Risk factors for metabolic syndrome in the premetabolic state assessed using hierarchical clustering study in a health screening group.

Early detection of a premetabolic status that is at risk for metabolic syndrome (MetS) but not meeting the criteria is crucial. This study examined 27,623 participants aged 20-50 (mean: 40.7) years who underwent initial health screening at Kangbuk Samsung Hospital (2011-2019), focusing on individuals with one or two MetS components.

A study on BReast cancer patient care using chAtbot and Video Education for radiation therapy (BRAVE): A randomized controlled trial.

Purpose: This study aimed to evaluate the use of chatbot and video education to reduce anxiety in patients with breast cancer undergoing RT.

Methods And Materials: This randomized controlled trial included patients with breast cancer scheduled for RT after surgery at an outpatient department of radiation oncology in a cancer center, randomly assigned to four groups: (1) Video+Chatbot, (2) Video+Paper, (3) Paper+Chatbot, and (4) Paper+Paper. In each group, patients received information regarding the treatment process and were frequently asked questions using the designated tool.

Synergistic two-step inhibition approach using a combination of trametinib and onvansertib in KRAS and TP53-mutated colorectal adenocarcinoma.

Colorectal malignancies associated with KRAS and TP53 mutations led us to investigate the effects of combination therapy targeting KRAS, MEK1, or PLK1 in colorectal cancer. MEK1 is downstream of RAS in the MAPK pathway, whereas PLK1 is a mitotic kinase of the cell cycle activated by MAPK and regulated by p53. Bioinformatics analysis revealed that patients with colorectal cancer had a high expression of MAP2K1 and PLK1.

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique, we aimed to formulate a model predicting antipsychotic treatment outcomes. Data were obtained from 299 patients with schizophrenia from three multicenter, open-label, non-comparative clinical trials.