| 1. |
- Kaushik, Sandeep S., et al.
(författare)
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Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network
- 2023
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Ingår i: Physics in Medicine and Biology. - : Institute of Physics (IOP). - 0031-9155 .- 1361-6560. ; 68:19
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Tidskriftsartikel (refereegranskat)abstract
- Objective: In MR-only clinical workflow, replacing CT with MR image is of advantage for workflow efficiency and reduces radiation to the patient. An important step required to eliminate CT scan from the workflow is to generate the information provided by CT via an MR image. In this work, we aim to demonstrate a method to generate accurate synthetic CT (sCT) from an MR image to suit the radiation therapy (RT) treatment planning workflow. We show the feasibility of the method and make way for a broader clinical evaluation.Approach: We present a machine learning method for sCT generation from zero-echo-time (ZTE) MRI aimed at structural and quantitative accuracies of the image, with a particular focus on the accurate bone density value prediction. The misestimation of bone density in the radiation path could lead to unintended dose delivery to the target volume and results in suboptimal treatment outcome. We propose a loss function that favors a spatially sparse bone region in the image. We harness the ability of the multi-task network to produce correlated outputs as a framework to enable localization of region of interest (RoI) via segmentation, emphasize regression of values within RoI and still retain the overall accuracy via global regression. The network is optimized by a composite loss function that combines a dedicated loss from each task.Main results: We have included 54 brain patient images in this study and tested the sCT images against reference CT on a subset of 20 cases. A pilot dose evaluation was performed on 9 of the 20 test cases to demonstrate the viability of the generated sCT in RT planning. The average quantitative metrics produced by the proposed method over the test set were-(a) mean absolute error (MAE) of 70 ± 8.6 HU; (b) peak signal-to-noise ratio (PSNR) of 29.4 ± 2.8 dB; structural similarity metric (SSIM) of 0.95 ± 0.02; and (d) Dice coefficient of the body region of 0.984 ± 0.Significance: We demonstrate that the proposed method generates sCT images that resemble visual characteristics of a real CT image and has a quantitative accuracy that suits RT dose planning application. We compare the dose calculation from the proposed sCT and the real CT in a radiation therapy treatment planning setup and show that sCT based planning falls within 0.5% target dose error. The method presented here with an initial dose evaluation makes an encouraging precursor to a broader clinical evaluation of sCT based RT planning on different anatomical regions.
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| 2. |
- Sharma, Shikha, et al.
(författare)
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Emerging challenges in antimicrobial resistance : implications for pathogenic microorganisms, novel antibiotics, and their impact on sustainability
- 2024
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Ingår i: Frontiers in Microbiology. - 1664-302X. ; 15
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Forskningsöversikt (refereegranskat)abstract
- Overuse of antibiotics is accelerating the antimicrobial resistance among pathogenic microbes which is a growing public health challenge at the global level. Higher resistance causes severe infections, high complications, longer stays at hospitals and even increased mortality rates. Antimicrobial resistance (AMR) has a significant impact on national economies and their health systems, as it affects the productivity of patients or caregivers due to prolonged hospital stays with high economic costs. The main factor of AMR includes improper and excessive use of antimicrobials; lack of access to clean water, sanitation, and hygiene for humans and animals; poor infection prevention and control measures in hospitals; poor access to medicines and vaccines; lack of awareness and knowledge; and irregularities with legislation. AMR represents a global public health problem, for which epidemiological surveillance systems have been established, aiming to promote collaborations directed at the well-being of human and animal health and the balance of the ecosystem. MDR bacteria such as E. coli, Staphylococcus aureus, Pseudomonas aeruginosa, Enterococcus spp., Acinetobacter spp., and Klebsiella pneumonia can even cause death. These microorganisms use a variety of antibiotic resistance mechanisms, such as the development of drug-deactivating targets, alterations in antibiotic targets, or a decrease in intracellular antibiotic concentration, to render themselves resistant to numerous antibiotics. In context, the United Nations issued the Sustainable Development Goals (SDGs) in 2015 to serve as a worldwide blueprint for a better, more equal, and more sustainable existence on our planet. The SDGs place antimicrobial resistance (AMR) in the context of global public health and socioeconomic issues; also, the continued growth of AMR may hinder the achievement of numerous SDGs. In this review, we discuss the role of environmental pollution in the rise of AMR, different mechanisms underlying the antibiotic resistance, the threats posed by pathogenic microbes, novel antibiotics, strategies such as One Health to combat AMR, and the impact of resistance on sustainability and sustainable development goals.
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| 3. |
- Singh Tuli, Hardeep, et al.
(författare)
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Luteolin, a Potent Anticancer Compound : From Chemistry to Cellular Interactions and Synergetic Perspectives
- 2022
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Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 14:21
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Forskningsöversikt (refereegranskat)abstract
- Increasing rates of cancer incidence and the toxicity concerns of existing chemotherapeutic agents have intensified the research to explore more alternative routes to combat tumor. Luteolin, a flavone found in numerous fruits, vegetables, and herbs, has exhibited a number of biological activities, such as anticancer and anti-inflammatory. Luteolin inhibits tumor growth by targeting cellular processes such as apoptosis, cell-cycle progression, angiogenesis and migration. Mechanistically, luteolin causes cell death by downregulating Akt, PLK-1, cyclin-B1, cyclin-A, CDC-2, CDK-2, Bcl-2, and Bcl-xL, while upregulating BAX, caspase-3, and p21. It has also been reported to inhibit STAT3 signaling by the suppression of STAT3 activation and enhanced STAT3 protein degradation in various cancer cells. Therefore, extensive studies on the anticancer properties of luteolin reveal its promising role in chemoprevention. The present review describes all the possible cellular interactions of luteolin in cancer, along with its synergistic mode of action and nanodelivery insight.
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