Search Results (1,725)

Background: Maple syrup urine disease (MSUD) is a genetic disorder caused by mutations in the branched-chain α-ketoacid dehydrogenase (BCKDH) complex, leading to toxic buildup of branched-chain amino acids (BCAAs) and their ketoacid derivatives. While newborn screening (NBS) and molecular testing are standard diagnostic tools, they face challenges such as delayed results and false positives. Untargeted metabolomics has emerged as a complementary approach, offering comprehensive metabolic profiling and potential for novel biomarker discovery. We previously applied untargeted metabolomics to neonates with MSUD, identifying distinct metabolic signatures. Objective: This follow-up study investigates metabolic changes and biomarkers in pediatric MSUD patients and explores shared dysregulated metabolites between neonatal and pediatric MSUD. Methods: Dried blood spot (DBS) samples from pediatric MSUD patients (n = 14) and matched healthy controls (n = 14) were analyzed using LC/MS-based untargeted metabolomics. Results: In pediatric MSUD, 3716 metabolites were upregulated and 4038 downregulated relative to controls. Among 1080 dysregulated endogenous metabolites, notable biomarkers included uric acid, hypoxanthine, and bilirubin diglucuronide. Affected pathways included sphingolipid, glycerophospholipid, purine, pyrimidine, nicotinate, and nicotinamide metabolism, and steroid hormone biosynthesis. Seventy-two metabolites overlapped with neonatal MSUD cases, some exhibiting inverse trends between age groups. Conclusion: Untargeted metabolomics reveals that the metabolic profiling of MCUD pediatric patients different from that of their controls. Also, there are valuable age-specific and shared metabolic alterations in MSUD, enhancing the understanding of disease progression in MSUD patients. This supports its utility in improving diagnostic precision and developing personalized treatment strategies across developmental stages. Full article

Drone-based target detection using visible and thermal (RGB-T) images is critical in disaster rescue, intelligent transportation, and wildlife monitoring. However, persons typically occupy fewer pixels and exhibit more varied postures than vehicles or large animals, making them difficult to detect in unmanned aerial vehicle (UAV) remote sensing images with complex backgrounds. We propose a novel progressive target-aware network (PTANet) for person detection using RGB-T images. A global adaptive feature fusion module (GAFFM) is designed to fuse the texture and thermal features of persons. A progressive focusing strategy is used. Specifically, we incorporate a person segmentation auxiliary branch (PSAB) during training to enhance target discrimination, while a cross-modality background mask (CMBM) is applied in the inference phase to suppress irrelevant background regions. Extensive experiments demonstrate that the proposed PTANet achieves high accuracy and generalization performance, reaching 79.5%, 47.8%, and 97.3% mean average precision (mAP)@50 on three drone-based person detection benchmarks (VTUAV-det, RGBTDronePerson, and VTSaR), with only 4.72 M parameters. PTANet deployed on an embedded edge device with TensorRT acceleration and quantization achieves an inference speed of 11.177 ms (640 × 640 pixels), indicating its promising potential for real-time onboard person detection. The source code is publicly available on GitHub. Full article

Background: Airborne transmission of bacteria, viruses, and fungal spores poses a major threat in enclosed settings, particularly nursing homes where residents are highly vulnerable. Compressive Heating Air Sterilization Technology (CHAST) applies compressive heating to inactivate microorganisms without reliance on filtration or chemicals. Methods: CHAST efficacy was evaluated in laboratory and deployed for a feasibility and performance validation study of air sterilization in a nursing home environment. Laboratory studies tested prototypes (300–5000 CFM; 220–247 °C) against aerosolized surrogates including Bacillus globigii (Bg), B. stearothermophilus (Bst), B. thuringiensis (Bt), Escherichia coli, and MS2 bacteriophage. Viral inactivation thresholds were further assessed by exposing MS2 to progressively lower treatment temperatures (64.5–143 °C). Feasibility and performance validation evaluation involved continuous operation of two CHAST units in a nursing home, with pre- and post-treatment air samples analyzed for bacterial and fungal burden. Results: Laboratory testing demonstrated consistent microbial inactivation, with most prototypes achieving > 6-log (99.9999%) reductions across bacterial spores, vegetative bacteria, and viruses. A 5000 CFM prototype achieved > 7-log (99.99999%) elimination of B. globigii. MS2 was completely inactivated at 240 °C, with modeling suggesting a threshold for total viral elimination near 170 °C. In the feasibility study, baseline sampling revealed bacterial (35 CFU/m³) and fungal (17 CFU/m³) contamination, dominated by Bacillus, Staphylococcus, Cladosporium, and Penicillium. After 72 h of CHAST operation, discharge air contained no detectable viable organisms, and fungal spore counts showed a 93% reduction relative to baseline return air. Units maintained stable operation (464 °F ± 2 °F; 329–335 CFM) throughout deployment. Conclusion: CHAST reproducibly and scalably inactivated airborne bacteria, viruses, and fungi under laboratory and feasibility field studies, supporting its potential as a chemical-free strategy to improve infection control and indoor air quality in healthcare facilities. Full article

Background: Chronic relapsing colitis involves immune dysregulation and oxidative stress, making monotherapies often insufficient. This study investigates a therapeutic strategy combining doxycycline (Dox), an immunomodulatory antibiotic, with Arthrospira platensis extracts to enhance anti-inflammatory and antioxidant effects, improving remission and controlling relapse. Methods: Ethanolic (ES) and aqueous (AS) extracts of A. platensis were chemically characterized by GC-MS after derivatization. Colitis was induced in mice using two intrarectal DNBS administrations spaced 7 days apart, with oral treatments (Dox, ES, AS, or combinations) given daily between doses. Disease progression was evaluated through clinical monitoring, histological scoring, and biochemical analysis, including MPO and CAT activities, as well as NO, MDA, and GSH levels. Results: GC-MS identified 16 bioactive compounds in each extract. ES contained mainly fatty acids and amino acids, whereas AS was rich in polysaccharides and phytol. Combined doxycycline and A. platensis extracts significantly enhanced recovery in reactivated DNBS colitis compared to monotherapies. Each treatment alone reduced disease severity, but their combination showed synergistic effects, significantly reducing disease activity index (p < 0.001), restoring mucosal integrity, and modulating inflammatory and oxidative markers (p < 0.001). Conclusion: Doxycycline potentiates the anti-colitic effects of A. platensis extracts via complementary mechanisms, offering a promising combination for managing relapsing colitis. Full article

Gasoline is the most common ignitable liquid used to initiate fires, making its detection and identification in fire debris crucial for determining incendiary origins. Fire debris is typically collected after extinguishment and safety clearance, often resulting in gasoline weathering, especially when delayed. Most research on gasoline weathering has been conducted in controlled laboratory settings in temperate climates. However, the effects of tropical conditions on the rate of gasoline weathering and the resulting chemical composition of volatiles remain largely unexplored. Understanding how tropical environmental factors alter gasoline weathering is essential for accurate fire debris interpretation in such regions. This study investigates how tropical climates impact gasoline weathering indoors and outdoors. Weathered samples were prepared by volume reduction method, gradually evaporating gasoline from 10% to 95%. Indoor samples were exposed to room temperature, while outdoor samples were left in open space under natural tropical conditions. Gas Chromatography/Mass Spectrometry (GC-MS) analysis revealed chromatographic shifts in heavier compounds (C₃–C₄ alkylbenzenes) compared to lighter ones like toluene as weathering progressed. Correlation between indoor and outdoor samples was high (>0.970) at 10–50% weathering but declined (<0.600) at 90–95%, indicating differing patterns. All target compounds remained detectable across all samples. Full article

