Search Results (47,896)

Tick infestation represents a significant constraint on livestock productivity in Saudi Arabia; however, there remains a substantial gap in research addressing tick species diversity, distribution, and their direct effects on milk production. This study aimed to morphologically and molecularly identify tick species infesting dairy cattle, quantify the impact of tick infestation on milk yield and composition, and contribute to the limited understanding of tick ecology and its economic implications in the region. Ticks were collected from infested cows and identified morphologically using taxonomic keys. Molecular identification was performed via PCR amplification of the mitochondrial cytochrome c oxidase subunit I (COI) gene. Milk production and quality parameters were assessed in tick-infested and healthy cows in Hafar Al-Batin, Eastern Saudi Arabia. Morphological and genetic analyses confirmed Hyalomma anatolicum as the predominant tick species in the study area, with COI sequences showing high similarity to regional isolates. Tick-infested cows exhibited substantial reductions in milk yield, fat, calcium, and potassium levels, indicating significant metabolic disruptions. Blood biochemical analysis revealed elevated levels of liver enzymes [aspartate aminotransferase (AST) increased by 238.6%, gamma-glutamyl transferase (GGT) by 155.7%], renal markers [creatinine increased by 788.9%, urea by 130.0%], and electrolyte imbalances [serum calcium decreased by 39.5%, potassium by 45.2%]. Hematological findings included increased white blood cell (WBC) and red blood cell (RBC) counts by 44.9% and 124.7%, respectively, along with a 53.1% decrease in hemoglobin (HGB), suggesting a systemic inflammatory response and possible anemia. This study is among the first to genetically confirm the presence of H. anatolicum in Hafar Al-Batin using molecular tools, thereby enhancing the accuracy of species-level identification and highlighting the physiological impact of tick burden on dairy productivity. Full article

Background: Chimeric Antigen Receptor (CAR) T-cell therapy has transformed the treatment of hematological malignancies, yet its application in solid tumors remains constrained by unique biological and logistical barriers. Objective: This review critically examines the evolving landscape of CAR T-cell therapy in solid malignancies, with a focus on antigen heterogeneity, the immunosuppressive tumor microenvironment, and risks of on-target, off-tumor toxicity. Methods: We outline recent advances in CAR engineering, including co-stimulatory optimization, dual- and multi-antigen targeting, armored CARs, and gene-edited constructs designed to enhance persistence and anti-tumor activity. Clinical progress is highlighted by recent FDA approvals of genetically modified T-cell therapies in synovial sarcoma and melanoma, underscoring the potential for broader solid tumor application. Additionally, we synthesize early-phase clinical trial findings across multiple solid tumor types (e.g., lung, colorectal, ovarian, glioblastoma), and discuss innovative approaches such as regional delivery, checkpoint blockade combinations, and incorporation of chemokine receptors for improved tumor infiltration. The review also considers future strategies, including artificial intelligence-guided target discovery and rational trial design to overcome translational bottlenecks. Conclusions: With expanding clinical experience and continued technological innovation, CAR T-cell therapy is steadily transitioning from an experimental strategy to a therapeutic reality in solid tumors, poised to reshape the future of cancer immunotherapy. Full article

