Treating acute lung injury through scavenging of cell-free DNA by cationic nanoparticles.

Acute lung injury (ALI) and acute respiratory distress syndrome are life-threatening conditions induced by inflammatory responses, in which cell-free DNA (cfDNA) plays a pivotal role. This study investigated the therapeutic potential of biodegradable cationic nanoparticles (cNPs) in alleviating ALI. Using a mouse model of lipopolysaccharide-induced ALI, we examined the impact of intravenously administered cNPs.

Dual-mode and multiplex lateral flow immunoassay: A powerful technique for simultaneous screening of respiratory viruses.

Respiratory viral infectious usually exhibit similar clinical symptoms, making a great challenge for their accurate diagnostic in early stages. Herein, we developed a dual-mode and multiplex lateral flow immunoassay (LFIA), in which near-infrared (NIR)-responsive Janus Au-FeO nanoparticles (NPs) were synthesized as new reporters for highly sensitive colorimetric/photothermal detection of H3N2 influenza and SARS-CoV-2 viruses in a single assay. The Janus Au-FeO well combined the NIR responsiveness and high molar absorption coefficient of Au and magnetic properties of FeO.

Factors driving the acceptance of COVID-19 pandemic mobile contact tracing apps: The influence of security and privacy concerns.

The acceptance of COVID-19 mobile contact tracing apps (MCTA) is crucial to curb the spread of the virus and decrease the number of infections. However, the security and privacy concerns (SPC) of COVID-19 MCTA have been called into question. Thus this paper examines the drivers of the acceptance of the COVID-19 pandemic MCTA under the auspices of the influence of SPC from the Chinese perspective based on the modified Unified Theory of Acceptance and Usage of Technology (UTAUT) model.

A review on direct regeneration of spent lithium iron phosphate: From waste to wealth.

Lithium iron phosphate (LFP) batteries are widely used due to their affordability, minimal environmental impact, structural stability, and exceptional safety features. However, as these batteries reach the end of their lifespan, the accumulation of waste LFP batteries poses environmental hazards. Recycling these batteries is crucial for mitigating pollution risks and enabling secondary resource utilization.

Toward high-quality pseudo masks from noisy or weak annotations for robust medical image segmentation.

Deep learning networks excel in image segmentation with abundant accurately annotated training samples. However, in medical applications, acquiring large quantities of high-quality labeled images is prohibitively expensive. Thus, learning from imperfect annotations (e.

Tetraphenylethene-Containing Nanofilm via Interfacial Confinement Self-Assembly for Fluorescent Monitoring of SOCl Leakage in a Li-SOCl Battery Electrolyte.

Lithium thionyl chloride (Li-SOCl) batteries are widely used due to their high energy density and long shelf life. However, the corrosive nature of SOCl poses a safety hazard, necessitating effective leak detection methods. We report an approach for real-time fluorescent detection of SOCl leakage in Li-SOCl batteries using a tetraphenylethene-based nanofilm.

Identifying and ranking causal association between gut microbiota and neuroticism.

Background: Recent studies had explored that gut microbiota was associated with personality traits through the gut-brain axis. Nevertheless, the fundamental causality remained obscure.

Methods: Mendelian randomization (MR) was used to investigate the causal relationship between gut microbiota, metabolites and neuroticism-related traits.

Impact of Artificial Intelligence-Based Autosegmentation of Organs at Risk in Low- and Middle-Income Countries.

Purpose: Radiation therapy (RT) processes require significant human resources and expertise, creating a barrier to rapid RT deployment in low- and middle-income countries (LMICs). Accurate segmentation of tumor targets and organs at risk (OARs) is crucial for optimal RT. This study assessed the impact of artificial intelligence (AI)-based autosegmentation of OARs in 2 LMICs.

Temporally Correlated Deep Learning-Based Horizontal Wind-Speed Prediction.

Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs).

Large-language-model empowered 3D dose prediction for intensity-modulated radiotherapy.

Background: Treatment planning is currently a patient specific, time-consuming, and resource demanding task in radiotherapy. Dose-volume histogram (DVH) prediction plays a critical role in automating this process. The geometric relationship between DVHs in radiotherapy plans and organs-at-risk (OAR) and planning target volume (PTV) has been well established.

Microbial strategies for effective microplastics biodegradation: Insights and innovations in environmental remediation.

Microplastics (MPs), diminutive yet ubiquitous fragments arising from the degradation of plastic waste, pervade environmental matrices, posing substantial risks to ecological systems and trophic dynamics. This review meticulously examines the origins, distribution, and biological impacts of MPs, with an incisive focus on elucidating the molecular and cellular mechanisms underpinning their toxicity. We highlight the indispensable role of microbial consortia and enzymatic pathways in the oxidative degradation of MPs, offering insights into enhanced biodegradation processes facilitated by innovative pretreatment methodologies.

Feasibility of simulation free abdominal stereotactic adaptive radiotherapy using an expedited pre-plan workflow.

Background And Purpose: Improved hounsfield-unit accuracy of on-board imaging may lead to direct-to-unit treatment approaches We aimed to demonstrate the feasibility of using only a diagnostic (dx) computed tomography (CT)-defined target pre-plan in an study of simulation-free abdominal stereotactic adaptive radiotherapy (ART).

Materials And Methods: Eight patients with abdominal treatment sites (five pancreatic cancer, three oligometastases) were treated using an integrated adaptive O-Ring gantry system. Each patient's target was delineated on a dxCT.

The effects of exercise on microRNA expression profiling in adipose tissue macrophages of mice.

Background: Exercise is recognized for its broad health benefits, influencing various physiological processes, including the behavior of adipose tissue macrophages (ATMs). While existing studies mainly associate ATM activity with obesity and metabolic syndrome, our study explores the impact of aerobic exercise on ATM microRNA expression profiling in a non-obese context, highlighting its general health-promoting mechanisms.

Methods: Sixty male C57BL/6 mice were randomly assigned to either a sedentary (S) or an exercise (E) group.

Response characterization of radiochromic OC-1 films in photon, electron, and proton beams.

Background: Radiochromic film (RCF) dosimeters with their high spatial resolution and tissue equivalent properties are conveniently used for two-dimensional and small-field dosimetry. OC-1 is a new model of RCF dosimeter that was commercially introduced recently. Due to its novelty there is a need to characterize its response in various radiation beam types.

Accumulation and depuration of tire wear particles in zebrafish (Danio rerio) and toxic effects on gill, liver, and gut.

The toxic effects of tire wear particles (TWPs) in the environment are a growing concern for a variety of aquatic organisms. However, studies about TWPs toxicity on aquatic organisms are limited. This study investigated the accumulation and depuration of TWPs in zebrafish at three different concentrations (5 mg/L, 10 mg/L, and 20 mg/L), as well as the toxic effects on the gill, liver, and gut.

Fast, faster, and the fastest structured illumination microscopy.

Parallel acquisition-readout structured-illumination microscopy (PAR-SIM) was designed for high-speed raw data acquisition. By utilizing an xy-scan galvo mirror set, the raw data is projected onto different areas of the camera, enabling a fundamentally stupendous information spatial-temporal flux.