SACM - United States of America
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Item Restricted Examination of the GEKO Turbulence Model for Hypersonic Turbulent Boundary Layer Applications(Saudi Digital Library, 2025) Kayshan, Faisal; Duan, LianThis study investigates the performance of Reynolds-Averaged Navier-Stokes (RANS) with the Generalized K-Omega turbulence model (GEKO) turbulence model in comparison with Menter's Shear Stress Transport (SST) model for predicting hypersonic turbulent boundary layer flows. Two configurations are considered: a Mach 4.9 zero-pressure-gradient (ZPG) flat-plate boundary layer and a Mach 4.9 curved-wall turbulent boundary layer. Both configurations were simulated using GEKO and SST models in ANSYS Fluent, and the relative accuracy of each model in predicting wall quantities, mean flow profiles, Reynolds Stresses, and turbulent heat fluxes was determined by comparing against the high-fidelity Direct Numerical Simulation (DNS). For the Mach 4.9 flat-plate case, the GEKO model demonstrated superior agreement with DNS for most flow quantities, particularly in capturing wall shear stress, thermal gradients, and Reynolds stresses. The SST model tended to overpredict wall heat transfer and skin friction values, offering more conservative estimates but less accuracy in matching DNS trends. In contrast, for the curved wall configuration, the SST model exhibited better consistency with DNS in regions influenced by pressure gradients, especially for wall-normal and shear stresses, though both models GEKO and SST produced nearly identical trends in mean profiles and turbulent fluxes. The analysis shows that GEKO offers flexibility through tunable parameters, it occasionally overestimates turbulent fluctuations and wall heat fluxes, particularly in regions of pressure gradient. Overall, both models capture the essential physics of hypersonic boundary layer development, though differences in quantitative accuracy emphasize the importance of turbulence model selection based on application needs. These findings support the applicability of GEKO for high-speed modeling and highlight the validation role of DNS.11 0Item Restricted Enzyme-Induced Carbonate Precipitation (EICP) for Erosion Control in Sandy Soils with Fines: A Multi-Scale Experimental and LiDAR-Based Quantification Study(Saudi Digital Library, 2025) Alothman, Saleh; Zapata, Claudia E; Kavazanjian, Edward; Khodadadi Tirkolaei, HamedA multi-scale experimental and LiDAR-based quantification study was conducted to evaluate the effectiveness of enzyme-induced carbonate precipitation (EICP) to mitigate soil erosion. EICP is an emerging biogeotechnical soil improvement technique that improves the mechanical properties of soil by precipitating CaCO3 via hydrolysis of urea in the presence of calcium ions, catalyzed by the urease enzyme extracted from plant sources. The precipitated calcium carbonate cements soil particles together, increasing strength, stiffness, and dilatancy. Laboratory experiments were conducted on manufactured sandy soils with varying fines content to examine the hydraulic properties of treated soil with an optimized EICP recipe. The study included testing soil water characteristic curves, measuring saturated hydraulic conductivity, and estimating unsaturated hydraulic conductivity functions. Furthermore, the compaction characteristics and the unconfined compression strength of the EICP-treated soils were compared to the untreated soils and the soils treated only with CaCl2 to distinguish the influence of unreacted CaCl2 in EICP treatment. The effectiveness of EICP was further evaluated for clayey-sands using a large-scale rainfall simulator with high-resolution LiDAR-based quantification methods on soil surfaces tilted at 20 degrees under three different rainfall intensities. Treatment methods included EICP spray application and EICP mix-and-compact followed by spray. The results demonstrated that the EICP treatment altered the hydraulic response of the soil, improved the compaction characteristics and strength. However, the effectiveness of the treatment decreased with increasing fine content. The EICP treatments significantly reduced the overall erosion potential, with reductions ranging from 57% to 7%.14 0Item Restricted Essays on Monetary Policy and Financial Markets(Saudi Digital Library, 2025) Alsuwayni, Abdulmalik; Barnett, William AThis dissertation investigates how U.S. monetary policy actions—and the uncertainty that surrounds them—shape equity-market behavior at home and abroad. Across three self-contained essays, I employ high-frequency surprise measures and news-based uncertainty indices to document heterogeneous responses that vary by risk factor, industry, and country. The first essay, "The Impact of Monetary Policy Surprises on Equity Risk Factors and Market Sectors," investigates