Computational Methods Research Articles

Repurposing of Empagliflozin as Cardioprotective Drug: An in-silico Approach.

Drug repurposing involves investigating new indications or uses for drugs that have already been approved for clinical use. Empagliflozin is a C-glycosyl compound characterized by the presence of a beta-glucosyl residue. It functions as a sodium-glucose co-transporter 2 inhibitor and is utilized to enhance glycemic control in adults diagnosed with type 2 diabetes mellitus. Additionally, it is indicated for the reduction of cardiovascular mortality risk in adult patients who have both type 2 diabetes mellitus and pre-existing cardiovascular disease. The study's objective revolves around exploring the repurposing potential of a novel SGLT2 inhibitor acting as an antidiabetic drug named Empagliflozin through computational methods, with a specific focus on its interaction with cardioprotective key target proteins. The study was performed by docking the empagliflozin with different target proteins (NHE1- CHP1, BIRC5, GLUT1, and XIAP) by using Autodock, and different values were recorded. The docked files were analysed by the BIOVIA Discovery Studio Visualizer. The in silico analysis conducted in this study examines the binding free energy values of Empagliflozin with key target proteins. Results revealed that NHE1-CHP1 exhibits the lowest binding free energy, followed by BIRC5, GLUT1, and XIAP, with the highest value. This descending order of binding energies suggests varying degrees of effectiveness in binding molecules, with lower energies indicative of more potent biological activity. The analysis underscores the importance of intermolecular interactions, particularly hydrogen bond formations facilitated by oxygen, nitrogen, and carbonyl groups in compound structures. Notably, NHE1-CHP1 demonstrates superior binding interactions with Empagliflozin compared to the other target proteins, highlighting its potential as a cardioprotective agent. These findings offer valuable insights into the therapeutic possibilities of Empagliflozin in cardioprotection, indicating promising avenues for further research and development in this domain.

Multi-slice electron ptychographic tomography for three-dimensional phase-contrast microscopy beyond the depth of focus limits

Abstract Electron ptychography is a powerful computational method for atomic-resolution imaging with high contrast for weakly and strongly scattering elements. Modern algorithms coupled with fast and efficient detectors allow imaging specimens with tens of nanometers thicknesses with sub-0.5 Ångstrom lateral resolution. However, the axial resolution in these approaches is currently limited to a few nanometers, limiting their ability to solve novel atomic structures ab initio. Here, we experimentally demonstrate multi-slice ptychographic electron tomography, which allows atomic resolution three-dimensional phase-contrast imaging in a volume surpassing the depth of field limits. We reconstruct tilt-series 4D-STEM measurements of a Co 3 O 4 nanocube, yielding 2 Å axial and 0.7 Å transverse resolution in a reconstructed volume of ( 18.2 nm ) 3 . Our results demonstrate a 13.5-fold improvement in axial resolution compared to multi-slice ptychography while retaining the atomic lateral resolution and the capability to image volumes beyond the depth of field limit. Multi-slice ptychographic electron tomography significantly expands the volume of materials accessible using high-resolution electron microscopy. We discuss further experimental and algorithmic improvements necessary to also resolve single weakly scattering atoms in 3D.

Leveraging the Thermodynamics of Protein Conformations in Drug Discovery.

As the name implies, structure-based drug design requires confidence in the holo complex structure. The ability to clarify which protein conformation to use when ambiguity arises would be incredibly useful. We present a large scale validation of the computational method Protein Reorganization Free Energy Perturbation (PReorg-FEP) and demonstrate its quantitative accuracy in selecting the correct protein conformation among candidate models in apo or ligand induced states for 14 different systems. These candidate conformations are pulled from various drug discovery related campaigns: cryptic conformations induced by novel hits in lead identification, binding site rearrangement during lead optimization, and conflicting structural biology models. We also show an example of a pH-dependent conformational change, relevant to protein design.

Enhancing risk and crisis communication with computational methods: A systematic literature review.

Recent developments in risk and crisis communication (RCC) research combine social science theory and data science tools to construct effective risk messages efficiently. However, current systematic literature reviews (SLRs) on RCC primarily focus on computationally assessing message efficacy as opposed to message efficiency. We conduct an SLR to highlight any current computational methods that improve message construction efficacy and efficiency. We found that most RCC research focuses on using theoretical frameworks and computational methods to analyze or classify message elements that improve efficacy. For improving message efficiency, computational and manual methods are only used in message classification. Specifying the computational methods used in message construction is sparse. We recommend that future RCC research apply computational methods toward improving efficacy and efficiency in message construction. By improving message construction efficacy and efficiency, RCC messaging would quickly warn and better inform affected communities impacted by current hazards. Such messaging has the potential to save as many lives as possible.

