Model-Free Rheo-AFM Probes the Viscoelasticity of Tunable DNA Soft Colloids.

Atomic force microscopy rheological measurements (Rheo-AFM) of the linear viscoelastic properties of single, charged colloids having a star-like architecture with a hard core and an extended, deformable double-stranded DNA (dsDNA) corona dispersed in aqueous saline solutions are reported. This is achieved by analyzing indentation and relaxation experiments performed on individual colloidal particles by means of a novel model-free Fourier transform method that allows a direct evaluation of the frequency-dependent linear viscoelastic moduli of the system under investigation. The method provides results that are consistent with those obtained via a conventional fitting procedure of the force-relaxation curves based on a modified Maxwell model. The outcomes show a pronounced softening of the dsDNA colloids, which is described by an exponential decay of both the Young's and the storage modulus as a function of the salt concentration within the dispersing medium. The strong softening is related to a critical reduction of the size of the dsDNA corona, down to ≈70% of its size in a salt-free solution. This can be correlated to significant topological changes of the dense star-like polyelectrolyte forming the corona, which are induced by variations in the density profile of the counterions. Similarly, a significant reduction of the stiffness is obtained by increasing the length of the dsDNA chains, which we attribute to a reduction of the DNA density in the outer region of the corona.

Predicting suicides after outpatient mental health visits in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS).

The 2013 US Veterans Administration/Department of Defense Clinical Practice Guidelines (VA/DoD CPG) require comprehensive suicide risk assessments for VA/DoD patients with mental disorders but provide minimal guidance on how to carry out these assessments. Given that clinician-based assessments are not known to be strong predictors of suicide, we investigated whether a precision medicine model using administrative data after outpatient mental health specialty visits could be developed to predict suicides among outpatients. We focused on male nondeployed Regular US Army soldiers because they account for the vast majority of such suicides. Four machine learning classifiers (naive Bayes, random forests, support vector regression and elastic net penalized regression) were explored. Of the Army suicides in 2004-2009, 41.5% occurred among 12.0% of soldiers seen as outpatient by mental health specialists, with risk especially high within 26 weeks of visits. An elastic net classifier with 10-14 predictors optimized sensitivity (45.6% of suicide deaths occurring after the 15% of visits with highest predicted risk). Good model stability was found for a model using 2004-2007 data to predict 2008-2009 suicides, although stability decreased in a model using 2008-2009 data to predict 2010-2012 suicides. The 5% of visits with highest risk included only 0.1% of soldiers (1047.1 suicides/100 000 person-years in the 5 weeks after the visit). This is a high enough concentration of risk to have implications for targeting preventive interventions. An even better model might be developed in the future by including the enriched information on clinician-evaluated suicide risk mandated by the VA/DoD CPG to be recorded.