Computational-Based Polyphenol Therapy for Nonsmall Cell Lung Cancer: Naringin Coamorphous Systems for Solubility and Bioavailability Enhancement.

In this research, we utilized molecular simulations to create co-amorphous materials (CAMs) of ceritinib (CRT) with the objective of improving its solubility and bioavailability. We identified naringin (NRG) as a suitable co-former for CRT CAMs based on binding energy and intermolecular interactions through computational modeling. We used the solvent evaporation method to produce CAMs of CRT and NRG, expecting to enhance both solubility and bioavailability simultaneously. The solid-state characterization using techniques like differential scanning calorimeter, X-ray powder diffraction, and Fourier-transform infrared spectroscopy affirmed the formation of a single amorphous phase and the presence of intermolecular interactions between CRT and NRG in the CAMs. These materials remained physically stable for up to six months under dry conditions at 40 °C. Moreover, the CAMs demonstrated significant improvements in the solubility and dissolution of CRT (specifically in the ratio CRT:NRG 1:2). This, in turn, led to an increase in cytotoxicity, apoptotic cells, and G0/G1 phase inhibition in A549 cells compared to CRT alone. Furthermore, CRT permeability is also improved twofold, as estimated by the everted gut sac method. The enhanced solubility of CAMs also positively affected the pharmacokinetic parameters. When compared to the physical mixture, the CAMs of CRT:NRG 2:1 exhibited a 2.1-fold increase in CRT exposure (AUC0-t) and a 2.4-fold increase in plasma concentration (Cmax).

Using Computational Simulations to Model Deleterious Variation and Genetic Load in Natural Populations.

AbstractDeleterious genetic variation is abundant in wild populations, and understanding the ecological and conservation implications of such variation is an area of active research. Genomic methods are increasingly used to quantify the impacts of deleterious variation in natural populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of mutations. Computational simulations of deleterious variation offer a complementary tool that can help overcome these limitations, although such approaches have yet to be widely employed. In this perspective article, we aim to encourage ecological and conservation genomics researchers to adopt greater use of computational simulations to aid in deepening our understanding of deleterious variation in natural populations. We first provide an overview of the components of a simulation of deleterious variation, describing the key parameters involved in such models. Next, we discuss several approaches for validating simulation models. Finally, we compare and validate several recently proposed deleterious mutation models, demonstrating that models based on estimates of selection parameters from experimental systems are biased toward highly deleterious mutations. We describe a new model that is supported by multiple orthogonal lines of evidence and provide example scripts for implementing this model (https://github.com/ckyriazis/simulations_review).

Shape-Configurable Mesh for Hernia Repair by Synchronizing Anisotropic Body Motion.

Continuous progress has been made in elucidating the relationship between material property, device design, and body function to develop surgical meshes. However, an unmet need still exists wherein the surgical mesh can handle the body motion and thereby promote the repair process. Here, the hernia mesh design and the advanced polymer properties are tailored to synchronize with the anisotropic abdominal motion through shape configuration. The thermomechanical property of shape configurable polymer enables molding of mesh shape to fit onto the abdominal structure upon temperature shift, followed by shape fixing with the release of the heat energy. The microstructural design of mesh is produced through finite element modeling to handle the abdominal motion efficiently through the anisotropic longitudinal and transverse directions. The design effects are validated through in vitro, ex vivo, and in vivo mechanical analyses using a self-configurable, body motion responsive (BMR) mesh. The regenerative function of BMR mesh leads to effective repair in a rat hernioplasty model by effectively handling the anisotropic abdomen motion. Subsequently, the device-tissue integration is promoted by promoting healthy collagen synthesis with fibroblast-to-myofibroblast differentiation. This study suggests a potential solution to promote hernia repair by fine-tuning the relationship between material property and mesh design.

Transformation of Graphitic Carbon Nitride by Reactive Chlorine Species: "Weak" Oxidants Are the Main Players.

