Atopic dermatitis (AD) is a chronic and persistent inflammatory skin disease characterized by eczematous lesions and itching, and it has become a serious health problem. However, the common clinical treatments provide limited relief and are accompanied by adverse effects. Therefore, there is a need to develop novel and effective therapies to treat AD. Neferine is a small molecule compound isolated from the green embryo of the mature seeds of lotus (Nelumbo nucifera). It has a bisbenzylisoquinoline alkaloid structure. Relevant studies have shown that neferine has many pharmacological and biological activities, including anti-inflammatory, anti-thrombotic, and anti-diabetic activities. However, there are very few studies on neferine in the skin, especially the related effects on inflammatory skin diseases. In this study, we proved that it has the potential to be used in the treatment of atopic dermatitis. Through in vitro studies, we found that neferine inhibited the expression of cytokines and chemokines in TNF-α/IFN-γ-stimulated human keratinocyte (HaCaT) cells, and it reduced the phosphorylation of MAPK and the NF-κB signaling pathway. Through in vivo experiments, we used 2,4-dinitrochlorobenzene (DNCB) to induce atopic dermatitis-like skin inflammation in a mouse model. Our results show that neferine significantly decreased the skin barrier damage, scratching responses, and epidermal hyperplasia induced by DNCB. It significantly decreased transepidermal water loss (TEWL), erythema, blood flow, and ear thickness and increased surface skin hydration. Moreover, it also inhibited the expression of cytokines and the activation of signaling pathways. These results indicate that neferine has good potential as an alternative medicine for the treatment of atopic dermatitis or other skin-related inflammatory diseases.

Insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) is an RNA-binding protein and serves as a post-transcriptional fine-tuner regulating the expression of mRNA targets. However, the clinicopathological roles of IGF2BP1 in colorectal cancer (CRC) remains limited. Thus, we aimed to elucidate the clinical significance and biomarker potentials of IGF2BP1 in CRC. A total of 266 specimens from two sets of CRC patients were collected. IGF2BP1 expression was studied by immunohistochemical (IHC) staining. The Kaplan-Meier survival plot and a log-rank test were used for survival analysis. The Cox proportional hazards model was applied to determine the survival impact of IGF2BP1. Public datasets sets from The Cancer Genome Atlas (TCGA) and Human Cancer Metastasis Database (HCMDB), receiver operating characteristic (ROC) plotter, and two CRC cell lines, HCT-116 and DLD-1, were used for validating our findings. We showed that IGF2BP1 was overexpressed in tumor specimens compared to 13 paired normal parts by examining the immunoreactivity of IGF2BP1 (p = 0.045). The increased expression of IGF2BP1 in primary tumor parts was observed regardless of metastatic status (p < 0.001) in HCMDB analysis. IGF2BP1 expression was significantly associated with young age (59.6% vs. 46.7%, p-value = 0.043) and advanced stage (61.3% vs. 40.0%, p-value = 0.001). After controlling for confounding factors, IGF2BP1 remained an independent prognostic factor (HR = 1.705, p-value = 0.005). TCGA datasets analysis indicated that high IGF2BP1 expression showed a lower 5-year survival rate (58% vs. 65%) in CRC patients. The increased expression of IGF2BP1 in chemotherapy non-responder rectal cancer patients was observed using a ROC plotter. Overexpression of IGF2BP1 promoted the colony-forming capacity and 5-fluorouracil and etoposide resistance in CRC cells. Here, IGF2BP1 was an independent poor prognostic marker in CRC patients and contributed to aggressive phenotypes in CRC cell lines.

Background: Uric acid (UA) is linked to gout, renal dysfunction, and cardiovascular disease. Prior studies often assume linear relationships, potentially oversimplifying physiological complexity. Methods: We analyzed data from 5200 healthy Taiwanese men. Demographic, biochemical, lifestyle, and inflammatory variables were assessed using Pearson correlation, multiple linear regression (MLR), and multivariate adaptive regression splines (MARS), an interpretable machine learning method for detecting nonlinear, threshold-based effects. Results: Pearson correlation showed broad linear associations, whereas MARS identified fewer but more physiologically meaningful predictors. Waist-to-hip ratio (WHR) had a strong threshold effect, influencing UA only below 0.969. Creatinine showed a nonlinear impact, becoming substantial above 0.97 mg/dL, suggesting a renal threshold within the “normal” range. Calcium and high-sensitivity C-reactive protein (hs-CRP) each displayed inflection points (9.5 mg/dL and 3.38 mg/L, respectively), indicating range-specific effects. Notably, betel nut exposure, nonsignificant in linear models, emerged in MARS as a predictor with a complex, non-binary association with UA metabolism. Predictive performance was comparable (RMSE: 1.6694 for MARS vs. 1.6666 for MLR), but MARS offered superior interpretability by highlighting localized nonlinear effects. Conclusions: MARS modeling revealed critical nonlinear, threshold-dependent associations between UA and WHR, creatinine, calcium, hs-CRP, and betel nut exposure, which were not captured by conventional methods. These findings underscore the value of interpretable machine learning in metabolic research and suggest precise thresholds for clinical risk stratification.

