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Postbiotics are defined as "preparation of inanimate microorganisms and/or their components that confers a health benefit on the host". Postbiotics have unique advantages over probiotics, such as stability, safety, and wide application. Although postbiotics are research hotspots, the research on them is still very limited. This review provides comprehensive information on the scope of postbiotics, the preparation methods of inanimate microorganisms, and the application and mechanisms of postbiotics in metabolic syndrome (MetS). Furthermore, the application trends of postbiotics in the food industry are reviewed. It was found that postbiotics mainly include inactivated microorganisms, microbial lysates, cell components, and metabolites. Thermal treatments are the main methods to prepare inanimate microorganisms as postbiotics, while non-thermal treatments, such as ionizing radiation, ultraviolet light, ultrasound, and supercritical CO2, show great potential in postbiotic preparation. Postbiotics could ameliorate MetS through multiple pathways including the modulation of gut microbiota, the enhancement of intestinal barrier, the regulation of inflammation and immunity, and the modulation of hormone homeostasis. Additionally, postbiotics have great potential in the food industry as functional food supplements, food quality improvers, and food preservatives. In addition, the SWOT analyses showed that the development of postbiotics in the food industry exists both opportunities and challenges. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the effects of low-frequency ultrasound (US) combined with microbubbles (MBs) on breast cancer xenografts and explain its underlying mechanisms. A total of 20 xenografted nude mice were randomly divided into four groups: a group treated with US plus MBs (the US + MBs group), a group treated with US alone (the US group), a group treated with MBs alone (the MBs group), and a control group. In different groups, mice were treated with different US and injection regimens on an alternate day, three times in total. Histological changes, apoptosis of cells, microvascular changes, and the apoptosis index (AI) and microvascular density (MVD) of the breast cancer xenograft were analyzed after the mice were sacrificed. Results indicated that the tumor volume in the US + MBs group was smaller than that in the other three groups ( p < 0.001 for all). The rate of tumor growth inhibition in the US + MBs group was significantly higher than that in the US and MBs groups ( p < 0.001 for both). There were no significant differences in histological changes among the four groups. However, the AI was higher in the US + MBs group than that in the other three groups while the MVD was lower ( p < 0.001 for all). All in all, low-frequency US combined with MBs can effectively slow down the growth of breast cancer in nude mice. In summary, low-frequency US combined with MBs has a significant effect on breast cancer treatment. Cavitation, thermal effects, and mechanical effects all play a vital role in the inhibition of tumor growth., Competing Interests: The authors have no conflicts of interest to disclose., (2024, National Center for Global Health and Medicine.)

Granular-ball support vector machine (GBSVM) is a significant attempt to construct a classifier using the coarse-to-fine granularity of a granular ball as input, rather than a single data point. It is the first classifier whose input contains no points. However, the existing model has some errors, and its dual model has not been derived. As a result, the current algorithm cannot be implemented or applied. To address these problems, we fix the errors of the original model of the existing GBSVM and derive its dual model. Furthermore, a particle swarm optimization (PSO) algorithm is designed to solve the dual problem. The sequential minimal optimization (SMO) algorithm is also carefully designed to solve the dual problem. The latter is faster and more stable. The experimental results on the UCI benchmark datasets demonstrate that GBSVM is more robust and efficient. All codes have been released in the open source library available at: http://www.cquptshuyinxia.com/GBSVM.html or https://github.com/syxiaa/GBSVM.