Search Results (167)

Type 2 diabetes (T2D) features insulin resistance that promotes cerebrovascular injury, yet the immune signals linking metabolic stress to vascular dysfunction remain unclear. We tested the hypothesis that insulin resistance and soluble vascular cell adhesion molecule-1 (sVCAM1) act through complementary pathways in mast cells (MCs) to raise circulating histamine levels and impair cerebral vascular function. In a high-fat diet (HFD) plus low-dose streptozotocin (STZ) model, plasma histamine rose sharply after the onset of insulin resistance and remained elevated. Plasma sVCAM1 levels also increased after insulin resistance. In vitro, recombinant sVCAM1 upregulated histidine decarboxylase (HDC) in native MCs in a dose-dependent manner, indicating a shift toward histamine synthesis, but did not enhance degranulation. In contrast, pharmacological inhibition of Akt with MK2206 activated Glycogen Synthase Kinase 3 beta (GSK3β) and increased MC degranulation without affecting HDC expression. Diabetic endothelial cell monolayers exhibited a ~twofold reduction in transendothelial electrical resistance consistent with impaired blood–brain barrier (BBB) integrity. Diabetic cerebral arteries showed receptor remodeling that favored constriction with histamine H1 receptor (H1R) expression increasing in vascular smooth muscle, while endothelial H1R and histamine H2 receptor (H2R) decreased. Functionally, insulin treatment lowered HOMA2-IR in T2D mice but did not restore cerebral artery myogenic tone or improve stroke outcomes after distal middle cerebral artery occlusion (dMCAO). Neutralizing VCAM1 with a monoclonal antibody reduced circulating sVCAM1 and histamine levels, and, together with the GSK3β inhibitor Tideglusib, stabilized MCs, normalized cerebral artery tone, and reduced post-MCAO infarct size and edema. These findings identify two distinct yet complementary mast cell pathways in T2D, highlight an immune-vascular interface that drives cerebrovascular dysfunction, and propose sVCAM1 blockade plus GSK3β inhibition as rational strategies to protect cerebral vascular function in the diabetic brain. Full article

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by pain, persistent synovial inflammation, progressive joint destruction, and systemic immune dysregulation. Increasing evidence has revealed that the microbiota–gut–joint axis represents a crucial communication network linking intestinal dysbiosis to aberrant immune responses in RA. Among the diverse gut-derived metabolites implicated in this axis, we propose that histamine may act as a central signaling node linking microbial alterations to joint inflammation. Both host- and microbiota-derived histamine, synthesized via histidine decarboxylase (HDC), regulate immune and stromal cell activity within the joint microenvironment through histamine receptors H1R, H2R, and H4R. In addition, histamine interacts with other microbial metabolites—such as short-chain fatty acids (SCFAs) and tryptophan derivatives—forming an intricate metabolic–inflammatory network that amplifies fibroblast-like synoviocyte activation, osteoclastogenesis, and chronic inflammation. Despite accumulating evidence supporting the immunomodulatory role of histamine, the precise molecular mechanisms mediating its crosstalk with microbial and host immune pathways remain incompletely defined. This review provides a comprehensive overview of histamine-mediated regulation within the microbiota–gut–joint axis, emphasizing its interplay with other microbial metabolites and its contribution to RA pathogenesis. A deeper understanding of this histamine-centered microbiota–gut–joint axis will help elucidate its mechanistic role in immune dysregulation and may ultimately inform future strategies for restoring immune balance and preventing joint damage in RA. Full article

The spatial heterogeneity of leaf traits within canopies is an important source of uncertainty in leaf parameter estimation from unmanned aerial vehicle (UAV) imagery, especially in structurally complex orchards. In this study, we combined three-dimensional (3D) radiative transfer simulations with field measurements from litchi orchards to quantify bidirectional reflectance factor (BRF) uncertainty under four leaf trait distribution patterns within the canopy. Whole-canopy leaf traits were represented using: (1) a homogeneous canopy (HC), (2) vertically divided canopy (VDC), (3) horizontally divided canopy (HDC), and (4) a canopy divided into nine sections (CD9s). Among the simplified schemes, HDC produced BRF values most consistent with the CD9s configuration, while the largest deviation between CD9s and HC was observed at 570 nm with a maximum BRF normalized difference of 65.29%. Relative contribution rate analysis based on the symmetric relative difference (SRD, %) showed that leaf trait distribution pattern dominated the variability of several VIs, including NDVI, NDRE, CCI, SIPI, LICI, and PVI. Meanwhile, other VIs (e.g., NIRv, SAVI, OSAVI and EVI) were more strongly influenced by illumination–viewing geometry. Using multiangle UAV multispectral data improved the estimation of proxy leaf chlorophyll content (LCC, max R²cv = 0.52), while nadir-only data yielded the best results for leaf nitrogen mass-based content (LNC, max R²cv = 0.41). These results emphasize that reliable UAV-based leaf trait retrieval is closely related to leaf trait distribution pattern within the canopy and its interaction with other factors (e.g., illumination–viewing geometry). Full article