Graphitic carbon nitride (g-C3N4) nanomaterials hold great promise in diverse applications; however, their stability in engineering systems and transformation in nature are largely underexplored. We evaluated the stability, aging, and environmental impact of g-C3N4 nanosheets under the attack of free chlorine and reactive chlorine species (RCS), a widely used oxidant/disinfectant and a class of ubiquitous radical species, respectively. g-C3N4 nanosheets were slowly oxidized by free chlorine even at a high concentration of 200-1200 mg L-1, but they decomposed rapidly when ClO· and/or Cl2•- were the key oxidants. Though Cl2•- and ClO· are considered weaker oxidants in previous studies due to their lower reduction potentials and slower reaction kinetics than ·OH and Cl·, our study highlighted that their electrophilic attack efficacy on g-C3N4 nanosheets was on par with ·OH and much higher than Cl·. A trace level of covalently bonded Cl (0.28-0.55 at%) was introduced to g-C3N4 nanosheets after free chlorine and RCS oxidation. Our study elucidates the environmental fate and transformation of g-C3N4 nanosheets, particularly under the oxidation of chlorine-containing species, and it also provides guidelines for designing reactive, robust, and safe nanomaterials for engineering applications.

The biomechanical efficacy of a hybrid support surface in protecting supine patients from sacral pressure ulcers.

Support surfaces are the most important pressure ulcer/injury prevention technology available to clinicians for protecting their at-risk patients. A hybrid support surface marries the benefits of reactive and active support surfaces, by using high-quality foam material inside inflatable air cells. When used in its "static mode", it is a constant low air pressure mattress which delivers pressure redistribution in response to patient bodyweight and movements, by maximising the immersion and envelopment performance of the support surface. When used in its powered "dynamic mode", this system further delivers alternating pressure care via the connected foam and air cells. Modes of action of hybrid support surfaces were never studied quantitatively before, excluding through the limited scope of interface pressure mapping. In this work, we developed a novel computational modelling framework and simulations to visualise and quantify the state of soft tissue loading at the buttocks of a supine patient positioned on a hybrid support surface, in both the static and dynamic modes. We found that the dynamic mode effectively shifts deep concentrated soft tissue loading from under the sacral bone (towards the sacral promontory) to the tip of the sacrum (coccyx) and vice versa, and thereby, generates a deep tissue offloading effect.

Regioselective Synthesis, Structural Characterization, and Antiproliferative Activity of Novel Tetra-Substituted Phenylaminopyrazole Derivatives.

A small library of highly functionalized phenylaminopyrazoles, bearing different substituents at position 1, 3, and 4 of the pyrazole ring, was prepared by the one-pot condensation of active methylene reagents, phenylisothiocyanate, and substituted hydrazine (namely, methyl- and benzyl-hydrazine). The identified reaction conditions proved to be versatile and efficient. Furthermore, the evaluation of alternative stepwise protocols affected the chemo- and regio-selectivity outcome of the one-pot procedure. The chemical identities of two N-methyl pyrazole isomers, selected as prototypes of the whole series, were unambiguously identified by means of NMR and mass spectrometry studies. Additionally, semiempirical calculations provided a structural rationale for the different chromatographic behavior of the two isomers. The prepared tetra-substituted phenylaminopyrazoles were tested in cell-based assays on a panel of cancer and normal cell lines. The tested compounds did not show any cytotoxic effect on the selected cell lines, thus supporting their pharmaceutical potentials.

Inhibitory Effects of Myriocin on Non-Enzymatic Glycation of Bovine Serum Albumin.

