Search

Tea contains a variety of bioactive components, including catechins, amino acids, tea pigments, caffeine and tea polysaccharides, which exhibit multiple biological activities. These functional components in tea provide a variety of unique flavors, such as bitterness, astringency, sourness, sweetness and umami, which meet the demand of people for natural plant drinks with health benefits and pleasant flavor. Meanwhile, the traditional process of tea plantation, manufacturing and circulation are often accompanied by the safety problems of pesticide residue, heavy metal, organic solvents and other exogenous risks. High-quality tea extract refers to the special tea extract obtained by enriching the specific components of tea. Through green and efficient extraction technologies, diversed high-quality tea extracts such as high-fragrance and high-amino acid tea extracts, low-caffeine and high-catechin tea extracts, high-bioavailability and high-theaflavin tea extracts, high-antioxidant and high-tea polysaccharide tea extracts, high-umami-taste and low-bitter and astringent taste tea extracts are produced. Furthermore, rapid detection, green control and intelligent processing are applied to monitor the quality of tea in real-time, which guarantee the stability and safety of high-quality tea extracts with enhanced efficiency. These emerging technologies will realize the functionalization and specialization of high-quality tea extracts, and promote the sustainable development of tea industry. HIGHLIGHTS: Main high-quality tea extracts and their preparation methods were introduced. Potential pollutants in the processing of tea extracts and their detection methods were proposed. Emerging intelligent processing technologies of tea extract were summarized. The applications of high-quality tea extracts in food industry were explored. Future trends of tea extracts and relevant suggestions were presented. [ABSTRACT FROM AUTHOR]

The emergence of multidrug-resistant bacteria is a critical global crisis that poses a serious threat to public health, particularly with the rise of multidrug-resistant Staphylococcus aureus. Accurate assessment of drug resistance is essential for appropriate treatment and prevention of transmission of these deadly pathogens. Early detection of drug resistance in patients is critical for providing timely treatment and reducing the spread of multidrug-resistant bacteria. This study aims to develop a novel risk assessment framework for S. aureus that can accurately determine the resistance to multiple antibiotics. The comprehensive 7-year study involved ˃20 000 isolates with susceptibility testing profiles of six antibiotics. By incorporating mass spectrometry and machine learning, the study was able to predict the susceptibility to four different antibiotics with high accuracy. To validate the accuracy of our models, we externally tested on an independent cohort and achieved impressive results with an area under the receiver operating characteristic curve of 0. 94, 0.90, 0.86 and 0.91, and an area under the precision–recall curve of 0.93, 0.87, 0.87 and 0.81, respectively, for oxacillin, clindamycin, erythromycin and trimethoprim-sulfamethoxazole. In addition, the framework evaluated the level of multidrug resistance of the isolates by using the predicted drug resistance probabilities, interpreting them in the context of a multidrug resistance risk score and analyzing the performance contribution of different sample groups. The results of this study provide an efficient method for early antibiotic decision-making and a better understanding of the multidrug resistance risk of S. aureus. [ABSTRACT FROM AUTHOR]

The emergence of drug resistance among pathogens has become a major challenge to human health on a global scale. Among them, antibiotic resistance is already a critical issue, and finding new therapeutic agents to address this problem is therefore urgent. One of the most promising alternatives to antibiotics are antibacterial peptides (ABPs), i.e., short peptides with antibacterial activity. In this study, we propose a novel ABP recognition method, called ABPCaps. It integrates a convolutional neural network (CNN), a long short-term memory (LSTM), and a new type of neural network named the capsule network. The capsule network can extract critical features automatically from both positive and negative samples, leading to superior performance of ABPCaps over all baseline models built on hand-crafted peptide descriptors. Evaluated on independent test sets, ABPCaps achieves an accuracy of 93.33% and an F1-score of 91.34%, and consistently outperforms the baseline models in other extensive experiments as well. Our study demonstrates that the proposed ABPCaps, built on the capsule network method, is a valuable addition to the current state-of-the-art in the field of ABP recognition and has significant potential for further development. [ABSTRACT FROM AUTHOR]