Background/Objectives: Decision-making under socially evaluative stress engages a dynamic interplay between cognitive control, emotional appraisal, and motivational systems. Contemporary models of multi-level co-regulation posit that these systems operate in reciprocal modulation, redistributing processing resources to prioritise either rapid socio-emotional alignment or deliberate evaluation depending on situational demands. Methods: Adopting a neurofunctional approach, a novel dual-task protocol combining the MetaCognition–Stress Convergence Paradigm (MSCP) and the Social Stress Test Neuro-Evaluation (SST-NeuroEval), a simulated social–evaluative speech task calibrated across progressive emotional intensities, was implemented. Twenty professionals from an HR consultancy firm participated in the study, with concurrent recording of frontal-temporoparietal electroencephalography (EEG) and bespoke psychometric indices: the MetaStress-Insight Index and the TimeSense Scale. Results: Findings revealed that decision contexts with higher socio-emotional salience elicited faster, emotionally guided choices (mean RT difference emotional vs. cognitive: −220 ms, p = 0.026), accompanied by oscillatory signatures (frontal delta: F(1,19) = 13.30, p = 0.002; gamma: F(3,57) = 14.93, p ≤ 0.001) consistent with intensified socio-emotional integration and contextual reconstruction. Under evaluative stress, oscillatory activity shifted across phases, reflecting the transition from anticipatory regulation to reactive engagement, in line with models of phase-dependent stress adaptation. Across paradigms, convergences emerged between decision orientation, subjective stress, and oscillatory patterns, supporting the view that cognitive–emotional regulation operates as a coordinated, multi-level system. Conclusions: These results underscore the importance of integrating behavioural, experiential, and neural indices to characterise how individuals adaptively regulate decision-making under socially evaluative stress and highlight the potential of dual-paradigm designs for advancing theory and application in cognitive–affective neuroscience. Full article

Extracellular signal-regulated kinase 1/2 (ERK1/2) inhibitors show therapeutic potential in triple-negative breast cancer (TNBC), but resistance through compensatory signaling limits their efficacy. We previously identified the secretory carrier membrane protein 3 (SCAMP3) as a regulator of TNBC progression and ERK1/2 activation. Here, we investigated the role of SCAMP3 in ERK1/2 signaling and therapeutic response using TMT-based LC-MS/MS phosphoproteomics of wild-type (WT) and SCAMP3 knockout (SC3KO) SUM-149 cells under basal conditions, after epidermal growth factor (EGF) stimulation, and during ERK1/2 inhibition with MK-8353. A total of 4408 phosphosites were quantified, with 1093 significantly changed. SC3KO abolished residual ERK activity under MK-8353 and affected the compensatory activation of oncogenic pathways observed in WT cells. SC3KO reduced the phosphorylation of ERK feedback regulators RAF proto-oncogene serine/threonine-protein kinase Raf-1 (S43) and the dual-specificity mitogen-activated protein kinase kinase 2 (MEK2) (T394), affected other ERK targets, including nucleoporins, transcription factors, and metabolic enzymes triosephosphate isomerase (TPI1) (S21) and ATP-citrate lyase (ACLY) (S455). SCAMP3 loss also impaired the mammalian target of rapamycin complex I (mTORC1) signaling and disrupted autophagic flux, evidenced by elevated sequestosome-1 (SQSTM1/p62) and microtubule-associated protein light chain 3 (LC3B-II) with reduced levels of the autophagosome lysosome maturation marker, Rab7A. Beyond ERK substrates, SC3KO affected phosphorylation events mediated by other kinases. These findings position SCAMP3 as a central coordinator of ERK signaling and autophagy. Our results support SCAMP3 as a potential therapeutic target to enhance ERK1/2 inhibitor clinical efficacy and overcome adaptive resistance mechanisms in TNBC. Full article

Today, science and medicine are striving to develop novel techniques for treating deadly diseases, including a wide range of cancers. Efforts are being made to better understand the molecular and biochemical mechanisms of tumor cell functioning, but a particular emphasis has recently been given to investigating immune cells residing in the tumor microenvironment, which may lead to revolutionary benefits in the design of new immunotherapies. Among these cells, tumor-associated macrophages (TAMs) are highly abundant and act as critical regulators of ovarian cancer progression, metastasis, and resistance to therapy. Their dual nature—as drivers of malignancy and as potential therapeutic mediators—has positioned them at the forefront of research into next-generation immunotherapies. As therapeutic targets, approaches include blocking macrophage recruitment (e.g., CSF-1/CSF-1R inhibitors), selectively depleting subsets of TAMs (e.g., via Folate Receptor Beta), or reprogramming immunosuppressive M2-like macrophages toward an anti-tumor M1 phenotype. On the other hand, macrophages can also serve as a therapeutic tool—they may be engineered to enhance anti-tumor immunity, as exemplified by the development of Chimeric Antigen Receptor Macrophages (CAR-Ms), or leveraged as delivery vehicles for targeted drug transport into the tumor microenvironment. A particularly innovative strategy involves Macrophage–Drug Conjugates (MDCs), which employs the transfer of iron-binding proteins (TRAIN) mechanism for precise intracellular delivery of therapeutic agents, thereby enhancing drug efficacy while minimizing systemic toxicity. This review integrates current knowledge of TAM biology, highlights emerging therapeutic approaches, and underscores the promise of macrophage-based interventions in ovarian cancer. By integrating macrophage-targeting strategies with advanced immunotherapeutic platforms, novel treatment paradigms may be determined that could substantially improve outcomes for patients with ovarian cancer and other solid tumors. Our work highlights that macrophages should be a particular area of research interest in the context of cancer treatment. Full article

