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Astaxanthin mitigates cardiac toxicity induced via doxorubicin by alleviating mitochondrial fission and autophagy in rats
Green synthesis of strontium stannate nanorods using extract of Juniperus communis L.: Structural characterization and evaluation of antibacterial, antifungal, and antioxidant activity
Engineered Nanofluidics for Molecular Recognition and Physical Perception
Abstract Nanofluidics has garnered significant attention as the ultra‐sensitive method for molecular recognition and physical perception that are not easily accessible through the traditional methods. The development of nanofluidic devices necessitates the integrated solid‐state nanochannels/nanopores with versatile surface modification strategies using precise nanofabrication techniques. This review systematically summarizes the development of the solid‐state nanochannels and nanopores, nanofabrication methods, sensing principles, transport characteristics, and the strategies employed to perceive molecules and physical stimuli. The discussion also emphasizes promising research directions and explores how the interaction between interface chemistry influenced by molecular recognition and physical stimuli, in conjunction with the exceptional ion transport properties of nanofluidic devices, significantly impacts the sensing performance of the nanofluidics. Lastly, we present the vision for the future prospects of biomimetic nanofluidic devices in ionic sensing applications.
Novel dataset and model for restroom sound event classification
Abstract This study presents a novel privacy-preserving deep learning framework for accurately classifying fine-grained hygiene and water-usage events in restroom environments. Leveraging a comprehensive, curated dataset comprising approximately 460 min of stereo audio recordings from five acoustically diverse bathrooms, our method robustly identifies 11 distinct events, including nuanced variations in faucet counts and flow rates, toilet flushing, and handwashing activities. Stereo audio inputs were transformed into triple-channel Mel spectrograms using an adaptive one-dimensional convolutional neural network (1D-CNN), dynamically synthesizing spatial cues to enhance discriminative power. Extensive experimentation identified the RegNetY-008 architecture as the most effective backbone, further improved by employing a semi-supervised learning strategy via pseudo-labeling and targeted data augmentation techniques such as XY masking and horizontal CutMix. The proposed ensemble model, combining RegNetY-008 networks with complementary third-channel generation strategies, achieved outstanding generalization performance, yielding an accuracy of 97.8% and macro-averaged F1-score of 0.966 across acoustically distinct test environments. Our publicly available dataset addresses critical gaps in existing resources, promoting future research in intelligent, privacy-conscious restroom monitoring
Pharmacokinetic analysis of selective TRPV2 inhibitor SET2 in rats
Cortico-subcortical converging organization at rest
Abstract Local segregation and global integration are the fundamnetal organizational principles of human brain. It is unknown how subcortex configures itself with respect to the segregation and integration dynamics at rest. Using resting state functional MRI data of 92 healthy adult participants, we revealed three non-overlapping segregated communities in subcortex, confining anatomically to thalamus, basal ganglia, and subcortical limbic structures, termed as subcortical networks. Further using network science, we analysed the topology of subcortex and found about 80% of subcortical regions acting as hubs, connecting with other cortical as well as subcortical communities. Next, using statistical modelling, we determined the role of subcortex (both at region-level and network-level) in cortical information integration and found multiple, widespread cortical regions (networks) converging onto individual subcortical regions (networks) (a many-to-one mapping). Individual subcortical networks showed varied extent of convergence, broadly from primary and association networks in cortex. We found functional diversity of cortex to be the major driving factor behind cortical convergence within subcortex and that the absence of subcortical regions significantly impacted the information transmission efficiency within the cortico-subcortical converging organization. Overall, our results provide insights into the subcortical organization at rest and underscore the subcortical contributions in shaping the large-scale brain dynamics.
Gender-specific factors affecting changes in physical function among older adults during the COVID-19 pandemic
Abstract This longitudinal study investigated gender-specific factors associated with changes in physical function among community-dwelling older adults during the COVID-19 pandemic. Although the impact of behavioral restrictions on older adults has been previously studied, few studies have examined individual-level longitudinal changes, especially with a focus on gender differences. A total of 242 older adults in Japan (111 men and 131 women) were followed from 2019 to 2021. Physical function was assessed using the Timed Up and Go (TUG) test and 5-m habitual walking speed. Associations between individual characteristics—such as education level (years), economic status, daily activity levels, and living arrangement—and changes in physical function were examined using linear mixed-effects models, adjusting for age, education level (years), economic status, and living arrangement. The results showed that among men, having less than 12 years of education level (years) and a higher pre-pandemic leisure activity score were significantly associated with a decline in TUG performance. Among women, living alone was associated with improved 5-m walking speed. These findings indicate that physical function changes during the pandemic varied by gender and were influenced by individual-level factors. The results highlight the importance of developing gender-sensitive and context-specific strategies to support older adults in maintaining physical function during public health emergencies.
