Frequency response function based structural damage detection using artificial neural networks

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, few attempts have been made to explore the structure damage with frequency response functions (FRFs). This paper illustrates the damage identification and condition assessment of a beam structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). In practice, usage of all available FRF data as an input to artificial neural networks makes the training and convergence impossible. Therefore one of the data reduction techniques Principal Component Analysis (PCA) is introduced in the algorithm. In the proposed procedure, a large set of FRFs are divided into sub-sets in order to find the damage indices for different frequency points of different damage scenarios. The basic idea of this method is to establish features of damaged structure using FRFs from different measurement points of different sub-sets of intact structure. Then using these features, damage indices of different damage cases of the structure are identified after reconstructing of available FRF data using PCA. The obtained damage indices corresponding to different damage locations and severities are introduced as input variable to developed artificial neural networks. Finally, the effectiveness of the proposed method is illustrated and validated by using the finite element modal of a beam structure. The illustrated results show that the PCA based damage index is suitable and effective for structural damage detection and condition assessment of building structures.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

Structural damage identification with noise polluted Frequency Response Functions (FRFs)

Damage detection in structures has become increasingly important in recent years. While a number of damage detection and localization methods have been proposed, very few attempts have been made to explore the structure damage with noise polluted data which is unavoidable effect in real world. The measurement data are contaminated by noise because of test environment as well as electronic devices and this noise tend to give error results with structural damage identification methods. Therefore it is important to investigate a method which can perform better with noise polluted data. This paper introduces a new damage index using principal component analysis (PCA) for damage detection of building structures being able to accept noise polluted frequency response functions (FRFs) as input. The FRF data are obtained from the function datagen of MATLAB program which is available on the web site of the IASC-ASCE (International Association for Structural Control– American Society of Civil Engineers) Structural Health Monitoring (SHM) Task Group. The proposed method involves a five-stage process: calculation of FRFs, calculation of damage index values using proposed algorithm, development of the artificial neural networks and introducing damage indices as input parameters and damage detection of the structure. This paper briefly describes the methodology and the results obtained in detecting damage in all six cases of the benchmark study with different noise levels. The proposed method is applied to a benchmark problem sponsored by the IASC-ASCE Task Group on Structural Health Monitoring, which was developed in order to facilitate the comparison of various damage identification methods. The illustrated results show that the PCA-based algorithm is effective for structural health monitoring with noise polluted FRFs which is of common occurrence when dealing with industrial structures.

Phenological changes in six Australian subalpine plants in response to experimental warming and year-to-year variation

The likely phenological responses of plants to climate warming can be measured through experimental manipulation of field sites, but results are rarely validated against year-to-year changes in climate. Here, we describe the response of 1-5 years of experimental warming on phenology (budding, flowering and seed maturation) of six common subalpine plant species in the Australian Alps using the International Tundra Experiment (ITEX) protocol.2. Phenological changes in some species (particularly the forb Craspedia jamesii) were detected in experimental plots within a year of warming, whereas changes in most other species (the forb Erigeron bellidioides, the shrub Asterolasia trymalioides and the graminoids Carex breviculmis and Poa hiemata) did not develop until after 2-4 years; thus, there appears to be a cumulative effect of warming for some species across multiple years.3. There was evidence of changes in the length of the period between flowering and seed maturity in one species (P. hiemata) that led to a similar timing of seed maturation, suggesting compensation.4. Year-to-year variation in phenology was greater than variation between warmed and control plots and could be related to differences in thawing degree days (particularly, for E. bellidioides) due to earlier timing of budding and other events under warmer conditions. However, in Carex breviculmis, there was no association between phenology and temperature changes across years.5. These findings indicate that, although phenological changes occurred earlier in response to warming in all six species, some species showed buffered rather than immediate responses.6. Synthesis. Warming in ITEX open-top chambers in the Australian Alps produced earlier budding, flowering and seed set in several alpine species. Species also altered the timing of these events, particularly budding, in response to year-to-year temperature variation. Some species responded immediately, whereas in others the cumulative effects of warming across several years were required before a response was detected.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Hybrid graphene/titania nanocomposite : interface charge transfer, hole doping, and sensitization for visible light response

We demonstrated for the first time by large-scale ab initio calculations that a graphene/titania interface in the ground electronic state forms a charge-transfer complex due to the large difference of work functions between graphene and titania, leading to substantial hole doping in graphene. Interestingly, electrons in the upper valence band can be directly excited from graphene to the conduction band, that is, the 3d orbitals of titania, under visible light irradiation. This should yield well-separated electron−hole pairs, with potentially high photocatalytic or photovoltaic performance in hybrid graphene and titania nanocomposites. Experimental wavelength-dependent photocurrent generation of the graphene/titania photoanode demonstrated noticeable visible light response and evidently verified our ab initio prediction.

