Digital twins for health: a scoping review.

The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.

Interpretable design of Ir-free trimetallic electrocatalysts for ammonia oxidation with graph neural networks.

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt3Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt3Ir, and Pt3Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d-band center metric of binding sites.

Novel covalent and non-covalent complex-based pharmacophore models of SARS-CoV-2 main protease (Mpro) elucidated by microsecond MD simulations.

As the world enters its second year of the pandemic caused by SARS-CoV-2, intense efforts have been directed to develop an effective diagnosis, prevention, and treatment strategies. One promising drug target to design COVID-19 treatments is the SARS-CoV-2 Mpro. To date, a comparative understanding of Mpro dynamic stereoelectronic interactions with either covalent or non-covalent inhibitors (depending on their interaction with a pocket called S1' or oxyanion hole) has not been still achieved. In this study, we seek to fill this knowledge gap using a cascade in silico protocol of docking, molecular dynamics simulations, and MM/PBSA in order to elucidate pharmacophore models for both types of inhibitors. After docking and MD analysis, a set of complex-based pharmacophore models was elucidated for covalent and non-covalent categories making use of the residue bonding point feature. The highest ranked models exhibited ROC-AUC values of 0.93 and 0.73, respectively for each category. Interestingly, we observed that the active site region of Mpro protein-ligand complex undergoes large conformational changes, especially within the S2 and S4 subsites. The results reported in this article may be helpful in virtual screening (VS) campaigns to guide the design and discovery of novel small-molecule therapeutic agents against SARS-CoV-2 Mpro protein.

Interpretable Machine Learning of Chemical Bonding at Solid Surfaces.

Understanding the nature of chemical bonding and its variation in strength across physically tunable factors is important for the development of novel catalytic materials. One way to speed up this process is to employ machine learning (ML) algorithms with online data repositories curated from high-throughput experiments or quantum-chemical simulations. Despite the reasonable predictive performance of ML models for predicting reactivity properties of solid surfaces, the ever-growing complexity of modern algorithms, e.g., deep learning, makes them black boxes with little to no explanation. In this Perspective, we discuss recent advances of interpretable ML for opening up these black boxes from the standpoints of feature engineering, algorithm development, and post hoc analysis. We underline the pivotal role of interpretability as the foundation of next-generation ML algorithms and emerging AI platforms for driving discoveries across scientific disciplines.

Infusing theory into deep learning for interpretable reactivity prediction.

Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d-band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties.

Modeling iontophoretic drug delivery in a microfluidic device.

Iontophoresis employs low-intensity electrical voltage and continuous constant current to direct a charged drug into a tissue. Iontophoretic drug delivery has recently been used as a novel method for cancer treatment in vivo. There is an urgent need to precisely model the low-intensity electric fields in cell culture systems to optimize iontophoretic drug delivery to tumors. Here, we present an iontophoresis-on-chip (IOC) platform to precisely quantify carboplatin drug delivery and its corresponding anti-cancer efficacy under various voltages and currents. In this study, we use an in vitro heparin-based hydrogel microfluidic device to model the movement of a charged drug across an extracellular matrix (ECM) and in MDA-MB-231 triple-negative breast cancer (TNBC) cells. Transport of the drug through the hydrogel was modeled based on diffusion and electrophoresis of charged drug molecules in the direction of an oppositely charged electrode. The drug concentration in the tumor extracellular matrix was computed using finite element modeling of transient drug transport in the heparin-based hydrogel. The model predictions were then validated using the IOC platform by comparing the predicted concentration of a fluorescent cationic dye (Alexa Fluor 594®) to the actual concentration in the microfluidic device. Alexa Fluor 594® was used because it has a molecular weight close to paclitaxel, the gold standard drug for treating TNBC, and carboplatin. Our results demonstrated that a 50 mV DC electric field and a 3 mA electrical current significantly increased drug delivery and tumor cell death by 48.12% ± 14.33 and 39.13% ± 12.86, respectively (n = 3, p-value <0.05). The IOC platform and mathematical drug delivery model of iontophoresis are promising tools for precise delivery of chemotherapeutic drugs into solid tumors. Further improvements to the IOC platform can be made by adding a layer of epidermal cells to model the skin.

Impact of the Mode of Extraction on the Lipidomic Profile of Oils Obtained from Selected Amazonian Fruits.

