Gyejigamcho-tang (GG) is documented as a treatment for abnormal heart palpitations in Korean medicine. In this study, we investigated the anti-inflammatory effects of a water extract of GG on lipopolysaccharide (LPS)-induced RAW 264.7 cells. We first measured the levels of nitric oxide (NO) and prostaglandin E₂ (PGE₂), which are key products involved in inflammatory processes. Additionally, we assessed the levels of enzymes, such as inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), and cytokines, including interleukin 6 (IL-6), tumor necrosis factor alpha (TNF-α), and interleukin 1 beta (IL-1β). Our results showed that GG suppressed the production of NO and PGE₂ in a dose-dependent manner and reduced both the protein levels and mRNA expression of iNOS and COX-2. Furthermore, GG decreased the mRNA expression of IL-6, TNF-α, and IL-1β. In conclusion, GG demonstrated the ability to reduce the production of LPS-induced inflammatory factors, suggesting its potential for clinical research into the treatment of inflammatory diseases.

With increasing research into the link between circadian rhythm disorders and Parkinson's disease (PD), understanding this connection is essential for early prevention and personalized treatment. We analyzed eight SNPs from DNA purified from the blood of 42 subjects diagnosed with PD. These include (1) circadian rhythm gene-related SNPs ARNTL rs900147, CLOCK rs1801260, PER1 rs2253820, CRY1 rs2287161, (2) a redox metabolism-related SNP NRF2 rs1469602964, and (3) other PD-related SNPs LRRK2 rs34778348, GBA rs421016, SNCA rs104893877. We then compared the minor allele frequencies (MAFs) of these SNPs in PD patients with MAFs from a genome-wide study of Koreans. In the PD group, the frequency of the ARNTL rs900147 G allele was significantly higher than in the general Korean population, while no significant difference was found for CLOCK rs1801260, PER1 rs2253820, and CRY1 rs2287161. For NRF2 rs1469602964 and SNCA rs104893877, all participants had the same allele. Additionally, allele frequencies for LRRK2 rs34778348 and GBA rs421016, associated with increased PD risk, were higher in this sample group than in the Korean population. The ARNTL rs900147 G allele was linked to higher Parkinson’s Disease Sleep Scale scores (r = 0.43, p < 0.01 for GG genotype), which indicates milder PD-related sleep symptoms, and tremor-dominant subtype (r = 0.50, p < 0.001 for GG genotype). The CLOCK rs1801260 G allele and PER1 rs2253820 T allele may also be associated with poor nighttime sleep quality in PD patients. However, given the small sample size, further research with larger cohorts is required to validate these findings.

Obesity, characterized by excessive fat accumulation, arises from an imbalance between energy consumption and expenditure. It is a major risk factor for cardiovascular diseases, type 2 diabetes, and metabolic syndrome. In this retrospective case-control study, we examined the relationship between obesity and iris features frequently utilized in iridology, a practice that studies iris features to assess health conditions. We analyzed the correlation between iris markers and obesity obesity-related test results in 197 adults (99 obese, 98 normal-weight) using anonymized medical records and iris images. Obesity was defined as a BMI of 25 or higher. Trained practitioners examined iris images for markers such as lacunae around the autonomic nerve wreath (ANW), toxic spots around ANW, protrusion around ANW, and lacunae in the cardiac region. They evaluated each marker using a 0-2 grading system. Obese individuals showed more pronounced features, including lacunae and toxic spots around ANW. Normal-weighted individuals showed thicker and more protruded ANW (suggesting increased sympathetic nervous system activity). These markers correlated with clinical measurements of obesity such as waist and hip circumference, visceral fat area, body fat mass, and percent body fat. Each iris marker tended to correlate with different test items. The results suggest that iridology could be utilized to identify obesity-related detailed pathophysiological factors. Iridology combined with advanced technologies such as deep learning and artificial intelligence may further improve early detection and personalized treatment approaches for obesity-related health issues, potentially making iridology a useful component in comprehensive obesity management programs.