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Objective This study explored the molecular mechanisms by which dexmedetomidine (Dex) alleviates cisplatin (CP)-induced acute kidney injury (AKI) in rats.Methods CP-induced AKI models were established, and Dex was intraperitoneally injected at different concentrations into rats in the model groups. Subsequently, rats were assigned to the control, CP, CP + Dex 10 μg/kg, and CP + Dex 25 μg/kg groups. After weighing the kidneys of the rats, the kidney arterial resistive index was calculated, and CP-induced AKI was evaluated. In addition, four serum biochemical indices were measured: histopathological damage in rat kidneys was detected; levels of inflammatory factors, interleukin (IL)-1β, IL-18, IL-6, and tumor necrosis factor alpha, in kidney tissue homogenate of rats were assessed through enzyme-linked immunosorbent assay (ELISA); and levels of NLRP-3, caspase-1, cleaved caspase-1, gasdermin D (GSDMD), and GSDMD-N in kidney tissues of rats were determined via western blotting.Results Dex treatment reduced nephromegaly and serum clinical marker upregulation caused by CP-induced AKI. In addition, hematoxylin and eosin staining revealed that Dex treatment relieved CP-induced kidney tissue injury in AKI rats. ELISA analyses demonstrated that Dex treatment reduced the upregulated levels of proinflammatory cytokines in the kidney tissue of AKI rats induced by CP, thereby alleviating kidney tissue injury. Western blotting indicated that Dex alleviated CP-induced AKI by inhibiting pyroptosis mediated by NLRP-3 and caspase-1.Conclusion Dex protected rats from CP-induced AKI, and the mechanism may be related to NLRP-3/Caspase-1-mediated pyroptosis.

In order to explore the microfloral succession in Takifugu flavidus during storage at 0 ℃, the spoilage bacteria were detected by 16S rDNA sequencing, the dominant spoilage bacteria in the late stage of storage were identified, and the changes in the microbial flora and the spoilage capacity of the dominant spoilage bacteria were analyzed. The results showed that the refrigerated shelf life of T. flavidus was about 5 days. In the early stage of storage (0–3 days), T. flavidus had the highest number of species of bacteria, including Pseudomonas, Streptophyta, and Shewanella, while in the late stage (6–15 days), the bacterial species were concentrated mainly in Pseudomonas and Shewanella. Ten dominant spoilage bacteria were isolated and identified, Shewanella and Pseudomonas accounting for a high proportion. Moreover, the spoilage capacity of several common dominant spoilage bacteria was analyzed, revealing that the decreasing order of the spoilage capacity was Pseudomonas fluorescent > P. azotoformans > Shewanella hafniensis > S. baltica. The results of this study provide a reference for understanding the spoilage mechanism of T. flavidus during cold storage and extending its shelf life.

Fractional vegetation coverage (FVC) is an important ecological parameter reflecting the growth of regional plants. Existing FVC estimation is often based on vegetation indices, especially the Normalized Difference Vegetation Index (NDVI). However, NDVI can be oversaturated and easily affected by problems such as ‘shadow’ in images, which leads to a decrease in the precision of FVC estimation. In this study, the Normalized Shaded Vegetation Index (NSVI) was used to comprehensively compare the estimation ability of FVC with NDVI, and the differences in FVC estimation ability between NSVI and NDVI were explored. Based on two dimensions of ‘bright’ and ‘shadow’ hierarchies and FVC ranks, four evaluation systems of signal-to-noise ratio (SNR), index range, statistical model and dimidiate pixel model were selected from Sentinel-2A MSI, Landsat-8 OLI and Resource-1-02D (ZY1-02D) images and covered a variety of topographic landscapes. The results showed that: (1) the ability of NSVI to resist soil background is slightly smaller than NDVI; (2) the range of NSVI and NDVI in hyperspectral images is slightly larger than multispectral images, and the ability of NSVI to detect vegetation information in medium-high-rank and high-rank FVC areas has obvious advantages; (3) in the same kind of regression model, the goodness of fit of NSVI, NDVI and FVC was slightly higher in bright areas than in shaded areas, and the goodness of fit of the five regression models obtained from NSVI showed a significant advantage in bright areas compared with NDVI, with the best fit of the cubic curve model; (4) the estimation accuracy of the dimidiate pixel model based on NSVI and NDVI is slightly higher in bright areas than in shaded areas, and the estimation ability of NSVI is slightly better than that of NDVI in bright areas with medium-high-rank and high-rank FVC. NSVI and NDVI have their own advantages in SNR, index range, statistical model and dimidiate pixel model, so it is suggested that when remote sensing estimation of FVC is carried out, NSVI should be preferred in medium-high-rank and high-rank FVC areas, and NDVI should be preferred in low-rank FVC areas in shaded areas.

