Machine Learning Model for Prediction of Development of Cancer Stem Cell Subpopulation in Tumurs Subjected to Polystyrene Nanoparticles.

Cancer stem cells (CSCs) play a key role in tumor progression, as they are often responsible for drug resistance and metastasis. Environmental pollution with polystyrene has a negative impact on human health. We investigated the effect of polystyrene nanoparticles (PSNPs) on cancer cell stemness using flow cytometric analysis of CD24, CD44, ABCG2, ALDH1 and their combinations. This study uses simultaneous in vitro cell lines and an in silico machine learning (ML) model to predict the progression of cancer stem cell (CSC) subpopulations in colon (HCT-116) and breast (MDA-MB-231) cancer cells. Our findings indicate a significant increase in cancer stemness induced by PSNPs. Exposure to polystyrene nanoparticles stimulated the development of less differentiated subpopulations of cells within the tumor, a marker of increased tumor aggressiveness. The experimental results were further used to train an ML model that accurately predicts the development of CSC markers. Machine learning, especially genetic algorithms, may be useful in predicting the development of cancer stem cells over time.

Effects of combustible cigarettes and heated tobacco products on immune cell-driven inflammation in chronic obstructive respiratory diseases.

Since long-term effects of heated tobacco products (HTP) on the progression of chronic obstructive pulmonary disease (COPD) are unknown, we used COPD mice model to compare immune cell-dependent pathological changes in the lungs of animals which were exposed to HTP or combustible cigarettes (CCs). We also performed intracellular staining and flow cytometry analysis of immune cells which were present in the blood of CCs and HTP users who suffered from immune cell-driven chronic obstructive respiratory diseases. CCs enhanced NLRP3 inflammasome-dependent production of inflammatory cytokines in lung-infiltrated neutrophils and macrophages and increased influx of cytotoxic Th1, Th2 and Th17 lymphocytes in the lungs of COPD mice. Similarly, CCs promoted generation of inflammatory phenotype in circulating leukocytes of COPD patients. Opposite to CCs, HTP favored expansion of immunosuppressive, IL-10-producing, FoxP3-expressing T, NK and NKT cells in inflamed lungs of COPD mice. Compared to CCs, HTP had weaker capacity to promote synthesis of inflammatory cytokines in lung-infiltrated immune cells. Significantly lower number of inflammatory neutrophils, monocytes, Th1, Th2 and Th17 lymphocytes were observed in the blood of patients who consumed HTP than in the blood of CCs users, indicating different effects of CCs and HTP on immune cells' phenotype and function.

Modeling 5-FU-Induced Chemotherapy Selection of a Drug-Resistant Cancer Stem Cell Subpopulation.

(1) Background: Cancer stem cells (CSCs) are a subpopulation of cells in a tumor that can self-regenerate and produce different types of cells with the ability to initiate tumor growth and dissemination. Chemotherapy resistance, caused by numerous mechanisms by which tumor tissue manages to overcome the effects of drugs, remains the main problem in cancer treatment. The identification of markers on the cell surface specific to CSCs is important for understanding this phenomenon. (2) Methods: The expression of markers CD24, CD44, ALDH1, and ABCG2 was analyzed on the surface of CSCs in two cancer cell lines, MDA-MB-231 and HCT-116, after treatment with 5-fluorouracil (5-FU) using flow cytometry analysis. A machine learning model (ML)-genetic algorithm (GA) was used for the in silico simulation of drug resistance. (3) Results: As evaluated through the use of flow cytometry, the percentage of CD24-CD44+ MDA-MB-231 and CD44, ALDH1 and ABCG2 HCT-116 in a group treated with 5-FU was significantly increased compared to untreated cells. The CSC population was enriched after treatment with chemotherapy, suggesting that these cells have enhanced drug resistance mechanisms. (4) Conclusions: Each individual GA prediction model achieved high accuracy in estimating the expression rate of CSC markers on cancer cells treated with 5-FU. Artificial intelligence can be used as a powerful tool for predicting drug resistance.

Effects of combustible cigarettes and electronic nicotine delivery systems on the development and progression of chronic lung inflammation in mice.

INTRODUCTION: Although detrimental effects of combustible cigarettes (CCs) on the progression of lung inflammatory diseases are well known, changes in electronic nicotine delivery systems (ENDS)-exposed lung-infiltrated immune cells are still unrevealed. METHODS: The analysis of blood gas parameters, descriptive and quantitative histology of lung tissues, determination of serum cytokines, intracellular staining and flow cytometry analysis of lung-infiltrated immune cells were used to determine the differences in the extent of lung injury and inflammation between mice from experimental (CC and ENDS-exposed animals) and control group (Air-exposed mice). RESULTS: Continuous exposition to either CCs or ENDS induced severe systemic inflammatory response, increased activation of NLRP3 inflammasome in neutrophils and macrophages and enhanced dendritic cell-dependent activation of Th1 and Th17 cells in the lungs. ENDS induced less severe immune response than CCs. Serum concentrations of inflammatory cytokines were significantly lower in the samples of ENDS-exposed mice. Compared to CCs, ENDS recruited lower number of circulating leukocytes in injured lungs and had less capacity to induce CD14/TLR-2-dependent activation of NLRP3 inflammasome in lung-infiltrated neutrophils and macrophages. ENDS-primed dendritic cells had reduced capacity for the generation of Th1 and Th17 cell-driven lung inflammation. Accordingly, extensive immune cell-driven lung injury resulted in severe respiratory dysfunction in CCs-exposed mice, while ENDS caused moderate respiratory dysfunction in experimental animals. CONCLUSIONS: Continuous exposition to either CCs or ENDS induced immune cell-driven lung damage in mice. ENDS triggered immune response which was less potent than inflammatory response elicited by CCs and, therefore, caused less severe lung injury and inflammation. IMPLICATIONS: This is the first study that compared the effects of CCs and ENDS on lung-infiltrated immune cells. Although both CCs and ENDS elicited systemic inflammatory response, immune cell-driven lung injury and inflammation were less severe in ENDS-exposed than in CC-exposed animals. Continuous exposition to ENDS-sourced aerosols was less harmful for respiratory function of experimental animals than CC-derived smoke.

Combined Biological and Numerical Modeling Approach for Better Understanding of the Cancer Viability and Apoptosis.

Nowadays, biomedicine is a multidisciplinary science that requires a very broad approach to the study and analysis of various phenomena essential for a better understanding of human health. This study deals with the use of numerical simulations to better understand the processes of cancer viability and apoptosis in treatment with commercial chemotherapeutics. Starting from many experiments examining cell viability in real-time, determining the type of cell death and genetic factors that control these processes, a lot of numerical results were obtained. These in vitro test results were used to create a numerical model that gives us a new angle of observation of the proposed problem. Model systems of colon and breast cancer cell lines (HCT-116 and MDA-MB-231), as well as a healthy lung fibroblast cell line (MRC-5), were treated with commercial chemotherapeutics in this study. The results indicate a decrease in viability and the appearance of predominantly late apoptosis in the treatment, a strong correlation between parameters. A mathematical model was created and employed for a better understanding of investigated processes. Such an approach is capable of accurately simulating the behavior of cancer cells and reliably predicting the growth of these cells.