Voltage-gated potassium channel Kv1.3 plays an important role in the regulation of survival and apoptosis in many cell types, including both normal and cancer cells. Inhibitors of these channels may potentially find clinical applications in the treatment of various diseases, including certain cancers characterized by the over-expression of Kv1.3. In this study, the effects of isobavachalcone (IBC) and two non-prenylated chalcones—2′-hydroxy-4,3′-dimethoxychalcone (HDC) and 2′-hydroxy-2-methoxychalcone (HMC)—on Kv1.3 channel activity were investigated in the Jurkat T cancer cell line using the whole-cell patch-clamp technique. The electrophysiological measurements were preceded by experiments assessing cell viability, and the patch-clamp data were consistent with results obtained from MTT-based assays. We observed an almost complete and irreversible inhibition of Kv1.3 in the presence of IBC. The non-prenylated chalcones also inhibited the channels, but with lower potency and in a reversible and incomplete manner. The inhibitory effect of IBC was significantly enhanced upon co-application with simvastatin (SIM) and mevastatin (MEV). In contrast, inhibition by the non-prenylated chalcones was significantly increased only in the presence of mevastatin, but not simvastatin. The channel inhibition may be related to the anti-proliferative and pro-apoptotic activities of these compounds in Kv1.3-expressing cancer cells. Altogether, our results indicate that both prenylated and non-prenylated chalcones, particularly in combination with statins, may represent biologically active scaffolds, warranting further optimization and preclinical evaluation. Full article

This study evaluated the viability, microbiological composition, functional traits, and safety of probiotic bacteria isolated from commercial products marketed as containing Limosilactobacillus reuteri. Viable cell counts, biochemical characterization, strain-level identification, functional properties, gastrointestinal tolerance, and safety attributes were assessed. Among the evaluated products, only four presented colony-forming units (CFU) counts consistent with label claims (products E, F, G, and H), while two showed no detectable viable microorganisms (products B and L). All isolates were Gram-positive, catalase-negative, and predominantly rod-shaped. rep-PCR analysis revealed strain homogeneity in most products, whereas others (products A and K) exhibited heterogeneous microbial compositions. Molecular identification based on 16S rRNA sequencing showed a predominance of Lmb. reuteri and Lacticaseibacillus rhamnosus, with some products containing additional species such as Lactiplantibacillus plantarum and Lactobacillus acidophilus. Functional assays demonstrated strain-dependent proteolytic and diacetyl-producing capacities, as well as variable tolerance to simulated gastrointestinal conditions. Most strains preferentially produced L-lactate, although some generated substantial amounts of D-lactate. All isolates were susceptible to antibiotics recommended by EFSA, except for intrinsic vancomycin resistance, and no transferable virulence markers, biogenic amine production, or Salmonella contamination were detected. Furthermore, virulence-related genes such as hdc, tdc, odc, hyl, cylA, and ace were not identified. Overall, the results highlight pronounced discrepancies between label claims and microbiological quality among commercial probiotic products and reinforce the importance of strain-level characterization to ensure safety, functional performance, and regulatory compliance. Full article

Hyperdimensional Computing (HDC) offers a robust and energy-efficient paradigm for edge intelligence; however, current hardware accelerators are often proprietary, tailored to the target learning task and tightly coupled to specific CPU microarchitectures, limiting portability and adoption. To address this, and democratize the deployment of HDC hardware, we present a general-purpose, plug-and-play accelerator IP that implements the Binary Spatter Code framework as a standalone, host-agnostic module. The design is compliant with the AMBA AXI4 standard and provides an AXI4-Lite control plane and DMA-driven AXI4-Stream datapaths coupled to a banked scratchpad memory. The architecture supports synthesis-time scalability, enabling high-throughput transfers independently of the host processor, while employing microarchitectural optimizations to minimize silicon area. A multi-layer C++ software (GitHub repository commit 3ae3b46) stack running in Linux userspace provides a unified programming model, abstracting low-level hardware interactions and enabling the composition of complex HDC pipelines. Implemented on a Xilinx Zynq XC7Z020 SoC, the accelerator achieves substantial gains over an ARM Cortex-A9 baseline, with primitive-level speedups of up to $431\times$. On end-to-end classification benchmarks, the system delivers average speedups of $68.45\times$ for training and $93.34\times$ for inference. The complete RTL and software stack are released as open-source hardware to support reproducible research and rapid adoption on heterogeneous SoCs. Full article

