Objective burden and emotional distress in parents of children with type 1 diabetes.

INTRODUCTION: The management of childhood type 1 diabetes requires the active participation of parents. The aim of the present study was to describe the main characteristics of parents of children with type 1 diabetes, including objective burden regarding time spent on diabetes care, emotional distress (exhaustion, need for respite, quality of life), and symptoms of depression as well as anxiety. METHODS: In this observational study, parents of children with type 1 diabetes completed a questionnaire, anonymously. Different questions were asked to the parent about the objective burden of diabetes and its repercussion, their exhaustion and need for respite. Two validated instruments (HADS, WHOQOL-BREF) have been integrated into the questionnaire. RESULTS: Eighty eight parents were included in the study. Among them, 76 (86%) were mothers. All the parents with a child aged 6 years or younger (10/10) reported having to take care of their child's diabetes twice or more a day, this was the case for 37/39 (94.9%) parents of children aged 7 to 13, and for 16/36 (44.4%) of parents of children aged 14 years or above. In the total population, 33/86 (38.4%) parents declared getting up every night because of their child's diabetes. The median daily time spent on diabetes management was 40 minutes. There were 54 parents (62.8%) who reported moderate-strong exhaustion, and 27 (30.7%) who expressed a moderate-strong need for respite. Regarding the result of the HADS, 46 parents (55.4%) reported symptoms of anxiety and/or depression. DISCUSSION/CONCLUSION: Parents of children with type 1 diabetes must carry out multiple daily care, at all times of day and night. Their emotional state can be impacted with, in particular, a risk of exhaustion. Screening for these difficulties should be a part of the overall management of a child with type 1 diabetes and his family to limit various complications.

Effect of Recombinant Gonadotropin on Testicular Function and Testicular Sperm Extraction in Five Cases of NR0B1 (DAX1) Pathogenic Variants.

Background: NR0B1 pathogenic variants can cause congenital adrenal hypoplasia or primary adrenal insufficiency in early childhood usually associated with hypogonadotropic hypogonadism. NR0B1 is necessary for organogenesis of the adrenal cortex and to maintain normal spermatogenesis. In humans, restoration of fertility in patients carrying NR0B1 pathogenic variants is challenging. Objective: The aim of the study was to investigate the clinical, hormonal, histological, spermiological, and molecular genetic characteristics of a cohort of patients with NR0B1 pathogenic variants, monitored for fertility preservation. Patients: We included five patients, including four teenagers, with NR0B1 pathogenic or likely pathogenic variants. They all had primary adrenal insufficiency and were receiving replacement therapy with glucocorticoids and mineralocorticoids. Patients received recombinant follicle-stimulating hormone and recombinant human chorionic gonadotropin in order to induce spermatogenesis. Combined gonadotropin treatment was initiated between 13 years and 15 years and 6 months for the four teenagers and at 31 years and 2 months for the only adult. Physical and hormonal assessments were performed just before starting gonadotropin treatment. After 12 months of gonadotropin treatment, physical examination and hormonal assessments were repeated, and semen analyses were performed. If no sperm cells were observed in at least 2 semen collections at 3-month interval, testicular biopsy for testicular sperm extraction was proposed. Results: Bilateral testicular volume increased from 8 ml (interquartile range, 6-9) to 12 ml (10-16) after gonadotropin treatment. Inhibin B levels were relatively stable: 110 ng/L (46-139) before and 91 ng/L (20-120) at the end of gonadotropin treatment. Azoospermia was observed in all semen analyses for all cases during gonadotropin treatment. Three patients agreed to testicular biopsy; no mature sperm cells could be retrieved in any. Conclusion: We characterized a cohort of patients with NR0B1 pathogenic or likely pathogenic variants for fertility preservation by recombinant gonadotropin treatment, which began either at puberty or in adulthood. No sperm cells could be retrieved in semen samples or testicular biopsy even after gonadotropin treatment, indicating that gonadotropin treatment, even when started at puberty, is ineffective for restoring fertility.

Semi-Automatic Spectral Image Stitching for a Compact Hybrid Linescan Hyperspectral Camera towards Near Field Remote Monitoring of Potato Crop Leaves.

