Dietary Intake of Cadmium, Lead and Mercury and Its Association with Bone Health in Healthy Premenopausal Women.

The bone is one of the relevant target organs of heavy metals, and heavy metal toxicity is associated with several degenerative processes, such osteoporosis and bone mineral alterations, that could lead to fractures. We aimed to study a presumed relationship between bone density, evaluated by quantitative bone ultrasound (QUS), dual-energy X-ray absorptiometry (DXA) and peripheral quantitative computed tomography (pQCT) and the dietary intake of cadmium, lead and mercury in healthy premenopausal women. A total of 158 healthy, non-smoking, premenopausal women were incorporated into the study. A validated food frequency questionnaire (FFQ) was administered to assess intake during the preceding seven days. The median predicted dietary cadmium intake among the 158 women studied was 25.29 µg/day (18.62-35.00) and 2.74 µg/kg body weight/week (b.w./w) (1.92-3.83). Dietary lead intake was 43.85 µg/day (35.09-51.45) and 4.82 µg/kg b.w./w (3.67-6.13). The observed dietary mercury intake was 9.55 µg/day (7.18-13.57) and 1.02 µg/kg b.w./w (0.71-1.48). Comparisons, in terms of heavy metal intake, showed no significant results after further adjusting for energy intake. No statistically significant correlations between heavy metal intake and the QUS, DXA and pQCT parameters were observed. Levels of dietary exposure of cadmium, lead and mercury were mostly within the recommendations. We did not find associations between the QUS, DXA and pQCT parameters and the dietary intake of the studied heavy metals in healthy premenopausal women.

Retreatment Predictions in Odontology by means of CBR Systems.

The field of odontology requires an appropriate adjustment of treatments according to the circumstances of each patient. A follow-up treatment for a patient experiencing problems from a previous procedure such as endodontic therapy, for example, may not necessarily preclude the possibility of extraction. It is therefore necessary to investigate new solutions aimed at analyzing data and, with regard to the given values, determine whether dental retreatment is required. In this work, we present a decision support system which applies the case-based reasoning (CBR) paradigm, specifically designed to predict the practicality of performing or not performing a retreatment. Thus, the system uses previous experiences to provide new predictions, which is completely innovative in the field of odontology. The proposed prediction technique includes an innovative combination of methods that minimizes false negatives to the greatest possible extent. False negatives refer to a prediction favoring a retreatment when in fact it would be ineffective. The combination of methods is performed by applying an optimization problem to reduce incorrect classifications and takes into account different parameters, such as precision, recall, and statistical probabilities. The proposed system was tested in a real environment and the results obtained are promising.

Modelling the longevity of dental restorations by means of a CBR system.

The lifespan of dental restorations is limited. Longevity depends on the material used and the different characteristics of the dental piece. However, it is not always the case that the best and longest lasting material is used since patients may prefer different treatments according to how noticeable the material is. Over the last 100 years, the most commonly used material has been silver amalgam, which, while very durable, is somewhat aesthetically displeasing. Our study is based on the collection of data from the charts, notes, and radiographic information of restorative treatments performed by Dr. Vera in 1993, the analysis of the information by computer artificial intelligence to determine the most appropriate restoration, and the monitoring of the evolution of the dental restoration. The data will be treated confidentially according to the Organic Law 15/1999 on 13 December on the Protection of Personal Data. This paper also presents a clustering technique capable of identifying the most significant cases with which to instantiate the case-base. In order to classify the cases, a mixture of experts is used which incorporates a Bayesian network and a multilayer perceptron; the combination of both classifiers is performed with a neural network.