Is diet related to skin condition? A Mendelian randomization study.

Observational studies have revealed associations between various dietary factors and skin conditions. However, the causal relationship between diet and skin condition is still unknown. Data on 17 dietary factors were obtained from the UK Biobank. Data on four skin conditions were derived from the UK Biobank and another large-scale GWAS study. Genetic predictions suggested that the intake of oily fish was associated with a lower risk of skin aging (OR: 0.962, P = 0.036) and skin pigmentation (OR: 0.973, P = 0.033); Tea intake was associated with a lower risk of skin pigmentation (OR: 0.972, P = 0.024); Salad/raw vegetables intake was associated with a lower risk of keratinocyte skin cancer (OR: 0.952, P = 0.007). Coffee intake was associated with increased risk of skin aging (OR: 1.040, P = 0.028); Pork intake was associated with increased risk of skin aging (OR: 1.134, P = 0.020); Beef intake was associated with increased risk of cutaneous melanoma (OR: 1.013, P = 0.016); Champagne plus white wine intake was associated with increased risk of cutaneous melanoma (OR: 1.033, P = 0.004); Bread intake was associated with increased risk of keratinocyte skin cancer (OR: 1.026, P = 0.013). Our study results indicate causal relationships between genetically predicted intake of oily fish, tea, salad/raw vegetables, coffee, pork, beef, champagne plus white wine, and bread and skin conditions.

Predicting the Efficacy of Novel Synthetic Compounds in the Treatment of Osteosarcoma via Anti-Receptor Activator of Nuclear Factor-κB Ligand (RANKL)/Receptor Activator of Nuclear Factor-κB (RANK) Targets.

BACKGROUND: Osteosarcoma (OS) currently demonstrates a rising incidence, ranking as the predominant primary malignant tumor in the adolescent demographic. Notwithstanding this trend, the pharmaceutical landscape lacks therapeutic agents that deliver satisfactory efficacy against OS. OBJECTIVE: This study aimed to authenticate the outcomes of prior research employing the HM and GEP algorithms, endeavoring to expedite the formulation of efficacious therapeutics for osteosarcoma. METHODS: A robust quantitative constitutive relationship model was engineered to prognosticate the IC50 values of innovative synthetic compounds, harnessing the power of gene expression programming. A total of 39 natural products underwent optimization via heuristic methodologies within the CODESSA software, resulting in the establishment of a linear model. Subsequent to this phase, a mere quintet of descriptors was curated for the generation of non-linear models through gene expression programming. RESULTS: The squared correlation coefficients and s2 values derived from the heuristics stood at 0.5516 and 0.0195, respectively. Gene expression programming yielded squared correlation coefficients and mean square errors for the training set at 0.78 and 0.0085, respectively. For the test set, these values were determined to be 0.71 and 0.0121, respectively. The s2 of the heuristics for the training set was discerned to be 0.0085. CONCLUSION: The analytic scrutiny of both algorithms underscores their commendable reliability in forecasting the efficacy of nascent compounds. A juxtaposition based on correlation coefficients elucidates that the GEP algorithm exhibits superior predictive prowess relative to the HM algorithm for novel synthetic compounds.