This study investigated the effects of encapsulated capsaicinoids (CAPs), containing 0.47% capsaicin and 0.22% dihydrocapsaicin, on growth, serum parameters, nutrient digestibility, and intestinal health in weaned piglets. A total of 168 piglets were randomly assigned to four groups: a basal diet or the same diet supplemented with 200 (LDC), 400 (MDC), or 600 (HDC) mg/kg of CAPs. The results indicated that CAPs improved lipid metabolism, evidenced by higher crude fat digestibility in the LDC and MDC groups and reduced serum low-density lipoprotein cholesterol in all CAP groups compared to the control. Glutathione peroxidase activity was significantly higher in the MDC and HDC groups. Histological analysis showed reduced hepatic vacuolation, enlarged fungiform papillae with shallower taste pores in the tongue epithelium, and deeper ileal crypts in the LDC group. At the molecular level, ZO-1 expression in the ileum was significantly upregulated in LDC piglets. Colonic microbiota analysis revealed decreased relative abundances of Lachnospiraceae_AC2044_group, Lachnospiraceae_XPB1014_group, and Rikenellaceae_RC9_gut, while Butyricicoccus was significantly enriched in the LDC group. In conclusion, CAPs supplementation enhanced fat digestibility, lipid metabolism, antioxidant capacity, intestinal development, and colonic microbiota composition, with the 200 mg/kg dose showing the most pronounced effects. Full article

In the realm of wearable technology, achieving robust continuous authentication requires balancing high security with the strict resource constraints of embedded platforms. Conventional machine learning approaches and deep learning-based biometrics often incur high computational costs, making them unsuitable for low-power edge devices. To address this challenge, we propose H-PPG, a lightweight authentication system that integrates photoplethysmography (PPG) and inertial measurement unit (IMU) signals for continuous user verification. Using Hyperdimensional Computing (HDC), a lightweight classification framework inspired by brain-like computing, H-PPG encodes user physiological and motion data into high-dimensional hypervectors that comprehensively represent individual identity, enabling robust, efficient and lightweight authentication. An adaptive learning process is employed to iteratively refine the user’s hypervector, allowing it to progressively capture discriminative information from physiological and behavioral samples. To further enhance identity representation, a dimension regeneration mechanism is introduced to maximize the information capacity of each dimension within the hypervector, ensuring that authentication accuracy is maintained under lightweight conditions. In addition, a user-defined security level scheme and an adaptive update strategy are proposed to ensure sustained authentication performance over prolonged usage. A wrist-worn prototype was developed to evaluate the effectiveness of the proposed approach and extensive experiments involving 15 participants were conducted under real-world conditions. The experimental results demonstrate that H-PPG achieves an average authentication accuracy of 93.5%. Compared to existing methods, H-PPG offers a lightweight and hardware-efficient solution suitable for resource-constrained wearable devices, highlighting its strong potential for integration into future smart wearable ecosystems. Full article

Pain in endometriosis involves not only nociceptive but also neuropathic and neurogenic components, reflecting its complex nature. Histamine, a biogenic amine, has emerged as a critical mediator connecting inflammation and nerve sensitization. This study aimed to characterize histamine receptor (HRH1–HRH4) expression, localization, and related inflammatory mediators in peritoneal, deep infiltrating, and ovarian endometriosis. Gene expression datasets were analyzed, and immunofluorescence staining of endometriotic lesions was performed using immune and neuronal markers. Histamine and its metabolite methylhistamine were quantified in serum, peritoneal fluid, and urine samples. HDC expression was significantly elevated in all endometriotic lesions compared with controls (all p < 0.01), paralleling increased IL-6, COX-2, NGF, and NGFR levels (p < 0.0001). In contrast, HRH1–HRH4 transcript levels showed no significant differences between groups. Immunofluorescence demonstrated robust HRH1–HRH4 protein expression in epithelial, immune, and nerve fibers, with subtype-specific colocalization patterns. Serum histamine concentrations were significantly higher in endometriosis patients than controls (0.484 vs. 0.153 ng/mg protein; p = 0.0014), whereas peritoneal histamine and urinary methylhistamine showed no group differences. Overall, these findings highlight histamine signaling as a potentially important component of endometriosis pathophysiology and point toward new directions for mechanistic studies and therapeutic exploration. Full article