The impact of biochar pyrolyzed at 450 ± 10 °C and made from date palm (D) and olive tree (O) wastes on the hydrophysical characteristics of sandy soil was assessed in this study through a laboratory column experiment. Two different application rates (wt/wt) were tested: 1% and 5%. The prepared biochars were added at 25 °C to the upper 10 cm layers of the soil columns. The outcome showed that, in comparison to O, D biochar possessed slightly less alkalinity and more salinity. The corresponding values for pH and EC in D and O biochars were 8.99 and 4.10 dS/m and 9.42 and 2.17 dS/m. Therefore, these biochars should be used cautiously as soil amendments in saline–sodic soils because of their excessive salinity, especially D biochar. On the other hand, they are safe to employ as amendments in acidic and non-saline soils. Cumulative evaporation (CE) decreased with all treatments, and the highest decrease of 10.2% (compared to control treatments after five cycles) was observed for D biochar and 5% application rate (D450, 5%). Moreover, the available water increased by 182%, 158%, 153%, and 29% for D450, 5%, D biochar and 1% application rate (D450, 1%), O biochar and 5% application rate (O450, 5%), and O biochar and 1% application rate (O450, 1%), respectively. The saturated hydraulic conductivity decreased by 94.8%, 87.0%, 76.6%, and 35.1% for D450, 5%, D450, 1%, O450, 5%, and O450, 1%, respectively. It was also found that the date palm biochar was more efficient than olive waste biochar in decreasing the cumulative infiltration and infiltration rate. Finally, this study showed the superiority of biochar prepared from date palm trees over that prepared from olive tree waste for improving the hydrophysical properties of sandy soil. Full article

The increasing number of catastrophic events has relentlessly disrupted production and distribution processes across the globe. To address this issue, the current study developed a research model that combines factors such as human capital, relational capital, structural capital, HR practices, risk management capability, and artificial intelligence to investigate logistic firm resilience capability. The research design was based on quantitative methods. Data were collected from logistic managers. A total of 213 questionnaires were retrieved for the research survey. Statistical findings revealed that human capital, relational capital, structural capital, HR practices, and artificial intelligence explained $R^2$ 86.5% of the variance in logistic firm resilience capability. Nevertheless, the relationship between risk management and resilience capabilities was found to be insignificant. On the other hand, logistic service quality and firm resilience capability explained $R^2$ 79.5% of the variance in logistic firm performance. Practically, this study suggests that adequate logistic service quality, appropriate intellectual capital, good HR practices, and the deployment of artificial intelligence in logistic operations could boost firm resilience capability, resulting in better performance during catastrophic events. The present study is original in that it investigated logistic firms’ resilience capability with intellectual capital, HR practices, and artificial intelligence. Another unique aspect of this study is that it established the moderating impact of logistic service quality on the relationship between logistic firm resilience capability and firm performance. Full article

Saudi Arabia is actively boosting women’s labor force participation, exceeding 2022 targets to enhance human capital. The purpose of this study is to examine the nexus between women’s empowerment and economic growth using secondary data from 1997 to 2022. Empowerment was proxied by Saudi women employed in government, with Gross Domestic Product (GDP) and female B.Sc. holders used as explanatory variables. The result of the Johansen test depicts a long-run equilibrium relationship between these variables. The Vector Error Correction Model (VECM) revealed a significant negative short-term impact of GDP on women’s empowerment at the 5% level, while female graduates had a positive short-term effect. The model results indicated rapid adjustment, correcting about 71% of disequilibrium per period towards long-run equilibrium. Importantly, a significant positive long-run relationship exists between women’s empowerment and economic growth. Diagnostic tests confirmed the VECM’s reliability, reflected by normally distributed residuals, with no significant autocorrelation, and overall model stability. The study findings contribute valuable insights for policymakers and stakeholders striving to achieve gender equity and sustainable economic development. Moreover, qualitative methods could be employed in future research to enhance the comprehensive understanding of the cultural and social barriers to maximize the long-run virtuous cycle of empowerment and economic growth. Full article