The miniaturization of hyperspectral cameras has opened a new path to capture spectral information. One such camera, called the hybrid linescan camera, requires accurate control of its movement. Contrary to classical linescan cameras, where one line is available for every band in one shot, the latter asks for multiple shots to fill a line with multiple bands. Unfortunately, the reconstruction is corrupted by a parallax effect, which affects each band differently. In this article, we propose a two-step procedure, which first reconstructs an approximate datacube in two different ways, and second, performs a corrective warping on each band based on a multiple homography framework. The second step combines different stitching methods to perform this reconstruction. A complete synthetic and experimental comparison is performed by using geometric indicators of reference points. It appears throughout the course of our experimentation that misalignment is significantly reduced but remains non-negligible at the potato leaf scale.

Transforming obstetric ultrasound into data science using eye tracking, voice recording, transducer motion and ultrasound video.

Ultrasound is the primary modality for obstetric imaging and is highly sonographer dependent. Long training period, insufficient recruitment and poor retention of sonographers are among the global challenges in the expansion of ultrasound use. For the past several decades, technical advancements in clinical obstetric ultrasound scanning have largely concerned improving image quality and processing speed. By contrast, sonographers have been acquiring ultrasound images in a similar fashion for several decades. The PULSE (Perception Ultrasound by Learning Sonographer Experience) project is an interdisciplinary multi-modal imaging study aiming to offer clinical sonography insights and transform the process of obstetric ultrasound acquisition and image analysis by applying deep learning to large-scale multi-modal clinical data. A key novelty of the study is that we record full-length ultrasound video with concurrent tracking of the sonographer's eyes, voice and the transducer while performing routine obstetric scans on pregnant women. We provide a detailed description of the novel acquisition system and illustrate how our data can be used to describe clinical ultrasound. Being able to measure different sonographer actions or model tasks will lead to a better understanding of several topics including how to effectively train new sonographers, monitor the learning progress, and enhance the scanning workflow of experts.

Gene expression signatures predict response to therapy with growth hormone.

Recombinant human growth hormone (r-hGH) is used as a therapeutic agent for disorders of growth including growth hormone deficiency (GHD) and Turner syndrome (TS). Treatment is costly and current methods to model response are inexact. GHD (n = 71) and TS patients (n = 43) were recruited to study response to r-hGH over 5 years. Analysis was performed using 1219 genetic markers and baseline (pre-treatment) blood transcriptome. Random forest was used to determine predictive value of transcriptomic data associated with growth response. No genetic marker passed the stringency criteria for prediction. However, we identified an identical set of genes in both GHD and TS whose expression could be used to classify therapeutic response to r-hGH with a high accuracy (AUC > 0.9). Combining transcriptomic markers with clinical phenotype was shown to significantly reduce predictive error. This work could be translated into a single genomic test linked to a prediction algorithm to improve clinical management. Trial registration numbers: NCT00256126 and NCT00699855.

Knowledge representation and learning of operator clinical workflow from full-length routine fetal ultrasound scan videos.

Ultrasound is a widely used imaging modality, yet it is well-known that scanning can be highly operator-dependent and difficult to perform, which limits its wider use in clinical practice. The literature on understanding what makes clinical sonography hard to learn and how sonography varies in the field is sparse, restricted to small-scale studies on the effectiveness of ultrasound training schemes, the role of ultrasound simulation in training, and the effect of introducing scanning guidelines and standards on diagnostic image quality. The Big Data era, and the recent and rapid emergence of machine learning as a more mainstream large-scale data analysis technique, presents a fresh opportunity to study sonography in the field at scale for the first time. Large-scale analysis of video recordings of full-length routine fetal ultrasound scans offers the potential to characterise differences between the scanning proficiency of experts and trainees that would be tedious and time-consuming to do manually due to the vast amounts of data. Such research would be informative to better understand operator clinical workflow when conducting ultrasound scans to support skills training, optimise scan times, and inform building better user-machine interfaces. This paper is to our knowledge the first to address sonography data science, which we consider in the context of second-trimester fetal sonography screening. Specifically, we present a fully-automatic framework to analyse operator clinical workflow solely from full-length routine second-trimester fetal ultrasound scan videos. An ultrasound video dataset containing more than 200 hours of scan recordings was generated for this study. We developed an original deep learning method to temporally segment the ultrasound video into semantically meaningful segments (the video description). The resulting semantic annotation was then used to depict operator clinical workflow (the knowledge representation). Machine learning was applied to the knowledge representation to characterise operator skills and assess operator variability. For video description, our best-performing deep spatio-temporal network shows favourable results in cross-validation (accuracy: 91.7%), statistical analysis (correlation: 0.98, p < 0.05) and retrospective manual validation (accuracy: 76.4%). For knowledge representation of operator clinical workflow, a three-level abstraction scheme consisting of a Subject-specific Timeline Model (STM), Summary of Timeline Features (STF), and an Operator Graph Model (OGM), was introduced that led to a significant decrease in dimensionality and computational complexity compared to raw video data. The workflow representations were learnt to discriminate between operator skills, where a proposed convolutional neural network-based model showed most promising performance (cross-validation accuracy: 98.5%, accuracy on unseen operators: 76.9%). These were further used to derive operator-specific scanning signatures and operator variability in terms of type, order and time distribution of constituent tasks.

