Browse Articles

Exploring the immunomodulatory potential of Lilium lancifolium in pneumonia: a combined study of network pharmacology, molecular docking, and in vitro experiments

Preterm birth and birth weight extremes are associated with periodontal disease and tooth loss in the ARIC study

Halodule uninervis ethanolic extract reduces inflammation in LPS-stimulated RAW 264.7 macrophages via NF-κB, STAT3, and MAPK modulation

The effects of meteorological factors on hemorrhagic fever with renal syndrome in Yichun, China: a major Apodemus-type endemic city

Hydrogen suppressed tumor growth during chronic intermittent hypoxia via modulating macrophage polarization

Synthesis and characterization of selenium-containing sodium phosphate glasses with enhanced physical and radiation shielding performance

Training set augmentation and biology-aware harmonization improve radiomic models for lung cancer prediction in indeterminate nodules

Abstract CT radiomics-based machine learning has potential to predict lung cancer in pulmonary nodules (PNs) earlier than standard-of-care methods. Low malignancy rates in early-development PNs and variable image acquisition hinder development of radiomic models for diagnosing these PNs. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We examine early-development benign and malignant PNs (n = 106) below the sensitivity of standard-of-care diagnosis. Classifiers predicting malignancy performed near chance when trained on ComBat-harmonized radiomic features from only early-development PNs. We then augmented training with later-development benign and malignant PNs (n = 225). We evaluated whether harmonization must incorporate biology that impacts acquisition effects in added training data. To correct variability from four acquisition protocols, we compared: (1) biology-unaware harmonization, (2) harmonizing with a covariate distinguishing early-development, later-development benign, later-development malignant datasets, (3) harmonizing each dataset separately. Models trained using augmentation, but biology-unaware harmonization, failed to improve consistently. Augmented training data harmonized with a covariate (ROC-AUC 0.74 [0.69–0.79]) or separately (ROC-AUC 0.71 [0.66–0.77]) yielded higher test ROC-AUC (Delong, p  ≤ 0.05) and PR-AUC (Wilcoxon, p  ≤ 0.05). In a proof-of-principle methodological study, we demonstrate with a small single-center dataset that combining radiomic features from later-development benign and malignant PNs requires biology-aware harmonization.

DFT studies of doped and encapsulated of B12N12 nanocage using nickel and platinum metals as carriers for Sunvozertinib drug

Social inequality and the mental health of Chinese youth

EMG-based hand gesture recognition using multi-scale deep residual network with SE-module

Solidago canadensis extract fractionation, phytochemical identification, and nematicidal/nematistatic activity against Meloidogyne incognita

Abstract The development of eco-friendly pest management strategies is crucial for sustainable agriculture. In this study, we investigated the bioactivity of Solidago canadensis leaf extract against Meloidogyne incognita juveniles (J2). In vitro assays showed that aqueous extracts exhibited greater nematistatic (immobilizing) activity than less polar solvent extracts. Fractionation of the aqueous decoction extract using column chromatography yielded 11 fractions (F), which were evaluated at 100, 200, and 300 mg L − 1 . The crude extract showed 100% nematistatic activity at all concentrations, while among the fractions, F5, F7, and F9 were the most effective. In the pot experiment, the crude extract and F7 reduced gall formation by 55.3%, whereas F8, despite low in vitro nematistatic activity (21.5%), achieved the highest gall reduction (58.7%). Unexpectedly, some fractions increased gall formation. GC-MS analysis of F7 and F8 revealed 20 phytochemical compounds, including several with reported nematode-suppressive properties. These findings underscore the potential of S. canadensis extracts and specific fractions as botanical nematistatic agents and emphasize the importance of combining the in vitro and pot bioassays when evaluating botanical nematicides.

