Search Results (113)

Hydroxychloroquine (HCQ), originally developed as an antimalarial agent, has been associated with hepatotoxic effects in experimental and clinical settings. Our study was designed to evaluate the effects of this agent on liver toxicity and to understand the protective roles of adenosine triphosphate (ATP), Liver-52 (Liv-52), and their combination. Male Wistar rats (250–280 g) were randomly assigned to five groups (n = 6): healthy control (C), HCQ only (H), ATP plus HCQ (AH), Liv-52 plus HCQ (LH), and ATP–Liv-52 plus HCQ (ALH). ATP (4 mg/kg) was administered intraperitoneally once daily, whereas Liv-52 (20 mg/kg) was administered orally via gavage. One hour later, all groups except C received HCQ (120 mg/kg, orally, twice daily). All treatments were continued for seven consecutive days. At the end of the experiment, serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels were measured, and liver tissues were analyzed for malondialdehyde (MDA), total glutathione (GSH), superoxide dismutase (SOD), and catalase (CAT) activities, along with histopathological evaluation. HCQ administration significantly increased oxidative stress, as evidenced by elevated MDA levels (p < 0.01) and reduced antioxidant parameters, including GSH, SOD, and CAT (p < 0.05), accompanied by prominent histopathological damage. Treatment with ATP or Liv-52 markedly ameliorated these alterations by decreasing MDA and restoring antioxidant markers. The combination treatment was observed to exhibit the most pronounced protective effect; it significantly reduced MDA levels, improved GSH, SOD, and CAT levels more effectively, and produced significant decreases in AST and ALT values (p < 0.05). Full article

The Lorentz invariance violation (LIV) predicted by some quantum gravity theories would manifest as an energy-dependent speed of light, which may potentially distort the observed temporal profile of photons from astrophysical sources at cosmological distances. The dispersion cancellation (DisCan) algorithm offers a powerful methodology for investigating such effects by employing quantities such as Shannon entropy, which reflects the initial temporal characteristics. In this study, we apply the DisCan algorithm to search for LIV effects in the LHAASO observations of GRB 221009A, combining data from both the Water Cherenkov Detector Array (WCDA) and Kilometer Squared Array (KM2A) detectors that collectively span an energy range of ∼0.2–13 TeV. Our analysis accounts for the uncertainties from both energy resolution and temporal binning. We derive $95\%$ confidence level lower limits on the LIV energy scale of $E_{\mathrm{QG},1}/{10}^{19}\mathrm{GeV}>14.6$ (11.2) for the first-order subluminal (superluminal) scenario, and $E_{\mathrm{QG},2}/{10}^{11}\mathrm{GeV}>13.7$ (12.5) for the second-order subluminal (superluminal) scenario. Full article

Background: Deep learning models have been used in the past for non-invasive liver fibrosis classification based on liver ultrasound scans. After numerous improvements in the network architectures, optimizers, and development of hybrid methods, the performance of these models has barely improved. This creates a need for a sophisticated method that helps improve this slow-improving performance. Methods: We propose LivSCP, a method to train liver fibrosis classification models for better accuracy than the traditional supervised learning (SL). Our method needs no changes in the network architecture, optimizer, etc. Results: The proposed method achieves state-of-the-art performance, with an accuracy, precision, recall, and F1-score of 98.10% each, and an AUROC of 0.9972. A major advantage of LivSCP is that it does not require any modification to the network architecture. Our method is particularly well-suited for scenarios with limited labeled data and computational resources. Conclusions: In this work, we successfully propose a training method for liver fibrosis classification models in low-data and computation settings. By comparing the proposed method with our baseline (Vision Transformer with SL) and multiple models, we demonstrate the state-of-the-art performance of our method. Full article

Background: Accurate deformity classification and fusion-level planning are essential in adolescent idiopathic scoliosis (AIS) surgery and are traditionally guided by Cobb angle measurement and the Lenke system. Multimodal large language models (LLMs) (e.g., ChatGPT-4.0; Claude 3.7 Sonnet, Gemini 2.5 Pro, DeepSeek-R1-0528 Chat) are increasingly used for image interpretation despite limited validation for radiographic decision-making. This study evaluated the agreement and reproducibility of contemporary multimodal LLMs for AIS assessment compared with expert spine surgeons. Methods: This single-center retrospective study included 125 AIS patients (94 females, 31 males; mean age 14.8 ± 1.9 years) who underwent posterior instrumentation (2020–2024). Two experienced spine surgeons independently performed Lenke classification (including lumbar and sagittal modifiers) and selected fusion levels (UIV–LIV) on standing AP, lateral, and side-bending radiographs; discrepancies were resolved by consensus to establish the reference standard. The same radiographs were analyzed by four paid multimodal LLMs using standardized zero-shot prompts. Because LLMs showed inconsistent end-vertebra selection, LLM-derived Cobb angles lacked a common anatomical reference frame and were excluded from quantitative analysis. Agreement with expert consensus and test–retest reproducibility (repeat analyses one week apart) were assessed using Cohen’s κ. Evaluation times were recorded. Results: Surgeon agreement was high for Lenke classification (92.0%, κ = 0.913) and fusion-level selection (88.8%, κ = 0.879). All LLMs demonstrated chance-level test–retest reproducibility and very low agreement with expert consensus (Lenke: 1.6–10.2%, κ = 0.001–0.036; fusion: 0.8–12.0%, κ = 0.003–0.053). Claude produced missing outputs in 17 Lenke and 29 fusion-level cases. Although LLMs completed assessments far faster than surgeons (seconds vs. ~11–12 min), speed did not translate into clinically acceptable reliability. Conclusions: Current general-purpose multimodal LLMs do not provide reliable Lenke classification or fusion-level planning in AIS. Their poor agreement with expert surgeons and marked internal inconsistency indicate that LLM-generated interpretations should not be used for surgical decision-making or patient self-assessment without task-specific validation. Full article

Dissolved organic matter (DOM) is a crucial carbon source for soil microorganisms and plays a vital role in nutrient cycling and carbon (C) sequestration in soils. However, the extent to which soil microbes mediate DOM transformation at the molecular level, and whether this is regulated by different organic fertilization, remains unclear. Here, we designed a field experiment to investigate the transformations of DOM under three types of organic fertilization (straw, biochar, and manure) using Fourier transform ion cyclotron resonance mass spectrometry and metagenomic analysis. Compared to the control, manure fertilization increased the molecular chemodiversity of DOM by 33.2%, with recalcitrant compounds (e.g., highly unsaturated phenolic compounds and lignins) increasing by 47.2%. In contrast, labile compounds (e.g., aliphatics) decreased by 73.5%. Compared to straw treatment, manure application significantly increased the average conversion rate of dissolved organic matter (DOM). This process was accompanied by a significant increase in the Shannon index of the soil microbial community (p < 0.05) and upregulation of ABC transporter-encoding genes (e.g., livK, livM). DOM composition directly governed transformation potential (p < 0.01), whereas functional genes enhanced transformation indirectly by modulating DOM composition. This study elucidates microbial-mediated DOM transformation mechanisms under varying organic fertilization practices, providing a scientific basis for optimizing soil organic matter management in paddy ecosystems. Full article

Fingerprint authentication systems encounter growing threats from presentation attacks, making strong liveness detection crucial. This work presents a deep learning-based framework integrating EfficientNetB0 with a Squeeze-and-Excitation (SE) attention approach, using transfer learning to enhance feature extraction. The LivDet 2015 dataset, composed of both real and fake fingerprints taken using four optical sensors and spoofs made using PlayDoh, Ecoflex, and Gelatine, is used to train and test the model architecture. Stratified splitting is performed once the images being input have been scaled and normalized to conform to EfficientNetB0’s format. The SE module adaptively improves appropriate features to competently differentiate live from fake inputs. The classification head comprises fully connected layers, dropout, batch normalization, and a sigmoid output. Empirical results exhibit accuracy between 98.50% and 99.50%, with an AUC varying from 0.978 to 0.9995, providing high precision and recall for genuine users, and robust generalization across unseen spoof types. Compared to existing methods like Slim-ResCNN and HyiPAD, the novelty of our model lies in the Squeeze-and-Excitation mechanism, which enhances feature discrimination by adaptively recalibrating the channels of the feature maps, thereby improving the model’s ability to differentiate between live and spoofed fingerprints. This model has practical implications for deployment in real-time biometric systems, including mobile authentication and secure access control, presenting an efficient solution for protecting against sophisticated spoofing methods. Future research will focus on sensor-invariant learning and adaptive thresholds to further enhance resilience against varying spoofing attacks. Full article

