Analytical and Clinical Interference of Sample Hemolysis in Evaluating Blood Biochemical and Endocrine Parameters in Cows.

Hemolysis is a common cause of errors in laboratory tests as it affects blood parameters and leads to a positive or negative bias. This study aims to examine the relationship between the level of hemolysis (expressed as cell-free hemoglobin concentration, g/L) and the variability of metabolic and endocrine parameters and to determine the threshold level of hemolysis that causes an analytically and clinically significant bias for the twenty most frequently examined blood parameters in cows. Paired blood samples of 10 mL each were obtained from 30 cows. One was subjected to mechanical trauma and plasma was extracted directly from the other. Hemolyzed and non-hemolyzed samples from the same animal were mixed to obtain final samples with cell-free hemoglobin concentrations of 0, 1, 2, 4, 6, 8, and 10 g/L. Metabolic and endocrine parameters were measured in the samples and their deviation and the linear equation between the level of hemolysis and the deviation were determined. The following threshold values of hemolysis were determined, which correspond to the acceptable analytical (lower value) and clinical (upper value) levels of parameter variability: BHB 0.96 and 4.81; NEFA 0.39 and 3.31; GLU 0.38 and 3.90; ALB 1.12 and 6.11; TPROT 1.40 and 6.80; UREA 6.62 and 20.1; TBIL 0.75 and 5.65; AST 0.11 and 2.18; GGT 1.71 and 8.90, LDH 0.01 and 0.11, ALP 0.97 and 2.95; TGC 1.56 and 15.5; CHOL 1.29 and 8.56; Ca 5.68 and 25.7; P 0.57 and 8.43; Mg 1.10 and 8.47; INS 1.15 and 3.89; T3 8.19 and 15.6; T4 8.97 and 18.5; and CORT 2.78 and 11.22 g/L cell-free hemoglobin. Three decision levels are available for each metabolic and endocrine parameter: if hemolysis is below the lower (analytical) threshold value, results can be reported without restriction; if hemolysis is between the lower and upper thresholds, the results can be issued with guidance in the form of corrective linear equations; and if hemolysis is above the upper (clinical) threshold, the results and sample must be discarded. This method contributes to an optimal approach to hemolysis interference with metabolic profile parameters in blood samples from cows.

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.

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.

Influence of Heat Stress on Body Surface Temperature and Blood Metabolic, Endocrine, and Inflammatory Parameters and Their Correlation in Cows.

This study aimed to determine whether heat stress affected the values and correlations of metabolic, endocrinological, and inflammatory parameters as well as the rectal and body surface temperature of cows in the early and middle stages of lactation. This experiment was conducted in May (thermoneutral period), June (mild heat stress), and July (moderate to severe heat stress). In each period we included 15 cows in early lactation and 15 in mid-lactation. The increase in rectal and body surface temperatures (°C) in moderate to severe heat stress compared to the thermoneutral period in different regions was significant (p < 0.01) and the results are presented as mean and [95%CI]: rectal + 0.9 [0.81-1.02], eye + 6 [5.74-6.25], ear + 13 [11.9-14.0], nose + 3.5 [3.22-3.71], forehead + 6.6 [6.43-6.75], whole head + 7.5 [7.36-7.68], abdomen + 8.5 [8.25-8.77], udder + 7.5 [7.38-7.65], front limb + 6 [5.89-6.12], hind limb + 3.6 [3.46-3.72], and whole body + 9 [8.80-9.21]. During heat stress (in both mild and moderate to severe stress compared to a thermoneutral period), an increase in the values of extracellular heat shock protein 70 (eHsp70), tumor necrosis factor α (TNFα), cortisol (CORT), insulin (INS), revised quantitative insulin sensitivity check index (RQUICKI), urea, creatinine, total bilirubin, aspartate transpaminase (AST), gamma-glutamyl transferase (GGT), lactate dehydrogenase (LDH), and creatin kinase (CK) occurred, as well as a decrease in the values of triiodothyronine (T3), thyroxine (T4), non-esterified fatty acids (NEFA), glucose (GLU), ß-Hydroxybutyrate (BHB), calcium, phosphorus, total protein (TPROT), albumin (ALB), triglycerides (TGCs), and cholesterol (CHOL). In cows in early lactation compared to cows in mid-lactation, there was a significantly larger increase (p < 0.01) in the values of eHsp70, TNFα, GLU, RQUICKI, and GGT, while the INS increase was smaller during the three experimental periods. The decrease in the values of Ca, CHOL, and TGC was more pronounced in cows in early lactation compared to cows in mid-lactation during the three experimental periods. Rectal temperature was related to eHsp70 (r = 0.38, p < 0.001) and TNFα (r = 0.36, p < 0.01) and showed non-significant poor correlations with other blood parameters. Blood parameters correlate with body surface temperature, with the following most common results: eHsp70 and TNFα showed a moderately to strongly significant positive correlation (r = 0.79-0.96, p < 0.001); CORT, INS, and Creat showed fairly to moderately significant positive correlations; T3, T4, NEFA and GLU showed fairly to moderately significant negative correlations (r = 0.3-0.79; p < 0.01); RQUICKI, urea, AST, and GGT showed fairly and significantly positive correlations; and TGC, CHOL, TPROT, and ALB showed fairly and significantly negative correlations (r = 0.3-0.59; p < 0.01). Measuring the surface temperature of the whole body or head can be a useful tool in evaluating the metabolic response of cows because it has demonstrated an association with inflammation (TNFα, eHsp70), endocrine response (CORT, T3, T4), the increased use of glucose and decreased use of lipids for energy purposes (INS, NEFA, GLU, and RQUICKI), and protein catabolism (ALB, TPROT, urea, Creat), which underlies thermolysis and thermogenesis in cows under heat stress. In future research, it is necessary to examine the causality between body surface area and metabolic parameters.