Public health insurance and cancer‐specific mortality risk among patients with breast cancer: A prospective cohort study in China

We thank all staff members working on the Breast Cancer Information Management System (BCIMS) for their contributions to data collection and management. We also thank Dr Bo Fu, Mr Yan Li and Mr Pei Liu at the University of Electronic Science and Technology of China for data cleaning and zip code mapping. Our study was supported by the Key Research and Development Project of Sichuan Province of China (grant number: 2017SZ00005) and Swedish Research Council (grant number: 2018‐00648).Little is known about how health insurance policies, particularly in developing countries, influence breast cancer prognosis. Here, we examined the association between individual health insurance and breast cancer-specific mortality in China. We included 7436 women diagnosed with invasive breast cancer between 2009 and 2016, at West China Hospital, Sichuan University. The health insurance plan of patient was classified as either urban or rural schemes and was also categorized as reimbursement rate (ie, the covered/total charge) below or above the median. Breast cancer-specific mortality was the primary outcome. Using Cox proportional hazards models, we calculated hazard ratios (HRs) for cancer-specific mortality, contrasting rates among patients with a rural insurance scheme or low reimbursement rate to that of those with an urban insurance scheme or high reimbursement rate, respectively. During a median follow-up of 3.1 years, we identified 326 deaths due to breast cancer. Compared to patients covered by urban insurance schemes, patients covered by rural insurance schemes had a 29% increased cancer-specific mortality (95% CI 0%-65%) after adjusting for demographics, tumor characteristics and treatment modes. Reimbursement rate below the median was associated with a 42% increased rate of cancer-specific mortality (95% CI 11%-82%). Every 10% increase in the reimbursement rate is associated with a 7% (95% CI 2%-12%) reduction in cancer-specific mortality risk, particularly in patients covered by rural insurance schemes (26%, 95% CI 9%-39%). Our findings suggest that underinsured patients face a higher risk of breast cancer-specific mortality in developing countries.VetenskapsrådetPeer Reviewe

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Prostate cancer prediction using the random forest algorithm that takes into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen

The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels (P < 0.001), as well as in all transrectal ultrasound characteristics (P < 0.05) except uneven echo (P = 0.609). The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy

Additional file 6: Figure S4. of Overexpression of G protein-coupled receptor GPR87 promotes pancreatic cancer aggressiveness and activates NF-κB signaling pathway

GPR87 up-regulation activates the NF-κB signaling pathway in pancreatic cancer. GSEA plot, indicating a significant correlation between the mRNA levels of GPR87 expression in pancreatic cancer and the NF-κB-activated gene signatures in multiple published datasets. (TIF 316 kb

Additional file 4: Figure S2. of Overexpression of G protein-coupled receptor GPR87 promotes pancreatic cancer aggressiveness and activates NF-κB signaling pathway

Annexin V-FITC and PI staining of indicated cells with or without Fluorouracil treatment. A. Representative images (left panel) and quantification (right panel) of Annexin V-FITC and PI staining of indicated cells with no treatment for 24 h. B. Representative images (left panel) and quantification (right panel) of Annexin V-FITC and PI staining of indicated cells treated with Fluorouracil for 24 h. Each bar represents the mean ± SD of three independent experiments. * p < 0.05. (TIF 508 kb