The biosynthesis of cell wall polymers involves enormous fluxes through central metabolism that are not fully delineated and whose regulation is poorly understood. We have established and validated a liquid chromatography tandem mass spectrometry method using multiple reaction monitoring mode to separate and quantify the levels of plant cell wall precursors. Target analytes were identified by their parent/daughter ions and retention times. The method allows the quantification of precursors at low picomole quantities with linear responses up to the nanomole quantity range. When applying the technique to Arabidopsis (Arabidopsis thaliana) T87 cell cultures, 16 hexose-phosphates (hexose-Ps) and nucleotide-sugars (NDP-sugars) involved in cell wall biosynthesis were separately quantified. Using hexose-P and NDPsugar standards, we have shown that hot water extraction allows good recovery of the target metabolites (over 86%). This method is applicable to quantifying the levels of hexose-Ps and NDP-sugars in different plant tissues, such as Arabidopsis T87 cells in culture and fenugreek (Trigonella foenum-graecum) endosperm tissue, showing higher levels of galacto-mannan precursors in fenugreek endosperm. In Arabidopsis cells incubated with [U-13 C Fru ]sucrose, the method was used to track the labeling pattern in cell wall precursors. As the fragmentation of hexose-Ps and NDP-sugars results in high yields of [PO 3 ] 2 and/or [H 2 PO 4 ] 2 ions, mass isotopomers can be quantified directly from the intensity of selected tandem mass spectrometry transitions. The ability to directly measure 13 C labeling in cell wall precursors makes possible metabolic flux analysis of cell wall biosynthesis based on dynamic labeling experiments.Plant cell walls are the most abundant renewable resources (Pauly and Keegstra, 2008a). Much of the current biotechnological research on plant cell wall synthesis involves manipulating these biosynthetic processes to obtain higher concentrations of starches or oil, which show much promise in biofuel production, or to alter cell wall composition for easier breakdown. A detailed knowledge of these processes is essential to understanding and utilizing plant cell wall materials as well as for progress in understanding plant growth and structural development (Pauly and Keegstra, 2008b). However, research into cell wall biosynthesis has been hindered by our limited understanding of the metabolic processes that produce cell walls and particularly their regulation. Progress in this area is limited by the difficulty of differentiating among the compounds involved and of analyzing the fluxes through the biochemical network of wall biosynthesis. Many of the metabolic steps involve isomeric sugars, including hexose-Ps and nucleotidesugars (NDP-sugars) that serve as direct precursors to plant cell wall biosynthesis. Separate quantification of these sugars has been difficult to achieve.Much of the current research on identifying and differentiating among different metabolic pathways involves the use of chromato...

Background The use of mobile health (mHealth) interventions, including smartphone apps, for the prevention of cardiovascular disease (CVD) has demonstrated mixed results for obesity, hypercholesterolemia, diabetes, and hypertension management. A major factor attributing to the variation in mHealth study results may be mHealth user engagement. Objective This systematic review aims to determine if user engagement with smartphone apps for the prevention and management of CVD is associated with improved CVD health behavior change and risk factor outcomes. Methods We conducted a comprehensive search of PubMed, CINAHL, and Embase databases from 2007 to 2020. Studies were eligible if they assessed whether user engagement with a smartphone app used by an individual to manage his or her CVD risk factors was associated with the CVD health behavior change or risk factor outcomes. For eligible studies, data were extracted on study and sample characteristics, intervention description, app user engagement measures, and the relationship between app user engagement and the CVD risk factor outcomes. App user engagement was operationalized as general usage (eg, number of log-ins or usage days per week) or self-monitoring within the app (eg, total number of entries made in the app). The quality of the studies was assessed. Results Of the 24 included studies, 17 used a randomized controlled trial design, 4 used a retrospective analysis, and 3 used a single-arm pre- and posttest design. Sample sizes ranged from 55 to 324,649 adults, with 19 studies recruiting participants from a community setting. Most of the studies assessed weight loss interventions, with 6 addressing additional CVD risk factors, including diabetes, sleep, stress, and alcohol consumption. Most of the studies that assessed the relationship between user engagement and reduction in weight (9/13, 69%), BMI (3/4, 75%), body fat percentage (1/2, 50%), waist circumference (2/3, 67%), and hemoglobin A1c (3/5, 60%) found statistically significant results, indicating that greater app user engagement was associated with better outcomes. Of 5 studies, 3 (60%) found a statistically significant relationship between higher user engagement and an increase in objectively measured physical activity. The studies assessing the relationship between user engagement and dietary and diabetes self-care behaviors, blood pressure, and lipid panel components did not find statistically significant results. Conclusions Increased app user engagement for prevention and management of CVD may be associated with improved weight and BMI; however, only a few studies assessed other outcomes, limiting the evidence beyond this. Additional studies are needed to assess user engagement with smartphone apps targeting other important CVD risk factors, including dietary behaviors, hypercholesterolemia, diabetes, and hypertension. Further research is needed to assess mHealth user engagement in both inpatient and outpatient settings to determine the effect of integrating mHealth interventions into the existing clinical workflow and on CVD outcomes.

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