DataSheet1_Effects of arch support doses on the center of pressure and pressure distribution of running using statistical parametric mapping.pdf

Insoles with an arch support have been used to address biomechanical risk factors of running. However, the relationship between the dose of support and running biomechanics remains unclear. The purpose of this study was to determine the effects of changing arch support doses on the center of pressure (COP) and pressure mapping using statistical parametric mapping (SPM). Nine arch support variations (3 heights * 3 widths) and a flat insole control were tested on fifteen healthy recreational runners using a 1-m Footscan pressure plate. The medial-lateral COP (COPML) coordinates and the total COP velocity (COPVtotal) were calculated throughout the entirety of stance. One-dimensional and two-dimensional SPM were performed to assess differences between the arch support and control conditions for time series of COP variables and pressure mapping at a pixel level, respectively. Two-way ANOVAs were performed to test the main effect of the arch support height and width, and their interaction on the peak values of the COPVtotal. The results showed that the COPVtotal during the forefoot contact and forefoot push off phases was increased by arch supports, while the COP medial-lateral coordinates remained unchanged. There was a dose-response effect of the arch support height on peak values of the COPVtotal, with a higher support increasing the first and third valleys but decreasing the third peak of the COPVtotal. Meanwhile, a higher arch support height shifted the peak pressure from the medial forefoot and rearfoot to the medial arch. It is concluded that changing arch support doses, primarily the height, systematically altered the COP velocities and peak plantar pressure at a pixel level during running. When assessing subtle modifications in the arch support, the COP velocity was a more sensitive variable than COP coordinates. SPM provides a high-resolution view of pressure comparisons, and is recommended for future insole/footwear investigations to better understand the underlying mechanisms and improve insole design.</p

Table_1_A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis.docx

BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.</p