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BackgroundPeutz-Jeghers syndrome (PJS) is a rare hereditary syndrome characterized by the occurrence of hamartomatous polyps in the gastrointestinal tract, mucocutaneous pigmentation and increased risk of cancer in multiple internal organs. Depending on the studied population, its incidence has been estimated to range from 1:200 000 even up to 1:50 000 births. Being an autosomal disease, PJS is caused in most cases by mutations in the STK11 gene.MethodsThe majority of causative DNA changes identified in patients with PJS are small mutations and, therefore, developing a method of their detection is a key aspect in the advancement of genetic diagnostics of PJS patients. We designed 13 pairs of primers, which amplify at the same temperature and enable examination of all coding exons of the STK11 gene by the HRM analysis.ResultsIn our group of 41 families with PJS small mutations of the STK11 gene were detected in 22 families (54%). In the remaining cases all of the coding exons were sequenced. However, this has not allowed to detect any additional mutations.ConclusionsThe developed methodology is a rapid and cost-effective screening tool for small mutations in PJS patients and makes it possible to detect all the STK11 gene sequence changes occurring in this group.

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Abstract. Spectral analysis is well-established analysis of vibrations used in diagnostics both in academia and industry. In general, one may identify components related to particular stages in the gearbox and analyze amplitudes of these components with a simple rule for decisionmaking: if amplitudes are increasing the condition becomes worse. However, usually one should analyze not single amplitude but at least several components, but: how to analyze them simultaneously? We have provided an example (case study) for planetary gearboxes in good and bad conditions (case B and case A). As diagnostic features we have used 15 amplitudes of spectral components related to fundamental planetary mesh frequency and its harmonics. Using Principal Component Analysis (PCA), it has been shown that amplitudes don't vary in the same way; change of condition affects not only amplitudes of all components in that sense, but also relation between them. We have investigated geometry of the data and it has been shown that the proportions of the explained total inertia of the three data sets ("good", "bad" and mixed good/bad) are different. We claim that it may be a novel diagnostic approach to employ multidimensional analysis for accounting not only directly observed values but also interrelations both within and between the two groups of data. Different structure of the data is associated with different condition of the machines and such assumption is specified for the first time in the literature. Obviously it requires more studies.

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