Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features.

The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rapid data-driven approaches to facilitate clinical decision making. We examined a machine learning process to predict inpatient mortality among COVID-19 patients using clinical and chest radiographic data. Modeling was performed with a de-identified dataset of encounters prior to widespread vaccine availability. Non-imaging predictors included demographics, pre-admission clinical history, and past medical history variables. Imaging features were extracted from chest radiographs by applying a deep convolutional neural network with transfer learning. A multi-layer perceptron combining 64 deep learning features from chest radiographs with 98 patient clinical features was trained to predict mortality. The Local Interpretable Model-Agnostic Explanations (LIME) method was used to explain model predictions. Non-imaging data alone predicted mortality with an ROC-AUC of 0.87 ± 0.03 (mean ± SD), while the addition of imaging data improved prediction slightly (ROC-AUC: 0.91 ± 0.02). The application of LIME to the combined imaging and clinical model found HbA1c values to contribute the most to model prediction (17.1 ± 1.7%), while imaging contributed 8.8 ± 2.8%. Age, gender, and BMI contributed 8.7%, 8.2%, and 7.1%, respectively. Our findings demonstrate a viable explainable AI approach to quantify the contributions of imaging and clinical data to COVID mortality predictions

Hippocampal overexpression of NOS1AP promotes endophenotypes related to mental disorders

BACKGROUND\nMETHODS\nFINDINGS\nINTERPRETATION\nFUNDING\nNitric oxide synthase 1 adaptor protein (NOS1AP; previously named CAPON) is linked to the glutamatergic postsynaptic density through interaction with neuronal nitric oxide synthase (nNOS). NOS1AP and its interaction with nNOS have been associated with several mental disorders. Despite the high levels of NOS1AP expression in the hippocampus and the relevance of this brain region in glutamatergic signalling as well as mental disorders, a potential role of hippocampal NOS1AP in the pathophysiology of these disorders has not been investigated yet.\nTo uncover the function of NOS1AP in hippocampus, we made use of recombinant adeno-associated viruses to overexpress murine full-length NOS1AP or the NOS1AP carboxyterminus in the hippocampus of mice. We investigated these mice for changes in gene expression, neuronal morphology, and relevant behavioural phenotypes.\nWe found that hippocampal overexpression of NOS1AP markedly increased the interaction of nNOS with PSD-95, reduced dendritic spine density, and changed dendritic spine morphology at CA1 synapses. At the behavioural level, we observed an impairment in social memory and decreased spatial working memory capacity.\nOur data provide a mechanistic explanation for a highly selective and specific contribution of hippocampal NOS1AP and its interaction with the glutamatergic postsynaptic density to cross-disorder pathophysiology. Our findings allude to therapeutic relevance due to the druggability of this molecule.\nThis study was funded in part by the DFG, the BMBF, the Academy of Finland, the NIH, the Japanese Society of Clinical Neuropsychopharmacology, the Ministry of Education of the Russian Federation, and the European Community

Multiatlas Segmentation Using Robust Feature-Based Registration

This paper presents a pipeline which uses a multiatlas approach for multiorgan segmentation in whole-body CT images. In order to obtain accurate registrations between the target and the atlas images, we develop an adapted feature-based method which uses organ-specific features. These features are learnt during an offline preprocessing step, and thus, the algorithm still benefits from the speed of feature-based registration methods. These feature sets are then used to obtain pairwise non-rigid transformations using RANSAC followed by a thin-plate spline refinement or NiftyReg. The fusion of the transferred atlas labels is performed using a random forest classifier, and finally, the segmentation is obtained using graph cuts with a Potts model as interaction term. Our pipeline was evaluated on 20 organs in 10 whole-body CT images at the VISCERAL Anatomy Challenge, in conjunction with the International Symposium on Biomedical Imaging, Brooklyn, New York, in April 2015. It performed best on majority of the organs, with respect to the Dice index

Efficiency analysis of dairy farms in the province of Izmir (Turkey): Data envelopment analysis (DEA)

WOS: 000237043100015The production efficiency of dairy farms based on cross section data of 2003 covering 80 farms chosen by the method of proportional sampling, was determined by Data Envelopment Analysis (DEA) using three outputs and seven inputs. Fortynine percent of the dairy farms appeared to be fully efficient according to the assumption of constant return to scale (CRS). The average efficiency indices obtained under CRS and variable return to scale (VRS) were 0.934 and 0.954, respectively. Mean scale efficiency, on the other hand, was 0.978. Out of the selected dairy farms 21.2% were observed to be efficient in measuring the efficiency of single output milk production. Average efficiency indices under CRS and VRS and scale efficiency index were measured to be 0.782, 0.832 and 0.938, respectively. This information will contribute to extensive dairy farm projects to be carried out in future

Abnormalities of thyroid function in children with newly diagnosed type 1 diabetes mellitus: are transient or permanent?

Aim: In this study, we aimed to determine the incidence and short term outcome of abnormal thyroid functions in children with newly diagnosed type 1 diabetes mellitus.Materials and Methods: Fourty-two patients with Type 1 diabetes mellitus who were diagnosed and observed in our department of pediatrics between 2001-2006 were retrospectively evaluated. The thyroid function tests of the patients were measured within four and a half days of the initial diagnosis of diabetes and at least one follow-up test median on day 180 after diagnosis.Results: Twenty-two (52.4%) of the patients were female and 20 (47.6%) were male. Mean age of the patients was 9.4 (+/- 3.6) years. Twenty-three patients (54.8%) were diagnosed as diabetic ketoacidosis, 15 (35.7%) as ketosis and 4 (9.5%) as hyperglycemia at the time of initial presentation. Thyroid functions were normal in 26 (61.9%) subjects. Thyroid function tests were abnormal in 16 (38.1%) subjects of whom 12 (75.0%) had biochemical findings compatible with sick euthyroid syndrome and of these 10 (83.3%) had diabetic ketoacidosis. All of these abnormalities were transient and thyroid function tests all returned to normal except for one patient. Antithyroid antibodies were positive in 7 (16.7%) subjects 2 (10.5%) with ketosis or hyperglycemia and 5 (21.7%) diabetic ketoacidosis. Thyroid function tests were abnormal in 6 (14.3.1%) subjects at follow-up. Thyroid disfunction rate decrased the 38.1% to 14.3% at the follow-up.Conclusion: This retrospective study revealed that abnormalities in thyroid function tests in subjects with newly diagnosed Type 1 diabetes mellitus were frequent and mostly transient. For this reason, in the absence of any clinical situation suggesting a thyroid disorder, we think it would be better to assess thyroid function tests at least one mouth after theinitial diagnosis of diabetes