COMPUTATIONAL-EXPERIMENTAL METHOD FOR STRESS-STRAIN CURVE CONSTRUCTING UNDER CONDITIONS OF INHOMOGENEOUS STRESS FIELDS

In order to ensure reliability of structures, it is necessary to study equilibrium damage accumulation which initiates softening zones in solids. It is considered appropriate to use postcritical deformation models when conducting strength analysis of structures. However, obtaining complete stress-strain curves in standard tests is difficult due to the strain localization in the form of a neck. At the same time, the use of true stresses taking into account changes in a specimen cross section is incorrect due to the implementation of a complex stress state. In this regard, it is necessary to develop computational and experimental methods for constructing material stress-strain curves under conditions of inhomogeneous stress fields. In this case it seems reasonable to use data on strain fields on the body’s surface, which can be obtained, for example, using non-contact optical video systems. The paper presents the computational-experimental method for constructing a stress-strain curve under conditions of inhomogeneous stress fields. An elastoplastic model of an isotropic material is considered. The initial data of the method are two elastic constants, the yield stress value, the load diagram of a body with a stress concentrator, and the maximum values of strain intensity corresponding to various states. The developed method was tested by a numerical simulation of deforming an hourglass specimen and a plate with a stress concentrator. Stress-strain curves with and without a yield plateau are considered. The results demonstrate a high agreement between the initially specified and reconstructed stress-strain curves. A conclusion is made about the rationality of using the developed method when constructing stress-strain diagrams and necessity for its modernization to describe the postcritical deformation stage.

Unsteady MHD Casson fluid flow past a vertical plate in the presence of viscid dissipation and Dufour effects

PurposeThis research investigates the impact of Dufour effects and viscous dissipation on unsteady magnetohydrodynamic (MHD) natural convection in an incompressible, viscous, and electrically conductive fluid over a vertically oscillating flat plate. The study highlights the significance of magnetic fields in influencing thermal and mass transfer, particularly in the context of thermal radiation. Computational fluid dynamics method including finite difference or finite element techniques can be used to crack the governing equations of the fluid flow. In this work, we used the finite element method (FEM) numerical technique to analyze the numerical behavior of unsteady boundary layer flow of Casson fluid with natural convection past an oscillating vertical plate. Key parameters such as skin friction, temperature, concentration, velocity and Sherwood numbers are derived and analyzed. The results demonstrate that viscous dissipation significantly elevates the fluid temperature, while an increase in the radiation parameter is associated with a decrease in internal friction at the plate. These findings provide critical insights into the interplay between thermal radiation and magnetic fields in MHD flows, with potential applications in engineering systems involving heat and mass transfer, such as cooling systems and material processing. This study underscores the importance of understanding these dynamics for optimizing the performance of MHD applications in various industrial settings.Design/methodology/approachThe mainly authorized and energetic FEM to explain the non-linear, dimensionless partial differential equations (11–13) via equation with boundary conditions (14) makes use of Bathe (36), Reddy (37), Connor (38) and Chung (39). Following are the key steps that make up the method: discretize the domain, derivation of element equation, assembly of element equation, imposition of boundary condition and solution of assembly equation.FindingsThis study examined the impact of viscid dissipative radiation and the Dufour effect on unsteady one-dimensional MHD natural convective flow of a viscous, incompressible, electrically conducting fluid past an infinite moving vertical flat plate with a chemical reaction. Numerically solving the governing equations using the FEM approach is efficient and precise, aiming to be applied to fluid mechanics and related problems. Along with their effects on temperature, concentration and velocity, the following parameters are included: the mass Grashof number, the Soret number, the Grashof number, the Prandtl number, chemical reaction, the Schmidt number, radiation and the Casson parameter. Both the Grashof numbers of thermal and mass rates (Gr, Gm) make an increment in the velocity region. The velocity decreases with an increase in the magnetic parameter. The velocity increases with an increase in the permeability of the porous medium parameter. The temperature flow rate is higher for both Dufour and Viscid dissipation, while a decrement is noted of both Prandtl number and radiation effects. The decrementing behavior of the concentration region is observed at supreme inputs of chemical reaction coefficient and Schmidt number.Originality/valueThis is an original paper and not submitted anywhere.

