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Clinical utility of NTproBNP as a fluid assessment and prognostic tool in PD patients
Exploring propolis-derived compounds as quorum sensing inhibitors for Candida albicans: a molecular docking and dynamics simulations study
Abstract The opportunistic fungal pathogen Candida albicans (C. albicans) employs quorum sensing (QS) to regulate virulence factors such as biofilm formation and morphological switching. Targeting QS offers a promising approach to attenuate pathogenicity without promoting resistance. The objective of the present study was to computationally screen a library of 106 propolis-derived compounds to identify natural quorum-sensing inhibitors targeting the CYC and RAS1 receptors. Advanced techniques, including molecular docking, molecular dynamics (MD) simulations, and ADMET predictions, were employed to establish a workflow for structure-based virtual screening. Farnesol, used as a reference compound, showed a good binding affinity (-7.0 kcal/mol) toward CYC and RAS1 receptors. Two propolis flavonoids, kaempferol-3-O-4-O-p-coumaryl-glucoside (KCG) and isorhamnetin-3-glucoside-7-rhamnoside (IGR), exhibited stronger binding affinities with CYC and RAS1 receptors (between − 8.4 and − 10.3 kcal/mol). Based on Prime MMGBSA binding free energy calculations, the RAS1-KCG complex demonstrated the most favorable interaction, driven by significant coulombic and Van der Waals contributions. The CYC-KCG complex also showed relatively strong binding. These results highlight the potential of KCG as a versatile inhibitor capable of interacting with both targets. MD simulations further confirmed the excellent conformational stability of both KCG-receptor complexes, as indicated by low RMSD values, suggesting favorable dynamic behavior. These findings provide a strong foundation for subsequent validation.
A hybrid multi-node QKD-ECC architecture for securing IoT networks
Abstract The rapid expansion of Internet of Things (IoT) applications in sectors like smart cities, healthcare, and industrial automation has introduced serious security challenges due to limited device resources and growing threats from quantum computing. Traditional cryptographic techniques such as RSA and AES are increasingly inadequate, particularly against quantum attacks, and face limitations in scalability and efficiency in multi-node environments. To overcome these challenges, this paper proposes a lightweight and quantum-resilient security framework for IoT networks based on Multi-Node Quantum Key Distribution (QKD) integrated with Elliptic Curve Cryptography (ECC), termed MNQ-ECC. The proposed architecture enables secure key generation and exchange across multiple nodes and includes four security phases: pre-deployment, registration, login, and authentication. Here, performance evaluation is carried out using Qiskit simulators under varying network conditions and key performance metrics such as key generation rate, entropy, latency, and communication overhead are analysed. The results demonstrate that MNQ-ECC achieves 99.5% resistance to quantum attacks, improves key generation efficiency by 30%, and reduces encryption overhead by 20% compared to standard ECC. These findings confirm the framework’s effectiveness in securing IoT networks with high scalability, low latency, and strong resilience against classical and quantum threats.
Trump team disbands controversial US climate panel
Influence of prophylactic antibiotics on incidence of urinary tract infections in acute-to-subacute patients with stroke and asymptomatic bacteriuria
Author Correction: A broad-spectrum lasso peptide antibiotic targeting the bacterial ribosome
Hierarchical reinforcement learning-based traffic signal control
Demonstration of an intrinsic circadian rhythm in bone resorption
Abstract Daily 24 h rhythms in bone turnover have been demonstrated but whether these rhythms are intrinsically generated circadian rhythms is not known. We thus aimed to investigate this using the commonly used constant routine protocol where external factors such as meals, activity, sleep/wake and light/dark are kept constant. Serum procollagen type I N-terminal propeptide (sPINP) (marker of bone formation) and C-terminal telopeptide of type 1 collagen (sCTX) (marker of bone resorption), were measured in 2 hourly blood samples taken sequentially across 26 h in healthy individuals (n = 22, aged 19–33 years, 50% female). Concentration of sCTX showed a cosine rhythm in all males (acrophase (peak) time (mean ± SEM) 02:48 ± 14 h:min, amplitude 0.15 ± 0.02 ng/mL). All of the females had a statistically significant cosine + linear fit (acrophase 03:24 ± 20 h:min, amplitude 0.05 ± 0.01 ng/mL). There was no sex difference in acrophase, but females had a significantly smaller amplitude (P < 0.001). For sP1NP, only 4 males (36%) and 1 female showed statistically significant rhythms for either cosine, or cosine + linear models. Overall, sCTX, but not sPINP, exhibited a robust circadian rhythm in both males and females. This finding suggests that the circadian clock regulation of bone resorption by osteoclasts is robust, whereas circadian clock regulation of bone formation by osteoblasts is minimal.
