Regulatory T cells (Treg) and T helper 17 cells (Th17), both derived from naïve T cells, play pivotal roles in modulating immune responses, and their dynamic balance is critical for maintaining immune homeostasis. Existing studies predominantly focus on the regulatory mechanisms of individual cell types and lack a systematic analysis of how multiparametric interactions and stochastic perturbations jointly influence cell-fate equilibrium. In this study, we investigate the gene regulatory network of Treg and Th17 cells in two major aspects: (i) elucidating the dynamical features of the network and (ii) examining the regulatory effects of Gaussian white noise on the balance between the two lineages. By integrating systems dynamics, non-equilibrium mechanics, and stochastic process theory, we propose a unified modeling framework that incorporates Gaussian white noise to simulate stochastic perturbations in gene expression, thereby establishing a mapping between parameter sets and cellular phenotypes and quantifying the regulatory weights of key factors. Our results demonstrate that parameters such as extracellular TGF-β input, foxp3 mRNA synthesis rate, and Stat3 protein degradation rate significantly modulate the differentiation balance between Treg and Th17 cells. Furthermore, within a certain range, stronger Gaussian white noise promotes the differentiation of naïve T cells toward the Th17 lineage, thereby enhancing immune responsiveness. This finding aligns with prior experimental evidence demonstrating that stochastic noise can amplify immune response efficacy. This framework uniquely couples static and dynamic perturbations, revealing stochasticity's role in cell-fate decisions and offering both a quantitative tool for studying Th17-Treg balance and a generalizable approach for other differentiation systems.

Antibiotic resistance in Escherichia coli (E. coli) poses a major public health threat. This study introduces a fractional-order differential equation model incorporating memory effects to analyze resistance and susceptibility dynamics in E. coli populations exposed to Ertapenem, Imipenem, and Meropenem, using real-world data from 2018 to 2023 from a hospital in Northern Cyprus. The model accounts for genetic mutations, horizontal gene transfer, and the decay of resistance. Results indicate a gradual increase in resistance, with higher fractional orders slowing growth rates. Basic reproduction number analysis identifies thresholds for resistance persistence or decline, suggesting that reducing mutation rates and enhancing decay factors can control resistance. Projections forecast an 800% rise in resistance cases by 2030 compared to 2018, underscoring the need for optimized antibiotic stewardship.

Since Waddington proposed the concept of the “epigenetic landscape” in 1957, researchers have developed various methodologies to represent it in diverse processes. Studying the epigenetic landscape provides valuable qualitative information regarding cell development and the stability of phenotypic and morphogenetic patterns. Although Waddington’s original idea was a visual metaphor, a contemporary perspective relates it to the landscape formed by the basins of attraction of a dynamical system describing the temporal evolution of protein concentrations driven by a gene regulatory network. Transitions among these attractors can be driven by stochastic perturbations, with the cell state more likely to transition to the nearest attractor or to the one that presents the path of least resistance. In this study, we define the epigenetic landscape using the free energy potential obtained from the solution of the Fokker-Planck equation on the regulatory network. Specifically, we obtained a numerical approximate solution of the Fokker-Planck equation describing the Arabidopsis thaliana flower morphogenesis process. We observed good agreement between the coexpression matrix obtained from the Fokker-Planck equation and the experimental coexpression matrix. This paper proposes a method for obtaining this landscape by solving the Fokker-Planck equation (FPE) associated with a dynamical system describing the temporal evolution of protein concentrations involved in the process of interest. As these systems are high-dimensional and analytical solutions are often unfeasible, we propose a gamma mixture model to solve the FPE, transforming this problem into an optimization problem. This methodology can enhance the analysis of gene regulatory networks by directly relating theoretical mathematical models with experimental observations of coexpression matrices, thus providing a discriminating technique for competing models.

Eyring-Powell nanofluids have great potential for use in biomedical engineering to create more effective medical procedures and treatments due to their special properties of fluidity, efficient heat transfer, and interaction with biological systems. This study investigates bioconvection flow and its heat transfer characteristics of the magnetohydrodynamic ternary hybrid nanofluid containing silver, copper, and aluminum nanoparticles with human blood. The forced convection in a porous media, radiation, and viscous dissipation have been considered. The governing equations are reduced to dimensionless partial differential equations and further simplified using the local non-similarity method to obtain ordinary differential equations, which were solved numerically using the BVP4C algorithm. The results indicate that the concentration profiles reduce with inertia coefficients and Schmidt numbers, while radiation parameters increase the surface temperature. A higher Lewis number accelerates thermal diffusion, in contrast to mass diffusion. Fast dissipation of temperature prevents microbial growth and is useful in applications dealing with medicine administration and wound healing. These results support existing research and provide recommendations for further improvement of industrial and biological processes. As the rises from to the Nusselt number declines as follows: for by around , for by and for by . The Nusselt number increases around as rises from to for , for by the Nusselt number increases by and for by . The article proposes non-similar transformations for solving complex problems on the movement of ternary nanofluids. This provides insight into medical applications such as drug delivery and diagnostic tools and advances nanofluidic dynamics in healthcare.