Multimodal Continual Learning with Sonographer Eye-Tracking in Fetal Ultrasound.

Deep networks have been shown to achieve impressive accuracy for some medical image analysis tasks where large datasets and annotations are available. However, tasks involving learning over new sets of classes arriving over extended time is a different and difficult challenge due to the tendency of reduction in performance over old classes while adapting to new ones. Controlling such a 'forgetting' is vital for deployed algorithms to evolve with new arrivals of data incrementally. Usually, incremental learning approaches rely on expert knowledge in the form of manual annotations or active feedback. In this paper, we explore the role that other forms of expert knowledge might play in making deep networks in medical image analysis immune to forgetting over extended time. We introduce a novel framework for mitigation of this forgetting effect in deep networks considering the case of combining ultrasound video with point-of-gaze tracked for expert sonographers during model training. This is used along with a novel weighted distillation strategy to reduce the propagation of effects due to class imbalance.

Spatio-temporal visual attention modelling of standard biometry plane-finding navigation.

We present a novel multi-task neural network called Temporal SonoEyeNet (TSEN) with a primary task to describe the visual navigation process of sonographers by learning to generate visual attention maps of ultrasound images around standard biometry planes of the fetal abdomen, head (trans-ventricular plane) and femur. TSEN has three components: a feature extractor, a temporal attention module (TAM), and an auxiliary video classification module (VCM). A soft dynamic time warping (sDTW) loss function is used to improve visual attention modelling. Variants of the model are trained on a dataset of 280 video clips, each containing one of the three biometry planes and lasting 3-7 seconds, with corresponding real-time recorded gaze tracking data of an experienced sonographer. We report the performances of the different variants of TSEN for visual attention prediction at standard biometry plane detection. The best model performance is achieved using bi-directional convolutional long-short term memory (biCLSTM) in both TAM and VCM, and it outperforms a previous spatial model on all static and dynamic saliency metrics. As an auxiliary task to validate the clinical relevance of the visual attention modelling, the predicted visual attention maps were used to guide standard biometry plane detection in consecutive US video frames. All spatio-temporal TSEN models achieve higher scores compared to a spatial-only baseline; the best performing TSEN model achieves F1 scores on these standard biometry planes of 83.7%, 89.9% and 81.1%, respectively.

Preoperative Topical Estrogen Treatment vs Placebo in 244 Children With Midshaft and Posterior Hypospadias.

PURPOSE: Urethral fistula and dehiscence are common after hypospadias surgery. Preoperative androgens have been considered to reduce these complications although this consideration is not evidence-based. Dermatologists have reported the benefits of topical estrogens on skin healing. We investigated whether the preoperative use of topical promestriene could reduce healing complications in hypospadias surgery. Our primary objective was to demonstrate a reduction of healing complications with promestriene vs placebo. Impact on reoperations and other complications, clinical tolerance, bone growth, and biological systemic effects of the treatment were also considered. METHODS: We conducted a prospective, randomized, placebo-controlled, double-blind, parallel group trial between 2011 and 2015 in 4 French centers. One-stage transverse preputial island flap urethroplasty (onlay urethroplasty) was selected for severe hypospadias. Promestriene or placebo was applied on the penis for 2 months prior to surgery. The primary outcome was the presence of postoperative urethral fistula or dehiscence in the first year postsurgery. For safety reasons, hormonal and anatomical screenings were performed. RESULTS: Out of 241 patients who received surgery, 122 patients were randomized to receive placebo, and 119 patients received promestriene. The primary outcome was unavailable for 11 patients. Healing complications were assessed at 16.4% (19/116) in the placebo vs 14.9% (17/114) in the promestriene arm, and the odds ratio adjusted on center was 0.93 (95% confidence interval 0.45-1.94), P = 0.86. CONCLUSIONS AND RELEVANCE: Although we observed an overall lower risk of complications compared to previous publications, postsurgery complications were not different between promestriene and placebo, because of a lack of power of the study or the inefficacy of promestriene.

