Browsing by Subject "discrimination"
Now showing 1 - 3 of 3
- Results Per Page
- Sort Options
Item A DNA-Based Multiplex Screening Tool for Separation of Fragmented and Commingled Skeletal Remains(2007-12-01) Ambers, Angie; Joseph Warren; John Planz; Arthur EisenbergAmbers, Angie, A DNA-based Multiplex Screening Tool for Separation of Fragmented and Commingled Skeletal Remains. Master of Science (Forensic Genetics), December, 2007, 63 pages, 13 tables, 19 figures, references, 38 titles. In mass death scenarios, human remains are often fragmented, scattered, and commingled. Ascertaining the number of victims and determining the victims’ identities in such scenarios is a challenging task. A DNA-based screening tool used early in the investigation of mass disasters or mass graves would provide a relatively quick way to initially assess casualty numbers and separate remains for further analysis. Such a tool would promote the most efficient allocation of resources and speed the identification process. The multiplex designed here incorporates a few genetic loci that show high variability in the human population, giving it sufficient discriminatory power for separation of commingled remains. Specifically, the multiplex includes the amelogenin sex-determining locus, D3S1358, and a 3’ (CA)n dinucleotide repeat in the mitochondrial D-loop. Further optimization/validation studies need to be conducted, and a fourth locus (D5S818) may need to be considered to increase the tool’s power of discrimination.Item Factors associated with COVID-19-related mental health among Asian Indians in the United States(Elsevier B.V., 2023-01-11) Ikram, Mohammad; Shaikh, Nazneen F.; Siddiqui, Zasim A.; Dwibedi, Nilanjana; Misra, Ranjita; Vishwanatha, Jamboor K.; Sambamoorthi, UshaBACKGROUND: In the United States, the COVID-19 pandemic has caused increased mental health symptoms and mental illness. Specific subgroups such as Asian Indians in the US have also been subject to additional stressors due to unprecedented loss of lives in their home country and increased Asian hate due to the misperception that Asians are to be blamed for the spread of the SARS-CoV-2. OBJECTIVE: We examined the various factors including discrimination associated with COVID-19-related mental health symptoms among Asian Indians. METHODS: We administered an online survey between May 2021 and July 2021 using convenient and snowball sampling methods to recruit Asian Indian adults (age > 18 years, N = 289). The survey included questions on mental health and the experience with unfair treatment in day-to-day life. Descriptive analysis and logistic regressions were performed. RESULTS: Overall, 46.0% reported feeling down, depressed, or lonely and feeling nervous, tense, or worried due to the COVID-19 pandemic; 90.0% had received at least one dose of vaccination and 74.7% reported some form of discrimination. In the fully-adjusted logistic regression, age (AOR = 0.95; 95%CI- 0.92, 0.97;p < 0.01) and general health (AOR=0.84; 95%CI- 0.73, 0.97; p < 0.015) were negatively associated with mental health symptoms. Participants who experienced discrimination were more likely (AOR=1.26; 95%CI- 1.08, 1.46; p < 0.01) to report mental health symptoms. CONCLUSION: In this highly vaccinated group of Asian Indians discriminatory behaviors were associated with mental health symptoms suggesting the need for novel institutional level policy responses to reduce anti-Asian racism.Item Leading Predictors of COVID-19-Related Poor Mental Health in Adult Asian Indians: An Application of Extreme Gradient Boosting and Shapley Additive Explanations(MDPI, 2023-01-09) Ikram, Mohammad; Shaikh, Nazneen F.; Vishwanatha, Jamboor K.; Sambamoorthi, UshaDuring the COVID-19 pandemic, an increase in poor mental health among Asian Indians was observed in the United States. However, the leading predictors of poor mental health during the COVID-19 pandemic in Asian Indians remained unknown. A cross-sectional online survey was administered to self-identified Asian Indians aged 18 and older (N = 289). Survey collected information on demographic and socio-economic characteristics and the COVID-19 burden. Two novel machine learning techniques-eXtreme Gradient Boosting and Shapley Additive exPlanations (SHAP) were used to identify the leading predictors and explain their associations with poor mental health. A majority of the study participants were female (65.1%), below 50 years of age (73.3%), and had income >/= $75,000 (81.0%). The six leading predictors of poor mental health among Asian Indians were sleep disturbance, age, general health, income, wearing a mask, and self-reported discrimination. SHAP plots indicated that higher age, wearing a mask, and maintaining social distancing all the time were negatively associated with poor mental health while having sleep disturbance and imputed income levels were positively associated with poor mental health. The model performance metrics indicated high accuracy (0.77), precision (0.78), F1 score (0.77), recall (0.77), and AUROC (0.87). Nearly one in two adults reported poor mental health, and one in five reported sleep disturbance. Findings from our study suggest a paradoxical relationship between income and poor mental health; further studies are needed to confirm our study findings. Sleep disturbance and perceived discrimination can be targeted through tailored intervention to reduce the risk of poor mental health in Asian Indians.