This study elucidated the evolution and catastrophic failure mechanisms of concrete’s mechanical properties under high-temperature and moisture-coupled environments. Specimens underwent hygrothermal shock simulation via constant-temperature drying (100 °C/200 °C, 4 h) followed by water quenching (20 °C, 30 min). Uniaxial compression tests were performed using a uniaxial compression test machine with synchronized multi-scale damage monitoring that integrated digital image correlation (DIC), acoustic emission (AE), and infrared thermography. The results demonstrated that hygrothermal coupling reduced concrete ductility significantly, in which the peak strain decreased from 0.36% (ambient) to 0.25% for both the 100 °C and 200 °C groups, while compressive strength declined to 42.8 MPa (−2.9%) and 40.3 MPa (−8.6%), respectively, with elevated elastic modulus. DIC analysis revealed the temperature-dependent failure mode reconstruction: progressive end cracking (max strain 0.48%) at ambient temperature transitioned to coordinated dual-end cracking with jump-type damage (abrupt principal strain to 0.1%) at 100 °C and degenerated to brittle fracture oriented along a singular path (principal strain band 0.015%) at 200 °C. AE monitoring indicated drastically reduced micro-damage energy barriers at 200 °C, where cumulative energy (4000 mV·ms) plummeted to merely 2% of the ambient group (200,000 mV·ms). Infrared thermography showed that energy aggregation shifted from “centralized” (ambient) to “edge-to-center migration” (200 °C), with intensified thermal shock effects in fracture zones (ΔT ≈ −7.2 °C). The study established that hygrothermal coupling weakens the aggregate-paste interfacial transition zone (ITZ) by concentrating the strain energy along singular weak paths and inducing brittle failure mode degeneration, which thereby provides theoretical foundations for fire-resistant design and catastrophic failure warning systems in concrete structures exposed to coupled environmental stressors. Full article

Deploying deep learning (DL) models in real-world environments remains a major challenge, particularly under resource-constrained conditions where achieving both high accuracy and compact architectures is essential. While effective, Conventional pruning methods often suffer from high computational overhead, accuracy degradation, or disruption of the end-to-end training process, limiting their practicality for embedded and real-time applications. We present Dynamic Attention-Guided Pruning (DAGP), a Dynamic Attention-Guided Soft Channel Pruning framework that overcomes these limitations by embedding learnable, differentiable pruning masks directly within convolutional neural networks (CNNs). These masks act as implicit attention mechanisms, adaptively suppressing non-informative channels during training. A progressively scheduled L1 regularization, activated after a warm-up phase, enables gradual sparsity while preserving early learning capacity. Unlike prior methods, DAGP is retraining-free, introduces minimal architectural overhead, and supports optional hard pruning for deployment efficiency. Joint optimization of classification and sparsity objectives ensures stable convergence and task-adaptive channel selection. Experiments on CIFAR-10 (VGG16, ResNet56) and PlantVillage (custom CNN) achieve up to 98.82% FLOPs reduction with accuracy gains over baselines. Real-world validation on an enhanced PlantDoc dataset for agricultural monitoring achieves 60 ms inference with only 2.00 MB RAM on a Raspberry Pi 4, confirming efficiency under field conditions. These results illustrate DAGP’s potential to scale beyond agriculture to diverse edge-intelligent systems requiring lightweight, accurate, and deployable models. Full article