the heterogeneous responses within the U.S. equity market. Utilizing high-frequency monetary policy surprises derived from interest rate futures around FOMC announcements, this study regresses daily returns of Fama-French equity risk factors (Market, Size, Value, Profitability, Investment, augmented with Momentum, Short-Term Reversal, and Long-Term Reversal) and detailed industry portfolios on these surprises. Key findings indicate a significant negative market-wide response to tightening surprises. Notably, style factors exhibit varied sensitivities: Size (SMB), Investment (CMA), Momentum (MOM), and Long-Term Reversal (LTR) show positive responses to tightening, while Short-Term Reversal (STR) reacts negatively. Value (HML) and Profitability (RMW) show insignificant immediate responses. Cyclical and discretionary industry sectors demonstrate pronounced negative reactions, whereas defensive sectors exhibit more muted impacts, underscoring the differential transmission of policy shocks across the U.S. equity landscape. The second essay, "The Heterogeneous Impact of Monetary Policy Uncertainty on Equity Market Sectors," examines how uncertainty surrounding U.S. monetary policy affects various U.S. equity sectors. Employing the Husted-Rogers-Sun (2020) news-based Monetary Policy Uncertainty (MPU) index, this essay uses OLS regressions to analyze the contemporaneous relationship between monthly MPU changes and the excess returns of the Fama-French 12 industry portfolios. The results consistently reveal a negative association between MPU and sectoral returns, with statistically significant adverse impacts observed for Non-Durables, Manufacturing, Chemicals, Telecommunications, Retail & Wholesale, and Health sectors. Financials and a miscellaneous "Other" category show weakly significant negative effects. While MPU demonstrates a pervasive negative influence, its explanatory power for monthly return variations is modest, suggesting it acts as a secondary or episodic driver. The third essay, "The Transmission of Monetary Policy Surprises to International Equity Markets," extends the analysis of U.S. monetary policy surprises to the global stage. Using the Bauer-Swanson (2023) high-frequency surprise dataset, this study assesses the spillover effects on equity markets in developed economies, emerging markets, and countries with fixed exchange rate regimes. The findings highlight significant heterogeneity: key emerging markets, particularly Brazil and Mexico, exhibit strong and statistically significant negative sensitivities to U.S. tightening surprises. In contrast, responses in most developed markets are generally subdued and statistically insignificant. Markets with fixed exchange rates to the USD, notably Dubai (UAE), can even display significant positive reactions, suggesting complex interactions between U.S. policy, local economic structures (such as commodity dependence), and regional financial dynamics. Collectively, these essays contribute to a deeper understanding of how monetary policy actions and the uncertainty surrounding them differentially affect various segments of domestic and international equity markets. The findings offer implications for investment strategy, risk management, and the formulation of monetary policy in an interconnected global financial system.21 0Item Restricted EXPRESSION AND CHARACTERIZATION OF HUMAN HEPARAN 6-O-ENDOSULFATASE SULF1(Saudi Digital Library, 2025) Aljuhani, Reem; Radoslav, GoldmanSULF1 is an extracellular sulfatase that modifies heparan sulfate proteoglycans (HSPGs) by selectively removing 6-O-sulfate groups from glucosamine residues. This enzymatic activity regulates the binding of growth factors, morphogens, and many other ligands to HSPGs, thereby modulating pathways such as FGF, WNT, and TGF-β. Despite its potential role in extracellular matrix remodeling and stromal regulation, SULF1 remains under-characterized at the biochemical level, due to difficulty in producing active full-length protein. In this study, we optimized a mammalian expression system using HEK293 cells in serum-free, suspension-adapted culture. Lentiviral transduction, combined with co-expression of the formylglycine- generating enzyme SUMF1, yielded over 2 mg/L of active full-length human SULF1, representing a significant improvement over previously reported expression systems. Using two in vitro assays, a general arylsulfatase substrate (4-MUS) and a defined synthetic heparan sulfate oligosaccharide (2S2-6S4) 6-O- endosulfatase assays, we verified that SULF1 has catalytic properties consistent with the activity of type I sulfatases. Specifically, we demonstrated that SULF1 activity in vitro requires the SUMF1-dependent formylglycine modification; Ca2+ but not Mg2+, as a divalent cofactor, and intact N-glycosylation. Partial enzymatic deglycosylation led to an 85–90% reduction