The advancement of genetic knowledge and the discovery of an increasing number of genetic disorders has made the role of the geneticist progressively more complex and fundamental. However, most genetic disorders present during childhood; thus, their early recognition is a challenge for the pediatrician, who will be also involved in the follow-up of these children, often establishing a close relationship with them and their families and becoming a referral figure. In this review, we aim to provide the pediatrician with a general knowledge of the approach to treating a child with a genetic syndrome associated with dysmorphic features. We will discuss the red flags, the most common manifestations, the analytic collection of the family and personal medical history, and the signs that should alert the pediatrician during the physical examination. We will offer an overview of the physical malformations most commonly associated with genetic defects and the way to describe dysmorphic facial features. We will provide hints about some tools that can support the pediatrician in clinical practice and that also represent a useful educational resource, either online or through apps downloaded on a smartphone. Eventually, we will offer an overview of genetic testing, the ethical considerations, the consequences of incidental findings, and the main indications and limitations of the principal technologies.

BACKGROUND: Noonan syndrome (NS) is a rare genetic disease, and patients who suffer from it exhibit a facial morphology that is characterized by a high forehead, hypertelorism, ptosis, inner epicanthal folds, down-slanting palpebral fissures, a highly arched palate, a round nasal tip, and posteriorly rotated ears. Facial analysis technology has recently been applied to identify many genetic syndromes (GSs). However, few studies have investigated the identification of NS based on the facial features of the subjects.
OBJECTIVE: This study develops advanced models to enhance the accuracy of diagnosis of NS.
METHODS: A total of 1,892 people were enrolled in this study, including 233 patients with NS, 863 patients with other GSs, and 796 healthy children. We took one to 10 frontal photos of each subject to build a dataset, and then applied the multi-task convolutional neural network (MTCNN) for data pre-processing to generate standardized outputs with five crucial facial landmarks. The ImageNet dataset was used to pre-train the network so that it could capture generalizable features and minimize data wastage. We subsequently constructed seven models for facial identification based on the VGG16, VGG19, VGG16-BN, VGG19-BN, ResNet50, MobileNet-V2, and squeeze-and-excitation network (SENet) architectures. The identification performance of seven models was evaluated and compared with that of six physicians.
RESULTS: All models exhibited a high accuracy, precision, and specificity in recognizing NS patients. The VGG19-BN model delivered the best overall performance, with an accuracy of 93.76%, precision of 91.40%, specificity of 98.73%, and F1 score of 78.34%. The VGG16-BN model achieved the highest AUC value of 0.9787, while all models based on VGG architectures were superior to the others on the whole. The highest scores of six physicians in terms of accuracy, precision, specificity, and the F1 score were 74.00%, 75.00%, 88.33%, and 61.76%, respectively. The performance of each model of facial recognition was superior to that of the best physician on all metrics.
CONCLUSIONS: Models of computer-assisted facial recognition can improve the rate of diagnosis of NS. The models based on VGG19-BN and VGG16-BN can play an important role in diagnosing NS in clinical practice.

Polydactyly-myopia syndrome is a rare genetic condition characterized by the co-occurrence of polydactyly and myopia. Herein, we present the case of a 28-year-old Muslim male, born of consanguineous parents, who presented with complaints of diminished vision since childhood. Ophthalmologic examination revealed severe myopia with characteristic fundus changes indicative of high myopia. Additionally, the patient exhibited polydactyly in all limbs, with a positive family history of both polydactyly and myopia. This case underscores the importance of recognizing and managing rare syndromes to provide appropriate genetic counseling and clinical care. Further research is warranted to elucidate the underlying genetic mechanisms and optimize therapeutic strategies for polydactyly-myopia syndrome. Awareness of this syndrome among healthcare providers is essential to facilitate early diagnosis and intervention for affected individuals and their families.

Despite having the same underlying genetic etiology, individuals with the same syndromic form of intellectual developmental disability (IDD) show a large degree of interindividual differences in cognition and IQ. Research indicates that up to 80% of the variation in IQ scores among individuals with syndromic IDDs is attributable to nongenetic effects, including social-environmental factors. In this narrative review, we summarize evidence of the influence that factors related to economic stability (focused on due to its prevalence in existing literature) have on IQ in individuals with syndromic IDDs. We also highlight the pathways through which economic stability is hypothesized to impact cognitive development and drive individual differences in IQ among individuals with syndromic IDDs. We also identify broader social-environmental factors (e.g., social determinants of health) that warrant consideration in future research, but that have not yet been explored in syndromic IDDs. We conclude by making recommendations to address the urgent need for further research into other salient factors associated with heterogeneity in IQ. These recommendations ultimately may shape individual- and community-level interventions and may inform systems-level public policy efforts to promote the cognitive development of and improve the lived experiences of individuals with syndromic IDDs.

