Henningsennoer4832

Children born extremely preterm (EP, less then 28 weeks' gestation) or extremely low birth weight (ELBW, less then 1,000 g) are a vulnerable population at high risk of working memory impairments. We aimed to examine changes in the brain structural connectivity networks thought to underlie working memory performance, after completion of a working memory training program (Cogmed) compared with a placebo program in EP/ELBW children. This was a double-blind, placebo-controlled randomized trial (the Improving Memory in a Preterm Randomised Intervention Trial). Children born EP/ELBW received either the Cogmed or placebo program at 7 years of age (n = 91). A subset of children had magnetic resonance imaging of the brain immediately pre- and 2 weeks post-training (Cogmed n = 28; placebo n = 27). T1 -weighted and diffusion-weighted images were used to perform graph theoretical analysis of structural connectivity networks. Changes from pre-training to post-training in structural connectivity metrics were generally similar between randomized groups. There was little evidence that changes in structural connectivity metrics were related to changes in working memory performance from pre- to post-training. check details Overall, our results provide little evidence that the Cogmed working memory training program has training-specific effects on structural connectivity networks in EP/ELBW children.

J-waves and fragmented QRS (fQRS) on surface ECGs have been associated with the occurrence of ventricular tachyarrhythmias. Whether these non-invasive parameters can also predict ventricular tachycardia (VT) recurrence after radiofrequency catheter ablation (RFCA) is unknown. Of interest, patients with a wide QRS-complex have been excluded from clinical studies on J-waves, although a J-wave like pattern has been described for wide QRS.

We retrospectively included 168 patients (67 ± 10 years; 146 men) who underwent RFCA of post-infarct VT. J-wave pattern were defined as J-point elevation≥0.1mV in at least two leads irrespective of QRS width. fQRS was defined as various RSR` pattern in patients with narrow QRS andmorethan two R wave in those with wide QRS. The primary endpoint was VT recurrence after RFCA up to 24 months.

J-wave pattern and fQRS were present in 27 and 28 patients, respectively. Overlap of J-wave pattern and fQRS was observed in nine. During a median follow-up of 20 (interquartile range 9-24) months, 46 (27%) patients had VT recurrence. Kaplan-Meier curves revealed that both J-wave pattern and fQRS were associated with VT recurrence. Multivariate Cox regression analysis demonstrated that the presence of J-wave pattern (hazard ratio [HR] 2.84; 95% confidence interval [CI] 1.45-5.58; P=.002) and greater number of induced VT (HR 1.29; 95% CI 1.15-1.45; P<.001) were the independent predictors of VT recurrence.

A J-wave pattern-but not fQRS-is independently associated with an increased risk of post-infarct VT recurrence after RFCA irrespective of QRS width. This simple non-invasive parameter may identify patients who require additional treatment.

A J-wave pattern-but not fQRS-is independently associated with an increased risk of post-infarct VT recurrence after RFCA irrespective of QRS width. This simple non-invasive parameter may identify patients who require additional treatment.

This work assesses the accuracy of the stretched exponential (SEM) and cylinder models of lung microstructural length scales that can be derived from hyperpolarized gas DWI. This was achieved by simulating

He and

Xe DWI signals within two micro-CT-derived realistic acinar airspace meshes that represent healthy and idiopathic pulmonary fibrosis lungs.

The healthy and idiopathic pulmonary fibrosis acinar airway meshes were derived from segmentations of 3D micro-CT images of excised human lungs and meshed for finite element simulations of the Bloch-Torrey equations.

He and

Xe multiple b value DWI experiments across a range of diffusion times (

He Δ = 1.6 ms;

Xe Δ = 5 to 20 ms) were simulated in each mesh. Global SEM mean diffusive length scale and cylinder model mean chord length value was derived from each finite element simulation and compared against each mesh's mean linear intercept length, calculated from intercept length measurements within micro-CT segmentation masks.

The SEM-derived mean diffusive length scale was within ±10% of the mean linear intercept length for simulations with both

He (Δ = 1.6 ms) and

Xe (Δ = 7 to 13 ms) in the healthy mesh, and with

Xe (Δ = 13 to 20 ms) for the idiopathic pulmonary fibrosis mesh, whereas for the cylinder model-derived mean chord length the closest agreement with mean linear intercept length (11.7% and 22.6% difference) was at

Xe Δ = 20 ms for both healthy and IPF meshes, respectively.

This work validates the use of the SEM for accurate estimation of acinar dimensions and indicates that the SEM is relatively robust across a range of experimental conditions and acinar length scales.

This work validates the use of the SEM for accurate estimation of acinar dimensions and indicates that the SEM is relatively robust across a range of experimental conditions and acinar length scales.

To identify and prioritize the root causes of adverse drug events (ADEs) in hospitals and to assess the ability of artificial intelligence (AI) capabilities to prevent ADEs.

A mixed method design was used.

A cross-sectional study for hospitals in Spain was carried out between February and April 2019 to identify and prioritize the root causes of ADEs. A nominal group technique was also used to assess the ability of AI capabilities to prevent ADEs.

The main root cause of ADEs was a lack of adherence to safety protocols (64.8%), followed by identification errors (57.4%), and fragile and polymedicated patients (44.4%). An analysis of the AI capabilities to prevent the root causes of ADEs showed that identification and reading are two potentially useful capabilities.

Identification error is one of the main root causes of drug adverse events and AI capabilities could potentially prevent drug adverse events.

This study highlights the role of AI capabilities in safely identifying both patients and drugs, which is a crucial part of the medication administration process, and how this can prevent ADEs in hospitals.