Comprehensive profiling of serum microRNAs in normal and non-alcoholic fatty liver disease (NAFLD) patients.

Pediatric non-alcoholic fatty liver disease (NAFLD) is emerging as a worldwide health concern with the potential to advance to cirrhosis and liver cancer. NAFLD can also directly contribute to heart problems through inflammation and insulin resistance, even in individuals without other risk factors. The pathological mechanisms of NAFLD are linked to functional differences of miRNAs in different biological environments.

Protective factors contributing to the health status of construction industry migrant workers in Chinese mainland.

Construction industry migrant workers (CIMWs) face high-intensity labor and harsh working environments, experiencing significant health issues. Studying these health issues can help to protect workers' rights, improve labor efficiency, and promote sustainable development in the construction industry. The objective of this study is to analyze the relationships among health status, social support, and marital adjustment in construction migrant workers.

Curcumin administration alleviates seminal vesicle damage in type 1 diabetic rats by promoting AQP8 expression through AR activation.

Diabetes is a detriment to male reproductive health, notably through its capacity to diminish secretion from accessory glands such as the seminal vesicles and prostate, which are crucial for reproductive function. Curcumin, a naturally derived polyphenol renowned for its anti-inflammatory and antioxidative attributes, has demonstrated potential in mitigating tissue damage across various organs in diabetic patients. Despite its established benefits, the specific impact of curcumin on seminal vesicle damage in the context of diabetes remains underexplored.

Secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification.

The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification.

EZH2 serves as a viable therapeutic target for myeloma-induced osteolytic bone destruction.

Myelomatous bone disease is a complication characterized by lytic bone lesions, reduced bone formation, bone pain, and increased fracture risk. Understanding these underlying mechanisms is crucial for developing effective therapeutic approaches. Here we show the role of enhancer of zeste homolog 2 (EZH2) in bone lesions induced by myeloma cells.

Risk factor assessment of prediabetes and diabetes based on epidemic characteristics in new urban areas: a retrospective and a machine learning study.

To explore in depth the characteristics of the risk factors for diabetes and prediabetes pathogenesis and progression in special regions. We investigated medical data from 160 thousand cases in the newly developing urban area of a large modern city from 2015 to 2021. After excluding the population with incomplete data, a total of 47,608 people who underwent physical examinations and blood tests were included in this study.

Noninvasive imaging biomarker reveals invisible microscopic variation in acute ischaemic stroke (≤ 24 h): a multicentre retrospective study.

To develop and validate non-contrast computed tomography (NCCT)-based radiomics method combines machine learning (ML) to investigate invisible microscopic acute ischaemic stroke (AIS) lesions. We retrospectively analyzed 1122 patients from August 2015 to July 2022, whose were later confirmed AIS by diffusion-weighted imaging (DWI). However, receiving a negative result was reported by radiologists according to the NCCT images.

Tunable control of Cas12 activity promotes universal and fast one-pot nucleic acid detection.

The CRISPR-based detection methods have been widely applied, yet they remain limited by the non-universal nature of one-pot diagnostic approaches. Here, we report a universal one-pot fluorescent method for the detection of epidemic pathogens, delivering results within 15-20 min. This method uses heparin sodium to precisely tunes the cis-cleavage capability of Cas12 via interference with the Cas12a-crRNA binding process, thereby generating significant fluorescence due to the accumulation of isothermal amplification products.

Liquid crystal monomers induce placental development and progesterone release dysregulation through transplacental transportation.

Embryonic and fetal development can be affected during gestation by exposure to xenobiotics that cross the placenta. Liquid crystal monomers (LCMs) are emerging contaminants commonly found in indoor environments; however, whether they can cross the placenta and affect placental development remains unexplored. Here, we develop an evaluation system that integrates human biomonitoring, uterine perfusion in pregnant rats, and placental cells.

An optimized lightweight real-time detection network model for IoT embedded devices.

With the rapid development of Internet of Things (IoT) technology, embedded devices in various computer vision scenarios can realize real-time target detection and recognition tasks, such as intelligent manufacturing, automatic driving, smart home, and so on. YOLOv8, as an advanced deep learning model in the field of target detection, has attracted much attention for its excellent detection speed, high precision, and multi-task processing capability. However, since IoT embedded devices typically own limited computing resources, direct deployment of YOLOv8 is a big challenge, especially for real-time detection tasks.

Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model.

Various modelling techniques are available to understand the temporal and spatial variations of the phenology of species. Scientists often rely on correlative models, which establish a statistical relationship between a response variable (such as species abundance or presence-absence) and a set of predominantly abiotic covariates. The choice of the modeling approach, i.

Machine learning-enhanced high-resolution exposure assessment of ultrafine particles.

