To address the challenges associated with defined control over matrix properties in 3D cell culture systems, we employed a peptide functionalized poly(ethylene glycol) (PEG) hydrogel matrix in which mechanical modulus and adhesive properties were tuned. An HT-1080 human fibrosarcoma cell line was chosen as a model for probing matrix influences on tumor cell migration using the PEG hydrogel platform. HT-1080 speed varied with a complex dependence on both matrix modulus and Cys-Arg-Gly-Asp-Ser (CRGDS) adhesion ligand concentration, with regimes in which motility increased, decreased, or was minimally altered being observed. We further investigated cell motility by forming matrix interfaces that mimic aspects of tissue boundaries that might be encountered during invasion by taking advantage of the spatial control of the thiol-ene photochemistry to form patterned regions of low and high cross-linking densities. HT-1080s in 100 Pa regions of patterned PEG hydrogels tended to reverse direction or aggregate at the interface when they encountered a 360 Pa boundary. In contrast, HT-1080s were apparently unimpeded when migrating from the stiff to the soft regions of PEG peptide hydrogels, which may indicate that cells are capable of “reverse durotaxis” within at least some matrix regimes. Taken together, our results identified matrix regimes in which HT-1080 motility was both positively and negatively influenced by cell adhesion or matrix modulus.

Introduction A common problem in metabolomics data analysis is the existence of a substantial number of missing values, which can complicate, bias, or even prevent certain downstream analyses. One of the most widely-used solutions to this problem is imputation of missing values using a k-nearest neighbors (kNN) algorithm to estimate missing metabolite abundances. kNN implicitly assumes that missing values are uniformly distributed at random in the dataset, but this is typically not true in metabolomics, where many values are missing because they are below the limit of detection (LOD) of the analytical instrumentation. Objectives Here, we explore the impact of nonuniformly distributed missing values (missing not at random, or MNAR) on imputation performance. We present a new model for generating synthetic missing data and a new algorithm, No-Skip kNN (NS-kNN), that accounts for MNAR values to provide more accurate imputations. Methods We compare the imputation errors of the original kNN algorithm using two distance metrics, NS-kNN, and a recently developed algorithm KNN-TN, when applied to multiple experimental datasets with different types and levels of missing data. Results Our results show that NS-kNN typically outperforms kNN when at least 20-30% of missing values in a dataset are MNAR. NS-kNN also has lower imputation errors than KNN-TN on realistic datasets when at least 50% of missing values are MNAR. Conclusion Accounting for the nonuniform distribution of missing values in metabolomics data can significantly improve the results of imputation algorithms. The NS-kNN method imputes missing metabolomics data more accurately than existing kNN-based approaches when used on realistic datasets.

Here, we describe an engineering approach to quantitatively compare migration, morphologies, and adhesion for tumorigenic human fibrosarcoma cells (HT-1080s) and primary human dermal fibroblasts (hDFs) with the aim of identifying distinguishing properties of the transformed phenotype. Relative adhesiveness was quantified using self-assembled monolayer (SAM) arrays and proteolytic 3-dimensional (3D) migration was investigated using matrix metalloproteinase (MMP)-degradable poly(ethylene glycol) (PEG) hydrogels (“synthetic extracellular matrix” or “synthetic ECM”). In synthetic ECM, hDFs were characterized by vinculin-containing features on the tips of protrusions, multipolar morphologies, and organized actomyosin filaments. In contrast, HT-1080s were characterized by diffuse vinculin expression, pronounced β1-integrin on the tips of protrusions, a cortically-organized F-actin cytoskeleton, and quantitatively more rounded morphologies, decreased adhesiveness, and increased directional motility compared to hDFs. Further, HT-1080s were characterized by contractility-dependent motility, pronounced blebbing, and cortical contraction waves or constriction rings, while quantified 3D motility was similar in matrices with a wide range of biochemical and biophysical properties (including collagen) despite substantial morphological changes. While HT-1080s were distinct from hDFs for each of the 2D and 3D properties investigated, several features were similar to WM239a melanoma cells, including rounded, proteolytic migration modes, cortical F-actin organization, and prominent uropod-like structures enriched with β1-integrin, F-actin, and melanoma cell adhesion molecule (MCAM/CD146/MUC18). Importantly, many of the features observed for HT-1080s were analogous to cellular changes induced by transformation, including cell rounding, a disorganized F-actin cytoskeleton, altered organization of focal adhesion proteins, and a weakly adherent phenotype. Based on our results, we propose that HT-1080s migrate in synthetic ECM with functional properties that are a direct consequence of their transformed phenotype.

High-cost healthcare users (HCUs) are a small proportion of the population who use a disproportionate amount of healthcare resources. Although the phenomenon occurs across the entire age spectrum, older adults represent the majority of HCUs. HCUs have drawn increasing attention internationally from clinicians, health policy-makers, and government administrators. Many experts have suggested that the short- and long-term sustainability of the healthcare system is threatened unless current approaches to the care and healthcare costs of this population are modified. Complex case management and care coordination models are being implemented internationally to address HCUs despite a lack of strong evidence to support their effectiveness in improving clinical outcomes or savings in costs of care. We review what is known about HCUs and the available evidence for the effectiveness of interventions designed to manage their high and costly healthcare use.

Clinically significant medication discrepancies occur commonly during internal hospital transfer. A structured, collaborative, and clearly defined medication reconciliation process is needed to prevent internal transfer discrepancies and patient harm.