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In human body, the endocrine system, nervous system and immune system jointly shoulder the important responsibility of life support. Hormones are the material basis for coordination of endocrine system. Steroid hormones, also known as steride hormone, belong to a large category of hormones. The synthesis process of steroid hormones takes cholesterol as the precursor. Through the participation of various metabolic enzymes, steroid hormones are synthesized in the human adrenal cortex, mitochondria of placental cells, smooth Endoplasmic reticulum and gonads, producing 21C, 19C or 18C-backbones with different chemical structures. According to pharmacological effects, steroid hormones are mainly divided into progestogens, corticosteroids and sex hormones, which play an important role in regulating human growth, development, reproduction and maintaining the stability of human body environment. In clinical practice, the level of steroid hormones is used as diagnostic indicator for adrenal and psychiatric diseases. Therefore, accurate determination of steroid hormone content is very important. Steroid hormones in the human body can be detected using various methods, such as radioimmunoassay, chemiluminescence immunoassay, enzyme-linked immunosorbent assay, gas chromatography-tandem mass spectrometry (GC-MS/MS), and liquid chromatography-tandem mass spectrometry (LC-MS/MS). Mass spectrometry has obvious advantages in terms of detection sensitivity and accuracy compared with immunoassay. GC-MS has been the main technique for steroid analysis due to its high specificity, wide analyte coverage, and sufficient sensitivity. However, the samples need to be derivatized, and the experimental processes are tedious. LC-MS/MS has the inherent advantages of high sensitivity and specificity, and has a high-throughput characteristic, thus it is an ideal tool for routine diagnosis. At present, common pretreatment techniques for serum samples include protein precipitation, liquid-liquid extraction, and solid phase extraction. Some new pretreatment methods have emerged in recent years, such as dispersed liquid-liquid extraction, ultrasonic-assisted extraction, and supramolecular extraction. Each pretreatment technique has its own advantages and disadvantages. For example, solid phase extraction (SPE) can achieve good recovery but requires more time and economic factors. In selection of pretreatment methods, the physical and chemical properties of analytes, the characteristics of matrices, the simplicity and durability of method establishment, and time, reliability, and cost required should be comprehensively considered. This article reviewed the application of LC-MS/MS in the determination of endogenous steroid hormones, especially in sample pretreatment, chromatographic and mass spectrometric conditions, so as to provide more accurate basis for clinical diagnosis and treatment based on steroid hormones.

Abstract Arterial occlusion-induced ischemic stroke (IS) is a highly frequent stroke subtype. Nuclear factor erythroid 2-related factor 2 (NRF2) is a transcription factor that modulates antioxidant genes. Its role in IS is still unelucidated. The current study focused on constructing a transient middle cerebral artery occlusion (tMCAO) model for investigating the NRF2-related mechanism underlying cerebral ischemia/reperfusion (I/R) injury. Each male C57BL/6 mouse was injected with/with no specific NRF2 activator post-tMCAO. Changes in blood–brain barrier (BBB)-associated molecule levels were analyzed using western-blotting, PCR, immunohistochemistry, and immunofluorescence analysis. NRF2 levels within cerebral I/R model decreased at 24-h post-ischemia. NRF2 activation improved brain edema, infarct volume, and neurological deficits after MCAO/R. Similarly, sulforaphane (SFN) prevented the down-regulated tight junction proteins occludin and zonula occludens 1 (ZO-1) and reduced the up-regulated aquaporin 4 (AQP4) and matrix metalloproteinase 9 (MMP9) after tMCAO. Collectively, NRF2 exerted a critical effect on preserving BBB integrity modulating ferroptosis and inflammation. Because NRF2 is related to BBB injury regulation following cerebral I/R, this provides a potential therapeutic target and throws light on the underlying mechanism for clinically treating IS.

