Prediction models for chronic postsurgical pain in patients with breast cancer based on machine learning approaches

PurposeThis study aimed to develop prediction models for chronic postsurgical pain (CPSP) after breast cancer surgery using machine learning approaches and evaluate their performance.MethodsThe study was a secondary analysis based on a high-quality dataset from a randomized controlled trial (NCT00418457), including patients with primary breast cancer undergoing mastectomy. The primary outcome was CPSP at 12 months after surgery, defined as modified Brief Pain Inventory > 0. The dataset was randomly split into a training dataset (90%) and a testing dataset (10%). Variables were selected using recursive feature elimination combined with clinical experience, and potential predictors were then incorporated into three machine learning models, including random forest, gradient boosting decision tree and extreme gradient boosting models for outcome prediction, as well as logistic regression. The performances of these four models were tested and compared.Results1152 patients were finally included, of which 22.1% developed CPSP at 12 months after breast cancer surgery. The 6 leading predictors were higher numerical rating scale within 2 days after surgery, post-menopausal status, urban medical insurance, history of at least one operation, under fentanyl with sevoflurane general anesthesia, and received axillary lymph node dissection. Compared with the multivariable logistic regression model, machine learning models showed better specificity, positive likelihood ratio and positive predictive value, helping to identify high-risk patients more accurately and create opportunities for early clinical intervention.ConclusionsOur study developed prediction models for CPSP after breast cancer surgery based on machine learning approaches, which may help to identify high-risk patients and improve patients’ management after breast cancer

Progress in Diagnosis and Treatment of Central Post-stroke Pain

Central post-stroke pain (CPSP), a neuropathic pain syndrome occurring after a cerebrovascular accident, is characterized by pain or paraesthesia in the part of the body dominated by the area of the brain where blood vessels are injured. CPSP patients are often accompanied by anxiety, depression and other emotional disorders, which have a serious negative impact on patients' quality of life. However, the pathogenesis of CPSP has not been fully elucidated, the clinical diagnosis rate is not high, and the commonly used treatment methods are not effective. This article reviews the clinical features, epidemiology, pathogenesis and treatment of CPSP in order to provide reference for the elucidation of CPSP mechanism and effective treatment

The changes of gene expression associated with the Toll-like receptor signaling in the three test groups of HOKs with reference to the control cells.

<p>The genes up-regulated over 2 folds are highlighted in bold, and those down-regulated over 2 folds are marked in bold italics.</p

Conformational flexibility of the dengue virus RNA-dependent RNA polymerase revealed by a complex with an inhibitor

We report a highly reproducible method to crystallize the RNA-dependent RNA polymerase (RdRp) domain of dengue virus serotype 3 (DENV-3), allowing structure refinement to a 1.79-Å resolution and revealing amino acids not seen previously. We also present a DENV-3 polymerase/inhibitor cocrystal structure at a 2.1-Å resolution. The inhibitor binds to the RdRp as a dimer and causes conformational changes in the protein. The improved crystallization conditions and new structural information should accelerate structure-based drug discovery.Published versio