Browse Articles
Discover research articles across all indexed journals
Stent-based and catheter-based endobiliary radiofrequency ablation for malignant biliary obstruction: an ex-vivo comparative study
Experimental evaluation on the influence of soot removal with novel sonic horn and supersonic air nozzle on heat transfer performance of boiler economizer
Bioinspired conductive and antibacterial hydrogel bionanocomposite based on tragacanth gum, polyaniline and osteoconductive SiO2 nanoparticles as a scaffold for bone tissue engineering application
Enoxaparin induces apoptosis and autophagy, modulates inflammatory signaling, and reduces oxidative DNA damage in breast and liver cancer cells
Temperature-dependent distribution and life history traits of the pestiferous slug Deroceras laeve in the Sikkim-Darjeeling Himalayas
Phenotypic and genetic factors that affect the likelihood of Standardbred foals achieving a race start
Vertical stratification of soil stoichiometric drivers across urban forest parks in Shenzhen, China
Debris variability and velocity estimation using Electrical Resistivity Tomography and DInSAR techniques of Durung Drung glacier, northwestern Himalaya
Unhealthy lifestyle and cardiovascular disease risk in older adults aged 75+ without prior cardiovascular events: a nationwide cohort study
A non-invasive targeting approach using RK-50 for stereotactic-guided focused ultrasound blood brain barrier opening in mice
Abstract Focused ultrasound (FUS) combined with micro/nanobubbles enables transient, localized blood brain barrier opening (BBBO) for targeted drug delivery to the brain. Although imaging-guided FUS methods provide accurate targeting, they require specialized infrastructure that increases cost and workflow complexity. Commercial stereotactic FUS platforms such as the RK-50 offer an accessible alternative, but standard procedures rely on a scalp incision to identify bregma for atlas registration, introducing tissue injury and potential confounds for repeat-BBBO studies. Here, we describe and validate a fully non-invasive targeting approach for FUS BBBO in mice using the RK-50 platform. Our novel targeting method achieved accurate, reproducible targeting without surgical exposure. Evans blue extravasation and atlas co-registration demonstrated sub-millimeter targeting accuracy at bregma and confirmed reliable BBBO in the cortex, hippocampus, and cerebellum. Across these anatomically distinct targets, passive cavitation detection showed broadly comparable acoustic emissions profiles. We further compared this non-invasive workflow with the standard incisional approach in a repeat-BBBO paradigm and observed similar acoustic emissions and comparable delivery of both small molecule and large molecule (antibody) reporters. Together, these findings establish a high-fidelity, incision-free targeting strategy that preserves the accessibility of stereotactic FUS while reducing procedural burden and improving suitability for longitudinal preclinical BBBO studies.
Effect of optimal control on SI epidemic model
Design and implementation of a high-sensitivity receiver channel for the BeiDou RDSS
Circulating epigenetic and epitranscriptomic biomarkers identify EGFR status, predict TKI response and reveal therapeutic vulnerabilities in NSCLC
Damage evolution and microscopic failure characteristics of dense sandstone at different water contents
Computational insights into the 5-fluorouracil loading efficiency of chitosan-PLGA nanocarrier in water
Abstract The therapeutic application of 5-fluorouracil (5-FU) is limited by its rapid metabolism, systemic toxicity, and drug resistance, which motivates the development of efficient polymer-based nanocarriers. In this study, the non-covalent interactions between 5-FU and a chitosan-conjugated poly(lactic-co-glycolic acid) (CS–PLGA) hybrid nanocarrier were investigated using density functional theory (DFT) at the PBE-D3/6–311 + G** level in an aqueous environment modeled via the Polarizable Continuum Model (PCM). Structural analysis indicates that adsorption of 5-FU induces minor elongations in PLGA carbonyl and ester bonds (up to ~ 0.01 Å), along with slight angular distortions, suggesting weak to moderate hydrogen bonding and electrostatic interactions while preserving the structural integrity of the polymer backbone. Adsorption energies ranging from -0.87 to -1.16 eV suggest energetically favorable adsorption of 5-FU on the CS–PLGA surface. Frontier molecular orbital analysis shows a moderate reduction in the HOMO–LUMO gap upon complex formation (ΔE g ≈ 15–17%), accompanied by an increase in dipole moment, indicating enhanced polarity of the drug–carrier system. UV–Vis simulations reveal bathochromic shifts in the absorption maxima (271–295 nm), reflecting electronic perturbations induced by drug adsorption, consistent with excited-state stabilization through hydrogen bonding and local electrostatic interactions. Charge transfer, ESP, and NBO analyses indicate partial electron redistribution from CS–PLGA to 5-FU, particularly in selected configurations, consistent with non-covalent adsorption. Infrared (IR) spectral shifts further support hydrogen-bond-mediated interactions between functional groups of the drug and polymer. Therefore, the results suggest that CS–PLGA provides a stable and responsive environment for 5-FU adsorption, supporting its potential application as a nanocarrier for controlled drug delivery.
