Modification Strategies and Prospects for Enhancing the Stability of Black Phosphorus.

Black phosphorus is a two-dimensional layer material with promising applications due to its many excellent physicochemical properties, including high carrier mobility, ambipolar field effect and unusual in-plane anisotropy. Currently, BP has been widely used in biomedical engineering, photocatalysis, semiconductor devices, and energy storage electrode materials. However, the unique structure of BP makes it highly chemically active, leading to its easy oxidation and degradation in air, which limits its practical applications.

Virulence gene polymorphisms in Shandong Helicobacter pylori strains and their relevance to gastric cancer.

Background: Helicobacter pylori (H. pylori) virulence factors, particularly the cagA and vacA genotypes, play important roles in the pathogenic process of gastrointestinal disease.

Methods: The cagA and vacA genotypes of 87 H.

Collagen protein-chitosan nerve conduits with neuroepithelial stem cells promote peripheral nerve regeneration.

The peripheral nervous system consists of ganglia, nerve trunks, plexuses, and nerve endings, that transmit afferent and efferent information. Regeneration after a peripheral nerve damage is sluggish and imperfect. Peripheral nerve injury frequently causes partial or complete loss of motor and sensory function, physical impairment, and neuropathic pain, all of which have a negative impact on patients' quality of life.

Adsorption of haem by magnetic chitosan microspheres: Optimal conditions, adsorption mechanisms and density functional theory analyses.

Magnetic chitosan microspheres (Al@CTS@FeO) were prepared for haem separation via chemical cross-linking of chitosan, FeO and AlCl·6HO. The properties of the Al@CTS@FeO microspheres were investigated through techniques including XRD, TEM, FTIR, BET analysis, SEM, TG, VSM, XPS and pHpzc analysis. The haem adsorption of Al@CTS@FeO was optimized via a Box-Behnken design (BBD) with three operating factors: FeO dose (0.

Cell-free analysis reveals the role of RG/RGG motifs in DDX3X phase separation and their potential link to cancer pathogenesis.

The DEAD-box RNA helicase DDX3X is a multifunctional protein involved in RNA metabolism and stress responses. In this study, we investigated the role of RG/RGG motifs in the dynamic process of liquid-liquid phase separation (LLPS) of DDX3X using cell-free assays and explored their potential link to cancer development through bioinformatic analysis. Our results demonstrate that the number, location, and composition of RG/RGG motifs significantly influence the ability of DDX3X to undergo phase separation and form self-aggregates.

Metabolomic and Transcriptomic Analyses Revealed Lipid Differentiation Mechanisms in at Ambient Conditions.

is one of the most popular mushroom species in the world; however, mushrooms are highly susceptible to browning due to the absence of a protective cuticle layer and high respiration rate. The molecular mechanism underlying the process of mushroom browning needs to be explored. Here, we analyzed the transcriptomic and metabolomic data from at ambient temperature.

Coupling dynamics of locally active memristor based neurons.

Brain-like dynamics require third-order or higher-order complexity. In order to investigate the coupling neuromorphic behaviors of identical third-order memristive neurons, this paper begins with the aim of exploring two identical neuron based dynamics under distinct operating regimes and coupling strengths. Without coupling, the single neuron can exhibit resting states, periodic spikes, or chaos depending on the bias condition.

SLC12A8 upregulation promotes colorectal cancer progression and chemoresistance.

Background: Colorectal cancer (CRC), a prevalent gastrointestinal malignant disease, causes substantial morbidity and mortality. Identification of novel prognostic biomarkers and therapeutic targets is critically needed to improve patient outcomes. Although solute carrier family 12 member 8 (SLC12A8) has high expression in various tumors and affects tumor progression, its role in CRC remains unclear.

Deep-learning radiomics based on ultrasound can objectively evaluate thyroid nodules and assist in improving the diagnostic level of ultrasound physicians.

Background: The incidence rate of thyroid nodules has reached 65%, but only 5-15% of these modules are malignant. Therefore, accurately determining the benign and malignant nature of thyroid nodules can prevent unnecessary treatment. We aimed to develop a deep-learning (DL) radiomics model based on ultrasound (US), explore its diagnostic efficacy for benign and malignant thyroid nodules, and verify whether it improved the diagnostic level of physicians.

The synergistic effect of Cl doping and Bi coupling to promote the carrier separation of BiOBr for efficient photocatalytic nitrogen reduction.

