Pingxiao capsule inhibits lung metastasis of triple-negative breast cancer and sensitizes breast cancer to radiotherapy.

OBJECTIVES: To investigate the anticancer effect of Pingxiao capsule (, PXC) on the treatment of breast cancer and . METHODS: The inhibition of PXC on cell viability and proliferation was determined by cell counting kit-8, EdU assay and colony formation assay, respectively. The effect of PXC on cell apoptosis was detected by using flow cytometry. The suppression of PXC on cell migration and invasion was investigated by chamber assay. To investigate the underlying molecular mechanisms, the expression of proteins related to epithelial to mesenchymal transition (EMT) was analyzed by Western blotting in breast cancer cells and by immunohistochemistry in tumor tissues. The anticancer effect of PXC was evaluated by using MDA-MB-231 xenograft model and 4T1 metastatic breast cancer model. RESULTS: Our results indicated that triple-negative breast cancer (TNBC) cell lines MDA-MB-231 and MDA-MB-468 were sensitive to PXC. PXC potently inhibited the proliferation, colony formation, migration, and invasion of MDA-MB-231 and MDA-MB-468 cells . Then, MDA-MB-231 xenograft model depicted that PXC significantly reduced tumor size and weight compared with Control. 4T1 lung metastasis model showed that PXC significantly inhibited breast cancer cell spreading to lungs in mice. Mechanistically, PXC inhibited EMT process by reducing cadherin turnover in TNBC. Furthermore, PXC in combination with 8 Gy X-ray treatment obviously promoted the induction of apoptosis, and suppressed cell proliferation. CONCLUSION: PXC could inhibit the proliferation and invasion of TNBC both and , and exerted its anti-metastatic effect by regulating cadherin turnover, Furthermore, it sensitized the TNBC cells to radiotherapy. The data supported further development of PXC as an adjuvant-therapy agent for TNBC.

Correction: Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA / 대한의료정보학회지

The original article contained errors.

Using Statistical and Machine Learning Methods to Evaluate the Prognostic Accuracy of SIRS and qSOFA / 대한의료정보학회지

OBJECTIVES: The objective of this study was to compare the performance of two popularly used early sepsis diagnostic criteria, systemic inflammatory response syndrome (SIRS) and quick Sepsis-related Organ Failure Assessment (qSOFA), using statistical and machine learning approaches. METHODS: This retrospective study examined patient visits in Emergency Department (ED) with sepsis related diagnosis. The outcome was 28-day in-hospital mortality. Using odds ratio (OR) and modeling methods (decision tree [DT], multivariate logistic regression [LR], and naïve Bayes [NB]), the relationships between diagnostic criteria and mortality were examined. RESULTS: Of 132,704 eligible patient visits, 14% died within 28 days of ED admission. The association of qSOFA ≥2 with mortality (OR = 3.06; 95% confidence interval [CI], 2.96–3.17) greater than the association of SIRS ≥2 with mortality (OR = 1.22; 95% CI, 1.18–1.26). The area under the ROC curve for qSOFA (AUROC = 0.70) was significantly greater than for SIRS (AUROC = 0.63). For qSOFA, the sensitivity and specificity were DT = 0.39, LR = 0.64, NB = 0.62 and DT = 0.89, LR = 0.63, NB = 0.66, respectively. For SIRS, the sensitivity and specificity were DT = 0.46, LR = 0.62, NB = 0.62 and DT = 0.70, LR = 0.59, NB = 0.58, respectively. CONCLUSIONS: The evidences suggest that qSOFA is a better diagnostic criteria than SIRS. The low sensitivity of qSOFA can be improved by carefully selecting the threshold to translate the predicted probabilities into labels. These findings can guide healthcare providers in selecting risk-stratification measures for patients presenting to an ED with sepsis.