Weekly Nowcasting of New COVID-19 Cases Using Past Viral Load Measurements.

The rapid spread of the coronavirus disease COVID-19 has imposed clinical and financial burdens on hospitals and governments attempting to provide patients with medical care and implement disease-controlling policies. The transmissibility of the disease was shown to be correlated with the patient's viral load, which can be measured during testing using the cycle threshold (Ct). Previous models have utilized Ct to forecast the trajectory of the spread, which can provide valuable information to better allocate resources and change policies. However, these models combined other variables specific to medical institutions or came in the form of compartmental models that rely on epidemiological assumptions, all of which could impose prediction uncertainties. In this study, we overcome these limitations using data-driven modeling that utilizes Ct and previous number of cases, two institution-independent variables. We collected three groups of patients (n = 6296, n = 3228, and n = 12,096) from different time periods to train, validate, and independently validate the models. We used three machine learning algorithms and three deep learning algorithms that can model the temporal dynamic behavior of the number of cases. The endpoint was 7-week forward number of cases, and the prediction was evaluated using mean square error (MSE). The sequence-to-sequence model showed the best prediction during validation (MSE = 0.025), while polynomial regression (OLS) and support vector machine regression (SVR) had better performance during independent validation (MSE = 0.1596, and MSE = 0.16754, respectively), which exhibited better generalizability of the latter. The OLS and SVR models were used on a dataset from an external institution and showed promise in predicting COVID-19 incidences across institutions. These models may support clinical and logistic decision-making after prospective validation.

Using Parallel Coordinates in Optimization of Nano-Particle Drug Delivery.

Nanoparticle drug delivery better targets neoplastic lesions than free drugs and thus has emerged as a safer form of cancer therapy. Nanoparticle design variables are important determinants of efficacy as they influence the drug biodistribution and pharmacokinetics. Previously, we determined optimal designs through mechanistic modeling and optimization. However, the numerical nature of the tumor model and numerous candidate nanoparticle designs hinder hypothesis generation and treatment personalization. In this paper, we utilize the parallel coordinates technique to visualize high-dimensional optimal solutions and extract correlations between nanoparticle design and treatment outcomes. We found that at optimality, two major design variables are dependent, and thus the optimization problem can be reduced. In addition, we obtained an analytical relationship between optimal nanoparticle sizes and optimal distribution, which could facilitate the utilization of tumors models in preclinical studies. Our approach has simplified the results of the previously integrated modeling and optimization framework developed for nanotherapy and enhanced the interpretation and utilization of findings. Integrated mathematical frameworks are increasing in the medical field, and our method can be applied outside nanotherapy to facilitate the clinical translation of computational methods.

A Dual Nanoparticle Delivery Strategy for Enhancing Drug Distribution in Cancerous Tissue.

Nanoparticle-mediated drug delivery may be a promising alternative to traditional chemotherapy of high systemic toxicity. Tumor tissue architecture poses a challenge to delivery of nanoparticles. Small and spherical nanoparticles have poor adherence to the tumor vasculature, while larger and more eccentric ones create high heterogeneity in tissue-to-drug exposure. In previous work, we quantified these tradeoffs using numerical optimization. In this study, we demonstrate that simultaneous delivery of multiple nanoparticle designs can enhance drug distribution in the cancerous tissue without compromising nanoparticle tumoral accumulation. We formulate and solve optimization problems to find the optimal constituent of the heterogeneous injection in terms of nanoparticle design diversity that increases drug distribution by 14%.

Multi-objective optimization of tumor response to drug release from vasculature-bound nanoparticles.

The pharmacokinetics of nanoparticle-borne drugs targeting tumors depends critically on nanoparticle design. Empirical approaches to evaluate such designs in order to maximize treatment efficacy are time- and cost-intensive. We have recently proposed the use of computational modeling of nanoparticle-mediated drug delivery targeting tumor vasculature coupled with numerical optimization to pursue optimal nanoparticle targeting and tumor uptake. Here, we build upon these studies to evaluate the effect of tumor size on optimal nanoparticle design by considering a cohort of heterogeneously-sized tumor lesions, as would be clinically expected. The results indicate that smaller nanoparticles yield higher tumor targeting and lesion regression for larger-sized tumors. We then augment the nanoparticle design optimization problem by considering drug diffusivity, which yields a two-fold tumor size decrease compared to optimizing nanoparticles without this consideration. We quantify the tradeoff between tumor targeting and size decrease using bi-objective optimization, and generate five Pareto-optimal nanoparticle designs. The results provide a spectrum of treatment outcomes - considering tumor targeting vs. antitumor effect - with the goal to enable therapy customization based on clinical need. This approach could be extended to other nanoparticle-based cancer therapies, and support the development of personalized nanomedicine in the longer term.

Sound simulation-based design optimization of brass wind instruments.