Cisplatin (CDDP) is a widely used chemotherapeutic agent, but its off-target toxicity, including testicular damage, limits clinical use. Bioactive compounds may help mitigate chemotherapy-induced reproductive toxicity. This study investigates thymol’s role in modulating ferritinophagy to preserve reproductive function and steroidogenesis. Male Wistar rats were randomized to control, CDDP, thymol, or CDDP + thymol groups. Thymol (60 mg/kg) was given orally for 14 days, and CDDP (8 mg/kg) was administered intraperitoneally on day 7. Testicular function was assessed through hormonal analysis, sperm evaluation, and histopathology. Ferritinophagy, oxidative stress, and inflammatory markers were assessed to elucidate thymol’s chemoprotective mechanisms. Thymol co-administration preserved steroidogenesis, restored sperm quality, and maintained testicular architecture in CDDP-treated rats. Thymol suppressed ferritinophagy, reducing iron overload and mitigating reactive oxygen species (ROS)-induced cellular damage. Additionally, thymol activated the Keap1/Nrf2/HO-1 pathway, enhancing antioxidant defenses while downregulating inflammatory mediators (TNF-α, IL-6). Additionally, thymol enhanced CDDP’s selectivity toward cancer cells while reducing its toxicity to normal cells. This study provides evidence that thymol modulates ferritinophagy to attenuate CDDP-induced testicular toxicity, helping preserve reproductive function via regulation of iron homeostasis. These findings highlight thymol’s potential as an adjunct therapy to mitigate chemotherapy-associated reproductive damage while maintaining CDDP’s anticancer efficacy. Full article

The rapid urban growth in Saudi Arabia has intensified challenges in sustainable solid waste management, particularly in selecting suitable landfill sites that minimize environmental risks and protect public health. Tabuk Province, located in the northwest of the Kingdom, represents a region where arid climatic conditions, fragile ecosystems, and increasing urbanization make landfill sitting highly complex. Traditional decision-making approaches often struggle to capture uncertainties in expert opinions and spatial data, leading to less reliable outcomes. While Geographic Information Systems and Multicriteria Decision-Making have been applied to this field, the explicit integration of fuzzy logic remains limited, especially in arid regions. This study addresses this gap by combining the Fuzzy Analytic Hierarchy Process with Geographic Information Systems to establish a more robust framework for landfill site selection in Tabuk. Seven critical criteria were considered, including distance from major roads, airports, urban centers, coastlines, wetlands, and protected areas, with expert assessments analyzed through fuzzy reasoning to improve decision reliability. The results generated a spatial suitability map highlighting priority zones for landfill development, particularly in the western and southwestern areas of the province, where environmental sensitivity is lower and accessibility to infrastructure is greater. The findings emphasize that proximity to urban areas and road networks are dominant factors influencing suitability. The novelty of this study lies in its methodological integration, which enhances transparency, adaptability, and objectivity in landfill sitting. By promoting environmentally responsible waste management, the framework directly supports the Sustainable Development Goal of Good Health and Well-Being and the Sustainable Development Goal of Sustainable Cities and Communities, ensuring safer urban development and healthier living conditions. Moreover, the approach is transferable to other arid and semi-arid regions, offering valuable insights for countries facing similar challenges in sustainable urban planning. Full article

Background/Objectives: Alzheimer’s disease (AD) is the most prevalent form of dementia and is characterized by a decline in cognition that may be due, in part, to deficient cholinergic signalling. Cholinesterase inhibitors (ChEIs) are the first-line pharmacotherapies for treating the diminished cholinergic function in AD patients. Plant phytochemicals may provide useful ChEIs and mitigate other elements of AD pathology, including oxidative stress. Methods: Herein, the phytochemicals present in Moringa oleifera aqueous and methanolic extracts were identified by LC-MS/MS and the potential of several phytochemicals (4-O-caffeoylquinic acid (4-CQA), quercetin 3-β-D-glucoside (Q3-β-D), chlorogenic acid (CGA), and rutin) to act as ChEIs and antioxidants was assessed. Results: The phytochemicals inhibited human acetylcholinesterase (AChE) in the following order of potency: 4-CQA > Q3-β-D > CGA > rutin; for AChE from Electrophorus electricus, the order of potency was Q3-β-D > 4-CQA > CGA > rutin. For human butyrylcholinesterase (hBuChE), the order of potency was rutin > 4-CQA > Q3-β-D > CGA and for equine serum BuChE, it was 4-CQA > Q3-β-D > rutin > CGA. Molecular docking validated the binding of the phytochemicals to cholinesterases, with binding affinities comparable to or higher than those of ChEI drugs. All the phytochemicals displayed potent radical-scavenging and antioxidant activities across six assays. 4-CQA was the most effective antioxidant in three of the assays. Conclusions: M. oleifera contains phytochemicals with weak ChEI activity and potent antioxidant capacity, with potential use as nutraceuticals to treat the cholinergic signalling deficit and oxidative stress that typifies AD pathology. Full article

