A Framework on A Computer Assisted and Systematic Methodology for Detection of Chronic Lower Back Pain using Artificial Intelligence and Computer Graphics Technologies

Back pain is one of the major musculoskeletal pain problems that can affect many people and is considered as one of the main causes of disability all over the world. Lower back pain, which is the most common type of back pain, is estimated to affect at least 60% to 80% of the adult population in the United Kingdom at some time in their lives. Some of those patients develop a more serious condition namely Chronic Lower Back Pain in which physicians must carry out a more involved diagnostic procedure to determine its cause. In most cases, this procedure involves a long and laborious task by the physicians to visually identify abnormalities from the patient’s Magnetic Resonance Images. Limited technological advances have been made in the past decades to support this process. This paper presents a comprehensive literature review on these technological advances and presents a framework of a methodology for diagnosing and predicting Chronic Lower Back Pain. This framework will combine current state-of-the-art computing technologies including those in the area of artificial intelligence, physics modelling, and computer graphics, and is argued to be able to improve the diagnosis process

Sinusoidal obstruction syndrome/veno-occlusive disease: current situation and perspectives—a position statement from the European Society for Blood and Marrow Transplantation (EBMT)

Sinusoidal obstruction syndrome or veno-occlusive disease (SOS/VOD) is a potentially life-threatening complication of hematopoietic SCT (HSCT). This review aims to highlight, on behalf of the European Society for Blood and Marrow Transplantation, the current knowledge on SOS/VOD pathophysiology, risk factors, diagnosis and treatments. Our perspectives on SOS/VOD are (i) to accurately identify its risk factors; (ii) to define new criteria for its diagnosis; (iii) to search for SOS/VOD biomarkers and (iv) to propose prospective studies evaluating SOS/VOD prevention and treatment in adults and children

Exploring the Nexus between Profitability, Dividend Policy and Share Prices in Kuwaiti Insurance Companies

The purpose of this study is to investigate the impact of dividend policy and profitability ratios on the share prices of insurance companies listed on Kuwait Stock Exchange (KSE) between 2014 and 2022. The study's findings demonstrated that 42.4% of share prices could be explained by factors related to profitability and dividend policy. Earnings per share (EPS) was the only variable that demonstrated a significant direct relationship with share prices when the individual effects of each variable were examined. While dividend payout ratio (DPR) exhibited a negative correlation with stock prices, it was not statistically significant. Other characteristics that were considered included dividend yield (DY) and interest rate (IR), both of which showed significant inverse relations. This study concludes that investors in Kuwait Stock Exchange (KSE) shares of the insurance sector favor unpredictable future capital gains over more assured dividends

Fuzzy Logical Algebra and Study of the Effectiveness of Medications for COVID-19

A fuzzy logical algebra has diverse applications in various domains such as engineering, economics, environment, medicine, and so on. However, the existing techniques in algebra do not apply to delta-algebra. Therefore, the purpose of this paper was to investigate new types of cubic soft algebras and study their applications, the representation of cubic soft sets with δ-algebras, and new types of cubic soft algebras, such as cubic soft δ-subalgebra based on the parameter λ (λ-CSδ-SA) and cubic soft δ-subalgebra (CSδ-SA) over η. This study explains why the P-union is not really a soft cubic δ-subalgebra of two soft cubic δ-subalgebras. We also reveal that any R/P-cubic soft subsets of (CSδ-SA) is not necessarily (CSδ-SA). Furthermore, we present the required conditions to prove that the R-union of two members is (CSδ-SA) if each one of them is (CSδ-SA). To illustrate our assumptions, the proposed (CSδ-SA) is applied to study the effectiveness of medications for COVID-19 using the python program

Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model

Microarray data examination is a relatively new technology that intends to determine the proper treatment for various diseases and a precise medical diagnosis by analyzing a massive number of genes in various experimental conditions. The conventional data classification techniques suffer from overfitting and the high dimensionality of gene expression data. Therefore, the feature (gene) selection approach plays a vital role in handling a high dimensionality of data. Data science concepts can be widely employed in several data classification problems, and they identify different class labels. In this aspect, we developed a novel red fox optimizer with deep-learning-enabled microarray gene expression classification (RFODL-MGEC) model. The presented RFODL-MGEC model aims to improve classification performance by selecting appropriate features. The RFODL-MGEC model uses a novel red fox optimizer (RFO)-based feature selection approach for deriving an optimal subset of features. Moreover, the RFODL-MGEC model involves a bidirectional cascaded deep neural network (BCDNN) for data classification. The parameters involved in the BCDNN technique were tuned using the chaos game optimization (CGO) algorithm. Comprehensive experiments on benchmark datasets indicated that the RFODL-MGEC model accomplished superior results for subtype classifications. Therefore, the RFODL-MGEC model was found to be effective for the identification of various classes for high-dimensional and small-scale microarray data

