Osteoarthritis (OA) is a degenerative joint disease characterized by cartilage degradation and inflammation. This study investigates the therapeutic potential of secretome derived from adipose tissue mesenchymal stem cells (ASCs) in mitigating inflammation and promoting cartilage repair in an in vitro model of OA. Our in vitro model comprised chondrocytes inflamed with TNF. To assess the therapeutic potential of secretome, inflamed chondrocytes were treated with it and concentrations of pro-inflammatory cytokines, metalloproteinases (MMPs) and extracellular matrix markers were measured. In addition, secretome-treated chondrocytes were subject to a microarray analysis to determine which genes were upregulated and which were downregulated. Treating TNF-inflamed chondrocytes with secretome in vitro inhibits the NF-κB pathway, thereby mediating anti-inflammatory and anti-catabolic effects. Additional protective effects of secretome on cartilage are revealed in the inhibition of hypertrophy markers such as RUNX2 and COL10A1, increased production of COL2A1 and ACAN and upregulation of SOX9. These findings suggest that ASC-derived secretome can effectively reduce inflammation, promote cartilage repair, and maintain chondrocyte phenotype. This study highlights the potential of ASC-derived secretome as a novel, non-cell-based therapeutic approach for OA, offering a promising alternative to current treatments by targeting inflammation and cartilage repair mechanisms.

Purpose: Accurate prediction of spine surgery outcomes is essential for optimizing treatment strategies. This study presents an enhanced machine learning approach to classify and predict the success of spine surgeries, incorporating advanced oversampling techniques and grid search optimization to improve model performance. Methods: Various machine learning models, including GaussianNB, ComplementNB, KNN, Decision Tree, KNN with RandomOverSampler, KNN with SMOTE, and grid-searched optimized versions of KNN and Decision Tree, were applied to a dataset of 244 spine surgery patients. The dataset, comprising pre-surgical, psychometric, socioeconomic, and analytical variables, was analyzed to determine the most efficient predictive model. The study explored the impact of different variable groupings and oversampling techniques. Results: Experimental results indicate that the KNN model, especially when enhanced with RandomOverSampler and SMOTE, demonstrated superior performance, achieving accuracy values as high as 76% and an F1-score of 67%. Grid-searched optimized versions of KNN and Decision Tree also yielded significant improvements in predictive accuracy and F1-score. Conclusions: The study highlights the potential of advanced machine learning techniques and oversampling methods in predicting spine surgery outcomes. The results underscore the importance of careful variable selection and model optimization to achieve optimal performance. This system holds promise as a tool to assist healthcare professionals in decision-making, thereby enhancing spine surgery outcomes. Future research should focus on further refining these models and exploring their application across larger datasets and diverse clinical settings.

The Distress Risk Assessment Method (DRAM) was presented by Main, Wood and Hillis in 1992 as a simple means of assessing the risk of failure due to psychosocial factors in spine surgery. To our knowledge, it has not been used in our setting. The aim of this study was to analyse the usefulness of the Spanish translation of this instrument to predict poor outcomes. Methods: A prospective blind study was conducted including 65 patients undergoing spine surgery. We created two groups of patients based on DRAM score: not distressed (NDRAM) or distressed (DDRAM). A visual analogue scale for pain and the 12-Item Short Form Health Survey (SF-12) were used at baseline, 6 weeks and 6 months. Results: 24 patients were classified as DDRAM and 38 as NDRAM, with 3 patients not completing the questionnaires. The analysis found no significant differences in the demographic or clinical variables at baseline. At 6 weeks and 6 months, the NDRAM group showed improvements in low back pain (p < 0.001; p = 0.005), leg pain (p < 0.001; p = 0.017), physical health (p = 0.031; p = 0.003) and mental health (p = 0.137; p = 0.049). In contrast, in the DDRAM group, though leg pain score improved (p < 0.001; p = 0.002), there was no improvement at 6 weeks or 6 months in low back pain (p = 0.108; p = 0.287), physical health (p = 0.620; p = 0.263) or mental health (p = 0.185; p = 0.329). Conclusions: In our setting, the DRAM is a useful screening tool, and it has allowed the creation of a program between psychiatry and spine surgery.