Photocatalytic nitrogen reduction reaction (NRR) is a sustainable process for ammonia synthesis under mild conditions. However, photocatalytic NRR activity and are generally limited by inefficient carrier separation and transfer. Therefore, parallel engineering of bulk phase doping and surface coupling is critical to achieving the goal of efficient NRR.

The intelligent evaluation in ice and snow tourism based on LSTM network.

In order to augment the efficacy of the intelligent evaluation model for assessing the suitability of ice and snow tourism, this study refines the model by incorporating the Long Short-Term Memory (LSTM) network within the framework of the Internet of Things (IoT). The investigation commences with an elucidation of the application of IoT technology in environmental detection. After this, an analysis is conducted on the structure of LSTM and its merits in the realm of time series prediction.

Metagenomics-based gene exploration and biochemical characterization of novel glucoamylases and α-amylases in Daqu and Pu-erh tea microorganisms.

α-Amylases and glucoamylases play a crucial role in starch degradation for various industrial applications. Further exploration of novel α-amylases and glucoamylases with diverse enzymatic characteristics is necessary. In this study, metagenomics analysis revealed a high abundance of these enzymes in the microorganisms of Daqu and Pu-erh tea, identifying 271 glucoamylases and 232 α-amylases with significant sequence identity to known enzymes.

Copper Single-Atom-Based Metal-Organic Framework for Ultrasound-Enhanced Nanocatalytic Therapy.

Chemodynamic therapy (CDT) is an emerging therapeutic modality triggered by endogenous substances in the tumor microenvironment (TME) to generate reactive oxygen species. However, the mild acid pH, low HO concentration, and overexpressed glutathione can suppress the CDT efficiency. Herein, ultrasound (US)-triggered Cu-based single-atom nanoenzymes (FA-NH-UiO-66-Cu, FNUC) are constructed with the performance of target and glutathione depletion.

Does the digital economy improve female employment? A cross-country panel data analysis.

The digital economy has had an impact on the female employment rate over time. Currently, the researchers are more interested in investigating the impacts of the digital economy by focusing on its various aspects of female employment. The current study is motivated by this renewed interest to investigate the impact of the digital economy on female employment rates in Asian Developing Countries from 1990 to 2021.

Interaction mechanism and compatibility studies of silk protein peptide (SPP) with the common surfactants SDS and DTAB.

The molecular interaction of low-molecular-weight SPP with common surfactants (SDS and DTAB) is a more complicated process than has been long believed. In this work, the interaction mechanism between SDS/DTAB and SPP was proposed using multiple methods including conductivity measurements, ST, UV-vis, FT-IR, DLS, fluorescence spectroscopy, and molecular docking simulations. Moreover, the foaming properties of the mixed systems were studied, and they were evaluated as cosmetics preservatives.

DSB-induced oxidative stress: Uncovering crosstalk between DNA damage response and cellular metabolism.

While that ROS causes DNA damage is well documented, there has been limited investigation into whether DNA damages and their repair processes can conversely induce oxidative stress. By generating a site-specific DNA double strand break (DSB) via I-SceI endonuclease expression in S. cerevisiae without damaging other cellular components, this study demonstrated that DNA repair does trigger oxidative stress.

A Tight-Connection -CN/BiOBr (001) S-Scheme Heterojunction Photocatalyst for Boosting Photocatalytic Degradation of Organic Pollutants.

The efficient separation of photogenerated charge carriers and strong oxidizing properties can improve photocatalytic performance. Here, we combine the construction of a tightly connected S-scheme heterojunction with the exposure of an active crystal plane to prepare -CN/BiOBr for the degradation of high-concentration organic pollutants. This strategy effectively improves the separation efficiency of photogenerated carriers and the number of active sites.

Exploring the Structural Stability of 1T-PdO and the Interface Properties of the 1T-PdO/Graphene Heterojunction.

Motivated by a recent study on the air stability of PdSe, which also reports the metastability of the PdO monolayer [Hoffman A. N..

CuO decorated TiCT MXene composites for non-enzymatic glucose sensing with large linear ranges.

TiCT MXene/CuO composites were prepared by acid etching combined with electrochemical technique. The abundant active sites on the surface of MXene greatly increase the loading of CuO nanoparticles, and the synergistic effect between the different components of the composite can accelerate the oxidation reaction of glucose. The results indicate that at the working potential of 0.

PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.

Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions.