Inflammatory bowel disease (IBD) includes ulcerative colitis (UC), Crohn's disease (CD) and indeterminate colitis. As these subtypes of IBD display important differences in the behavior of the natural course of the disease, the identification of non-invasive markers for IBD is important. The aim of the present study was to evaluate the serum levels of 10 adipokines and their association with endoscopic activity in IBD. The 10-protein profile (C-peptide, ghrelin, gastric inhibitory polypeptide, glucagon-like peptide-1, glucagon, insulin, leptin, plasminogen activator inhibitor-1, resistin and visfatin) was evaluated using serum from 53 participants (23 UC and 11 CD patients, as well as 19 controls) from Zacatecas (Mexico) by using the Bio-Plex Pro Human Diabetes 10-Plex Panel (Bio-Rad Laboratories, Inc.). Compared with those in the controls, leptin levels were significantly lower in patients with IBD (P=4.9×10−4). In addition, serum leptin displayed differences between groups with and without disease activity on endoscopy (P<0.001). Among the study population, serum leptin levels of <5,494 pg/ml significantly increased the odds of IBD by 12.8-fold [odds ratio (OR)=12.8, 95% confidence interval (CI)=3.04–53.9, P=0.001]. In addition, patients with serum leptin levels of <2,498 pg/ml displayed 5.8-fold greater odds of disease activity on endoscopy among the study population (OR=5.8, 95% CI=1.52–22.4, P=0.013). No differences in the serum levels of the remaining proteins were identified between the groups. Among the study population, serum leptin was associated with an increased risk of IBD and with disease activity on endoscopy. Additional studies will be necessary to validate the use of leptin as a non-invasive biomarker of IBD severity.

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Serum hsa-miRs 512-3p, 518f-3p, 520c-3p, and 520d-3p, are differentially expressed between WWD-PE and controls and their role in the development of preeclampsia should be investigated further.

One of the main problems in greenhouse crop production is the presence of pests. Detection and classification of insects are priorities in integrated pest management (IPM). This document describes a machine vision system able to detect whiteflies (Bemisia tabaci Genn.) in a greenhouse by sensing their presence using hunting traps. The extracted features corresponding to the eccentricity and area of the whiteflies projections allow to establish differences among pests and other insects on both the trap surfaces and dust generated artefacts. Because of whiteflies geometrical characteristics, it was possible to design an efficient (related to manual counting) machine vision algorithm to scout and count units of this pest within a greenhouse environment. These algorithm results show high correlation indexes for both, sticky screens (R2 = 0.97) and plant leaf situations (R2 = 1.0). The machine vision algorithm reduces the scouting time and the associated human error for IPM‐related activities.