Advancing a biomechanical framework for cellular interaction and mechanotransduction analysis using data mining techniques

As biomechanics advances, understanding the structural and functional dynamics of biological systems at molecular and cellular levels becomes increasingly critical. This study investigates a biomechanical framework for analyzing cellular interaction forces and information flow in mechanotransduction processes. Leveraging Data Mining (DM) technology, we evaluate cellular system efficiency and adaptation mechanisms under varying biomechanical stimuli, simulating stress conditions akin to high parallel processing loads. The proposed algorithm achieves an accuracy of 95.27% in predicting cellular response behaviors, with system stability maintained above 90% under simulated mechanical stress. These findings highlight the potential of computational methods to enhance our understanding of cellular resilience and adaptation in biomechanical contexts. Ultimately, this work contributes to the development of robust, adaptive models for studying the mechanobiology of cells and tissues, advancing diagnostic and therapeutic approaches in molecular and cellular biomechanics.

Peeking into the future: inferring mechanics in dynamical tissues.

Cells exert forces on each other and their environment, shaping the tissue. The resulting mechanical stresses can be determined experimentally or estimated computationally using stress inference methods. Over the years, mechanical stress inference has become a non-invasive, low-cost computational method for estimating the relative intercellular stresses and intracellular pressures of tissues. This mini-review introduces and compares the static and dynamic modalities of stress inference, considering their advantages and limitations. To date, most software has focused on static inference, which requires only a single microscopy image as input. Although applicable in quasi-equilibrium states, this approach neglects the influence that cell rearrangements might have on the inference. In contrast, dynamic stress inference relies on a time series of microscopy images to estimate stresses and pressures. Here, we discuss both static and dynamic mechanical stress inference in terms of their physical, mathematical, and computational foundations and then outline what we believe are promising avenues for in silico inference of the mechanical states of tissues.

Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single-cell resolution.

Dissecting human neurobiology at high resolution and with mechanistic precision requires a major leap in scalability, given the need for experimental designs that include multiple individuals and, prospectively, population cohorts. To lay the foundation for this, we have developed and benchmarked complementary strategies to multiplex brain organoids by pooling cells from different pluripotent stem cell (PSC) lines either during organoid generation (mosaic models) or before single-cell RNA sequencing (scRNA-seq) library preparation (downstream multiplexing). We have also developed a new computational method, SCanSNP, and a consensus call to deconvolve cell identities, overcoming current criticalities in doublets and low-quality cell identification. We validated both multiplexing methods for charting neurodevelopmental trajectories at high resolution, thus linking specific individuals' trajectories to genetic variation. Finally, we modeled their scalability across different multiplexing combinations and showed that mosaic organoids represent an enabling method for high-throughput settings. Together, this multiplexing suite of experimental and computational methods provides a highly scalable resource for brain disease and neurodiversity modeling.

A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade

Identifying breast masses is relevant in early cancer detection. Automatic identification using computational methods helps assist medical experts with this task. Although high values have been reported in breast mass classification from digital mammograms, most results have focused on a general benign/malignant classification. According to the BI-RADS standard, masses are associated with cancer risk by grade depending on their specific shape, margin, and density characteristics. This work presents a methodology of testing several descriptors on the INbreast dataset, finding those better related to clinical assessment. The analysis provides a description based on BI-RADS for mass classification by combining neural networks and image processing. The results show that masses associated with grades BI-RADS-2 to BI-RADS-5 can be identified, reaching a general accuracy and sensitivity of 0.88±0.07. While this initial study is limited to a single dataset, it demonstrates the possibility of generating a description for automatic classification that is directly linked to the information analyzed by medical experts in clinical practice.

Drug Repurposing Using Hypergraph Embedding Based on Common Therapeutic Targets of a Drug.

Developing a new drug is a long and expensive process that typically takes 10-15 years and costs billions of dollars. This has led to an increasing interest in drug repositioning, which involves finding new therapeutic uses for existing drugs. Computational methods become an increasingly important tool for identifying associations between drugs and new diseases. Graph- and hypergraph-based approaches are a type of computational method that can be used to identify potential associations between drugs and new diseases. Here, we present a drug repurposing method based on hypergraph neural network for predicting drug-disease association in three stages. First, it constructs a heterogeneous graph that contains drug and disease nodes and links between them; in the second stage, it converts the heterogeneous simple graph to a hypergraph with only disease nodes. This is achieved by grouping diseases that use the same drug into a hyperedge. Indeed, all the diseases that are the common therapeutic goal of a drug are placed on a hyperedge. Finally, a graph neural network is used to predict drug-disease association based on the structure of the hypergraph. This model is more efficient than other methods because it uses a hypergraph to model relationships more effectively than graphs. Furthermore, it constructs the hypergraph using only a drug-disease association matrix, eliminating the need for extensive amounts of data. Experimental results show that the hypergraph-based approach effectively captures complex interrelationships between drugs and diseases, leading to improved accuracy of drug-disease association prediction compared to state-of-the-art methods.

