Imam Mohammad Ibn Saud Islamic University Journal of Applied Sciences

Phytochemical Evaluation of Six Medicinal Plant Species Traditionally Used in Makkah to Treat Skin Diseases

Afnan Alqethami — 2025-09-07

Introduction: Ethnomedicinal studies indicate that herbal medicine can be used as a treatment alternative for skin disorders. Skin diseases have a significant influence on life quality. Medicinal plants are important in healthcare and have a major role in enhancing community security and health. Here for the first time, the potential therapeutic benefits and confirm the effectiveness of six medicinal plants used in Makkah to treat skin diseases are investigated. Therefore, the present paper aims to screen of 6 medicinal plants used in traditional medicine in Makkah to treat skin, diseases for the presence of alkaloids, glycosides, flavonoids and tannins, and to answer the following question: Is the ethnomedicinal importance of medicinal plants used in Makkah to treat skin diseases conform to their secondary metabolite content.

Methods: Fieldwork took place in Makkah during May 2023. Six different plant species that are members of six families were identified. Using standard methods, six medicinal plants were screened for the presence of alkaloids, flavonoids,glycosides, and tannins.

Results: As a result of this work, the components found in medicinal plants that are most frequently distributed were flavonoids (100%; 6 species). All the 4 groups of chemical compounds were found in leaves of Aloe vera, which is one flora of Saudi Arabia.

Conclusions: The existence of these groups appears to confirm the effectiveness of using these medicinal herbs in Makkah's ethnomedical practice to cure skin diseases. Moreover, ethnobotanical investigations require phytochemical screening in order to complete traditional medicine research, which results in the development of novel medications.

Advancing Drought Tolerance in Plants: Integrating Morphological and Genomic Mechanisms

Amal Alyamani — 2025-09-04

Drought is a major abiotic stress that significantly limits water availability, adversely affecting critical plant processes such as photosynthesis, nutrient uptake, and growth. These effects lead to decreased crop productivity and pose economic challenges, particularly in agriculture-dependent regions. Plants respond to drought stress through intricate biological mechanisms, including changes in metabolite profiles and gene expression, which help mitigate damage and maintain functionality.

Central to these responses are transcription factors, which regulate stress-responsive gene expression by mediating signal transduction pathways. Their role is pivotal in linking drought signals to physiological and molecular adaptations. Understanding these complex networks of physiological, metabolic, and gene regulatory responses is essential for developing crops with improved drought resilience.

Marker-assisted selection (MAS) provides a more efficient method for assessing the value of various genomic regions in crops under stress conditions. Numerous crops contain quantitative trait loci (QTLs) linked to drought tolerance and other traits. The development of detailed molecular linkage maps and MAS techniques has enabled the integration of favorable characteristics, enhancing agricultural resilience to drought. Despite these advancements, challenges persist in achieving accurate and precise QTL identification.

This review incorporates insights into signal transduction for developing drought-resistant crop cultivars or lines. It also explores recent progress in understanding transcription factors (TFs), with a particular focus on their role in orchestrating plant responses to abiotic stress. Additionally, it delves into novel molecular mechanisms underlying their function in stress conditions. These insights are crucial for understanding regulatory processes and developing stress-resistant crop varieties.

Advanced multilevel analysis of crash counts by severity type on multilane arterial segments with multiple intersections

Omar Almutairi — 2025-09-07

This paper employs advanced multilevel modeling to predict crash counts on multilane arterial segments with numerous intersections, providing actionable insights into traffic safety. Using crash data from Ohio (2016–2017), two-level (univariate) and three-level (multivariate) models are constructed to analyze fatal and injury (FI) crashes and property damage-only (PDO) crashes both separately and jointly. The multivariate model demonstrates superior predictive performance, reducing root mean square error from 11.017 to 5.615 and explaining 92% of the variance in out-of-sample 2017 data. Key findings reveal significant heterogeneity across state routes and strong correlations between FI and PDO crash counts. The analysis identifies eight critical factors influencing FI crashes and five for PDO crashes. For example, adding a lane in a multilane arterial segment increases FI crashes by an average of 1.546 crashes, while divided segments decrease FI and PDO crashes by 3 and 9 crashes, respectively. Additionally, each additional signalized intersection raises FI and PDO crashes counts by 0.992 and 3.840 crashes, respectively.

Application of the Grey System Theory in Vehicles Marketing

Rabab Alzahrani — 2025-09-20

In this paper, we use the fuzzy soft set theory and apply the grey theory to marketing electric vehicles so that the optimal option is chosen according to high standards.