Mass spectrometry protein expression profiles in colorectal cancer tissue associated with clinico-pathological features of disease.

Background: Studies of several tumour types have shown that expression profiling of cellular protein extracted from surgical tissue specimens by direct mass spectrometry analysis can accurately discriminate tumour from normal tissue and in some cases can sub-classify disease. We have evaluated the potential value of this approach to classify various clinico-pathological features in colorectal cancer by employing matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS). Methods: Protein extracts from 31 tumour and 33 normal mucosa specimens were purified, subjected to MALDI-Tof MS and then analysed using the `GenePattern' suite of computational tools (Broad Institute, MIT, USA). Comparative Gene Marker Selection with either a t-test or a signal-to-noise ratio (SNR) test statistic was used to identify and rank differentially expressed marker peaks. The k-nearest neighbours algorithm was used to build classification models either using separate training and test datasets or else by using an iterative, `leave-one-out' cross-validation method. Results: 73 protein peaks in the mass range 1800-16000Da were differentially expressed in tumour verses adjacent normal mucosa tissue (P <= 0.01, false discovery rate <= 0.05). Unsupervised hierarchical cluster analysis classified most tumour and normal mucosa into distinct cluster groups. Supervised prediction correctly classified the tumour/normal mucosa status of specimens in an independent test spectra dataset with 100\% sensitivity and specificity (95\% confidence interval: 67.9-99.2\%). Supervised prediction using `leave-one-out' cross validation algorithms for tumour spectra correctly classified 10/13 poorly differentiated and 16/18 well/moderately differentiated tumours (P = < 0.001; receiver-operator characteristics - ROC - error, 0.171); disease recurrence was correctly predicted in 5/6 cases and disease-free survival (median follow-up time, 25 months) was correctly predicted in 22/23 cases (P = < 0.001; ROC error, 0.105). A similar analysis of normal mucosa spectra correctly predicted 11/14 patients with, and 15/19 patients without lymph node involvement (P = 0.001; ROC error, 0.212). Conclusions: Protein expression profiling of surgically resected CRC tissue extracts by MALDI-TOF MS has potential value in studies aimed at improved molecular classification of this disease. Further studies, with longer follow-up times and larger patient cohorts, that would permit independent validation of supervised classification models, would be required to confirm the predictive value of tumour spectra for disease recurrence/patient survival

Analysis of post-operative changes in serum protein expression profiles from colorectal cancer patients by MALDI-TOF mass spectrometry: a pilot methodological study.

Background: Mass spectrometry-based protein expression profiling of blood sera can be used to discriminate colorectal cancer (CRC) patients from unaffected individuals. In a pilot methodological study, we have evaluated the changes in protein expression profiles of sera from CRC patients that occur following surgery to establish the potential of this approach for monitoring post-surgical response and possible early prediction of disease recurrence. Methods: In this initial pilot study, serum specimens from 11 cancer patients taken immediately prior to surgery and at approximately 6 weeks following surgery were analysed alongside 10 normal control sera by matrix-assisted laser desorption ionisation time of-flight-mass spectrometry (MALDI-TOF MS). Using a two-sided t-test the top 20 ranked protein peaks that discriminate normal from pre-operative sera were identified. These were used to classify postoperative sera by hierarchical clustering analysis (Spearman's Rank correlation) and, as an independent `test' dataset, by k-nearest neighbour and weighted voting supervised learning algorithms. Results: Hierarchical cluster analysis classified post-operative sera from all six early Dukes' stage (A and B) patients as normal. The remaining five post-operative sera from more advanced Dukes' stages (C1 and C2) were classified as cancer. Analysis by supervised learning algorithms similarly grouped all advanced Dukes' stages as cancer, with four of the six post-operative sera from early Dukes' stages being classified as normal (P = 0.045; Fisher's exact test). Conclusions: The results of this pilot methodological study illustrate the proof-of-concept of using protein expression profiling of post-surgical blood sera from individual patients to monitor disease course. Further validation on a larger patient cohort and using an independent post-operative sera dataset would be required to evaluate the potential clinical relevance of this approach. Prospective data, including follow-up on patient survival, could in the future, then be evaluated to inform decisions on individualised treatment modalities

A hazard model of the probability of medical school dropout in the United Kingdom

From individual level longitudinal data for two entire cohorts of medical students in UK universities, we use multilevel models to analyse the probability that an individual student will drop out of medical school. We find that academic preparedness—both in terms of previous subjects studied and levels of attainment therein—is the major influence on withdrawal by medical students. Additionally, males and more mature students are more likely to withdraw than females or younger students respectively. We find evidence that the factors influencing the decision to transfer course differ from those affecting the decision to drop out for other reasons

An advanced Bayesian model for the visual tracking of multiple interacting objects

Visual tracking of multiple objects is a key component of many visual-based systems. While there are reliable algorithms for tracking a single object in constrained scenarios, the object tracking is still a challenge in uncontrolled situations involving multiple interacting objects that have a complex dynamics. In this article, a novel Bayesian model for tracking multiple interacting objects in unrestricted situations is proposed. This is accomplished by means of an advanced object dynamic model that predicts possible interactive behaviors, which in turn depend on the inference of potential events of object occlusion. The proposed tracking model can also handle false and missing detections that are typical from visual object detectors operating in uncontrolled scenarios. On the other hand, a Rao-Blackwellization technique has been used to improve the accuracy of the estimated object trajectories, which is a fundamental aspect in the tracking of multiple objects due to its high dimensionality. Excellent results have been obtained using a publicly available database, proving the efficiency of the proposed approach

Blood pressure and cholesterol level checks as dynamic interrelated screening examinations

This study analysed the determinants of screening uptake for blood pressure and cholesterol level checks. Furthermore, it investigated the presence of possible spillover effects from one type of cardiovascular screening to another type of cardiovascular screening. A dynamic random effects bivariate panel probit model with initial conditions (Wooldridge-type estimator) was adopted for the estimation. The outcome variables were the participation in blood pressure and cholesterol level checks by individuals in a given year. The balanced panel sample of 21,138 observations was constructed from 1,626 individuals from the British Household Panel Survey (BHPS) between 1996 and 2008. The analysis showed the significance of past screening behaviour for both cardiovascular screening examinations. For both cardiovascular screening examinations state dependence exist. The study also shows a significant spillover effect of the cholesterol level check on the blood pressure check and vice versa. Also a poorer health status led to a higher uptake for both types of screening examinations. Changes in recommendations have to consider the fact that taking part in one type of cardiovascular screening examination can influence the decision to take part in the other type of cardiovascular screening examination

Using texture analysis in the development of a potential radiomic signature for early identification of hepatic metastasis in colorectal cancer

Background: Radiomics allows information not readily available to the naked eye to be extracted from high resolution imaging modalities such as CT. Identifying that a cancer has already metastasised at the time of presentation through a radiomic signature will affect the treatment pathway. The ability to recognise the existence of metastases earlier will have a significant impact on the survival outcomes. / Aim: To create a novel radiomic signature using textural analysis in the evaluation of synchronous liver metastases in colorectal cancer. / Methods: CT images at baseline and subsequent surveillance over a 5-year period of patients with colorectal cancer were processed using textural analysis software. Comparison was made between those patients who developed liver metastases and those that remained disease free to detect differences in the ‘texture’ of the liver. / Results: A total of 24 patients were divided into two matched groups for comparison. Significant differences between the two groups scores when using the textural analysis programme were found on coarse filtration (p = 0.044). Patients that went on to develop metastases an average of 18 months after presentation had higher levels of hepatic heterogeneity on CT. / Conclusion: This initial study demonstrates the potential of using a textural analysis programme to build a radiomic signature to predict the development of hepatic metastases in rectal cancer patients otherwise thought to have clear staging CT scans at time of presentation

Corporate philanthropy through the lens of ethical subjectivity

The dynamic organisational processes in businesses dilute the boundaries between the individual, organisational, and societal drivers of corporate philanthropy. This creates a complex framework in which charitable project selection occurs. Using the example of European tour operators, this study investigates the mechanisms through which companies invest in charitable projects in overseas destinations. Inextricably linked to this is the increasing contestation by local communities as to how they are able to engage effectively with tourism in order to realise the benefits tourism development can bring. This research furthers such debates by exploring the processes through which tour operators facilitate community development through charitable giving. Findings show, with no formal frameworks in existence, project selection depends upon emergent strategies that connect the professional with the personal, with trust being positioned as a central driver of these informal processes. Discretionary responsibilities are reworked through business leaders’ commitment to responsible business practises and the ethical subjectivity guiding these processes

Beyond the mean gender wage gap : decomposition of differences in wage distributions using quantile regression

Using linked employer-employee data, this study measures and decomposes the differences in the earnings distribution between male and female employees in Germany. I extend the traditional decomposition to disentangle the effect of human capital characteristics and the effect of firm characteristics in explaining the gender wage gap. Furthermore, I implement the decomposition across the whole wage distribution with the method proposed by Machado and Mata (2005). Thereby, I take into account the dependence between the human capital endowment of individuals and workplace characteristics. The selection of women into less successful and productive firms explains a sizeable part of the gap. This selection is more pronounced in the lower part of the wage distribution than in the upper tail. In addition, women also benefit from the success of firms by rent-sharing to a lesser extent than their male colleagues. This is the source of the largest part of the pay gap. Gender differences in human capital endowment as well s differences in returns to human capital are less responsible for the wage differential