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by heterogeneous behavioral and neurological patterns, complicating timely and accurate diagnosis. Behavioral datasets are commonly used to diagnose ASD. In clinical practice, it is difficult to identify ASD because of the complexity of the behavioral symptoms, overlap of neurological disorders, and individual heterogeneity. Correct and timely identification is dependent on the presence of skilled professionals to perform thorough neurological examinations. Nevertheless, with developments in deep learning techniques, the diagnostic process can be significantly improved by automatically identifying and automatically classifying patterns of ASD-related behaviors and neuroimaging features. Method: This study introduces a novel multimodal diagnostic paradigm that combines structured behavioral phenotypes and structural magnetic resonance imaging (sMRI) into an interpretable and personalized framework. A Generalized Additive Model with Interactions (GAMI-Net) is used to process behavioral data for transparent embedding of clinical phenotypes. Structural brain characteristics are extracted via a hybrid CNN–GNN model, which retains voxel-level patterns and region-based connectivity through the Harvard–Oxford atlas. The embeddings are then fused using an Autoencoder, compressing cross-modal data into a common latent space. A Hyper Network-based MLP classifier produces subject-specific weights to make the final classification. Results: On the held-out test set drawn from the ABIDE-I dataset, a 20% split with about 247 subjects, the constructed system achieved an accuracy of 99.40%, precision of 100%, recall of 98.84%, an F1-score of 99.42%, and an ROC-AUC of 99.99%. For another test of generalizability, five-fold stratified cross-validation on the entire dataset yielded a mean accuracy of 98.56%, an F1-score of 98.61%, precision of 98.13%, recall of 99.12%, and an ROC-AUC of 99.62%. Conclusions: These results suggest that interpretable and personalized multimodal fusion can be useful in aiding practitioners in performing effective and accurate ASD diagnosis. Nevertheless, as the test was performed on stratified cross-validation and a single held-out split, future research should seek to validate the framework on larger, multi-site datasets and different partitioning schemes to guarantee robustness over heterogeneous populations. Full article

Background and Objectives: Vitiligo is a chronic autoimmune skin disorder characterized by the loss of pigmentation, resulting in depigmented patches due to the destruction of melanocytes. This condition can lead to considerable psychological distress, and research indicates a possible connection with major depressive disorder (MDD). Nonetheless, the depth and nature of this association, particularly within the Saudi population, have not been thoroughly investigated. This case–control study seeks to explore the relationship between vitiligo and depressive symptomatology, evaluate the severity of depressive symptoms in vitiligo patients compared to control subjects, and examine the association between the clinical severity of vitiligo (assessed using the Vitiligo Area Scoring Index, VASI) and depressive symptoms (measured using the PHQ-9, a validated screening instrument based on DSM criteria). Materials and Methods: A total of 700 participants were included, comprising 340 individuals with vitiligo and 360 controls with other skin conditions. Participants completed a web-based questionnaire that collected sociodemographic data and included the PHQ-9 assessment. The severity of vitiligo was determined using the VASI. Statistical analysis involved using the computer program IBM Statistical Program for Social Sciences (SPSS) (version 26.0). Results: The average PHQ-9 score was significantly higher for patients with vitiligo (8.28 ± 7.36) compared to controls (6.30 ± 4.70, p = 0.028). While the overall rates of mild to severe depression were comparable (41.5% in vitiligo patients versus 40.3% in controls, p = 0.748), vitiligo patients exhibited higher occurrences of moderately severe depression (11.8%) and severe depression (10.9%) compared to controls (5.8% and 0.8%, respectively; p < 0.001). A weak, yet significant, positive correlation was found between VASI and PHQ-9 scores (ρ = 0.184, p < 0.001). The vulgaris and segmental types of vitiligo exhibited the highest median depression scores (PHQ-9: 11 and 9, respectively; p < 0.001). Logistic regression analysis indicated that those with genital vitiligo had greater odds of experiencing depression (OR = 12.10, p = 0.039), while those with universalis vitiligo faced even higher odds (OR ≈ 26,837.84, p = 0.001). Interestingly, higher VASI scores were linked to lower odds of depression (OR = 0.927, p = 0.029). Additionally, the risk of depression significantly increased with higher income levels and among individuals aged 50 years and older. Conclusions: Although the overall prevalence of depression was not significantly different between vitiligo patients and controls, the degree of depressive symptoms was notably more severe in those with vitiligo. Specific clinical subtypes, particularly genital and universalis vitiligo, were found to be more closely associated with an increased risk of depression. These results highlight the importance of regular mental health screenings and customized psychosocial support in dermatological care, especially for high-risk groups. Full article