Ultrafine particles (UFPs) under 100 nm pose significant health risks inadequately addressed by traditional mass-based metrics. The WHO emphasizes particle number concentration (PNC) for assessing UFP exposure, but large-scale evaluations remain scarce. In this study, we developed a stacking-based machine learning framework integrating data-driven and physical-chemical models for a national-scale UFP exposure assessment at 1 km spatial and 1-hour temporal resolutions, leveraging long-term standardized PNC measurements in Switzerland.

Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease.

The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge.

A study on the effect of blue light on kidney stone formation in rats via the brain-kidney axis.

Kidney stones, a common urological disease, may involve the brain-kidney axis in their formation, though the specific mechanism remains unclear. This study aimed to investigate the effects of blue light on relevant metabolic indicators and oxidative stress status in rats with kidney stones through the brain-kidney axis. A rat model of kidney stones was established by administering 1% ethylene glycol and 2% ammonium chloride.

Chromosome level genome assembly of 'Wanfeng' almond (Prunus dulcis).

We assembled a chromosome-level genome of Chinese native 'Wanfeng' almond, with a size of 288.53 Mb and a contig N50 of 30.48 Mb.

Cross-talk-free, high extinction ratio, and ultra-compact all‑optical 4 × 2 encoder using graphene-based plasmonic waveguides.

This paper presents an all-optical 4 × 2 encoder based on graphene-plasmonic waveguides for operation in the wavelength range of 8-12 μm. The basic plasmonic waveguide consists of a silicon (Si) strip and a graphene sheet supported by two dielectric ridges. Surface plasmon polaritons (SPPs) are stimulated in the spatial gap between the graphene sheet and the Si strip.

Coherent harmonic generation of magnons in spin textures.

Harmonic generation, a notable non-linear phenomenon, has promising applications in information processing. For spin-waves in ferromagnetic materials, great progress has been made in the generation higher harmonics, however probing the coherence of these higher harmonics is challenging. Here, using in-situ diamond sensors, we study the coherent harmonic generation of spin waves in a soft ferromagnet.

Association between triglyceride-glucose index and carotid atherosclerosis in Chinese steelworkers: a cross-sectional study.

This study aims to explore the association between the triglyceride-glucose (TyG) index and the risk of carotid atherosclerosis (CAS) among Chinese steelworkers. This is a cross-sectional study involving a total of 4,203 Chinese steelworkers. The TyG index was calculated using the formula: TyG = Ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL) / 2].

Dynamic urban morphology mapping in Chinese cities based on local climate zone approach.

Urbanization have been significantly reshaping the form of urban areas and natural landscapes, leading to complex urban morphologies. In 2012, the Local Climate Zone (LCZ) classification was proposed to address this issue and has since been widely adopted in urban climate studies globally. Despite its prevalence, literature on dynamic mapping of urban morphology remains sparse, making it difficult to delve into the study of urban renewal year by year.

Superconducting quantum computing optimization based on multi-objective deep reinforcement learning.

Deep reinforcement learning is considered an effective technology in quantum optimization and can provide strategies for optimal control of complex quantum systems. More precise measurements require simulation control at multiple experimental stages. Based on this, we improved a multi-objective deep reinforcement learning method in mathematical convex optimization theory for multi-process quantum optimal control optimization.

Dynamic characteristics and safe operation speed threshold of metro train passing through curved bridge considering resilient wheel.

To investigate the dynamic characteristics and safe operation speed threshold of metro train passing through curved bridge (CB) considering resilient wheels, the mechanical connection characteristics of rim and web are discussed firstly. Based on the train-track-bridge interaction theory, the coupled dynamic model of metro train-CB considering resilient wheels is established. Then, the vehicle-bridge coupled dynamic characteristics under the excitation of long-short wave track irregularity are researched.

Fire-driven disruptions of global soil biochemical relationships.

Fires alter the stability of organic matter and promote soil erosion which threatens the fundamental coupling of soil biogeochemical cycles. Yet, how soil biogeochemistry and its environmental drivers respond to fire remain virtually unknown globally. Here, we integrate experimental observations and random forest model, and reveal significant divergence in the responses of soil biogeochemical attributes to fire, including soil carbon (C), nitrogen (N), and phosphorus (P) contents worldwide.

Degeneracy-breaking and long-lived multimode microwave electromechanical systems enabled by cubic silicon-carbide membrane crystals.

Cubic silicon-carbide crystals (3C-SiC), known for their high thermal conductivity and in-plane stress, hold significant promise for the development of high-quality (Q) mechanical oscillators. We reveal degeneracy-breaking phenomena in 3C-phase crystalline silicon-carbide membrane and present high-Q mechanical modes in pairs or clusters. The 3C-SiC material demonstrates excellent microwave compatibility with superconducting circuits.