Unbiased next generation sequencing analysis confirms the existence of autosomal dominant Alport syndrome in a relevant fraction of cases.

The mode of inheritance of Alport syndrome (ATS) has long been controversial. In 1927, the disease was hypothesized as a dominant condition in which males were more severely affected than females. In 1990, it was considered an X-linked (XL) semidominant condition, due to COL4A5 mutations. Later on, a rare autosomal recessive (AR) form due to COL4A3/COL4A4 mutations was identified. An autosomal dominant (AD) form was testified more recently by the description of some large pedigrees but the real existence of this form is still questioned by many and its exact prevalence is unknown. The introduction of next generation sequencing (NGS) allowed us to perform an unbiased simultaneous COL4A3-COL4A4-COL4A5 analysis in 87 Italian families (273 individuals) with clinical suspicion of ATS. In 48 of them (55%), a mutation in one of the three genes was identified: the inheritance was XL semidominant in 65%, recessive in 4% and most interestingly AD in 31% (15 families). The AD form must therefore be seriously taken into account in all pedigrees with affected individuals in each generation. Furthermore, a high frequency of mutations (>50%) was shown in patients with only 1 or 2 clinical criteria, suggesting NGS as first-level analysis in cases with a clinical suspicion of ATS.

Autosomal Dominant Pattern of Inheritance in Type 2 Diabetes in Nigeria

We investigated 212 patients attending 3 diabetic clinics centres in Lagos; Nigeria; and recorded family history; age of onset of diabetes; and body mass index. Analysis of family pedigrees showed that 3of sibships followed an autosomal dominant pattern of inheritance. The mean age of diabetes onset among the dominant group was 25 years and obesity was uncommon. This suggests that these patients may have Maturity Onset Diabetes of the Young (MODY)

Automatic patient registration in computer assisted maxillofacial surgery.

In this paper, a new approach for patient registration in computer assisted maxillofacial surgery is presented. The method uses a unique structure of markers embedded in a reference frame for the automatic detection of the coordinate system of the medical imaging data during the surgical intervention. With the new method, the inaccurate and time consuming process of manually identifying markers in the data volume and manually teaching them to a navigation system can be replaced. The method and algorithms for the automatic marker detection are described in this paper. Experiments with 45 data sets of patients proove the robustness, usability and safety of the new method. The method has been integrated into the navigation system RoboDent for dental implant surgery.