This study presents the complete analysis of a (2 + 1)-dimensional nonlinear wave-type partial differential equation with anisotropic power-law nonlinearities and a general power-law source term, which arises in physical domains such as fluid dynamics, nonlinear acoustics, and wave propagation in elastic media, yet their symmetry properties and exact solution structures remain largely unexplored for arbitrary nonlinearity exponents. To fill this gap, a complete Lie symmetry classification of the equation is performed for arbitrary values of m and n, providing all admissible symmetry generators. These generators are then employed to systematically reduce the PDE to ordinary differential equations, enabling the construction of exact analytical solutions. Traveling wave and soliton solutions are derived using Jacobi elliptic function and sine-cosine methods, revealing rich nonlinear dynamics and wave patterns under anisotropic conditions. Additionally, conservation laws associated with variational symmetries are obtained via Noether’s theorem, yielding invariant physical quantities such as energy-like integrals. The results extend the existing literature by providing, for the first time, a full symmetry classification for arbitrary m and n, new families of soliton and traveling wave solutions in multidimensional settings, and associated conserved quantities. The findings contribute both computationally and theoretically to the study of nonlinear wave phenomena in multidimensional cases, extending the catalog of exact solutions and conserved dynamics of a broad class of nonlinear partial differential equations. Full article

This study investigates the public acceptance and willingness to pay (WTP) for nuclear energy in Saudi Arabia, a country seeking to diversify its energy portfolio under Vision 2030. Utilizing a cross-sectional survey of 403 residents, the research employs descriptive statistics, regression analysis, and a SWOT analysis to explore the socio-economic and perceptual drivers of public attitudes. The findings reveal that 82.4% of participants support nuclear energy, with a mean WTP of 38.2% of their monthly electricity bill for its development. Key factors positively influencing acceptance include age and knowledge about nuclear energy, while environmental concerns and being married are associated with lower support. Notably, trust in government oversight (64.8%) and the prospect of a new energy source (62.7%) are major reasons for support, whereas health and environmental risks (74.6%) are the primary concerns for opponents. This study provides crucial, evidence-based insights for policymakers, marking the first Saudi-specific research to jointly examine WTP, acceptance determinants through econometric modeling, and strategic implications via SWOT analysis, highlighting the need for targeted public engagement and transparent communication strategies to address public concerns and ensure the successful and sustainable integration of nuclear energy into Saudi Arabia’s energy mix. Full article

Background/Objectives: Existing research has highlighted the stress associated with the transition from student to practitioner among newly hired nurses, often resulting in diminished professional quality of life (ProQOL). However, there remains a dearth of understanding regarding the impact of the teaching methods during the COVID-19 pandemic on this transition period. This study aims to test a model assessing the mediating role of ProQOL in the association between structural empowerment and successful transition among newly hired nurses who underwent education during the COVID-19 pandemic. Methods: This study utilized a cross-sectional correlational design and was conducted in two university hospitals and four government hospitals in Saudi Arabia. The study sample was selected using purposive sampling. The Casey–Fink Graduate Nurse Experience Survey, the Arabic version of the ProQOL version 5, and the Conditions for Workplace Effectiveness Questionnaire Second Arabic version were used in the study. Data were analyzed using the Statistical Package for Social Sciences (SPSS) Version 28.0.1.1. The model was examined using Hayes’ process macro. Results: Structural empowerment significantly predicts successful transitions, both directly and indirectly through its impact on ProQOL. Conclusions: Nurse managers should employ optimal strategies and innovative structures within orientation programs to effectively facilitate the transition of Saudi graduate nurses. Moreover, nursing leaders and policymakers should leverage the increased attention garnered during the COVID-19 pandemic to enhance structural empowerment among newly hired nurses, thereby improving their transition and overall well-being. Structural empowerment was a direct and indirect predictor of successful transitions. Full article

Emotion recognition from speech is essential for human–computer interaction (HCI) and affective computing, with applications in virtual assistants, healthcare, and education. Although deep learning has made significant advancements in Automatic Speech Emotion Recognition (ASER), the challenge still exists in the task given variation in speakers, subtle emotional expressions, and environmental noise. Practical deployment in this context depends on a strong, fast, scalable recognition system. This work introduces a new framework combining DenseNet121, especially fine-tuned for the crowd-sourced emotional multimodal actors dataset (CREMA-D), with the convolutional block attention module (CBAM). While DenseNet121’s effective feature propagation captures rich, hierarchical patterns in the speech data, CBAM improves the focus of the model on emotionally significant elements by applying both spatial and channel-wise attention. Furthermore, enhancing the input spectrograms and strengthening resistance against environmental noise is an advanced preprocessing pipeline including log-Mel spectrogram transformation and normalization. The proposed model demonstrates superior performance. To make sure the evaluation is strong even if there is a class imbalance, we point out important metrics like an Unweighted Average Recall (UAR) of 71.01% and an F1 score of 71.25%. The model also gets a test accuracy of 71.26% and a precision of 71.30%. These results establish the model as a promising solution for real-world speech emotion detection, highlighting its strong generalization capabilities, computational efficiency, and focus on emotion-specific features compared to recent work. The improvements demonstrate practical flexibility, enabling the integration of established image recognition techniques and allowing for substantial adaptability in various application contexts. Full article

Background: Three-dimensional (3D) printing has emerged as a valuable tool in dentistry for producing provisional restorations with high precision and reduced costs. However, the limited mechanical strength of temporary 3D-printed resins remains a clinical concern. This in vitro study aimed to enhance the mechanical properties of a 3D-printed temporary resin by incorporating functionalized niobium (Nb) nanoparticles and to compare its performance with a conventional resin composite and a permanent 3D-printed resin. Methods: Six groups were evaluated: bisacrylic resin (Protemp), resin composite (Z350), temporary 3D resin (Temp 3D), permanent 3D resin (Perm 3D), Temp 3D + 0.05% Nb, and Temp 3D + 0.1% Nb. Niobium oxyhydroxide nanoparticles were synthesized using a hydrothermal method, silanized, and incorporated into the Temp 3D at 0.05% and 0.1% by weight. The tested variables included flexural strength (FS), elastic modulus (EM), surface hardness (SH), and color stability (ΔE). Results: The Z350 resin showed the best mechanical results. The addition of 0.1% Nb nanoparticles significantly improved the FS, EM, and SH of the Temp 3D, reaching values comparable to the Perm 3D (p > 0.05). Color stability remained unaffected across all groups. Conclusions: These findings suggest that Nb reinforcement at a low concentration is a promising strategy for improving the performance of 3D-printed temporary restorations. Full article