portrait analysis is then used to illustrate the transient evolution and critical characteristics of first commutation failure (FCF). The critical commutation voltage is predicted under different fault severities and further converted into a dynamic CFPREV start-up threshold. Simulations based on the CIGRE LCC-HVDC benchmark model verify the prediction accuracy. They also show that the improved CFPREV strategy suppresses FCF mainly by starting up at an appropriate instant rather than increased compensation strength. Full article

Cancer is still one of the world’s major causes of morbidity and mortality; thus, safer and more efficient treatment approaches are required. The structural variety, multitargeted mechanisms, and generally good safety profiles of plant-derived polyphenols have made them attractive anticancer medicines. Flavonoids (like quercetin), stilbenes (like resveratrol), phenolic acids and curcuminoids (like curcumin) are major classes that have shown strong anticancer action against a variety of cancers, including prostate, colorectal and breast cancers. Through targets including PI3K/Akt, MAPK, NF-κB, and p53 signaling networks, these substances influence important molecular pathways involved in tumor initiation and development, including oxidative stress, inflammation, apoptosis, cell cycle control, angiogenesis and metastasis. The clinical translation of polyphenols is still constrained by poor bioavailability, fast metabolism, low aqueous solubility and inefficient pharmacokinetic characteristics, which lead to insufficient systemic exposure and therapeutic efficacy despite strong preclinical data. Their therapeutic applicability is further complicated by variations in absorption and possible dose-related restrictions. To overcome these limitations, the anticancer efficacy of polyphenols has been enhanced via delivery technologies like polymeric nanoparticles, lipid-based carriers, nanoemulsions and phytosome complexes, which have shown improved stability, increased bioavailability and targeted delivery to tumor tissues. This review provides a comprehensive and integrative analysis of plant-derived polyphenols by linking molecular mechanisms, pharmacokinetic limitations and emerging delivery strategies within a translational framework. By bridging these interconnected domains, this review highlights the potential of polyphenols as viable candidates in next-generation cancer therapeutics and underscores the need for well-designed clinical studies to facilitate their successful integration into oncology practice. Full article

High sound pressure level (SPL) acoustic sensing requires miniaturized microphones that can operate under large acoustic loading while maintaining mechanical linearity, sufficient sensing response, and broadband audio frequency behavior. This work targets high-SPL operation and numerically investigates a graphene piezoresistive MEMS microphone based on a membrane-supported beam–island diaphragm. The proposed structure retains a continuous membrane for acoustic load bearing, while the upper beam–island topology redirects deformation-induced strain toward beam root regions where graphene piezoresistors are placed. This design is intended to increase the local strain available for piezoresistive readout without simply relying on larger global diaphragm deflection. Finite-element analysis was used to optimize the diaphragm geometry and evaluate strain enhancement, pressure response linearity, modal behavior, and harmonic response. Under the 170 dB SPL reference condition, the optimized structure increases the peak structural strain from 47.83 με in a thickness-equivalent solid diaphragm to 562.53 με, achieving an approximately 11.8-fold enhancement in local sensing strain while maintaining a highly linear pressure response (R2 > 0.9999). Additionally, the results also show that the sensor exhibits a high first natural frequency of 64.07 kHz and a small response variation of approximately 0.94 dB within the 0–20 kHz target frequency range, indicating excellent dynamic stability and high-fidelity signal transduction characteristics. To connect the structural response with piezoresistive readout, first-order electromechanical output estimation was further performed using representative graphene gauge factors, quarter-bridge readout assumptions, contact resistance correction, and Johnson-noise-limited signal-to-noise ratio estimation. A ±5% geometric tolerance check further indicates that the membrane side length is the most fabrication-sensitive parameter, while the selected design remains generally robust except for reduced linearity margin under positive membrane side-length deviation. These results demonstrate the potential of the proposed graphene-based MEMS microphone for high-SPL broadband acoustic sensing applications in harsh and high-intensity acoustic environments. Full article

