Human mutations often cause amino acid changes (variants) that can alter protein function or stability. Some variants fall at protein positions that experimentally exhibit "rheostatic" mutation outcomes (different amino acid substitutions lead to a range of functional outcomes). In ongoing studies of rheostat positions, we encountered the need to aggregate experimental results from multiple variants, to describe the overall roles of individual positions. Here, we present "RheoScale" which generates quantitative scores to discriminate rheostat positions from those with "toggle" (most substitutions abolish function) or "neutral" (most substitutions have wild-type function) outcomes. RheoScale scores facilitate correlations of experimental data (such as binding affinity or stability) with structural and bioinformatic analyses. The RheoScale calculator is encoded into a Microsoft Excel workbook and an R script. Example analyses are shown for three model protein systems, including one assessed via deep mutational scanning. The RheoScale calculator quickly and efficiently provided quantitative descriptions that were in good agreement with prior qualitative observations. As an example application, scores were compared to the example proteins' structures; strong rheostat positions tended to occur in dynamic locations. In the future, RheoScale scores can be easily integrated into computational studies to facilitate improved algorithms for predicting outcomes of human variants.

Clinical diagnosis requiring central facilities and site visits can be burdensome for patients in resource-limited or rural areas. Therefore, development of a lowcost test that utilizes smartphone data collection and transmission would beneficially enable disease self-management and point-of-care (POC) diagnosis. In this paper, we introduce a low-cost iPOC 3D diagnostic strategy which integrates 3D design and printing of microfluidic POC device with smartphone-based disease diagnosis in one process as a stand-alone system, offering strong adaptability for establishing diagnostic capacity in resource-limited areas and low-income countries. We employ smartphone output (AutoCAD 360 app) and readout (color-scale analytical app written in-house) functionalities for rapid 3D printing of microfluidic auto-mixers and colorimetric detection of blood hemoglobin levels. The automixing of reagents with blood via capillary force has been demonstrated in 1 second without the requirement of external pumps. We employed this iPOC 3D system for point-of-care diagnosis of anemia using a training set of patients (n anemia ¼ 16 and n healthy ¼ 6), which showed consistent measurements of blood hemoglobin levels (a.u.c. ¼ 0.97) and comparable diagnostic sensitivity and specificity, compared with standard clinical hematology analyzer. Capable of 3D fabrication flexibility and smartphone compatibility, this work presents a novel diagnostic strategy for advancing personalized medicine and mobile healthcare. Published by AIP Publishing. [http://dx