Dataset Information

A Large Turkish Cohort of Williams Syndrome: The Evaluation of Facial, Cardiovascular, and Neuropsychiatric Features.

ABSTRACT:

Objective

Williams syndrome is caused by a microdeletion at 7q11.23 and is characterized by a distinctive face, cardiovascular disease, and intellectual disability with a specific cognitive and behavioral profile. This study aims to evaluate the clinical features and obtain important information that can guide early diagnoses and correct follow-up.

Materials and methods

The study included 78 patients whose diagnoses were confirmed by fluorescent in situ hybridization. Facial features, anthropometric measurements, and neurocognitive, endocrine, and urinary system evaluations were obtained from the medical records, and photographs of the patients were evaluated retrospectively.

Results

The most common complaints at admission were cardiovascular disease and atypical face. The mean age at admission was 39 ± 4.8 months. The mean age of patients presenting with atypical face was 41 ± 5.6 months, while it was 11 ± 3.1 months in patients presenting with cardiovascular disease. Short nose/bulbous nasal type with anteverted nares and periorbital fullness, which are diagnostic facial features, were present in all patients in the infantile/ early childhood period. 80% of the patients had cardiovascular disease; supravalvular aortic stenosis (53.8%) and peripheral pulmonary artery stenosis (41%) were the most common cardiac anomalies.Intel lectu al/de velop menta l disability was present in 75.6% of the patients. Behavioral disorders including autism spectrum disorder and attention deficit hyperactivity disorder were detected in 50% of our patients. Hypersensitivity to loud and/or sudden sounds was present in all patients.

Conclusion

We highlighted that recognition of facial findings is important for early diagnosis, especially in patients without cardiovascular disease. The frequency of cardiovascular, endocrinological, renal anomalies, and intellectual disab ility /deve lopme ntal delay was described that provide valuable information in the follow-up of patients.

SUBMITTER: Bozlak S

PROVIDER: S-EPMC10081020 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Large Turkish Cohort of Williams Syndrome: The Evaluation of Facial, Cardiovascular, and Neuropsychiatric Features.

Bozlak Serdar S Uludağ Alkaya Dilek D Kasap Büşra B Yüksel Ülker Aylin A Yıldız Ceren Ayça CA Altındağ Veysel V Ülkersoy İpek İ Çifçi Sunamak Evrim E Dedeoğlu Reyhan R Tüysüz Beyhan B

Turkish archives of pediatrics 20230301 2

<h4>Objective</h4>Williams syndrome is caused by a microdeletion at 7q11.23 and is characterized by a distinctive face, cardiovascular disease, and intellectual disability with a specific cognitive and behavioral profile. This study aims to evaluate the clinical features and obtain important information that can guide early diagnoses and correct follow-up.<h4>Materials and methods</h4>The study included 78 patients whose diagnoses were confirmed by fluorescent in situ hybridization. Facial featu ...[more]

PMID: 36856356

Similar Datasets

Project description:Objectives: Pediatric acute-onset neuropsychiatric syndrome (PANS) is a descriptive clinical entity defined by the abrupt onset of psychiatric and somatic symptoms leading to significant loss of function. Data on well-characterized PANS patients are limited, biomarkers have yet to be identified, and a solid evidence base to guide treatment is lacking. In this study, we present our experience of a systematic evaluation of the first 45 patients included in a Swedish cohort. Methods: During the period 2014-2018, our clinic received 100 referrals regarding suspected PANS. All patients underwent a standardized psychiatric/medical evaluation by a child/adolescent psychiatrist and a clinical psychologist or a nurse. Those with severe symptoms were also assessed by a pediatric neurologist and a pediatric rheumatologist. Laboratory tests were obtained at different time points in an attempt to capture an active disease state. Results: Of the 100 referrals, 45 met strict PANS criteria and consented to participate in a long-term follow-up study. The median age at intake was 7.2 years (range 3.0-13.1) and 56% were male. Ninety-three percent fulfilled both criteria for acute/atypical onset of PANS symptoms and having had an infection in relation to onset. Sixteen percent had an onset of an autoimmune or inflammatory disorder in temporal relation to the onset of PANS-related symptoms. The most common onset symptoms were obsessive-compulsive disorder (89%), anxiety (78%), and emotional lability (71%). Twenty-four percent had a preexisting autoimmune disease (AD) and 18% a preexisting psychiatric/neuropsychiatric diagnosis. Sixty-four percent of biological relatives had at least one psychiatric disorder and 76% at least one AD or inflammatory disorder. Complement activation (37%), leukopenia (20%), positive antinuclear antibodies (17%), and elevated thyroid antibodies (11%) were the most common laboratory findings. Conclusions: In our PANS cohort, there was a strong indication of an association with AD. Further work is needed to establish whether any of the potential biomarkers identified will be clinically useful. Long-term follow-up of these patients using the Swedish national registers will enable a deeper understanding of the course of this patient group.

Project description: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.

Dataset Information

A Large Turkish Cohort of Williams Syndrome: The Evaluation of Facial, Cardiovascular, and Neuropsychiatric Features.

Objective

Materials and methods

Results

Conclusion

Publications

A Large Turkish Cohort of Williams Syndrome: The Evaluation of Facial, Cardiovascular, and Neuropsychiatric Features.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets