The present study aimed to evaluate the significance of post-mastectomy radiotherapy (PMRT) in patients with early stage (T1-2) breast cancer. The Surveillance, Epidemiology, and End Results database was searched, and data on female patients with early stage (T1-2) breast cancer with 1-3 positive axillary lymph nodes (LNs) were extracted. Patients were subdivided into two groups: Those who had received PMRT and those who had not (no PMRT). Data from the two groups were analyzed in order to identify associations between PMRT status, breast cancer-specific survival (BCSS) probability and overall survival (OS) probability using multivariate Cox proportional hazards regression and propensity score matching models. A total of 7,316 patients were included in the analysis. Prior to propensity score matching, outcome probabilities were increased in the PMRT group, compared with the no PMRT group (BCSS probabilities: 92.0 vs. 90.1%, respectively, P= 0.015; OS probabilities: 89.8 vs. 86.0%, respectively, P<0.001). In multivariate analyses, tumor location was not identified as being a risk factor for BCSS (hazard ratio, 0.917; 95% confidence interval, 0.772-1.090; P= 0.326). Following propensity score matching, differences between the two treatment groups (PMRT and no PMRT) in terms of their BCSS scores remained significant (93.7 vs. 90.1%, respectively; P=0.007). Compared with the no PMRT group, the OS probabilities of the PMRT group were increased (89.4 vs. 86.0%; P=0.025). In conclusion, the present results indicated that PMRT may benefit the prognosis of patients with breast cancer with early stage disease (T1-2), and those with one to three positive axillary LNs.

Background Compared with typical visceral fat deposits in obesity and metabolic syndrome, perirenal adipose tissue (PRAT) dysfunction is more closely linked to obesity-related chronic kidney disease (OB-CKD). The myokine irisin reportedly promotes positive outcomes in metabolic disease. This study investigated whether irisin could reduce urinary albumin excretion and demonstrate renoprotective effects through the regulation of PRAT function in obese mice. Methods C57BL/6 J mice received a high-fat diet (HFD) with or without concurrent administration of irisin. Glucose tolerance, plasma levels of free fatty acids, and urinary albumin excretion were assessed, along with renal morphology. The vascular endothelial growth factor and nitric oxide in glomeruli were also analyzed, in addition to PRAT function-associated proteins. Results Irisin administration significantly reduced the final body weight, fat mass, and free fatty acids, without reducing PRAT mass, in HFD mice. Furthermore, irisin decreased urinary albumin excretion and attenuated both renal fibrosis and lipid accumulation. Irisin administration led to increases in PRAT function-associated proteins, including sirtuin1, uncoupling protein-1, and heme-oxygenase-1. Ex vivo treatment of PRAT and glomeruli with irisin also restored PRAT function. Finally, irisin treatment restored the vascular endothelial growth factor–nitric oxide axis. Conclusions Irisin attenuated metabolic disorders and protected against OB-CKD by normalizing the PRAT–kidney axis. These results suggest that agents targeting PRAT activation might be useful for treatment of OB-CKD.

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BackgroundSepsis-associated acute kidney injury (S-AKI) is a major contributor to mortality in intensive care units (ICU). Early prediction of mortality risk is crucial to enhance prognosis and optimize clinical decisions. This study aims to develop a 28-day mortality risk prediction model for S-AKI utilizing an explainable ensemble machine learning (ML) algorithm.MethodsThis study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.0) database to gather information on patients with S-AKI. Univariate regression, correlation analysis and Boruta were combined for feature selection. To construct the four ML models, hyperparameters were tuned via random search and five-fold cross-validation. To evaluate the performance of all models, ROC, K-S, and LIFT curves were used. The discrimination of ML models and traditional scoring systems was compared using area under the receiver operating characteristic curve (AUC). Additionally, the SHapley Additive exPlanation (SHAP) was utilized to interpret the ML model and identify essential variables. To investigate the relationship between the top nine continuous variables and the risk of 28-day mortality. COX regression-restricted cubic splines were utilized while controlling for age and comorbidities.ResultsThe study analyzed data from 9,158 patients with S-AKI, dividing them into a 28-day mortality group of 1,940 and a survival group of 7,578. The results showed that XGBoost was the best performing model of the four ML models with AUC of 0.873. All models outperformed APS-III 0.713 and SAPS-II 0.681. The K-S and LIFT curves indicated XGBoost as the most effective predictor for 28-day mortality risk. The model’s performance was evaluated using ROCpr curves, calibration curves, accuracy, precision, and F1 scores. SHAP force plots were utilized to interpret and visualize the personalized predictive power of the 28-day mortality risk model. Additionally, COX regression restricted cubic splines revealed an interesting non-linear relationship between the top nine variables and 28-day mortality.ConclusionThe use of ensemble ML models has shown to be more effective than the LR model and conventional scoring systems in predicting 28-day mortality risk in S-AKI patients. By visualizing the XGBoost model with the best predictive performance, clinicians are able to identify high-risk patients early on and improve prognosis.

Benzo[a]pyrene (B[a]P) and polybrominated diphenyl ethers (PBDEs) are persistent environmental contaminants. The effects in organisms of exposures to binary mixtures of such contaminants remain obscure. Attenuated total reflection Fourier‐transform infrared (ATR‐FTIR) spectroscopy is a label‐free, non‐destructive analytical technique allowing spectrochemical analysis of macromolecular components, and alterations thereof, within tissue samples. Herein, we employed ATR‐FTIR spectroscopy to identify biomolecular changes in rat liver post‐exposure to B[a]P and BDE‐47 (2,2′,4,4′‐tetrabromodiphenyl ether) congener mixtures. Our results demonstrate that significant separation occurs between spectra of tissue samples derived from control versus exposure categories (accuracy = 87%; sensitivity = 95%; specificity = 79%). Additionally, there is significant spectral separation between exposed categories (accuracy = 91%; sensitivity = 98%; specificity = 90%). Segregation between control and all exposure categories were primarily associated with wavenumbers ranging from 1600 to 1700 cm−1. B[a]P and BDE‐47 alone, or in combination, induces liver damage in female rats. However, it is suggested that binary exposure apparently attenuates the toxic effects in rat liver of the individual contaminants. This is supported by morphological observations of liver tissue architecture on hematoxylin and eosin (H&E)‐stained liver sections. Such observations highlight the difficulties in predicting the endpoint effects in target tissues of exposures to mixtures of environmental contaminants.