Protein translation rate determines neocortical neuron fate.

The mammalian neocortex comprises an enormous diversity regarding cell types, morphology, and connectivity. In this work, we discover a post-transcriptional mechanism of gene expression regulation, protein translation, as a determinant of cortical neuron identity. We find specific upregulation of protein synthesis in the progenitors of later-born neurons and show that translation rates and concomitantly protein half-lives are inherent features of cortical neuron subtypes. In a small molecule screening, we identify Ire1α as a regulator of Satb2 expression and neuronal polarity. In the developing brain, Ire1α regulates global translation rates, coordinates ribosome traffic, and the expression of eIF4A1. Furthermore, we demonstrate that the Satb2 mRNA translation requires eIF4A1 helicase activity towards its 5'-untranslated region. Altogether, we show that cortical neuron diversity is generated by mechanisms operating beyond gene transcription, with Ire1α-safeguarded proteostasis serving as an essential regulator of brain development.

An overview of reviews and synthesis across 440 studies examines the importance of hospital readmission predictors across various patient populations.

OBJECTIVES: The scientific literature contains an abundance of prediction models for hospital readmissions. However, no review has yet synthesized their predictors across various patient populations. Therefore, our aim was to examine predictors of hospital readmissions across 13 patient populations. STUDY DESIGN AND SETTING: An overview of systematic reviews was combined with a meta-analytical approach. Two thousand five hundred four different predictors were categorized using common ontologies to pool and examine their odds ratios and frequencies of use in prediction models across and within different patient populations. RESULTS: Twenty-eight systematic reviews with 440 primary studies were included. Numerous predictors related to prior use of healthcare services (odds ratio; 95% confidence interval: 1.64; 1.42-1.89), diagnoses (1.41; 1.31-1.51), health status (1.35; 1.20-1.52), medications (1.28; 1.13-1.44), administrative information about the index hospitalization (1.23; 1.14-1.33), clinical procedures (1.20; 1.07-1.35), laboratory results (1.18; 1.11-1.25), demographic information (1.10; 1.06-1.14), and socioeconomic status (1.07; 1.02-1.11) were analyzed. Diagnoses were frequently used (in 37.38%) and displayed large effect sizes across all populations. Prior use of healthcare services showed the largest effect sizes but were seldomly used (in 2.57%), whereas demographic information (in 13.18%) was frequently used but displayed small effect sizes. CONCLUSION: Diagnoses and patients' prior use of healthcare services showed large effects both across and within different populations. These results can serve as a foundation for future prediction modeling.

Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults.

Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor "AIDE-MOI" was developed. "AIDE-MOI" senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as "fall" or "non-fall". The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively.

Ultrasound point shear wave elastography of the pancreas: comparison of patients with type 1 diabetes and healthy volunteers - results from a pilot study.

BACKGROUND: The aims of this study were to establish shear wave elastography of the pancreas by comparing measurements in patients with type 1 diabetes (T1D) and healthy volunteers and to consider whether this method could contribute to the screening or prevention of T1D. METHODS: This pilot study included 15 patients with T1D (10 men, 5 women) and 15 healthy volunteers (10 men, 5 women) as controls. Measurements were performed with a Siemens Acuson S3000 (Siemens Healthcare, Erlangen, Germany) using a 6C1 convex transducer and the Virtual Touch™ tissue quantification (VTQ) method. RESULTS: The mean shear wave velocity of the head of the pancreas was 1.0 ± 0.2 m/s (median: 1.1 m/s) for the study group and likewise 1.0 ± 0.2 m/s (median: 0.9 m/s) for the control group. Velocities of 1.2 ± 0.2 m/s (median: 1.2 m/s) were measured in the body of the pancreas in both groups. There was a significant difference between the values obtained in the tail of the pancreas: patients 1.1 ± 0.1 m/s (median: 1.0 m/s) versus controls 0.9 ± 0.1 m/s (median: 0.8 m/s) (p = 0.0474). The mean value in the whole pancreas of the study group was not significantly above that of the control group: 1.1 ± 0.1 m/s (median: 1.0 m/s) versus 1.0 ± 0.1 m/s (median: 1.0 m/s) (p = 0.2453). CONCLUSIONS: Sonoelastography of the pancreas revealed no overall difference between patients with T1D and healthy volunteers. Patients with T1D showed higher values only in the tail segment. Future studies need to determine whether specific regional differences can be found in a larger study population.