Current theories of multiple sclerosis (MS) induction and progression place autoreactive T cells in the focus of the pathogenesis. Mesenchymal/stromal stem cells (MSC) have become a promising alternative approach for pathogenic therapy of MS due to their immunomodulatory properties, underlying mechanisms of which are intensive study. The objective of the research was to investigate the contribution of PGE 2 to MSC-mediated suppression in patients with MS using in vitro model of mitogen-and myelin-stimulated T cell cocultivation with autologous/allogeneic MSC. We have showed that PGE 2 production depends on cell-to-cell contact of MSC and lymphocytes. The antigenic stimulation did not affect PGE 2 production following cocultivation of MSC and PBMC, and it is the presence of MSC in cell culture that significantly increases PGE 2 production irrespective of antigenic cultivation conditions. Simultaneously, PGE 2 synthesis correlated with indexes of MSC-mediated suppression of mitogen-and myelinstimulated T cell proliferation in patients with MS. No significant differences in PGE 2 production by autologous and allogeneic MSC have been established. These results have demonstrated that in patients with MS, PGE 2 is one of the possible factors of MSC immunosuppression. The interrelation between PGE 2 concentrations and T cell proliferation suppression mediated by MSC may explain one of the immune mechanisms of cell therapy, which is crucial for the further proper use of MSC in MS research and pathogenic treatment.

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This article aims to explore the use of machine learning (ML) methods for mapping the distribution of mercury (Hg) content in topsoil, using the city of Ufa (Russia) and adjacent areas as an example. For this purpose, a soil dataset of 250 points sampled from a 0–20 cm depth on different land uses, including residential, industrial and undisturbed (forests and parks), was used. Random Forest (RF), Extreme Gradient Boosting (XGboost), Cubist and k-Nearest Neighbor (kNN) ML techniques were employed to model and map the Hg concentrations. We used remote sensing data (RSD) and topographic attributes as explanatory variables. ML models were calibrated and validated using the leave-one-out cross-validation approach. The Hg content varied from 0.005 to 0.58 mg/kg and was characterized by very high variability. According to the MAE and RMSE metrics, the RF method resulted in the most accurate spatial prediction for the Hg content (0.029 and 0.065 mg/kg, respectively), while the XGBoost approach showed the lowest prediction efficiency (0.032 and 0.073 mg/kg, respectively). The results showed that the slope map, spectral index MSI and Sentinel-2A band B11 were the key variables in explaining the variability of Hg content. We found that higher uncertainty values of soil Hg were found in croplands, urban residential and industrial areas, which supports the view that spatial modelling of HM in urban landscapes is challenging. The present study provides insights into the potential of digital soil mapping techniques in combination with RSD and terrain variables for identifying areas at risk of Hg contamination in urban areas, which can inform land-use planning and management strategies to protect human health and the environment.