Deep Belief Networks (DBN) with IoT-Based Alzheimer’s Disease Detection and Classification

Dementias that develop in older people test the limits of modern medicine. As far as dementia in older people goes, Alzheimer’s disease (AD) is by far the most prevalent form. For over fifty years, medical and exclusion criteria were used to diagnose AD, with an accuracy of only 85 per cent. This did not allow for a correct diagnosis, which could be validated only through postmortem examination. Diagnosis of AD can be sped up, and the course of the disease can be predicted by applying machine learning (ML) techniques to Magnetic Resonance Imaging (MRI) techniques. Dementia in specific seniors could be predicted using data from AD screenings and ML classifiers. Classifier performance for AD subjects can be enhanced by including demographic information from the MRI and the patient’s preexisting conditions. In this article, we have used the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. In addition, we proposed a framework for the AD/non-AD classification of dementia patients using longitudinal brain MRI features and Deep Belief Network (DBN) trained with the Mayfly Optimization Algorithm (MOA). An IoT-enabled portable MR imaging device is used to capture real-time patient MR images and identify anomalies in MRI scans to detect and classify AD. Our experiments validate that the predictive power of all models is greatly enhanced by including early information about comorbidities and medication characteristics. The random forest model outclasses other models in terms of precision. This research is the first to examine how AD forecasting can benefit from using multimodal time-series data. The ability to distinguish between healthy and diseased patients is demonstrated by the DBN-MOA accuracy of 97.456%, f-Score of 93.187 %, recall of 95.789 % and precision of 94.621% achieved by the proposed technique. The experimental results of this research demonstrate the efficacy, superiority, and applicability of the DBN-MOA algorithm developed for the purpose of AD diagnosis

Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain

The emergence of the Internet of Things (IoT) and its subsequent evolution into the Internet of Everything (IoE) is a result of the rapid growth of information and communication technologies (ICT). However, implementing these technologies comes with certain obstacles, such as the limited availability of energy resources and processing power. Consequently, there is a need for energy-efficient and intelligent load-balancing models, particularly in healthcare, where real-time applications generate large volumes of data. This paper proposes a novel, energy-aware artificial intelligence (AI)-based load balancing model that employs the Chaotic Horse Ride Optimization Algorithm (CHROA) and big data analytics (BDA) for cloud-enabled IoT environments. The CHROA technique enhances the optimization capacity of the Horse Ride Optimization Algorithm (HROA) using chaotic principles. The proposed CHROA model balances the load, optimizes available energy resources using AI techniques, and is evaluated using various metrics. Experimental results show that the CHROA model outperforms existing models. For instance, while the Artificial Bee Colony (ABC), Gravitational Search Algorithm (GSA), and Whale Defense Algorithm with Firefly Algorithm (WD-FA) techniques attain average throughputs of 58.247 Kbps, 59.957 Kbps, and 60.819 Kbps, respectively, the CHROA model achieves an average throughput of 70.122 Kbps. The proposed CHROA-based model presents an innovative approach to intelligent load balancing and energy optimization in cloud-enabled IoT environments. The results highlight its potential to address critical challenges and contribute to developing efficient and sustainable IoT/IoE solutions

Sickle Cell Illness Awareness among the General Public

Background: Lifelong ickle cell disease (SCD), a group of inherited blood disorders, afflicts millions of individuals. Sickle cell disease (SCD), with a global prevalence of 112 cases per 100,000 individuals, frequently gives rise to this condition. Sickle Cell Disease (SCD) exhibits a high prevalence in various regions, including Sub-Saharan Africa, Saudi Arabia, India, South and Central America, as well as the Mediterranean. We conducted a study in Tabuk, Saudi Arabia to assess the level of public knowledge and awareness of Sickle Cell Disease (SCD). Methods: The present study employed a cross-sectional observational design, encompassing a sample of 386 individuals residing in Tabuk, who were over the age of 18 and represented both genders and various nationalities. Demographic data and sickle cell disease awareness were obtained through the utilization of a structured questionnaire that was developed from previous research. Results: The present study included a total of 386 adults residing in Tabuk, Saudi Arabia, who satisfied the predetermined inclusion criteria. Among the participants, 47.4% fell between the age range of 18 to 25 years. The majority of participants had a satisfactory level of knowledge, with 24.1% of individuals aged 18-25, 10.1% of those aged 26-35, 7.3% and 6.55% of individuals aged 36-45, and a significant proportion of participants aged over 45. Conclusion: The survey participants demonstrated a satisfactory degree of understanding on the prevalence of sickle cell disease (SCD) in the Kingdom of Saudi Arabia (KSA).&nbsp