Clarifying the role of SONIA: supporting academic evidence on CDK4/6 inhibitor timing
Visual motion thresholds mapped to midget and parasol ganglion cell topography in the human retina
Abstract Motion in visual images can be described in terms of changes in phases of Fourier components (phase cues), or displacements in the position of specific features (position cues) over time. Human observers are able to perceive motion using both cues, where perceived direction of motion is biased in favour of phase cues at higher spatial and temporal frequencies, and in favour of position cues at lower spatial and temporal frequencies. This suggests the existence of separable mechanisms for processing phase and position cues. We propose that these mechanisms receive separate inputs from the parasol (magnocellular) and midget (parvocellular) retinal ganglion cells. Using two-frame apparent motion Gabor stimuli that isolated phase and position cues, we measured displacement thresholds for motion direction discrimination across the visual field (from 0 to 15 degrees eccentricity) for 7 observers. Thresholds for positional displacements decreased significantly more steeply with eccentricity than those for phase displacements, mirroring precisely the decline with increasing eccentricity of the linear densities of the midget and parasol retinal ganglion cell populations respectively. These results suggest that the magnocellular and parvocellular visual pathways could constitute separable neural substrates for first-order (Fourier) and third-order (feature-tracking) motion perception.
Determination of Cd(II) and Zn(II) ions in honey samples by in-situ produced CO2-assisted dispersive micro solid phase based on covalent organic framework
Impact of Enterococcus faecium 129 BIO 3B-R on Helicobacter pylori eradication therapy side effects in adult patients: a randomized, double-blind, placebo-controlled study
Evolving roles of MET as a therapeutic target in NSCLC and beyond
Author Correction: MicroRNA-33b inhibits breast cancer metastasis by targeting HMGA2, SALL4 and Twist1
The microbiota in radiotherapy-induced cancer immunosurveillance
Author Correction: Evaluation of novel and traditional anthropometric indices for predicting metabolic syndrome and its components: a cross-sectional study of the Nepali adult population
Hybrid Active Sites in Coordination Polymers Enable Ampere‐Level Acetylene Semihydrogenation in Membrane Electrode Assembly Systems
Abstract Electrocatalytic acetylene semihydrogenation in membrane electrode assembly systems promises a sustainable pathway for ethylene production, yet faces challenges in catalyst performance and durability. Herein, we developed a Cu coordination polymer with hybrid sites that synergistically integrate open Cu sites and N‐heterocyclic carbenes. These hybrid sites bestow the coordination polymer with acetylene gasophilicity, hydrophobicity toward water, and readily accessible active Cu sites, which energetically facilitate acetylene absorption and vinyl intermediate formation, thereby enabling efficient ethylene production at ampere‐level current densities. In a membrane electrode assembly electrolyzer with pure acetylene, this polymeric catalyst achieved high ethylene Faradaic efficiency of 93.1% at −0.5 A cm −2 and 83.3% at −1.0 A cm −2 , with stable operation for 100 h at −0.5 A cm −2 . Notably, even with a 15% coal‐derived acetylene at a flow rate of 60 standard cubic centimeters per minute, this catalyst system demonstrated 64.4% ethylene energy efficiency and durable performance over 200 h at −0.5 A. This work advances the design of highly stable and active polymeric catalysts for electrocatalytic acetylene semihydrogenation.
Decoding and spatial mapping of acoustic noise in the neonatal intensive care unit
Abstract The neonatal intensive care unit (NICU) is a critical care setting where premature infants face continuous exposure to elevated noise levels, often exceeding international safety guidelines. While the risks of excessive acoustic exposure are well established, strategies for real-time noise monitoring and mitigation in operational NICUs remain underexplored. In this study, we propose an exploratory framework that integrates spatially distributed sound sensors, acoustic heatmap visualization, and machine learning-based classification to analyze and categorize noise events in a high-density NICU setting. The analysis identified persistent high-noise zones near incubators and entryways, with staff movement and alarm-related activities causing significant sound level spikes—particularly during the noon shift. Additionally, a random forest classifier achieved 85.5% accuracy in distinguishing clinical activity patterns based on environmental acoustic data. While not intended for urgent alerting, this framework demonstrates the potential of using ambient sound profiles for non-critical event recognition and environmental monitoring in the NICU.