Targeting STAT3 signaling pathway by curcumin and its analogues for breast cancer: A narrative review.

BACKGROUND: Breast cancer (BC) continues to be a significant global health issue, with a rising number of cases requiring ongoing research and innovation in treatment strategies. Curcumin (CUR), a natural compound derived from Curcuma longa, and similar compounds have shown potential in targeting the STAT3 signaling pathway, which plays a crucial role in BC progression. AIMS: The aim of this study was to investigate the effects of curcumin and its analogues on BC based on cellular and molecular mechanisms. MATERIALS & METHODS: The literature search conducted for this study involved utilizing the Scopus, ScienceDirect, PubMed, and Google Scholar databases in order to identify pertinent articles. RESULTS: This narrative review explores the potential of CUR and similar compounds in inhibiting STAT3 activation, thereby suppressing the proliferation of cancer cells, inducing apoptosis, and inhibiting metastasis. The review demonstrates that CUR directly inhibits the phosphorylation of STAT3, preventing its movement into the nucleus and its ability to bind to DNA, thereby hindering the survival and proliferation of cancer cells. CUR also enhances the effectiveness of other therapeutic agents and modulates the tumor microenvironment by affecting tumor-associated macrophages (TAMs). CUR analogues, such as hydrazinocurcumin (HC), FLLL11, FLLL12, and GO-Y030, show improved bioavailability and potency in inhibiting STAT3, resulting in reduced cell proliferation and increased apoptosis. CONCLUSION: CUR and its analogues hold promise as effective adjuvant treatments for BC by targeting the STAT3 signaling pathway. These compounds provide new insights into the mechanisms of action of CUR and its potential to enhance the effectiveness of BC therapies.

An artificial intelligence approach to predict infants' health status at birth.

BACKGROUND: Machine learning could be used for prognosis/diagnosis of maternal and neonates' diseases by analyzing the data sets and profiles obtained from a pregnant mother. PURPOSE: We aimed to develop a prediction model based on machine learning algorithms to determine important maternal characteristics and neonates' anthropometric profiles as the predictors of neonates' health status. METHODS: This study was conducted among 1280 pregnant women referred to healthcare centers to receive antenatal care. We evaluated several machine learning methods, including support vector machine (SVM), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Decision tree classifiers, to predict newborn health state. RESULTS: The minimum redundancy-maximum relevance (MRMR) algorithm revealed that variables, including head circumference of neonates, pregnancy intention, and drug consumption history during pregnancy, were top-scored features for classifying normal and unhealthy infants. Among the different classification methods, the SVM classifier had the best performance. The average values of accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC) in the test group were 75%, 75%, 76%, 76%, and 65%, respectively, for SVM model. CONCLUSION: Machine learning methods can efficiently forecast the neonate's health status among pregnant women. This study proposed a new approach toward the integration of maternal data and neonate profiles to facilitate the prediction of neonates' health status.