Explainable Machine Learning Model for Alzheimer Detection Using Genetic Data: A Genome-Wide Association Study Approach

Recent research has revealed that using machine learning systems for the analysis of genetic data could reliably detect Alzheimer's disease. The interpretability of these models, however, has been a challenge, as they frequently provided little insight into the features that contribute to their predictions. Explainable machine learning has been presented as a solution to this problem since it enables the identification of significant attributes and gives a clearer method of making predictions. In this study, Genome-Wide Association Studies were used to recognize genetic variants associated with Alzheimer's disease, utilizing the Alzheimer's Disease Neuroimaging Initiative dataset and quality control methods to ensure the validity and reliability of the findings. The results indicate strong connections between certain genetic variations and Alzheimer's disease, highlighting the potential of Genome-Wide Association Studies as a valuable tool for identifying and predicting this disease. After studying and analyzing the genetic data, machine learning algorithms are utilized to train a model to detect Alzheimer. The Support Vector Machine achieved 89% accuracy as the best-performing model. Explainable machine learning has the potential to increase the accuracy and interpretability of Alzheimer's disease detection models, giving significant insights for both academics and physicians. The explanation of the support vector machine model reveals that rs4821510 is the most important SNP in detecting AD. On top of that, the SHAP method shows that rs429358 is an indication for Alzheimer's disease and rs4821510 presents in the healthy ones. These findings suggest that explainable machine learning can play an important role in accurately detecting Alzheimer's disease and identifying critical genetic markers associated with the disease

Using the health belief model to predict breast self examination among Saudi women

BACKGROUND: In the Kingdom of Saudi Arabia, breast cancer (BC) usually presents at advanced stages and more frequently in young pre-menopausal women in comparison to western countries. There is controversy surrounding the efficacy of breast self examination (BSE) for early detection of BC in countries where other methods are available. This study aims to explore the perception towards breast cancer and towards BSE among Saudi women, using the Health Belief Model (HBM). METHODS: A convenient sample of adult Saudi female employees, working at King Abdulaziz Medical City, Riyadh, Saudi Arabia (n = 225), and their non-working adult female family members (n = 208), were subjected to the Arabic version of revised Champion’s Health Belief Model Scale (CHBMS) and the Arabic version of Breast Cancer Awareness Measure (CAM), to assess their knowledge and attitude on BC respectively. Percentage mean score (PMS) for each HBM domain was calculated. Significant predictors of BSE practice were identified using logistic regression analysis and significance was considered at p < 0.05. RESULTS: The majority of women heard about BSE (91.2 %), only 41.6 % reported ever practicing BSE and 21 % performed it regularly. Reported reasons for not doing BSE were: not knowing how to examine their breast (54.9 %), or untrusting themselves able to do it (24.5 %). Women were less knowledgeable about BC in general, its risk factors, warning signs, nature and screening measures (PMS:54.2 %, 44.5 %, 61.4 %, 53.2 %, 57.6 % respectively). They reported low scores of; perceived susceptibility, seriousness, confidence and barriers (PMS: 44.8 %, 55.6 %, 56.5 % & 41.7 % respectively), and high scores of perceived benefits and motivation (PMS: 73 % & 73.2 % respectively) to perform BSE. Significant predictors of  BSE performance were: levels of perceived barriers (p = 0.046) and perceived confidence (p = 0.001) to BSE, overall knowledge on BC (p < 0.001), work status (p = 0.032) and family history of BC (p = 0.011). CONCLUSIONS: Saudi women had poor knowledge on BC, reported negative attitude towards BSE and their practice was poor. Working women and those with family history of BC, higher perceived confidence and lower perceived barriers on HBM, and those with high level of knowledge on BC were more likely to perform BSE. Breast awareness as an alternative to BSE needs further investigations. HBM was shown as a valid tool to predict BSE practice among Saudi women

