Electronic clinical decision support algorithms incorporating point-of-care diagnostic tests in low-resource settings: a target product profile.

Health workers in low-resource settings often lack the support and tools to follow evidence-based clinical recommendations for diagnosing, treating and managing sick patients. Digital technologies, by combining patient health information and point-of-care diagnostics with evidence-based clinical protocols, can help improve the quality of care and the rational use of resources, and save patient lives. A growing number of electronic clinical decision support algorithms (CDSAs) on mobile devices are being developed and piloted without evidence of safety or impact. Here, we present a target product profile (TPP) for CDSAs aimed at guiding preventive or curative consultations in low-resource settings. This document will help align developer and implementer processes and product specifications with the needs of end users, in terms of quality, safety, performance and operational functionality. To identify the characteristics of CDSAs, a multidisciplinary group of experts (academia, industry and policy makers) with expertise in diagnostic and CDSA development and implementation in low-income and middle-income countries were convened to discuss a draft TPP. The TPP was finalised through a Delphi process to facilitate consensus building. An agreement greater than 75% was reached for all 40 TPP characteristics. In general, experts were in overwhelming agreement that, given that CDSAs provide patient management recommendations, the underlying clinical algorithms should be human-interpretable and evidence-based. Whenever possible, the algorithm's patient management output should take into account pretest disease probabilities and likelihood ratios of clinical and diagnostic predictors. In addition, validation processes should at a minimum show that CDSAs are implementing faithfully the evidence they are based on, and ideally the impact on patient health outcomes. In terms of operational needs, CDSAs should be designed to fit within clinic workflows and function in connectivity-challenged and high-volume settings. Data collected through the tool should conform to local patient privacy regulations and international data standards.

Fructose-induced stress signaling in the liver involves methylglyoxal.

BACKGROUND: Fructose produces hepatic insulin resistance in humans and animals. We have proposed that the selective metabolism of fructose by the liver can, under conditions of elevated fructose delivery, inflict a metabolic insult that is localized to the hepatocyte. The present study was designed to identify potential cellular effectors of this insult. METHODS: Primary hepatocytes were incubated with 8 mM glucose and 0.12% inulin (G, n = 6) or 8 mM glucose, 0.12% inulin and 28 mU of inulinase (GF, n = 6) in the presence or absence of insulin for 0, 2, or 4 h. RESULTS: GF produced fructose concentrations of ~0.7 mM over the 4 h experiment. GF induced phosphorylation of MKK7 and JNK, phosphorylation of serine307 on IRS-1, and reduced tyrosine phosphorylation of IRS-1 and -2. GF increased ceramide levels and reactive oxygen species (ROS); however inhibitors of ceramide synthesis or ROS accumulation did not prevent GF-mediated changes in MKK7, JNK or IRS proteins. GF increased cellular methylglyoxal concentrations and a selective increase in methylglyoxal recapitulated the GF-induced changes in MKK7, JNK and IRS proteins. CONCLUSIONS: We hypothesize that GF-mediated changes in stress signaling involve methylglyoxal in primary hepatocytes.