Safety Indices of Ultrasound: Adherence to Recommendations and Awareness During Routine Obstetric Ultrasound Scanning.

PURPOSE: To analyze bioeffect safety indices and assess how often operators look at these indices during routine obstetric ultrasound. MATERIALS AND METHODS: Automated analysis of prospectively collected data including video recordings of full-length ultrasound scans coupled with operator eye tracking was performed. Using optical recognition, we extracted the Mechanical Index (MI), Thermal Index in soft tissue (TIs), and Thermal Index in bone (TIb) values and ultrasound mode. This allowed us to report the bioeffect safety indices during routine obstetric scans and assess adherence to professional organization recommendations. Eye-tracking analysis allowed us to assess how often operators look at the displayed bioeffect safety indices. RESULTS: A total of 637 ultrasound scans performed by 17 operators were included, of which 178, 216, and 243 scans were first, second, and third-trimester scans, respectively. During live scanning, the mean and range were 0.14 (0.1 to 3.0) for TIb, 0.2 (0.1 to 1.2) for TIs, and 0.9 (0.1 to 1.3) for MI. The mean and standard deviation of TIb were 0.15 ±â€Š0.03, 0.23 ±â€Š0.09, 0.32 ±â€Š0.24 in the first, second, and third trimester, respectively. For B-mode, the highest TIb was 0.8 in all trimesters. The highest TIb was recorded for pulsed-wave Doppler mode in all trimesters. The recommended exposure times were maintained in all scans. Analysis of eye tracking suggested that operators looked at bioeffect safety indices in only 27 (4.2 %) of the scans. CONCLUSION: In this study, recommended bioeffect indices were adhered to in all routine scans. However, eye tracking showed that operators rarely assessed safety indices during scanning.

Evaluation of Gaze Tracking Calibration for Longitudinal Biomedical Imaging Studies.

Gaze tracking is a promising technology for studying the visual perception of clinicians during image-based medical exams. It could be used in longitudinal studies to analyze their perceptive process, explore human-machine interactions, and develop innovative computer-aided imaging systems. However, using a remote eye tracker in an unconstrained environment and over time periods of weeks requires a certain guarantee of performance to ensure that collected gaze data are fit for purpose. We report the results of evaluating eye tracking calibration for longitudinal studies. First, we tested the performance of an eye tracker on a cohort of 13 users over a period of one month. For each participant, the eye tracker was calibrated during the first session. The participants were asked to sit in front of a monitor equipped with the eye tracker, but their position was not constrained. Second, we tested the performance of the eye tracker on sonographers positioned in front of a cart-based ultrasound scanner. Experimental results show a decrease of accuracy between calibration and later testing of 0.30° and a further degradation over time at a rate of 0.13°. month-1. The overall median accuracy was 1.00° (50.9 pixels) and the overall median precision was 0.16° (8.3 pixels). The results from the ultrasonography setting show a decrease of accuracy of 0.16° between calibration and later testing. This slow degradation of gaze tracking accuracy could impact the data quality in long-term studies. Therefore, the results we present here can help in planning such long-term gaze tracking studies.

Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.

Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architecture design. This is the approach considered in the paper where we propose to combine ultrasound video with point-of-gaze tracked for expert sonographers as they scan to train memory-efficient ultrasound image analysis models. Specifically we develop teacher-student knowledge transfer models for the exemplar task of frame classification for the fetal abdomen, head, and femur. The best performing memory-efficient models attain performance within 5% of conventional models that are 1000× larger in size.

Captioning Ultrasound Images Automatically.

We describe an automatic natural language processing (NLP)-based image captioning method to describe fetal ultrasound video content by modelling the vocabulary commonly used by sonographers and sonologists. The generated captions are similar to the words spoken by a sonographer when describing the scan experience in terms of visual content and performed scanning actions. Using full-length second-trimester fetal ultrasound videos and text derived from accompanying expert voice-over audio recordings, we train deep learning models consisting of convolutional neural networks and recurrent neural networks in merged configurations to generate captions for ultrasound video frames. We evaluate different model architectures using established general metrics (BLEU, ROUGE-L) and application-specific metrics. Results show that the proposed models can learn joint representations of image and text to generate relevant and descriptive captions for anatomies, such as the spine, the abdomen, the heart, and the head, in clinical fetal ultrasound scans.

Ultrasound Image Representation Learning by Modeling Sonographer Visual Attention.

Image representations are commonly learned from class labels, which are a simplistic approximation of human image understanding. In this paper we demonstrate that transferable representations of images can be learned without manual annotations by modeling human visual attention. The basis of our analyses is a unique gaze tracking dataset of sonographers performing routine clinical fetal anomaly screenings. Models of sonographer visual attention are learned by training a convolutional neural network (CNN) to predict gaze on ultrasound video frames through visual saliency prediction or gaze-point regression. We evaluate the transferability of the learned representations to the task of ultrasound standard plane detection in two contexts. Firstly, we perform transfer learning by fine-tuning the CNN with a limited number of labeled standard plane images. We find that fine-tuning the saliency predictor is superior to training from random initialization, with an average F1-score improvement of 9.6% overall and 15.3% for the cardiac planes. Secondly, we train a simple softmax regression on the feature activations of each CNN layer in order to evaluate the representations independently of transfer learning hyper-parameters. We find that the attention models derive strong representations, approaching the precision of a fully-supervised baseline model for all but the last layer.

Transcriptomics and machine learning predict diagnosis and severity of growth hormone deficiency.

BACKGROUND: The effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and random forest analysis. METHODS: Prepubertal treatment-naive children with GHD (n = 98) were enrolled from the PREDICT study, and controls (n = 26) were acquired from online data sets. Whole blood gene expression was correlated with peak growth hormone (GH) using rank regression and a random forest algorithm tested for prediction of the presence of GHD and in classification of GHD as severe (peak GH <4 µg/l) and nonsevere (peak ≥4 µg/l). Performance was assessed using area under the receiver operating characteristic curve (AUC-ROC). RESULTS: Rank regression identified 347 probe sets in which gene expression correlated with peak GH concentrations (r = ± 0.28, P < 0.01). These 347 probe sets yielded an AUC-ROC of 0.95 for prediction of GHD status versus controls and an AUC-ROC of 0.93 for prediction of GHD severity. CONCLUSION: This study demonstrates highly accurate diagnosis and disease classification for GHD using a combination of transcriptomics and random forest analysis. TRIAL REGISTRATION: NCT00256126 and NCT00699855. FUNDING: Merck and the National Institute for Health Research (CL-2012-06-005).

Multi-task SonoEyeNet: Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps.

We present a novel multi-task convolutional neural network called Multi-task SonoEyeNet (M-SEN) that learns to generate clinically relevant visual attention maps using sonographer gaze tracking data on input ultrasound (US) video frames so as to assist standardized abdominal circumference (AC) plane detection. Our architecture consists of a generator and a discriminator, which are trained in an adversarial scheme. The generator learns sonographer attention on a given US video frame to predict the frame label (standardized AC plane / background). The discriminator further fine-tunes the predicted attention map by encouraging it to mimick the ground-truth sonographer attention map. The novel model expands the potential clinical usefulness of a previous model by eliminating the requirement of input gaze tracking data during inference without compromising its plane detection performance (Precision: 96.8, Recall: 96.2, F-1 score: 96.5).

The human bitter taste receptor T2R38 is broadly tuned for bacterial compounds.