Underwater acoustic (UWA) channels are inherently complex, with pronounced variability arising from multipath propagation, time variability, Doppler effects, and nonstationary ocean conditions. Such variability often leads to unstable communication reliability when conventional single-carrier signaling and fixed reception strategies are employed. In practical UWA environments, performance degradation may occur when channel characteristics deviate from the assumed regime, thereby limiting system robustness. To address this reliability challenge, this study develops an unequal-rate frequency–phase keying (URFPK) signaling strategy that combines a low-rate frequency component with a high-rate phase component. A corresponding receiver structure is designed, employing parallel coherent and noncoherent processing to enhance robustness under dynamic channel conditions. In addition, a reduced-complexity noncoherent procedure is introduced to improve computational efficiency. Simulation results demonstrate substantially improved robustness under severe UWA distortions. Full-scale sea trials further validate the engineering effectiveness of the proposed approach, achieving communication success rate improvements of $18.62$% and $9.39$% over baseline schemes within short intervals and maintaining an overall success rate exceeding 91% over extended transmissions. These results indicate that the URFPK signaling strategy provides a practical and robust mechanism for improving UWA link reliability in dynamic UWA channels. Full article

Background: Age at diagnosis and histologic differentiation are clinically relevant in early gastric cancer (GC), as poorly differentiated tumors and those diagnosed in younger patients often demonstrate more aggressive characteristics. Serum microRNAs (miRNAs) may provide insights into the molecular basis of these features. Methods: We compared expression profiles between undifferentiated and differentiated early GC cases to identify differentially expressed miRNAs (DEmiRNAs) and associated enriched pathways. Using Lasso regression, we developed and cross-validated a histologic differentiation classifier based on miRNA profiles from 1399 early GC serum samples. Finally, cancer-specific miRNA differences between adolescent and young adult (AYA) and non-AYA patients were evaluated using samples from cancer cases and normal controls. Results: We identified 75 differentiation-associated DEmiRNAs targeting genes enriched in cancer hallmark pathways such as TP53 and PI3K/AKT/mTOR signaling. In the validation set, the combined Lasso model predicted differentiation status with a sensitivity of 69.2%, specificity of 75.3%, positive predictive value (PPV) of 66.9%, negative predictive value (NPV) of 77.2%, an overall accuracy of 73.1%, and an area under the curve (AUC) of 79.7%. Comparison of AYA and non-AYA groups identified 52 cancer-specific and age-related miRNAs. Notably, three components of a previously reported four-miRNA GC diagnostic signature were significantly associated with age. Conclusions: Age-related variation in miRNA expression suggests that patient age may influence the performance of the existing four-miRNA diagnostic signature in early GC. Overall, our findings demonstrate the utility of miRNA profiling for predicting differentiation status in early GC and reveal age-associated variation in cancer-specific miRNAs. Full article

Grouting and sealing in gas drainage boreholes are two of the critical measures to ensure efficient coal seam gas extraction. However, traditional cement grouting often leads to debonding and cracking of the slurry–coal cemented body under external load, resulting in poor sealing performance. To suppress crack propagation and achieve borehole reinforcement and efficient sealing, this study compares the mechanical properties and crack evolution characteristics of slurry–coal cemented samples grouted with different modified materials. Five types of cement-based sealing materials, including ordinary Portland cement, were used for grouting coal rock in boreholes. By employing an acoustic emission signal acquisition system and a non-contact full-field strain measurement system, the tensile mechanical properties of coal before and after grouting were compared. The influence of material properties on the reinforcement capacity of borehole coal was analyzed, along with the failure process characteristics and final failure morphology of the slurry–coal cemented body under Brazilian splitting load. Finally, the effects of material toughness and bond strength on the brittleness index and failure mode of the slurry–coal cemented samples under Brazilian splitting conditions were discussed. The results show that the tensile strength improvement rates of the samples were 26.9%, 55.3%, 48.4%, 8.6%, and 45.6%, respectively. Distinct from previous studies focusing on fractured grouting or intact coal rock, this work for the first time systematically reveals the non-monotonic influence of the combination of material toughness and bond strength on the reinforcement effect of borehole coal samples and proposes an evaluation framework based on quantitative acoustic emission crack type analysis and the concept of effectiveness threshold. The varying degrees of tensile strength enhancement indicate differences in the reinforcement capabilities of grouting materials with different properties. The acoustic emission signals during the failure process of the slurry–coal cemented body exhibited typical stage-specific characteristics, though material properties altered the failure modes. By quantifying the intrinsic properties and crack characteristics of the slurry–coal cemented body using the brittleness index and grayscale histograms, this study provides a theoretical basis for guiding efficient sealing of gas drainage boreholes through an in-depth understanding of the mechanical behavior and crack evolution of borehole coal samples before and after grouting under Brazilian splitting conditions. Full article