Oils and fats are important raw materials in food products, animal feed, cosmetics, and pharmaceuticals among others. The market today is dominated by oils derive, d from African palm, soybean, oilseed and animal fats. Colombia's Amazon region has endemic palms such as Euterpe precatoria (açai), Oenocarpus bataua (patawa), and Mauritia flexuosa (buriti) which grow in abundance and produce a large amount of ethereal extract. However, as these oils have never been used for any economic purpose, little is known about their chemical composition or their potential as natural ingredients for the cosmetics or food industries. In order to fill this gap, we decided to characterize the lipids present in the fruits of these palms. We began by extracting the oils using mechanical and solvent-based approaches. The oils were evaluated by quantifying the quality indices and their lipidomic profiles. The main components of these profiles were triglycerides, followed by diglycerides, fatty acids, acylcarnitine, ceramides, ergosterol, lysophosphatidylcholine, phosphatidyl ethanolamine, and sphingolipids. The results suggest that solvent extraction helped increase the diglyceride concentration in the three analyzed fruits. Unsaturated lipids were predominant in all three fruits and triolein was the most abundant compound. Characterization of the oils provides important insights into the way they might behave as potential ingredients of a range of products. The sustainable use of these oils may have considerable economic potential.

A Machine Learning Strategy for Autism Screening in Toddlers.

OBJECTIVE: Autism spectrum disorder (ASD) screening can improve prognosis via early diagnosis and intervention, but lack of time and training can deter pediatric screening. The Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) is a widely used screener but requires follow-up questions and error-prone human scoring and interpretation. We consider an automated machine learning (ML) method for overcoming barriers to ASD screening, specifically using the feedforward neural network (fNN). METHODS: The fNN technique was applied using archival M-CHAT-R data of 14,995 toddlers (age 16-30 months, 46.51% male). The 20 M-CHAT-R items were inputs, and ASD diagnosis after follow-up and diagnostic evaluation (i.e., ASD or not ASD) was the output. The sample was divided into subgroups by race (i.e., white and black), sex (i.e., boys and girls), and maternal education (i.e., below and above 15 years of education completed) to examine subgroup differences. Each subgroup was evaluated for best-performing fNN models. RESULTS: For the total sample, best results yielded 99.72% correct classification using 18 items. Best results yielded 99.92% correct classification using 14 items for white toddlers and 99.79% correct classification using 18 items for black toddlers. In boys, best results yielded 99.64% correct classification using 18 items, whereas best results yielded 99.95% correct classification using 18 items in girls. For the case when maternal education is 15 years or less (i.e., associate degree and below), best results were 99.75% correct classification when using 16 items. Results were essentially the same when maternal education was 16 years or more (i.e., above associate degree); that is, 99.70% correct classification was obtained using 16 items. CONCLUSION: The ML method was comparable to the M-CHAT-R with follow-up items in accuracy of ASD diagnosis while using fewer items. Therefore, ML may be a beneficial tool in implementing automatic, efficient scoring that negates the need for labor-intensive follow-up and circumvents human error, providing an advantage over previous screening methods.

A Temperature-, pH- and Voltage-Responsive Nanogate with a Remarkably High Factor of Change in Ion Currents due to ON/OFF Switching.

In biological cells, nuclear pore complexes (NPCs) embedded in cell membranes are capable of controlling the flow of ions, for example, Na+ , K+ , and Ca2+ by responding to stimuli, for example, pH and voltage. Inspired by NPCs, researchers have been endeavoring to develop nanogates to achieve the control of ion transport, but the developed nanogates only have a low factor of change in ion currents due to ON/OFF switching. As such nanopores with high temperature and pH responsivities were developed in this work. According to the experimental results, at a voltage of 3 V, the change in ion currents due to pH change is up to a factor of 170, which is remarkably high compared to other nanogates reported. Quantum chemical (QC) calculation results show that a protonated cytosine molecule (C+ ) and an unprotonated cytosine molecule (C) form three pairs of hydrogen bonds and consequently a nucleobase pair, CC+ , leading to the binding of various strands, assembly of a strand net, and blockage of ion transport. The nanogate developed is capable of responding to temperature change. At a voltage of 3 V, the factor of change in ion currents in response to temperature variation is as high as 110. Further experiments were performed to investigate the influence of the NaCl concentrations and small opening diameters exerted on nanogate performance.

Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations.

Molecular functionalization of porphyrins opens countless new opportunities in tailoring their physicochemical properties for light-harvesting applications. However, the immense materials space spanned by a vast number of substituent ligands and chelating metal ions prohibits high-throughput screening of combinatorial libraries. In this work, machine-learning algorithms equipped with the domain knowledge of chemical graph theory were employed for predicting the energy gaps of >12 000 porphyrins from the Computational Materials Repository. Among a variety of graph-based molecular descriptors, the electrotopological-state index, which encodes electronic and topological structure information, captures the energy gaps of porphyrins with a prediction RMSE < 0.06 eV. Importantly, feature sensitivity analysis suggests that the carbon structural motif in methine bridges connected to the anchor group predominantly influences the energy gaps of porphyrins, consistent with the spatial distribution of their frontier molecular orbitals from quantum-chemical calculations.

