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Nuclear Terrorism
Published in Robert A. Burke, Counter-Terrorism for Emergency Responders, 2017
Victims who show symptoms of central nervous system damage within the first hour have likely received a superlethal dose. The prodromal phase lasts only a few hours. While the preliminary symptoms of nausea, vomiting, and malaise are nonspecific, when nuclear exposure is suspected and in the absence of other causes, radiation exposure should be considered. A triage of suspected radiation victims can be placed in three categories: radiation injury unlikely, radiation injury probable, and radiation injury severe. The latent phase may last for 2–6 weeks, where the victim will not show any symptoms. Following the latent phase, the manifest phase develops in which the bone marrow, intestines, and neurovascular systems become affected by the radiation exposure. Diagnosis of radiation sickness will be based on the symptoms exhibited by the patient. Exposure history will also be helpful if available. Certainly patients exposed to a conventional explosion who develop radiation sickness symptoms should be evaluated for radiation exposure. The explosive device may have been used to disseminate nuclear materials.
Risk of cognitive impairment from exposure to incense smoke
Published in International Journal of Environmental Health Research, 2023
Sun-Wung Hsieh, Szu-Chia Chen, Chun-Hung Chen, Ming-Tsang Wu, Chih-Hsing Hung
AD is a worldwide neurodegenerative disease, with preceding from prodromal stages of subjective cognitive impairment (SCI) and mild cognitive impairment (MCI) (Petersen et al. 2001; Jessen et al. 2014). It is estimated that 42.5% incidence rate at 5 years from the progression of MCI to AD if without effective and preventive therapy (Duara et al. 2011). SCI or MCI are often under-diagnosed and it is crucial for the start of preventive therapy. Although the MMSE was observed within normal range in our study, it indicated MMSE and registration ability were slightly worse in participants with incense exposure. Our study did not show higher proportion of vascular risk factors including diabetes, hypertension, hyperlipidemia, gouts or smoking in those with incense exposure, but incense burning was considered to link with vascular risk factors to predispose poor cognitive function and registration ability. This might contribute the role of incense burning to be the potential risk of vascular cognitive impairment. Accordingly, incense burning should be practiced with cautions in elder vulnerable population with high prevalence of vascular diseases. Long-term follow-up in detecting vascular events and cognitive function were suggested for those with incense exposure.
Study of onset in brain dementia using hierarchical wolf colony optimization and dual deep learning technique
Published in The Imaging Science Journal, 2022
Ahana Priyanka Nedunchellian, Kavitha Ganesan
Dementia is said to cause a rapid deterioration in the cognitive ability of the brain. Globally, 90 million people are claimed to be affected, and it is predicted to increase massively by 2050 as per the Alzheimer’s disease international report [1]. The prodromal stages of this mitigating disorder are Early mild cognitive impairment (EMCI), Late mild cognitive impairment (LMCI), Mild cognitive impairment (MCI), and Alzheimer’s disease (AD). The detection of each progressive stage is difficult because of the prominent aetiology and unclear overlap in severity levels [2]. The clinical diagnosis tests widely considered to determine the range of atrophy for dementia are Clinical Dementia Rate (CDR) and Mini mental State Examination (MMSE) [3]. The Magnetic Resonance (MR) images effectively characterize the structural changes that can serve as prominent biomarkers.
Analyzing the dynamics of sleep electroencephalographic (EEG) signals with different pathologies using threshold-dependent symbolic entropy
Published in Waves in Random and Complex Media, 2021
Lal Hussain, Saeed Arif Shah, Wajid Aziz, Syed Nadeem Haider Bukhari, Kashif Javed Lone, Quratul-Ain Chaudhary
To understand the dynamics of physiological signals and systems, researchers employed different information -theoretic approaches Hussain [37] employed threshold-dependent symbolic entropy to study the dynamics of neurophysiological signals. Recently, MSE is used for the analysis of speech signals to quantify the disorders in vocal patterns indicative of sleep apnea [56]. Based on the variability, spectral analysis, nonlinearity, and nonstationarity of physiological signals, the researchers extracted multimodal features [36,37,57–65] and employed robust complexity-based information-theoretic approaches and machine learning methods to quantifying the nonlinear dynamics in physiological signals. The interactions between different brain regions during resting states were extensively studied by [65] to determine the relationship and coupling. Hussain [34] extracted hybrid features based on refined fuzzy entropy to detect the Arrhythmia. Likewise, [61] used the biomarkers for the early detection of Alzheimer’s disease (AD) to improve the accuracy of imaging-based prediction of AD and its prodromal stage that is a mild cognitive impairment (MCI). A comparision of findings with previous studies is reflected in Table 2.