Machine learning models for early prediction of mortality risk in patients with burns: A single center experience.

Mortality rate is considered as the most important outcome measure for assessing the severity of burn injury. A scale or model that accurately predicts burn mortality can be useful to determine the clinical course of burn injuries, discuss treatment options and rehabilitation with patients and their families, and evaluate novel, innovative interventions for the injuries. This study aimed to use machine learning models to predict the mortality risk of patients with burns after their first admission to the center and to compare the performances of these models.

To evaluate the effects of antidepressant drugs on pregnancy outcomes in a university hospital of Turkey.

Objective: To assess the effects of antidepressant use on pregnancy outcomes.

Methods: The cross-sectional study was conducted at the Department of Pharmacology, Manisa Celal Bayar University, Manisa, Turkey, and comprised pregnant women who were admitted to the Department of Gynaecology between 2008 and 2017 who had been prescribed antidepressant drugs before pregnancy and continued to use them during any week of their respective pregnancies. The women were contacted by telephone after delivery to obtain information about the pregnancy outcomes.

Mesenchymal Stem Cells: a Potential Treatment Approach for Refractory Chronic Spontaneous Urticaria.

The etiopathogenesis of chronic spontaneous urticaria (CSU) is not fully elucidated, and almost 30-40% of patients are resistant to treatments; therefore, there is still a need for the development of new and effective treatments. This study aimed to develop experimental cellular therapy for CSU patients resistant to current treatment options. Autologous adipose tissue mesenchymal stem cells (MSC) were administered to 10 refractory CSU patients who were then followed up for six months.

Automatic Segmentation of Meniscus in Multispectral MRI Using Regions with Convolutional Neural Network (R-CNN).

The meniscus has a significant function in human anatomy, and Magnetic Resonance Imaging (MRI) has an essential role in meniscus examination. Due to a variety of MRI data, it is excessively difficult to segment the meniscus with image processing methods. An MRI data sequence contains multiple images, and the region features we are looking for may vary from each image in the sequence.

CuO-CuO ball like/multiwalled carbon nanotube hybrid for fast and effective ultrasound-assisted solid phase extraction of uranium at ultra-trace level prior to ICP-MS detection.

Multiwalled carbon nanotube/CuO-CuO ball like hybrid material (MWCNTs/CuO-CuO) synthesized using of a green hydrothermal process was evaluated as a new sorbent for solid phase extraction of uranium with inductively coupled plasma mass spectrometry (ICP-MS). According to our best knowledge, this new material was synthesized and used as solid phase extraction adsorbent for the first time in the literature. The characterization of new hybrid material was carried out using Raman spectroscopy, X-ray diffraction (XRD) and scanning electronic microscopy techniques (SEM, SEM-EDX and SEM-Mapping).