Ovatodiolide, isolated from Anisomeles indica, suppresses bladder carcinogenesis through suppression of mTOR/ß-catenin/CDK6 and exosomal miR-21 derived from M2 tumor-associated macrophages.

Bladder cancer (BCa) is the fourth leading cause of cancer deaths worldwide due to its aggressiveness and resistance against therapies. Intricate interactions between cancer cells and the tumor microenvironment (TME) are essential for both disease progression and regression. Thus, interrupting molecular communications within the TME could potentially provide improved therapeutic efficacies. M2-polarized tumor-associated macrophages (M2 TAMs) were shown to contribute to BCa progression and drug resistance. We attempted to provide evidence for ovatodiolide (OV) as a potential therapeutic agent that targets both TME and BCa cells. First, tumor-suppressing functions of OV were determined by cell viability, colony, and tumor-sphere formation assays using a coculture system composed of M2 TAMs/BCa cells. Subsequently, we demonstrated that extracellular vesicles (EVs) isolated from M2 TAMs containing oncomiR-21 and mRNAs, including Akt, STAT3, mTOR, and ß-catenin, promoted cisplatin (CDDP) resistance, migration, and tumor-sphere generation in BCa cells, through increasing CDK6, mTOR, STAT3, and ß-catenin expression. OV treatment also prevented M2 polarization and reduced EV cargos from M2 TAMs. Finally, in vivo data demonstrated that OV treatment overcame CDDP resistance. OV only and the OV + CDDP combination both resulted in significant reductions in mTOR, ß-catenin, CDK6, and miR-21 expression in tumor samples and EVs isolated from serum. Collectively, we demonstrated that M2 TAMs induced malignant properties in BCa cells, in part via oncogenic EVs. OV treatment prevented M2 TAM polarization, reduced EV cargos derived from M2 TAMs, and suppressed ß-catenin/mTOR/CDK6 signaling. These findings provide preclinical evidence for OV as a single or adjuvant agent for treating drug-resistant BCa.

Artificial intelligence-assisted prediction of preeclampsia: Development and external validation of a nationwide health insurance dataset of the BPJS Kesehatan in Indonesia.

BACKGROUND: We developed and validated an artificial intelligence (AI)-assisted prediction of preeclampsia applied to a nationwide health insurance dataset in Indonesia. METHODS: The BPJS Kesehatan dataset have been preprocessed using a nested case-control design into preeclampsia/eclampsia (n = 3318) and normotensive pregnant women (n = 19,883) from all women with one pregnancy. The dataset provided 95 features consisting of demographic variables and medical histories started from 24 months to event and ended by delivery as the event. Six algorithms were compared by area under the receiver operating characteristics curve (AUROC) with a subgroup analysis by time to the event. We compared our model to similar prediction models from systematically reviewed studies. In addition, we conducted a text mining analysis based on natural language processing techniques to interpret our modeling results. FINDINGS: The best model consisted of 17 predictors extracted by a random forest algorithm. Nine∼12 months to the event was the period that had the best AUROC in external validation by either geographical (0.88, 95% confidence interval (CI) 0.88-0.89) or temporal split (0.86, 95% CI 0.85-0.86). We compared this model to prediction models in seven studies from 869 records in PUBMED, EMBASE, and SCOPUS. This model outperformed the previous models in terms of the precision, sensitivity, and specificity in all validation sets. INTERPRETATION: Our low-cost model improved preliminary prediction to decide pregnant women that will be predicted by the models with high specificity and advanced predictors. FUNDING: This work was supported by grant no. MOST108-2221-E-038-018 from the Ministry of Science and Technology of Taiwan.