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Traditional toxicological assessment relied heavily on 2D cell cultures and animal models of study, which were inadequate for the precise prediction of human response to chemicals. Researchers have now shifted focus on organoids for toxicological assessment. Organoids are 3D structures produced from stem cells that mimic the shape and functionality of human organs and have a number of advantages compared to traditional models of study. They have the capacity to replicate the intricate cellular microenvironment and in vivo interactions . They offer a physiologically pertinent platform that is useful for the researchers to monitor cellular responses in a more realistic manner and evaluate drug toxicity. Additionally, organoids can be created from cells unique to a patient, allowing for individualized toxicological research and providing understanding of the inter-individual heterogeneity in drug responses. Recent developments in the use of gut and liver organoids for assessment of the xenobiotics (environmental toxins and drugs) is reviewed in this article. Gut organoids can reveal potential damage to the digestive system and how xenobiotics affect nutrient absorption and barrier function. Liver is the primary site of detoxification and metabolism of xenobiotics, usually routed from the gut. Hence, these are linked and crucial for evaluating chemical or pollutant induced organ toxicity, forecasting their metabolism and pharmacokinetics. When incorporated into the drug development process, organoid models have the potential to improve the accuracy and efficiency of drug safety assessments, leading to safer and more effective treatments. We also discuss the limitations of using organoid-based toxicological assays, and future prospects, including the need for standardized protocols for overcoming reproducibility issues.

Six decades have passed since the foundational recognition of the primary properties of the stem cells. Research on stem cells has since remained at the forefront of efforts to combat a spectrum of diseases, most notably cancer. Despite remarkable progress in medical science, a definitive cure for cancer has remained elusive, spurring the pursuit of diverse therapeutic strategies, among which stem cell therapy is a particularly promising avenue. Moreover, the utilization of cancer stem cells as a therapeutic source holds immense potential for addressing intractable diseases. The strategy of targeting cancer stem cells is beset with challenges, including immune rejection and disease relapse. Additionally, the capacity to inadvertently generate cancer stem cells upon transplantation underscores the critical need to eliminate these cells to ensure the efficacy of cell-based therapies. This paper underscores the pivotal role of cancer stem cells in onco-therapeutics and their potential to aid in early cancer diagnosis. With the proliferation of tissue banks and their collection of malignant tissue types, a renewable source of medications to combat cancer is on the horizon. While cancer stem cell-based therapy presents sophisticated and significant challenges, it offers unprecedented opportunities to extend human life. Continued technological advancements in stem cell therapy promise to provide new insights and refine approaches for cancer treatment, ushering in a new era of hope and innovation in the fight against this formidable disease.

In 2012, Rivaie et al. introduced RMIL conjugate gradient (CG) method which is globally convergent under the exact line search. Later, Dai (2016) pointed out abnormality in the convergence result and thus, imposed certain restricted RMIL CG parameter as a remedy. In this paper, we suggest an efficient RMIL spectral CG method. The remarkable feature of this method is that, the convergence result is free from additional condition usually imposed on RMIL. Subsequently, the search direction is sufficiently descent independent of any line search technique. Thus, numerical experiments on some set of benchmark problems indicate that the method is promising and efficient. Furthermore, the efficiency of the proposed method is demonstrated on applications arising from arm robotic model and image restoration problems.

Genetic toxicology is crucial for evaluating the potential risks of chemicals and drugs to human health and the environment. The emergence of high-throughput technologies has transformed this field, providing more efficient, cost-effective, and ethically sound methods for genotoxicity testing. It utilizes advanced screening techniques, including automated in vitro assays and computational models to rapidly assess the genotoxic potential of thousands of compounds simultaneously. This review explores the transformation of traditional in vitro and in vivo methods into computational models for genotoxicity assessment. By leveraging advances in machine learning, artificial intelligence, and high-throughput screening, computational approaches are increasingly replacing conventional methods. Coupling conventional screening with artificial intelligence (AI) and machine learning (ML) models has significantly enhanced their predictive capabilities, enabling the identification of genotoxicity signatures tied to molecular structures and biological pathways. Regulatory agencies increasingly support such methodologies as humane alternatives to traditional animal models, provided they are validated and exhibit strong predictive power. Standardization efforts, including the establishment of common endpoints across testing approaches, are pivotal for enhancing comparability and fostering consensus in toxicological assessments. Initiatives like ToxCast exemplify the successful incorporation of HTS data into regulatory decision-making, demonstrating that well-interpreted in vitro results can align with in vivo outcomes. Innovations in testing methodologies, global data sharing, and real-time monitoring continue to refine the precision and personalization of risk assessments, promising a transformative impact on safety evaluations and regulatory frameworks.