A method for optimizing the inner shape of brass instruments using sound simulations is presented. This study considers different objective functions and constraints (representative of both the intonation and the spectrum of the instrument) for a relatively large number of design variables. A complete physics-based model, taking into account the instrument and the musician's embouchure, is used to simulate steady regimes of sounds by means of the harmonic balance technique, the instrument being represented by its input impedance. The design optimization variables are related to the geometrical dimensions of the resonator. The embouchure's parameters are varied during the optimization procedure to obtain an average behavior of the instrument. The objective and constraint functions of the optimization problem are evaluated using the physics-based simulation model, which is computationally expensive. Moreover, the gradients of the objective and constraint functions can be discontinuous, unavailable, or hard to approximate reliably. Therefore, a surrogate-assisted derivative-free optimization strategy using the mesh adaptive direct search algorithm was employed. One example of a B♭ trumpet's bore is used to demonstrate the effectiveness of the design optimization approach: the obtained results improve previously reported objective function values significantly.

Design Optimization of Tumor Vasculature-Bound Nanoparticles.

Nanotherapy may constitute a promising approach to target tumors with anticancer drugs while minimizing systemic toxicity. Computational modeling can enable rapid evaluation of nanoparticle (NP) designs and numerical optimization. Here, an optimization study was performed using an existing tumor model to find NP size and ligand density that maximize tumoral NP accumulation while minimizing tumor size. Optimal NP avidity lies at lower bound of feasible values, suggesting reduced ligand density to prolong NP circulation. For the given set of tumor parameters, optimal NP diameters were 288 nm to maximize NP accumulation and 334 nm to minimize tumor diameter, leading to uniform NP distribution and adequate drug load. Results further show higher dependence of NP biodistribution on the NP design than on tumor morphological parameters. A parametric study with respect to drug potency was performed. The lower the potency of the drug, the bigger the difference is between the maximizer of NP accumulation and the minimizer of tumor size, indicating the existence of a specific drug potency that minimizes the differential between the two optimal solutions. This study shows the feasibility of applying optimization to NP designs to achieve efficacious cancer nanotherapy, and offers a first step towards a quantitative tool to support clinical decision making.

Assessment of structural and hemodynamic performance of vascular stents modelled as periodic lattices.

This work considers vascular stents with tubular geometry assumed to follow a periodic arrangement of repeating unit cells. Structural and hemodynamic metrics are presented to assess alternative stent geometries, each defined by the topology of the unit cell. Structural metrics include foreshortening, elastic recoil and radial stiffness, whereas hemodynamic performance is described by a wall shear stress index quantifying the impact of in-stent restenosis. A representative volume element (RVE) modelling approach is used, and results are compared to those obtained from full simulations of entire stents. We demonstrate that the RVE approach can be used to quantify the impact of the topology of the repeating unit on the structural and hemodynamic properties of a stent, and thus support clinicians in making proper choices among alternative stent geometries.

Nanoparticle Optimization for Enhanced Targeted Anticancer Drug Delivery.

Nanoparticle (NP)-based drug delivery is a promising method to increase the therapeutic index of anticancer agents with low median toxic dose. The delivery efficiency, corresponding to the fraction of the injected NPs that adhere to the tumor site, depends on NP size a and aspect ratio AR. Values for these variables are currently chosen empirically, which may not result in optimal targeted drug delivery. This study applies rigorous optimization to the design of NPs. A preliminary investigation revealed that delivery efficiency increases monotonically with a and AR. However, maximizing a and AR results in nonuniform drug distribution, which impairs tumor regression. Therefore, a multiobjective optimization (MO) problem is formulated to quantify the trade-off between NPs accumulation and distribution. The MO is solved using the derivative-free mesh adaptive direct search algorithm. Theoretically, the Pareto-optimal set consists of an infinite number of mathematically equivalent solutions to the MO problem. However, interesting design solutions can be identified subjectively, e.g., the ellipsoid with a major axis of 720 nm and an aspect ratio of 7.45, as the solution closest to the utopia point. The MO problem formulation is then extended to optimize NP biochemical properties: ligand-receptor binding affinity and ligand density. Optimizing physical and chemical properties simultaneously results in optimal designs with reduced NP sizes and thus enhanced cellular uptake. The presented study provides an insight into NP structures that have potential for producing desirable drug delivery.

Development and validation of a modified Hybrid-III six-year-old dummy model for simulating submarining in motor-vehicle crashes.

In motor-vehicle crashes, young school-aged children restrained by vehicle seat belt systems often suffer from abdominal injuries due to submarining. However, the current anthropomorphic test device, so-called "crash dummy", is not adequate for proper simulation of submarining. In this study, a modified Hybrid-III six-year-old dummy model capable of simulating and predicting submarining was developed using MADYMO (TNO Automotive Safety Solutions). The model incorporated improved pelvis and abdomen geometry and properties previously tested in a modified physical dummy. The model was calibrated and validated against four sled tests under two test conditions with and without submarining using a multi-objective optimization method. A sensitivity analysis using this validated child dummy model showed that dummy knee excursion, torso rotation angle, and the difference between head and knee excursions were good predictors for submarining status. It was also shown that restraint system design variables, such as lap belt angle, D-ring height, and seat coefficient of friction (COF), may have opposite effects on head and abdomen injury risks; therefore child dummies and dummy models capable of simulating submarining are crucial for future restraint system design optimization for young school-aged children.