Prognostic Value of Admission Neutrophil Count in Asthma Patients with COVID-19: A Comparative Analysis with other Systemic Inflammation Indices for In-Hospital Mortality Prediction.

Despite studies indicating that asthma patients do not exhibit a higher mortality rate or severity compared to the general population when infected with COVID-19, there have been few reports on predictive factors for mortality in this context. This study aimed to assess the predictive value of systemic inflammation indices including neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte (PLR), systemic inflammation response index (SIR-I), and systemic inflammation index (SII) in determining mortality rate among patients with COVID-19 and asthma. In this prospective study, the laboratory parameters of 1792 COVID-19 patients were examined, with a subgroup consisting of 112 patients with asthma and 1680 patients without asthma.

Aggregate index of systemic inflammation (AISI) in admission as a reliable predictor of mortality in COPD patients with COVID-19.

Background: The role of leukocytes and systemic inflammation indicators in predicting the severity and mortality of inflammatory diseases has been well reported, such as the neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR), neutrophil/lymphocyte*platelet ratio (NLPR), derived neutrophil/lymphocyte ratio (dNLR), aggregate index of systemic inflammation (AISI), as well as systemic inflammation response index (SIRI) and systemic inflammation index (SII). The purpose of the present study was to investigate the prognostic role of systemic inflammatory indicators in the mortality of chronic obstructive pulmonary disease (COPD) patients with COVID-19.

Methods: This retrospective study included 169 COPD patients hospitalized with COVID-19.

Multi-mask self-supervised learning for physics-guided neural networks in highly accelerated magnetic resonance imaging.

Self-supervised learning has shown great promise because of its ability to train deep learning (DL) magnetic resonance imaging (MRI) reconstruction methods without fully sampled data. Current self-supervised learning methods for physics-guided reconstruction networks split acquired undersampled data into two disjoint sets, where one is used for data consistency (DC) in the unrolled network, while the other is used to define the training loss. In this study, we propose an improved self-supervised learning strategy that more efficiently uses the acquired data to train a physics-guided reconstruction network without a database of fully sampled data.

Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms.

Inverse problems for accelerated MRI typically incorporate domain-specific knowledge about the forward encoding operator in a regularized reconstruction framework. Recently physics-driven deep learning (DL) methods have been proposed to use neural networks for data-driven regularization. These methods unroll iterative optimization algorithms to solve the inverse problem objective function, by alternating between domain-specific data consistency and data-driven regularization via neural networks.

High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks.

Long scan duration remains a challenge for high-resolution MRI. Deep learning has emerged as a powerful means for accelerated MRI reconstruction by providing data-driven regularizers that are directly learned from data. These data-driven priors typically remain unchanged for future data in the testing phase once they are learned during training.

Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data.

Purpose: To develop a strategy for training a physics-guided MRI reconstruction neural network without a database of fully sampled data sets.

Methods: Self-supervised learning via data undersampling (SSDU) for physics-guided deep learning reconstruction partitions available measurements into two disjoint sets, one of which is used in the data consistency (DC) units in the unrolled network and the other is used to define the loss for training. The proposed training without fully sampled data is compared with fully supervised training with ground-truth data, as well as conventional compressed-sensing and parallel imaging methods using the publicly available fastMRI knee database.

Accelerated coronary MRI with sRAKI: A database-free self-consistent neural network k-space reconstruction for arbitrary undersampling.

Purpose: To accelerate coronary MRI acquisitions with arbitrary undersampling patterns by using a novel reconstruction algorithm that applies coil self-consistency using subject-specific neural networks.

Methods: Self-consistent robust artificial-neural-networks for k-space interpolation (sRAKI) performs iterative parallel imaging reconstruction by enforcing self-consistency among coils. The approach bears similarity to SPIRiT, but extends the linear convolutions in SPIRiT to nonlinear interpolation using convolutional neural networks (CNNs).

ACCELERATED CORONARY MRI USING 3D SPIRIT-RAKI WITH SPARSITY REGULARIZATION.

Coronary MRI is a non-invasive radiation-free imaging tool for the diagnosis of coronary artery disease. One of its limitations is the long scan time, due to the need for high resolution imaging in the presence of respiratory and cardiac motions. Machine learning (ML) methods have been recently utilized to accelerate MRI.

Optimized fast GPU implementation of robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction.

Background: Robust Artificial-neural-networks for k-space Interpolation (RAKI) is a recently proposed deep-learning-based reconstruction algorithm for parallel imaging. Its main premise is to perform k-space interpolation using convolutional neural networks (CNNs) trained on subject-specific autocalibration signal (ACS) data. Since training is performed individually for each subject, the reconstruction time is longer than approaches that pre-train on databases.

Electromagnetic Brain Source Imaging by Means of a Robust Minimum Variance Beamformer.

Objective: Adaptive beamformer methods that have been extensively used for functional brain imaging using EEG/MEG (magnetoencephalography) signals are sensitive to model mismatches. We propose a robust minimum variance beamformer (RMVB) technique, which explicitly incorporates the uncertainty of the lead field matrix into the estimation of spatial-filter weights that are subsequently used to perform the imaging.

Methods: The uncertainty of the lead field is modeled by ellipsoids in the RMVB method; these hyperellipsoids (ellipsoids in higher dimensions) define regions of uncertainty for a given nominal lead field vector.

Electromagnetic source imaging using simultaneous scalp EEG and intracranial EEG: An emerging tool for interacting with pathological brain networks.

Objective: The goal of this study is to investigate the performance, merits and limitations of source imaging using intracranial EEG (iEEG) recordings and to compare its accuracy to the results of EEG source imaging. Accuracy in this study, is measured both by determining the location and inter-nodal connectivity of underlying brain networks.

Methods: Systematic computer simulation studies are conducted to evaluate iEEG-based source imaging vs.

Radiosensitive effect of curcumin on thyroid cancer cell death induced by radioiodine-131.

Curcumin is a natural product widely consumed by humans. It has many biological properties. In this study, we investigated the radiosensitive effect of curcumin on thyroid cancer cells against cellular toxicity induced by 131-I.

Protective effects of thymol against nephrotoxicity induced by cisplatin with using 99mTc-DMSA in mice.

Background: In this study, we investigated the protective effect of thymol as a natural compound against cisplatin-induced nephrotoxicity by quantitative renal 99mTc-DMSA uptake and compared its effect with histopathology in mice.

Materials And Methods: Mice were divided into six groups as control, cisplatin (7.5 mg/kg, intraperitoneally), thymol+cisplatin (thymol; 50 and 150 mg/kg+cisplatin; 7.

Resveratrol sensitizes selectively thyroid cancer cell to 131-iodine toxicity.

Background. In this study, the radiosensitizing effect of resveratrol as a natural product was investigated on cell toxicity induced by (131)I in thyroid cancer cell. Methods.