Explore the vast range of titles available.

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.

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.

Tangent hyperbolic fluids characterized by shear-thinning behavior, are widely utilized in diverse industrial and scientific fields such as polymer engineering, inkjet printing, biofluids modeling, thermal insulation materials, and chemical manufacturing. Additionally, double-diffusive convection involving simultaneous heat and mass transfer driven by temperature and concentration gradients plays a critical role in many natural and industrial systems, including oceanic circulation, geothermal energy extraction, crystal solidification, alloy formation, and enhanced oil recovery. The current work examines the peristaltic transport of a tangent hyperbolic nanofluid under the concurrent effects of thermal radiation, electroosmotic forces, slip boundary conditions, and double diffusion. The governing nonlinear equations are numerically solved using Mathematica’s NDSolve command after being simplified under the presumptions of a long wavelength, a low Reynolds number, and Debye-Huckel linearization. The analysis reveals that a rise in the velocity slip parameter decreases the core fluid velocity but increases it closer to channel walls, while increased solutal Grashof number and electroosmotic parameter result in non-uniform velocity distributions, reducing the flow towards the left wall and increasing it towards the right. The pressure gradient increases with higher electroosmotic effects and Helmholtz-Smoluchowski velocity,but decreases under more intense thermal radiation and increased Prandtl number. The magnetic field increases pressure in the retrograde area and moves the enhanced zone towards the right wall, emphasizing increased flow resistance. Also, the trapping effects intensify with increasing solutal Grashof number and Helmholtz-Smoluchowski velocity, providing better particle transport and mixing in microfluidic devices.

This paper presents a mathematical analysis on our proposed physiologically structured PDE model that incorporates multiscale and nonlinear features. The model accounts for both mutated and healthy populations of quiescent and proliferating cells at the macroscale, as well as the microscale dynamics of cell cycle proteins. A reversible transition between quiescent and proliferating cell populations is assumed. The growth factors generated from the total cell population of proliferating and quiescent cells influence cell cycle dynamics. As feedback from the microscale, Cyclin D/CDK 4-6 protein concentration determines the transition rates between quiescent and proliferating cell populations. Using semigroup and spectral theory, we investigate the well-posedness of the model, derive steady-state solutions, and find sufficient conditions of stability for derived solutions. In the end, we executed numerical simulations to observe the impact of the parameters on the model's nonlinear dynamics.

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.

This study synthesized NiAl-layered double hydroxide (LDH)/Cu-MOF photocatalyst using a simple impregnation method involving NiAl-LDH and Cu-MOF. The successful synthesis was confirmed through Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), zeta potential measurements, thermogravimetric analysis (TGA), ultraviolet diffuse reflectance spectroscopy (UV-DRS), N2 adsorption at −196 °C, and electrochemical impedance spectroscopy (EIS). Photocatalysts based on NiAl-LDH, Cu-MOF, and NiAl-LDH/Cu-MOF were used to remove methyl orange (MO) dye from contaminated water. The impact of various factors, including pH, dye concentration, and photocatalyst amount, on MO degradation efficiency was assessed. FTIR analysis was conducted both before and after dye degradation. The optimal degradation conditions were a photocatalyst dose of 25 mg and a pH of 3. Kinetic studies indicated that the degradation of MO dye onto NiAl-LDH/Cu-MOF followed a pseudo-first-order and an L–H or Langmuir–Hinshelwood model. The value of R2 = 0.94 confirms the validity of pseudo-first-order and Langmuir–Hinshelwood (L–H) kinetic models for the photocatalytic degradation of MO dye. This study highlights the importance of developing novel photocatalysts with improved degradation efficiency to protect the water environment. Antibacterial activity was also performed with antibacterial sensibility testing by disk diffusion to determine minimal inhibitory and bactericidal concentrations. In short, NiAl-LDH/Cu-MOF can be helpful for various biomedical and industrial applications.

