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Cover image for IAbstract. Stem cell (SC) transplantation
IAbstract. Stem cell (SC) transplantation has shown potential as a therapeutic approach for premature ovarian failure (POF). Despite this, no quantitative analysis has been conducted on the efficacy of SC therapy for POF in humans. To address this gap, the present study conducted a meta-analysis to evaluate the effectiveness of the transplantation of SC in improving ovarian function among POF patients. A systematic review in this regard by searching PubMed, ScienceDirect, clinicalTrial.gov (http://clinicalTrial.gov), and Cochrane's library databases was conducted to identify relevant studies, while associated reviews were also considered. The extracted data included parameters such as estradiol (E2), follicle-stimulating hormone (FSH), follicle count (FC), ovarian weight (OW), number of pregnancies, and live birth. As per the combined effect taking the last follow-up time, the level of FSH and AMH for the SC group was lower than these were at the baseline as (SMD: 1.58, 95% CI: 0.76 to 3.92, P-value: 0.185 > 0.05, I2 : 94.03%) and (SMD: 1.34, 95% CI: 0.77 to 1.92, P-value: 0.001 < 0.05, I2 : 0%) respectively. While the means of E2 and OW for the SC group was higher than these were at the baseline as (SMD: 0.47, 95% CI: 0.73 to 0.21, P-value: 0.001 < 0.01, I2 : 38.23%) and (SMD: 1.18, 95% CI: 2.62 to 0.26, P-value: 0.108 > 0.05, I2 : 76.68%) respectively. The overall effect size measured with proportion of pregnancy and live birth at a 5% level of significance expected SC transplantation results were as (combined proportion: 0.09, 95% CI: 0.03 to 0.15, P-value: 0.002 < 0.05, I2 : 46.29%) and (SMD: 0.09, 95% CI: 0.03 to 0.15, P-value: 0.003 < 0.05, I2 : 1.76%) respectively. Based on the fixed-effects model, the estimated average log odds ratio of Follicles count was 1.0234 (95% CI: 0.1252 to 1.9216). Therefore, the average outcome differed significantly from zero (Pvalue: 0.0255 < 0.05) due to SC transplantation. These results suggest that using SCs to restore ovarian function may be viable for treating POF. However, larger and better-quality investigations would need to be conducted in the future due to the heterogeneity of the examined studies.
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Cover image for Quantitative Statistical Analysis & Mediation
Quantitative Statistical Analysis & Mediation Study Objective Explore how mind-body practices relate to people's overall quality of life, and whether personality traits help explain this relationship. Dataset 1,092 valid responses were analyzed after data screening and preparation. Analysis Performed Data cleaning & screening Descriptive statistics Reliability testing Normality & assumption testing Correlation analysis Structural Equation Modeling (SEM) Mediation analysis Tools & Methods JASP • Pearson & Spearman Correlation • SEM • DWLS • Bootstrap Analysis Key Findings Higher neuroticism was significantly associated with lower self-reported health/quality of life. Mind-body practice showed a significant direct relationship with quality of life. Neuroticism did not significantly explain/mediate the relationship between mind-body practice and quality of life. The final model explained 17.1% of the variation in quality of life. Deliverable A complete statistical analysis report with tables, visualizations, statistical results, interpretation, and research findings.
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Cover image for Global Superstore: Strategic Sales &
Global Superstore: Strategic Sales & Profitability Analysis I performed a comprehensive analysis of the Global Superstore dataset to uncover hidden trends in sales performance, customer behavior, and regional profitability. This project demonstrates my ability to transform raw data into actionable business intelligence using modern data science techniques. Key Technical Features: Data Wrangling: Performed extensive data cleaning and preprocessing using Pandas to handle missing values and inconsistent formatting. Exploratory Data Analysis (EDA): Conducted deep-dive analysis to identify top-performing product categories and high-growth geographic regions. Visual Intelligence: Created advanced visualizations using Matplotlib and Seaborn to represent complex sales trends and profit margins clearly. Business Impact: Developed a data-driven model to identify the key drivers of customer churn and profitability bottlenecks.
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