Advanced glycation end products (AGEs) are the compounds produced by non-enzymatic glycation of proteins, which are involved in diabetic-related complications. To investigate the potential anti-glycation activity of Myriocin (Myr), a fungal metabolite of Cordyceps, the effect of Myr on the formation of AGEs resulted from the glycation of bovine serum albumin (BSA) and the interaction between Myr and BSA were studied by multiple spectroscopic techniques and computational simulations. We found that Myr inhibited the formation of AGEs at the end stage of glycation reaction and exhibited strong anti-fibrillation activity. Spectroscopic analysis revealed that Myr quenched the fluorescence of BSA in a static process, with the possible formation of a complex (approximate molar ratio of 1:1). The binding between BSA and Myr mainly depended on van der Waals interaction, hydrophobic interactions and hydrogen bond. The synchronous fluorescence and UV-visible (UV-vis) spectra results indicated that the conformation of BSA altered in the presence of Myr. The fluorescent probe displacement experiments and molecular docking suggested that Myr primarily bound to binding site 1 (subdomain IIA) of BSA. These findings demonstrate that Myr is a potential anti-glycation agent and provide a theoretical basis for the further functional research of Myr in the prevention and treatment of AGEs-related diseases.

An exploration of error-driven learning in simple two-layer networks from a discriminative learning perspective.

Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to current complex deep learning models dominating discussions in machine learning and artificial intelligence. However, despite the ubiquity of this mechanism, detailed analyses of its basic workings uninfluenced by existing theories or specific research goals are rare in the literature. To address this, we present an exposition of error-driven learning - focusing on its simplest form for clarity - and relate this to the historical development of error-driven learning models in the cognitive sciences. Although historically error-driven models have been thought of as associative, such that learning is thought to combine preexisting elemental representations, our analysis will highlight the discriminative nature of learning in these models and the implications of this for the way how learning is conceptualized. We complement our theoretical introduction to error-driven learning with a practical guide to the application of simple error-driven learning models in which we discuss a number of example simulations, that are also presented in detail in an accompanying tutorial.

Synthesis, in vitro evaluation, and computational simulations studies of 1,2,3-triazole analogues as DPP-4 inhibitors.

Novel 1,2,3-triazole analogues (S7 ~ S10) were synthesized and evaluated for their inhibitory activity against hDPP-4. All the 1,2,3-triazole analogues exhibited moderate in vitro hDPP-4 inhibitory activities (265 ~ 780 nM). These results are somewhat less potent compared to those of known 1,2,3-triazole analogues (S1 ~ S6, 14 ~ 254 nM). S2 and S3 manifested excellent potency against hDPP-4 with IC50s of 28 and 14 nM, respectively. The role of the 1,2,3-triazole moiety in binding the molecule to the target was investigated using combined 10 1,2,3-triazole analogues (S1 ~ S10). Molecular dynamics (MD) simulations following the aforementioned docking phase were performed to elucidate potential binding modes of sitagliptin's 1,2,3-triazole analogues in hDPP-4, with the use of a cocrystal structure of hDPP-4 with sitagliptin (PDB ID: 1X70). Docking and MD simulations of the complexes of hDPP-4 with sitagliptin, S2 and S3 suggest that Glu205, Glu206, Tyr662, and Tyr666 would be the key amino acid residues for the binding of the molecules with the receptor. Especially, S2 and S3 showed additional strong π-π interaction between Phe357 and 1,2,3-triazole. Same strong π-π interaction is also observed between Phe357 and the 1,2,4-triazole ring of sitagliptin. Furthermore, additional interactions with Tyr547, Cys551, and especially Arg358 would enhance the binding affinity of the compounds for the pocket of the enzyme. In overall, in vitro hDPP-4 inhibitory activities of synthetic 1,2,3-triazole analogues were well matched with results of computational simulations studies.

Agent-Based Modeling in Molecular Systems Biology.