Improvement of the thalamocortical white matter network in people with stable treated relapsing-remitting multiple sclerosis over time

Advanced imaging techniques (tractography) enable the mapping of white matter (WM) pathways and the understanding of brain connectivity patterns. We combined tractography with a network-based approach to examine WM microstructure on a network level in people with relapsing–remitting multiple sclerosis (pw-RRMS) and healthy controls (HCs) over 2 years. Seventy-six pw-RRMS matched with 43 HCs underwent clinical assessments and 3T MRI scans at baseline (BL) and 2-year follow-up (2-YFU). Probabilistic tractography was performed, accounting for the effect of lesions, producing connectomes of 25 million streamlines. Network differences in fibre density across pw-RRMS and HCs at BL and 2-YFU were quantified using network-based statistics (NBS). Longitudinal network differences in fibre density were quantified using NBS in pw-RRMS, and were tested for correlations with disability, cognition and fatigue scores. Widespread network reductions in fibre density were found in pw-RRMS compared with HCs at BL in cortical regions, with more reductions detected at 2-YFU. Pw-RRMS had reduced fibre density at BL in the thalamocortical network compared to 2-YFU. This effect appeared after correction for age, was robust across different thresholds, and did not correlate with lesion volume or disease duration. Pw-RRMS demonstrated a robust and long-distance improvement in the thalamocortical WM network, regardless of age, disease burden, duration or therapy, suggesting a potential locus of neuroplasticity in MS. This network's role over the disease's lifespan and its potential implications in prognosis and treatment warrants further investigation.</p

Stability of longitudinal DTI metrics in MS with treatment of injectables, fingolimod and dimethyl fumarate

Background and purpose: Diffusion MRI (dMRI) is sensitive to microstructural changes in white matter of people with relapse-remitting multiple sclerosis (pw-RRMS) that lead to progressive disability. The role of diffusion in assessing the efficacy of different therapies requires more investigation. This study aimed to evaluate selected dMRI metrics in normal-appearing white matter and white matter-lesion in pw-RRMS and healthy controls longitudinally and compare the effect of therapies given. Material and methods: Structural and dMRI scans were acquired from 78 pw-RRMS (29 injectables, 36 fingolimod, 13 dimethyl fumarate) and 43 HCs at baseline and 2-years follow-up. Changes in dMRI metrics and correlation with clinical parameters were evaluated. Results: Differences were observed in most clinical parameters between pw-RRMS and HCs at both timepoints (p ≤ 0.01). No significant differences in average changes over time were observed for any dMRI metric between treatment groups in either tissue type. Diffusion metrics in NAWM and WML correlated negatively with most cognitive domains, while FA correlated positively at baseline but only for NAWM at follow-up (p ≤ 0.05). FA correlated negatively with disability in NAWM and WML over time, while MD and RD correlated positively only in NAWM. Conclusions: This is the first DTI study comparing the effect of different treatments on dMRI parameters over time in a stable cohort of pw-RRMS. The results suggest that brain microstructural changes in a stable MS cohort are similar to HCs independent of the therapies used.</p

CEST 2022 - Differences in APT-weighted signal in T1 weighted isointense lesions, black holes and normal-appearing white matter in people with relapsing-remitting multiple sclerosis

Purpose: To evaluate amide proton transfer weighted (APTw) signal differences between multiple sclerosis (MS) lesions and contralateral normal-appearing white matter (cNAWM). Cellular changes during the demyelination process were also assessed by comparing APTw signal intensity in T1weighted isointense (ISO) and hypointense (black hole -BH) MS lesions in relation to cNAWM. Methods: Twenty-four people with relapsing-remitting MS (pw-RRMS) on stable therapy were recruited. MRI/APTw acquisitions were undertaken on a 3 T MRI scanner. The pre and post-processing, analysis, co-registration with structural MRI maps, and identification of regions of interest (ROIs) were all performed with Olea Sphere 3.0 software. Generalized linear model (GLM) univariate ANOVA was undertaken to test the hypotheses that differences in mean APTw were entered as dependent variables. ROIs were entered as random effect variables, which allowed all data to be included. Regions (lesions and cNAWM) and/or structure (ISO and BH) were the main factor variables. The models also included age, sex, disease duration, EDSS, and ROI volumes as covariates. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic performance of these comparisons. Results: A total of 502 MS lesions manually identified on T2-FLAIR from twenty-four pw-RRMS were subcategorized as 359 ISO and 143 BH with reference to the T1-MPRAGE cerebral cortex signal. Also, 490 ROIs of cNAWM were manually delineated to match the MS lesion positions. A two-tailed t-test showed that mean APTw values were higher in females than in males (t = 3.52, p 75% (AUC = 0.79, SE = 0.014). Discrimination between ISO lesions and cNAWM was accomplished with an accuracy of >69% (AUC = 0.74, SE = 0.018), while discrimination between BH lesions and cNAWM was achieved at an accuracy of >80% (AUC = 0.87, SE = 0.021). Conclusions: Our results highlight the potential of APTw imaging for use as a non-invasive technique that is able to provide essential molecular information to clinicians and researchers so that the stages of inflammation and degeneration in MS lesions can be better characterized.</p

