Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

A field study on EROD activity and quantitative hepatocytological changes in an immature demersal fish

Demersal fish, Solea ovata, were sampled from a reference site and a site where highly contaminated sediment is dumped. Sexually immature fish from the contaminated site exhibited significantly higher EROD activity compared with counterparts sampled from the reference site. No significant difference in EROD activity could be found for sexually mature males and females between sites. The relationship between EROD activity and quantitative changes in hepatic lipofuscin/ceroid, as well as peroxisome, was investigated for immature S. ovata. A significant correlation was found between EROD activity and volume density of lipofuscin/ceroid in fish hepatocyte (r=0.750; P<0.05), but no significant correlation was discernible between EROD activity and peroxisomes. Results from this field study corroborate our earlier laboratory findings, in which induction of EROD activity by intraperitoneal injection of benzo[a]pyrene was associated with increase in absolute volume and absolute number of lipofuscin/ceroid in hepatocytes. The present study provides further evidence that induction of EROD activity is associated with an increase in hepatic lipofuscin/ceroid and possibly cytological damages in immature S. ovata. This cytological change may serve as a potential marker for exposure to PAHs and PCBs. Copyright © 2001 Elsevier Science Ltd.link_to_subscribed_fulltex

Performance of the generalized delta rule in structural damage detection

The paper examines the suitability of the generalized data rule in training artificial neural networks (ANN) for damage identification in structures. Several multilayer perceptron architectures are investigated for a typical bridge truss structure with simulated damage stares generated randomly. The training samples have been generated in terms of measurable structural parameters (displacements and strains) at suitable selected locations in the structure. Issues related to the performance of the network with reference to hidden layers and hidden. neurons are examined. Some heuristics are proposed for the design of neural networks for damage identification in structures. These are further supported by an investigation conducted on five other bridge truss configurations

Structural sensitivity as a measure of redundancy

The conventional definition of redundancy is applicable to skeletal structural systems only, whereas the concept of redundancy has never been discussed in the context of a continuum. Generally, structures in civil engineering constitute a combination of both skeletal and continuum segments. Hence, this gaper presents a generalized definition of redundancy that has been defined in terms of structural response sensitivity, which is applicable to both continuum and discrete structures. In contrast to the conventional definition of redundancy, which is assumed to be fixed for a given structure and is believed to be independent of loading and material properties, the new definition would depend on strength and response of the structure at a given stage of its service life. The redundancy measure proposed in this paper is linked to the structural response sensitivities. Thus, the structure can have different degrees of redundancy during its lifetime, depending on the response sensitivity under consideration It is believed that this new redundancy measure would be more relevant in structural evaluation, damage assessment, and reliability analysis of structures at large

Vibration Signature Analysis Using Artificial Neural Networks

Damage detection by measuring and analyzing vibration signals in a machine component is an established procedure in mechanical and aerospace engineering. This paper presents vibration signature analysis of steel bridge structures in a nonconventional way using artificial neural networks (ANN). Multilayer perceptrons have been adopted using the back-propagation algorithm for network training. The training patterns in terms of vibration signature are generated analytically for a moving load traveling on a trussed bridge structure at a constant speed to simulate the inspection vehicle. Using the finite-element technique, the moving forces are converted into stationary time-dependent force functions in order to generate vibration signals in the structure and the same is used to train the network. The performance of the trained networks is examined for their capability to detect damage from unknown signatures taken independently at one, three, and five nodes. It has been observed that the prediction using the trained network with single-node signature measurement at a suitability chosen location is even better than that of three-node and five-node measurement data

Time-delay neural networks in damage detection of railway bridges

The recent developments in multilayer perceptron using the backpropagation algorithm, has opened up new possibilities in structural identification. Limitation of traditional neural networks (TNN) in dealing with patterns that may vary in time domain has given birth to time-delay neural networks (TDNN). In the present paper the TNN and the TDNN have been implemented in detecting the damage in bridge structure using vibration signature analysis. A comparative study has been carried out for the various cases of complete as well as incomplete measurement data. It has been observed that TDNNs have performed better than Tows in this application

Multilayer Perceptron in Damage Detection of Bridge Structures

Recent developments in artificial neural networks (ANN) have opened up new possibilities in the domain of structural engineering. For inverse problems like structural identification of large civil engineering structures such as bridges and buildings where the in sifu measured data are expected to be imprecise and often incomplete, the ANN holds greater promise. The detection of structural damage and identification of damaged element in a large complex structure is a challenging task indeed. This paper presents an application of multilayer perceptron in the damage detection of steel bridge structures, The issues relating to the design of network and learning paradigm are addressed and network architectures have been developed with reference to trussed bridge structures. The training patterns are generated for multiple damaged zones in a structure and performance of the networks with one and two hidden layers are examined. It has been observed that the performance of the network with two hidden layers was better than that of a single-layer architecture in general. The engineering importance of the whole exercise is demonstrated from the fact that measured inout at only a few locations in the structure is needed in the identification process using the ANN.

Synthesis of multi-wall carbon nanotubes by simple pyrolysis

A new pyrolysis technique has been developed for the synthesis of multi-walled carbon nanotubes (MWCNTs). In this simple method diethyl ether and nickelocene is pyrolysized in a reaction quartz tube without using carrier gas. The samples are prepared at pyrolysis temperatures of 650 and $950^0C$ and the effect of temperature on the tube morphology investigated. Purification has been done following the standard oxidation and acid bath treatment The as-synthesized and purified nanotubes have been characterized by X-ray diffraction (XRD), Scanning electron microscope (SEM), transmission electron microscope (TEM), thermogravimetric analysis (TGA) and micro-Raman spectroscopy. The technique has great advantages such as low cost and easy operation for the production of CNTs