T2R38 has been shown to be a specific bacterial detector implicated in innate immune defense mechanism of human upper airway. Several clinical studies have demonstrated that this receptor is associated with the development of chronic rhinosinusitis (CRS). T2R38 was previously reported to bind to homoserine lactones (HSL), quorum sensing molecules specific of Pseudomonas Aeruginosa and other gram negative species. Nevertheless, these bacteria are not the major pathogens found in CRS. Here we report on the identification of bacterial metabolites acting as new agonists of T2R38 based on a single cell calcium imaging study. Two quorum sensing molecules (Agr D1 thiolactone from Staphylococcus Aureus and CSP-1 from Streptococcus Pneumoniae) and a list of 32 bacterial metabolites from pathogens frequently implicated in CRS were tested. First, we observed that HSL failed to activate T2R38 in our experimental system, but that the dimethylsulfoxide (DMSO), used as a solvent for these lactones may, by itself, account for the agonistic effect previously described. Secondly, we showed that both Agr D1 thiolactone and CSP-1 are inactive but that at least 7 bacterial metabolites (acetone, 2-butanone, 2-pentanone, 2-methylpropanal, dimethyl disulfide, methylmercaptan, γ-butyrolactone) are able to specifically trigger this receptor. T2R38 is thus much more broadly tuned for bacterial compounds than previously thought.

Assessment of informal caregiver's needs by self-administered instruments: a literature review.

Background: Clinicians, researchers and politicians are seeking to better assess caregiver's needs. Challenges exist in broadly implementing this so as to provide appropriate support. The aim of this review was to compile self-administered instruments for assessment of caregiver's needs that are deemed to be scientifically robust. Methods: The Medline database was searched for publications reporting self-administered instruments assessing caregiver's needs with acceptable psychometric properties. These instruments were analyzed in terms of the development context, target population, concept, purpose, structure, content and psychometric properties. The dimensions of the needs were listed and categorized. Results: A total of nine self-administered instruments were analyzed. They averaged 32 items, they were specifically developed for a targeted subpopulation of caregivers and dedicated to epidemiological research. Response devices were based on Likert scales. The main dimensions of the needs identified were 'Health and Care', 'Psychological - Emotional Support', 'Information-Knowledge', 'Social Life-Work-Finance'. None was specifically geared toward caregivers for the elderly, children or teenagers. In the absence of transcultural validation, no instrument was directly usable in Europe. Conclusions: Assessing caregivers' needs is a key part in providing caregivers with appropriate support. The development of self-administered instruments constitutes a complex field that is still underexplored at the international level; strict specifications with psychometric validation are essential. To be efficient, the instrument should be integrated in a larger process including: upstream, recognition, identification and assessment of the overall situation of the caregiver; and downstream, guidance, establishment and follow-up of a suitable action plan.

A Randomized Phase 2 Study of Long-Acting TransCon GH vs Daily GH in Childhood GH Deficiency.

Context: TransCon Growth Hormone (GH) (Ascendis Pharma) is a long-acting recombinant sustained-release human GH prodrug in development for children with GH deficiency (GHD). Objective: To compare the pharmacokinetics, pharmacodynamics, safety, and efficacy of weekly TransCon GH to that of daily GH in prepubertal children with GHD. Design: Randomized, open-label, active-controlled study of three doses of weekly TransCon GH versus daily Genotropin (Pfizer). Setting: Thirty-eight centers in 14 European countries and Egypt. Patients: Prepubertal male and female treatment-naïve children with GHD (n = 53). Interventions: Subjects received one of three TransCon GH doses (0.14, 0.21, or 0.30 mg GH/kg/wk) or Genotropin 0.03 mg GH/kg/d for 26 weeks. Main Outcome Measures: GH and insulinlike growth factor-1 (IGF-1) levels, growth, adverse events, and immunogenicity. Results: Both GH maximum concentration and area under the curve were similar following TransCon GH or Genotropin administration at comparable doses. A dose response was observed, with IGF-1 standard deviation scores increasing into the normal range for all three TransCon GH doses. Annualized mean height velocity for the three TransCon GH doses ranged from 11.9 cm to 13.9 cm, which was not statistically different from 11.6 cm for Genotropin. Adverse events were mild to moderate, and most were unrelated to the study drug. Injection site tolerance was good. One TransCon GH subject developed a low-titer, nonneutralizing antibody response to GH. Conclusions: The results suggest that long-acting TransCon GH is comparable to daily Genotropin for GH (pharmacokinetics) and IGF-1 (pharmacodynamics) levels, safety, and efficacy and support advancement into phase 3 development.