Williams-Beuren syndrome (WBS) is a rare genetic disorder characterized by special facial gestalt, delayed development, and supravalvular aortic stenosis or/and stenosis of the branches of the pulmonary artery. We aim to develop and optimize accurate models of facial recognition to assist in the diagnosis of WBS, and to evaluate their effectiveness by using both five-fold cross-validation and an external test set. We used a total of 954 images from 135 patients with WBS, 124 patients suffering from other genetic disorders, and 183 healthy children. The training set comprised 852 images of 104 WBS cases, 91 cases of other genetic disorders, and 145 healthy children from September 2017 to December 2021 at the Guangdong Provincial People\'s Hospital. We constructed six binary classification models of facial recognition for WBS by using EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN. Transfer learning was used to pre-train the models, and each model was modified with a variable cosine learning rate. Each model was first evaluated by using five-fold cross-validation and then assessed on the external test set. The latter contained 102 images of 31 children suffering from WBS, 33 children with other genetic disorders, and 38 healthy children. To compare the capabilities of these models of recognition with those of human experts in terms of identifying cases of WBS, we recruited two pediatricians, a pediatric cardiologist, and a pediatric geneticist to identify the WBS patients based solely on their facial images. We constructed six models of facial recognition for diagnosing WBS using EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN. The model based on VGG-19BN achieved the best performance in terms of five-fold cross-validation, with an accuracy of 93.74% ± 3.18%, precision of 94.93% ± 4.53%, specificity of 96.10% ± 4.30%, and F1 score of 91.65% ± 4.28%, while the VGG-16BN model achieved the highest recall value of 91.63% ± 5.96%. The VGG-19BN model also achieved the best performance on the external test set, with an accuracy of 95.10%, precision of 100%, recall of 83.87%, specificity of 93.42%, and F1 score of 91.23%. The best performance by human experts on the external test set yielded values of accuracy, precision, recall, specificity, and F1 scores of 77.45%, 60.53%, 77.42%, 83.10%, and 66.67%, respectively. The F1 score of each human expert was lower than those of the EfficientNet-b3 (84.21%), ResNet-50 (74.51%), VGG-16 (85.71%), VGG-16BN (85.71%), VGG-19 (83.02%), and VGG-19BN (91.23%) models.
CONCLUSIONS: The results showed that facial recognition technology can be used to accurately diagnose patients with WBS. Facial recognition models based on VGG-19BN can play a crucial role in its clinical diagnosis. Their performance can be improved by expanding the size of the training dataset, optimizing the CNN architectures applied, and modifying them with a variable cosine learning rate.
BACKGROUND: • The facial gestalt of WBS, often described as \"elfin,\" includes a broad forehead, periorbital puffiness, a flat nasal bridge, full cheeks, and a small chin. • Recent studies have demonstrated the potential of deep convolutional neural networks for facial recognition as a diagnostic tool for WBS.
BACKGROUND: • This study develops six models of facial recognition, EfficientNet-b3, ResNet-50, VGG-16, VGG-16BN, VGG-19, and VGG-19BN, to improve WBS diagnosis. • The VGG-19BN model achieved the best performance, with an accuracy of 95.10% and specificity of 93.42%. The facial recognition model based on VGG-19BN can play a crucial role in the clinical diagnosis of WBS.

BACKGROUND: There are a few literature reports of prenatal ultrasound manifestations of Williams-Beuren syndrome. We aimed to explore the prenatal diagnosis of Williams-Beuren syndrome by ultrasound and chromosomal microarray analysis and describe the prenatal ultrasound performance of this syndrome.
METHODS: In this retrospective study, we reported eight cases of Williams-Beuren syndrome diagnosed at our prenatal diagnostic center from 2016 to 2021. We systematically reviewed clinical data from these cases, including indications for invasive testing, sonographic findings, QF-PCR results, chromosomal microarray analysis results, and pregnancy outcomes.
RESULTS: In this study, the common ultrasound features were ventricular septal defect (37.5%), intrauterine growth retardation (25%), and aortic coarctation (25%). Moreover, all patients were found to have a common deletion in the Williams-Beuren syndrome chromosome region at the 7q11.23 locus, which contained the elastin gene. Deletion sizes ranged from 1.42 to 2.07 Mb. Seven parents asked for termination of pregnancy, and one patient was lost to follow-up.
CONCLUSIONS: This study is the most extensive prenatal study using chromosomal microarray analysis technology for detailed molecular analysis of Williams-Beuren syndrome cases. We reported three cases combined with first-reported ultrasound manifestations. Case 1 was concomitant with multicystic dysplastic kidney and duodenal atresia combined with case 3. Notably, case 4 was combined with multiple cardiovascular malformations: Tetralogy of Fallot, right aortic arch, and supravalvar aortic stenosis. These manifestations expand the intrauterine ultrasound phenotype of Williams-Beuren syndrome in previous literature reports.

