Minimal intervention dentistry and older patients part 2: minimally invasive operative interventions

As described in the first paper of this two part series, the expansion of our older population and the concomitant reduction in levels of edentulism will result in an increase in the number of patients presenting in general practice with complex restorative challenges. The application of the concepts of minimal intervention dentistry and minimally invasive operative techniques may offer a powerful armamentarium to the general dentist to provide ethical and conservative treatment to older patients.

Clinical Relevance: When it is unavoidable, operative intervention should be as minimally invasive as practicable in older patients to preserve the longevity of their natural dentition.

Minimal intervention dentistry and older patients Part 1: Risk assessment and caries prevention

Ten million people in the UK today are aged over 65. The latest projections estimate that there will be 5 1/2 million more people aged 65 and older in the next 20 years. This projected pattern of population ageing will have profound consequences for dentistry. Minimal intervention dentistry (MID) is a modern evidence-based approach to caries management in dentate patients that uses the 'medical model' whereby disease is controlled by the 'oral physician'. This approach offers considerable benefits over conventional dentistry for older patients. It encourages patients to be responsible for their oral health through the provision of both knowledge and motivation. MID encompasses risk assessment for dental disease, early detection and control of disease processes, and minimally invasive treatment. 

Clinical Relevance: Risk assessment tools can aid the general dental practitioner and the patient to develop a suitable caries prevention programme for that individual and reduce the need for future operative intervention.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

A tree augmented classifier based on Extreme Imprecise Dirichlet Model

We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

NP-hardness of MAP in Ternary Tree Bayesian Networks

This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem in Bayesian networks with topology of trees (every variable has at most one parent) and variable cardinality at most three. MAP is the problem of querying the most probable state configuration of some (not necessarily all) of the network variables given evidence. It is demonstrated that the problem remains hard even in such simplistic networks.

Losses of bioactive polyacetylenes during minimal processing of carrots

Vegetables of the Apiaceae plant family such as carrots, parsnip, celery and parsley,contain in minor quantities, a group of bioactive aliphatic C17-polyacetylenes (falcarinol,falcarindiol, falcarindiol-3- acetate). Recent studies have highlighted important biologicalfunctions in vitro and in vivo (animal studies) although the beneficial effect in humannutrition attributable to an increased in polyacetylenes diet are yet to be confirmed(Lund, 1990).Carrots not only contain relatively high polyacetylene content but also form a significantpart of many countries dietary habits. Carrots are also present in some ready-to-eat foodssuch as chilled freshly prepared salads, as part of the increasingly popular minimallyprocessed foods. Whereas the effect of conventional processing (boiling, vacuum processing) on the levels of polyacetylenes has been relatively well studied, the effect of minimal mechanical operations such as “peeling”, “mechanical cutting” and “chlorine washing” remains unknown.

When innovation fails: An institutional perspective of the (non)adoption of boundary spanning IT innovation

We combine the concepts of legitimacy, institutional (mis)alignments, strategic responses and organizing visions to develop a conceptual framework to analyse the adoption of innovations that span organisational fields. We apply the framework to examine a telehealth innovation connecting a public sector hospital-based eye clinic with private sector optometry practices. We find that while compromise strategies were successful in encouraging adoption within each field, the innovation ultimately failed as fields developed different organising visions which could not be reconciled. The findings suggest that institutional misalignments within and between fields interact to amplify their overall effect on the adoption of hybrid innovations.

John Birks: Pioneer in quantitative palaeoecology

We describe the career of John Birks as a pioneering scientist who has, over a career spanning five decades, transformed palaeoecology from a largely descriptive to a rigorous quantitative science relevant to contemporary questions in ecology and environmental change. We review his influence on students and colleagues not only at Cambridge and Bergen Universities, his places of primary employment, but also on individuals and research groups in Europe and North America. We also introduce the collection of papers that we have assembled in his honour. The papers are written by his former students and close colleagues and span many of the areas of palaeoecology to which John himself has made major contributions. These include the relationship between ecology and palaeoecology, late-glacial and Holocene palaeoecology, ecological succession, climate change and vegetation history, the role of palaeoecological techniques in reconstructing and understanding the impact of human activity on terrestrial and freshwater ecosystems and numerical analysis of multivariate palaeoecological data.

