Frontiers in Chemistry最新文献

Cyclic di-guanosine monophosphate (c-di-GMP) acts as a second messenger regulating bacterial behaviors including cell cycling, biofilm formation, adhesion, and virulence. Monitoring c-di-GMP levels is crucial for understanding these processes and designing inhibitors to combat biofilm-related antibiotic resistance. Here, we developed a genetically encoded biosensor, cdiGEBS, based on the transcriptional activity of the c-di-GMP-responsive transcription factor MrkH. Notably, cdiGEBS can detect both low and high cellular c-di-GMP levels, with a high fluorescence dynamic change of 23-fold. Moreover, it can detect subtle changes in c-di-GMP concentrations due to variations in the expression of c-di-GMP synthesis or degradation enzymes and can distinguish different synthesis activities among WspR mutants. These capabilities allow us to apply cdiGEBS for identifying new diguanylate cyclases and evaluating chemicals that modulate c-di-GMP levels, highlighting its potential as a high-throughput tool for screening inhibitors of c-di-GMP synthesis enzymes. Overall, cdiGEBS enhances the study of c-di-GMP-regulated functions and holds the potential for screening antimicrobials targeting c-di-GMP or its synthesis enzymes.

Gas sensors are now widely employed in many industries due to the rapid speed of industrialization and the growth of the Internet of Things. However, the wearability and mobility of traditional gas sensors are limited by their high reliance on external power sources. Nanogenerators (NGs) can compensate for their power source limitations when paired with gas sensors by transforming the environment's widely dispersed low-frequency energy into electrical energy, allowing for self-powered gas detection. The paper thoroughly examines the advancements made in the field of NG-based self-powered gas sensor research in recent years. A systematic description is given of the two main types of NG-based self-powered gas sensors. Lastly, the evolution of sensor use in a few typical gas sensing applications is highlighted, and the field's future development trend is anticipated.

Triboelectric nanogenerators (TENGs) have attracted great attention due to the simple manufacturing process, low cost, and diverse forms of energy harvesting. However, the energy collected by individual TENG is relatively limited, making it necessary to develop new method to enhance the energy harvesting capability of TENG. Here, we design a hybridized TENG that integrates a droplet-driven TENG and a wind-driven TENG, which exhibits excellent electrical performance. Under the simulated environment of medium rain with medium breeze, the hybridized TENG generates an output voltage of 95.10 V, a maximum average power of 18.15 μW, an energy of 181.54 μJ in 10 s, and charges a 1 μF capacitor to 43.29 V in 120 s. This work enables the harvesting of dispersed wind and droplet energy from the environment, providing new ideas and possibilities for online monitoring in remote areas and the construction of Internet of Things systems.

Introduction: Treatment of type 2 diabetes (T2D) remains a significant challenge because of its multifactorial nature and complex metabolic pathways. There is growing interest in finding new therapeutic targets that could lead to safer and more effective treatment options. Takeda G protein-coupled receptor 5 (TGR5) is a promising antidiabetic target that plays a key role in metabolic regulation, especially in glucose homeostasis and energy expenditure. TGR5 agonists are attractive candidates for T2D therapy because of their ability to improve glycemic control. This study used machine learning-based models (ML), molecular docking (MD), and molecular dynamics simulations (MDS) to explore novel small molecules as potential TGR5 agonists.

Methods: Bioactivity data for known TGR5 agonists were obtained from the ChEMBL database. The dataset was cleaned and molecular descriptors based on Lipinski's rule of five were selected as input features for the ML model, which was built using the Random Forest algorithm. The optimized ML model was used to screen the COCONUT database and predict potential TGR5 agonists based on their molecular features. 6,656 compounds predicted from the COCONUT database were docked within the active site of TGR5 to calculate their binding energies. The four top-scoring compounds with the lowest binding energies were selected and their activities were compared to those of the co-crystallized ligand. A 100 ns MDS was used to assess the binding stability of the compounds to TGR5.

Results: Molecular docking results showed that the lead compounds had a stronger affinity for TGR5 than the cocrystallized ligand. MDS revealed that the lead compounds were stable within the TGR5 binding pocket.

Discussion: The combination of ML, MD, and MDS provides a powerful approach for predicting new TGR5 agonists that can be optimised for T2D treatment.

Since ancient times, plants have provided humans with important bioactive compounds for the treatment of various diseases. Nine compounds were isolated from the roots and rhizomes of Caulophyllum robustum (a plant in the family Panaxaceae), including two new saponins C. Spanion A and C. Spanion B (1-2) and seven known saponins (3-9). The cytotoxicity of these compounds on human cancer cell lines was analyzed using MTT method. Compounds 6 and 9 exhibit cytotoxicity towards these three types of human cancer cells (<10 μM). By utilizing the SEA platform for target prediction, a common tumor related target CD81 was identified. The molecular docking of saponins 1, 2, 6, and 9 with CD81 protein showed strong binding affinities ranging from -4.5 to -7.1 kcal/mol. Research has shown that these compounds can become potential anti-tumor drugs. Further research is still recommended to understand its exact molecular mechanism and toxicological effects.

Noninvasive diagnosis of human diseases relies on the detection of molecular markers (probes) in a painless manner. Although extrinsic and intrinsic molecular markers are often used, intrinsic disease probes (molecular markers) are preferable because they are naturally present in our body, and deviation in their concentration from normal levels clearly indicates anomalies in human bodies, that is, diseases. In this study, we report noninvasive spectroscopic measurements of total haemoglobin (Hb), bilirubin, and the ratio of oxy- and deoxyhaemoglobin as disease markers for anaemia, jaundice, and oxygen deficiency, respectively, using a meticulously designed optical fibre probe. The challenges in designing the fibre probe for simultaneous noninvasive detection, including optical power, spectral density of the probing light, and resolution of the spectrometer, were found to be critical to accurate measurements. Finally, a fibre-less, highly portable, and low-cost prototype was developed and tested in human clinical trials for the diagnosis of diseases, and these results were compared with conventional techniques (blood tests).

A highly efficient and widely applicable adsorbent for the removal of methylene blue (MB) was created using nitrogen-doped and reduced graphene oxide (NRGO). The effects of NRGO mass, pH, contact time, and the initial MB concentration on the adsorption properties of MB onto NRGO were investigated. The results showed that the adsorption behavior remained stable within the pH range of 2.0-10.0, and the adsorption process gradually reached equilibrium after 24 h. Additionally, the adsorption kinetics and adsorption isotherms were discussed to propose a theoretical adsorption mechanism. Meanwhile, some characterizations including Scanning Electron Microscopy, Energy Disperse X-ray Spectroscopy, X-ray Photoelectron Spectroscopy, X-ray Powder Diffraction, Fourier Transform Infrared Spectroscopy, etc. were used to explore potential adsorption mechanism, which indicated the physisorption caused by π-π bonds was the main adsorption mechanism. NRGO exhibits efficient MB absorption and holds significant potential application for the wastewater treatment.