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 131I in thyroid cancer cell. Methods. Human thyroid cancer cell and human nonmalignant fibroblast cell (HFFF2) were treated with 131I and/or resveratrol at different concentrations for 48 h. The cell proliferation was measured by determination of the percent of the survival cells using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Results. Findings of this study show that resveratrol enhanced the cell death induced by 131I on thyroid cancer cell. Also, resveratrol exhibited a protective effect on normal cells against 131I toxicity. Conclusion. This result indicates a promising effect of resveratrol on improvement of cellular toxicity during iodine therapy

Zero-Shot Self-Supervised Learning for MRI Reconstruction

Deep learning (DL) has emerged as a powerful tool for accelerated MRI reconstruction, but these methods often necessitate a database of fully-sampled measurements for training. Recent self-supervised and unsupervised learning approaches enable training without fully-sampled data. However, a database of undersampled measurements may not be available in many scenarios, especially for scans involving contrast or recently developed translational acquisitions. Moreover, database-trained models may not generalize well when the unseen measurements differ in terms of sampling pattern, acceleration rate, SNR, image contrast, and anatomy. Such challenges necessitate a new methodology that can enable scan-specific DL MRI reconstruction without any external training datasets. In this work, we propose a zero-shot self-supervised learning approach to perform scan-specific accelerated MRI reconstruction to tackle these issues. The proposed approach splits available measurements for each scan into three disjoint sets. Two of these sets are used to enforce data consistency and define loss during training, while the last set is used to establish an early stopping criterion. In the presence of models pre-trained on a database with different image characteristics, we show that the proposed approach can be combined with transfer learning to further improve reconstruction quality

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. In this study, we propose to use a transfer learning approach to fine-tune these regularizers for new subjects using a self-supervision approach. While the proposed approach can compromise the extremely fast reconstruction time of deep learning MRI methods, our results on knee MRI indicate that such adaptation can substantially reduce the remaining artifacts in reconstructed images. In addition, the proposed approach has the potential to reduce the risks of generalization to rare pathological conditions, which may be unavailable in the training data

The Role of Interleukin (IL-22) in immune response to human diseases

Background and aims: IL-22 is an alpha- helical cytokine. IL-22 binds to a heterodimeric cell surface receptor composed of IL-10R2 and IL-22R1subunits. IL-22R is expressed on tissue cells, and it is absent on immune cells. L-22 and IL-10 receptor chains play a role in cellular targeting and signal transduction to selectively initiate and regulate immune responses. The aim of this study was to investigate the Role of Interleukin (IL-22) in Immune Response in human diseases. Methods: This study was a mini-review research to investigate the role of T helper 22 (Th22) in immune response. Results: IL-22 contributes to immune disease through the stimulation of inflammatory responses, S100s and defensins. IL-22 also promotes hepatocyte survival in the liver and epithelial cells in the lung and gut similar to IL-10. In some contexts, the pro-inflammatory versus tissue-protective functions of IL-22 are regulated by the often co-expressed cytokine IL-17A. IL-22 confirms regulation of antimicrobial proteins. Targeting the IL-22–IL-22R pathway may yield new therapeutic potential for the treatment of certain human diseases. Conclusion: IL-22 is expressed constitutively by LTi-like cells within the small intestine, a tissue that is under the careful immune balance between inflammation and tolerance. Gaining a better understanding of the expression and role of IL-22 in health and disease is important for development of IL-22 as a potential drug target IL-22 is expressed constitutively by LTi-like cells within the small intestine, a tissue that is under the careful immune balance between inflammation and tolerance. Obtaining a better understanding of the expression and role of IL-22 in health and disease is important for development of IL-22 as a potential drug target

Strategic Environment Analysis Using DEMATEL Method Through Systematic Approach:

A combined model for Environmental Analysis (EA) in strategy formulation process is presented in this paper. EA is the critical element in strategic planning. Because of direct effect on quality of results, different quantitative or qualitative approaches have been developed. In this paper, steps of EA using values tools such as System Dynamics, expert panels, DEMATEL method, designed and explained in the integrated model. In first step, all different factors are identified and classified, and then using a questionnaire, related factors are listed. The causal model identifies the main causes and effects. DEMATEL method specifies priorities of each factor. By using the influenced-influencing matrix, key factors will be determined. In all stages, panel of experts plays complementary and approval role. Finally we applied this model in strategic planning processes of an energy research institute in Iran as a case study. Key words: Environmental Analysis; Systems Approach; Causal loop diagram; DEMATEL Method; Expert Panel

Self-Supervised Physics-Based Deep Learning MRI Reconstruction Without Fully-Sampled Data

Deep learning (DL) has emerged as a tool for improving accelerated MRI reconstruction. A common strategy among DL methods is the physics-based approach, where a regularized iterative algorithm alternating between data consistency and a regularizer is unrolled for a finite number of iterations. This unrolled network is then trained end-to-end in a supervised manner, using fully-sampled data as ground truth for the network output. However, in a number of scenarios, it is difficult to obtain fully-sampled datasets, due to physiological constraints such as organ motion or physical constraints such as signal decay. In this work, we tackle this issue and propose a self-supervised learning strategy that enables physics-based DL reconstruction without fully-sampled data. Our approach is to divide the acquired sub-sampled points for each scan into training and validation subsets. During training, data consistency is enforced over the training subset, while the validation subset is used to define the loss function. Results show that the proposed self-supervised learning method successfully reconstructs images without fully-sampled data, performing similarly to the supervised approach that is trained with fully-sampled references. This has implications for physics-based inverse problem approaches for other settings, where fully-sampled data is not available or possible to acquire.Comment: 5 Pages, 5 Figure

Multi-Mask Self-Supervised Learning for Physics-Guided Neural Networks in Highly Accelerated MRI

Purpose: To develop an improved self-supervised learning strategy that efficiently uses the acquired data for training a physics-guided reconstruction network without a database of fully-sampled data. Methods: Currently self-supervised learning for physics-guided reconstruction networks splits acquired undersampled data into two disjoint sets, where one is used for data consistency (DC) in the unrolled network and the other to define the training loss. The proposed multi-mask self-supervised learning via data undersampling (SSDU) splits acquired measurements into multiple pairs of disjoint sets for each training sample, while using one of these sets for DC units and the other for defining loss, thereby more efficiently using the undersampled data. Multi-mask SSDU is applied on fully-sampled 3D knee and prospectively undersampled 3D brain MRI datasets, which are retrospectively subsampled to acceleration rate (R)=8, and compared to CG-SENSE and single-mask SSDU DL-MRI, as well as supervised DL-MRI when fully-sampled data is available. Results: Results on knee MRI show that the proposed multi-mask SSDU outperforms SSDU and performs closely with supervised DL-MRI, while significantly outperforming CG-SENSE. A clinical reader study further ranks the multi-mask SSDU higher than supervised DL-MRI in terms of SNR and aliasing artifacts. Results on brain MRI show that multi-mask SSDU achieves better reconstruction quality compared to SSDU and CG-SENSE. Reader study demonstrates that multi-mask SSDU at R=8 significantly improves reconstruction compared to single-mask SSDU at R=8, as well as CG-SENSE at R=2. Conclusion: The proposed multi-mask SSDU approach enables improved training of physics-guided neural networks without fully-sampled data, by enabling efficient use of the undersampled data with multiple masks