Single-cell RNA sequencing in stroke and traumatic brain injury: Current achievements, challenges, and future perspectives on transcriptomic profiling.

Single-cell RNA sequencing (scRNA-seq) is a high-throughput transcriptomic approach with the power to identify rare cells, discover new cellular subclusters, and describe novel genes. scRNA-seq can simultaneously reveal dynamic shifts in cellular phenotypes and heterogeneities in cellular subtypes. Since the publication of the first protocol on scRNA-seq in 2009, this evolving technology has continued to improve, through the use of cell-specific barcodes, adoption of droplet-based systems, and development of advanced computational methods. Despite induction of the cellular stress response during the tissue dissociation process, scRNA-seq remains a popular technology, and commercially available scRNA-seq methods have been applied to the brain. Recent advances in spatial transcriptomics now allow the researcher to capture the positional context of transcriptional activity, strengthening our knowledge of cellular organization and cell-cell interactions in spatially intact tissues. A combination of spatial transcriptomic data with proteomic, metabolomic, or chromatin accessibility data is a promising direction for future research. Herein, we provide an overview of the workflow, data analyses methods, and pros and cons of scRNA-seq technology. We also summarize the latest achievements of scRNA-seq in stroke and acute traumatic brain injury, and describe future applications of scRNA-seq and spatial transcriptomics.

A fast calculation method for CCGHs applicable to various planar computer-generated hologram computation methods

A fast calculation method for CCGHs applicable to various planar computer-generated hologram computation methods

Artificial intelligence’s current involvement in urology and future implementation in clinical environments

Artificial intelligence (AI) is a set of computational methods that interprets the data given, uncovers the underlying patterns associated with the complexity of data, and provides the prediction of outcomes that have become increasingly relevant in urology. The current application of AI in urology predominately focuses on disease diagnosis and risk factor analysis in urologic oncology and male infertility. While many candidate models have been proposed in the literature, efforts to construct clinically meaningful data by incorporating patient-specific and multidisciplinary approaches should be carried out to improve the clinical applicability of AI in driving personalised treatment planning and disease prognosis. Looking forward, AI has the potential to drive targeted training in urology, from surgical techniques to patient-specific surgical procedure simulation, in combination with other technologies such as augmented reality. In order to achieve this, patient involvement should be considered in the model development stage, which also addresses issues surrounding the ethical deployment of AI in the clinical environment. It is possible to see AI playing a collaborative role with surgeons in improving clinical efficiency in the future. Level of evidence: Not Applicable

Computational Insight in the Identification of Non-Synonymous Single-Nucleotide Polymorphism Affecting the Structure and Function of Interleukin-4.

IL4 is a versatile cytokine essentially known for differentiation, proliferation and cell death in cells. Its dysregulation has been found to be associated with the development of inflammatory disorders. The goal of the current investigation is to identify and select non-synonymous single-nucleotide polymorphisms (nsSNPs) in the IL-4 gene by employing computational methods which may have a potential functional impact on the occurrence of disease. Six different nsSNPs were predicted to be deleterious based on the consensus of different algorithms: SIFT, Polyphen2 (Humdiv and HumVar), PredictSNP and SNP&GO. I-mutant and MuPro assessment revealed a decrease in the stability of these mutants except K150M. Modelling was then carried out to build the wild type along with its mutants, followed by superimposition of the wild type with mutants to evaluate the RMSD value, which lies between 0.26 and 0.34. Simulation results of mutant models, along with wild type, showed that four of the mutants (N113Y, A118G, R109W and K150M) deviated most and were unstable. A118G showed a significant deviation from the wild type, while V53A and C123R were stable. The finding establishes the evidence that the identified six nsSNPs of IL-4 can be the new entrant presenting their candidature for genetic testing.

Optimization of cargo distribution for high-speed freight trains to overcome strong wind conditions

The high-speed freight train (HSFT) is an emerging freight transportation mode that can play a significant role in the development of remote cities. In this study, we propose an optimization method for cargo distribution to prevent HSFT from overturning in strong winds. The multibody dynamics (MBD) HSFT model is built and imposed with aerodynamic loads derived through the computational fluid dynamics method. Based on the MBD simulation results, the prediction model of overturning safety for HSFT is established using gradient-boosting decision tree to make the value of cargo height continuous. Consequently, a nonlinear programming model for the cargo distribution problem is built and solved. The MBD simulation results reveal that the outer side crosswind is good for large vehicle velocity and the vehicle in the rear position of HSFT is safer. The case study demonstrates that the proposed optimization method can decrease the overturning risk for HSFT by more than 10% compared with the even distribution scheme. This research will contribute to making the loading plan for the rail company, ensuring the operational safety of HSFT in the windy area.