Explainable AI for Unraveling the Significance of Visual Cues in High Stakes Deception Detection

Deception, a widespread aspect of human behavior, has significant implications in fields like law enforcement, security, judicial proceedings, and social areas. Detecting deception accurately, especially in high-stakes environments, is critical for ensuring justice and security. Recently, machine learning has significantly enhanced deception detection capabilities by analyzing various behavioral and visual cues. However, machine learning models often operate as opaque "black boxes,"offering high predictive accuracy without explaining the reasoning behind the decisions. This lack of transparency necessitates the integration of Explainable Artificial Intelligence to make the models' decisions understandable and trustworthy. This study proposes the implementation of existing model-agnostic Explainable Artificial Intelligence techniques - Permutation Importance, Partial Dependence Plots, and SHapley Additive exPlanations - to showcase the contributions of visual features in deception detection. Using Real-Life Trial dataset, recognized as the most valuable high-stake dataset, we demonstrate that Multi-layer Perceptron achieved the highest accuracy of 88% and a recall of 92.86%. Along with the aforementioned existing techniques, Real-Life Trial dataset inspired us to develop a novel technique: 'set-of-features permutation importance'. Additionally, this study is novel in the sense of that it extensively applies XAI techniques in the field of deception detection on Real-Life Trial dataset. Experimental results shows that the visual cues related to eyebrow movements are most indicative of deceptive behavior. Along with the new findings, our work underscores the importance of making machine learning models more transparent and explainable, thereby enhancing their utility for human-in-loop AI and ethical acceptability

Development of Eco-Friendly Concrete Mix Using Recycled Aggregates: Structural Performance and Pore Feature Study Using Image Analysis.

The shortage of natural aggregates has compelled the developers to devote their efforts to finding alternative aggregates. On the other hand, demolition waste from old constructions creates huge land acquisition problems and environmental pollution. Both these problems can be solved by recycling waste materials. The current study aims to use recycled brick aggregates (RBA) to develop eco-friendly pervious concrete (PC) and investigate the new concrete's structural performance and pore structure distributions. Through laboratory testing and image processing techniques, the effects of replacement ratio (0%, 20%, 40%, 60%, 80%, and 100%) and particle size (4.75 mm, 9.5 mm, and 12.5 mm) on both structural performance and pore feature were analyzed. The obtained results showed that the smallest aggregate size (size = 4.75 mm) provides the best strength compared to the large sizes. The image analysis method has shown the average pore sizes of PC mixes made with smaller aggregates (size = 4.75 mm) as 1.8-2 mm, whereas the mixes prepared with an aggregate size of 9.5 mm and 12.5 mm can provide pore sizes of 2.9-3.1 mm and 3.7-4.2 mm, respectively. In summary, the results confirmed that 40-60% of the natural aggregates could be replaced with RBA without influencing both strength and pore features

Phenotypic and Functional Characterization of Mesenchymal Stem/Multipotent Stromal Cells from Decidua Basalis

Mesenchymal stem cell (MSC) therapies for the treatment of diseases associated with inflammation and oxidative stress employ primarily bone marrow MSCs (BMMSCs) and other MSC types such as MSC from the chorionic villi of human term placentae (pMSCs). These MSCs are not derived from microenvironments associated with inflammation and oxidative stress, unlike MSCs from the decidua basalis of the human term placenta (DBMSCs). DBMSCs were isolated and then extensively characterized. Differentiation of DBMSCs into three mesenchymal lineages (adipocytes, osteocytes, and chondrocytes) was performed. Real-time polymerase chain reaction (PCR) and flow cytometry techniques were also used to characterize the gene and protein expression profiles of DBMSCs, respectively. In addition, sandwich enzyme-linked immunosorbent assay (ELISA) was performed to detect proteins secreted by DBMSCs. Finally, the migration and proliferation abilities of DBMSCs were also determined. DBMSCs were positive for MSC markers and HLA-ABC. DBMSCs were negative for hematopoietic and endothelial markers, costimulatory molecules, and HLA-DR. Functionally, DBMSCs differentiated into three mesenchymal lineages, proliferated, and migrated in response to a number of stimuli. Most importantly, these cells express and secrete a distinct combination of cytokines, growth factors, and immune molecules that reflect their unique microenvironment. Therefore, DBMSCs could be attractive, alternative candidates for MSC-based therapies that treat diseases associated with inflammation and oxidative stress