To develop and validate a machine learning model incorporating dietary antioxidants to predict cardiovascular disease (CVD)-cancer comorbidity and to elucidate the role of antioxidants in disease prediction. Data were sourced from the National Health and Nutrition Examination Survey. Antioxidants, including vitamins, minerals, and polyphenols, were selected as key features. Additionally, demographic, lifestyle, and health condition features were incorporated to improve model accuracy. Feature preprocessing included removing collinear features, addressing class imbalance, and normalizing data. Models constructed within the mlr3 framework included recursive partitioning and regression trees, random forest, kernel k-nearest neighbors, naïve bayes, and light gradient boosting machine (LightGBM). Benchmarking provided a systematic approach to evaluating and comparing model performance. SHapley Additive exPlanation (SHAP) values were calculated to determine the prediction role of each feature in the model with the highest predictive performance. This analysis included 10,064 participants, with 353 identified as having comorbid CVD and cancer. After excluding collinear features, the machine learning model retained 29 dietary antioxidant features and 9 baseline features. LightGBM achieved the highest predictive accuracy at 87.9 %, a classification error rate of 12.1 %, and the top area under the receiver operating characteristic curve (0.951) and the precision-recall curve (0.930). LightGBM also demonstrated balanced sensitivity and specificity, both close to 88 %. SHAP analysis indicated that naringenin, magnesium, theaflavin, kaempferol, hesperetin, selenium, malvidin, and vitamin C were the most influential contributors. LightGBM exhibited the best performance for predicting CVD-cancer comorbidity. SHAP values highlighted the importance of antioxidants, with naringenin and magnesium emerging as primary factors in this model.

Supercritical anti-solvent fluidized bed (SAS-FB) coating technology has the advantages of reducing particle size, preventing high surface energy particle aggregation, improving the dissolution performance and bioavailability of insoluble drugs. The poor solubility of Biopharmaceutics Classification System (BCS) class IV drugs poses challenges in achieving optimal bioavailability. Numerous anti-cancer drugs including paclitaxel (PTX) belong to the BCS class IV, hindering their therapeutic efficacy. To address this concern, our study explored SAS-FB technology to coat PTX with D-α-tocopherol polyethylene glycol 1000 succinate (TPGS) onto lactose. Under our optimized conditions, we achieved a PTX coating efficiency of 96.8%. Further characterization confirmed the crystalline state of PTX in the lactose surface coating by scanning electron microscopy and X-ray powder diffraction. Dissolution studies indicated that SAS-FB processed samples release over 95% of the drug within 1 min. Moreover, cell transmembrane transport assays demonstrated that SAS-FB processed PTX samples co-coated with TPGS had an enhanced PTX internalization into cells and a higher permeability coefficient compared to those without TPGS. Finally, compared to unprocessed PTX, SAS-FB (TPGS) and SAS-FB processed samples showed a 2.66- and 1.49-fold increase in oral bioavailability in vivo, respectively. Our study highlights the efficacy of SAS-FB co-coating for PTX and TPGS as a promising strategy to overcome bioavailability challenges inherent in BCS class IV drugs. Our approach holds broader implications for enhancing the performance of similarly classified medications.

Introduction: Obesity has long been considered an independent risk factor for cardiovascular diseases (CVD), even in the COVID-19 pandemic. However, recent studies have found that a certain degree of obesity may be beneficial for patients who have already suffered from CVD, which is called the “obesity paradox”. Our objective was to investigate whether the obesity paradox existed in coronary care unit (CCU) patients and the relationship between body mass index (BMI) and short- and long-term mortality.Methods: We performed a cohort analysis of 3,502 adult CCU patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The patients were divided into four groups according to the WHO BMI categories. Both multivariable logistic regression and Cox regression were used to reveal the relation between BMI and mortality. Subgroup analyses were performed based on Simplified Acute Physiology Score (SAPS) and age.Results: After adjusting for confounders, obese patients had 33% and 30% lower mortality risk at 30-day and 1-year (OR 0.67, 95% CI 0.51 to 0.89; HR 0.70, 95% CI 0.59 to 0.83; respectively) compared with normal-weight patients, while the underweight group were opposite, with 141% and 81% higher in short- and long-term (OR 2.41, 95% CI 1.37 to 4.12; HR 1.81, 95% CI 1.34 to 2.46; respectively). Overweight patients did not have a significant survival advantage at 30-day (OR 0.91, 95% CI 0.70 to 1.17), but did have a 22% lower mortality risk at 1-year (HR 0.78; 95% CI 0.67 to 0.91). The results were consistent after being stratified by SAPS and age.Conclusion: Our study supports that obesity improved survival at both 30-day and 1-year after CCU admission, and the obesity paradox existed in CCU patients.