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Inflammation is a fundamental defensive response to harmful stimuli, but the overactivation of inflammatory responses is associated with most human diseases. Reactive oxygen species (ROS) are a class of chemicals that are generated after the incomplete reduction of molecular oxygen. At moderate levels, ROS function as critical signaling molecules in the modulation of various physiological functions, including inflammatory responses. However, at excessive levels, ROS exert toxic effects and directly oxidize biological macromolecules, such as proteins, nucleic acids and lipids, further exacerbating the development of inflammatory responses and causing various inflammatory diseases. Therefore, designing and manufacturing biomaterials that scavenge ROS has emerged an important approach for restoring ROS homeostasis, limiting inflammatory responses and protecting the host against damage. This review systematically outlines the dynamic balance of ROS production and clearance under physiological conditions. We focus on the mechanisms by which ROS regulate cell signaling proteins and how these cell signaling proteins further affect inflammation. Furthermore, we discuss the use of potential and currently available-biomaterials that scavenge ROS, including agents that were engineered to reduce ROS levels by blocking ROS generation, directly chemically reacting with ROS, or catalytically accelerating ROS clearance, in the treatment of inflammatory diseases. Finally, we evaluate the challenges and prospects for the controlled production and material design of ROS scavenging biomaterials.

Cancer cells characterized by uncontrolled growth and proliferation require altered metabolic processes to maintain this characteristic. Metabolic reprogramming is a process mediated by various factors, including oncogenes, tumor suppressor genes, changes in growth factors, and tumor–host cell interactions, which help to meet the needs of cancer cell anabolism and promote tumor development. Metabolic reprogramming in tumor cells is dynamically variable, depending on the tumor type and microenvironment, and reprogramming involves multiple metabolic pathways. These metabolic pathways have complex mechanisms and involve the coordination of various signaling molecules, proteins, and enzymes, which increases the resistance of tumor cells to traditional antitumor therapies. With the development of cancer therapies, metabolic reprogramming has been recognized as a new therapeutic target for metabolic changes in tumor cells. Therefore, understanding how multiple metabolic pathways in cancer cells change can provide a reference for the development of new therapies for tumor treatment. Here, we systemically reviewed the metabolic changes and their alteration factors, together with the current tumor regulation treatments and other possible treatments that are still under investigation. Continuous efforts are needed to further explore the mechanism of cancer metabolism reprogramming and corresponding metabolic treatments. This article presented the metabolic reprogramming of tumor cells to adapt to their microenvironment distinct from normal cells. And this study reviewed the driving factors of metabolic reprogramming, the specific metabolic pathway changes affected, and the related treatment options, in order to provide some suggestions for future tumor treatment.

Angiotensin-converting enzyme (ACE), a zinc metalloprotein, is a central component of the renin–angiotensin system (RAS). It degrades bradykinin and other vasoactive peptides. Angiotensin-converting-enzyme inhibitors (ACE inhibitors, ACEIs) decrease the formation of angiotensin II and increase the level of bradykinin, thus relaxing blood vessels as well as reducing blood volume, lowering blood pressure and reducing oxygen consumption by the heart, which can be used to prevent and treat cardiovascular diseases and kidney diseases. Nevertheless, ACEIs are associated with a range of adverse effects such as renal insufficiency, which limits their use. In recent years, researchers have attempted to reduce the adverse effects of ACEIs by improving the selectivity of ACEIs for structural domains based on conformational relationships, and have developed a series of novel ACEIs. In this review, we have summarized the research advances of ACE inhibitors, focusing on the development sources, design strategies and analysis of structure-activity relationships and the biological activities of ACE inhibitors.

Objective The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces. Methods A retrospective analysis was conducted with 208 patients aged 14 to 44. A total of 624 high-quality digital images captured under standardized conditions were used to construct a deep learning model based on the Mask region-based convolutional neural network (Mask R-CNN). The model was trained to automate the detection of enamel demineralization. Its performance was compared to two junior dentists' diagnostic abilities. Results The model achieved an F1-score of 0.856 for detecting demineralized teeth on the validation set, a metric that reflects comprehensive diagnostic performance, demonstrating performance close to that of senior dentists. With the the model's assistance, the junior dentists' average F1-scores improved significantly-from 0.713 and 0.689 to 0.897 and 0.949, respectively (p < 0.05). The model accurately segmented tooth surfaces and detected demineralized areas, allowing for precise detection of demineralized areas and monitoring of lesion progression. Conclusion Deep learning can accurately segment tooth surfaces and lesion contours, enhancing the precision, accuracy, and efficiency of enamel demineralization diagnosis and area delineation.

