P080 Acroosteolysis: a rare complication or a co-occurrence with Juvenile Idiopathic Arthritis?

Background Acroosteolysis refers to a destructive process involving the distal phalanges of the fingers and toes that may extend to metacarpals or metatarsals. Rarely idiopathic, the diagnosis of primary acroosteolysis requires ruling out other causes. Juvenile idiopathic arthritis is an exceptional aetiology of acroosteolysis occurring mainly in psoriatic arthritis. Here by a case of juvenile idiopathic arthritis associated with acroosteolysis of the toes. Methods A 13-year-old girl with no past medical history, presented to our department of rheumatology with oligoarthritis affecting both wrists and knees. She had no familiar history of psoriasis nor rheumatic diseases. She described a dull ache and recurring swelling of knees evolving for >6 years associated with a macular rash of the chest without fever. On examination, the knees were swollen with a limited range of motion of < 90°. Examination of the spine and sacroiliac joints was unremarkable. There was no deformity, no dysmorphic syndrome nor ligamentous hyper laxity. The mucocutaneous examination was normal. Similarly, there was no hepatosplenomegaly or swollen lymph nodes. Laboratory investigations showed high acute phase reactants and normal blood count. Rheumatoid factor, anti-cyclic citrullinated peptide antibodies and antinuclear antibodies were also negative. Besides, she was negative for HLAB-27. Ophthalmic examination did not show any sequelae of uveitis. Results Plain radiograph of the feet revealed bone resorption of the second and fifth distal phalanges without signs of reconstruction. Other secondary causes of acroosteolysis were ruled out. The diagnosis of oligoarticular juvenile idiopathic arthritis was made. In view of the involvement of the distal phalanges, the phenotype of psoriatic arthritis was probable. The patient was initially treated with non-steroidal anti-inflammatory drugs as well as intraarticular injections of corticosteroids in knees. As the flares persisted, she was put on Methotrexate at a dosage of 15 mg per week with marked clinical improvement. Conclusion Our case illustrates a possible occurrence of acroosteolysis of the feet in the field of an active juvenile idiopathic arthritis. It is important to rule out other causes and make a rapid diagnosis in order to ensure appropriate management decisions.

Psychology of Hair Loss Patients and Importance of Counseling

Androgenetic alopecia (AGA) is highly prevalent in society, affecting both men and women. More than the sociological meaning of hair loss, it has become a very important part of self-identity or “body image.” A psychological concept of body image refers to one's thoughts, feelings, perceptions, and behavioral changes related to one's physical looks. In spite of alopecia's common occurrence, it often leads to psychological disturbance and distress. Hair thinning and perceived hair loss also has a very important negative impact on the psyche of the individual. The common emotional aspects associated are self-consciousness, embarrassment, frustration, and jealousy. Knowledge of these effects among the clinicians managing hair loss patients is beneficial. The clinician must make an active effort to identify the borderline group of patients with body dysmorphic syndrome so as to manage them with psychotherapeutic medication for their hair loss prior to hair transplantation. This article aims to provide important information and an understanding of how the psychology gets affected due to hair loss, particularly AGA and its management to the practicing hair transplant surgeons.

The Role of the Reanalysis of Genetic Test Results in the Diagnosis of Dysmorphic Syndrome Caused by Inherited Xq24 Deletion including the UBE2A and CXorf56 Genes

Psychomotor delay, hypotonia, and intellectual disability, as well as heart defects, urogenital malformations, and characteristic cranio-facial dysmorphism are the main symptoms of dysmorphic syndrome associated with intergenic deletion in the Xq24 chromosome region including the UBE2A and CXorf56 genes. To date, there is limited information in the literature about the symptoms and clinical course of the Xq24 deletion. Here, we present a case of Xq24 deletion including the UBE2A and CXorf56 genes in a nine-year-old boy, in whom the array comparative genomic hybridization (array-CGH) and whole exome sequencing (WES) tests were performed in 2015 with normal results. The WES results were reanalyzed in 2019. Intergenic, hemizygous deletion in the Xq24 chromosome region including the UBE2A and CXorf56 genes was revealed and subsequently confirmed in the array-CGH study as the deletion of 35kb in the Xq24 region. Additionally, the carriership of deletion in the mother of the child was confirmed.