The role of perceived usefulness and attitude on electronic health record acceptance : an empirical investigation using response surface analysis

Information and communications technologies are a significant component of the healthcare domain and electronic health records play a major role within it. As a result, it is important that they are accepted en masse by healthcare professionals. How healthcare professionals perceive the usefulness of electronic health records and their attitudes towards them have been shown to have significant effects on their overall acceptance. This paper investigates the role of perceived usefulness and attitude on the intention to use electronic health records by future healthcare professionals using polynomial regression with response surface analysis. Results show that the relationship is more complex than predicted in prior research. The paper concludes that the predicting properties of the above determinants must be further investigated to clearly understand their role in predicting the intention to use electronic health records and in designing systems that are better adopted by healthcare professionals of the future.

Use of an on-line student response system : an analysis of adoption and continuance

The goal of this project was to initiate the use of an internet-based student response system in a large, first year chemistry class at a typical Australian university, and to verify its popularity and utility. A secondary goal was to influence other academic staff to adopt the system, initiating change at the discipline and Faculty level. The first goal was achieved with a high response rate using a commercial on-line system; however, the number of students engaging with the system dropped gradually during each class and over the course of the semester. Factors affecting student and staff adoption and continuance with technology are explored using established models.

The function and mechanisms of action of ghrelin and obestatin in ovarian cancer

In this study, we have demonstrated that the preproghrelin derived hormones, ghrelin and obestatin, may play a role in ovarian cancer. Ghrelin and obestatin stimulated an increase in cell migration in ovarian cancer cell lines and may play a role in cancer progression. Ovarian cancer is the leading cause of death among gynaecological cancers and is the sixth most common cause of cancer-related deaths in women in developed countries. As ovarian cancer is difficult to diagnose at a low tumour grade, two thirds of ovarian cancers are not diagnosed until the late stages of cancer development resulting in a poor prognosis for the patient. As a result, current treatment methods are limited and not ideal. There is an urgent need for improved diagnostic markers, as well better therapeutic approaches and adjunctive therapies for this disease. Ghrelin has a number of important physiological effects, including roles in appetite regulation and the stimulation of growth hormone release. It is also involved in regulating the immune, cardiovascular and reproductive systems and regulates sleep, memory and anxiety, and energy metabolism. Over the last decade, the ghrelin axis, (which includes the hormones ghrelin and obestatin and their receptors), has been implicated in the pathogenesis of many human diseases and it may t may also play an important role in the development of cancer. Ghrelin is a 28 amino acid peptide hormone that exists in two forms. Acyl ghrelin (usually referred to as ghrelin), has a unique n-octanoic acid post-translational modification (which is catalysed by ghrelin O-acyltransferase, GOAT), and desacyl ghrelin, which is a non-octanoylated form. Octanoylated ghrelin acts through the growth hormone secretagogue receptor type 1a (GHSR1a). GHSR1b, an alternatively spliced isoform of GHSR, is C-terminally truncated and does not bind ghrelin. Ghrelin has been implicated in the pathophysiology of a number of diseases Obestatin is a 23 amino acid, C-terminally amidated peptide which is derived from preproghrelin. Although GPR39 was originally thought to be the obestatin receptor this has been disproven, and its receptor remains unknown. Obestatin may have as diverse range of roles as ghrelin. Obestatin improves memory, inhibits thirst and anxiety, increases pancreatic juice secretion and has cardioprotective effects. Obestatin also has been shown to regulate cell proliferation, differentiation and apoptosis in some cell types. Prior to this study, little was known regarding the functions and mechanisms of action ghrelin and obestatin in ovarian cancer. In this study it was demonstrated that the full length ghrelin, GHSR1b and GOAT mRNA transcripts were expressed in all of the ovarian-derived cell lines examined (SKOV3, OV-MZ-6 and hOSE 17.1), however, these cell lines did not express GHSR1a. Ovarian cancer tissue of varying stages and normal ovarian tissue expressed the coding region for ghrelin, obestatin, and GOAT, but not GHSR1a, or GHSR1b. No correlations between cancer grade and the level of expression of these transcripts were observed. This study demonstrated for the first time that both ghrelin and obestatin increase cell migration in ovarian cancer cell lines. Treatment with ghrelin (for 72 hours) significantly increased cell migration in the SKOV3 and OV-MZ-6 ovarian cancer cell lines. Ghrelin (100 nM) stimulated cell migration in the SKOV3 (2.64 +/- 1.08 fold, p <0.05) and OV-MZ-6 (1.65 +/- 0.31 fold, p <0.05) ovarian cancer cell lines, but not in the representative normal cell line hOSE 17.1. This increase in migration was not accompanied by an increase in cell invasion through Matrigel. In contrast to other cancer types, ghrelin had no effect on proliferation. Ghrelin treatment (10nM) significantly decreased attachment of the SKOV3 ovarian cancer cell line to collagen IV (24.7 +/- 10.0 %, p <0.05), however, there were no changes in attachment to the other extracellular matrix molecules (ECM) tested (fibronectin, vitronectin and collagen I), and there were no changes in attachment to any of the ECM molecules in the OV-MZ-6 or hOSE 17.1 cell lines. It is, therefore, unclear if ghrelin plays a role in cell attachment in ovarian cancer. As ghrelin has previously been demonstrated to signal through the ERK1/2 pathway in cancer, we investigated ERK1/2 signalling in ovarian cancer cell lines. In the SKOV3 ovarian cancer cell line, a reduction in ERK1/2 phosphorylation (0.58 fold +/- 0.23, p <0.05) in response to 100 nM ghrelin treatment was observed, while no significant change in ERK1/2 signalling was seen in the OV-MZ-6 cell line with treatment. This suggests that this pathway is unlikely to be involved in mediating the increased migration seen in the ovarian cancer cell lines with ghrelin treatment. In this study ovarian cancer tissue of varying stages and normal ovarian tissue expressed the coding region for obestatin, however, no correlation between cancer grade and level of obestatin transcript expression was observed. In the ovarian-derived cell lines studied (SKOV3, OV-MZ-6 and hOSE 17.1) it was demonstrated that the full length preproghrelin mRNA transcripts were expressed in all cell lines, suggesting they have the ability to produce mature obestatin. This is the first study to demonstrate that obestatin stimulates cell migration and cell invasion. Obestatin induced a significant increase in migration in the SKOV3 ovarian cancer cell line with 10 nM (2.80 +/- 0.52 fold, p <0.05) and 100 nM treatments (3.12 +/- 0.68 fold, p <0.05) and in the OV-MZ-6 cancer cell line with 10 nM (2.04 +/- 0.10 fold, p <0.01) and 100 nM treatments (2.00 +/- 0.37 fold, p <0.05). Obestatin treatment did no affect cell migration in the hOSE 17.1normal ovarian epithelial cell line. Obestatin treatment (100 nM) also stimulated a significant increase in cell invasion in the OV-MZ-6 ovarian cancer cell line (1.45 fold +/- 0.13, p <0.05) and in the hOSE17.1 normal ovarian cell line cells (1.40 fold +/- 0.04 and 1.55 fold +/- 0.05 respectively, p <0.01) with 10 nM and 100 nM treatments. Obestatin treatment did not stimulate cell invasion in the SKOV3 ovarian cancer cell line. This lack of obestatin-stimulated invasion in the SKOV3 cell line may be a cell line specific result. In this study, obestatin did not stimulate cell proliferation in the ovarian cell lines and it has previously been shown to have no effect on cell proliferation in the BON-1 pancreatic neuroendocrine and GC rat somatotroph tumour cell lines. In contrast, obestatin has been shown to affect cell proliferation in gastric and thyroid cancer cell lines, and in some normal cell lines. Obestatin also had no effect on attachment of any of the cell lines to any of the ECM components tested (fibronectin, vitronectin, collagen I and collagen IV). The mechanism of action of obestatin was investigated further using a two dimensional-difference in gel electrophoresis (2D-DIGE) proteomic approach. After treatment with obestating (0, 10 and 100 nM), SKOV3 ovarian cancer and hOSE 17.1 normal ovarian cell lines were collected and 2D-DIGE analysis and mass spectrometry were performed to identify proteins that were differentially expressed in response to treatment. Twenty-six differentially expressed proteins were identified and analysed using Ingenuity Pathway Analysis (IPA). This linked 16 of these proteins in a network. The analysis suggested that the ERK1/2 MAPK pathway was a major mediator of obestatin action. ERK1/2 has previously been shown to be associated with obestatin-stimulated cell proliferation and with the anti-apoptotic effects of obestatin. Activation of the ERK1/2 signalling pathway by obestatin was, therefore, investigated in the SKOV3 and OV-MZ-6 ovarian cancer cell lines using anti-active antibodies and Western immunoblots. Obestatin treatment significantly decreased ERK1/2 phosphorylation at higher obestatin concentrations in both the SKOV3 (100 nM and 1000 nM) and OV-MZ-6 (1000 nM) cell lines compared to the untreated controls. Currently, very little is known about obestatin signalling in cancer. This thesis has demonstrated for the first time that the ghrelin axis may play a role in ovarian cancer migration. Ghrelin and obestatin increased cell migration in ovarian cancer cell lines, indicating that they may be a useful target for therapies that reduce ovarian cancer progression. Further studies investigating the role of the ghrelin axis using in vivo ovarian cancer metastasis models are warranted.