in activity, indicating that N-glycosylation contributes significantly to enzyme activity. AlphaFold-based structural modeling, revealed conserved residues predicted to coordinate Ca2+ binding at the active site and enabled visualization of electrostatic potential and N-glycosylation site distribution across the protein surface, supporting the biochemical findings. The improved expression system also facilitated the development and validation of monoclonal antibodies targeting human SULF1. Among the panel screened, three clones (5D1, 6E12, 4D2) demonstrated high specificity and affinity, supporting their utility in detection and quantification assays. Lastly, we evaluated the marine-derived fucosylated chondroitin sulfate (HfFucCS) as a candidate glycan-based SULF1 inhibitor. In vitro assays revealed dose-dependent, non-competitive inhibition. In a head and neck squamous cell carcinoma (HNSCC) xenograft model, high-dose HfFucCS treatment modestly reduced stromal content without significantly affecting tumor growth. Collectively, this work overcomes key technical limitations in the biochemical study of SULF1, introduces validated reagents for its detection and inhibition, and provides a robust starting point for future investigations of its functional roles in the extracellular matrix and tumor stroma.10 0Item Restricted Expanding the Performance and Functionality of Air-Jet Dry Powder Inhalers Across Multiple Applications(Saudi Digital Library, 2025) Aladwani, Ghali H; Longest, WorthThis dissertation aims to expand the performance and functionality of air-jet dry powder inhaler (DPI) applications through the development of a next-generation, mesh-nebulizer-based, and scalable spray dryer for the production of pharmaceutical aerosols, along with new spray-dried formulation production methods, novel formulations, and new air-jet DPI components for aerosol delivery to both infants and adults. The first aim focused on the development of a custom small-particle spray drying system capable of integrating vibrating mesh nebulizer technologies in various configurations for the production of excipient enhanced growth (EEG) formulations. This flexible system enabled efficient production of both small- and large-particle powders for pulmonary and nasal-targeted drug delivery. Additional advantages of the developed spray dryer included a significant increase in production rates using dual- and triple-mesh nebulizer configurations. Formulations produced in this work included a synthetic lung surfactant formulation (SLS-EEG), which demonstrated improved aerosol performance with a fine particle fraction (FPF < 5 µm) of 89.9% and a reduction in pre-separator loss from 30% to 6% when tested using the Next Generation Impactor (NGI) and compared with powder previously produced using the commercial Buchi B-90 Nano Spray Dryer. The second aim of this work focused on the development and evaluation of an infant manual cyclic (MC) air source for operating air-jet DPIs, delivering consistent and repeatable low air volumes (up to 17 mL) in compliance with Lung Ventilator Code ISO 10651-4 when the device is paired with an infant air-jet DPI. A compact version of the MC air source was also developed for various air-jet DPI applications, including nasal-targeted delivery. Together, these aims advanced the performance of air-jet DPIs through the development of a scalable and mesh-nebulizer based spray dryer capable of producing efficient EEG formulations, and the development of new DPI components suitable for both pediatric and adult populations.15 0Item Restricted Protection of Journalists in Areas of Armed Conflicts and Wars Under Principles of International Law(Saudi Digital Library, 2025) Alanazi, Amal.D; Grena, EileenReporting on armed conflicts and the violent situations has become more increasingly dangerous, with many journalists and media professionals being killed or deliberately targeted due to their work. These threats come from both government forces and Non-State Actors (NSAs), raising concerns about sufficiency and enforcement of existing international legal protections. The classification of a violent situation under international law significantly impacts the legal status and treatment of journalists, whether they are war correspondents, embedded reporters, or independent media professionals. International Humanitarian Law (IHL) provides protection for journalists during armed conflicts, whether these conflicts are international or non-international. This differs from peacetime, during which journalists are safeguarded under International Human Rights Law (IHRL). Various international conventions, including ICCPR, outline human rights protection mechanisms for journalists. While, IHL establishes specific protections for journalists through treaties such as the “Hague Conventions of 1899 and 1907, the