Quercetin in semen extender improves frozen-thawed spermatozoa quality and in-vivo fertility in crossbred Kamori goats.

This study investigated the antioxidant effect of quercetin-treated semen on frozen-thawed spermatozoa quality and in-vivo fertility in crossbred Kamori goats. In total, 32 ejaculates from four fertile bucks were diluted in Tris-based egg yolk extender with varying levels of quercetin (0, 1, 5, 10, and 15 µM). Qualified semen samples were pooled and frozen in French straws. The results revealed that the addition of quercetin in the semen extender increased (p < 0.05) frozen-thawed sperm total motility (TM), progressive motility (PM), rapid velocity (RV), average path velocity (VAP), straight line velocity (VSL), curvilinear velocity (VCL), and amplitude of lateral head (ALH) displacement in contrast to the control group. Quercetin supplementation had no effect on beat cross frequency (BCF), straightness (STR), and linearity (LIN) (p > 0.05). Quercetin showed significantly higher (p < 0.05) plasma membrane and acrosome integrity and viability (p < 0.05) of spermatozoa in contrast to the control group. Quercetin in the semen extender significantly increased (p < 0.05) superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), ascorbate peroxidase (APX), and total antioxidant capacity (TAC) levels while reduced (p < 0.05) the contents of total oxidant status (TOS) and malondialdehyde (MDA), which were in contrast to the control group. Ultrasound results revealed that 24 out of 30 (80%) goats were found pregnant when semen was treated with 5 µM quercetin while the control group showed 18 out of 30 (60%) animals were pregnant. Thus, the study concluded that 5 µM quercetin-treated semen was found to be efficient, showed increased antioxidant status, and reduced oxidant production, leading to improved spermatozoa quality and in-vivo fertility in goats.

Enzyme-triggered approach to reduce water bodies' contamination using peroxidase-immobilized ZnO/SnO2/alginate nanocomposite.

Enzyme immobilization on solid support offers advantages over free enzymes by overcoming characteristic limitations. To synthesize new stable and hyperactive nano-biocatalysts (co-precipitation method), ginger peroxidase (GP) was surface immobilized (adsorption) on ZnO/SnO2 and ZnO/SnO2/SA nanocomposite with immobilization efficacy of 94 % and 99 %, respectively. Thereafter, catalytic and biochemical characteristics of free and immobilized GP were investigated by deploying various techniques, i.e., FTIR, PXRD, SEM, and PL. Diffraction peaks emerged at 2θ values of 26°, 33°, 37°, 51°, 31°, 34°, 36°, 56°, indicating the formation of SnO2 and ZnO. The OH stretching of the H2O molecules was attributed to broad peaks between 3200 and 3500 cm-1, whereas ZnO/SnO2 spikes occurred in the 1626-1637 cm-1 range. SnO stretching mode and ZnO terminal vibrational patterns have been verified at corresponding wavelengths of 625 cm-1 and 560 cm-1. Enzyme entrapment onto substrate was verified via interactions between GP and ZnO/SnO2/SA as corroborated by signals beneath 1100 cm-1. GP-immobilized fractions were optimally active at pH 5, 50 °C, and retained maximum activity after storage of 4 weeks at -4 °C. Kinetic parameters were determined by using a Lineweaver-Burk plot and Vmax for free GP, ZnO/SnO2/GP and ZnO/SnO2/SA/GP with guaiacol as a substrate, were found to be 322.58, 49.01 and 11.45 (µM/min) respectively. A decrease in values of Vmax and KM indicates strong adsorption of peroxidase on support and maximum affinity between nano support and enzyme, respectively. For environmental remediation, free ginger peroxidase (GP), ZnO/SnO2/GP and ZnO/SnO2/SA/GP fractions effectively eradicated highly intricate dye. Multiple scavengers had a significant impact on the depletion of the dye. In conclusion, ZnO/SnO2 and ZnO/SnO2/SA nanostructures comprise an ecologically acceptable and intriguing carrier for enzyme immobilization.

Stability analysis of a multiscale model of cell cycle dynamics coupled with quiescent and proliferating cell populations.

In this paper, we perform a mathematical analysis of our proposed nonlinear, multiscale mathematical model of physiologically structured quiescent and proliferating cell populations at the macroscale and cell-cycle proteins at the microscale. Cell cycle dynamics (microscale) are driven by growth factors derived from the total cell population of quiescent and proliferating cells. Cell-cycle protein concentrations, on the other hand, determine the rates of transition between the two subpopulations. Our model demonstrates the underlying impact of cell cycle dynamics on the evolution of cell population in a tissue. We study the model's well-posedness, derive steady-state solutions, and find sufficient conditions for the stability of steady-state solutions using semigroup and spectral theory. Finally, we performed numerical simulations to see how the parameters affect the model's nonlinear dynamics.

Evolution of cancer stem cell lineage involving feedback regulation.

Tumor emergence and progression is a complex phenomenon that assumes special molecular and cellular interactions. The hierarchical structuring and communication via feedback signaling of different cell types, which are categorized as the stem, progenitor, and differentiated cells in dependence of their maturity level, plays an important role. Under healthy conditions, these cells build a dynamical system that is responsible for facilitating the homeostatic regulation of the tissue. Generally, in this hierarchical setting, stem and progenitor cells are yet likely to undergo a mutation, when a cell divides into two daughter cells. This may lead to the development of abnormal characteristics, i.e. mutation in the cell, yielding an unrestrained number of cells. Therefore, the regulation of a stem cell's proliferation and differentiation rate is crucial for maintaining the balance in the overall cell population. In this paper, a maturity based mathematical model with feedback regulation is formulated for healthy and mutated cell lineages. It is given in the form of coupled ordinary and partial differential equations. The focus is laid on the dynamical effects resulting from acquiring a mutation in the hierarchical structure of stem, progenitor and fully differentiated cells. Additionally, the effects of nonlinear feedback regulation from mature cells into both stem and progenitor cell populations have been inspected. The steady-state solutions of the model are derived analytically. Numerical simulations and results based on a finite volume scheme underpin various expected behavioral patterns of the homeostatic regulation and cancer evolution. For instance, it has been found that the mutated cells can experience significant growth even with a single somatic mutation, but under homeostatic regulation acquire a steady-state and thus, ensuing healthy cell population to either a steady-state or a lower cell concentration. Furthermore, the model behavior has been validated with different experimentally measured tumor values from the literature.

BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm.

Breast cancer is one of the leading causes of death in the current age. It often results in subpar living conditions for a patient as they have to go through expensive and painful treatments to fight this cancer. One in eight women all over the world is affected by this disease. Almost half a million women annually do not survive this fight and die from this disease. Machine learning algorithms have proven to outperform all existing solutions for the prediction of breast cancer using models built on the previously available data. In this paper, a novel approach named BCD-WERT is proposed that utilizes the Extremely Randomized Tree and Whale Optimization Algorithm (WOA) for efficient feature selection and classification. WOA reduces the dimensionality of the dataset and extracts the relevant features for accurate classification. Experimental results on state-of-the-art comprehensive dataset demonstrated improved performance in comparison with eight other machine learning algorithms: Support Vector Machine (SVM), Random Forest, Kernel Support Vector Machine, Decision Tree, Logistic Regression, Stochastic Gradient Descent, Gaussian Naive Bayes and k-Nearest Neighbor. BCD-WERT outperformed all with the highest accuracy rate of 99.30% followed by SVM achieving 98.60% accuracy. Experimental results also reveal the effectiveness of feature selection techniques in improving prediction accuracy.