Background: Alpha-tectorin is a noncollagenous component of the tectorial membrane which plays an essential role in auditory transduction. In several DFNA12 families mutations in TECTA, the gene encoding alpha-tectorin, were shown to cause hearing impairment (HI) with different phenotypes depending on the location of the mutation. Methods/Results: Here we report a Turkish family displaying autosomal dominant inherited HI. Linkage analysis revealed significant cosegregation (LOD score: 4.6) of the disease to markers on chromosome 11q23.3- q24. This region contains the TECTA gene which was subsequently sequenced. A nucleotide change in exon 13, 4526T>G, was detected leading to a substitution from cysteine to glycine at codon 1509 of the TECTA protein. This cysteine is located in vWFD4 domain, a protein domain which is supposed to be involved in disulfide bonds and protein-protein interactions. Conclusions: It is conspicuous that the phenotype in this family correlates with other families, also displaying mutations involving conserved cysteines. In all three families these mutations result in progressive HI involving high frequencies. In contrast, mutations which are not affecting the vWFD domains seem to provoke mid-frequency sensorineural HI. Furthermore, evaluation of clinical data in our family revealed a gender effect for the severity of hearing impairment. Males were significantly more affected than females. The identification of the third family displaying a missense mutation in the vWFD domain of alpha- tectorin underlines the phenotype-genotype correlation based on different mutations in TECTA.

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Information systems are important references aiming to support the decisions of decisionmakers. Information reliability depends on the accuracy and efficacy of data and models. Therefore, some risks may emerge in information systems concerning models, data, and humans. It is important to identify and extract outliers in decision support systems developed for the health information systems such as the detection system of Covid-19 symptoms. In this study, the risks that are important in decision making in Covid-19 symptom detection were determined by the statistical time series (ARMA) approach. Potential solutions are proposed in this way. Moreover, outliers are detected by software developed by using the Box-Jenkins model, and the reliability and accuracy of data are increased by using estimated data instead of outliers. In the implementation of this study, time-series-based data obtained from laboratory examinations of Covid-19 test devices can be used. With the method revealed here, outliers originating from healthcare workers or test apparatus can be detected and more accurate results can be obtained by replacing these outliers with estimated values.

Kronik insomni, en az 3 ay boyunca devam eden ve haftada en az 3 gece sıklığında ortaya çıkan uykusuzluk şikâyetleri olarak tanımlanır. 1 Uykusuzluk şi-kâyeti, uykuyu başlatma zorluğu, sürdürme zorluğu veya sabah istenilen saatten daha erken uyanma ve tekrar uykuya dalamama şeklinde olabilir. İnsomni