Background: Williams-Beuren syndrome (WBS) is a rare genetic syndrome with a characteristic \"elfin\" facial gestalt. The \"elfin\" facial characteristics include a broad forehead, periorbital puffiness, flat nasal bridge, short upturned nose, wide mouth, thick lips, and pointed chin. Recently, deep convolutional neural networks (CNNs) have been successfully applied to facial recognition for diagnosing genetic syndromes. However, there is little research on WBS facial recognition using deep CNNs. Objective: The purpose of this study was to construct an automatic facial recognition model for WBS diagnosis based on deep CNNs. Methods: The study enrolled 104 WBS children, 91 cases with other genetic syndromes, and 145 healthy children. The photo dataset used only one frontal facial photo from each participant. Five face recognition frameworks for WBS were constructed by adopting the VGG-16, VGG-19, ResNet-18, ResNet-34, and MobileNet-V2 architectures, respectively. ImageNet transfer learning was used to avoid over-fitting. The classification performance of the facial recognition models was assessed by five-fold cross validation, and comparison with human experts was performed. Results: The five face recognition frameworks for WBS were constructed. The VGG-19 model achieved the best performance. The accuracy, precision, recall, F1 score, and area under curve (AUC) of the VGG-19 model were 92.7 ± 1.3%, 94.0 ± 5.6%, 81.7 ± 3.6%, 87.2 ± 2.0%, and 89.6 ± 1.3%, respectively. The highest accuracy, precision, recall, F1 score, and AUC of human experts were 82.1, 65.9, 85.6, 74.5, and 83.0%, respectively. The AUCs of each human expert were inferior to the AUCs of the VGG-16 (88.6 ± 3.5%), VGG-19 (89.6 ± 1.3%), ResNet-18 (83.6 ± 8.2%), and ResNet-34 (86.3 ± 4.9%) models. Conclusions: This study highlighted the possibility of using deep CNNs for diagnosing WBS in clinical practice. The facial recognition framework based on VGG-19 could play a prominent role in WBS diagnosis. Transfer learning technology can help to construct facial recognition models of genetic syndromes with small-scale datasets.

Williams-Beuren syndrome (WBS) is an autosomal dominant disorder caused by a gene deletion on chromosome 7q11.23. Patients with WBS usually show a group of features such as developmental delay, cardiovascular anomalies, mental retardation, and characteristic facial appearance. It occurs in 1:7,500 live births and affects males and females equally. Recent studies showed that lower urinary tract symptoms were also frequent in WBS patients. However, there is extremely rare study report non-monosymptomatic nocturnal enuresis as the main manifestation of Williams syndrome in children. We reported a child with non-monosymptomatic nocturnal enuresis and multiple bladder diverticula as the main implications of Williams syndrome. A 7.6-year-old girl was admitted to our hospital due to frequent micturition, urgency, and nocturnal enuresis for 4 years, and B ultrasound of urinary system revealed multiple bladder diverticula. The patient was found to have 7q11.23 deletion that involves the elastin gene for WBS. Multiple bladder diverticula in WBS patients can lead to many lower urinary tract symptoms. The treatment for the lower urinary tract symptoms in WBS patients with multiple bladder diverticula is lacking. Lower urinary tract symptoms should be considered as a significant indicator of the clinical diagnosis of WBS and have a significant negative impact on patient\'s quality of life.

Williams-Beuren syndrome (WBS) is a well-defined multisystem chromosomal disorder that is caused by a chromosome 7q11.23 region heterozygous deletion. We explored prenatal diagnosis of WBS by ultrasound as well as multiple genetic methods to characterize the structural variants of WBS prenatally. Expanded noninvasive prenatal testing (NIPT-plus) was elected as a regular prenatal advanced screen for risk assessments of fetal chromosomal aneuploidy and genome-wide microdeletion/microduplication syndromes at the first trimester. At the second and three trimester, seven prenatal cases of WBS were evaluated for the indication of the invasive testing, the ultrasound features, cytogenetic, single-nucleotide polymorphism array (SNP array), and fluorescent quantitative PCR (QF-PCR) results. The NIPT-plus results for seven fetuses were low risk. All cryptic aberrations were detected by the SNP array as karyotyping analyses were negative. Subsequently, QF-PCR further confirmed the seven deletions. Combining our cases with 10 prenatal cases from the literature, the most common sonographic features were intrauterine growth retardation (82.35%, 14/17) and congenital cardiovascular abnormalities (58.82%, 10/17). The manifestations of cardiovascular defects mainly involve supravalvar aortic stenosis (40%, 4/10), ventricular septal defect (30%, 3/10), aortic coarctation (20%, 2/10), and peripheral pulmonary artery stenosis (20%, 2/10). To the best of our knowledge, this is the first largest prenatal study of WBS cases with detailed molecular analysis. Aortic coarctation combined with persistent left superior vena cava and right aortic arch cardiovascular defects were first reported in prenatal WBS cases by our study.

