Effects of Greenshell™ mussel intervention on biomarkers of cartilage metabolism, inflammatory markers and joint symptoms in overweight/obese postmenopausal women: A randomized, double-blind, and placebo-controlled trial

Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models

INTRODUCTION

Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP.

METHODS

The transcriptomic data of patients with LBP from peripheral immune cells were retrieved from the GEO database. Patients with LBP were recruited, and treatment outcomes were assessed after 3 months. Patients were classified into two groups: those with resolved pain and those with persistent pain. Differentially expressed genes (DEGs) between the two groups were identified through bioinformatic analysis. Key genes were selected using five machine learning models, including Lasso, Elastic Net, Random Forest, SVM, and GBM. These key genes were then used to train 45 machine learning models by combining nine different algorithms: Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting Machine, Multilayer Perceptron, Naive Bayes, and Linear Discriminant Analysis. Five-fold cross-validation was employed to ensure robust model evaluation and minimize overfitting. In each fold, the dataset was split into training and validation sets, with model performance assessed using multiple metrics including accuracy, precision, recall, and F1 score. The final model performance was reported as the mean and standard deviation across all five folds, providing a more reliable estimate of the models' ability to predict LBP treatment outcomes using gene expression data from peripheral immune cells.

RESULTS

A total of 61 DEGs were identified between patients with resolved and persistent pain. From these genes, 45 machine learning models were constructed using different combinations of feature selection methods and classification algorithms. The Elastic Net with Logistic Regression achieved the highest accuracy of 88.7% ± 8.0% (mean ± standard deviation), followed closely by Elastic Net with Linear Discriminant Analysis (88.7% ± 7.5%) and Lasso with Multilayer Perceptron (87.7% ± 6.7%). Overall, 15 models demonstrated robust performance with accuracy > 80%, suggesting the reliability of our machine learning approach in predicting LBP treatment outcomes. The SHapley Additive exPlanations (SHAP) method was used to visualize the contribution of core genes to model performance, highlighting their roles in predicting treatment outcomes.

CONCLUSION

The study demonstrates the potential of using transcriptomic data from peripheral immune cells and machine learning models to predict treatment outcomes in patients with LBP. The identification of key genes and the high accuracy of certain models provide a basis for future personalized treatment strategies in LBP management. Visualizing gene importance with SHAP adds interpretability to the predictive models, enhancing their clinical relevance.

Medical treatment of osteoarthritis: botanical pharmacologic aspect

Osteoarthritis (OA) is the most common form of arthritis, and its prevalence is expected to further increase as our society ages. Despite many approaches to cure OA, no drugs are currently proven to modulate the progression of OA. Nowadays, new OA treatment options are holistically developed and one of the approaches of treatment option is botanical drugs. Some botanical drugs for OA have shown both therapeutic effect comparable to refined drugs in small studies and fewer side effects. Hence, there are various health functional foods which are known to relieve symptoms of OA. However, since there are many botanical products, clinicians are not familiar to the efficacy of each botanical product, making it challenging to use them appropriately in clinical practice. Here, we summarize the botanical products available for treating OA, including prescription botanical drugs and health functional foods available in Korea. Further studies and the purification of effective molecules from botanical products will be necessary in future.

The Efficacy of New Zealand Greenshell™ Mussel Powder Supplementation in Supporting Muscle Recovery Following Eccentric Exercise-Induced Muscle Damage in Healthy, Untrained Adult Males

Unaccustomed eccentric exercise results in muscle damage limiting physical performance for several days. This study investigated if Greenshell™ mussel (GSM) powder consumption expedited muscle recovery from eccentric exercise-induced muscle damage (EIMD). Methods: Twenty untrained adult men were recruited into a double-blind, placebo-controlled, cross-over study and were randomly assigned to receive the GSM powder or placebo treatment first. Participants consumed their allocated intervention for four weeks then completed a bench-stepping exercise that induced muscle damage to the eccentrically exercised leg. Muscle function, soreness and biomarkers of muscle damage, oxidative stress and inflammation were measured before exercise, immediately after exercise and 24, 48 and 72 h post exercise. GSM powder promoted muscle function recovery, significantly improving (p < 0.05) isometric and concentric peak torque at 48 h and 72 h post exercise, respectively. Participants on the GSM treatment had faster dissipation of soreness, with significant treatment × time interactions for affective (p = 0.007) and Visual Analogue Scale-assessed pain (p = 0.018). At 72 h, plasma creatine kinase concentrations in the GSM group were lower (p < 0.05) compared with the placebo group. This study provides evidence for GSM powder being effective in supporting muscle recovery from EIMD.