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The GMC patient and colleague questionnaires offer a reliable basis for the assessment of professionalism among UK doctors. If used in the revalidation of doctors' registration, they would be capable of discriminating a range of professional performance among doctors, and potentially identifying a minority whose practice should to subjected to further scrutiny.

Recent advances in biology (namely, DNA arrays) allow an unprecedented view of the biochemical mechanisms contained within a cell. However, this technology raises new challenges for computer scientists and biologists alike, as the data created by these arrays is often highly complex. One of the challenges is the elucidation of the regulatory connections and interactions between genes, proteins and other gene products. In this paper, a novel method is described for determining gene interactions in temporal gene expression data using genetic algorithms combined with a neural network component. Experiments conducted on real-world temporal gene expression data sets confirm that the approach is capable of finding gene networks that fit the data. A further repeated approach shows that those genes significantly involved in interaction with other genes can be highlighted and hypothetical gene networks and circuits proposed for further laboratory testing.

Our results show that artificial neural networks and symbolic learning techniques (See5) capture some fundamental and new substrate attributes, but neural networks outperform their symbolic counterpart.

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Feedback from colleagues and patients is a core element of the revalidation process being developed by the General Medical Council. However, there are few feedback tools which have been specifically developed and validated for doctors in primary care. This paper presents data demonstrating the reliability and validity of one such tool. The CFEP360 tool combines feedback from the Colleague Feedback Evaluation Tool (CFET) and the Doctor's Interpersonal Skills Questionnaire (DISQ). The analysis of over 10 000 completed questionnaires presented here identifies that colleague feedback is essentially two-dimensional (i.e. clinical and non-clinical skills) and that patient feedback is one-dimensional. However, items from both scales also effectively predict combined global ratings, indicating that colleagues and patients are identifying similar levels of performance as accessed by the feedback. Doctors who receive low feedback scores may require further attention, meaning the feedback potentially has diagnostic value. Reliable feedback on this tool, as indicated by this analysis, requires 14 colleague responses and 25 patient responses, figures comparable to other MSF tools if CFEP360 is to be used for a high stakes performance evaluation and possible revalidation (generalisability statistic G> or =0.80). For lower stakes performance evaluations, such as personal development, responses from 11 colleagues and 16 patients will still return reliable results (G> or =0.70).

BackgroundNSCLC exhibits considerable heterogeneity in its sensitivity to chemotherapy and similar heterogeneity is noted in vitro in a variety of model systems. This study has tested the hypothesis that the molecular basis of the observed in vitro chemosensitivity of NSCLC lies within the known resistance mechanisms inherent to these patients' tumors.MethodsThe chemosensitivity of a series of 49 NSCLC tumors was assessed using the ATP-based tumor chemosensitivity assay (ATP-TCA) and compared with quantitative expression of resistance genes measured by RT-PCR in a Taqman Array™ following extraction of RNA from formalin-fixed paraffin-embedded (FFPE) tissue.ResultsThere was considerable heterogeneity between tumors within the ATP-TCA, and while this showed no direct correlation with individual gene expression, there was strong correlation of multi-gene signatures for many of the single agents and combinations tested. For instance, docetaxel activity showed some dependence on the expression of drug pumps, while cisplatin activity showed some dependence on DNA repair enzyme expression. Activity of both drugs was influenced more strongly still by the expression of anti- and pro-apoptotic genes by the tumor for both docetaxel and cisplatin. The doublet combinations of cisplatin with gemcitabine and cisplatin with docetaxel showed gene expression signatures incorporating resistance mechanisms for both agents.ConclusionGenes predicted to be involved in known mechanisms drug sensitivity and resistance correlate well with in vitro chemosensitivity and may allow the definition of predictive signatures to guide individualized chemotherapy in lung cancer.