Automatic Modulation Classification (AMC) is essential for waveform-level signal characterization. It supports spectrum sensing, signal identification, and adaptive resource allocation in cognitive radio and next-generation wireless systems. However, channel impairments such as multipath propagation, frequency offset, fast fading, and noise degrade modulation signatures, making reliable AMC challenging. Existing deep learning-based approaches often rely on purely data-driven learning, leading to insufficient modeling of modulation-relevant features, loss of transient characteristics, and limited exploitation of hierarchical relationships among modulation types. To address these issues, this paper proposes PHM-Net, a physics-informed hierarchical multi-scale network for robust AMC. The model employs a hierarchical backbone with residual encoder blocks. A Transient Feature Gating (TFG) module enhances modulation-relevant representations, a Cross-Resolution Signal Aggregation (CRSA) module fuses multi-stage features, and a Physics-Informed Hierarchical Loss (PI-HL) enforces consistency between coarse- and fine-grained predictions. Experimental results on three benchmark datasets (RML2016.10a, RML2016.10b, and RML2018.01a) show that PHM-Net consistently achieves the highest average accuracy among all compared models. On RML2018.01a, which contains 1024-sample sequences and 24 classes, PHM-Net achieves an average accuracy of 64.59% and a best-case accuracy of 98.42%, surpassing AMC_Net by 11.14 and 17.09 percentage points and CNN-Transformer by 9.43 and 11.15 percentage points, respectively. PHM-Net provides a robust and interpretable solution for AMC under complex channel conditions. Full article

Background: Hypertrophic olivary degeneration (HOD) is a rare neurological condition resulting from trans-synaptic degeneration of the inferior olivary nucleus (ION) following disruption of the dentato-rubro-olivary pathway, also known as the Guillain–Mollaret triangle (GMT). Although the clinical and radiologic features of HOD have been previously described, the precise temporal correlation between clinical symptom onset and manifestations on magnetic resonance imaging (MRI) remains difficult to establish, and the factors contributing to accelerated disease progression are poorly understood. Case Presentation: A 43-year-old male presented with intracerebral hemorrhage involving the left midbrain, bilateral pons, and cerebellum. Serial MRI was prospectively performed starting four weeks post-hemorrhage, at which time no signal abnormalities were detected in the ION. However, at 9 weeks, T2 hyperintensity first emerged in the bilateral ION. Approximately 2 weeks after this finding, the patient developed characteristic palatal and lingual tremors, accompanied by a dissociated vertical pendular nystagmus that was predominantly monocular (right eye). In addition, severe dysphagia was also noted, with videofluoroscopic swallowing study (VFSS) showing aspiration across all diets. A subsequent MRI obtained at 13 weeks post-insult (two weeks after tremor onset) revealed newly developed bilateral ION hypertrophy, with the maximal diameter increasing from a 5 mm baseline to 7 mm. Follow-up MRI at 17 weeks post-hemorrhage revealed further progression with increased hypertrophy and signal intensity. Dysphagia persisted throughout the clinical course, ultimately necessitating percutaneous endoscopic gastrostomy (PEG) tube insertion. Conclusions:This case provides rare, longitudinal documentation of the clinico-radiologic progression of HOD, facilitated by a pre-insult baseline MRI and prospective serial imaging. Our findings provide a detailed timeline of the transition from signal abnormality to hypertrophy in correlation with clinical symptom emergence. Full article