Williams-Beuren syndrome (WBS; OMIM #194,050) is a rare multisystem disorder of a variable phenotypic spectrum caused by a heterozygous microdeletion in the WBS chromosome region (WBSCR) in 7q11.23.
We screened 38 Chinese Han patients with suspected WBS using chromosomal microarray analysis (CMA).
Pathogenic CNVs were identified in 34 of the patients, including 29 cases with a typical 7q11.23 microdeletion, three cases with atypical copy number variations (CNVs) within the WBS chromosome region and two cases with CNVs associated with other known syndromes. All 29 WBS patients with a typical microdeletion exhibited distinctive facial dysmorphisms and developmental delay. We observed that the incidence of pulmonary abnormalities was slightly higher than that of aortic abnormalities. We also found long philtrum and prominent lips with a thick lip that may warrant suspicion of WBS in the Chinese Han patients.
CMA facilitates diagnosis in individuals with classic/nonclassic features of WBS and demonstrated that when Chinese Han patients present with a less classical phenotype, such as pulmonary abnormalities, this may raise suspicion for a WBS diagnosis and suggest a referral for a genetics evaluation for a differential diagnosis.

Williams-Beuren syndrome (WBS) is a microdeletion disorder with cognitive phenotype. NSUN5 gene, which encodes a cytosine-5 RNA methyltransferase, is located in WBS deletion locus. To investigate the influence of NSUN5 deletion on cognitive behaviors, we produced single-gene Nsun5 knockout (Nsun5-KO) mice. Here, we report that adult Nsun5-KO mice showed spatial cognitive deficits. Size of the brain and hippocampal structures and the number of CA1 or CA3 pyramidal cells in Nsun5-KO mice did not differ from WT mice. Basal properties of Schaffer collateral-CA1 synaptic transmission in Nsun5-KO mice were unchanged, but NMDA receptor (NMDAr)-dependent long-term potentiation (LTP) was not induced. The NMDA-evoked current in CA1 pyramidal cells was reduced in Nsun5-KO mice without the changes in expression and phosphorylation of NMDAr subunits NR2A and NR2B. Although the protein level of AMPA receptor subunit GluR2 was attenuated in Nsun5-KO mice, the AMPA-evoked current was not altered. Hippocampal immuno-staining showed the selective expression of Nsun5 in NG2 or PDGFRα labeled oligodendrocyte precursor cells (OPCs), but not in pyramidal cells or astrocytes. Analysis of RT-PCR determined the Nsun5 expression in purified populations of OPCs rather than neurons or astrocytes. The Nsun5 deficiency led to decreases in the number and neurite outgrowth of OPCs in the hippocampal CA1 and DG, with the decline in NG2 expression and OPCs proliferation. These findings indicate that the Nsun5 deletion suppresses NMDAr activity in neuronal cells probably through the disrupted development and function of OPCs, leading to deficits in NMDAr-dependent LTP and spatial cognitive abilities.

Williams-Beuren syndrome is a genetic disorder characterized by physiological and mental abnormalities, and is caused by hemizygous deletion of several genes in chromosome 7. One of the removed genes encodes the WBSCR27 protein. Bioinformatic analysis of the sequence of WBSCR27 indicates that it belongs to the family of SAM-dependent methyltransferases. However, exact cellular functions of this protein or phenotypic consequences of its deficiency are still unknown. Here we report nearly complete 1H, 15N, and 13C chemical shifts assignments of the 26 kDa WBSCR27 protein from Mus musculus in complex with the cofactor S-adenosyl-L-methionine (SAM). Analysis of the assigned chemical shifts allowed us to characterize the protein\'s secondary structure and backbone dynamics. The topology of the protein\'s fold confirms the assumption that the WBSCR27 protein belongs to the family of class I methyltransferases.

BACKGROUND: Williams-Beuren syndrome (WBS) is caused by a microdeletion of chromosome arm 7q11.23. A rapid and inexpensive genotyping method to detect microdeletion on 7q11.23 needs to be developed for the diagnosis of WBS. This study describes the development of a new type of molecular diagnosis method to detect microdeletion on 7q11.23 based upon high-resolution melting (HRM).
METHODS: Four genes on 7q11.23 were selected as the target genes for the deletion genotyping. dNTP-limited duplex PCR was used to amplify the reference gene, CFTR, and one of the four genes respectively on 7q11.23. An HRM assay was performed on the PCR products, and the height ratio of the negative derivative peaks between the target gene and reference gene was employed to analyze the copy number variation of the target region.
RESULTS: A new genotyping method for detecting 7q11.23 deletion was developed based upon dNTP-limited PCR and HRM, which cost only 96 min. Samples from 15 WBS patients and 12 healthy individuals were genotyped by this method in a blinded fashion, and the sensitivity and specificity was 100% (95% CI, 0.80-1, and 95% CI, 0.75-1, respectively) which was proved by CytoScan HD array.
CONCLUSIONS: The HRM assay we developed is an rapid, inexpensive, and highly accurate method for genotyping 7q11.23 deletion. It is potentially useful in the clinical diagnosis of WBS.