Disturbances of the gut microbiota-derived tryptophan metabolites as key actors in vagotomy-induced mastitis in mice.

Previous studies have demonstrated that gut microbiota dysbiosis promotes the development of mastitis. The interaction of the vagus nerve and gut microbiota endows host homeostasis and regulates disease development, but whether the vagus nerve participates in the pathogenesis of mastitis is unclear. Here, vagotomized mice exhibit disruption of the blood-milk barrier and mammary gland inflammation. Notably, mastitis and barrier damage caused by vagotomy are dependent on the gut microbiota, as evidenced by antibiotic treatment and fecal microbiota transplantation. Vagotomy significantly alters the gut microbial composition and tryptophan metabolism and reduces the 5-hydroxyindole acetic acid (5-HIAA) level. Supplementation with 5-HIAA alleviates vagotomy-induced mastitis, which is associated with the activation of the aryl hydrocarbon receptor (AhR) and subsequent inhibition of the NF-κB pathway. Collectively, our findings indicate the important role of the vagus-mediated gut-mammary axis in the pathogenesis of mastitis and imply a potential strategy for the treatment of mastitis by targeting the vagus-gut microbiota interaction.

A rolling-mode triboelectric nanogenerator with multi-tunnel grating electrodes and opposite-charge-enhancement for wave energy harvesting.

In light of the crucial role of marine ecosystems and the escalating environmental conservation challenges, it is essential to conduct marine monitoring to help implement targeted environmental protection measures efficiently. Energy harvesting technologies, particularly triboelectric nanogenerators (TENGs), have great potential for prolonging the lifespan and enhancing the reliability of sensors in remote areas. However, the high internal resistance, low current, and friction-induced abrasion issues of TENGs limit their performance in practical applications. This work presents a rolling mode triboelectric nanogenerator that utilizes multi-tunnel grating electrodes and the opposite-charge-enhancement mechanism to harvest wave energy efficiently. The device achieves significant instantaneous and root mean square power density of 185.4 W/(m3·Hz) and 10.92 W/(m3·Hz), respectively. By utilizing stacked devices and an exclusively designed power management module, a self-powered ocean sensing system including computing and long-range wireless communication (0.8 km) capabilities was developed. Laboratory and in-situ ocean tests were conducted to assess and validate the system. This work offers a potential solution for the challenging deployment of marine self-powered sensing nodes.

Design, Synthesis, and Evaluation of 18F-Labeling CYP1B1 PET Tracer Based on 2-Phenylquinazolin.

Cytochrome P450 (CYP)1B1 has been identified to be specifically overexpressed in several solid tumors, thus it's a potential target for the detection of tumors. Based on the 2-Phenylquinazolin CYP1B1 inhibitors, we designed and synthesized several positron emission computed tomography (PET) imaging probes targeting CYP1B1. Through IC50 determinations, most of these probes exhibited good affinity and selectivity to CYP1B1. Considering their affinity, solubility, and their 18F labeling methods, we chose compound 5c as the best candidate. The 18F radiolabeling of [18F] 5c was easy to handle with good radiolabeling yield and radiochemical purity. In vitro and in vivo stability study indicated that probe [18F]5c has good stability. In cell binding assay, [18F]5c could be specifically taken up by tumor cells, especially HCT-116 cells. Although the tumor-blood (T/B) and tumor-muscle (T/M) values and PET imaging results were unsatisfied, it is still possible to develop PET probes targeting CYP1B1 by structural modification on the basis of 5c in the future.

ZEA mediates autophagy through the ROS-AMPK-m-TOR pathway to enhance the susceptibility of mastitis induced by Staphylococcus aureus in mice.

Mastitis is an inflammatory response of the mammary tissue caused by pathogenic bacterial infections, especially Staphylococcus aureus (S. aureus). Zearalenone (ZEA) is one of the common mycotoxins in moldy feed, which usually affects the cow's resistance to pathogenic microorganisms. However, it is not well understood whether ZEA affects the development of mastitis. Therefore, this study aimed to investigate the role of ZEA in the development of S. aureus-induced mastitis in mice. The results showed that administered daily by gavage for one week of ZEA (40 mg/kg) aggravated the severity of mastitis induced by S. aureus. Furthermore, we found that ZEA promotes the adhesion and invasion of S. aureus into mouse mammary epithelial cells (MMEC) by activating autophagy, and the activation of autophagy mediated by ROS-AMPK-m-TOR pathway. Taken together, the results showed that ZEA enhances S. aureus-induced mastitis susceptibility through activating autophagy mediated by ROS-AMPK-mTOR signaling pathway.

Flexible Fusion Network for Multi-Modal Brain Tumor Segmentation.

Automated brain tumor segmentation is crucial for aiding brain disease diagnosis and evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely adopted approach in the field of brain tumor segmentation that can provide different modality images. It is critical to leverage multi-modal images to boost brain tumor segmentation performance. Existing works commonly concentrate on generating a shared representation by fusing multi-modal data, while few methods take into account modality-specific characteristics. Besides, how to efficiently fuse arbitrary numbers of modalities is still a difficult task. In this study, we present a flexible fusion network (termed F 2Net) for multi-modal brain tumor segmentation, which can flexibly fuse arbitrary numbers of multi-modal information to explore complementary information while maintaining the specific characteristics of each modality. Our F 2Net is based on the encoder-decoder structure, which utilizes two Transformer-based feature learning streams and a cross-modal shared learning network to extract individual and shared feature representations. To effectively integrate the knowledge from the multi-modality data, we propose a cross-modal feature-enhanced module (CFM) and a multi-modal collaboration module (MCM), which aims at fusing the multi-modal features into the shared learning network and incorporating the features from encoders into the shared decoder, respectively. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our F 2Net over other state-of-the-art segmentation methods.

Deep Learning-Assisted Triboelectric Smart Mats for Personnel Comprehensive Monitoring toward Maritime Safety.

Monitoring the crew of a ship can be performed by combining sensors and artificial intelligence methods to process sensing data. In this study, we developed a deep learning (DL)-assisted minimalist structure triboelectric smart mat system for obtaining abundant crew information without the privacy concerns of taking video. The smart mat system is fabricated using a conductive sponge with different filling rates and a fluorinated ethylene propylene membrane. The proposed dual-channel measurement method improves the stability of the generated signal. Comprehensive crew and cargo monitoring, including personnel and status identification, as well as positioning and counting functions are realized by the DL-assisted triboelectric smart mat system according to the analysis of instant sensory data. Real-time monitoring of crews through fixed and mobile devices improves the ability and efficiency of handling emergencies. The smart mat system provides privacy concerns and an effective way to build ship Internet of Things and ensure personnel safety.