MicroRNA (miRNA)–target interaction (MTI) plays a substantial role in various cell activities, molecular regulations and physiological processes. Published biomedical literature is the carrier of high-confidence MTI knowledge. However, digging out this knowledge in an efficient manner from large-scale published articles remains challenging. To address this issue, we were motivated to construct a deep learning-based model. We applied the pre-trained language models to biomedical text to obtain the representation, and subsequently fed them into a deep neural network with gate mechanism layers and a fully connected layer for the extraction of MTI information sentences. Performances of the proposed models were evaluated using two datasets constructed on the basis of text data obtained from miRTarBase. The validation and test results revealed that incorporating both PubMedBERT and SciBERT for sentence level encoding with the long short-term memory (LSTM)-based deep neural network can yield an outstanding performance, with both F1 and accuracy being higher than 80% on validation data and test data. Additionally, the proposed deep learning method outperformed the following machine learning methods: random forest, support vector machine, logistic regression and bidirectional LSTM. This work would greatly facilitate studies on MTI analysis and regulations. It is anticipated that this work can assist in large-scale screening of miRNAs, thereby revealing their functional roles in various diseases, which is important for the development of highly specific drugs with fewer side effects. Source code and corpus are publicly available at https://github.com/qi29. [ABSTRACT FROM AUTHOR]

Deferoxamine (DFO) is an iron chelator with FDA approval for the clinical treatment of iron excess. As a well-established stabilizer of hypoxia-inducible factor-1α (HIF-1α), DFO can efficiently upregulate HIF-1α and relevant downstream angiogenic factors, leading to accelerated vascularization. Moreover, as increasing studies have focused on DFO as a hypoxia-mimetic agent in recent years, it has been shown that DFO exhibited multiple functions, including stem cell regulation, immunoregulation, provascularization, and pro-osteogenesis. On the contrary, DFO can bind excess iron ions in wounds of chronic inflammation, while serving as an antioxidant with the characteristic of removing reactive oxygen species. Collectively, these characteristics make DFO a potent modulator in tissue engineering for increasing tissue integration of biomaterials in vivo and facilitating wound healing. This review outlines the activity of DFO as a representative hypoxia-mimetic agent in cells as well as the evolution of its application in tissue engineering. It can be concluded that DFO is a medication with tremendous promise and application value in future trends, which can optimize biomaterials and existing tissue engineering techniques for tissue regeneration.

An open critical-size bone defect is a major medical problem because of the difficulty in self-healing, leading to an increased risk of bacterial infection owing to wound exposure, resulting in treatment failure. Herein, a composite hydrogel was synthesized by chitosan, gallic acid, and hyaluronic acid, termed "CGH." Hydroxyapatite was modified with polydopamine (PDA@HAP) and introduced to CGH to obtain a mussel-inspired mineralized hydrogel (CGH/PDA@HAP). The CGH/PDA@HAP hydrogel exhibited excellent mechanical performances, including self-healing and injectable properties. Owing to its three-dimensional porous structure and polydopamine modifications, the cellular affinity of the hydrogel was enhanced. When adding PDA@HAP into CGH, Ca 2+ and PO 4 3- could release and then promoted differentiation of BMSCs into osteoblasts. Without any osteogenic agent or stem cells, the area of new bone at the site of defect was enhanced and the newly formed bone had a dense trabecular structure after implanting of the CGH/PDA@HAP hydrogel for 4 and 8 weeks. Moreover, the growth of Staphylococcus aureus and Escherichia coli was effectively inhibited through the grafting of gallic acid onto chitosan. Above, this study provides a reasonable alternative strategy to manage open bone defects., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Pang, Guan, Zhu, Niu, Zhu and Lin.)