Development and validation of anthropometric prediction equations for estimation of lean body mass and appendicular lean soft tissue in Indian men and women

Lean body mass (LBM) and muscle mass remains difficult to quantify in large epidemiological studies due to non-availability of inexpensive methods. We therefore developed anthropometric prediction equations to estimate the LBM and appendicular lean soft tissue (ALST) using dual energy X-ray absorptiometry (DXA) as a reference method. Healthy volunteers (n= 2220; 36% females; age 18-79 y) representing a wide range of body mass index (14-44 kg/m2) participated in this study. Their LBM including ALST was assessed by DXA along with anthropometric measurements. The sample was divided into prediction (60%) and validation (40%) sets. In the prediction set, a number of prediction models were constructed using DXA measured LBM and ALST estimates as dependent variables and a combination of anthropometric indices as independent variables. These equations were cross-validated in the validation set. Simple equations using age, height and weight explained > 90% variation in the LBM and ALST in both men and women. Additional variables (hip and limb circumferences and sum of SFTs) increased the explained variation by 5-8% in the fully adjusted models predicting LBM and ALST. More complex equations using all the above anthropometric variables could predict the DXA measured LBM and ALST accurately as indicated by low standard error of the estimate (LBM: 1.47 kg and 1.63 kg for men and women, respectively) as well as good agreement by Bland Altman analyses. These equations could be a valuable tool in large epidemiological studies assessing these body compartments in Indians and other population groups with similar body composition.

Molecular approaches to diagnosis and management of ovarian cancer

The recent advances in the understanding of the pathogenesis of ovarian cancer have been helpful in addressing issues in diagnosis, prognosis and management. The study of ovarian tumours by novel techniques such as immunohistochemistry, fluorescent in situ hybridisation, comparative genomic hybridisation, polymerase chain reaction and new tumour markers have aided the evaluation and application of new concepts into clinical practice. The correlation of novel surrogate tumour specific features with response to treatment and outcome in patients has defined prognostic factors which may allow the future design of tailored therapy based on a molecular profile of the tumour. These have also been used to design new approaches to therapy such as antibody targeting and gene therapy. The delineation of roles of c-erbB2, c-fms and other novel receptor kinases in the pathogenesis of ovarian cancer has led initially to the development of anti-c-erbB2 monoclonal antibody therapy. The discovery of BRCA1 and BRCA2 genes will have an impact in the diagnosis and the prevention of familial ovarian cancer. The important role played by recessive genes such as p53 in cancer has raised the possibility of restoration of gene function by gene therapy. Although the pathological diagnosis of ovarian cancer is still confirmed principally on morphological features, addition of newer investigations will increasingly be useful in addressing difficult diagnostic problems. The increasingly rapid pace of discovery of genes important in disease, makes it imperative that the evaluation of their contribution in the pathogenesis of ovarian cancer is undertaken swiftly, thus improving the overall management of patients and their outcome.

Dynamic contrast-enhanced magnetic resonance imaging as a biomarker for the pharmacological response of PTK787/ZK 222584, an inhibitor of the vascular endothelial growth factor receptor tyrosine kinases, in patients with advanced colorectal cancer and liver metastases: Results from two phase I studies

Purpose: PTK787/ZK 222584 (PTK/ZK), an orally active inhibitor of vascular endothelial growth factor (VEGF) receptor tyrosine kinases, inhibits VEGF-mediated angiogenesis. The pharmacodynamic effects of PTK/ZK were evaluated by assessing changes in contrast-enhancement parameters of metastatic liver lesions using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with advanced colorectal cancer treated in two ongoing, dose-escalating phase I studies. Patients and Methods: Twenty-six patients had DCE-MRI performed at baseline, day 2, and at the end of each 28-day cycle. Doses of oral PTK/ZK ranged from 50 to 2000 mg once daily. Tumor permeability and vascularity were assessed by calculating the bidirectional transfer constant (Ki). The percentage of baseline Ki (% of baseline Ki) at each time point was compared with pharmacokinetic and clinical end points. Results: A significant negative correlation exists between the % of baseline Ki and increase in PTK/ZK oral dose and plasma levels (P = .01 for oral dose; P = .0001 for area under the plasma concentration curve at day 2). Patients with a best response of stable disease had a significantly greater reduction in Ki at both day 2 and at the end of cycle 1 compared with progressors (mean difference in % of baseline Ki, 47%, P = .004%; and 51%, P = .006; respectively). The difference in % of baseline Ki remained statistically significant after adjusting for baseline WHO performance status. Conclusion: These findings should help to define a biologically active dose of PTK/ZK. These results suggest that DCE-MRI may be a useful biomarker for defining the pharmacological response and dose of angiogenesis inhibitiors, such as PTK/ZK, for further clinical development. © 2003 by American Society of Clinical Oncology.

