Rascholsson8052

e data can be used by the PAH scientific and clinical community to enhance the reliability and value of research findings, to inform quality improvement initiatives, and ultimately to improve health for PAH patients.

Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation (MV) may aid in delivering timely treatment.

Can a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24h in advance?

We trained and externally validated a transparent DL algorithm to predict the future need for MV in hospitalized patients, including those with COVID-19, using commonly available data in electronic health records. Additionally, commonly used clinical criteria (heart rate, oxygen saturation, respiratory rate, Fio

, and pH) were used to assess future need for MV. Performance of the algorithm was evaluated using the area under receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value.

We obtained data from more than 30,000 ICU patients (including more than 700 patients with COVID-19) from two academic medical centers. The performance of the model with a 24-h prediction horizon at the development and validation sites was comparable (AUC, 0.895 vs0.882, respectively), providing significant improvement over traditional clinical criteria (P< .001). Prospective validation of the algorithm among patients with COVID-19 yielded AUCs in the range of 0.918 to 0.943.

A transparent DL algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. Such an algorithm may help clinicians to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.

A transparent DL algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. Such an algorithm may help clinicians to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.Bronchoscopic lung volume reduction with one-way endobronchial valves is a guideline treatment option for patients with advanced emphysema that is supported by extensive scientific data. Patients limited by severe hyperinflation, with a suitable emphysema treatment target lobe and with absence of collateral ventilation, are the responders to this treatment. Detailed patient selection, a professional treatment performance, and dedicated follow up of the valve treatment, including management of complications, are key ingredients to success. This treatment does not stand alone; it especially requires extensive knowledge of COPD for which the most appropriate treatment is discussed in a multidisciplinary approach. We discuss the endobronchial valve treatment for emphysema and provide a guideline for patient selection, treatment guidance, and practice tools, based on our own experience and literature.

Three-dimensional (3D) in vitro models have been developed into more in vivo resembling structures. In particular, there is a need for human-based models for neuronal tissue engineering (TE). To produce such a model with organized microenvironment for cells in central nervous system (CNS), a 3D layered scaffold composed of hydrogel and cell guiding fibers has been proposed.

Here, we describe a novel method for producing a layered 3D scaffold consisting of electrospun poly (L,D-lactide) fibers embedded into collagen 1 hydrogel to achieve better resemblance of cells' natural microenvironment for human pluripotent stem cell (hPSC)-derived neurons. The scaffold was constructed via a single layer-by-layer process using an electrospinning technique with a unique collector design.

The method enabled the production of layered 3D cell-containing scaffold in a single process. HPSC-derived neurons were found in all layers of the scaffold and exhibited a typical neuronal phenotype. The guiding fiber layers supported the directed cell growth and extension of the neurites inside the scaffold without additional functionalization.

Previous methods have required several process steps to construct 3D layer-by-layer scaffolds.

We introduced a method to produce layered 3D scaffolds to mimic the cell guiding cues in CNS by alternating the soft hydrogel matrix and fibrous guidance cues. The produced scaffold successfully enabled the long-term culture of hPSC-derived neuronal cells. This layered 3D scaffold is a useful model for in vitro and in vivo neuronal TE applications.

We introduced a method to produce layered 3D scaffolds to mimic the cell guiding cues in CNS by alternating the soft hydrogel matrix and fibrous guidance cues. The produced scaffold successfully enabled the long-term culture of hPSC-derived neuronal cells. This layered 3D scaffold is a useful model for in vitro and in vivo neuronal TE applications.

This study explores how mild cognitive impairment (MCI) and Alzheimer's disease (AD) develop over time. NEW METHOD this study involves a new application of latent curve models (LCM) to examine the development trajectory of a healthy, MCI, and AD groups on a series of clinical and neural measures. Wnt pathway Multiple-group latent curve models were used to compare the parameters of the trajectories across groups.

LCM results showed that a linear functional form of growth was adequate for all the clinical and neural measures. Positive and significant differences in initial levels were seen across groups on all of the clinical and neural measures. In all groups, the following measures increased slightly, or considerably, over time Clinical Dementia Rating, Alzheimer's disease Cognitive Assessment, and Montreal Assessment Test for Dementia. In contrast, a slight or a greatly decreasing trajectory was observed on the following measures Fluorodeoxyglucose, Mini-Mental State Exam, Rey Auditory Verbal Learning Test as well as Hippocampus, Fusiform and Entorhinal Cortex volume measures. However, a constant mean trajectory was seen on Cognition Self Report Memory and languages scores. COMPARISION WITH EXISTING METHODS there are no prior studies that applied LCM on large AD datasets.

cognitive decline occurs in the cognitively normal (CN), MCI, and AD groups but at different rates. Further, some important cognitive, neural, and clinical variables that (a) best differentiate between CN, MCI, and AD as well as (b) differentially change over time in MCI and AD, which may explain disease progression.

cognitive decline occurs in the cognitively normal (CN), MCI, and AD groups but at different rates. Further, some important cognitive, neural, and clinical variables that (a) best differentiate between CN, MCI, and AD as well as (b) differentially change over time in MCI and AD, which may explain disease progression.