Overall survival and cancer-specific survival were improved in local treatment of metastatic prostate cancer

BackgroundFor metastatic prostate cancer (mPCa), radical prostatectomy (RP) and radiation therapy (RT) may improve overall survival (OS) and cancer-specific survival (CSS). Compared with RT, RP shows significant advantages in improving patient outcomes. External beam radiation therapy (EBRT) even slightly elevates CSM with no statistical difference in OS compared with no local treatment (NLT).ObjectiveTo evaluate OS and CSS after local treatment (LT) (including RP and RT) versus NLT in mPCa.Design, setting, and participantsWithin the Surveillance, Epidemiology and End Results (SEER) database (2000-2018), 20098 patients with metastatic prostate cancer were selected in this study, of which 19433 patients had no local treatment, 377 patients with radical prostate treatment, and 288 patients with RT.Outcome measurements and statistical analysisMultivariable competing risks regression analysis after propensity score matching (PSM) was used to calculate CSM. Multivariable Cox regression analysis was used to identify the risk factors. Kaplan-Meier methods were used to calculate OS.Results and limitationsA total of 20098 patients were included: NLT (n = 19433), RP (n=377) and RT (n=288). In a competing risk regression analysis after PSM (ratio 1:1), RP resulted in a significantly lower CSM (hazard ratio [HR] 0.36, 95% confidence interval [CI] 0.29-0.45) than NLT, while RT showed a slightly lower CSM (HR 0.77, 95% CI 0.63-0.95). In a competing risk regression analysis after PSM (ratio 1:1), RP led to a lower CSM (HR 0.56, 95% CI 0.41-0.76) versus RT. As for all-cause mortality (ACM), RP (HR 0.37, 95% CI 0.31-0.45) and RT (HR 0.66, 95% CI 0.56-0.79). also showed a downward trend. In terms of OS, RP and RT significantly improved the survival probability compared with NLT, with the effect of RP being more pronounced. Obviously, older age, Gleason scores ≥8, AJCC T3-T4 stage, AJCC N1, AJCC M1b-M1c were all associated with higher CSM (P <0.05). The same results held true for ACM. The limitation of this article is that it is not possible to assess the effect of differences in systemic therapy on CSM in mPCa patients and clinical trials are needed to verify the results.ConclusionsFor patients with mPCa, both RP and RT are beneficial to patients, and the efficacy of RP is better than RT from the perspective of CSM and ACM. Older age, higher gleason scores and the more advanced AJCC TNM stage all put patients at higher risk of dying.Patient summaryA large population-based cancer database showed that in addition to first-line therapy (hormonal treatment), RP and radiotherapy can also benefit patients with mPCa

Visible-Light Active and Magnetically Recyclable Nanocomposites for the Degradation of Organic Dye

Recyclable visible-light photocatalyst Fe3O4@TiO2 with core-shell structure was prepared by a simple synthetic strategy using solvothermal crystallization of titanium precursor on preformed Fe3O4 nanopartiles. The photo-degradation reaction of neutral red aqueous solution was tested to evaluate the visible-light photocatalytic activity of the as prepared Fe3O4@TiO2 nanoparticles, which show excellent photocatalytic activity compared with commercial P25 catalyst. Moreover, the Fe3O4@TiO2 nanocomposites can be easily separated from the reaction mixture, and maintain favorable photocatalytic activity after five cycles. The high visible light absorption of the Fe3O4@TiO2 nanocomposites may originate from the absence of electronic heterojunction, excellently dispersity and the high specific surface area of the as-synthesized Fe3O4@TiO2 samples

Facile Synthesis of CeO2-LaFeO3 Perovskite Composite and Its Application for 4-(Methylnitrosamino)-1-(3-Pyridyl)-1-Butanone (NNK) Degradation

A facile and environmentally friendly surface-ion adsorption method using CeCO3OH@C as template was demonstrated to synthesize CeO2-LaFeO3 perovskite composite material. The obtained composite was characterized by X-ray diffraction (XRD), fourier transform infrared spectra (FT-IR), field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), thermo-gravimetric analysis and differential scanning calorimetry (TG-DSC), N2 adsorption/desorption isotherms and X-ray photoelectron spectra (XPS) measurements. The catalytic degradation of nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) was tested to evaluate catalytic activity of the CeO2-LaFeO3 composite. Much better activity was observed for the CeO2-LaFeO3 composite comparing with CeO2 and LaFeO3. These results suggested that perovskite composite materials are a promising candidate for the degradation of tobacco-specific nitrosamines (TSNAs)

Developing an Interpretable Machine Learning Model to Predict in-Hospital Mortality in Sepsis Patients: A Retrospective Temporal Validation Study

Background: Risk stratification plays an essential role in the decision making for sepsis management, as existing approaches can hardly satisfy the need to assess this heterogeneous population. We aimed to develop and validate a machine learning model to predict in-hospital mortality in critically ill patients with sepsis. Methods: Adult patients fulfilling the definition of Sepsis-3 were included at a large tertiary medical center. Relevant clinical features were extracted within the first 24 h in ICU, re-classified into different genres, and utilized for model development under three strategies: “Basic + Lab”, “Basic + Intervention”, and “Whole” feature sets. Extreme gradient boosting (XGBoost) was compared with logistic regression (LR) and established severity scores. Temporal validation was conducted using admissions from 2017 to 2019. Results: The final cohort included 24,272 patients, of which 4013 patients formed the test cohort for temporal validation. The trained and fine-tuned XGBoost model with the whole feature set showed the best discriminatory ability in the test cohort with AUROC as 0.85, significantly higher than the XGBoost “Basic + Lab” model (0.83), the LR “Whole” model (0.82), SOFA (0.63), SAPS-II (0.73), and LODS score (0.74). The performance in varying subgroups remained robust, and predictors, such as increased urine output and supplemental oxygen therapy, were crucially correlated with improved survival when interpretability was explored. Conclusions: We developed and validated a novel XGBoost-based model and demonstrated significantly improved performance to LR and other scores in predicting the mortality risks of sepsis patients in the hospital using features in the first 24 h