Phase II trial of Oxaliplatin and 5-Fluorouracil/Leucovorin combination in epithelial ovarian carcinoma relapsing within 2 years of platinum-based therapy

Objective To evaluate the efficacy and toxicity of Oxaliplatin and 5-Fluorouracil (5-FU)/Leucovorin (LV) combination in ovarian cancer relapsing within 2 years of prior platinum-based chemotherapy in a phase II trial. Methods Eligible patients had at least one prior platinum-based chemotherapy regimen, elevated CA-125 ≥ 60 IU/l, radiological evidence of disease progression and adequate hepatic, renal and bone marrow function. Patients with raised CA-125 levels alone as marker of disease relapse were not eligible. Oxaliplatin (85 mg/m 2) was given on day 1, and 5-Fluorouracil (370 mg/m 2) and Leucovorin (30 mg) was given on days 1 and 8 of a 14-day cycle. Results Twenty-seven patients were enrolled. The median age was 57 years (range 42-74 years). The median platinum-free interval (PFI) was 5 months (range 0-17 months) with only 30% of patients being platinum sensitive (PFI > 6 months). Six patients (22%) had two prior regimens of chemotherapy. A total of 191 cycles were administered (median 7; range 2-12). All patients were evaluable for toxicity. The following grade 3/4 toxicities were noted: anemia 4%; neutropenia 15%; thrombocytopenia 11%; neurotoxicity 8%; lethargy 4%; diarrhea 4%; hypokalemia 11%; hypomagnesemia 11%. Among 27 enrolled patients, 20 patients were evaluable for response by WHO criteria and 25 patients were evaluable by Rustin's CA-125 criteria. The overall response rate (RR) by WHO criteria was 30% (95% CI: 15- 52) [three complete responses (CRs) and three partial responses (PRs)]. The CA-125 response rate was 56% (95% CI: 37-73). Significantly, a 25% (95% CI: 9-53) radiological and a 50% (95% CI: 28-72) CA-125 response rate were noted in platinum resistant patients (PFI < 6 months). The median response duration was 4 months (range 3-12) and the median overall survival was 10 months. Conclusion Oxaliplatin and 5-Fluorouracil/ Leucovorin combination has a good safety profile and is active in platinum-pretreated advanced epithelial ovarian cancer. © 2004 Elsevier Inc. All rights reserved.

The adaptive metabolic response to exercise-induced weight loss influences both energy expenditure and energy intake

BACKGROUND/OBJECTIVEs A decline in resting energy expenditure (REE) beyond that predicted from changes in body composition has been noted following dietary-induced weight loss. However, it is unknown whether a compensatory downregulation in REE also accompanies exercise (EX)-induced weight loss, or whether this adaptive metabolic response influences energy intake (EI). SUBJECTS/METHODS Thirty overweight and obese women (body mass index (BMI)=30.6±3.6 kg/m2) completed 12 weeks of supervised aerobic EX. Body composition, metabolism, EI and metabolic-related hormones were measured at baseline, week 6 and post intervention. The metabolic adaptation (MA), that is, difference between predicted and measured REE was also calculated post intervention (MApost), with REE predicted using a regression equation generated in an independent sample of 66 overweight and obese women (BMI=31.0±3.9 kg/m2). RESULTS Although mean predicted and measured REE did not differ post intervention, 43% of participants experienced a greater-than-expected decline in REE (−102.9±77.5 kcal per day). MApost was associated with the change in leptin (r=0.47; P=0.04), and the change in resting fat (r=0.52; P=0.01) and carbohydrate oxidation (r=−0.44; P=0.02). Furthermore, MApost was also associated with the change in EI following EX (r=−0.44; P=0.01). CONCLUSIONS Marked variability existed in the adaptive metabolic response to EX. Importantly, those who experienced a downregulation in REE also experienced an upregulation in EI, indicating that the adaptive metabolic response to EX influences both physiological and behavioural components of energy balance.