Fat deposition plays an important role in yak metabolism, reproduction, and meat quality, and male yaks are often castrated to facilitate management and improve production performance. The effect of castration on the characteristics of fat deposition in male yaks and the molecular mechanisms of action was explored in this study. The subcutaneous fat thickness in castrated and common male yaks was measured, further the content of fatty acids in yak subcutaneous fat was detected using gas chromatography-mass spectrometer (GC-MS); the transcriptome, metabolome in the yak subcutaneous fat were detected using mRNA-Sequencing, ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), respectively; the integrative analyses of differentially expressed genes (DEGs), different metabolites (DMs), fatty acids and fat thickness were carried out. The results showed that castration can strengthen the ability of fat deposition and improve the content of fatty acids, especially PUFAs, in male yaks, and both transcriptome and metabolome were significantly different between castrated male yaks and common male yaks. The effect of castration on the male yak fat deposition was closely related to the PPAR signaling pathway, citrate cycle, and insulin resistance. Data suggests that FASN, ACACA, AGPAT2, ACLY, ACSL5, SCD, GSK3B, and SLC2A4 may be the crucial control genes for the fat amount in yaks, and that FADS2, LPL, and ACSL4 may be the crucial control genes for the polyunsaturated fatty acids (PUFAs) content in yak adipose tissue. Further functional studies will be conducted to determine the specific role of each gene in regulating fat deposition and fatty acid composition in yaks. Full article

Background/Objectives: Post-Endoscopic Retrograde Cholangiopancreatography Pancreatitis (PEP) is a clinically significant complication of ERCP, occurring in approximately 2–10% of general cases and at higher rates in high-risk patients. Early prediction of PEP risk may support timely intervention and improved patient management. This retrospective single-center study comparatively evaluated tabular clinical data, endoscopic image data, and multimodal fusion approaches for PEP prediction. Methods: Retrospective data collected from the Sindh Institute of Advanced Endoscopy and Gastroenterology were analyzed using machine learning and deep learning techniques. XGBoost(version 3.2.0) was applied to tabular clinical data, while EfficientNet-B0, ResNet50, and DenseNet201 were used for endoscopic image analysis. A multimodal contrastive learning (MMCL)-based framework combining ResNet50 image features with multilayer perceptron (MLP)-based tabular features was additionally implemented for binary PEP prediction. Class imbalance mitigation techniques, including data augmentation and balancing strategies, were applied during training. Model performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, F1-score, and precision. SHAP analysis was performed to identify important predictive features. Results: The tabular XGBoost model achieved the best predictive performance with an AUC of 0.95 and a sensitivity of 0.50, while five-fold cross-validation yielded an AUC of 0.79 and a sensitivity of 0.48. Among image-based models, ResNet50 achieved the highest performance, with an AUC of 0.76 and a sensitivity of 0.40. The multimodal model achieved an AUC of 0.57 and a sensitivity of 0.20. SHAP analysis identified cannulation time, ampulla type, and age as prominent features associated with PEP prediction. Conclusions: This exploratory study suggests that structured clinical data currently provide stronger predictive signals for PEP prediction than the available image and multimodal data within this limited cohort. The relatively low occurrence of PEP contributed to class imbalance despite mitigation strategies. Future multicenter studies with larger datasets, improved image availability, synthetic data generation, and advanced multimodal fusion techniques may improve predictive performance and clinical applicability. Full article