Transforming drug discovery: the impact of AI and molecular simulation on R&D efficiency.

The process of developing new drugs in the pharmaceutical industry is both time-consuming and costly, making efficiency crucial. Recent advances in hardware and computational methods have led to the widespread application of computational science approaches in drug discovery. These approaches, including artificial intelligence and molecular simulations, span from target identification to pharmacokinetics research, aiming to reduce the likelihood of failure and present lower costs. Machine learning-based methods predict new applications for developing new drugs based on accumulated knowledge, while molecular simulations estimate interactions between drugs and target proteins at the atomic level based on physical laws. Each approach has its advantages and disadvantages, and they complement each other. As a result, the future of computational science approaches in drug discovery is expected to focus on developing new methodologies that integrate these two techniques to enhance the efficiency of drug discovery.

On the role of aurophilic interactions in determining geometries in [Pn(AuL)4]+ complexes (Pn = N, P, As)

The nature of Au···Au interactions in poly‐gold complexes has been a fervent field of research since the term ‘aurophilicity’ was coined by H. Schmidbaur in the late 80s. Here we look to address the importance of such interactions in defining the solid‐state geometries of discreet gold‐pnictonium clusters [Pn(Au·PMe3)4]+ (Pn = N‐As), using in‐depth computational methods such as Energy‐Decomposition Analysis and Electron Localization Functions. We find that predicted geometries of simplified congeners of known tetrahedral N and square‐planar As derivatives align with those reported structures, whilst the as‐yet unknown square‐pyramidal geometry is predicted for P. The tetrahedral to square‐pyramidal switch arises from shorter Au···Au contacts in the latter geometry, which allows for increased metal‐metal dispersion interactions, i.e. aurophilic interactions, which overcome the high electrostatic interactions predicted for the same species in the tetrahedral conformation.

Optimization of sustainable polymer composites for surface metamorphosis in FDM processes

The demand for the development of sustainable manufacturing processes is enhanced by the necessity to optimize polymer composites, particularly in the context of fused deposition modeling (FDM). This research aims to enhance sustainable polymer composites to improve the surface metamorphosis during FDM processes. Various eco-friendly polymer matrices were integrated with novel composite reinforcements to evaluate their impact on surface quality, structural integrity, and the performance of FDM-printed components. Key surface features, including roughness (Ra), texture, and function, were quantified through both experimental and computational methods. The optimized composites led to a significant reduction in surface roughness, with Ra values improving by up to 45% compared to standard filaments. In addition, tensile strength was increased by 30% and flexural strength by 20% relative to unmodified polymer composites. Optimization strategies, guided by green chemistry principles and materials science, successfully enhanced surface finishes and functional properties, aligning with sustainability goals. The results demonstrate that optimized sustainable polymer composites can significantly improve the quality and performance of FDM prints, supporting more efficient and environmentally friendly manufacturing practices. This study contributes to advancing materials and processes in line with sustainability principles and surface engineering.

Study on Public Perceptions and Disaster Prevention Framework of Tunnel Fires Based on Social Media and Artificial Intelligence

To investigate public perceptions regarding tunnel fire disasters and optimize the tunnel fire disaster prevention framework, this study takes the emerging social media platform Douyin as a case study, conducting an in-depth analysis of 2133 short videos related to tunnel fires on the platform. A computational communication method was used for analysis, Latent Dirichlet Allocation was used to cluster the discussion topics of these tunnel fire short videos, and a spatiotemporal evolution analysis of the number of videos posted, user comments, and emotional inclinations across different topics was performed. The findings reveal that there is a noticeable divergence in public opinion regarding emergency decision making in tunnel fires, related to the complexity of tunnel fire incidents, ethical dilemmas in tunnel fire escape scenarios, and insufficient knowledge popularization of fire safety practices. The study elucidates the public’s actual needs during tunnel fire incidents, and a dynamic disaster prevention framework for tunnel fires based on social media and artificial intelligence is proposed on this basis to enhance emergency response capabilities. Utilizing short videos on social media, the study constructs a critical target dataset under real tunnel fire scenarios. It proposes a computer vision-based model for identifying critical targets in tunnel fires. This model can accurately and in real-time identify key targets such as fires, smoke, vehicles, emergency exits, and people in real tunnel fire environments, achieving an average detection precision of 77.3%. This research bridges the cognitive differences between the general public and professionally knowledgeable tunnel engineers regarding tunnel fire evacuation, guiding tunnel fire emergency responses and personnel evacuation.