Molecular systems orchestrating the biology of the cell typically involve a complex web of interactions among various components and span a vast range of spatial and temporal scales. Computational methods have advanced our understanding of the behavior of molecular systems by enabling us to test assumptions and hypotheses, explore the effect of different parameters on the outcome, and eventually guide experiments. While several different mathematical and computational methods are developed to study molecular systems at different spatiotemporal scales, there is still a need for methods that bridge the gap between spatially-detailed and computationally-efficient approaches. In this review, we summarize the capabilities of agent-based modeling (ABM) as an emerging molecular systems biology technique that provides researchers with a new tool in exploring the dynamics of molecular systems/pathways in health and disease.

A quantitative and qualitative analysis of the COVID-19 pandemic model.

Global efforts around the world are focused on to discuss several health care strategies for minimizing the impact of the new coronavirus (COVID-19) on the community. As it is clear that this virus becomes a public health threat and spreading easily among individuals. Mathematical models with computational simulations are effective tools that help global efforts to estimate key transmission parameters and further improvements for controlling this disease. This is an infectious disease and can be modeled as a system of non-linear differential equations with reaction rates. This work reviews and develops some suggested models for the COVID-19 that can address important questions about global health care and suggest important notes. Then, we suggest an updated model that includes a system of differential equations with transmission parameters. Some key computational simulations and sensitivity analysis are investigated. Also, the local sensitivities for each model state concerning the model parameters are computed using three different techniques: non-normalizations, half normalizations, and full normalizations. Results based on the computational simulations show that the model dynamics are significantly changed for different key model parameters. Interestingly, we identify that transition rates between asymptomatic infected with both reported and unreported symptomatic infected individuals are very sensitive parameters concerning model variables in spreading this disease. This helps international efforts to reduce the number of infected individuals from the disease and to prevent the propagation of new coronavirus more widely on the community. Another novelty of this paper is the identification of the critical model parameters, which makes it easy to be used by biologists with less knowledge of mathematical modeling and also facilitates the improvement of the model for future development theoretically and practically.

A Systematic Test of Receptor Binding Kinetics for Ligands in Tumor Necrosis Factor Superfamily by Computational Simulations.

Ligands in the tumor necrosis factor (TNF) superfamily are one major class of cytokines that bind to their corresponding receptors in the tumor necrosis factor receptor (TNFR) superfamily and initiate multiple intracellular signaling pathways during inflammation, tissue homeostasis, and cell differentiation. Mutations in the genes that encode TNF ligands or TNFR receptors result in a large variety of diseases. The development of therapeutic treatment for these diseases can be greatly benefitted from the knowledge on binding properties of these ligand-receptor interactions. In order to complement the limitations in the current experimental methods that measure the binding constants of TNF/TNFR interactions, we developed a new simulation strategy to computationally estimate the association and dissociation between a ligand and its receptor. We systematically tested this strategy to a comprehensive dataset that contained structures of diverse complexes between TNF ligands and their corresponding receptors in the TNFR superfamily. We demonstrated that the binding stabilities inferred from our simulation results were compatible with existing experimental data. We further compared the binding kinetics of different TNF/TNFR systems, and explored their potential functional implication. We suggest that the transient binding between ligands and cell surface receptors leads into a dynamic nature of cross-membrane signal transduction, whereas the slow but strong binding of these ligands to the soluble decoy receptors is naturally designed to fulfill their functions as inhibitors of signal activation. Therefore, our computational approach serves as a useful addition to current experimental techniques for the quantitatively comparison of interactions across different members in the TNF and TNFR superfamily. It also provides a mechanistic understanding to the functions of TNF-associated cell signaling pathways.

Unravelling the Enigma of Nonoxidative Conversion of Methane on Iron Single-Atom Catalysts.

