NLoed: A Python Package for Nonlinear Optimal Experimental Design in Systems Biology.

Modeling in systems and synthetic biology relies on accurate parameter estimates and predictions. Accurate model calibration relies, in turn, on data and on how well suited the available data are to a particular modeling task. Optimal experimental design (OED) techniques can be used to identify experiments and data collection procedures that will most efficiently contribute to a given modeling objective. However, implementation of OED is limited by currently available software tools that are not well suited for the diversity of nonlinear models and non-normal data commonly encountered in biological research. Moreover, existing OED tools do not make use of the state-of-the-art numerical tools, resulting in inefficient computation. Here, we present the NLoed software package and demonstrate its use with in vivo data from an optogenetic system in Escherichia coli. NLoed is an open-source Python library providing convenient access to OED methods, with particular emphasis on experimental design for systems biology research. NLoed supports a wide variety of nonlinear, multi-input/output, and dynamic models and facilitates modeling and design of experiments over a wide variety of data types. To support OED investigations, the NLoed package implements maximum likelihood fitting and diagnostic tools, providing a comprehensive modeling workflow. NLoed offers an accessible, modular, and flexible OED tool set suited to the wide variety of experimental scenarios encountered in systems biology research. We demonstrate NLoed's capabilities by applying it to experimental design for characterization of a bacterial optogenetic system.

Overpriced? Are Hospital Prices Associated with the Quality of Care?

In most consumer markets, higher prices generally imply increased quality. For example, in the automobile, restaurant, hospitality, and airline industries, higher pricing generally conveys a signal of complexity and superiority of a service or product. However, in the healthcare industry, there is room to challenge the price-quality connection as both health prices and health quality can be difficult to interpret. In the best of circumstances, health care costs, prices, and quality can often be difficult to isolate and measure. Recent efforts by the Trump Administration and the Center for Medicare and Medicaid Services (CMS) have required the pricing of hospital services to be more transparent. Specifically, hospital chargemaster (retail) prices must now be available to the public. However, many continue to question if the pricing of health care services reflects the quality of service delivery. This research focuses on investigating the prices hospitals charge for their services in relation to the costs incurred and the association with the quality of care provided. By analyzing data from a nationwide sample of U.S. hospitals, this study considers the relationship between hospital pricing (as measured by the charge-to-cost ratio) and hospital quality performance as measured by the Value Based Purchasing Total Performance Score (TPS) and its associated sub-domains. Results of the study indicate that hospital prices, as measured by our primary independent variable of interest, the charge-to-cost ratio, are significantly and negatively associated with Total Performance Score, Patient Experience, and the Efficiency and Cost Reduction domains. A marginal statistically significant positive association is shown in the Clinical Care domain. The findings indicate that unlike most other industries, in medicine, higher pricing compared to cost does not necessarily associate with higher quality and, in fact, might indicate the opposite. The results of this study suggest that purchasers of healthcare, at all levels, have justification in challenging the pricing of healthcare services considering the quality scores available in the public domain.

Development of selective colorimetric probes for hydrogen sulfide based on nucleophilic aromatic substitution.

Hydrogen sulfide is an important biological signaling molecule and an important environmental target for detection. A major challenge in developing H2S detection methods is separating the often similar reactivity of thiols and other nucleophiles from H2S. To address this need, the nucleophilic aromatic substitution (SNAr) reaction of H2S with electron-poor aromatic electrophiles was developed as a strategy to separate H2S and thiol reactivity. Treatment of aqueous solutions of nitrobenzofurazan (7-nitro-1,2,3-benzoxadiazole, NBD) thioethers with H2S resulted in thiol extrusion and formation of nitrobenzofurazan thiol (λmax = 534 nm). This reactivity allows for unwanted thioether products to be converted to the desired nitrobenzofurazan thiol upon reaction with H2S. The scope of the reaction was investigated using a Hammett linear free energy relationship study, and the determined ρ = +0.34 is consistent with the proposed SN2Ar reaction mechanism. The efficacy of the developed probes was demonstrated in buffer and in serum with associated submicromolar detection limits as low as 190 nM (buffer) and 380 nM (serum). Furthermore, the sigmoidal response of nitrobenzofurazan electrophiles with H2S can be fit to accurately quantify H2S. The developed detection strategy offers a manifold for H2S detection that we foresee being applied in various future applications.