Exploration of bulk and interface behavior of gas molecules and 1-butyl-3-methylimidazolium tetrafluoroborate ionic liquid using equilibrium and nonequilibrium molecular dynamics simulation and quantum chemical calculation.

Ionic liquids (ILs) show brilliant performance in separating gas impurities, but few researchers have performed an in-depth exploration of the bulk and interface behavior of penetrants and ILs thoroughly. In this research, we have performed a study on both molecular dynamics (MD) simulation and quantum chemical (QC) calculation to explore the transport of acetylene and ethylene in the bulk and interface regions of 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM]-[BF4]). The diffusivity, solubility and permeability of gas molecules in the bulk were researched with MD simulation first. The subdiffusion behavior of gas molecules is induced by coupling between the motion of gas molecules and the ions, and the relaxation processes of the ions after the disturbance caused by gas molecules. Then, QC calculation was performed to explore the optical geometry of ions, ion pairs and complexes of ions and penetrants, and interaction potential for pairs and complexes. Finally, nonequilibrium MD simulation was performed to explore the interface structure and properties of the IL-gas system and gas molecule behavior in the interface region. The research results may be used in the design of IL separation media.

Peptides design based on transmembrane Escherichia coli's OmpA protein through molecular dynamics simulations in water-dodecane interfaces.

Recent research efforts have focused on the production of environmentally nonthreatening products, including identifying biosurfactants that can replace conventional surfactants. In order to utilize biosurfactants in different industries such as cosmetic, food or petroleum, it is necessary to understand the underpinnings behind the interactions that could take place for biosurfactants which display potential for interface activity. This work aimed to use molecular dynamics simulations to understand the interactions of rationally obtained peptide sequences from the original sequence of the OmpA gene in Escherichia coli, based on the free energy change (ΔG) during peptide insertion at the water-dodecane interface. Seventeen OmpA-based peptide sequences were selected and analyzed based on their hydropathy index profiles. We found that free energy change due to Columbic interactions and SASA (ΔGCoul/SASA), total free energy change and MW (ΔG/MW), and free energy change due to Coulombic and van der Waals interactions (ΔGCoul/ΔGvdW) ratios could provide a better understating in the contribution of the free energy decrease at the interface. The results indicated that the peptide sequences GKNHDTGVSPVFA and THENQLGAGAFG display biosurfactant potential based on low ΔG per square nanometer, high ΔGCoul/ΔGvdW ratio, clearly defined moieties along its hydrophobic surface and sequence, and the presence of charged residues in the polar head. Clearly defined moieties and SASA were determinant for electrostatic interactions between oil-water interfaces. Experimental validations exhibited that the emulsions prepared remained stable between 3 and 27h, respectively. Even though the peptide GKNHDTGVSPVFA displays strong interactions at the interface, stabilization times showed that the peptide THENQLGAGAFG exhibited the best performance suggesting that the stability can be better described by kinetic rather than thermodynamic criteria once the emulsion is formed.

Machine-Learning-Augmented Chemisorption Model for CO2 Electroreduction Catalyst Screening.

We present a machine-learning-augmented chemisorption model that enables fast and accurate prediction of the surface reactivity of metal alloys within a broad chemical space. Specifically, we show that artificial neural networks, a family of biologically inspired learning algorithms, trained with a set of ab initio adsorption energies and electronic fingerprints of idealized bimetallic surfaces, can capture complex, nonlinear interactions of adsorbates (e.g., *CO) on multimetallics with ∼0.1 eV error, outperforming the two-level interaction model in prediction. By leveraging scaling relations between adsorption energies of similar adsorbates, we illustrate that this integrated approach greatly facilitates high-throughput catalyst screening and, as a specific case, suggests promising {100}-terminated multimetallic alloys with improved efficiency and selectivity for CO2 electrochemical reduction to C2 species. Statistical analysis of the network response to perturbations of input features underpins our fundamental understanding of chemical bonding on metal surfaces.

Diffusion-based microfluidic PCR for "one-pot" analysis of cells.

Genetic analysis starting with cell samples often requires multi-step processing including cell lysis, DNA isolation/purification, and polymerase chain reaction (PCR) based assays. When conducted on a microfluidic platform, the compatibility among various steps often demands a complicated procedure and a complex device structure. Here we present a microfluidic device that permits a "one-pot" strategy for multi-step PCR analysis starting from cells. Taking advantage of the diffusivity difference, we replace the smaller molecules in the reaction chamber by diffusion while retaining DNA molecules inside. This simple scheme effectively removes reagents from the previous step to avoid interference and thus permits multi-step processing in the same reaction chamber. Our approach shows high efficiency for PCR and potential for a wide range of genetic analysis including assays based on single cells.

