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.

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.

Smoke derived from combustible cigarettes (CCs) contains numerous harmful chemicals that can impair the viability, proliferation, and activation of immune cells, affecting the progression of chronic inflammatory diseases. In order to avoid the detrimental effects of cigarette smoking, many CC users have replaced CCs with heated tobacco products (HTPs). Due to different methods of tobacco processing, CC-sourced smoke and HTP-derived aerosols contain different chemical constituents. With the exception of nicotine, HTP-sourced aerosols contain significantly lower amounts of harmful constituents than CC-derived smoke. Since HTP-dependent effects on immune-cell-driven inflammation are still unknown, herein we used flow cytometry analysis, intracellular staining, and an enzyme-linked immunosorbent assay to determine the impact of CCs and HTPs on systemic inflammatory response in patients suffering from ulcerative colitis (UC), diabetes mellitus (DM), and chronic obstructive pulmonary disease (COPD). Both CCs and HTPs significantly modulated cytokine production in circulating immune cells, affecting the systemic inflammatory response in COPD, DM, and UC patients. Compared to CCs, HTPs had weaker capacity to induce the synthesis of inflammatory cytokines (IFN-γ, IL-1β, IL-5, IL-6, IL-12, IL-23, IL-17, TNF-α), but more efficiently induced the production of immunosuppressive IL-10 and IL-35. Additionally, HTPs significantly enhanced the synthesis of pro-fibrotic TGF-β. The continuous use of CCs and HTPs aggravated immune-cell-driven systemic inflammation in COPD and DM patients, but not in UC patients, suggesting that the immunomodulatory effects of CC-derived smoke and HTP-sourced aerosols are disease-specific, and need to be determined for specific immune-cell-driven inflammatory diseases.

(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.

Electrospun polymer-based nanofibers are of increasing interest in contemporary applied biomedicine. The challenge regarding modern surgery and tissue engineering is to discover a variety of manufactured scaffolds with improved properties that can replace and regenerate damaged skin and organs. The unique properties of polymer nanofibers, such as submicron and nanoscale diameters, large surface area, flexibility, etc., make them attractive objects for a wide range of applications. In this study, a combination of chitosan as a natural polymer and poly(lactic) acid as a synthetic polymer is studied with the aim of improving and accelerating the healing of skin wounds. Chitosan (Chi) is one of the most promising polymers for scaffold design, due to its high biodegradability, non-toxic, and antibacterial properties. On the other hand, poly(lactic) acid (PLA) possesses enhanced electrospinability potential and desirable mechanical strength. Therefore, the combination of Chi and PLA enhances the mutually superior properties of both. After optimizing the process parameters, imaging, and determining the diameter of the nanofibers, the scaffold potential for wound healing was investigated by in vitro scratch test on a healthy fibroblast cell line. The study concludes that ultrafine Chi:PLA nanofiber scaffolds have significant potential to regenerate and restore damaged tissue under in vitro conditions.

Royal jelly (RJ) is a natural product, consumed as a functional food and in the form of a food supplement with multiple biological potentials. Apitherapy presents a complementary medical approach using bee products in the treatment of diseases, including cancer. Cancer metastasis implies the acquisition of migratory potential of cancer cells, and RJ already showed remarkable antimetastatic effects. We aimed to investigate the effects of RJ on the migration of colorectal cancer cells and key proteins involved in this process, E- and N-cadherin. Experiments were done 24 h after treatment with two selected concentrations. RJ suppressed the migratory potential of colorectal cancer cell line HCT-116, and enhanced the expression of anti-migratory protein, E-cadherin, while significantly inhibiting the promigratory marker, N-cadherin.