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study

Background Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. Objective The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. Methods Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. Results We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). Conclusions DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.

Bartter Syndrome in Children; A Cause of Severe Hypokalemic Metabolic Alkalosis: Clinical Case Report and Literature Review

Bartter syndrome is an inherited renal tubular disorder caused by a defective salt reabsorption in the thick ascending limb of loop of Henle. It characterized by urinary loss of sodium, potassium, and chloride; hypokalemic metabolic alkalosis; normal blood pressure, high plasma levels of renin and aldosterone. There is phenotypical and genetic variability of Bartter syndrome since were identified five genes responsible for five different forms of Bartter syndrome. The objective of this work is to report a clinical case to study the pathophysiological, clinical, biological and therapeutic features of this syndrome. Materials and Methods: We reported a case of 04-month-old male infant admitted for acute dehydration secondary to polyuro-polydipsia syndrome and vomiting. In clinical presentation the patient had a dysmorphic syndrome with triangular face, protruding ears and flattened nasal root. Laboratory tests revealed hypokalemia, hyponatremia, metabolic alkalosis and hypercalciuria. Treatment with indomethacin was started at 1 mg/kg per day with favorable outcome.

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study (Preprint)

BACKGROUND Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt’s quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. OBJECTIVE The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning–based framework for the automated differentiation of DeepGestalt’s output on such images. METHODS Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. RESULTS We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt’s high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt’s syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt’s top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; <i>P</i>&lt;.001). A linear SVM running on DeepGestalt’s result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; <i>P</i>&lt;.001). CONCLUSIONS DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt’s results and may help enhance it and similar computer-aided facial phenotyping tools.

Psychomotor Retardation, Low Set Ears, Retrognathia, Facial Dysmorphism and Schizencephaly: A New Dysmorphic Syndrome

Background: Schizencephaly is a rare primary congenital brain defect of heterogeneous nature resulting from abnormal morphogenesis with a very early disruption of the grey matter migration during embryogenesis. Braga et al (2018) reviewed 156 articles including 734 patients with schizencephaly, and none of them had facial dysmorphism, low set ears or micrognathia Patients and methods A dysmorphic male infant who was referred to the neuropsychiatric consultation clinic of the Children Teaching hospital of Baghdad medical city was studied. Results Four month male infant presented with psychomotor retardation with no interaction with the mother and no recognition of her face. He had low set ears, retrognathia, and facial dysmorphism with narrow and upslanting palpebral fissures and thin upper lips. Family history was negative for a similar condition. Brain CT-scan showed open limb bilateral schizencephaly and karyotype showed normal finding. Conclusion: A new dysmorphic syndrome associated with schizencephaly, facial dysmorphism, low set ears and micrognathia is reported.

Mixed Phenotype of Langer–Giedion's and Cornelia de Lange's Syndromes in an 8q23.3-q24.1 Microdeletion without TRPS1 Deletion

Langer–Giedion's syndrome (LGS) or trichorhinophalangeal syndrome type II (TRPS II; MIM:150230) is a contiguous gene deletion syndrome caused by the haploinsufficiency of the TRPS1 and EXT1 genes. Cornelia de Lange's syndrome (CdLS) is a genetically heterogeneous dysmorphic syndrome where heterozygous mutations of RAD21 gene have been associated with a mild clinical presentation (CDLS type 4; MIM: 614701). We report a female patient with a 2.3-Mb interstitial deletion at 8q23.3-q24.1 encompassing EXT1 and RAD21 genes but not TRPS1. Clinical findings in this patient are correlated with a mixed phenotype of LGS and CdLS type 4.