Zinc is an essential trace element involved in critical physiological functions such as gene expression, immune regulation, and cellular proliferation. This review explores the link between zinc homeostasis and cancer, with a specific focus on LIV-1 zinc transporters and their potential relevance to cancer research and treatment priorities in Saudi Arabia, as informed by global data. Zinc homeostasis is maintained by two major transporter families: ZIP (SLC39A) and ZnT (SLC30A). The dysregulation of specific ZIP transporters, particularly ZIP4, ZIP7, ZIP6, and ZIP10, has been implicated in cancer progression. Bioinformatic analyses revealed the significant overexpression of ZIP4, ZIP7, and ZIP6 in breast cancer and ZIP4 in colorectal cancer, which are the most common cancers among Saudi women and men, respectively. Notably, ZIP4 and ZIP7 upregulation correlated with poorer clinical outcomes, whereas ZIP6 was positively associated with survival in breast cancer. These findings underscore the potential of zinc transporters as prognostic biomarkers and therapeutic targets. Despite the substantial global evidence, research on zinc transporters in the Saudi population remains limited. Considering the Kingdom’s rising cancer burden and unique genetic, environmental, and dietary factors, understanding zinc metabolism in this context is important. Targeted research may support precision medicine strategies and improve outcomes in line with Saudi Arabia’s healthcare transformation goals. Full article

To determine if exposing embryos to light during incubation affects antibody titer and corticosterone immediately following hatch, we incubated layer eggs and exposed them to light or darkness and vaccinated a subset of each treatment against Newcastle Disease Virus (NDV) using in ovo administration on ED 18, spray application at hatch (d 0), or not at all. There were six treatments: light incubated and non-vaccinated (LNV), light incubated and in ovo vaccinated (LIV), light incubated and post-hatch vaccinated (LPHV), dark incubated and non-vaccinated (DNV), dark incubated and in ovo-vaccinated (DIV), and dark incubated and post-hatch vaccinated (DPHV). Plasma corticosterone (CORT) and NDV antibody titers were measured on d 0, 7, and 14. Light-incubated chicks had lower (p < 0.05) plasma CORT on d 0. NDV titers did not differ (p > 0.05) between light- and dark-incubated chicks on d 0, 7, or 14. However, LIV chicks had higher antibody titers than LPHV on d 14. Exposing embryos to continuous green light during incubation may reduce stress during the early post-hatch period. Vaccination method, rather than exposure to continuous green light during incubation, may have a greater impact on humoral immune response post-hatch. Full article

Existing Simultaneous Localization and Mapping (SLAM) algorithms provide precise local pose estimation and real-time scene reconstruction, widely applied in autonomous navigation for land vehicles. However, the odometry of SLAM algorithms exhibits localization drift and error divergence over long-distance operations due to the lack of inherent global constraints. In this paper, we propose a real-time geo-localization method for land vehicles, which only relies on a LiDAR-inertial-visual SLAM (LIV-SLAM) and a referenced image. The proposed method enables long-distance navigation without requiring GPS or loop closure, while eliminating accumulated localization errors. To achieve this, the local map constructed by SLAM is real-timely projected onto a downward-view image, and a highly efficient cross modal matching algorithm is proposed to estimate the global position by aligning the projected local image to a geo-referenced satellite image. The cross-modal algorithm leverages dense texture orientation features, ensuring robustness against cross-modal distortion and local scene changes, and supports efficient correlation in the frequency domain for real-time performance. We also propose a novel adaptive Kalman filter (AKF) to integrate the global position provided by the cross-modal matching and the pose estimated by LIV-SLAM. The proposed AKF is designed to effectively handle observation delays and asynchronous updates while simultaneously rejecting the impact of erroneous matches through an Observation-Aware Gain Scaling (OAGS) mechanism. We verify the proposed algorithm through R³LIVE and NCLT datasets, demonstrating superior computational efficiency, reliability, and accuracy compared to existing methods. Full article