Primary thyroid adenoid cystic carcinoma (PTACC) is an extremely rare type of mucin-secreting adenocarcinoma. Currently, it is difficult to diagnose, and it lacks standard treatment protocols. We report the case of a 53-year-old female patient with PTACC who underwent additional intensity-modulated radiotherapy 1 month after surgical treatment with an uneventful course. No invasion or distant metastasis was detected at the 7-month follow-up after radiotherapy, and the prognosis was favorable. In this case, herein, we have summarized the diagnostic features of the disease and proposed that postoperative adjuvant radiotherapy can significantly improve the patient’s prognosis. Finally, we further confirmed the important role of radiotherapy in PTACC by reviewing relevant literature, which may provide clinicians with valuable treatment experience.

At present, there are many types of drugs for clinical treatment of type 2 diabetes mellitus (T2DM) , there are differences in the hypoglycemic mechanisms and safety of traditional hypoglycemic drugs such as thiazolidinediones, sulfonylureas, biguanides, and insulin. In recent years, an assortment of new target drugs has emerged in an endless stream.New target drugs such as glucokinase activators, multi-site receptor agonists for GLP-1, GIP and glucagon, glucagon receptor inhibitors have gradually assembled the necessities of personalized clinical treatment through different mechanisms. This article reviews the current research progress of new target drugs for T2DM, and analyzes the advantages and disadvantages of clinical research of various new target drugs, in order to provide basic guidance for the clinical treatment of T2DM.

Unmanned aerial vehicle (UAV) hyperspectral remote sensing technology has developed rapidly in recent years, providing a new scheme for intelligent monitoring of forest resources. This study aims to deeply mining the UAV hyperspectral information, and construct the best model for extracting Moso bamboo (Phyllostachys pubescens) forest information. Firstly, the spectral information features of Moso bamboo, broadleaf, bare area and shadow were obtained and their spectral anisotropies were analyzed. Secondly, multiple machine learning algorithms were combined with the recursive feature elimination (RFE) to obtain three sets of optimized feature subsets. Eventually, three sets of optimization features were substituted into multiple machine learning algorithms to build nine models of Moso bamboo information extraction, comparing their classification effects and generalization capabilities. The results showed that: (1) The subset of features optimized by different base classifiers in combination with RFE and the ranking results of optimized features’ importance were different, but all the three sets of optimized feature subsets showed relatively obvious feature difference patterns; (2) The nine models based on SVM-RFE, RF-RFE and XGBoost-RFE were all capable of extracting Moso bamboo information, among which, the classification effect of the XGBoost-RFE-XGBoost model was the best and had significant advantages, with the overall classification accuracy OA, Kappa coefficient and R2 of 98.75%, 0.983 and 0.980, respectively, and the classification accuracy for Moso bamboo was 98.34%. Thus, XGBoost was the most applicable algorithm for feature optimization in combination with RFE, and the most effective algorithm with a better generalization ability for extracting Moso bamboo information from UAV hyperspectral images. This study is crucial for Moso bamboo’s large area monitoring and fine identification, providing a technical reference for obtaining information about the spatial and temporal distribution of Moso bamboo resources timely and accurately, facilitating the monitoring and management of them, and giving full play to their ecological and economic benefits.