The direct, nonoxidative conversion of methane on a silica-confined single-atom iron catalyst is a landmark discovery in catalysis, but the proposed gas-phase reaction mechanism is still open to discussion. Here, we report a surface reaction mechanism by computational modeling and simulations. The activation of methane occurs at the single iron site, whereas the dissociated methyl disfavors desorption into gas phase under the reactive conditions. In contrast, the dissociated methyl prefers transferring to adjacent carbon sites of the active center (Fe1 ©SiC2 ), followed by C-C coupling and hydrogen transfer to produce the main product (ethylene) via a key -CH-CH2 intermediate. We find a quasi Mars-van Krevelen (quasi-MvK) surface reaction mechanism involving extracting and refilling the surface carbon atoms for the nonoxidative conversion of methane on Fe1 ©SiO2 and this surface process is identified to be more plausible than the alternative gas-phase reaction mechanism.

Multiscale Molecular Dynamics Simulation of Multiple Protein Adsorption on Gold Nanoparticles.

A multiscale molecular dynamics simulation study has been carried out in order to provide in-depth information on the adsorption of hemoglobin, myoglobin, and trypsin over citrate-capped AuNPs of 15 nm diameter. In particular, determinants for single proteins adsorption and simultaneous adsorption of the three types of proteins considered have been studied by Coarse-Grained and Meso-Scale molecular simulations, respectively. The results, discussed in the light of the controversial experimental data reported in the current experimental literature, have provided a detailed description of the (i) recognition process, (ii) number of proteins involved in the early stages of corona formation, (iii) protein competition for AuNP adsorption, (iv) interaction modalities between AuNP and protein binding sites, and (v) protein structural preservation and alteration.

Release of sodium pyruvate from sacral prophylactic dressings: A computational model.

The use of sacral dressings for pressure ulcer prevention is growing rapidly. In addition to their passive biomechanical role in pressure and shear reduction, in the near future, prophylactic dressings may also provide active tissue protection by releasing preventive agents or drugs into skin and deeper tissues. We investigated delivery of sodium pyruvate (NaPy) from an active dressing to potentially protect the sacral skin and underlying tissues in addition. We used four finite element model variants describing different skin roughness levels to determine time profiles of NaPy diffusion from the dressing into the skin layers. The NaPy concentrations for the different modelled cases stabilised after 1 to 6.5 hours from the time of application of the dressings, at 1% to 3% of the NaPy concentration in the dressing reservoir, which is considered potent. We conclude that prophylactic sacral dressings have the potential to deliver NaPy into skin and subdermally, to potentially increase soft tissue tolerance to sustained bodyweight-caused cell and tissue deformations. The time durations to achieve the steady-state potent NaPy dermal concentrations are clinically feasible, for example, for preparation of patients for surgery or for use in intensive care units.

Infrared Fingerprint Engineering: A Molecular-Design Approach to Long-Wave Infrared Transparency with Polymeric Materials.

Optical technologies in the long-wave infrared (LWIR) spectrum (7-14 µm) offer important advantages for high-resolution thermal imaging in near or complete darkness. The use of polymeric transmissive materials for IR imaging offers numerous cost and processing advantages but suffers from inferior optical properties in the LWIR spectrum. A major challenge in the design of LWIR-transparent organic materials is that nearly all organic molecules absorb in this spectral window which lies within the so-called IR-fingerprint region. We report on a new molecular-design approach to prepare high refractive index polymers with enhanced LWIR transparency. Computational methods were used to accelerate the design of novel molecules and polymers. Using this approach, we have prepared chalcogenide hybrid inorganic/organic polymers (CHIPs) with enhanced LWIR transparency and thermomechanical properties via inverse vulcanization of elemental sulfur with new organic co-monomers.

Computational Modelling of Cancer Development and Growth: Modelling at Multiple Scales and Multiscale Modelling.

In this paper, we present two mathematical models related to different aspects and scales of cancer growth. The first model is a stochastic spatiotemporal model of both a synthetic gene regulatory network (the example of a three-gene repressilator is given) and an actual gene regulatory network, the NF-[Formula: see text]B pathway. The second model is a force-based individual-based model of the development of a solid avascular tumour with specific application to tumour cords, i.e. a mass of cancer cells growing around a central blood vessel. In each case, we compare our computational simulation results with experimental data. In the final discussion section, we outline how to take the work forward through the development of a multiscale model focussed at the cell level. This would incorporate key intracellular signalling pathways associated with cancer within each cell (e.g. p53-Mdm2, NF-[Formula: see text]B) and through the use of high-performance computing be capable of simulating up to [Formula: see text] cells, i.e. the tissue scale. In this way, mathematical models at multiple scales would be combined to formulate a multiscale computational model.

