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Skeletal dysplasias collectively affect a large number of patients worldwide. The majority of these disorders cause growth anomalies. Hence, assessing skeletal maturity via determining the bone age (BA) is one of the most valuable tools for their diagnoses. Moreover, consecutive BA assessments are crucial for monitoring the pediatric growth of patients with such disorders, especially for timing hormone treatments or orthopedic interventions. However, manual BA assessment is time-consuming and suffers from high intra-and inter-rater variability. This is further exacerbated by genetic disorders causing severe skeletal malformations. While numerous approaches to automatize BA assessment were proposed, few were validated for BA assessment on children with abnormal development. In this work, we present Deeplasia, an open-source prior-free deep-learning ensemble approach. After training on the public RSNA BA dataset, we achieve state-of-the-art performance with a mean absolute difference (MAD) of 3.87 months based on the average of six different reference ratings. Next, we demonstrate that Deeplasia generalizes to an unseen dataset of 568 X-ray images from 189 patients with molecularly confirmed diagnoses of seven different genetic bone disorders (including Achondroplasia and Hypochondroplasia) achieving a MAD of 5.84 months w.r.t. to the average of two references. Further, using longitudinal data from a subset of the cohort (149 images), we estimate the test-retest precision of our model ensemble to be at least at the human expert level (2.74 months). We conclude that Deeplasia suits assessing and monitoring the BA in patients with skeletal dysplasias.

Context Prediction of adult height (AH) is important in clinical management of short children. The conventional methods of Bayley-Pinneau (BP) or Roche-Wainer-Thissen (RWT) have limitations. Objective We aimed to develop a set of algorithms for AH prediction in patients with idiopathic short stature (ISS) which are specific for combinations of predicting variables. Methods Demographic and auxologic data were collected in childhood (1980s) and at AH (1990s). Data were collected by Dutch and German referral centers for pediatric endocrinology. A total of 292 subjects with ISS (219 male, 73 female) were enrolled. The population was randomly split into modeling (n = 235) and validation (n = 57) cohorts. Linear multi-regression analysis was performed with predicted AH (PAH) as response variable and combinations of chronological age (CA), baseline height, parental heights, relative bone age (BA/CA), birth weight, and sex as exploratory variables. Results Ten models including different exploratory variables were selected with adjusted R² ranging from 0.84 to 0.78 and prediction errors from 3.16 to 3.68 cm. Applied to the validation cohort, mean residuals (PAH minus observed AH) ranged from −0.29 to −0.82 cm, while the conventional methods showed some overprediction (BP: +0.53 cm; RWT: +1.33 cm; projected AH: +3.81 cm). There was no significant trend of residuals with PAH or any exploratory variables, in contrast to BP and projected AH. Conclusion This set of 10 multi-regression algorithms, developed specifically for children with ISS, provides a flexible tool for AH prediction with better accuracy than the conventional methods.

In most patients with chronic myeloid leukemia (CML) clonal cells can be kept under control by BCR::ABL1 tyrosine kinase inhibitors (TKI). However, overt resistance or intolerance against these TKI may occur. We identified the epigenetic reader BRD4 and its downstream-effector MYC as growth regulators and therapeutic targets in CML cells. BRD4 and MYC were found to be expressed in primary CML cells, CD34

In Osterreich erkrankt fast jeder 25. Spitalspatient an einem Krankenhauskeim. Eine Studie sieht die Nation im Kampf gegen nosokomiale Infektionen im guten Mittelfeld. Ojan Assadian, Prasident der Osterreichischen Gesellschaft fur Krankenhaushygiene, erklart, warum die Umsetzung von wirksamen Hygienemasnahmen so muhsam ist.