Person:

Wiltschko, Alexander Bame

Iron deficiency anemia (IDA) is a nutritional disorder that impacts over one billion people worldwide, it causes permanent cognitive impairment in children, fatigue in adults, and suboptimal outcomes in pregnancy. IDA can be diagnosed by detection of red blood cells (RBCs) that are characteristically small (microcytic) and deficient in hemoglobin (hypochromic), typically by examining the results of a complete blood count performed by a hematology analyzer. These instruments are expensive, not portable, and require trained personnel; they are therefore, unavailable in many low-resource settings. This paper describes a low-cost and rapid method to diagnose IDA using aqueous multiphase systems (AMPS)—thermodynamically stable mixtures of biocompatible polymers and salt that spontaneously form discrete layers having sharp steps in density. AMPS are preloaded into a microhematocrit tube and used with a drop of blood from a fingerstick. After only two minutes in a low-cost centrifuge, the tests (n = 152) were read by eye with a sensitivity of 84% (72-93%) and a specificity of 78% (68-86%), corresponding to an area under the curve (AUC) of 0.89. The AMPS test outperforms diagnosis by hemoglobin alone (AUC = 0.73) and is comparable to methods used in clinics like reticulocyte hemoglobin concentration (AUC = 0.91). Standard machine learning tools were used to analyze images of the resulting tests captured by a standard desktop scanner to 1) slightly improve diagnosis of IDA—sensitivity of 90% (83-96%) and a specificity of 77% (64-87%), and 2) predict several important red blood cell parameters, such as mean corpuscular hemoglobin concentration. These results suggest that the use of AMPS combined with machine learning provides an approach to developing point-of-care hematology.

Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that three-dimensional (3D) mouse pose dynamics are structured at the sub-second timescale by using a newly developed 3D imaging and machine learning-based automated phenotyping system, which we term Motion Sequencing (MoSeq). Computational modeling of these fast postural dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities, which collectively encapsulate the underlying structure of mouse behavior within a given experiment.

By deploying MoSeq in a variety of experimental contexts, we show that it unmasks strategies employed by the brain to generate specific adaptations to changes in the environment, and captures both predicted and previously-hidden phenotypes induced by genetic, neural, and pharmacological manipulations. We directly compare the predictive power of behavioral representations built by MoSeq against traditional measurements of behavior, including speed, length, and allocentric position, and demonstrate MoSeq is able to discriminate between subtle pharmacological manipulations of behavior, while traditional methods are not. This work demonstrates that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes a framework for characterizing the influence of environmental cues, genes, neural activity and pharmacology on behavior.