Weifang Peoples Hospital

Acute cerebral infarction poses a significant health risk and effective treatment is crucial. This randomized controlled trial assessed the impact of combining Butylphthalide and Atorvastatin on neurological function, quality of life and vascular endothelial function in 124 individuals with acute cerebral infarction. Participants were randomized into control and experimental groups, with the latter receiving the combination therapy. Objective assessments using NIHSS, SS-QOL, MBI and MoCA scales, along with biochemical markers, demonstrated improved outcomes in the experimental group. This study provides evidence for the clinical benefits of the drug combination in managing acute cerebral infarction.

We aimed to identify critical clinical features to develop an accurate webbased prediction model for estimating the overall survival (OS) of primary breast diffuse large Bcell lymphoma (PB-DLBCL) adult patients.

We first included all PB-DLBCL cases with available covariates retrieved from the Surveillance, Epidemiology, and End Results database. We sequentially performed univariate and multivariate Cox regression approaches to identify the predictors independently associated with prognosis, and all the predictors that passed these tests were then constructed to build a nomogram for predicting 3-, 5-, and 10-year survival rates of patients. The C-index and the receiver operating characteristic curve (ROC) were used to evaluate the prediction discrimination, and the calibration curve was applied to estimate the calibration.

A total of PB-DLBCL adult patients were included (median age was 69 with the interquartile range [IQR] of 57-79 years), of which 466 (70%) were randomly allocated to the development cohort, and the remaining cases were collected for validation. Using three identified independent predictors (i.e., age, stage, and radiation), an accurate nomogram for predicting OS was developed and validated. The C-indices of our nomogram were both relatively acceptable, with 0.74 (95% CI: 0.71-0.78) and 0.72 (95% CI: 0.70-0.75) for the development and validation cohorts, respectively. The calibration curves also accurately predicted the prognosis of PB-DLBCL in all cases. In addition, ROC curves showed our nomogram to possess superior predictive ability compared to any single variable. To visually present this prediction model, a convenient webbased tool was implemented based on our prognostic nomogram.

For patients with PB-DLBCL, a more convenient and accurate web-based prediction model was developed and validated, which showed relatively good performances in both discrimination and calibration during model development and validation. External evaluation and validation are warranted by further independent studies.

The clinical significance of cancer‐related stigma on patients' well‐being has been widely established. Stigma can be perceived and internalised by cancer patients or implemented by the general population and healthcare workers. Various interventions have been carried out to reduce cancer‐related stigma, but their effectiveness is not well‐understood. This review aims to synthesise evidence on the effectiveness of interventions to reduce cancer‐related stigma.

An integrative review.

This integrative review combined both qualitative and quantitative studies and followed five steps to identify problems, search for the literature, appraise the literature quality, analyse data, and present data. Mixed Methods Appraisal Tool (version 2018) was applied to evaluate the quality of the included studies.

Databases included Web of Science, MEDLINE, SpringerLink, Wiley Online Journals, Cochrane Library, ScienceDirect, OVID, and China National Knowledge Infrastructure (from the inception of each database to 30 April 2021).

Eighteen quantitative, six qualitative, and five mixed‐methods studies were included in this review. Cultural factors should be considered when conducting interventions to reduce cancer‐related stigma. For cancer patients, multi‐component interventions have demonstrated a positive effect on their perceived stigma. For general population, interactive interventions show promise to reduce their implemented stigma towards cancer patients. For healthcare workers, there is a paucity of studies to reduce their implemented stigma. Existing studies reported inconclusive evidence, partially due to the lack of a robust study design with an adequate sample size.

Multi‐component and interactive interventions show promise to relieve cancer‐related stigma. More methodologically robust studies should be conducted in different cultures to elucidate the most appropriate interventions for different populations to reduce cancer‐related stigma.

These findings will facilitate healthcare workers to design and implement interventions to reduce cancer‐related stigma, thus improving the quality of life for cancer patients.

No patient and public contribution.