Diffusion tensor imaging changes of the cortico-thalamic-striatal tracts correlate with fatigue and disability in people with relapsing-remitting MS

Purpose: To investigate how the microstructural neural integrity of cortico-thalamic-striatal (CTS) tracts correlate with fatigue and disability over time. The primary outcome was diffusion tensor imaging (DTI) metrics change over time, and the secondary outcome was correlations with fatigue and disability in people with RRMS (pw-RRMS). Methods: 76 clinically stable pw-RRMS and 43 matched healthy controls (HCs). The pw-RRMS cohort consisted of three different treatment subgroups. All participants underwent disability, cognitive, fatigue and mental health assessments. Structural and diffusion scans were performed at baseline (BL) and 2-year follow-up (2-YFU) for all participants. Fractional anisotropy (FA), mean, radial and axial diffusivities (MD, RD, AD) of normal-appearing white matter (NAWM) and white matter lesion (WML) in nine tracts-of-interests (TOIs) were estimated using our MRtrix3 in-house pipeline. Results: We found significant BL and 2-YFU differences in most diffusion metrics in TOIs in pw-RRMS compared to HCs (pFDR ≤ 0.001; false-detection-rate (FDR)-corrected). There was a significant decrease in WML diffusivities and an increase in FA over the follow-up period in most TOIs (pFDR ≤ 0.001). Additionally, there were no differences in DTI parameters across treatment groups. AD and MD were positively correlated with fatigue scores (r ≤ 0.33, p ≤ 0.01) in NAWM-TOIs, while disability (EDSS) was negatively correlated with FA in most NAWM-TOIs (|r|≤0.31, p ≤ 0.01) at both time points. Disability scores correlated with all diffusivity parameters (r ≤ 0.29, p ≤ 0.01) in most WML-TOIs at both time points. Conclusion: Statistically significant changes in diffusion metrics in WML might be indicative of integrity improvement over two years in CTS tracts in clinically stable pw-RRMS. This finding represents structural changes within lesioned tracts. Measuring diffusivity in pw-RRMS affected tracts might be a relevant measure for future remyelination clinical trials.</p

Additional file 1: of Adherence to Brain Trauma Foundation guidelines for management of traumatic brain injury patients: study protocol for a systematic review and meta-analysis

Appendix 1. Proposed Medline search strategy; Appendix 2. Proposed Embase search strategy; Appendix 3. Proposed EBM Reviews—Cochrane Database of Systematic Reviews search strategy. (DOCX 24 kb

Knowledge and attitude toward biological warfare among health-related students: A cross-sectional questionnaire-based survey

Purpose: Many types of research have been published on the history of biological warfare, the agents used, and the medical implications. However, no studies measure how people are aware of the magnitude of these health problems and international threats. The present study aimed to produce and make background about biological warfare information for health college students to be used as a basis for future studies or research and prepare the hospitals' bases for similar disasters. Methods: This observational, cross-sectional, descriptive study was conducted among undergraduate students (N = 626) enrolled in health-related colleges at Jazan University, Saudi Arabia. A preliminary survey of 30 participants was then undertaken to improve the questionnaire's understanding and validity. The questionnaire encompassed three primary sections, including (1) sociodemographic characteristics, (2) knowledge, and (3) awareness. Sociodemographic characteristics consisted of age, gender, college type, academic level, and specialty. All data were gathered using an online self-reported questionnaire using Google Forms and participants were recruited using a random sampling strategy. Results: The total participants were 626 students; 514 were females, whereas 112 were males. Knowledge and attitude indices were 3.8650 ± 0.48 and 4.06 ± 0.51 (maximum is 5). The indices showed variable statistical differences among sociodemographic factors. With adjusted and crude odds ratios of 0.53 and 0.54, attitude score is the sole significant (P = 0.05) predictor of knowledge as analyzed using logistic regression. Conclusion: The results of the present study are the first of their kind in the region and can be used to shape public awareness among specialists and decision-makers, especially in light of the recent pandemic