Current studies were performed to evaluate the physico-chemical characteristics and therapeutic potential of Chutrun thermal springs located in the North-west of Shigar Valley, Gilgit-Baltistan (Pakistan). Thermal springs with different mineral contents have been used by people for bathing and health purposes since old timings. The mineral water of these springs contains elements like sodium, potassium, calcium, magnesium as chlorides, fluorides, sulphates, phosphates and bicarbonates which may be responsible for cure of various diseases. Chutrun hot springs have 7.21–7.8 pH, 40–42° C Temperature, 300–310 ppm TDS, 3.1–6.7 ppm DO, 278–285 ppm hardness, 1.62–2.42 ppm turbidity, 250–260 ppm alkalinity, 500–516 ppm conductivity, 12–18 ppm sodium, 3.8–4.1 ppm potassium, 80–82 ppm calcium, 20 ppm magnesium, 9.6–12 ppm chlorides, 3.4–3.9 fluorides, 260–282 bicarbonates and 80–85 ppm sulphates. Absence of E.Coli and faecal coliforms indicated that waters from thermal springs are free from organic wastes contaminations. Water from thermal springs of Chutrun was unsuitable for drinking purposes due to the presence of high fluoride content and also small amount of total coliforms which may be due to the presence of environmental bacteria and non-protective measures during sampling but it was found suitable for bathing and other body contact activities.

Diabetes mellitus remains a major global health challenge, affecting over 537 million adults worldwide and this number is expected to rise 783 million by 2045, underscoring the need for improved therapeutic strategies. In this study, we synthesized and evaluated ellagic-acid-loaded gold nanoparticles (EA-AuNPs) prepared from Syzygium cumini seed extract as a potential antidiabetic intervention. EA-AuNPs exhibited a spherical morphology with an average size of 68.4 ± 5.2 nm, zeta potential of −24.6 mV, and encapsulation efficiency of 81.3 ± 2.7 %, indicating stability and optimal drug-loading capacity. In vivo testing in streptozotocin-induced diabetic mice (n = 40) revealed that EA-AuNPs (25 mg/kg, orally, for 28 days) reduced fasting blood glucose by 68.3 % (p < 0.001) and HbA1c by 43.5 % (p < 0.01), while enhancing serum insulin by 2.8-fold. Histological assessment showed restoration of islet morphology with an increase in β-cell area, consistent with protective and recovery-associated effects. Multi-omics analyses supported modulation of metabolic and inflammatory pathways, including PI3K/AKT, AMPK, and NF-κB, consistent with improved glucose homeostasis and reduced oxidative stress (MDA ↓52.4 %, SOD ↑2.1-fold). Collectively, these findings suggest that EA-AuNPs may offer a promising nanophytochemical approach for managing Type 2 diabetes, warranting further mechanistic and translational investigation. [Display omitted] •Syzygium cumini seeds identified as a rich source of ellagic acid (32.4 ± 1.6 mg/g).•EA-AuNP treatment reduced fasting blood glucose by 68.3 % in diabetic mice.•Treatment improved insulin secretion, preserved islet morphology, and increased β-cell area.•Multi-omics analyses indicated modulation of metabolic and inflammatory pathways, including PI3K/AKT and NF-κB.•EA-AuNPs were well tolerated with no observable hepato-renal or hematological toxicity.

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.

Software fault prediction (SFP) techniques identify faults at the early stages of the software development life cycle (SDLC). We find machine learning techniques commonly used for SFP compared to deep learning methods, which can produce more accurate results. Deep learning offers exceptional results in various domains, such as computer vision, natural language processing, and speech recognition. In this study, we use three deep learning methods, namely, long short-term memory (LSTM), bidirectional LSTM (BILSTM), and radial basis function network (RBFN) to predict software faults and compare our results with existing models to show how our results are more accurate. Our study uses Chidamber and Kemerer (CK) metrics-based datasets to conduct experiments and test our proposed algorithm. We conclude that LSTM and BILSTM perform better, whereas RBFN is faster in producing the required results. We use k-fold cross-validation to do the model evaluation. Our proposed models provide software developers with a more accurate and efficient SFP mechanism.