A control engineering model of calcium regulation.

CONTEXT: A control engineering perspective provides a framework for representing important mechanistic details of the calcium (Ca) regulatory system efficiently. The resulting model facilitates the testing of hypotheses about mechanisms underlying the emergence of known Ca-related pathologies. OBJECTIVE: The objective of this work is to develop a comprehensive computational model that will enable quantitative understanding of plasma Ca regulation under normal and pathological conditions. DESIGN: Ca regulation is represented as an engineering control system where physiological subprocesses are mapped onto corresponding block components (sensor, controller, actuator, and process), and underlying mechanisms are represented by differential equations. The resulting model is validated with clinical observations of induced hypo- or hypercalcemia in healthy subjects, and its applicability is demonstrated by comparing model predictions of Ca-related pathologies to corresponding clinical data. RESULTS: Our model accurately predicts clinical responses to induced hypo- and hypercalcemia in healthy subjects within a framework that facilitates the representation of Ca-related pathologies in terms of control system component defects. The model also enables a deeper understanding of the emergence of pathologies and the testing of hypotheses about related features of Ca regulation-for example, why primary hyperparathyroidism and hypoparathyroidism arise from "controller defects." CONCLUSIONS: The control engineering framework provides an efficient means of organizing the subprocesses constituting Ca regulation, thereby facilitating a fundamental understanding of this complex process. The resulting validated model's predictions are consistent with clinically observed short- and long-term dynamic characteristics of the Ca regulatory system in both healthy and diseased patients. The model also enables simulation of currently infeasible clinical tests and generates predictions of physiological variables that are currently not measurable.

The new and computationally efficient MIL-SOM algorithm: potential benefits for visualization and analysis of a large-scale high-dimensional clinically acquired geographic data.

The objective of this paper is to introduce an efficient algorithm, namely, the mathematically improved learning-self organizing map (MIL-SOM) algorithm, which speeds up the self-organizing map (SOM) training process. In the proposed MIL-SOM algorithm, the weights of Kohonen's SOM are based on the proportional-integral-derivative (PID) controller. Thus, in a typical SOM learning setting, this improvement translates to faster convergence. The basic idea is primarily motivated by the urgent need to develop algorithms with the competence to converge faster and more efficiently than conventional techniques. The MIL-SOM algorithm is tested on four training geographic datasets representing biomedical and disease informatics application domains. Experimental results show that the MIL-SOM algorithm provides a competitive, better updating procedure and performance, good robustness, and it runs faster than Kohonen's SOM.

Escherichia coli autoinducer-2 uptake network does not display hysteretic behavior but AI-2 synthesis rate controls transient bifurcation.

Analysis of different architectures of quorum sensing networks has been the center of attention in recent times. The approach employs mathematical models to uncover the factors behind the dynamics. Quorum sensing networks mostly display autoregulation such as Pseudomonas aeruginosa and Vibrio cholerae. However, Escherichia coli autoinducer-2 (AI-2) synthesis does not display autoinduction (i.e. autoregulation). This and other features have raised questions about the actual function of AI-2 inside the cell. In this paper we propose a model for lsr operon regulation which explains or at least is consistent with AI-2 uptake in E. coli. The model was employed to determine the main factors that control the concentration of the signal and the uptake activation. We investigated deterministic and stochastic variants of the network model and we found no states that could lead to the typical bistability in quorum sensing systems. However, stochastic simulations predict a transient bifurcation (positively regulated by AI-2 synthesis) that could provide some advantage in adapting to new environments. LsrR inactivation was found to play a crucial role in the uptake activation compared to AI-2 synthesis, lsr transcription and AI-2 excretion. Our hypothesis is that positive regulation of the level of expression is the main factor in understanding the function of the lsr operon. This is in contrast to the conventionally held belief that the main factor is the onset of activation.

Computational models in plant-pathogen interactions: the case of Phytophthora infestans.

BACKGROUND: Phytophthora infestans is a devastating oomycete pathogen of potato production worldwide. This review explores the use of computational models for studying the molecular interactions between P. infestans and one of its hosts, Solanum tuberosum. MODELING AND CONCLUSION: Deterministic logistics models have been widely used to study pathogenicity mechanisms since the early 1950s, and have focused on processes at higher biological resolution levels. In recent years, owing to the availability of high throughput biological data and computational resources, interest in stochastic modeling of plant-pathogen interactions has grown. Stochastic models better reflect the behavior of biological systems. Most modern approaches to plant pathology modeling require molecular kinetics information. Unfortunately, this information is not available for many plant pathogens, including P. infestans. Boolean formalism has compensated for the lack of kinetics; this is especially the case where comparative genomics, protein-protein interactions and differential gene expression are the most common data resources.