The Living Well with Dementia (LivDem) group intervention aims to support people to adjust following a diagnosis of dementia and is delivered across the UK and abroad. However, LivDem was designed for older people with dementia and may not address the needs of younger adults. This study aimed to identify the perspectives of LivDem facilitators on adapting the LivDem course for younger adults. Data was collected as part of an online facilitator survey and included questions requiring either ordinal or free-text responses. Responses from fifteen facilitators were analysed using descriptive statistics and Reflexive Thematic Analysis. The former indicated that participants believed that LivDem could be beneficial for younger adults and were in favour of it being adapted. Qualitative analysis generated two main themes, the first of which (‘The domino effect’: Unique Challenges for Younger Adults) had two subthemes: ‘Life and opportunities stripped away’ and ‘Impacting on everyone’. Theme 2, ‘Good to be with peers’: The Importance of Age-Appropriate Support, also had two subthemes: Groups ‘full of old people’ and Groups ‘specifically for younger people’. These findings reinforce the argument for creating age-appropriate services for people with young-onset dementia and will inform an adapted version of LivDem that provides age-appropriate support. Full article

Lightning-induced voltages (LIVs) computation is crucial for lightning protection of power systems and equipment, yet the effect of complex terrain on LIVs remains not fully evaluated. This study establishes a three-dimensional finite-difference time-domain model to investigate the LIVs over Gaussian-shaped mountainous terrain, considering different lightning strike locations. Simulation results show that the influence of Gaussian-shaped mountains on LIVs is directly related to the lightning strike location. Compared with the flat ground scenario, the LIVs’ amplitude can increase by approximately 56% when lightning strikes the mountain top. However, for lightning strikes to the ground adjacent to the mountain, the LIVs’ amplitude is attenuated to varying degrees due to the shielding effect of the mountain. Additionally, the influences of line configuration, as well as mountain height and width on the LIVs, are evaluated. Full article

We reanalyze the spectral lag data for 56 Gamma-Ray Bursts (GRBs) in the cosmological rest frame to search for Lorentz Invariance Violation (LIV) using frequentist inference. For this purpose, we use the technique of profile likelihood to deal with the nuisance parameters, corresponding to a constant time lag in the GRB rest frame and an unknown intrinsic scatter, while the parameter of interest is the energy scale for LIV ($E_{QG}$). With this method, we do not obtain a global minimum for ${\chi }^2$ as a function of $E_{QG}$ up to the Planck scale. Thus, we can obtain one-sided lower limits on $E_{QG}$ in a seamless manner. Therefore, the 95% c.l. lower limits which we thus obtain on $E_{QG}$ are then given by $E_{QG}\ge 2.07\times {10}^{14}$ GeV and $E_{QG}\ge 3.71\times {10}^5$ GeV, for linear and quadratic LIV, respectively. Full article

Background/Objectives: Proximal junctional kyphosis (PJK) is one of the most frequently discussed complications following corrective surgery in patients with neuromuscular scoliosis (NMS). Despite its clinical relevance, the etiology of PJK remains incompletely understood and appears to be multifactorial. Biomechanical and limited clinical studies suggest that preoperative hyperkyphosis, resection of the spinous processes with consequent disruption of posterior ligamentous structures, and rod contouring parameters may contribute as risk factors. Methods: To validate these findings, we retrospectively analyzed 99 NMS patients who underwent posterior spinal fusion using a standardized screw-rod system between 2009 and 2017. Radiographic assessments were conducted at three time points: preoperatively (preOP), postoperatively (postOP), and at a mean follow-up (FU) of 29 months. Clinical variables collected included patient age, weight, height, sex, and Risser sign. Radiographic evaluations encompassed Cobb angles, thoracic kyphosis (TK), lumbar lordosis, the levels of the upper (UIV) and lower (LIV) instrumented vertebrae, the total number of fused segments, parameters of sagittal alignment, the rod contour angle (RCA), and the postoperative mismatch between RCA and the proximal junctional angle (PJA). Based on the development of proximal junctional kyphosis, patients were categorized into PJK and non-PJK groups. Results: The overall incidence of PJK was 23.2%. In line with previous biomechanical findings, spinous process resection was significantly associated with PJK development. Furthermore, the PJK group demonstrated significantly higher preoperative TK (59.3° ± 29.04° vs. 34.5° ± 26.76°, p < 0.001), greater RCA (10.2° ± 4.01° vs. 7.7° ± 4.34°, p = 0.021), and a larger postoperative mismatch between PJA and RCA (PJA−RCA: 3.8° ± 6.76° vs. −1.8° ± 6.55°, p < 0.001) compared to the non-PJK group. Conclusions: Spinous process resection, a pronounced mismatch between postoperative PJA and RCA (odds ratio [OR] = 1.19, p = 0.002), excessive rod bending (i.e., high RCA), and severe preoperative thoracic hyperkyphosis with an expected increase in the risk of PJK of approximately 6.5% per degree of increase in preoperative TK are significant risk factors for PJK. These variables should be carefully considered during the surgical planning and execution of deformity correction in NMS patients. Full article

The spread of biometric systems utilizing fingerprints has increased the need for advanced spoof detection techniques, but training convolutional neural networks (CNNs) with the limited number of images available in fingerprint datasets poses significant challenges. In this paper, we propose a lightweight network architecture which addresses the challenges inherent in small fingerprint datasets by employing a moderately deep network architecture which is sufficient for extracting essential features from fingerprint images. We apply a hyperbolic tangent activation to the final feature map, which has features from local receptive fields, and average the responses into a single value. Thus, our architecture reduces overfitting by increasing the number of effective labels during training. Additionally, the incorporation of the spatial attention module enhances feature representation, culminating in improved accuracy. The evaluation results show that the proposed model, with only 0.14 million parameters, outperforms existing techniques including lightweight models and transfer-learning-based models, achieving superior average test accuracies of 98.30% and 95.57% on the LivDet-2015 and -2017 datasets, respectively. It also delivers state-of-the-art cross-material performance, with corresponding average classification error values of 0.81% and 1.91%, making it highly effective for on-device fingerprint authentication. Full article

Ground segmentation in LiDAR point clouds is a foundational capability for autonomous systems, enabling safe navigation in applications ranging from urban self-driving vehicles to planetary exploration rovers. Reliably distinguishing traversable surfaces in geometrically irregular or sensor-sparse environments remains a critical challenge. This paper introduces a hybrid framework that synergizes multi-resolution polar discretization with sparse convolutional neural networks (SCNNs) to address these challenges. The method hierarchically partitions point clouds into adaptive sectors, leveraging PCA-derived geometric features and dynamic variance thresholds for robust terrain modeling, while a SCNN resolves ambiguities in data-sparse regions. Evaluated in structured (SemanticKITTI) and unstructured (Rellis-3D) environments, two different versions of the proposed method are studied, including a purely geometric method and a hybrid approach that exploits deep learning techniques. A comparison of the proposed method with its purely geometric version is made for the purpose of highlighting the strengths of each approach. The hybrid approach achieves state-of-the-art performance, attaining an F1-score of 95.4% in urban environments, surpassing the purely geometric (91.4%) and learning-based baselines. Conversely, in unstructured terrains, the geometric variant demonstrates superior metric balance (80.8% F1) compared to the hybrid method (75.8% F1), highlighting context-dependent trade-offs between precision and recall. The framework’s generalization is further validated on custom datasets (UMH-Gardens, Coimbra-Liv), showcasing robustness to sensor variations and environmental complexity. The code and datasets are openly available to facilitate reproducibility. Full article