Speech is one of the earliest-affected modalities in Parkinson’s disease (PD). For more reliable PD evaluation, speech-based telediagnosis studies often use multiple samples from the same subject to capture variability in speech recordings. However, many existing studies split samples—rather than subjects—between training and testing, creating a biased experimental setup that allows data (samples) from the same subject to appear in both sets. This raises concerns for reliable PD evaluation due to data leakage, which results in over-optimistic performance (often close to 100%). In addition, detecting subtle vocal impairments from speech recordings using multiple feature extraction techniques often increases data dimensionality, although only some features are discriminative while others are redundant or non-informative. To address this and build a reliable speech-based PD telediagnosis system, the key contributions of this work are two-fold: (1) a uniform (fair) experimental setup employing subject-wise symmetric (stratified) splitting in 5-fold cross-validation to ensure better generalization in PD prediction, and (2) a novel hybrid data complexity-aware (HDC) feature selection method that improves class separability. This work further contributes to the research community by releasing a publicly accessible five-fold benchmark version of the Parkinson’s speech dataset for consistent and reproducible evaluation. The proposed HDC method analyzes multiple aspects of class separability to select discriminative features, resulting in reduced data complexity in the feature space. In particular, it uses data complexity measures (F4, F1, F3) to assess minimal feature overlap and ReliefF to evaluate the separation of borderline points. Experimental results show that the top-50 discriminative features selected by the proposed HDC outperform existing feature selection algorithms on five out of six classifiers, achieving the highest performance with 0.86 accuracy, 0.70 G-mean, 0.91 F1-score, and 0.58 MCC using an SVM (RBF) classifier. Full article

Remote sensing image change captioning (RSICC) fuses computer vision and natural language processing to translate visual differences between bi-temporal remote sensing images into interpretable text, with applications in environmental monitoring, urban planning, and disaster assessment. Multimodal Large Language Models (MLLMs) boost RSICC performance but suffer from inefficient inference due to massive parameters, whereas lightweight models enable fast inference yet lack generalization across diverse scenes, which creates a critical timeliness-generalization trade-off. To address this, we propose the Dual-Constrained Transformer (DCT), an end-to-end lightweight RSICC model with three core modules and a decoder. Full-Level Feature Distillation (FLFD) transfers hierarchical knowledge from a pre-trained Dinov3 teacher to a Generalizable Lightweight Visual Encoder (GLVE), enhancing generalization while retaining compactness. Key Change Region Adaptive Weighting (KCR-AW) generates Region Difference Weights (RDW) to emphasize critical changes and suppress backgrounds. Hierarchical encoding and Difference weight Constrained Attention (HDC-Attention) refine multi-scale features via hierarchical encoding and RDW-guided noise suppression; these features are fused by multi-head self-attention and fed into a Transformer decoder for accurate descriptions. The DCT resolves three core issues: lightweight encoder generalization, key change recognition, and multi-scale feature-text association noise, achieving a dynamic balance between inference efficiency and description quality. Experiments on the public LEVIR-CC dataset show our method attains SOTA among lightweight approaches and matches advanced MLLM-based methods with only 0.98% of their parameters. Full article

Aging is associated with chronic low-grade inflammation of exocrine glands, such as the lacrimal glands. Histamine, synthesized by histidine decarboxylase (HDC), is implicated in immune modulation; however, its role in age-related lacrimal gland inflammation remains unclear. To explore the role of histamine in age-related lacrimal gland inflammation, we compared wild-type and histidine decarboxylase knockout (HDC-KO) C57BL/6 mice at 6 weeks and 12 months of age (10 males and 10 females in each group). Histological and immunohistochemical analyses were performed to assess lymphocytic infiltration, mast cells, and the expression of cytokines and adhesion molecules. Gene expression levels were quantified using reverse transcriptase quantitative PCR (RT-qPCR). Aged wild-type mice showed significant upregulation of mRNA transcription of HDC and histamine H1 receptor, along with increased infiltration of B220-positive B cells and CD3-positive T cells in the lacrimal gland. The mRNA expression levels of pro-inflammatory cytokines (TNF-α, IL-1β, and IL-6) and ICAM-1 were elevated with age, whereas these changes were attenuated in HDC-KO mice. The mRNA expression of PPARγ, an anti-inflammatory factor, was upregulated in the aged HDC-KO mice. Mast cell numbers increased with age but did not differ according to sex. These findings suggest that histamine, via HDC and H1 receptor signaling, contributes to age-associated lacrimal gland inflammation by enhancing cytokine and ICAM-1 expression. HDC deficiency suppresses this inflammatory response, potentially through the upregulation of PPARγ. Thus, histamine may be a key mediator of age-related inflammation in the lacrimal gland and a potential therapeutic target. Full article

Epileptic seizure (ES) detection is critical for improving clinical outcomes in epilepsy management. While intracranial EEG (iEEG) provides high-quality neural recordings, existing detection methods often rely on large amounts of data, involve high computational complexity, or fail to generalize in low-data settings. In this paper, we propose a lightweight, data-efficient, and high-performance approach for ES detection based on hyperdimensional computing (HDC). Our method first extracts local binary patterns (LBPs) from each iEEG channel to capture temporal–spatial dynamics. These binary sequences are then mapped into a high-dimensional space via HDC for robust representation, followed by a binary Naive Bayes classifier to distinguish ictal and inter-ictal states. The proposed design enables fast inference, low memory requirements, and suitability for hardware implementation. We evaluate the method on the SWEC-ETHZ iEEG short-term dataset. In one-shot learning, it achieves 100% sensitivity and specificity for most patients. In few-shot learning, it maintains 98.88% sensitivity and 93.09% specificity on average. The average latency is 4.31 s, demonstrating that it is much better than state-of-the-art methods. These results demonstrate the method’s potential for efficient, low-resource, and high-performance ES detection. Full article

Organic chlorine (Org-Cl) in crude oil poses continuous operational and environmental risks during production, trading, and refining processes. This article reviews the management of Org-Cl from its origin assumptions to analysis and mitigation measures and proposes a practical closed-loop framework. Quantitative merit value indicators (typical detection limit/quantitative limit, accuracy, and repeatability) and greenness indicators are used to compare standard methods and advanced methods, and to guide the selection of applicable methods. Corresponding technical maturity levels (TRLs) are assigned to mitigation measures (protective beds/adsorption, HDC, and emerging electrochemical/photochemical routes). Technical economic indicators with reference values (relative capital expenditure/operating expenditure levels) are summarized to assist decision-making. The main findings are as follows: (i) Evidence of secondary formation of organic chlorine under distillation-related conditions still relies on the matrix and requires independent verification; (ii) MWDXRF can achieve rapid screening (usually only 5 to 10 min), while CIC/D5808 supports quality balance arbitration; (iii) adsorption can remove a considerable portion of organic chlorine in light fractions under laboratory conditions, while the survival ability of HDC related to crude oil depends on the durability of the catalyst and the tail gas treatment capacity; and (iv) minimum viable implementation (MVI) combined with online total-chlorine monitoring and a physical principle-based digital twin technology can provide auditable closed-loop control. The limitations of this review include partial reliance on laboratory-scale data, inconsistent reports among studies, and the lack of standardized public datasets for model benchmarking. Prioritization should be given to analysis quality control, process durability indicators, and data governance to achieve reliable digital deployment. Full article

Structural Health Monitoring (SHM) systems increasingly require efficient and scalable methods for identifying structural damage under dynamic loading. Traditional learning-based SHM models often rely on high-dimensional features or deep architectures, which may be computationally intensive and difficult to deploy in real-time applications, especially in scenarios with limited resources or bandwidth constraints. In this work, we propose a lightweight classification framework based on Hyperdimensional Computing (HDC) to detect structural damage using vibration-induced features, aiming to reduce complexity while maintaining detection performance. The proposed method encodes a rich feature set, including time-domain, frequency-domain, and autoregressive (AR) model features into high-dimensional binary vectors through a sliding window approach, capturing temporal variations and local patterns within the signal. A supervised HDC classifier is trained to distinguish between healthy and damaged structural states using these compact encodings. The framework enables fast learning and low memory usage, making it particularly suitable for edge-level SHM applications where real-time processing is required. To evaluate the feasibility and effectiveness of the proposed method, experiments are conducted on vibration data collected from controlled lateral impact tests on a concrete-filled steel tubular structure. The results validate the method ability to detect the damage-induced variations in modal frequencies and highlight its potential as a compact, robust, and efficient solution for future SHM systems based on modal data. Full article