The purpose of this study was to analyze the main green marketing approaches and their impact on consumer behavior towards the environment in the United Arab Emirates (UAE). We reviewed the current consumption patterns of green products using a questionnaire approach. For this study, 359 consumers that used any type of green product were selected in various shopping malls in the UAE. For the assessment of questionnaire responses, measurement models, such as confirmatory factor analysis (CFA), and structural equation modeling (SEM) approaches were applied. The findings of the study suggest interesting inferences regarding eco-labeling (EL), green packaging and branding (GPB), green products, premium, and pricing (GPPP), and the environmental concerns and beliefs (ECB) of consumers that affect their perceptions of the environment. Factor analysis provided a goodness of fit for the selected items. It was found that key factors of green marketing, such as EL and GPPP, have a significant positive influence on consumer beliefs towards the environment (CBTE). In addition, environmental concerns and beliefs (ECB) also have a significant and positive influence on CBTE in the UAE. The practical significance of this study is that it will help the progress of the integration of green marketing and consumer behavior theories about the environment. The managerial implications of this study include the provision of significant findings for both domestic and international firms for the promotion of green products through focusing on consumer behavior towards the environment. This study provides important guidelines to boost positive attitudes in society towards green marketing and helps companies develop effective strategies to promote their green products.

Human body cells are stem cell (SC) derivatives originating from bone marrow. Their special characteristics include their capacity to support the formation and self-repair of the cells. Cancer cells multiply uncontrollably and invade healthy tissues, making stem cell transplants a viable option for cancer patients undergoing high-dose chemotherapy (HDC). When chemotherapy is used at very high doses to eradicate all cancer cells from aggressive tumors, blood-forming cells and leukocytes are either completely or partially destroyed. Autologous stem cell transplantation (ASCT) is necessary for patients in those circumstances. The patients who undergo autologous transplants receive their own stem cells (SCs). The transplanted stem cells first come into contact with the bone marrow and then undergo engraftment, before differentiating into blood cells. ASCT is one of the most significant and innovative strategies for treating diseases. Here we focus on the treatment of Hodgkin’s lymphoma, non-Hodgkin’s lymphoma, multiple myeloma, and AL amyloidosis, using ASCT. This review provides a comprehensive picture of the effectiveness and the safety of ASCT as a therapeutic approach for these diseases, based on the currently available evidence.

Wireless sensor networks (WSNs) have grown considerably in recent years and have a significant potential in different applications including health, environment, and military. Despite their powerful capabilities, the successful development of WSN is still a challenging task. In current real-world WSN deployments, several programming approaches have been proposed, which focus on low-level system issues. In order to simplify the design of the WSN and abstract from technical low-level details, high-level approaches have been recognized and several solutions have been proposed. In particular, the model-driven engineering (MDE) approach is becoming a promising solution. In this paper, we present a survey of existing programming methodologies and model-based approaches for the development of sensor networks. We recall and classify existing related WSN development approaches. The main objective of our research is to investigate the feasibility and the application of high-level-based approaches to ease WSN design. We concentrate on a set of criteria to highlight the shortcomings of the relevant approaches. Finally, we present our future directions to cope with the limits of existing solutions.

