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Gastrointestinal cancers
Published in Ruijiang Li, Lei Xing, Sandy Napel, Daniel L. Rubin, Radiomics and Radiogenomics, 2019
Radiomics can be applied to a large number of conditions in gastrointestinal cancers, with emerging research that aims to aid in tumor characterization, staging, treatment planning, assessment of prognosis, and longitudinal response monitoring. In this section, a detailed review of the clinical application of radiomics features in gastrointestinal cancers will be summarized and categorized according to its major topics. The largest proportion of these studies have concentrated on the therapeutic response or clinical outcome predictions in patients with a specific treatment because heterogeneity of the diseases is a potential mechanism (Holzel et al. 2013, Junttila and de Sauvage 2013, McGranahan and Swanton 2017) that accounts for recurrence or treatment failure in a specific therapy and requires patient selection for better management. Histological grading and subtyping using radiomics features are also common because they are associated with tumor characterization and risk stratification. While ostensibly for the application of differential diagnoses, most studies were preliminary research aimed at testing a new classifier or algorithm.
The Amazing Architecture of the Human Immune System
Published in Rocky Dr. Termanini, The Nano Age of Digital Immunity Infrastructure Fundamentals and Applications, 2018
The application of nanotechnology in cancer research has provided hope within the scientific community for the development of novel cancer therapeutic strategies. As gastrointestinal cancers contribute to more than 55% of deaths associated with cancer, tremendous efforts have been made toward the development of novel diagnostic and therapeutic methods for improving patient quality of life and lengthening survival. Advances in image-based detection, targeted drug delivery, and metastases ablation could go a long way to improve patient outcomes. Classical approaches generally do not meet patients’ expectations due to a lack of specificity and poor patient stratification. More highly targeted and customized treatments are needed. Toward this goal, nanotechnologies and nano devices have been explored for their potential utilities in advancing targeted therapeutic approaches.
Human Health Studies
Published in Barry L. Johnson, Impact of Hazardous Waste on Human Health, 2020
New Jersey Municipalities: Clusters of cancer mortality in New Jersey municipalities were investigated by Najem et al. (1985). Their previous work had shown elevated cancer mortality rates in 20 of New Jersey’s counties in comparison with U.S. national rates for several cancers (Najem et al., 1983). In the 1985 study, Najem et al. obtained death and birth certificates and other data from the vital statistics records of the state of New Jersey. The period 1968–1977 was selected for study because of the availability of vital statistics data. Thirteen anatomical major cancer sites were studied; annual age-adjusted mortality raters (per 100,000 population) were calculated for 194 municipalities (10,000 or more population) listed on death certificates as the community of usual residence. The investigators arbitrarily defined a cancer cluster if a community had: (1) two or more age-adjusted cancer rates that were at least 50% greater than corresponding national rates at a significance level of at least 0.01 and (2) one or more cancer rates that was significantly (set at p< 0.0005) higher than national rates. In 10 New Jersey counties, 23 municipalities met the criteria for cancer clusters; 16 of the 23 were located in the heavily industrial northeast area of the state. Of the cancers in the municipalities with clusters of excessive mortality, 72% were gastrointestinal cancers, especially the stomach, rectum, and colon. Correlation analyses indicated that most of the cancer rates were negatively associated with annual per capita income and positively with the density of chemical toxic waste disposal sites.
Health risk assessment in different age-group due to nitrate, fluoride, nitrite and geo-chemical parameters in drinking water in Ahmadpur East, Punjab, Pakistan
Published in Human and Ecological Risk Assessment: An International Journal, 2021
Iftikhar Alam, Jalil Ur Rehman, S. Nazir, Aalia Nazeer, Muhammad Akram, Zahida Batool, Hafeez Ullah, Aslam Hameed, Altaf Hussain, Abid Hussain, M. Bilal Tahir
Drinking water (DW) is an important exigency for human life-style. It has significant impact on human health in both quantitative and qualitative levels. DW quality is affecting worldwide due to climate change, increase in population and anthropic activities (Hadi Rezaeia et al. 2019). According to World Health Organization (WHO) report, 50% populations in developed countries and 60% in under developing countries have health issues due to contaminated and/or deficiencies of drinking water (Kazmi and Khan 2005). Biologically and chemically, unsafe drinking water causes serious difficulties in surviving human being and animals lives. One of reasons of pollutant DW is high concentration of nitrite, fluoride and nitrate in the water. Nitrate ion (NO3−) and nitrite ion (NO2−) being components of the nitrogen cycle exist naturally in water. Nitrate is relatively more stable ion of combined nitrogen than nitrite in oxygenated systems. Reactivity of nitrite is greater than nitrate (ICAIR 1987). Fertilizer soil and organic nitrogen wastes are main sources of nitrate and nitrite. Both formed ammonia by decomposition then create nitrate and nitrite by oxidation that move toward ground water (USEPA 1987). Experimentally, it is concluded that surface water is low polluted by nitrite, fluoride and nitrate than ground water (Lee 1992). Non-permissible stage of concentrations of nitrite, fluoride and nitrate in DW is caused due to excess usage of pesticide in agricultural land, sewerage water or waste water of factories. In Pakistan, in 1986, W.H.O. reported that standard values of fluoride, nitrate and nitrite in DW are 1.5, 50 and 3 mg/L, respectively (Kazmi and Khan 2005). Methemoglobinemia (disease in infant) is due to nitrate-exposure in DW that is deficiency of oxygen in respiratory system in infant. Large amount of nitrate causes stomach cancer, women abortion, gastrointestinal-cancer, oral cancer and rectum cancer in human and animals (Liyan Liu 2019). High dose intake of nitrite in DW and food causes carcinogenic diseases in human and animal. Ground water contains many ions including fluoride in different concentrations in different regions. WHO recommended the permissible level of fluoride ions in DW from 0.8 to 1.2 L-1 and if this range exceeds can cause dental and bone diseases. Neurological symptoms like headache, sleepiness, etc. are raised due to high level of fluoride ions in drinking water (Panezai et al. 2018).
A fast and robust OSRAD filter for telemedicine applications
Published in International Journal of Computers and Applications, 2021
Amira Hadj Fredj, Jihene Malek
For endoscopic applications, the main objective of a real-time image processing system is to produce a video output that supplements the normal endoscope video used by the physician [8]. This is achieved by performing some combinations of noise filtering, contrast enhancement, and abnormal tissue recognition on the endoscope video. To identify and remove an abnormal tissue mass that grows on the mucous membranes of the gastrointestinal system, endoscopy is an effective preventative measure against gastrointestinal cancer. Moreover, the physician must scrutinize the video for abnormalities while taking care of navigating the endoscope through body cavities without causing harm to the patient. There is also an impetus to perform the procedure quickly due to the discomfort caused to the patient. Therefore, a fast processing of videos is needed to ensure the treatment of 25 high definition frames per second (25 fps). To overcome these constraints, several Graphics Processing Unit (GPU) computing approaches have recently been proposed. Although they present a great potential of a GPU platform, any one is able to process high definition video sequences efficiently. Thus, a need has arisen to develop a tool being able to address the outlined problem. In the real-time system community, GPUs have been studied actively in recent years because of their potential benefits in demanding data-parallel real-time applications. Integrated CPU-GPU architecture-based System-On-a-Chip (SOC) platforms are increasingly demanded for performance medical image processing [9, 10]. The medical image processing such as filtering requires high computing performance to process the vast amount of data flowing from a variety of images in real-time while satisfying a number of size, weight and power constraints. This makes systems with integrated CPU-GPU architectures an attractive option for medical image processing since such systems can provide high performance while meeting requirements (size, weight and power). The contribution of this work is the fast implementation of a filtering algorithm for different images and video sequences using the Compute Unified Device Architecture (CUDA) language. CUDA is one of the parallel computing language through which we can make programs faster. It is a model specific to GPUs which have distinctly different architectures compared to the traditional CPUs. There are plenty of others–TBB, OpenCL, OpenMP, MPI, Cilk, etc. In fact, MPI and OpenMP are SPMD (single program, multiple data) models to control and synchronize sets of independent computations. CUDA is a SIMT (Single Instruction, Multiple Thread) model to control graphics coprocessors, and it is pretty customized to its purpose.