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Machine learning for retinopathy prediction: Unveiling the importance of age and HbA1c with XGBoost

Ramachandran et al. | Sep 05, 2024

Machine learning for retinopathy prediction: Unveiling the importance of age and HbA1c with XGBoost

The purpose of our study was to examine the correlation of glycosylated hemoglobin (HbA1c), blood pressure (BP) readings, and lipid levels with retinopathy. Our main hypothesis was that poor glycemic control, as evident by high HbA1c levels, high blood pressure, and abnormal lipid levels, causes an increased risk of retinopathy. We identified the top two features that were most important to the model as age and HbA1c. This indicates that older patients with poor glycemic control are more likely to show presence of retinopathy.

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The impact of genetic analysis on the early detection of colorectal cancer

Agrawal et al. | Aug 24, 2023

The impact of genetic analysis on the early detection of colorectal cancer

Although the 5-year survival rate for colorectal cancer is below 10%, it increases to greater than 90% if it is diagnosed early. We hypothesized from our research that analyzing non-synonymous single nucleotide variants (SNVs) in a patient's exome sequence would be an indicator for high genetic risk of developing colorectal cancer.

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Refinement of Single Nucleotide Polymorphisms of Atopic Dermatitis related Filaggrin through R packages

Naravane et al. | Oct 12, 2022

Refinement of Single Nucleotide Polymorphisms of Atopic Dermatitis related Filaggrin through R packages

In the United States, there are currently 17.8 million affected by atopic dermatitis (AD), commonly known as eczema. It is characterized by itching and skin inflammation. AD patients are at higher risk for infections, depression, cancer, and suicide. Genetics, environment, and stress are some of the causes of the disease. With the rise of personalized medicine and the acceptance of gene-editing technologies, AD-related variations need to be identified for treatment. Genome-wide association studies (GWAS) have associated the Filaggrin (FLG) gene with AD but have not identified specific problematic single nucleotide polymorphisms (SNPs). This research aimed to refine known SNPs of FLG for gene editing technologies to establish a causal link between specific SNPs and the diseases and to target the polymorphisms. The research utilized R and its Bioconductor packages to refine data from the National Center for Biotechnology Information's (NCBI's) Variation Viewer. The algorithm filtered the dataset by coding regions and conserved domains. The algorithm also removed synonymous variations and treated non-synonymous, frameshift, and nonsense separately. The non-synonymous variations were refined and ordered by the BLOSUM62 substitution matrix. Overall, the analysis removed 96.65% of data, which was redundant or not the focus of the research and ordered the remaining relevant data by impact. The code for the project can also be repurposed as a tool for other diseases. The research can help solve GWAS's imprecise identification challenge. This research is the first step in providing the refined databases required for gene-editing treatment.

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Correlation of socioeconomic status and lead concentration in tap water in Missouri

Rabbani et al. | Feb 03, 2022

Correlation of socioeconomic status and lead concentration in tap water in Missouri

Organic and non-organic contaminants in tap water have been linked to adverse health effects. Tap water is a major source of lead, which is neurotoxic and poses a major health risk, particularly to children and pregnant women. Using publicly available annual water quality reports data for the state of Missouri, the authors show that communities with lower median household income and lower per capita incomes had significantly higher lead levels in their tap water.

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An analysis of the distribution of microplastics along the South Shore of Long Island, NY

Sanderson et al. | Sep 21, 2020

An analysis of the distribution of microplastics along the South Shore of Long Island, NY

This study is focused on the distribution of microplastics in Long Island, NY. Microplastics are plastic particles that measure less than 5 mm in length and pose an environmental risk due to their size, composition, and ubiquitous location in the marine environment. Focusing on the South Shore of Long Island, the authors investigated the locations and concentrations of microplastics at four locations along the shore line. While they did not find significant differences in the number of microplastics per location, there were microplastics at all four locations. This finding is important to drive future research and environmental policy as well.

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Mendelian randomization reveals shared genetic landscape in autism spectrum disorder and Alzheimer's disease

Lee et al. | Nov 04, 2024

Mendelian randomization reveals shared genetic landscape in autism spectrum disorder and Alzheimer's disease

Autism Spectrum Disorder (ASD) and Alzheimer's Disease (AD) are distinct conditions, but research suggests a link, as individuals with ASD are 2.5 times more likely to develop AD. A study employing genome-wide association studies and Mendelian randomization revealed shared genetic factors, particularly in synaptic regulation pathways, that may increase the risk of AD in those with ASD. These findings provide insights into the genetic underpinnings connecting the two disorders.

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Fitness social media is positively associated with the use of performance-enhancing drugs among young men

Tamaki et al. | Feb 01, 2024

Fitness social media is positively associated with the use of performance-enhancing drugs among young men
Image credit: Samuel Girven

Here the authors investigated the relationship between fitness-related social media and the high usage of performance-enhancing drugs (PEDs) specifically by men in the US age 18-35. In a survey with 149 participants they identified that young men that use fitness-related social media are more likely to use PEDs. Their results suggest the necessity to consider potential risk behaviors which may be related to social media consumption.

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Using machine learning to develop a global coral bleaching predictor

Madireddy et al. | Feb 21, 2023

Using machine learning to develop a global coral bleaching predictor
Image credit: Madireddy, Bosch, and McCalla

Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.

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The novel function of PMS2 mutation on ovarian cancer proliferation

Cho et al. | Dec 18, 2022

The novel function of <em>PMS2</em> mutation on ovarian cancer proliferation

With disruption of DNA repair pathways pertinent to the timeline of cancer, thorough evaluation of mutations relevant to DNA repair proteins is crucial within cancer research. One such mutation includes S815L PMS2 - a mutation that results in significant decrease of DNA repair function by PMS2 protein. While mutation of PMS2 is associated with significantly increased colorectal and endometrial cancer risk, much work is left to do to establish the functional effects of the S815L PMS2 mutation in ovarian cancer progression. In this article, researchers contribute to this essential area of research by uncovering the tumor-progressive effects of the S815L PMS2 mutation in the context of ovarian cancer cell lines.

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The influence of experience on the perception of homelessness in individuals

Dua et al. | Jun 30, 2022

The influence of experience on the perception of homelessness in individuals

Economic disruptions and housing instabilities have for long propelled a homelessness epidemic among adults and youth in the United States. The COVID-19 pandemic has accelerated this phenomenon with a 2.2% increase in the number of homeless individuals and more than 70% of Americans fearing this outcome for themselves. This study aimed to analyze the perception of homelessness in two groups: Those who have previously experienced and overcome homelessness and those who are at-risk for experiencing the same. The study analyzed publicly available Reddit posts by people in both groups and found that at-risk individuals tended to associate primarily fearful emotions with the event, and those who had overcome homelessness tended to view the event in a negative context. These results may encourage the establishment of resources to support the currently homeless and help them transition into society, and services to help them cope with negative emotions, as negative attitudes have been shown to decrease life expectancy.

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