Objective: To analyze and synthesize the information available from five pivotal, large-scale, multicenter, observational studies (CHECK, OAI, FNIH Biomarkers Consortium, IMI-APPROACH, and MOST) focusing on knee osteoarthritis (OA), which can be used to elucidate disease progression, risk factors, and the effectiveness of potential interventions. Design: For this narrative review, a comprehensive literature search and data extraction from official web pages and scientific databases were conducted to compare methodologies, in- and exclusion criteria, outcomes, and cohort characteristics across the studies. Thematic, comparative, and qualitative analyses were employed to identify trends, commonalities, and disparities among the findings. Results: The studies collectively enhanced understanding of the onset and progression of knee OA, and in several of the studies, hip OA, emphasizing the importance of both systemic and local risk factors. Advanced imaging and biomarkers are important components in all the cohorts, with the goal of aiding early diagnosis and tracking disease progression. All cohorts evaluated unique markers generally not available in the other cohorts, while other factors overlap, suggesting possibilities for combining or cross-validating between cohorts. Conclusions: The collaborative efforts of major OA research significantly advance our understanding of knee OA. These studies highlight the importance of a multifaceted approach, integrating advanced imaging, biomarkers, and longitudinal data to tackle the complexities of OA. By synthesizing findings and addressing knowledge gaps such as heterogeneity of patients and used measurements, and use of novel pain measures, future research can develop more effective diagnostic tools and treatments, ultimately enhancing the quality of life for OA patients.

Recent technologies for recording and storing data, as well as advancements in data processing techniques, have opened up novel possibilities for urban planners to design a more optimal public transport network. This study aims to initially develop a robust framework for making an insightful understanding of already recorded and available data sets using machine learning approaches. This will give transportation planners a powerful framework to use great recorded datasets to understand the network better and make datasets more meaningful for transport planners. And then introduces an approach to use Machine Learning algorithms and extract hidden patterns for predicting financial loss during any crisis, which is a novel perspective and application. To do this, seven alternative machine learning algorithms were developed to predict ridership: Multiple Linear Regression, Decision Tree, Random Forest, Bayesian Ridge Regression, Neural Networks, Support Vector Regression, and k-Nearest Neighbors. The developed framework was applied to the available 10 years of historical recorded data from the blue bus line number 4 in Stockholm, Sweden. The best model, kNN, with an average R-squared of 0.65 in 10-fold cross-validation, was accepted as the best model. This model is then used to estimate the financial loss of the network during the pandemic in 2020 and 2021. Results reveal a decline of 49% in 2020 and 82% in 2021 in the studied line. Finally, the results were validated with a similar study that analyzed the ticket validations and passenger counts during the spring of 2020.