Long-term evaluation of the ventricular defibrillation energy requirement

INTRODUCTION: Defibrillation energy requirements in patients with nonthoracotomy defibrillators may increase within several months after implantation. However, the stability of the defibrillation energy requirement beyond 1 year has not been reported. The purpose of this study was to characterize the defibrillation energy requirement during 2 years of clinical follow-up. METHODS AND RESULTS: Thirty-one consecutive patients with a biphasic nonthoracotomy defibrillation system underwent defibrillation energy requirement testing using a step-down technique (20, 15, 12, 10, 8, 6, 5, 4, 3, 2, and 1 J) during defibrillator implantation, and then 24 hours, 2 months, 1 year, and 2 years after implantation. The mean defibrillation energy requirement during these evaluations was 10.9+/-5.5 J, 12.3+/-7.3 J, 11.7+/-5.6 J, 10.2+/-4.0 J, and 11.7+/-7.4 J, respectively (P = 0.4). The defibrillation energy requirement was noted to have increased by 10 J or more after 2 years of follow-up in five patients. In one of these patients, the defibrillation energy requirement was no longer associated with an adequate safety margin, necessitating revision of the defibrillation system. There were no identifiable clinical characteristics that distinguished patients who did and did not develop a 10-J or more increase in the defibrillation energy requirement. CONCLUSION: The mean defibrillation energy requirement does not change significantly after 2 years of biphasic nonthoracotomy defibrillator system implantation. However, approximately 15% of patients develop a 10-J or greater elevation in the defibrillation energy requirement, and 3% may require a defibrillation system revision. Therefore, a yearly evaluation of the defibrillation energy requirement may be appropriate.     

Use of the ARPE-19 cell line as a model of RPE polarity: basolateral secretion of FGF5

PURPOSE: To determine the polarity of fibroblast growth factor 5 (FGF5) secretions from retinal pigment epithelium (RPE) cells and to examine the viability and utility of the ARPE-19 cell line as a model for the study of RPE polarity. METHODS: Influenza infection and adenovirus-mediated gene transfer were used to deliver and express genes encoding influenza hemagglutinin (HA), p75-NTR (a neurotrophin receptor), low-density lipoprotein (LDL) receptor (LDLR), and FGF5 in confluent monolayers of ARPE-19 cells. The localization of HA, p75-NTR, and LDLR was determined by confocal microscopy. Domain selective biotinylation assays were used to quantitatively determine the polarities of p75-NTR and LDLR. The secretion of FGF5 into the apical and basal media of ARPE-19 cultures was examined by immunoblot analysis of conditioned media. RESULTS: Hemagglutinin and p75-NTR were found to be localized on the apical surface of infected and transduced ARPE-19 cells. In contrast, LDLR was associated preferentially with the basolateral membrane of ARPE-19 cells. Biotinylation studies indicated that 84% of p75-NTR was present on the apical surface, and 79% of LDLR was basolaterally polarized. Over the course of 6 hours, more than 90% of the total secreted FGF5 protein accumulated in the basolateral media. CONCLUSIONS: ARPE-19 cells exhibit a polarized distribution of cell surface markers when examined by either confocal microscopy or surface-labeling assays. This indicates that the ARPE-19 cell line is a valid model for studies of RPE cell polarity. FGF5, a secreted protein normally produced by RPE cells, is accumulated preferentially in the basal media after only 6 hours, suggesting that it is vectorially secreted from the basolateral surface of ARPE-19 cells.     

Prenatal diagnosis of trisomy 13 on fetal cells obtained from maternal blood after minor enrichment

In a pilot study to establish fetal nucleated red blood cell (NRBC) detection in maternal blood, trisomy 13 was diagnosed by FISH analysis at 11 weeks' gestation. The NRBCs were detected after a single-step ficoll density gradient enrichment. In blood samples taken both before and after CVS, 52 and 80 NRBCs, respectively, were found to be positive for fetal haemoglobin. In 47 per cent of these cells, FISH analysis for X and Y chromosomes confirmed the fetal sex. Moreover, 48 per cent of these NRBCs showed three fluorescent signals for a chromosome 13 probe, which confirmed the diagnosis of trisomy 13, previously detected at CVS karyotyping. This is the first report of non-invasive prenatal diagnosis of trisomy 13, i.e., pre-CVS, in the first trimester. The high number of fetal NRBCs detected indicates a connection with aneuploidy, probably due to early impairment of the feto-maternal barrier.     

A fully autonomous kernel-based online learning neural network model and its application to building cooling load prediction

Building cooling load prediction is critical to the success of energy-saving measures. While many of the computational models currently available in the industry have been developed for this purpose, most require extensive computer resources and involve lengthy computational processes. Artificial neural networks (ANNs) have recently been adopted for prediction, and pioneering works have confirmed the feasibility of this approach. However, users are required to predetermine an ANN model’s parameters. This hinders the applicability of the ANN approach in actual engineering problems, as most engineers may be unfamiliar with soft computing. This paper proposes a fully autonomous kernel-based neural network (AKNN) model for noisy data regression prediction. No part of the model’s mechanism requires human intervention; rather, it self-organises its structure according to the training samples presented. Unlike the other existing autonomous models, the AKNN model is an online learning model. It is particularly suitable for online steps-ahead prediction. In this paper, we benchmark the AKNN model’s performance according to other ANN models. It is also successfully applied to predicting the cooling load of a commercial building in Hong Kong. The occupancy areas and concentration of carbon dioxide inside the building are successfully adopted to mimic the building’s internal cooling load. Training data was adopted from actual measurements taken inside the building. Its results show reasonable agreement with actual cooling loads.