130 research outputs found
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
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