Theoretical and experimental research on the self-assembled system of molecularly imprinted polymers formed by salbutamol and methacrylic acid.

The quantum chemical method was applied for screening functional monomers in the rational design of salbutamol-imprinted polymers. Salbutamol was the template molecule, and methacrylic acid was the single functional monomer. The LC-WPBE/6-31G(d,p) method was used to investigate the geometry optimization, active sites, natural bond orbital charges, binding energies of the imprinted molecule, and solvation energy. The mechanism of action between salbutamol and methacrylic acid was also discussed. The theoretical results show that salbutamol interacts with functional monomers by hydrogen bonds, and the salbutamol-imprinted polymers with a ratio of 1:4 (salbutamol/methacrylic acid) in acetonitrile had the highest stability. The salbutamol-imprinted polymers were prepared by precipitation polymerization. The experimental results indicated that the maximum adsorption capacity for salbutamol toward molecularly imprinted polymers was 7.33 mg/g, and the molecularly imprinted polymers had a higher selectivity for salbutamol than for norepinephrine and terbutaline sulfate. Herein, the studies can provide theoretical and experimental references for the salbutamol molecular imprinted system.

A Series of Computational Neuroscience Labs Increases Comfort with MATLAB.

Computational simulations allow for a low-cost, reliable means to demonstrate complex and often times inaccessible concepts to undergraduates. However, students without prior computer programming training may find working with code-based simulations to be intimidating and distracting. A series of computational neuroscience labs involving the Hodgkin-Huxley equations, an Integrate-and-Fire model, and a Hopfield Memory network were used in an undergraduate neuroscience laboratory component of an introductory level course. Using short focused surveys before and after each lab, student comfort levels were shown to increase drastically from a majority of students being uncomfortable or with neutral feelings about working in the MATLAB environment to a vast majority of students being comfortable working in the environment. Though change was reported within each lab, a series of labs was necessary in order to establish a lasting high level of comfort. Comfort working with code is important as a first step in acquiring computational skills that are required to address many questions within neuroscience.

Tumour Growth Mechanisms Determine Effectiveness of Adaptive Therapy in Glandular Tumours.

In cancer treatment, adaptive therapy holds promise for delaying the onset of recurrence through regulating the competition between drug-sensitive and drug-resistant cells. Adaptive therapy has been studied in well-mixed models assuming free mixing of all cells and spatial models considering the interactions of single cells with their immediate adjacent cells. Both models do not reflect the spatial structure in glandular tumours where intra-gland cellular interaction is high, while inter-gland interaction is limited. Here, we use mathematical modelling to study the effects of adaptive therapy on glandular tumours that expand using either glandular fission or invasive growth. A two-dimensional, lattice-based model of sites containing sensitive and resistant cells within individual glands is developed to study the evolution of glandular tumour cells under continuous and adaptive therapies. We found that although both growth models benefit from adaptive therapy's ability to prevent recurrence, invasive growth benefits more from it than fission growth. This difference is due to the migration of daughter cells into neighboring glands that is absent in fission but present in invasive growth. The migration resulted in greater mixing of cells, enhancing competition induced by adaptive therapy. By varying the initial spatial spread and location of the resistant cells within the tumour, we found that modifying the conditions within the resistant cells containing glands affect both fission and invasive growth. However, modifying the conditions surrounding these glands affect invasive growth only. Our work reveals the interplay between growth mechanism and tumour topology in modulating the effectiveness of cancer therapy.