Minimal residual disease detection in childhood acute lymphoblastic leukaemia patients at multiple time-points reveals high levels of concordance between molecular and immunophenotypic approaches

In this single centre study of childhood acute lymphoblastic leukaemia (ALL) patients treated on the Medical Research Council UKALL 97/99 protocols, it was determined that minimal residual disease (MRD) detected by real time quantitative polymerase chain reaction (RQ-PCR) and 3-colour flow cytometry (FC) displayed high levels of qualitative concordance when evaluated at multiple time-points during treatment (93.38%), and a combined use of both approaches allowed a multi time-point evaluation of MRD kinetics for 90% (53/59) of the initial cohort. At diagnosis, MRD markers with sensitivity of at least 0.01% were identified by RQ-PCR detection of fusion gene transcripts, IGH/TRG rearrangements, and FC. Using a combined RQ-PCR and FC approach, the evaluation of 367 follow-up BM samples revealed that the detection of MRD >1% at Day 15 (P = 0.04), >0.01% at the end of induction (P = 0.02), >0.01% at the end of consolidation (P = 0.01), >0.01% prior to the first delayed intensification (P = 0.01), and >0.1% prior to the second delayed intensification and continued maintenance (P = 0.001) were all associated with relapse and, based on early time-points (end of induction and consolidation) a significant log-rank trend (P = 0.0091) was noted between survival curves for patients stratified into high, intermediate and low-risk MRD groups.

Wilms' tumor gene 1 (WT1) expression in childhood acute lymphoblastic leukemia:a wide range of WT1 expression levels, its impact on prognosis and minimal residual disease monitoring

Wilms' tumor gene 1 (WT1) is overexpressed in the majority (70-90%) of acute leukemias and has been identified as an independent adverse prognostic factor, a convenient minimal residual disease (MRD) marker and potential therapeutic target in acute leukemia. We examined WT1 expression patterns in childhood acute lymphoblastic leukemia (ALL), where its clinical implication remains unclear. Using a real-time quantitative PCR designed according to Europe Against Cancer Program recommendations, we evaluated WT1 expression in 125 consecutively enrolled patients with childhood ALL (106 BCP-ALL, 19 T-ALL) and compared it with physiologic WT1 expression in normal and regenerating bone marrow (BM). In childhood B-cell precursor (BCP)-ALL, we detected a wide range of WT1 levels (5 logs) with a median WT1 expression close to that of normal BM. WT1 expression in childhood T-ALL was significantly higher than in BCP-ALL (P<0.001). Patients with MLL-AF4 translocation showed high WT1 overexpression (P<0.01) compared to patients with other or no chromosomal aberrations. Older children (> or =10 years) expressed higher WT1 levels than children under 10 years of age (P<0.001), while there was no difference in WT1 expression in patients with peripheral blood leukocyte count (WBC) > or =50 x 10(9)/l and lower. Analysis of relapsed cases (14/125) indicated that an abnormal increase or decrease in WT1 expression was associated with a significantly increased risk of relapse (P=0.0006), and this prognostic impact of WT1 was independent of other main risk factors (P=0.0012). In summary, our study suggests that WT1 expression in childhood ALL is very variable and much lower than in AML or adult ALL. WT1, thus, will not be a useful marker for MRD detection in childhood ALL, however, it does represent a potential independent risk factor in childhood ALL. Interestingly, a proportion of childhood ALL patients express WT1 at levels below the normal physiological BM WT1 expression, and this reduced WT1 expression appears to be associated with a higher risk of relapse.