RNA analysis by biosynthetic tagging using 4-thiouracil and uracil phosphoribosyltransferase.

RNA analysis by biosynthetic tagging (RABT) enables sensitive and specific queries of (a) how gene expression is regulated on a genome-wide scale and (b) transcriptional profiling of a single cell or tissue type in vivo. RABT can be achieved by exploiting unique properties of Toxoplasma gondii uracil phosphoribosyltransferase (TgUPRT), a pyrimidine salvage enzyme that couples ribose-5-phosphate to the N1 nitrogen of uracil to yield uridine monophosphate (UMP). When 4-thiouracil is provided as a TgUPRT substrate, the resultant product is 4-thiouridine monophosphate which can, ultimately, be incorporated into RNA. Thio-substituted nucleotides are not a natural component of nucleic acids and are readily tagged, detected, and purified with commercially available reagents. Thus, one can do pulse/chase experiments to measure synthesis and decay rates and/or use cell-specific expression of the TgUPRT to tag only RNA synthesized in a given cell type. This chapter updates the original RABT protocol (1) and addresses methodological details associated with RABT that were beyond the scope or space allotment of the initial report.

Optimal intensive care outcome prediction over time using machine learning.

BACKGROUND: Prognostication is an essential tool for risk adjustment and decision making in the intensive care unit (ICU). Research into prognostication in ICU has so far been limited to data from admission or the first 24 hours. Most ICU admissions last longer than this, decisions are made throughout an admission, and some admissions are explicitly intended as time-limited prognostic trials. Despite this, temporal changes in prognostic ability during ICU admission has received little attention to date. Current predictive models, in the form of prognostic clinical tools, are typically derived from linear models and do not explicitly handle incremental information from trends. Machine learning (ML) allows predictive models to be developed which use non-linear predictors and complex interactions between variables, thus allowing incorporation of trends in measured variables over time; this has made it possible to investigate prognosis throughout an admission. METHODS AND FINDINGS: This study uses ML to assess the predictability of ICU mortality as a function of time. Logistic regression against physiological data alone outperformed APACHE-II and demonstrated several important interactions including between lactate & noradrenaline dose, between lactate & MAP, and between age & MAP consistent with the current sepsis definitions. ML models consistently outperformed logistic regression with Deep Learning giving the best results. Predictive power was maximal on the second day and was further improved by incorporating trend data. Using a limited range of physiological and demographic variables, the best machine learning model on the first day showed an area under the receiver-operator characteristic curve (AUC) of 0.883 (σ = 0.008), compared to 0.846 (σ = 0.010) for a logistic regression from the same predictors and 0.836 (σ = 0.007) for a logistic regression based on the APACHE-II score. Adding information gathered on the second day of admission improved the maximum AUC to 0.895 (σ = 0.008). Beyond the second day, predictive ability declined. CONCLUSION: This has implications for decision making in intensive care and provides a justification for time-limited trials of ICU therapy; the assessment of prognosis over more than one day may be a valuable strategy as new information on the second day helps to differentiate outcomes. New ML models based on trend data beyond the first day could greatly improve upon current risk stratification tools.