The impact of Paulownia–buckwheat intercropping on the biodiversity of different living biota

Abstract Intercropping has emerged as a promising strategy to improve agroecosystem biodiversity and mitigate some adverse effects associated with intensive monoculture systems. This study evaluated weed infestation and biodiversity responses in a Paulownia –buckwheat intercropping system compared with buckwheat monoculture under the environmental conditions of southwestern Poland. Weed species composition, abundance, and biomass were assessed at different growth stages of buckwheat. Selected agroecosystem components, including soil microorganisms, soil mesofauna, and pollinator abundance, were evaluated. The intercropping system increased Collembola diversity and Acari abundance, while higher diversity and richness of segetal plant species were also observed compared with monoculture. Higher bacterial abundance and dehydrogenase activity were recorded under intercropping, whereas fungal community composition remained generally stable between cultivation systems. Several melliferous weed species were identified within the intercropping system, potentially supporting pollinator activity. Although no statistically significant differences were observed in pollinator-related parameters, nectar productivity and sugar availability tended to be higher under intercropping conditions, suggesting that the Paulownia –buckwheat system may contribute to maintaining pollinator activity and supporting agroecosystem biodiversity. No significant differences in buckwheat yield or biometric traits were found between cultivation systems. Overall, the results indicate that Paulownia –buckwheat intercropping may enhance selected components of agroecosystem biodiversity without reducing crop productivity.

Design of a Fano-resonance-enhanced dielectric grating for ultralow-filling-factor superconducting nanowire single-photon detector

Abstract Superconducting nanowire single photon detectors (SNSPDs) exhibit excellent performance in the near-infrared band, but their application range is limited by the detection efficiency and detection speed. In this paper, we propose a high-efficient and ultralow-filling-factor design scheme based on Fano resonance which is excited by a one-dimensional silicon dielectric grating. This design can reduce the kinetic inductance of the nanowires while maintaining the active sensing area of SNSPDs, which is beneficial for improving the recovery time of the detector. Meanwhile, the absorption efficiency of the nanowires can be enhanced by the Fano resonance. Taking λ = 1550 nm as an example, numerical simulations are performed using commercial simulation software based on the finite-difference time-domain method. Under idealized structural conditions, the absorption efficiency of the Niobium Nitride (NbN) superconducting nanowires can exceed 98% when the filling factor of the nanowire is only 10%.

Effects of anchor characteristics and product attributes on consumer purchase decisions in live streaming sales: evidence from an eye-tracking study

A dual track predictive model for assessing crow’s feet aging across different clinical severity grades

Multi-criteria-based determination of optimal probability distribution with four parameter estimation methods using wind speed data from ten tropical sites in the South Pacific

Microscale bioturbation as a first order control on distal prodelta to offshore mudrock reservoirs

Structure-aware acoustic scene classification: a feature decoupling framework using HPSS and asymmetric convolutions

RUL-DBRS: A Novel Energy Efficient and Robust Protocol for Enhanced Communication in Underwater Wireless Sensor Networks

A blockchain-enabled trust-aware authentication framework for secure communication in VANETs

Abstract Vehicular Ad Hoc Networks (VANETs) are essential for intelligent transportation systems; however, their dynamic topology and open wireless communication environment expose them to impersonation, Sybil, replay, and message modification attacks. Existing authentication schemes mainly rely on centralized authorities and conventional cryptographic mechanisms, which lack dynamic trust evaluation and fail to ensure secure authentication during Roadside Unit (RSU) handover and cluster mobility. To address these challenges, this paper proposes a blockchain-enabled trust-aware authentication framework for secure VANET communication. The framework integrates a Joint Probability and Error Unit-based Deep Learning Neural Network (JPEU-DLNN) for dynamic trust assessment and Log-based Edwards Curve Cryptography (LECC) for lightweight key generation. In addition, Gini Indexed Farthest First Clustering (GIFFC) ensures stable cluster formation, while the Directional Gannet Optimization Algorithm (DGOA) supports optimal routing. Blockchain technology provides decentralized and immutable credential storage, eliminating single-point failure and certificate forgery. Simulation results show that the proposed framework achieves 95.62% authentication accuracy and 96.94% packet delivery ratio, with reduced end-to-end delay and improved network lifetime compared to existing blockchain-based authentication schemes. Security evaluation confirms strong resistance against active and passive attacks. These results demonstrate the practical applicability of the framework for secure and reliable VANET deployments.