Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.

Dietary supplements (DSs) have been widely used in the U.S. and evaluated in clinical trials as potential interventions for various diseases. However, many clinical trials face challenges in recruiting enough eligible patients in a timely fashion, causing delays or even early termination. Using electronic health records to find eligible patients who meet clinical trial eligibility criteria has been shown as a promising way to assess recruitment feasibility and accelerate the recruitment process. In this study, we analyzed the eligibility criteria of 100 randomly selected DS clinical trials and identified both computable and non-computable criteria. We mapped annotated entities to OMOP Common Data Model (CDM) with novel entities (e.g., DS). We also evaluated a deep learning model (Bi-LSTM-CRF) for extracting these entities on CLAMP platform, with an average F1 measure of 0.601. This study shows the feasibility of automatic parsing of the eligibility criteria following OMOP CDM for future cohort identification.

Effective flocculation of Microcystis aeruginosa with simultaneous nutrient precipitation from hydrolyzed human urine.

Mechanical harvest of massive harmful algal blooms is an effective measure for bloom mitigation. Yet subsequent processing of the resulting water from algae water separation after the harvesting becomes a new problem since individual algal cells or small algal aggregates are still present in the water. Here, we proposed a novel approach for effectively flocculating the cyanobacteria Microcystis aeruginosa with a removal efficiency of 97% in 6 h using hydrolyzed urine. Nitrogen and phosphorus were simultaneously reclaimed through struvite formation. The addition of Mg2+ promoted the flocculation efficiency and nutrient removal as well as the yield of struvite. Ca2+ could enhance the flocculation efficiency by forming calcium phosphate. During the flocculation process, no significant damage in algal cells was observed. This study provides a novel and sustainable potential for subsequent processing of the resulting water after algae water separation with simultaneous nutrient precipitation and reducing nutrient loads to wastewater treatment plants.