Dioxin-like (DL-) polychlorinated biphenyls induced immunotoxicity through apoptosis in mice splenocytes via the AhR mediated mitochondria dependent signaling pathways.

Polychlorinated biphenyls (PCBs) would do serious damage to multiple systems, while coplanar polychlorinated biphenyls, the most toxic member of the family, has been widely taken into consideration. In this study, ICR mice were fed with different doses of PCB126 to explore the underlying molecular mechanisms on immunotoxicity. The results showed that PCB126 caused immunosuppression as evidenced by inhibiting the ratios of thymus and spleen weights, changing the organizational structure and decreasing levels and mRNA expression of TNF-α, IFN-γ and IL-2. PCB126 inhibited the SOD activity and spurred the accumulation of MDA in spleen and thymus. Meanwhile, it also disturbed the Nrf2 signaling pathway as evidenced by up-regulating the mRNA expression of Nrf2 and Keap1. Additionally, a remarkable reduction in the mRNA expression of AhR and enhancement in the mRNA expression of Cyp1 enzymes (Cyp1a1, Cyp1a2 and Cyp1b1) were observed, which increased the ROS levels. PCB126 could increase protein expression of Bax, Caspase-3, Caspase-8 and Caspase-9, while the protein expression of Bcl-2 was decreased. In summary, the results indicated that PCB126 modulated the AhR signaling pathway, which interacted with apoptosis and oxidative stress to induce immunotoxicity, enrich the immunotoxicological mechanisms of PCB126.

Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers.

BACKGROUND: The incidence of pancreatic neuroendocrine tumors (PNETs) is now increasing rapidly. The tumor grade of PNETs significantly affects the treatment strategy and prognosis. However, there is still no effective way to non-invasively classify PNET grades. Machine learning (ML) algorithms have shown potential in improving the prediction accuracy using comprehensive data. AIM: To provide a ML approach to predict PNET tumor grade using clinical data. METHODS: The clinical data of histologically confirmed PNET cases between 2012 and 2018 were collected. A method of minimum P for the Chi-square test was used to divide the continuous variables into binary variables. The continuous variables were transformed into binary variables according to the cutoff value, while the P value was minimum. Four classical supervised ML models, including logistic regression, support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) were trained by clinical data, and the models were labeled with the pathological tumor grade of each PNET patient. The performance of each model, including the weight of the different parameters, were evaluated. RESULTS: In total, 91 PNET cases were included in this study, in which 32 were G1, 48 were G2 and 11 were G3. The results showed that there were significant differences among the clinical parameters of patients with different grades. Patients with higher grades tended to have higher values of total bilirubin, alpha fetoprotein, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4. Among the models we used, LDA performed best in predicting the PNET tumor grade. Meanwhile, MLP had the highest recall rate for G3 cases. All of the models stabilized when the sample size was over 70 percent of the total, except for SVM. Different parameters varied in affecting the outcomes of the models. Overall, alanine transaminase, total bilirubin, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4 affected the outcome greater than other parameters. CONCLUSION: ML could be a simple and effective method in non-invasively predicting PNET grades by using the routine data obtained from the results of biochemical and tumor markers.

Combining Exosomes Derived from Immature DCs with Donor Antigen-Specific Treg Cells Induces Tolerance in a Rat Liver Allograft Model.

Allograft tolerance is the ultimate goal in the field of transplantation immunology. Immature dendritic cells (imDCs) play an important role in establishing tolerance but have limitations, including potential for maturation, short lifespan in vivo and short storage times in vitro. However, exosomes (generally 30-100 nm) from imDCs (imDex) retain many source cell properties and may overcome these limitations. In previous reports, imDex prolonged the survival time of heart or intestine allografts. However, tolerance or long-term survival was not achieved unless immune suppressants were used. Regulatory T cells (Tregs) can protect allografts from immune rejection, and our previous study showed that the effects of imDex were significantly associated with Tregs. Therefore, we incorporated Tregs into the treatment protocol to further reduce or avoid suppressant use. We defined the optimal exosome dose as approximately 20 µg (per treatment before, during and after transplantation) in rat liver transplantation and the antigen-specific role of Tregs in protecting liver allografts. In the co-treatment group, recipients achieved long-term survival, and tolerance was induced. Moreover, imDex amplified Tregs, which required recipient DCs and were enhanced by IL-2. Fortunately, the expanded Tregs retained their regulatory ability and donor-specificity. Thus, imDex and donor-specific Tregs can collaboratively induce graft tolerance.