Cytomegalovirus (CMV) is a significant clinical pathogen posing risks, especially for immunocompromised individuals. This study investigates the transport dynamics of CMV within viscoelastic saliva, focusing on factors influencing viral mobility. We employed mathematical models, including the Basset-Boussinesq-Oseen (BBO) equation, to analyze how viral density, particle diameter, saliva viscosity, and external electric and magnetic fields affect CMV movement in the oesophagus. Novel insights include the discovery that smaller CMV particles move significantly faster compared to larger ones, highlighting a critical aspect of viral infectivity. Additionally, we found that increased peristaltic wave amplitudes in the oesophagus greatly enhance viral velocity. More notably, our investigation reveals that the application of external magnetic fields can manipulate CMV transport by exerting forces that reduce viral mobility, thus potentially lowering infection rates through electromagnetic interactions. These findings underscore the complex interplay between fluid rheology, particle shape, and external fields in viral dynamics, suggesting novel therapeutic interventions aimed at controlling CMV spread based on saliva viscosity and electromagnetic manipulation. Our research paves the way for innovative strategies in viral infection management and therapeutic development.

Magnesium-based implants are highly valued in the biomedical field for biocompatibility and biodegradability, though their inherent low strength in body fluids is a limitation. This study addresses this by alloying magnesium with zinc and titanium to enhance its properties. Mechanical alloying was used to synthesize binary (Mg-Zn, Mg-Ti) and ternary (Mg-Zn-Ti) alloys, which were then compacted and sintered. The alloy powders, composed of 10 wt% Zn and 5 wt% Ti, were milled at 360 rpm for 10 h. Microstructural analysis revealed uniformly dispersed particles, with SEM confirming spherical and fine particles alongside laminates. XRD identified intermetallic compound formation. The ternary alloy demonstrated superior micro-hardness and Young's modulus similar to human bone, making it particularly promising for biomedical applications. Incorporating zinc and titanium into the magnesium matrix resulted in a ternary alloy that outperformed its binary counterparts.

Cell fate decision is crucial in biological development and plays fundamental roles in normal development and functional maintenance of organisms. By identifying key regulatory interactions and molecules involved in these fate decisions, we can shed light on the intricate mechanisms underlying the cell fates. This understanding ultimately reveals the fundamental principles driving biological development and the origins of various diseases. In this study, we present an overarching framework which integrates pseudo-trajectory inference and differential analysis to determine critical regulatory interactions and molecules during cell fate transitions. To demonstrate feasibility and reliability of the approach, we employ the differentiation networks of hepatobiliary system and embryonic stem cells as representative model systems. By applying pseudo-trajectory inference to biological data, we aim to identify critical regulatory interactions and molecules during the cell fate transition processes. Consistent with experimental observations, the approach can allow us to infer dynamical cell fate decision processes and gain insights into the underlying mechanisms which govern cell state decisions.

Methyl damage to DNA bases is common in the cell nucleus. O6-alkylguanine-DNA alkyl transferase (AGT) may be a promising candidate for direct damage reversal in methylated DNA (mDNA) at the O6 point of the guanine. Indeed, atomic-level investigations in the contact region of AGT-DNA complex can provide an in-depth understanding of their binding mechanism, allowing to evaluate the silico-drug nature of AGT and its utility in removing methyl damage in DNA. In this study, molecular dynamics (MD) simulation was utilized to examine the flipping of methylated nucleotide, the binding mechanism between mDNA and AGT, and the comparison of binding strength prior and post methyl transfer to AGT. The study reveals that methylation at the O6 atom of guanine weakens the hydrogen bond (H-bond) between guanine and cytosine, permitting for the flipping of such nucleotide. The formation of a H-bond between the base pair of methylated nucleotide (i.e., cytosine) and the intercalated arginine of AGT also forces the nucleotide to rotate. Following that, electrostatics and van der Waals contacts as well as hydrogen bonding contribute to form the complex of DNA and protein. The stronger binding of AGT with DNA before methyl transfer creates the suitable condition to transfer methyl adduct from DNA to AGT.

Weakly reversible chemical reaction networks with zero deficiency associated with mass-action kinetics admit, within each positive stoichiometric compatibility class, one positive steady state which is locally asymptotically stable and this irrespective of the values of the kinetics constants. Networks which do not enjoy these structural properties potentially exhibit more diverse dynamical behaviors. In this article, we consider a chemical reaction network associated with mass-action kinetics which is not weakly reversible and has a deficiency larger than one. The chemical reactions are at most bimolecular, but inflow and outflow reactions are present. Our results are as follows. We establish the existence of positive steady-state solutions and obtain their analytic expressions in the concentration space and in convex coordinates. We show that the system fulfills necessary conditions for a saddle-node and for a bifurcation into a saddle and a node. We apply a constructive approach to obtain a set of numerical values for the state variables and kinetic parameters, not reported previously, such that the reduced Jacobian is characterized by a zero eigenvalue with all other eigenvalues having negative real parts. The bifurcation diagram confirms the presence of the switch-like behavior.

In this study, scanning electron microscopy (SEM) was used to study the cell structure of SARS-CoV-2 infected cells. Our measurements revealed infection remodeling caused by infection, including the emergence of new specialized areas where viral morphogenesis occurs at the cell membrane. Intercellular extensions for viral cell surfing have also been observed. Our results expand knowledge of SARS-CoV-2 interactions with cells, its spread from cell to cell, and their size distribution. Our findings suggest that SEM is a useful microscopic method for intracellular ultrastructure analysis of cells exhibiting specific surface modifications that could also be applied to studying other important biological processes.