Histology is a foundational discipline in medical education that connects basic biomedical sciences with clinical practice. However, its perceived relevance may diminish as students progress academically, especially when integration with clinical teaching is limited. This study aimed to assess medical students' perceptions of histology's value, teaching quality, and clinical integration at Imam Mohammad Ibn Saud Islamic University across different academic levels. A cross-sectional survey was conducted in January 2025 among 235 undergraduate medical students. A validated, self-administered questionnaire measured students' perceptions of histology's importance, teaching effectiveness, clinical relevance, curricular integration, and overall satisfaction. Quantitative data were analyzed using descriptive statistics, ANOVA, logistic regression, and Pearson correlation. Most students rated histology as important or very important (73.6%) and acknowledged its contribution to understanding disease mechanisms (88.5%). Satisfaction was strongly associated with perceived applicability, visualization, and integration. Lectures and laboratory sessions were rated as effective or very effective by 75.7% and 76.2% of students, respectively. Additionally, 80.9% supported stronger clinical integration, and 83% preferred the inclusion of clinical examples. Perceptions varied across academic stages, with preclinical students expressing more favorable views. Regression analysis identified teaching quality and integration with clinical content as significant predictors of positive perceptions. Histology continues to be a valued component of medical education when delivered through effective teaching and clinically relevant integration. Strengthening instructional design and embedding clinical context throughout the curriculum may enhance student engagement, improve learning outcomes, and sustain the relevance of histology across all stages of medical training.

Background. Asthma is the most common reason for emergency visits to hospital and loss of productive hours. In Saudi Arabia, asthma affects more than 2 million people and majority of them have uncontrolled asthma with their quality of life adversely being impacted. It is well known that the prevalence of asthma has been increasing in many places around the world in the last few decades. The present review attempted to identify studies on asthma and associated factors in Saudi Arabian population and assess their time trends and regional variation. Methods. The titles and abstracts of retrieved articles were compared to delete duplication and irrelevant data. A data collection form was designed to extract several key components from selected articles like bibliographic information on the article, study population, and sample size. Extracted information was grouped appropriately for data analysis. Database search retrieved 71 articles. Applying inclusion and exclusion criteria, 40 articles were excluded and 31 qualified full articles were included for the review. Results. Among 31 retained studies, Riyadh had the highest publication output followed by Jeddah. Ten studies were based on ISAAC and 5 on non-ISAAC questionnaires while 5 studies were genetic studies conducted to unravel the genetic basis of asthma. Most of the studies were conducted on pediatric subjects of less than 16 years of age. Sample sizes ranged from less than 150 to more than 10000 with study settings being predominantly urban (22 studies). The pooled weighted prevalence rates of asthma, lifetime wheeze, and rhinitis were 14.3% (95% CI: 13.4–15.2), 16.5 (95% CI 15.5–17.4), and 21.4 (95% CI 20.5–22.3), respectively. An increase in asthma prevalence from 1990 to 2000 along with a stabilized or not so significant decline in the prevalence from 2010 to 2016 was observed. Conclusion. The prevalence of asthma varied in different regions without any disparity in prevalence in the rural and urban areas of Saudi Arabia. The understanding of genetic variability and recognition of risk factors in asthma patients can greatly help in individualizing the therapy for the management and control of asthma.

This work introduce a new high dimensional 10-D hyperchaotic system with high complexity and many of coexisting attractors. With the adjustment of its parameters and initial points, the novel system can generate periodic, quasi-periodic, chaotic, and hyperchaotic behaviours. For special values of parameters, we show that the proposed 10-D system has a very high Kaplan-Yorke fractal dimension, which can reach up to 9.067 indicating the very complexity of the 10-D system dynamics. In addition, the proposed system is shown to exhibit at least six varied attractors for the same values of parameters due to its multistability. Regions of multistability are identified by analysing the bifurcation diagrams of the proposed model versus its parameters and for six different values of initial points. Many of numerical plots are given to show the appearance of different dynamical behaviours and the existence of multiple coexisting attractors. The main problem with controlling chaos/hyperchaos systems is that they are not always fully synchronized. therefore, some powerful synchronization techniques should be considered. The synchronization between the high-dimensional 10-D system and a set of three low-dimensional chaotic and hyperchaotic systems is proposed. Ten control functions are designed using the active control method, ensuring synchronisation between the collection of systems and the 10-D hyperchaotic system. Finally, using Multisim 13.0 software to construct the new system’s electronic circuit, the feasibility of the new system with its extremely complicated dynamics is verified. Therefore, the novel 10-D hyperchaotic system can be applied to different chaotic-based application due to its large dimension, complex dynamics, and simple circuit architecture.