A comparative study of two different regression methods for radiographs in Polish youngsters estimating chronological age on third molars.

AIM: The aim of this study was to establish a third molar developmental database to model dental age of Polish youngsters, to investigate the rating level of the scores when dividing a year interval into a quarter of a year and to examine sex differences, left-right and upper-lower jaw asymmetry. MATERIAL AND METHODS: A cross-sectional sample of 1048 orthopantomograms of 644 females and 404 males aged between 12 and 26 years was investigated using the scoring system of Gleiser and Hunt modified by Köhler. Reference tables according to age were split in a whole year and in quarters of a year using descriptive statistics. The various developmental stages between males and females were analyzed with a paired t-test and the cusum method. Differences in mineralization between the quadrants were analyzed with a two-factor ANOVA and the Duncan post hoc test. The single quadratic and support vector regression were performed to describe the relationship between score and age. RESULTS: Dividing age classes in quarters of a year discriminated better between individuals provided that there is a sufficient sampling size for all age classes. The mineralization tempo occurred significantly at a faster rate in males. The maturational events in the upper arch developed significantly at earlier ages for both genders. Obtained chronological age had nearly the same standard error of estimate when calculated with both regression methods. DISCUSSION AND CONCLUSION: Comparing the results of the present study with those of other population groups suggests that there are differences in the ageing process of the wisdom tooth. This is the first database of Polish youngsters (15-24 years) with their respective regression equations to yield age estimations.

Polymorphisms in the ficolin 1 gene (FCN1) are associated with susceptibility to the development of rheumatoid arthritis.

OBJECTIVES: We investigated the possible association of rheumatoid arthritis (RA) with single nucleotide polymorphisms (SNP) within the ficolin (FCN) genes. Two SNPs in the FCN1 gene, four SNPs in the FCN2 gene and one SNP in the FCN3 gene were studied. METHODS: The SNPs within the FCN genes were detected by an experimental INNO-LiPA methodology (Innogenetics, Belgium) in a population consisting of 338 RA patients and 595 controls. The significant SNPs were further evaluated in two subpopulations and related to carriage of the human leukocyte antigen-shared epitope (HLA-SE), rheumatoid factor (RF) and the presence of anti-citrullinated protein/peptide antibodies (ACPA). RESULTS: Two SNPs in the FCN1 gene were significantly associated with RA: the A allele rs2989727 was significantly increased in RA patients (67%) compared with controls (60%) (P = 0.002). Also, the frequency of the G allele of rs1071583 was increased in RA patients (68%) compared with controls (61%) (P = 0.003). Analysis of agreement between SNPs suggested strong linkage between rs2989727 and rs1071583. Carriage of a FCN1 SNP was independent of carriage of the HLA-SE, RF status and ACPA positivity. CONCLUSIONS: We describe two linked SNPs in the FCN1 gene that are associated with the development of RA.

Rheumatoid factor and anticitrullinated protein antibodies in rheumatoid arthritis: diagnostic value, associations with radiological progression rate, and extra-articular manifestations.

BACKGROUND: Autoantibodies such as rheumatoid factor (RF) and anticitrullinated protein antibodies can be detected in rheumatoid arthritis (RA) sera. OBJECTIVE: To determine the diagnostic values of RF, anticitrullinated protein antibodies, and the shared epitope (SE), and their associations with radiological progression rates and extra-articular manifestations. METHODS: Population 1 consisted of sera from 315 patients, consecutively sent for detection of anticitrullinated protein antibodies, of which 264 were used to determine the sensitivity and specificity of RF and of antibodies against three synthetic citrullinated peptides: peptide A (pepA), peptide B (pepB), and CCP2. Population 2 consisted of sera from 180 longstanding RA patients and was used to determine associations of RA associated antibodies and the SE with radiological progression rates and extra-articular manifestations. Antibodies to pepA and pepB were detected by line immunoassay, and antibodies to CCP2 by ELISA. HLA Class II typing was performed by LiPA. RESULTS: In population 1, we defined adapted cut offs corresponding to a specificity of >/=98.5%. This yielded the following sensitivities: RF 12.8%; anti-pepA antibodies 63.6%; anti-pepB antibodies 54.2%; and anti-CCP2 antibodies 73.7%. In population 2, significant differences in radiological progression rates were found between positive and negative patients for different RA antibodies and the SE. RF, but not anticitrullinated protein antibodies or the SE, were more frequent in patients with extra-articular manifestations. CONCLUSION: A valid comparison of RA associated antibodies shows superior sensitivity of the anticitrullinated protein antibodies compared with RF. The presence of RA associated antibodies and the SE are indicative for poorer radiological outcome, and presence of extra-articular manifestations is associated with RF but not with anticitrullinated protein antibodies.

Prediction of diagnosis in patients with early arthritis using a combined Kohonen mapping and instance-based evaluation criterion.

Rheumatoid arthritis (RA) and spondyloarthropathy (SpA) are the two most frequent forms of chronic autoimmune arthritis. These diseases lead to important inflammatory symptoms resulting in an important functional impairment. This paper introduces a self-organizing artificial neural network combined with a case-based reasoning evaluation criterion to predict diagnosis in patients with early arthritis. Results show that 47.2% of the sample space can be predicted with an accuracy of 84.0% and attaining a high confidence level. 37.7% of the sample space is classified with an overall accuracy of 65.0%. The remaining group was labeled as "undetermined". A general prediction accuracy of 75.6% is reached, exceeding the performance of other approaches such as a backpropagation neural network and the Quest decision tree program. Furthermore, by using this new method, more specifically case-based reasoning, as a helpful tool to classify patients with early arthritis, the possibility of a confidence measure is given, indicating a degree of "belief" of the system in its results. This is often an important feature when dealing with diagnosis in human patients.

The development of a decision support system for the pathological diagnosis of human cerebral tumours based on a neural network classifier.

This study describes the use of a topological mapping system in the classification of cerebral tumours and the development of a decision support system based upon that classifier. Fourteen pathological parameters from two hundred primary cerebral tumours are presented as vectors to a topological map. The map, consisting of a grid of neurones, learns the features of each tumour by means of a shortest Euclidean distance algorithm, after which self adaptation of the neurons occurs. An LVQ algorithm performs the final classification. Study of the map reveals that it can correctly classify tumors following their malignancy potential and their cytogenesis. The decision support system uses the network at its core and helps not only in reaching a diagnosis but also in finding the optimal way to reach that diagnosis. The usefulness of such a mapping system lies in the field of education, clinical research and medically acceptable cost reduction.