Role of VEGF165b/VEGFTOTAL ratio in gestational diabetes mellitus.

Proper vascular function is important for well-being of mother and growing fetus. VEGFTOTAL, and VEGF165b levels and its vascular endothelial complications in gestational diabetes mellitus (GDM) together with the association of inflammation and advanced glycation end products (AGEs) are less studied. VEGF165b/VEGFTOTAL (VEGF RATIO) in GDM pregnant women was investigated in this study. Plasma VEGFTOTAL was lower in GDM (17.68 ± 1.30 pg/mL) compared to non-GDM (25.69 ± 1.40 pg/mL). VEGF165b, ICAM-1, and AGEs were higher in GDM (9.9 ± 1.4 pg/mL, 201.04 ± 7.85 µg/mL, and 10.40 ± 0.98 µg/mL, respectively) and lower in non-GDM (6.47 ± 0.70 pg/mL, 174.1 ± 7.11 µg/mL, and 4.71 ± 0.39 µg/mL, respectively). Compared to non GDM (0.25 ± 0.02), VEGF RATIO was higher in GDM (0.45 ± 0.04) and correlated with -ICAM-1 (r = 0.375, p < .001) and AGEs (r = 0.199, p < .05). Tertile stratification of VEGF RATIO implied that frequency of GDM increases with increasing tertiles of VEGF RATIO (p for trend <.001). Association of VEGF RATIO with GDM was significant even after adjusting for AGEs (OR = 1.279, CI = 1.118-1.462, p < .0010) but it lost its significance when adjusted for ICAM-1 (OR = 1.006, CI = 0.995-1.017, p = .308). VEGF RATIO plays an important role in GDM in association with vascular inflammation.

Hyperglycaemia cause vascular inflammation through advanced glycation end products/early growth response-1 axis in gestational diabetes mellitus.

Hyperglycaemia during pregnancy is the main reason for developing diabetes mediated vascular complications. Advanced glycation end products (AGEs) are formed due to non-enzymatic glycation of proteins, lipids and nucleic acids during hyperglycaemia. It has the potential to damage vasculature by modifying the substrate or by means of AGEs and receptor of AGE (RAGE) interaction. It has been linked with the pathogenesis of various vascular diseases including coronary heart disease, atherosclerosis, restenosis etc. This study was carried out to investigate the role of AGEs-EGR-1 pathway in gestational diabetes mellitus (GDM) vascular inflammation. Human umbilical vein endothelial cells (HuVECs) isolated from normal glucose tolerant mothers were subjected to various treatments including high glucose, silencing of early growth response (EGR)-1, blockade of protein kinase C (PKC) ß, blocking extracellular signal-regulated protein kinases 1 and 2 (ERK1/2), and treatment with AGEs and assayed for EGR-1, tissue factor (TF) and soluble intercellular adhesion molecule (sICAM)-1. Similarly, umbilical vein endothelial cells isolated from normal and GDM mothers were assayed for EGR-1, TF, and sICAM-1. There was a significant increase in EGR-1 and TF levels in HuVECs isolated form GDM mother's umbilical cord and normal HuVECs treated with high glucose condition. This was accompanied by elevated levels of sICAM-1 in high glucose treated cells. Our results revealed AGE-mediated activation of EGR-1 and its downstream genes via PKC ßII and ERK1/2 signaling pathway. The present study demonstrated a novel mechanism of AGEs/ PKC ßII/ ERK1/2/EGR-1 pathway in inducing vascular inflammation in GDM.

Predictive modeling for corrective maintenance of imaging devices from machine logs.

In the cost sensitive healthcare industry, an unplanned downtime of diagnostic and therapy imaging devices can be a burden on the financials of both the hospitals as well as the original equipment manufacturers (OEMs). In the current era of connectivity, it is easier to get these devices connected to a standard monitoring station. Once the system is connected, OEMs can monitor the health of these devices remotely and take corrective actions by providing preventive maintenance thereby avoiding major unplanned downtime. In this article, we present an overall methodology of predicting failure of these devices well before customer experiences it. We use data-driven approach based on machine learning to predict failures in turn resulting in reduced machine downtime, improved customer satisfaction and cost savings for the OEMs. One of the use-case of predicting component failure of PHILIPS iXR system is explained in this article.

Temporal and spectral analysis of internal carotid artery Doppler signal for normal and abnormal flow detection.

Detection of carotid artery stenosis is presently highly dependent on ultrasound imaging systems. This work presents a method that can detect the normal and abnormal blood flow in the carotid structure independent of Doppler angle by analysing the time and spectral domain representation of Doppler signal. In the proposed approach, time and spectral domain based features are extracted from the Doppler signals of internal carotid arteries. Further, these features are used in supervised machine learning approach to identify the presence of abnormal blood flow. The proposed method is evaluated on 100 subjects (200 signals) with equal number of normal and abnormal flow profiles. Experimental results show that the maximum classification accuracies of 79.3% and 82.9% are observed with k-nearest neighbours and support vector machine classifiers, respectively.