No abstract

Examining specific patterns of major cranio-facial alterations through cephalometric measurements in order to improve the Prader–Willi (PWS) syndrome diagnostic poses a major challenge of identifying interlinkages between numerous credentials. These interactions can be captured through probabilistic models of conditional independence between heterogeneous variables. Our research included 18 subjects (aged 4 to 28 years) genetically diagnosed with Prader–Willi syndrome and a healthy control group (matched age and sex). A morphometric and cephalometric analysis was performed upon all the subjects in order to obtain the needed specific data. We have, therefore, firstly deployed several integrated Gaussian graphical models (GGMs) to capture the positive and negative partial correlations and the intensity of the connections between numerous credentials configured to determine specific cranio-facial characteristics of patients with PWS compared to others without this genetic disorder (case-control analysis). Afterwards, we applied structural equation modelling (SEM) with latent class analysis to assess the impact of these coordinates on the prevalence of the Prader–Willi diagnostic. We found that there are latent interactions of features affected by external variables, and the interlinkages are strapping particularly between cranial base (with an important role in craniofacial disharmonies) and facial heights, as important characteristic patterns in determining the Prader–Willi diagnostic, while the overall patterns are significantly different in PWS and the control group. These results impact the field by providing an enhanced comprehensive perspective on cephalometric characteristics and specific patterns associated with Prader–Willi syndrome that can be used as benchmarks in determining the diagnostic of this rare genetic disorder. Furthermore, the two innovative exploratory research tools applied in this paper are very useful to the craniofacial field to infer the connections/dependencies between variables (particularly biological variables and genes) on cephalometric characteristics and specific patterns associated with Prader–Willi syndrome.

We report two siblings with congenital generalized hypertrichosis and distinctive facial appearance consistent with the dysmorphic facial features described in Ambras syndrome. The patients were born to non-consanguineous, phenotypically normal parents. This is the first report of affected siblings and could be explained by either autosomal recessive inheritance or by germline mosaicism for an autosomal dominant gene. We compared the phenotype of our patients to descriptions of reported cases and discuss phenotypic variability.

Fetal aneuploidies screening was based for a long time on ultrasonographic and biochemical markers measurement. The risk calculated in accordance with second trimester biochemical markers (STBM) values relies on calculation of corrected MoM values. MoM (multiple of Medians) signify the deviation of a measured value from the expected value (Median). The Median is measured at the same gestational age in pregnancies which involve healthy fetuses. The correction of MoM includes an adjustment for certain parameters that influence the STBM value: demographical (ethnicity), behavioral (smoking status, weight), and others (mode of conceiving, etc.). In our article we aim to analyze: (1) the accuracy of software to calculate STBM corrected MoM values, (2) the effect of weight of pregnant women on STBM and (3) the capability of software to counterbalance this influence. Pregnant women (n=1242) were screened for aneuploidies based on an integrated test: first trimester ultrasound and STBM (AFP, hCG and uE3). The absolute value, multiple of median (MoM) and corrected multiple of median (MoMc) values were 33.94�0.45, 1.04�0.12 and 0.98�0.01 for AFP, 22530�477, 0.87�0.01 and 0.85�0.01 for hCG, respectively 0.97�0.03, 0.99�0.01 and 0.98�0.01 for uE3. The weight of pregnant women inversely correlates with absolute and MoM AFP, hCG and uE3 values. No correlation was found with AFP and hCG MoMc values. A very weak inverse correlation was found between weight and uE3 corrected MoM values. Our study confirms that there is a difference between provider and own calculated hCG MoMc values. The weight of pregnant women inversely correlates with STBM values. The software used for aneuploidies risk evaluation corrects the influence of weight of pregnant women, but a minimal influence on uE3 corrected MoM values is still present.