BACKGROUND: While characteristic facial features provide important clues for finding the correct diagnosis in genetic syndromes, valid assessment can be challenging. The next-generation phenotyping algorithm DeepGestalt analyzes patient images and provides syndrome suggestions. GestaltMatcher matches patient images with similar facial features. The new D-Score provides a score for the degree of facial dysmorphism.
OBJECTIVE: We aimed to test state-of-the-art facial phenotyping tools by benchmarking GestaltMatcher and D-Score and comparing them to DeepGestalt.
METHODS: Using a retrospective sample of 4796 images of patients with 486 different genetic syndromes (London Medical Database, GestaltMatcher Database, and literature images) and 323 inconspicuous control images, we determined the clinical use of D-Score, GestaltMatcher, and DeepGestalt, evaluating sensitivity; specificity; accuracy; the number of supported diagnoses; and potential biases such as age, sex, and ethnicity.
RESULTS: DeepGestalt suggested 340 distinct syndromes and GestaltMatcher suggested 1128 syndromes. The top-30 sensitivity was higher for DeepGestalt (88%, SD 18%) than for GestaltMatcher (76%, SD 26%). DeepGestalt generally assigned lower scores but provided higher scores for patient images than for inconspicuous control images, thus allowing the 2 cohorts to be separated with an area under the receiver operating characteristic curve (AUROC) of 0.73. GestaltMatcher could not separate the 2 classes (AUROC 0.55). Trained for this purpose, D-Score achieved the highest discriminatory power (AUROC 0.86). D-Score\'s levels increased with the age of the depicted individuals. Male individuals yielded higher D-scores than female individuals. Ethnicity did not appear to influence D-scores.
CONCLUSIONS: If used with caution, algorithms such as D-score could help clinicians with constrained resources or limited experience in syndromology to decide whether a patient needs further genetic evaluation. Algorithms such as DeepGestalt could support diagnosing rather common genetic syndromes with facial abnormalities, whereas algorithms such as GestaltMatcher could suggest rare diagnoses that are unknown to the clinician in patients with a characteristic, dysmorphic face.

Individuals with Kabuki syndrome type 1 (KS1) often have hearing loss recognized in middle childhood. Current clinical dogma suggests that this phenotype is caused by frequent infections due to the immune deficiency in KS1 and/or secondary to structural abnormalities of the ear. To clarify some aspects of hearing loss, we collected information on hearing status from 21 individuals with KS1 and found that individuals have both sensorineural and conductive hearing loss, with the average age of presentation being 7 years. Our data suggest that while ear infections and structural abnormalities contribute to the observed hearing loss, these factors do not explain all loss. Using a KS1 mouse model, we found hearing abnormalities from hearing onset, as indicated by auditory brainstem response measurements. In contrast to mouse and human data for CHARGE syndrome, a disorder possessing overlapping clinical features with KS and a well-known cause of hearing loss and structural inner ear abnormalities, there are no apparent structural abnormalities of the cochlea in KS1 mice. The KS1 mice also display diminished distortion product otoacoustic emission levels, which suggests outer hair cell dysfunction. Combining these findings, our data suggests that KMT2D dysfunction causes sensorineural hearing loss compounded with external factors, such as infection.

Perceptual and statistical evidence has highlighted voice characteristics of individuals affected by genetic syndromes that differ from those of normophonic subjects. In this paper, we propose a procedure for systematically collecting such pathological voices and developing AI-based automated tools to support differential diagnosis. Guidelines on the most appropriate recording devices, vocal tasks, and acoustical parameters are provided to simplify, speed up, and make the whole procedure homogeneous and reproducible. The proposed procedure was applied to a group of 56 subjects affected by Costello syndrome (CS), Down syndrome (DS), Noonan syndrome (NS), and Smith-Magenis syndrome (SMS). The entire database was divided into three groups: pediatric subjects (PS; individuals < 12 years of age), female adults (FA), and male adults (MA). In line with the literature results, the Kruskal-Wallis test and post hoc analysis with Dunn-Bonferroni test revealed several significant differences in the acoustical features not only between healthy subjects and patients but also between syndromes within the PS, FA, and MA groups. Machine learning provided a k-nearest-neighbor classifier with 86% accuracy for the PS group, a support vector machine (SVM) model with 77% accuracy for the FA group, and an SVM model with 84% accuracy for the MA group. These preliminary results suggest that the proposed method based on acoustical analysis and AI could be useful for an effective, non-invasive automatic characterization of genetic syndromes. In addition, clinicians could benefit in the case of genetic syndromes that are extremely rare or present multiple variants and facial phenotypes.