Wheat yield and soil physicochemical properties through mineral nitrogen and vermicompost application in Lasta district, North Ethiopia
Assessment of freeze-dried Sargassum polycystum as a nutritional feed component for red tilapia (Oreochromis spp.) fingerlings
Steel surface defect detection algorithm based on improved YOLOv10
Association between stress hyperglycemia ratio and all-cause mortality in patients with chronic obstructive pulmonary disease
Unveiling community structure, antimicrobial resistance, and virulence factor of a wastewater sample of dairy farm located in mayurbhanj, odisha, India
Decoding of image properties from single-trial visual evoked potentials recorded by ultra-high-density EEG
Design and fabrication of tailored Dy2O3 PVA nanocomposites with optical characterization for advanced optoelectronic applications
An optimized YOLO NAS based framework for realtime object detection
Abstract An enhanced version of the YOLO-NAS object detection network model has been presented in this paper, and MISH activation and Artificial Bee Colony (ABC) optimization algorithms are integrated. MISH functional adds non-monotonic behavior, which at the same time enhances the feature representation and complements the gradient flow. ABC optimization that assists in the optimization of the hyperparameters to a ground truth and resistance to the models. The given model is tested on the dataset that is introduced by the researchers themselves, and it shows better results compared to baselines based on the YOLO-NAS variants in precision, recall, and mean average precision (mAP) measures. Experiments prove the fact that a combination of a biologically inspired optimizer and a contemporary activation function helps to make training more stable and predictions more accurate. The results show that the proposed fine-tuned YOLO-NAS model outperformed the other tested models, that is, YOLOv6, YOLOv7, and YOLOv8, in the three metrics of accuracy, recall, precision, F1 score, and mAP at 0.50, 0.75, and 0.95 on the test dataset. The proposed model achieved an accuracy of 98% while recognizing real-time objects.
A multinational study of deep learning-based image enhancement for multiparametric glioma MRI
Advanced battery diagnostics for electric vehicles using CAN based BMS data with EKF and data driven predictive models
Practice testing enhances learning but not attitude change from persuasive texts
Abstract We examined whether practice testing enhances learning from persuasive texts and influences attitude change. In two online experiments, participants read texts on biodiversity loss (Study 1, n = 454) or wolf recolonization (Study 2, n = 400) and were assigned to one of three conditions: pretesting (guessing before reading), posttesting (retrieving after reading), or no-testing. Both testing conditions improved knowledge retention compared to no-testing, with no consistent differences between pre- and posttesting. Across all conditions, attitudes shifted in the expected direction—biodiversity loss was perceived as more severe, and wolf recolonization more positively after reading. However, neither testing condition led to greater attitude change than no-testing. A systematic association between final knowledge and attitude emerged only in the pretesting group, suggesting that generating responses before learning may have promoted attitude change in some individuals. These findings demonstrate that practice testing enhances knowledge acquisition from persuasive texts but does not reliably amplify attitude change. This suggests that while retrieval-based learning strengthens memory for persuasive content, additional factors may be required to translate knowledge gains into belief revision. Our results highlight the complex relationship between knowledge and attitudes and contribute to understanding the role of test-enhanced learning in persuasion.