Computerized clinical decision support systems for prescribing in primary care: main characteristics and implementation impact-protocol of an evidence and gap map.

BACKGROUND: Computerized clinical decision support systems are used by clinicians at the point of care to improve quality of healthcare processes (prescribing error prevention, adherence to clinical guidelines, etc.) and clinical outcomes (preventive, therapeutic, and diagnostics). Attempts to summarize results of computerized clinical decision support systems to support prescription in primary care have been challenging, and most systematic reviews and meta-analyses failed due to an extremely high degree of heterogeneity present among the included primary studies. The aim of our study will be to synthesize the evidence, considering all methodological factors that could explain these differences, and build an evidence and gap map to identify important remaining research questions. METHODS: A literature search will be conducted from January 2010 onwards in MEDLINE, Embase, the Cochrane Library, and Web of Science databases. Two reviewers will independently screen all citations, full text, and abstract data. The study methodological quality and risk of bias will be appraised using appropriate tools if applicable. A flow diagram with the screened studies will be presented, and all included studies will be displayed using interactive evidence and gap maps. Results will be reported in accordance with recommendations from the Campbell Collaboration on the development of evidence and gap maps. DISCUSSION: Evidence behind computerized clinical decision support systems to support prescription use in primary care has so far been difficult to be synthesized. Evidence and gap maps represent an innovative approach that has emerged and is increasingly being used to address a broader research question, where multiple types of intervention and outcomes reported may be evaluated. Broad inclusion criteria have been chosen with regard to study designs, in order to collect all available information. Regarding the limitations, we will only include English and Spanish language studies from the last 10 years, we will not perform a grey literature search, and we will not carry out a meta-analysis due to the predictable heterogeneity of available studies. SYSTEMATIC REVIEW REGISTRATION: This study is registered in Open Science Framework https://bit.ly/2RqKrWp.

Scalp and serum profiling of frontal fibrosing alopecia reveals scalp immune and fibrosis dysregulation with no systemic involvement.

BACKGROUND: Frontal fibrosing alopecia (FFA) is a progressive, scarring alopecia of the frontotemporal scalp that poses a substantial burden on quality of life. Large-scale global profiling of FFA is lacking, preventing the development of effective therapeutics. OBJECTIVE: To characterize FFA compared to normal and alopecia areata using broad molecular profiling and to identify biomarkers linked to disease severity. METHODS: This cross-sectional study assessed 33,118 genes in scalp using RNA sequencing and 350 proteins in serum using OLINK high-throughput proteomics. Disease biomarkers were also correlated with clinical severity and a fibrosis gene set. RESULTS: Genes differentially expressed in lesional FFA included markers related to Th1 (IFNγ/CXCL9/CXCL10), T-cell activation (CD2/CD3/CCL19/ICOS), fibrosis (CXCR3/FGF14/FGF22/VIM/FN1), T-regulatory (FOXP3/TGFB1/TGFB3), and Janus kinase/JAK (JAK3/STAT1/STAT4) (Fold changes [FCH]>1.5, FDR<.05 for all). Only one protein, ADM, was differentially expressed in FFA serum compared to normal (FCH>1.3, FDR<.05). Significant correlations were found between scalp biomarkers (IL-36RN/IL-25) and FFA severity, as well as between JAK/STAT and fibrosis gene-sets (r>.6; P <.05). LIMITATIONS: This study was limited by a small sample size and predominantly female FFA patients. CONCLUSION: Our data characterize FFA as an inflammatory condition limited to scalp, involving Th1/JAK skewing, with associated fibrosis and elevated T-regulatory markers, suggesting the potential for disease reversibility with JAK/STAT inhibition.

Fructose modifies the hormonal response and modulates lipid metabolism during aerobic exercise after glucose supplementation.

The metabolic response when aerobic exercise is performed after the ingestion of glucose plus fructose is unclear. In the present study, we administered two beverages containing GluF (glucose+fructose) or Glu (glucose alone) in a randomized cross-over design to 20 healthy aerobically trained volunteers to compare the hormonal and lipid responses provoked during aerobic exercise and the recovery phase. After ingesting the beverages and a 15-min resting period, volunteers performed 30 min of moderate aerobic exercise. Urinary and blood samples were taken at baseline (t(-15)), during the exercise (t(0), t(15) and t(30)) and during the recovery phase (t(45), t(75) and t(105)). Plasma insulin concentrations were higher halfway through the exercise period and during acute recuperation (t(15) and t(75); P<0.05) following ingestion of GluF than after Glu alone, without any differences between the effects of either intervention on plasma glucose concentrations. Towards the end of the exercise period, urinary catecholamine concentrations were lower following GluF (t(45); P<0.05). Plasma triacylglycerol (triglyceride) concentrations were higher after the ingestion of GluF compared with Glu (t(15), t(30), t(45) and t(105); P<0.05). Furthermore, with GluF, we observed higher levels of lipoperoxides (t(15), t(30), t(45) and t(105); P<0.05) and oxidized LDL (low-density lipoprotein; t(30); P<0.05) compared with after the ingestion of Glu alone. In conclusion, hormonal and lipid alterations are provoked during aerobic exercise and recovery by the addition of a dose of fructose to the pre-exercise ingestion of glucose.