Gonadotropin administration to mimic mini-puberty in hypogonadotropic males: pump or injections?

Objective: Newborns with congenital hypogonadotropic hypogonadism (CHH) have an impaired postnatal activation of the gonadotropic axis. Substitutive therapy with recombinant gonadotropins can be proposed to mimic physiological male mini-puberty during the first months of life. The aim of this study was to co mpare the clinical and biological efficacy of two treatment modalities of gonadotropins administration during mini-puberty in CHH neonates. Design: Multicenter retrospective analytical epidemiological study comparing two treatments, pump vs injection, between 2004 and 2019. Methods: Clinical (penile size, testis size, testicular descent) and biological parameters (serum concentrations of testosterone, anti-Müllerian hormone (AMH) and Inhibin B) were compared between the two groups by multivariate analyses. Results: Thirty-five patients were included. A significantly higher incre ase in penile length and testosterone level was observed in the injection group compared to the pump group (+0.16 ± 0.02 mm vs +0.10 ± 0.02 mm per day, P = 0.002; and +0.04 ± 0.007 ng/mL vs +0.01 ± 0.008 ng/mL per day, P = 0.001). In both groups, significant increases in penile length and width, testosterone, AMH, and Inhibin B levels were observed, as well as improved testicular descent (odds ratio of not being in a scrotal position at the end of treatment = 0.97 (0.96; 0.99)). Conclusions: Early postnatal administration of recombinant gonadotropins in CHH boys is effective in stimulating penile growth, Sertoli cell proliferati on, and testicular descent, with both treatment modalities

Maxillary shape after primary cleft closure and before alveolar bone graft in two different management protocols: A comparative morphometric study

AIM AND SCOPE: Result assessment in cleft surgery is a technical challenge and requires the development of dedicated morphometric tools. Two cohorts of patients managed according to two different protocols were assessed at similar ages and their palatal shape was compared using geometric morphometrics. MATERIAL AND METHODS: Ten patients (protocol No. 1) benefited from early lip closure (1-3 months) and secondary combined soft and hard palate closure (6-9 months); 11 patients (protocol No. 2) benefited from later combined lip and soft palate closure (6 months) followed by hard palate closure (18 months). Cone-Beam Computed Tomography (CBCT) images were acquired at 5 years of age and palatal shapes were compared between protocols No. 1 and No. 2 using geometric morphometrics. RESULTS: Protocols No. 1 and No. 2 had a significantly different timing in their surgical steps but were assessed at a similar age (5 years). The inter-canine distance was significantly narrower in protocol No. 1. Geometric morphometrics showed that the premaxillary region was located more inferiorly in protocol No. 1. CONCLUSION: Functional approaches to cleft surgery (protocol No. 2) allow obtaining larger inter-canine distances and more anatomical premaxillary positions at 5 years of age when compared to protocols involving early lip closure (protocol No. 1). This is the first study comparing the intermediate results of two cleft management protocols using 3D CBCT data and geometric morphometrics. Similar assessments at the end of puberty are required in order to compare the long-term benefits of functional protocols

AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes

IntroductionMandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes.MethodsThe training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set.ResultsWe trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] (p < 0.001) and an AUC (Area Under the Curve) of 0.975 within controls (binary classification). Balanced accuracies were 0.811 [0.648–0.920] (p = 0.002) in a first multiclass design (MFDM vs. controls and differential diagnoses) and 0.813 [0.544–0.960] (p = 0.003) in a second multiclass design (MFDM vs. differential diagnoses).ConclusionThis is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers

Evaluation of Electric Scooter Head and Neck Injuries in Paris, 2017-2019

International audienc

Oral migration of

We report an autochthonous case of oral dirofilariasis in a 46-year-old female patient exposed in South-Eastern France. The patient first presented eyelid creeping dermatitis of one-week duration, then a sub-mucosal nodule appeared in the cheek. The entire nodule was removed surgically. Histologically, the nodule appeared as inflammatory tissue in which a worm was seen. The molecular analysis, based on cox1 and 12S sequences, identified Dirofilaria repens. Ivermectin treatment was given prior to diagnosis, while taking into consideration the most common causes of creeping dermatitis, but treatment was ineffective. The oral form of dirofilariasis is uncommon and could lead to diagnostic wandering

Intentional craniofacial remodelling in Europe in the XIXth century: Quantitative evidence of soft tissue modifications from Toulouse, France

International audienc

Next generation phenotyping for diagnosis and phenotype–genotype correlations in Kabuki syndrome

Abstract The field of dysmorphology has been changed by the use Artificial Intelligence (AI) and the development of Next Generation Phenotyping (NGP). The aim of this study was to propose a new NGP model for predicting KS (Kabuki Syndrome) on 2D facial photographs and distinguish KS1 (KS type 1, KMT2D-related) from KS2 (KS type 2, KDM6A-related). We included retrospectively and prospectively, from 1998 to 2023, all frontal and lateral pictures of patients with a molecular confirmation of KS. After automatic preprocessing, we extracted geometric and textural features. After incorporation of age, gender, and ethnicity, we used XGboost (eXtreme Gradient Boosting), a supervised machine learning classifier. The model was tested on an independent validation set. Finally, we compared the performances of our model with DeepGestalt (Face2Gene). The study included 1448 frontal and lateral facial photographs from 6 centers, corresponding to 634 patients (527 controls, 107 KS); 82 (78%) of KS patients had a variation in the KMT2D gene (KS1) and 23 (22%) in the KDM6A gene (KS2). We were able to distinguish KS from controls in the independent validation group with an accuracy of 95.8% (78.9–99.9%, p < 0.001) and distinguish KS1 from KS2 with an empirical Area Under the Curve (AUC) of 0.805 (0.729–0.880, p < 0.001). We report an automatic detection model for KS with high performances (AUC 0.993 and accuracy 95.8%). We were able to distinguish patients with KS1 from KS2, with an AUC of 0.805. These results outperform the current commercial AI-based solutions and expert clinicians

Table1_AI-based diagnosis in mandibulofacial dysostosis with microcephaly using external ear shapes.docx

IntroductionMandibulo-Facial Dysostosis with Microcephaly (MFDM) is a rare disease with a broad spectrum of symptoms, characterized by zygomatic and mandibular hypoplasia, microcephaly, and ear abnormalities. Here, we aimed at describing the external ear phenotype of MFDM patients, and train an Artificial Intelligence (AI)-based model to differentiate MFDM ears from non-syndromic control ears (binary classification), and from ears of the main differential diagnoses of this condition (multi-class classification): Treacher Collins (TC), Nager (NAFD) and CHARGE syndromes.MethodsThe training set contained 1,592 ear photographs, corresponding to 550 patients. We extracted 48 patients completely independent of the training set, with only one photograph per ear per patient. After a CNN-(Convolutional Neural Network) based ear detection, the images were automatically landmarked. Generalized Procrustes Analysis was then performed, along with a dimension reduction using PCA (Principal Component Analysis). The principal components were used as inputs in an eXtreme Gradient Boosting (XGBoost) model, optimized using a 5-fold cross-validation. Finally, the model was tested on an independent validation set.ResultsWe trained the model on 1,592 ear photographs, corresponding to 1,296 control ears, 105 MFDM, 33 NAFD, 70 TC and 88 CHARGE syndrome ears. The model detected MFDM with an accuracy of 0.969 [0.838–0.999] (p ConclusionThis is the first AI-based syndrome detection model in dysmorphology based on the external ear, opening promising clinical applications both for local care and referral, and for expert centers.</p