Aortic valve stenosis, is a prevalent cardiovascular disease, narrows the valve orifice and restricts blood flow, resulting in abnormal high velocities and shear stresses. The progression of these hemodynamic abnormalities and their link with stenosis severity remain incompletely understood, which are critical for early detection and intervention. Computational Fluid Dynamics (CFD) was employed to characterize aortic hemodynamics across healthy, mild, moderate, and severe stenosis using a 3D steady-state model with idealized leaflet geometries. Key flow parameters, including velocity distribution, wall shear stress (WSS), pressure loss coefficient, and helicity, were evaluated. Results show a non-linear increase in velocity and WSS with stenosis severity, with peak jet velocities of 1.08, 1.82, 2.73, and 4.7 m/s and peak WSS of 11, 35, 80, and 122 Pa at the aortic arch, respectively. Severe stenosis produced a highly eccentric jet along the anterior of aortic arch, accompanied by a narrower jet, increased turbulence intensity and expanded recirculation zones. A significant increase in helicity and pressure loss coefficient was also observed for higher stenosis severities. These findings highlight the influence of valve leaflets on aortic flow dynamics, providing physiologically relevant insights into stenosis-induced mechanical stresses that may drive endothelial dysfunction and support earlier detection of disease progression. Full article

Background: The progression of cognitive decline is accelerated in older adults with sarcopenia, but the protective dietary factors have remained uncertain. Objective: This study aims to investigate the association between dietary factors and cognitive decline in older adults, and to explore the potential mediating effects of sarcopenic components. Methods: Data from the Mr. OS and Ms. OS cohort study in Hong Kong (N = 3146, aged ≥65 years) were used. Cognitive function was assessed based on the Mini-Mental State Examination (MMSE). Sarcopenic status was assessed according to the Asian Working Group for Sarcopenia 2019 updated consensus. Dietary protein intake and adherence to dietary patterns were assessed using a food frequency questionnaire. Linear regression was used to examine the associations between dietary factors and MMSE scores. Mediation analysis was conducted to identify the possible mediators in the diet–cognition associations. Results: Sarcopenia and its components were associated with baseline MMSE and MMSE changes. Positive associations were observed for plant protein intake (β = 0.79, 95% CI 0.24–1.35) and dietary patterns such as the Dietary Approaches to Stop Hypertension (DASH) diet (β = 0.14, 95% CI 0.02–0.26) and diets with lower Dietary Inflammatory Index (DII) scores (β = −0.18, 95% CI −0.26–−0.09) with better MMSE outcomes. Protective effects were more profound in participants with sarcopenia/severe sarcopenia. The effects of the DASH diet and DII were more profound in female participants, while higher adherence to the Mediterranean–DASH Intervention for Neurodegenerative Delay (MIND) diet was associated with an increment in MMSE score in male participants with sarcopenia. Handgrip strength and physical performance are significant mediators in the diet–cognition associations. Conclusions: The protective effects of healthy dietary patterns were beneficial, especially for participants with sarcopenia, while handgrip strength and walking speed potentially mediated the associations. Full article

Background: Recent hypotheses suggest that mutations associated with alpha1 antitrypsin (AAT) deficiency (AATD) may influence the clinical presentation and progression of cystic fibrosis (CF). This study employs a longitudinal design to determine the prevalence of AATD mutations and assess their impact on CF. Methods: The study Finding AAT Deficiency in Obstructive Lung Diseases: Cystic Fibrosis (FADO-CF) is a retrospective cohort study evaluating people with CF from November 2020 to February 2024. On the date of inclusion, serum levels of AAT were measured and a genotyping of 14 mutations associated with AATD was performed. Historical information, including data on exacerbations, microbiological sputum isolations, and lung function, was obtained from the medical records, aiming at a temporal lag of 10 years. Results: The sample consisted of 369 people with CF (40.9% pediatrics). Of these, 58 (15.7%) cases presented at least one AATD mutation. The AATD allelic combinations identified were PI*MS in 47 (12.7%) cases, PI*MZ in 5 (1.4%) cases, PI*SS in 3 (0.8%) cases, PI*SZ in 2 (0.5%) cases, and PI*M/P_lowell in 1 (0.3%) case. The optimal cutoff value for AAT levels to detect AATD-associated mutation carriers was 129 mg/dL in the overall cohort (sensitivity of 73.0%; specificity 69.2%) and 99.5 mg/dL when excluding PI*MS cases (sensitivity 98.0%; specificity 90.9%), highlighting the need for lower thresholds in clinically severe genotypes to improve case detection. The number of mild exacerbations during the follow-up appeared to be associated with AATD mutations. Conclusions: AATD mutations are prevalent in CF and may impact certain clinical outcomes. If systematic screening was to be planned, we recommend considering the proposed cut-off points to select the population for genetic studies. Full article

