SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted PyZoBot: A Platform for Conversational Information Extraction and Synthesis from Curated Zotero Reference Libraries through Advanced Retrieval-Augmented Generation. A(Virginia Commonwealth University, 2025) Alshammari, Suad; Wijesinghe, DayanjanThis work presents a systematic evaluation of PyZoBot, an AI-powered platform for literature-based question answering, using the Retrieval-Augmented Generation Assessment Scores (RAGAS) framework. The study focuses on a subset of 49 cardiology-related questions extracted from the BioASQ benchmark dataset. PyZoBot's performance was assessed across 32 configurations, including standard Retrieval-Augmented Generation (RAG) and GraphRAG pipelines, implemented with both OpenAI-based models (GPT-3.5-Turbo, GPT-4o) and open-source models (LLaMA 3.1, Mistral). To establish a comparative benchmark, responses generated by PyZoBot were evaluated alongside answers manually written by six PhD students and recent graduates from the pharmacotherapy field, using a curated Zotero library containing BioASQ-referenced documents. The evaluation applied four key RAGAS metrics—faithfulness, answer relevancy, context recall, and context precision—along with a composite harmonic score to determine overall performance. The findings reveal that 22 PyZoBot configurations surpassed the highest-performing human participant, with the top pipeline (GPT-3.5-Turbo + layout-aware chunking, k=10) achieving a 129 harmonic RAGAS score of 0.6944. Statistical analysis using Kruskal-Wallis and Dunn’s post hoc tests confirmed significant differences across all metrics, especially in faithfulness and time efficiency. These results validate PyZoBot’s ability to support high-quality biomedical information synthesis and demonstrate the system’s potential to meet or exceed human performance in complex, evidence-based academic tasks.8 0Item Restricted Sickle cell disease associated lipid changes and their relevance towards the disease pathogenesis And Lipid Biomarkers and Embryo Quality in In Vitro Fertilization; Pregnancy Success Differentially Expressed by Body Weight(Virginia Commonwealth University, 2028) Alshammari, Suad; Wijesinghe, Dayanjan SAbstract 1: Sickle cell disease (SCD) is a group of genetic disorder that occurs due to genetic mutation of a beta-globin gene that lead to production of pathogenic hemoglobin S (HB S). Genotypes of SCD include Hb SS (sickle cell anemia) which is the most common and severe form of SCD affects about 20 to 25 million people worldwide, HbSC, Hb Sβ+- thalassemia, and Hb Sβ0-thalassemia. SCD is characterized by multiorgan complications that, in turn, affect lipids composition. During hypoxia a sequence of changes will take place such as HbS polymerization, erythrocyte rigidity and stickiness, and oxidative stress. The combination of these changes will affect lipids components such as polyunsaturated fatty acids (PUFA), which are substrates for a significant number of bioactive lipids such as the eicosanoids and some of the endocannabinoids. For example, vaso-occlusion crisis, the most common cause of SDC hospitalization, is found to be accompanied by changes in PUFA components of RBCs cell membrane encompassing Omega-3 and Omega-6. This comprehensive review outlines lipid changes that accompany SCD and also identify the gaps in our knowledge. This review will also allow us to devise better treatment options to manage the different pathophysiology and complications of SCD. Abstract 2: Introduction: A common risk factor for infertility is obesity. The global rise of obesity accompanied with infertility has led to widespread adoption of assisted reproductive technologies such as in vitro fertilization (IVF) to achieve pregnancy. However, pregnancy outcomes such as embryo quality vary after IVF, possibly due to disruptions in metabolism. Previous metabolomic studies investigating embryo quality were limited to characterizing broadly lipid classes, or a few molecular lipid species. Here, we sought to determine specific circulating lipids and metabolites with matrix-specific effects that could serve as putative biomarkers of embryo quality, correlated with BMI, and predicted clinical pregnancy subsequent IVF. Methods: Electronic health record (EHR) data, as well as lipids and metabolites obtained from follicular fluid (FF) and platelet poor plasma (PPP), were collected from women (n = 26) undergoing IVF. Lipids and metabolites were acquired via untargeted mass spectrometry. For embryo quality and BMI, we performed multiple linear regression analysis to find correlates. For 6 weeks pregnancy, we applied a linear discriminant analysis to select lipids and metabolites that allowed for group determination. Results: Several lipids and metabolites were selected from both matrices (FF and PPP) that either outperformed models containing only EHR or added value to EHR models. In predicting embryo quality, glycerophospholipids obtained from PPP produced the best fit model. The predicted values include (LPC) 22:6 , phosphatidylcholine (PC) 16:1/22:6, and phosphatidylethanolamine-plasmalogen (PE-P) 16:0/22:6 were negatively correlated with 2PN while Phosphatidylethanolamine (PE) 18:0/20:3, lysophosphatidylethanolamine (LPE) 18:1, PC 14:0/16:1, and PE-P 16:0/20:5 were positively correlated with 2PN (R adjusted = 0.730, RMSE = 0.329). For rLDA of 6-weeks of pregnancy, the best model was the metabolite model obtained from Platelet Poor Plasma (misclassification = 3.85%, Entropy R-squared = 0.809). The BMI multicomponent domain model obtained from FF, LPC 18:1, PC 16:1/22:6, and malic acid were negatively associated with BMI while Fasting insulin and PC 16:0/22:4 were positively correlated with BMI values (R-square adjusted = 0.819, RMSE = 0.127). However, the combined data model for FF has the best prediction of BMI values. In this model, PE-P 16:0/22:6, aspartic acid, and fasting insulin as positively correlated variables with BMI values, whereas indole-3-propionic acid was negatively correlated with BMI (R-squared adjusted = 0.856, RMSE = 0.113). Conclusion: High BMI is known to have detrimental effects on the development and success of pregnancy. It is also known to disrupt the endocrine pathways involved in the pregnancy process. This study provides information about metabolic signatures associated with BMI and pregnancy outcomes. Also, it shows the effect of 2PN value on embryo quality and the success of pregnancy.87 0