Search

Simulation-based approaches to disease progression allow us to make counterfactual predictions about the effects of an untried series of treatment choices. However, building accurate simulators of disease progression is challenging, limiting the utility of these approaches for real world treatment planning. In this work, we present a novel simulation-based reinforcement learning approach that mixes between models and kernel-based approaches to make its forward predictions. On two real world tasks, managing sepsis and treating HIV, we demonstrate that our approach both learns state-of-the-art treatment policies and can make accurate forward predictions about the effects of treatments on unseen patients.

Peptide dendrimers are a class of molecules that exhibit a large array of biological effects including antiviral activity. In this report, we analyzed the antiviral activity of the peptide-derivatized SB105-A10 dendrimer, which is a tetra-branched dendrimer synthetized on a lysine core, in activated peripheral blood mononuclear cells (PBMCs) that were challenged with reference and wild-type human immunodeficiency virus type 1 (HIV-1) strains. SB105-A10 inhibited infections by HIV-1 X4 and R5 strains, interfering with the early phases of the viral replication cycle. SB105-A10 targets heparan sulfate proteoglycans (HSPGs) and, importantly, the surface plasmon resonance (SPR) assay revealed that SB105-A10 strongly binds gp41 and gp120, most likely preventing HIV-1 attachment/entry through multiple mechanisms. Interestingly, the antiviral activity of SB105-A10 was also detectable in an organ-like structure of human cervicovaginal tissue, in which SB105-A10 inhibited the HIV-1ada R5 strain infection without altering the tissue viability. These results demonstrated the strong antiviral activity of SB105-A10 and suggest a potential microbicide use of this dendrimer to prevent the heterosexual transmission of HIV-1.

OBJECTIVE:ICU severity scores such as the Sequential Organ Failure Assessment (SOFA) determine neurologic dysfunction based on the Glasgow Coma Scale, a tool that may be limited in a critically ill population. It remains unknown whether alternative methods to assess for neurologic dysfunction, such as FOUR and RASS, are superior. This study aimed to determine the predictive performance of a modified SOFA tool in a large Brazilian ICU cohort. DESIGN:Prospective cohort single center study. SETTING:Mixed surgical and medical ICU in Salvador, Bahia, Brazil between August 2015 and December 2018. PATIENTS:All acutely ill ICU admissions, other than postoperative patients or those with insufficient data, were eligible for study inclusion. MEASUREMENTS AND MAIN RESULTS:2147 patients were admitted to the ICU, of which 999 meeting inclusion criteria were included in the final analysis with a median age of 72 years (IQR 58-83) and a female predominance 545 (54%). The SOFA score using GCS, RASS and FOUR for the neurologic component performed marginally in the ability to predict general ICU mortality (SOFAGCS AUC 0.74 vs SOFARASS AUC 0.71 and SOFAFOUR AUC 0.67), with SOFAFOUR performing significantly lower compared to either SOFARASS and SOFAGCS (p