A 9.4T brain MRI is the highest resolution MRI scanner in the public market. It offers submillimeter brain imaging with exceptional anatomical detail, making it one of the most powerful tools for detecting subtle structural changes associated with neurological conditions. Current segmentation models are optimized for lower-field MRI (1.5T–3T), and they struggle to perform well on 9.4T data. In this study, we present the GA-MS-UNet++, the world’s first deep learning-based model specifically designed for 9.4T brain MRI segmentation. Our model integrates multi-scale residual blocks, gated skip connections, and spatial channel attention mechanisms to improve both local and global feature extraction. The model was trained and evaluated on 12 patients in the UltraCortex 9.4T dataset and benchmarked against four leading segmentation models (Attention U-Net, Nested U-Net, VDSR, and R2UNet). The GA-MS-UNet++ achieved a state-of-the-art performance across both evaluation sets. When tested against manual, radiologist-reviewed ground truth masks, the model achieved a Dice score of 0.93. On a separate test set using SynthSeg-generated masks as the ground truth, the Dice score was 0.89. Across both evaluations, the model achieved an overall accuracy of 97.29%, precision of 90.02%, and recall of 94.00%. Statistical validation using the Wilcoxon signed-rank test (p < 1 × 10⁻⁵) and Kruskal–Wallis test (H = 26,281.98, p < 1 × 10⁻⁵) confirmed the significance of these results. Qualitative comparisons also showed a near-exact alignment with ground truth masks, particularly in areas such as the ventricles and gray–white matter interfaces. Volumetric validation further demonstrated a high correlation (R² = 0.90) between the predicted and ground truth brain volumes. Despite the limited annotated data, the GA-MS-UNet++ maintained a strong performance and has the potential for clinical use. This algorithm represents the first publicly available segmentation model for 9.4T imaging, providing a powerful tool for high-resolution brain segmentation and driving progress in automated neuroimaging analysis. Full article

Pansharpening fuses multispectral (MS) and panchromatic (PAN) remote sensing images to generate outputs with high spatial resolution and spectral fidelity. Nevertheless, conventional methods relying primarily on convolutional neural networks or unimodal fusion strategies frequently fail to bridge the sensor modality gap between MS and PAN data. Consequently, spectral distortion and spatial degradation often occur, limiting high-precision downstream applications. To address these issues, this work proposes a Hybrid Spectral–Structural Transformer Network (HSSTN) that enhances multi-level collaboration through comprehensive modelling of spectral–structural feature complementarity. Specifically, the HSSTN implements a three-tier fusion framework. First, an asymmetric dual-stream feature extractor employs a residual block with channel attention (RBCA) in the MS branch to strengthen spectral representation, while a Transformer architecture in the PAN branch extracts high-frequency spatial details, thereby reducing modality discrepancy at the input stage. Subsequently, a target-driven hierarchical fusion network utilises progressive crossmodal attention across scales, ranging from local textures to multi-scale structures, to enable efficient spectral–structural aggregation. Finally, a novel collaborative optimisation loss function preserves spectral integrity while enhancing structural details. Comprehensive experiments conducted on QuickBird, GaoFen-2, and WorldView-3 datasets demonstrate that HSSTN outperforms existing methods in both quantitative metrics and visual quality. Consequently, the resulting images exhibit sharper details and fewer spectral artefacts, showcasing significant advantages in high-fidelity remote sensing image fusion. Full article