Background: Myocardial perfusion scintigraphy (MPS) is an important tool for evaluating ischemia in diabetic populations. However, applications of advanced predictive models like multivariate adaptive regression splines (MARS) to estimate summed stress scores (SSS) are lacking. Methods: In this study, 1028 diabetic women undergoing Thallium-201 MPS were analyzed. The dataset was split into training (80%) and testing (20%) subsets. MARS and multiple linear regression (MLR) models were constructed to predict SSS, and their performance was evaluated using root mean square error (RMSE), relative absolute error (RAE), root relative squared error (RRSE), Mean Absolute Percentage Error (MAPE), and Symmetric Mean Absolute Percentage Error (SMAPE). Results: On the testing dataset, the MARS model outperformed the MLR model across all metrics, with an RMSE of 3.25 compared to 3.89 for MLR, an RAE of 0.52 vs. 0.64, and an RRSE of 0.53 vs. 0.67. Similar trends were observed in MAPE (18.7% vs. 22.1%) and SMAPE (17.3% vs. 20.5%). Conclusions: The superior predictive accuracy of the MARS model suggests its potential to enhance non-invasive myocardial risk stratification in diabetic women.

Background: Biological age (BA) is a better representative of health status than chronological age (CA), as it uses different biological markers to quantify cellular and systemic change status. However, BA can be difficult to accurately estimate using current methods. This study uses multiple adaptive regression spline (MARS) to build an equation to estimate BA among healthy postmenopausal women, thereby potentially improving the efficiency and accuracy of BA assessment. Methods: A total of 11,837 healthy women were enrolled (≥51 years old), excluding participants with metabolic syndrome variable values outside two standard deviations. MARS was applied, with the results compared to traditional multiple linear regression (MLR). The method with the smaller degree of estimation error was considered to be more accurate. The lower prediction errors yielded by MARS compared to the MLR method suggest that MARS performs better than MLR. Results: The equation derived from MARS is depicted. It could be noted that BA could be determined by marriage, systolic blood pressure (SBP), diastolic blood pressure (DBP), waist–hip ratio (WHR), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), creatinine (Cr), carcinoembryonic antigen (CEA), bone mineral density (BMD), education level, and income. The MARS equation is generated. Conclusions: Using MARS, an equation was built to estimate biological age among healthy postmenopausal women in Taiwan. This equation could be used as a reference for calculating BA in general. Our equation showed that the most important factor was BMD, followed by WHR, Cr, marital status, education level, income, CEA, blood pressure, ALP, and LDH.

Background: Non-alcoholic fatty liver disease (NAFLD) represents the most widespread chronic liver disorder globally, impacting roughly 30% of the general population. Numerous factors have been linked to NAFLD, including obesity, type 2 diabetes, diet, physical inactivity, age, sex, genetic factors, and metabolic syndrome. Previous research predominantly treated NAFLD as a categorical outcome, providing less granular data compared to the continuous fatty liver index (FLI). This investigation enrolled healthy young Taiwanese men and applied multivariate adaptive regression spline (MARS) modeling to develop a predictive equation. Our aims were twofold: 1. To assess the predictive accuracy of traditional multiple linear regression (MLR) versus MARS. 2. To construct a MARS-derived equation for estimating FLI in this demographic. Methods: Data originated from the Taiwan MJ Cohort, comprising 5496 men aged 20–50 years not using medications for metabolic syndrome. MARS was used to formulate the FLI estimation equation. Model performance was compared using symmetric mean absolute percentage error (SMAPE), relative absolute error (RAE), root relative squared error (RRSE), and root mean squared error (RMSE). Results: Evaluation indicated that MARS yielded lower estimation errors than MLR, demonstrating its superior performance. The derived equation is: FLI = 65.224 − 0.436 × B1 − 0.490 × B2 + 0.252 × B3 − 2.962 × B4 + 2.231 × B5 − 0.292 × B6 + 0.189 × B7 − 0.361 × B8 − 0.699 × B9 + 0.160 × B10 − 2.715 × B11 + 0.799 × B12 − 0.153 × B13 + 0.084 × B14 − 35.274 × B15 − 4.424 × B16. Conclusions: Using MLR as a benchmark, our analysis revealed that MARS delivered better predictive performance. The presented equation explains 62.7% of the variance in FLI (r² = 0.627). Based on standardized variable importance scores (nsubsets metric), CRP emerged as the most influential predictor, followed by WBC, UA, HDL-C, AST, age, ALT, FPG, SBP, and LDL in this cohort of healthy young Taiwanese men.