Understanding Parkinson's: The microbiome and machine learning approach.

Objective: Given that Parkinson's disease is a progressive disorder, with symptoms that worsen over time, our goal is to enhance the diagnosis of Parkinson's disease by utilizing machine learning techniques and microbiome analysis. The primary objective is to identify specific microbiome signatures that can reproducibly differentiate patients with Parkinson's disease from healthy controls.

Methods: We used four Parkinson-related datasets from the NCBI repository, focusing on stool samples.

A bibliometric analysis of teledentistry published in the category of dentistry, oral surgery and medicine.

Background/purpose: Telemedicine has gained the popularity during COVID-19 pandemic. "Teledentistry" as the dental application of telemedicine was also with increased attention. The bibliometric analysis was employed to examine the global research trends and the current implementation status of teledentistry.

SLC11A1 predicts the overall survival of patients with colorectal cancer.

Colorectal cancer (CRC) remains a significant contributor to cancer-related mortality, emphasizing the critical need for identifying biomarkers that can improve clinical management and patient outcomes. In this retrospective study, we analyzed tumor samples from 25 patients with metastatic CRC, categorized based on long-term (> 50 months) or short-term (< 10 months) survival. Employing the PanCancer Immune Profile Panel, encompassing 770 genes, in the discovery dataset, we identified 54 differentially expressed genes (DEGs) within the tumor microenvironment of metastatic CRC.