Distinct conformational dynamics of the type-2 Angiotensin II receptor bound to Angiotensin II and Angiotensin (1–7)
Abstract The Angiotensin II type-2 receptor (AGTR2) is a G protein-coupled receptor (GPCR) that mediates vasodilatory, anti-proliferative, and cardioprotective responses as part of the renin–angiotensin system (RAS). However, the precise structural dynamics underlying its ligand-specific activation remain incompletely understood. In this study, we performed long-timescale molecular dynamics (MD) simulations to investigate how Angiotensin peptides differentially modulate the structural dynamics of AGTR2 in both inactive- and active-like conformations and to elucidate ligand-specific modulation of AGTR2 in the RAS protective arm. Ang II stabilized a ‘Locked Active State’ through compact TM3–TM6 distances, persistent hydrogen bonding (PHE8–LYS215 5.42 ), salt bridges (ARG2-ASP279 6.58 , ARG2-ASP297 7.32 ), hydrophobic contacts (TRP100 2.60 , MET128 3.36 ), and rotameric locking of micro-switches. These interactions stabilized Helix 8, enhancing dynamic network connectivity and supporting ligand-dependent activation-related conformational tendencies rather than a full canonical GPCR activation transition. Conversely, Ang1–7 induced a ‘Flexible Intermediate State’ characterized by weaker interactions (PRO7–THR125 3.33 ), dynamic instability at the Helix 8 interface, and increased conformational sampling. Activation motif analyses (CWxP, PIF, E/DRY, NPxxY), DCC maps, CP, DRIN metrics, and PCA confirmed distinct signaling features for each ligand. This study provides a dual functional profile for AGTR2, where Ang II acts as a strong conformational stabilizer promoting activation-related dynamic features, while Ang 1–7 serves as a dynamic modulator. These findings contribute to a structural and dynamic framework for understanding AGTR2 signaling and supporting its therapeutic potential in fine-tuning cardiovascular responses within the RAS.
PureChain web-based energy predictor with federated learning Dirichlet for real-time energy consumption forecasting
A geospatial machine-learning framework for regional electrification estimation mapping and trend analysis using demographic and infrastructure features
Abstract Many rural communities in Ethiopia’s Amhara region lack electricity access despite substantial urban-rural disparities. Traditional surveys are costly and inconsistent, while Nighttime Light (NTL)-population threshold methods suffer from arbitrary thresholds, off-grid detection failures, and light-access conflation. This study develops a geospatial machine learning framework using Visible Infrared Imaging Radiometer Suite—Day/Night Band (VIIRS-DNB) nighttime lights, population density, and distances to grid lines and roads. Random Forest (RF) outperforms Support Vector Machine (SVM) and Decision Tree (DT), achieving 86.8% overall accuracy and a 71.3% Kappa coefficient for operational mapping of electrification status and estimation of regional electrification rates from 2018 to 2024. Validation against GPS-recorded transformer locations demonstrates that the Random Forest model reconstructs a realistic, corridor-type electrified network and outperforms a calibrated NTL–population threshold approach, which tends to miss grid-connected rural corridors and overestimate urban brightness effects. The resulting maps reveal zone-level disparities and steady growth to 41% electrification by 2024, with population density (27.49% importance) emerging as the leading predictor among balanced variables. This framework supports SDG 7 planning and investment prioritization in underserved areas.