Dental mesenchymal stem cells (DMSCs) are multipotent progenitor cells that can differentiate into multiple lineages including odontoblasts, osteoblasts, chondrocytes, neural cells, myocytes, cardiomyocytes, adipocytes, endothelial cells, melanocytes, and hepatocytes. Odontoblastic differentiation of DMSCs is pivotal in dentinogenesis, a delicate and dynamic process regulated at the molecular level by signaling pathways, transcription factors, and posttranscriptional and epigenetic regulation. Mutations or dysregulation of related genes may contribute to genetic diseases with dentin defects caused by impaired odontoblastic differentiation, including tricho-dento-osseous (TDO) syndrome, X-linked hypophosphatemic rickets (XLH), Raine syndrome (RS), hypophosphatasia (HPP), Schimke immuno-osseous dysplasia (SIOD), and Elsahy-Waters syndrome (EWS). Herein, recent progress in the molecular regulation of the odontoblastic differentiation of DMSCs is summarized. In addition, genetic syndromes associated with disorders of odontoblastic differentiation of DMSCs are discussed. An improved understanding of the molecular regulation and related genetic syndromes may help clinicians better understand the etiology and pathogenesis of dentin lesions in systematic diseases and identify novel treatment targets.

The aim of our study was to investigate the causes of fetal growth <10th centile diagnosed <26 weeks\' gestation in singleton pregnancies and compare pregnancy outcomes in relation to the identified etiology.
Historical cohort study conducted in two Italian hospitals which included all small-for-gestational-age fetuses diagnosed between 18+0 and 26+0 weeks over a 10-year period. Fetuses were divided into three groups depending on the prenatally suspected etiology: chromosomal abnormalities (Group 1), malformations (Group 2) and isolated (Group 3). These groups were compared regarding pregnancy outcomes. Fetuses in Group 3 were divided into small-for-gestational-age and fetal growth restriction following the Delphi Consensus criteria and the outcomes were further compared. Fisher\'s Exact or Mann-Whitney test were used for comparison of groups.
In all, 435 fetuses were included. Of these, 20 cases (4.6%) were associated with chromosomal abnormalities (Group 1), 98 (22.5%) with fetal malformations (Group 2) and 317 (72.9%) were isolated (Group 3). A higher percentage of live births was reported for Group 3 (P < 0.001). Termination of pregnancy was more common in Group 1 (P < 0.001). No differences in gestational age at delivery, birthweight, intrauterine death or neonatal death were detected within groups. Growth-restricted fetuses had lower gestational age at delivery, birthweight and number of live births (P < 0.001), higher rates of termination of pregnancy, intrauterine death (P < 0.001) and neonatal death <10 days (P = 0.002) compared to small-for-gestational-age. In 17 cases a chromosomal abnormality, genetic syndrome or adverse neurological outcome was diagnosed after birth: six from Group 2 (11.3% of live births in this group) and 11 from Group 3 (4.3%).
We report that fetal growth <10th percentile diagnosed before 26 weeks is not isolated before birth in 27% of cases. Malformations and chromosomal abnormalities are common etiologies; therefore, detailed anomaly scans and invasive testing should be offered. In addition, there is a residual risk of neonatal death and postnatal diagnosis of a genetic syndrome or neurodevelopmental impairment despite normal prenatal tests. These results expand the small amount of information on the outcome of cases with very early diagnosis of impaired fetal growth currently available and highlight the importance of detailed counseling with couples.

Alpha-fetoprotein (AFP) is a protein commonly found during fetal development, but its role extends beyond birth. Throughout the first year of life, AFP levels can remain high, which can potentially mask various conditions from the neurological, metabolic, hematological, endocrine, and early childhood cancer groups. Although AFP reference values and clinical utility have been established in adults, evaluating AFP levels in children during the diagnostic process, treatment, and post-treatment surveillance is still associated with numerous diagnostic pitfalls. These challenges arise from the presence of physiologically elevated AFP levels, inconsistent data obtained from different laboratory tests, and the limited population of children with oncologic diseases that have been studied. To address these issues, it is essential to establish updated reference ranges for AFP in this specific age group. A population-based study involving a statistically representative group of patients could serve as a valuable solution for this purpose.