Classified detection of antibiotics using FeO@Eu(BDC) as a fluorescent probe.

Background: In recent years, fluorescence sensing technology by rare-earth metal-organic frameworks (Ln-MOFs) as probes has garnered extensive attention in the domains of environmental quality testing, pollutant reduction, and biomolecule analysis because of its non-disruptive nature, rapid response, and high sensitivity. The research on aided magnetic controlling has further advanced the industrial value of Ln-MOFs, but the accomplishment of high specificity and rapid recovery still is a challenge for the magnetic Ln-MOFs in practical applications.

Results: A magnetic Ln-MOFs, FeO@Eu(BDC), doped with FeO using HBDC as the ligand and Eu as the central ion through co-precipitation, has been successfully synthesized.

Porcine deltacoronavirus nucleocapsid protein antagonizes JAK-STAT signaling pathway by targeting STAT1 through KPNA2 degradation.

Porcine deltacoronavirus (PDCoV) is an enteric pathogenic coronavirus that causes acute and severe watery diarrhea in piglets and has the ability of cross-species transmission, posing a great threat to swine production and public health. The interferon (IFN)-mediated signal transduction represents an important component of virus-host interactions and plays an essential role in regulating viral infection. Previous studies have suggested that multifunctional viral proteins encoded by coronaviruses antagonize the production of IFN via various means.

Analysis of Factors Affecting Concentrations and Concentration-To-Dose Ratios of Trazodone.

Background: Trazodone is prescribed for several clinical conditions. Multiple factors may affect trazodone to reach its therapeutic reference range. The concentration-to-dose (C/D) ratio can be used to facilitate the therapeutic drug monitoring of trazodone.

Artificial intelligence-based model for dose prediction of sertraline in adolescents: a real-world study.

Background: Variability exists in sertraline pharmacokinetic parameters in individuals, especially obvious in adolescents. We aimed to establish an individualized dosing model of sertraline for adolescents with depression based on artificial intelligence (AI) techniques.

Methods: Data were collected from 258 adolescent patients treated at the First Hospital of Hebei Medical University between December 2019 to July 2022.

Obscuring effect of income inequality and moderating role of financial literacy in the relationship between digital finance and China's household carbon emissions.

Households have emerged as one of the primary sources for carbon emissions in China, thus posing challenges to the "dual carbon" objectives. Digital finance, an emergent form of industry that fused advanced technology with financial services, had a pronounced impact on household carbon emissions stemming from daily consumption. However, the mechanisms driving this impact have not been adequately examined.

TFRegNCI: Interpretable Noncovalent Interaction Correction Multimodal Based on Transformer Encoder Fusion.

The interpretability is an important issue for end-to-end learning models. Motivated by computer vision algorithms, an interpretable noncovalent interaction (NCI) correction multimodal (TFRegNCI) is proposed for NCI prediction. TFRegNCI is based on RegNet feature extraction and a transformer encoder fusion strategy.

Separate and combined effects of cadmium (Cd) and nonylphenol (NP) on growth and antioxidative enzymes in Hydrocharis dubia (Bl.) Backer.

Cadmium (Cd) is considered a priority pollutant, and nonylphenol (NP) is a common organic pollutant in water environments. However, the ecological risks of combined Cd and NP pollution have not been fully elucidated. In this study, the effects of Cd, NP, and Cd-NP on the growth and physiology of Hydrocharis dubia (Bl.

Hepatoprotective effect and structural analysis of Hedysarum polysaccharides in vivo and in vitro.

The crude Hedysarum polysaccharides (HPS: HPS-50 and HPS-80) obtained from Radix Hedysari exhibited great pharmacological activities in our previous research. This study investigated the effects of HPS on lipopolysaccharide (LPS)/D-galactosamine (D-GalN)-induced acute liver injury (ALI) in mice and LPS-induced injury in LO2 cells, as well as the relationship between structural characteristics and hepatoprotective activities. The in vivo results showed that compared with HPS-80, HPS-50 showed stronger hepatoprotection, which improved histopathological changes to normal levels.

Green synthesis, characterization of Radix -mediated silver nanoparticles and their use for sensitive colorimetric detection of Pb in the Yellow River medium.

In this study, a safe, rapid, and environment-friendly green synthesis of silver nanoparticles using the alcohol extract of Radix (RH-AgNPs) was developed, the alcohol extract of Radix (RH) acted as the reducing agent, stabilizer, and modifier. The main components of RH were determined using high-performance liquid chromatography (HPLC). The particle size and morphology of RH-AgNPs were optimized and characterized by a series of techniques.

DeepNCI: DFT Noncovalent Interaction Correction with Transferable Multimodal Three-Dimensional Convolutional Neural Networks.

A multimodal deep learning model, DeepNCI, is proposed for improving noncovalent interactions (NCIs) calculated via density functional theory (DFT). DeepNCI is composed of a three-dimensional convolutional neural network (3D CNN) for abstracting critical and comprehensive features from 3D electron density, and a neural network for modeling one-dimensional quantum chemical properties. By merging features from two networks, DeepNCI is able to reduce the root-mean-square error of DFT-calculated NCI from 1.

Efficient prediction for high precision CO-N potential energy surface by stacking ensemble DNN.

High-dimensional potential energy surface (PES) for van der Waals systems with spectroscopic accuracy, is of great importance for quantum dynamics and an extremely challenge job. CO-N is a typical van der Waals system and its high-precision PES may help elucidate weak interaction mechanisms. Taking CO-N potential energies calculated by CCSD(T)-F12b/aug-cc-pVQZ as the benchmark, we establish an accurate, robust, and efficient machine learning model by using only four molecular structure descriptors based on 7966 benchmark potential energies.

Structural characterization of a polysaccharide from Radix Hedysari and its protective effects against HO-induced injury in human gastric epithelium cells.

The gastroprotective effects of polysaccharides had become a hot topic in the field of functional polysaccharides research. Three polysaccharides, namely HPS-80-1, HPS-80-2, and HPS-80-3 were purified by DEAE-52 column chromatography. The thermodynamic characteristics, scanning electron microscopy, and Congo red experimental results of the above polysaccharides were greatly distinctive.

The amelioration of ulcerative colitis induced by Dinitrobenzenesulfonic acid with Radix Hedysari.

Ulcerative colitis (UC) is a chronic inflammatory disease with an unknown precise etiology. This study proves that Radix Hedysari (RH) ameliorates UC. Four RH extracts were used to ameliorate UC induced by 2,4-Dinitrobenzenesulfonic acid by 7 days intervention in agreement to preliminary studies.

Responses of Hydrocharis dubia (Bl.) Backer and Trapa bispinosa roxb. to tetracycline exposure.

The presence of tetracycline is ubiquitous and has adverse effects on aquatic systems. A hydroponic experiment was conducted to investigate the ecological sensitivity of Hydrocharis dubia (Bl.) Backer and Trapa bispinosa Roxb.

Learning temporal rules to forecast instability in continuously monitored patients.

Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity.

Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

Objective: The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability.

Design: Observational cohort study.

Setting: Twenty-four-bed trauma step-down unit.