Geneva Convention of 1929 (GC 1929), the Four Geneva Conventions of 1949, and the Additional Protocols of 1977 (AP-I &AP-II)”. IHL classifies journalists into two categories. The first category includes war correspondents, who are officially accredited by armed forces. Their status is defined under the “1949 Geneva Conventions”. The second category consists of journalists engaged in dangerous professional tasks during armed conflicts, as recognized in “Article 79 of the First Additional Protocol of 1977”, which applies to international armed conflict zones. In event of capture, war correspondents are granted prisoner-of-war status under Third GC of 1949. The journalists who 10 undertake dangerous tasks in conflict zones are legally recognized as “civilians under Article 79 of AP-I”, ensuring their protection under international law. This dissertation investigates whether the existing international framework adequately safeguards journalists and media personnel reporting from armed conflict zones. The analysis encompasses current legal provisions under IHL, along with recommendations from international, regional, and non-governmental organizations. A key objective is to assess whether violence against journalists should be classified as war crimes or crimes against humanity, granting the ICC automatic jurisdiction in cases where national courts fail or refuse to prosecute such offenses. In addition, this research aims to propose measures to strengthen protection of journalists and media professionals in conflict zones. Findings indicate that current international legislative framework for journalists in war zones are inadequate, with little to no commitment from states to address this gap. Relying solely on IHRL and IHL to protect journalists—who play a vital role in upholding democracy and rule of law, particularly in Western democracies—has proven ineffective. It is imperative that states act swiftly to establish a dedicated treaty ensuring protection of journalists and media workers in conflict areas. UNGA should direct UNILC, under Article 13(1) of the UN Charter, to begin an urgent study on the development of international legal protections for journalists and media professionals in conflict zones. Findings of “International Law Commission Draft Articles on the Protection of Journalists and Media” should be formally adopted by United Nations, ultimately leading to a new international convention aimed at safeguarding journalists and media personnel covering armed conflicts15 0Item Restricted Graph Neural Network Architectures for Multi-Omics-Based Cancer Classification with Emphasis on Interpretability and Biomarker Discovery(Saudi Digital Library, 2025) Alharbi, Fadi; Vakanski, AleksandarCancer describes a class of diseases in which malignant cells form inside the human body due to genetic change. These cells divide indiscriminately upon development, extend throughout the organs, and in many cases, they can result in loss of life. Cancer is the second leading cause of mortality globally after cardiovascular illnesses. Recent studies on integrating multiple omics data highlighted the potential to advance our understanding of the cancer disease process. Graph neural networks (GNNs) have emerged as powerful computational models for cancer classification tasks, particularly when applied to high-dimensional and heterogeneous multi-omics datasets. GNNs differ from classic neural models MLPs, CNNs, RNNs through their capability to handle complex biological network relationships by mapping biological entities as graph nodes which they analyze using network structure information. They perfectly suit PPI networks or gene regulatory networks because they can effectively capture the natural biological interactions present in these networks. GNNs address key challenges in multi-omics data analysis, including data sparsity and complexity, by learning node embeddings that integrate both omics features and topological information. Attention-based GNNs have advanced both model interpretability and predictive accuracy which leads to more precise biomarker and cancer type classification. These advantages make GNNs as effective approaches to optimizing precision oncology especially when they use integrated omics data as input features. Graph Attention Networks (GATs) improve attention-based GNNs by implementing dynamic weights for neighboring nodes which depend on their relevance to the model learning process. The selective attention mechanism proves highly effective when analyzing multiomics data because different biological relationships have varying degrees of informative value. Building upon the strengths of GATs in emphasizing important interactions, the Graph Kolmogorov–Arnold Network (GKAN) introduces new interpretability through its combination of Kolmogorov–Arnold representation theorem with graph structures. The univariate functions of GKAN provide effective non-linear modeling capacity for multi-omics data structures which maintain their network connections. Our work introduces three key innovations: (1) LASSO-MOGAT, a novel Graph Attention Network that integrates LASSO-based feature selection with multi-omics graph learning, demonstrating superior performance in classifying 31 cancer types; (2) An interpretable Graph Kolmogorov–Arnold Network (GKAN) that identifies pan-omics biomarker signatures through learnable activation functions; and (3) A systematic comparison of graph construction methods, proving that multi-omics correlation networks outperform single-omics approaches.10 0Item Restricted TRANSMEMBRANE PROTEIN RESIDUE CONTACT PREDICTION USING NOVEL MACHINE LEARNING APPROACHES AND PRETRAINED PROTEIN LANGUAGE MODELS(Saudi Digital Library, 2025) Almalki, Bander; Li, LiaoTransmembrane (TM) proteins represent a significant portion of all known proteins and play a crucial role in many biological processes, such as facilitating thetransport of molecules, ions, and information between a cell and its external environment. It’s estimated that they account for approximately 20-30% of all protein-coding genes in humans. Despite their abundance and importance, only a very small portion has been determined experimentally because of the difficulty of obtaining well-ordered crystals and the high cost of conducting in vitro experiments. Such a difficulty might hinder the understanding of their function. The need for developing computational tools to determine their structure, therefore, becomes essential. However, compared to globular proteins, computationally determining the structure of TM proteins can be harder due to the limited availability of high-resolution structures to be used as templates and training examples for structural modeling and prediction. Residue contact prediction is one of the most successful computational approaches to reduce the huge search space for the TM protein fold and generate a high-quality 3D structure. Determining the structure of the protein can reveal invaluable information about its function. In this work, we explore the current advances in the field, investigate the effectiveness of different learning approaches, propose and develop novel machine/deep learning techniques for generating a high-quality contact map for both inter-chain and intra-chain residues in alpha-helical TM proteins. In addition, we assess the accuracy of the different proposed models and show that the proposed work can enhance the contact map prediction and ultimately produce a better 3D structure. In the first chapter, we investigate the contact map prediction task of alpha-helical TM proteins from a different angle using a transductive learning approach. Identifying the interaction between the helices within the membrane greatly affects their tilt angle and relative position, thus impacting the overall protein structure. We utilize transductive learning by incorporating the unlabeled test data during training to address the scarcity of labeled data, which is common in the prediction of amino acid residue contacts, and to improve the model accuracy. Using features derived from protein structures, we compare the predictive performance of transductive support vector machine (SVM) and inductive SVM in identifying helix-helix residue contacts, with the aim of determining the specific conditions and limitations under which TSVMs excel. Then, we explore potential solutions to mitigate the performance degradation of the transductive model. We introduce an early stop technique TSVMES that produces a more accurate model, outperforming the state-of-the-art TSVM by 5% when tested on a set of benchmarks of transmembrane proteins. In the second chapter, we investigate the feasibility of incorporating structural features into the classifier. Most current TM protein residue contact predictors rely solely on features extracted from protein sequences to predict residue contacts. However, using these features alone leads to a low-accuracy contact map and, subsequently, to a poor 3D structure. Other models attempt to exploit features extracted from 3D protein structures to produce a better representative contact model. Nevertheless, this approach is not applicable in real-life scenarios where the structure is not available during the model testing phase, making it a chicken-and-egg dilemma. Therefore, we propose a novel approach that utilizes atomic features extracted from known TM protein 3D structures to enable the model to train on these features and transfer this knowledge to the test data, which lack atomic features, to improve the prediction of the contact of the TM protein residue. Our proposed method, AT-TSVM, employs Transductive Support Vector Machines with transfer and active learning to improve contact prediction accuracy. The results indicate that our proposed model can boost the accuracy of contact prediction by an average of 5 to 6% on the inductive classifier and 3 to 4% over the transductive classifier. In the third chapter, we utilize large protein language models to generate an accurate contact map for alpha-helical TM proteins. The majority of previous studies employ techniques that rely on statistical analysis of the sequence to infer connections between residues. A few recent techniques, which are based on natural language processing models, have been successful in achieving this goal. Nevertheless, the majority of these techniques and models are designed for globular proteins and are not tailored for specific protein types like Transmembrane Proteins. Therefore, we propose a Transmembrane Protein Helices Contacts predictor (TMHC-MSA) that utilizes features extracted by a protein language model (MSA Transformer) and incorporates neighborhood information using a feature window to enhance the quality of the produced contact map. Our proposed model demonstrates superior performance by successfully outperforming the state-of-the-art method by an average of 7% in terms of L precision and even surpassing the MSA Transformer by an average of 2.5% on the same metric. Furthermore, we demonstrate that the more accurate contact map produced by our model can be used to generate a more accurate 3D structure. In the fourth chapter, we dive deeper to explore the dimerization of bitopic TM proteins. Most bitopic transmembrane proteins associate with each other to stabilize their structure by forming dimers. This association leads to the activation of downstream specific cellular functions. Therefore, being able to accurately identify interface residues in a given dimer is important to understand its function, and has been a challenging pursuit of many computational methods. In this chapter, we break down the dimerization residue contact prediction into two tasks. In the first task, we propose a model that leverages structural features extracted from the field of molecular dynamics alongside other features from various domains to predict interface residues in α-helical TM dimers. The accurate prediction of interface residues has potential applications in pharmaceutical drug design. The results reveal key limitations in the ability of state-of-the-art multimer models, including AlphaFold2-Multimer and RoseTTAFold2, to precisely identify these interface residues. Therefore, we introduced TMH-ID, a novel machine learning model which integrates various sequence-based features, including large protein language model coupling scores and TM-specific motifs, in addition to structure-based features extracted from the predicted structure of PREDDIMER. In particular, our proposed model achieved the highest mean F1 score, outperforming several advanced baselines such as THOIPA, MSA Transformer, and ProteinBERT. Furthermore, TMH-ID outperforms other multimer structure predictors RoseTTAFold2, AlphaFold2Multimer, and PREDDIMER in interface residue prediction across the Crystal subset. In the fifth chapter, we explore the prediction of inter-chain residue contacts in TM homodimers and present ICM-MD, a novel machine learning framework for predicting inter-chain residue contacts in α-helical TM homodimers. In this chapter, we propose two models to address the scarcity of available structures necessary to develop an accurate classifier to identify these contacts. This is achieved by training the models on a large database of molecular dynamics simulated dimers (Membranome) and integrating transmembrane-specific sequence and structural features. Our models adopt a residue-pair-centric learning paradigm to address the limited availability of training data and to enhance generalizability to unseen examples. The first model employs a lightweight and interpretable feed-forward neural network architecture that is com- putationally efficient and scalable. The second model implements a more advanced architecture based on graph convolutional networks (GCNs), enabling the effective integration of information from neighboring residues to capture richer structural context. This is achieved through the message-passing mechanism, which facilitates the exchange of information between contacting residues, thereby enabling each residue to incorporate context from its interaction partners. To the best of our knowledge, this is the first study to leverage molecular dynamics–based structural models as a surrogate ground truth for training an interchain contact predictor in TM proteins. The results show that the proposed simple model consistently outperforms state-of-the-art models, including DeepHomo1, DeepHomo2, Glinter, and DeepTMP in multiple evaluation metrics. Moreover, the advanced GCN-based model surpasses all the other models, delivering consistently stable performance across all evaluation metrics.6 0Item Restricted HEALTHCARE UTILIZATION PATTERNS, ASSOCIATED FACTORS, AND READMISSION RATES AMONG CHILDREN WITH SICKLE CELL DISEASE(Saudi Digital Library, 2025) Sabrah, Daniya; Karimi, SeyedSickle Cell Disease (SCD) is common in Saudi Arabia, with a prevalence rate of 2.3%. It is an expensive disease to manage due to the extensive utilization of healthcare resources, including hospital readmissions, extended hospital stays, and frequent emergency department visits, which cost approximately $395 million annually in U.S. dollars. The primary objective of this study was to conduct a systematic literature review of healthcare utilization and cost of patients with SCD, following the PICO framework and PRISMA guidelines. The second aim utilized count models, including the negative binomial and Poisson, to identify factors associated with emergency, inpatient, and outpatient hospital visits among children with SCD. The third aim employed a multi-episode survival analysis to assess the association between bone marrow transplantation and the risk of emergency and inpatient readmission rates among children. The Andersen-Aday and Donabedian conceptual models informed the statistical variables for aims two and three, respectively. De-identified data provided by the King Abdullah International Medical Research Center (KAIMRC) were used in this study. Factors affecting the average annual number of inpatient visits were the total number of complications (incidence rate ratio, IRR = 1.23), crisis episodes (IRR = 1.03), and living in the Eastern (IRR = 2.65) and Western and Southern (IRR = 1.98) regions of Saudi Arabia. Similarly, for emergency visits, the total number of complications (IRR = 1.52), total crisis episodes (IRR = 1.04), bone marrow treatment (IRR = 0.33), hydroxyurea (IRR = 1.72), and living in the Eastern Saudi Arabia region (IRR = 3.41) were examined. For an outpatient visit, the Charlson Comorbidity Index (CCI) score (IRR = 1.33), bone marrow treatment (IRR = 1.81), age (IRR = 1.02), and living in the Eastern Saudi Arabia region (IRR = 0.65) were significant factors. The association between bone marrow treatment and the risk of emergency readmission was estimated and compared to those who did not receive bone marrow treatment. Health policies regarding bone marrow treatment receipt and preventive healthcare can help alleviate the burden of healthcare costs.12 0Item Restricted Effect of bonding agents used as lubricants on color stability of composite restorations(Saudi Digital Library, 2025) Badeeb, Bayan; Kose Junior, CarlosObjective: This in vitro study aimed to evaluate the effect of different bonding agents used as lubricants during composite placement on the color stability of resin-based restorations when exposed to common staining agents over time. Materials and Methods: A total of 120 Tetric EvoCeram composite specimens were prepared and divided into four groups: Control (no lubricant), Scotchbond Universal, Excite F, and Wetting Resin. Each specimen was subjected to staining solutions (water, coffee, or red wine) and stored at 37 °C. Color measurements were recorded using a digital spectrophotometer (VITA Easyshade® V) at baseline, 2 weeks, and 4 weeks, following ISO/TR 28642:2016 guidelines. Color change (ΔE*) was calculated using the CIELAB formula. Statistical analysis was performed using one-way ANOVA, post hoc tests, and paired t-tests (p < 0.05). Results: At 2 weeks, significant differences in ΔE values were observed between groups under coffee staining (p = 0.010), with Wetting Resin showing the lowest discoloration, a followed by Scotchbond Universal and Excite F, while the Control group exhibited the highest color change. Wine caused the highest ΔE values, followed by coffee and water. By 4 weeks, although ΔE values increased in all groups, differences between bonding agents were no longer statistically significant. Time-dependent discoloration was observed across all groups, with the greatest ΔE increases occurring in red wine, followed by coffee and then water. Conclusion: Bonding agents used as lubricants can significantly influence early color stability of composite restorations, particularly under aggressive staining. Wetting Resin provided the best resistance to discoloration, suggesting its potential advantage in esthetically sensitive restorations. However, the protective effect diminishes over time, highlighting the importance of long-term evaluation. Significance: This study suggests that using bonding agents as lubricants during composite placement does not adversely affect color stability and may offer a protective effect under staining conditions. While these findings are limited to in vitro settings, they support the incorporation of this technique with composite restorations.12 0