Interfaces within batteries, such as the widely studied solid electrolyte interface (SEI), profoundly influence battery performance. Among these interfaces, the solid–solid interface between electrode materials and current collectors is crucial to battery performance but has received less discussion and attention. This review highlights the latest research advancements on the solid–solid interface between lithium metal (the next-generation anode) and current collectors (typically copper), focusing on factors affecting the Li-current collector interface and improvement strategies from perspectives of current collector substrate (lithiophilicity, crystal facets, mechanical properties, and topological structure), electrolyte chemistry, current density, stacking pressure, SEI, electric field and temperature, and provides a future directions and opportunities on this topic.

Enantioselective recognition and separation are the most important issues in the fields of chemistry, pharmacy, agrochemical, and food science. Here, we developed two optically active diamines showing aggregation‐induced emission (AIE) that can discriminate 5 kinds of chiral acids with high enantioselectivity. Especially, a very high fluorescence intensity ratio (Il/Id) of 281 for (±)‐Dibenzoyl‐d/l‐tartaric acid was obtained through the collection of fluorescence change after interaction with chiral AIE‐active diamine. By virtue of AIE property and intermolecular acid‐base interaction, enantioselective separation was facilely realized by simple filtration of the precipitates formed by chiral AIE luminogen (AIEgen) and one enantiomer in the racemic solution. The chiral HPLC data indicated that the precipitates of AIEgen/chiral acid possessed 82% l‐analyte (the enantiomeric excess value was assessed to be 64% ee). Therefore, this method can serve as a simple, convenient, and low‐cost tool for chiral detection and separation. A pair of optically active diamine with AIE characteristics were developed which can discriminate 5 kinds of chiral acids. Especially, a very high fluorescence intensity ratio (Il/Id) of 281 for (±)‐Dibenzoyl‐d/l‐tartaric acid through formation AIEgen/acid complexes. The enantioselective separation was carried out by using the chiral AIEgens, which enantiomer separation efficiency of (−)‐Dibenzoyl‐l‐tartaric acid was calculated to be 82% in the precipitates.

Hepatocellular carcinoma (HCC) has a poor prognosis due to the rapid disease progression and early metastasis. The metabolism program determines the proliferation and metastasis of HCC; however, the metabolic approach to treat HCC remains uncovered. Here, by analyzing the liver cell single-cell sequencing data from HCC patients and healthy individuals, we found that 6-phosphogluconolactonase (PGLS), a cytosolic enzyme in the oxidative phase of the pentose phosphate pathway (PPP), expressing cells are associated with undifferentiated HCC subtypes. The Cancer Genome Atlas database showed that high PGLS expression was correlated with the poor prognosis in HCC patients. Knockdown or pharmaceutical inhibition of PGLS impaired the proliferation, migration, and invasion capacities of HCC cell lines, Hep3b and Huh7. Mechanistically, PGLS inhibition repressed the PPP, resulting in increased reactive oxygen species level that decreased proliferation and metastasis and increased apoptosis in HCC cells. Overall, our study showed that PGLS is a potential therapeutic target for HCC treatment through impacting the metabolic program in HCC cells.

Breast cancer is a heterogeneous disease and ranks as one of the most lethal and frequently detected disease in the world. It poses significant challenges for precision therapy. To better decipher the patterns of heterogeneous nature in human genome and converge them into common functionals, mutational signatures are introduced to define the types of DNA damage, repair and replicative mechanisms that shape the genomic landscape of each cancer patient. In this study, we developed a deep learning (DL) model, MetaWise 2.0, based on pruning technology that improved model generalization with deep sparsity. We applied it to patient samples from multiple sequencing studies, and identified statistically significant mutational signatures associated with metastatic progression using Shapley additive explanations (SHAP). We also employed gene cumulative contribution abundance analysis to link the mutational signatures with relevant genes, which could unearth the shared molecular mechanisms behind tumorigenesis and metastasis of each patient and lead to novel therapeutic target identification. Our study illustrates that MetaWise 2.0 is an effective DL tool for discovering clinically meaningful mutational signatures in metastatic breast cancer (MBC) and relating them directly to relevant biological functions and gene targets. These findings could facilitate the development of novel therapeutic strategies and improve the clinical outcomes for individual patients.