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The authors use known Parkinson's disease-associated genetic variants to compare the prevalence of sleep dysfunction between males and females diagnosed with Parkinson's disease.

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.

Human cytomegalovirus (HCMV) causes serious infections in immunocompromised patients and therapies to inhibit latent HCMV are not developed. Using CRISPR/Cas9, the authors were able to delete an important promoter region in HCMV.

Each year, over 100,000 patients die from Sudden Unexpected Death in Epilepsy (SUDEP). A reliable seizure warning system can help patients stay safe. This work presents a comprehensive, comparative analysis of three different signal processing algorithms for automated seizure/ictal detection. The experimental results show that the proposed methods can be effective for accurate automated seizure detection and monitoring in clinical care.

Pediatric cancers pose unique challenges due to their rarity and distinct biological factors, emphasizing the need for accurate survival prediction to guide treatment. This study integrated generative AI and machine learning, including synthetic data, to analyze 9,184 pediatric cancer patients, identifying age at diagnosis, cancer types, and anatomical sites as significant survival predictors. The findings highlight the potential of AI-driven approaches to improve survival prediction and inform personalized treatment strategies, with broader implications for innovative healthcare applications.

In a 10-year period in the early 2000’s, hospital-based (nosocomial) infections increased by 123%, and this number is increasing as time goes on. The purpose of this experiment was to use hyaluronic acid, silver nanoparticles, and a bacteriophage cocktail to create a hydrogel that promotes wound healing by increasing cell proliferation while simultaneously disrupting biofilm formation and breaking down Staphylococcus aureus and Pseudomonas aeruginosa, which are two strains of bacteria that attribute to nosocomial infections and are increasing in antibiotic resistance.

The study explored the role of testosterone beyond its well-established effects on male sex characteristics, focusing on its association with non-communicable diseases (NCDs) like obesity and type 2 diabetes (T2D), using Mendelian randomization (MR) analysis on genomic data.

A central challenge of cancer therapy is identifying treatments that will effectively target cancer cells while minimizing effects on healthy cells. To identify potential targets for treating a multiple myeloma, a frequently incurable cancer, Kochenderfer and Kochenderfer analyze RNA sequencing data from the Cancer Cell Line Encyclopedia to find genes with high expression in multiple myeloma cells and low expression in normal tissues

The authors investigated whether the physician-patient relationship affected patient perceptions and treatment adherence.

The authors investigate how improper disposal of medication can be mitigated through community education efforts.