Identifying significant structural factors associated with knee pain severity in patients with osteoarthritis using machine learning.

Our main objective was to use machine learning methods to identify significant structural factors associated with pain severity in knee osteoarthritis patients. Additionally, we assessed the potential of various classes of imaging data using machine learning techniques to gauge knee pain severity. The data of semi-quantitative assessments of knee radiographs, semi-quantitative assessments of knee magnetic resonance imaging (MRI), and MRI images from 567 individuals in the Osteoarthritis Initiative (OAI) were utilized to train a series of machine learning models. Models were constructed using five machine learning methods: random forests (RF), support vector machines (SVM), logistic regression (LR), decision tree (DT), and Bayesian (Bayes). Employing tenfold cross-validation, we selected the best-performing models based on the area under the curve (AUC). The study results indicate no significant difference in performance among models using different imaging data. Subsequently, we employed a convolutional neural network (CNN) to extract features from magnetic resonance imaging (MRI), and class activation mapping (CAM) was utilized to generate saliency maps, highlighting regions associated with knee pain severity. A radiologist reviewed the images, identifying specific lesions colocalized with the CAM. The review of 421 knees revealed that effusion/synovitis (30.9%) and cartilage loss (30.6%) were the most frequent abnormalities associated with pain severity. Our study suggests cartilage loss and synovitis/effusion lesions as significant structural factors affecting pain severity in patients with knee osteoarthritis. Furthermore, our study highlights the potential of machine learning for assessing knee pain severity using radiographs.

Exploring the potential of the sit-to-stand test for self-assessment of physical condition in advanced knee osteoarthritis patients using computer vision.

Introduction: Knee osteoarthritis (KOA) is a prevalent condition often associated with a decline in patients' physical function. Objective self-assessment of physical conditions poses challenges for many advanced KOA patients. To address this, we explored the potential of a computer vision method to facilitate home-based physical function self-assessments. Methods: We developed and validated a simple at-home artificial intelligence approach to recognize joint stiffness levels and physical function in individuals with advanced KOA. One hundred and four knee osteoarthritis (KOA) patients were enrolled, and we employed the WOMAC score to evaluate their physical function and joint stiffness. Subsequently, patients independently recorded videos of five sit-to-stand tests in a home setting. Leveraging the AlphaPose and VideoPose algorithms, we extracted time-series data from these videos, capturing three-dimensional spatiotemporal information reflecting changes in key joint angles over time. To deepen our study, we conducted a quantitative analysis using the discrete wavelet transform (DWT), resulting in two wavelet coefficients: the approximation coefficients (cA) and the detail coefficients (cD). Results: Our analysis specifically focused on four crucial joint angles: "the right hip," "right knee," "left hip," and "left knee." Qualitative analysis revealed distinctions in the time-series data related to functional limitations and stiffness among patients with varying levels of KOA. In quantitative analysis, we observed variations in the cA among advanced KOA patients with different levels of physical function and joint stiffness. Furthermore, there were no significant differences in the cD between advanced KOA patients, demonstrating different levels of physical function and joint stiffness. It suggests that the primary difference in overall movement patterns lies in the varying degrees of joint stiffness and physical function among advanced KOA patients. Discussion: Our method, designed to be low-cost and user-friendly, effectively captures spatiotemporal information distinctions among advanced KOA patients with varying stiffness levels and functional limitations utilizing smartphones. This study provides compelling evidence for the potential of our approach in enabling self-assessment of physical condition in individuals with advanced knee osteoarthritis.

Gut microbiota: a potential target for improved cancer therapy.

Drug resistance and toxicity are major challenges observed during cancer treatment. In recent years, gut microbiota has been found to be strongly associated with the efficacy, toxicity, and side effects of chemotherapy, radiotherapy, and immunotherapy. Both preclinical studies and clinical trials have demonstrated the potential of microbiota modulation for cancer treatment. The human gut microbiota has exciting prospects for developing biomarkers to predict the outcome of cancer treatment. Moreover, multiple approaches can alter the gut microbiota composition, including faecal microbiota transplantation (FMT), probiotics, antibiotics (ATB), and diet. We describe the mechanisms by which the gut microbiota influences the efficacy and toxicity of cancer therapy, disease-related biomarkers, and methods to target the gut microbiota to improve outcomes. The purpose of this review is to provide new ideas for optimising cancer therapy by providing up-to-date information on the relationship between gut microbiota and cancer therapy, and hopes to find new targets for cancer treatment from human microbiota.

Effects of nursing support workers participation on negative emotions, quality of life and life satisfaction of patients with cerebral hemorrhage: a quasi-experimental study.

BACKGROUND: Due to the high nursing pressure of patients with cerebral hemorrhage and the general shortage of clinical nurses, nursing support workers often participate in clinical nursing work, but the influence of nursing support workers' participation on the negative emotion, quality of life and life satisfaction of patients with intracerebral hemorrhage is unknown. METHODS: This quasi-experimental study was conducted with a pretest-posttest design. A total of 181 ICH patients admitted to our hospital from January 2022 to April 2022 were enrolled, including 81 patients receiving conventional care (CG control group) and 80 patients receiving nursing support worker participation (RG research group). All patients were recorded with self-perceived Burden Scale (SPBS), Hamilton Depression Scale (HAMD), Quality of Life Scale (SF-36), Somatic Self rating Scale (SSS), Patient self-care ability assessment scale (Barthel) and Satisfaction with life scale (SWLS) scores. RESULTS: Patients with high negative emotion were more willing to participate in clinical nursing work (p < 0.05). Nursing support workers involved in cerebral hemorrhage patients can alleviate negative emotions, improve life quality, improve life satisfaction (p < 0.05). CONCLUSION: The participation of nursing support workers can alleviate the negative emotions of ICH patients, enhance their self-management ability, and improve their life quality.