In our research, we developed a Distributed Relaxation Spectrum Delay Differential Equation (DRSDDE) model to simulate viscoelastic responses exhibited by materials with multiple-scale relaxation mechanisms and finite delay times. Our model expanded upon traditional integer-order viscoelastic models to include a continuum relaxation process using a log-time-space Gaussian distribution representing a continuum of relaxation processes, including a direct representation of the effect of delayed feedback via an explicit time delay term. Consequently, the resultant model can be viewed as a generalized Maxwell-type formulation where the viscoelastic behavior exhibits distributed relaxation dynamics and has finite signal propagation characteristics. We then used experimental data obtained from three representative materials: PDMS Sylgard 184, bovine brain white matter, and polyurethane foam to calibrate the model. Calibration was achieved by estimating model parameters through the use of Gauss-Legendre quadrature combined with non-linear optimization of the relaxation spectrum. The results indicate that the coefficients of determination for each of the materials exceeded R² > 0.83. Therefore, the proposed DRSDDE model outperformed the classical Zener model when simulating materials that exhibit a wide relaxation spectrum. The parameter values estimated for each of the examined materials provided additional insight into their physical behaviors. Specifically, the characteristic relaxation times for the studied materials were determined based upon \(\tau\)_c = 10^µ ranging from about 63 s to 158 s. These results illustrate different dominant relaxation regimes for the investigated materials. Additionally, both characteristic equations and frequency domain analyses were utilized to study the stability and bifurcation properties of the DRSDDE model. A significant finding resulted from identifying a delay-insensitive stability regime for materials with \(\tilde{K} < 1\) (as illustrated by bovine brain white matter). For materials with \(\tilde{K} > 1\), the analysis revealed Hopf bifurcation results illustrating critical delay thresholds and frequencies for the onset of oscillations. Further, it was established that all calibrated delay values were significantly less than these threshold values. This indicates that all identified models functioned well below the oscillation thresholds at realistic delay times. Ultimately, the proposed DRSDDE model represents a physically intuitive, robust, and flexible method for modeling complex viscoelastic systems. Future research will involve investigating temperature-dependent effects, nonlinear bifurcations, and experimental validations of predicted oscillatory dynamics Full article

This study examines how green bond financing costs in China are jointly shaped by market pricing mechanisms and institutional investor behavior. It develops an integrated two-level framework linking issuance-level bond characteristics with investor decision-making to explain green bond pricing in an emerging market. Using a comprehensive dataset of Chinese green bond issuances, the results show that financing costs are driven mainly by conventional credit-related signals, including issuer and bond ratings, guarantee structures, issuance size, and maturity. However, market frictions such as liquidity constraints and rating inertia weaken the capitalization of environmental attributes in yields. A survey-based Logit analysis of institutional investors in Shanghai further shows that green bond investment is influenced more by trading activity, information transparency, and risk management than by environmental awareness alone. Institutional heterogeneity also suggests that securities firms display stronger participation than investment companies, reflecting differences in bond-market exposure, product familiarity, and institutional investment mandates. Overall, the findings reveal a feedback mechanism in which market signals shape investor behavior, which in turn reinforces or moderates pricing dynamics. The study clarifies the structural and behavioral drivers of green bond pricing and offers policy implications for improving transparency, liquidity, and investor incentives. Full article

Mechanical Wear particle recognition is an important approach for equipment health monitoring and fault early warning. However, flow-field disturbances and high-speed particle motion in high-speed fluid environments can lead to image degradation, non-stationary electrostatic signals, and insufficient reliability of single-source recognition methods. Therefore, this study proposes a wear particle recognition method based on multi-source information fusion for high-speed fluid environments. The method establishes a multi-scale electrostatic sensing model to characterize the coupling relationship among particle material properties, motion states, and electrostatic response characteristics. Empirical mode decomposition and independent component analysis are combined for adaptive electrostatic signal denoising, and a Transformer network is used to extract multi-domain features. Meanwhile, an ECA-CNN model with an efficient channel attention mechanism is introduced to enhance the feature representation of degraded particle images. On this basis, a meta-learning-based sample-adaptive decision fusion framework is developed to achieve dynamic and complementary fusion of electrostatic and visual information. The experimental results demonstrate that the proposed method exhibits excellent recognition accuracy and robustness in the tested high-speed fluid environment of 10 m/s, achieving a fusion recognition accuracy of 96.0%, which is significantly superior to single-source recognition methods. Ablation experiments further show that removing the global scaling factor, guidance loss, interpolation loss, and category-specific weight generator decreases the average recognition accuracy by 0.7%, 1.2%, 0.4%, and 1.8%, respectively, confirming the contribution of each key module to fusion recognition performance. These findings provide a new technical approach for the online intelligent recognition of wear particles under high-speed fluid conditions and offer theoretical support and methodological guidance for condition monitoring, health assessment, and intelligent operation and maintenance of large-scale equipment. Full article