3,3′,4,4′,5-Pentachlorobiphenyl (PCB126) is the most toxic congener of dioxin-like polychlorinated biphenyls (DL PCBs), while nanoplastics (NPs) have recently emerged as significant marine pollutants, both posing threats to aquatic organisms and human health. They coexist in the environment, but their comprehensive toxicological effects remain unclear. In this study, zebrafish embryos were simultaneously exposed to PCB126 and 80-nanometer nanoplastyrene (NPS). Researchers utilized fluorescence microscopy, qPCR, histopathological examination, and transcriptomic sequencing to investigate the developmental toxicity of different concentrations of PCB126 and NPS individually or in combination on zebrafish embryos and larvae. Results indicate that the chorion significantly impedes the accumulation of NPS (p < 0.05). It is noteworthy that this barrier effect diminishes upon simultaneous exposure to PCB126. In this experiment, the semi-lethal concentration of PCB126 for larvae was determined to be 6.33 μg/L. Exposure to PCB126 induces various deformities, primarily mediated through the aryl hydrocarbon receptor (AHR). Similarly, exposure to NPS also activates AHR, leading to developmental impairments. Furthermore, transcriptomic sequencing revealed similar effects of PCB126 and NPS on the gene expression trends in zebrafish larvae, but combined exposure to both exacerbates the risk of cancer and induces more severe cardiac toxicity. At this level, co-exposure to PCB126 and NPS adversely affects the development of zebrafish larvae. This study contributes to a deeper understanding of the in vivo accumulation of DL polychlorinated biphenyls and microplastics in actual aquatic environments and their impact on fish development.

Polychlorinated biphenyls (PCBs) are lipophilic environmental toxicants. Epidemiological studies have established a link between PCBs and both metabolic syndrome and nonalcoholic fatty liver disease (NAFLD). Multiple studies have reported that exposure to both PCB156 and PCB126 among the 12 dioxin-like PCBs leads to the development of NAFLD. However, studies to elucidate whether PCB169 induces the development of NAFLD by constructing in vivo models have not been reported. Therefore, we evaluated the effects of exposure to PCB169 (5 mg/kg-bw) on hepatic lipid metabolism in C57BL/6 mice from control diet and high-fat diet cohorts. The results showed that PCB169 exposure reduced body weight and intraperitoneal fat mass in mice on the control diet, but the liver lipid levels were significantly increased, exacerbating NAFLD in mice on a high-fat diet. Through transcriptomics studies, it was found that PCB169 exposure induced significant up-regulation of Pparγ, Fasn, and Aacs genes involved in hepatic lipogenesis, as well as remarkable up-regulation of Hmgcr, Lss, and Sqle genes involved in cholesterol synthesis. Additionally, there was notable down-regulation of Pparα and Cpt1 genes involved in lipid β-oxidation, leading to abnormal lipid accumulation in the liver. In addition, we found that PCB169 exposure significantly activated the Arachidonic acid metabolism, PPAR signaling pathway, Metabolism of xenobiotics by cytochrome P450, and Retinol metabolism pathways, and so on. Our study suggests that PCB169 can modify gene expression related to lipid metabolism, augument lipid accumulation in the liver, and further contribute to the development of NAFLD, thereby revealing the detrimental effects associated with PCB exposure on animal growth and metabolism.

Objective: Solve the problems of low detection efficiency and poor accuracy in existing food freshness recognition methods. Methods: Based on the food production line image acquisition system, an improved residual neural network model was proposed for food freshness recognition on the production line. The improved LRELU activation function was introduced to improve the recognition performance of the model, the batch normalization layer was introduced to improve the training efficiency of the model, and the Dropout layer was introduced to discard a certain proportion of neurons to reduce the impact of over fitting. Results: Compared with conventional food freshness recognition methods, the experimental method could accurately and efficiently achieve food freshness recognition, with an overall freshness recognition accuracy of ＞97%, average recognition time of 9.8 ms, which meet the needs of food production lines for freshness recognition. Conclusion: The detection method based on deep learning is a non-destructive, efficient, and high-precision method for recognizing the freshness of food images.