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Ovarian cancer is one of the common tumors in female reproductive organs and accounts for about 4% of all malignant tumors in women. It is also the leading cause of death among various gynecological tumors. Surgery combined with chemotherapy is the frequently used treatment for ovarian. Chemotherapy resistance is one of the main reasons for the recurrence and poor prognosis of ovarian cancer. Various components of the MAPK pathway, also known as the RAS-RAF-MEK-ERK signaling cascade, are related to cancer, and ERK1/2 is an indispensable key site in this pathway. Continuous research has found that ERK1/2 is a "ferryman" involved in regulating the occurrence, development, and drug resistance mechanisms of ovarian cancer. This article briefly introduces the activation mechanism and pathway of the ERK1/2 signaling pathway, summarizes its relationship to ovarian cancer, and emphasizes that ERK1/2 inhibitors may be a new treatment strategy with potential advantages in improving poor prognosis in patients with ovarian cancer.

Incremental learning is key to bridging the enormous gap between artificial intelligence and human intelligence,mea-ning that agents can learn several tasks sequentially from a continuous stream of correlated data without forgetting,just as humans do.Object detection is one of the core tasks in the field of computer vision and the cornerstone of computer images understanding.Therefore,the incremental object detection has important research and practical significance.Although incremental learning has achieved good results in image classification,the research on incremental learning based on object detection is still in its infancy.This is because object detection is more complex than image classification,which needs to solve both classification and bounding box regression problems.Many researchers have made great efforts to solve this problem,but most of the work only focuses on how to retain previous learning,ignoring fast adaptability to new tasks,which is a critical requirement for incremental learning.Based on the memory mechanism of the brain,humans can constantly extract knowledge during learning,so as to learn new tasks better and faster without forgetting.Inspired by this,an incremental meta-learning method that integrates the codec memory replay mechanism is proposed.This method encodes,stores,decodes and replays the feature vectors of learned samples,so as to approximate the dynamic learning environment as a local stationary environment and avoid catastrophic forgetting.Besides,a double-loop online meta-learning strategy is designed,which can help model to extract common structures of tasks and improve generalization performance on new tasks encountered during learning.The model is respectively updated by SGD with multiple batches of old and new mixed data in the inner loop,and is meta-updated in the outer loop.We evaluate the proposed approach on three incremental object detection settings defined on PASCAL VOC and MS COCO datasets,where the proposed algorithm performs favorably well against state-of-the-art methods.It proves that it can help the model to resist forgetting better and have better generalization performance on new tasks.The proposed algorithm is gradient-based and model-agnostic,so it has strongadaptability and can be applied on more complex detection frameworks.

Intrauterine adhesion (IUA) is the clinical manifestation of endometrial fibrosis. The dysregulation of microRNAs (miRNAs) has been confirmed to implicate in a diversity of human diseases, including IUA. Nevertheless, the specific function of miR-223-3p in IUA remains to be clarified. Reverse transcription quantitative polymerase chain reaction analysis displayed the downregulation of miR-223-3p in IUA tissues and endometrial epithelial cells (EECs). Results from wound healing assay, Transwell assay and western blotting showed that TGF-β facilitated the migration and invasion of EECs and induced epithelial-mesenchymal transition (EMT) process as well as extracellular matrix (ECM) deposition. Overexpression of miR-223-3p in EECs was shown to suppress the effects induced by TGF-β. Bioinformatics analysis and luciferase reporter assay revealed the binding relation between miR-223-3p and SP3. SP3 was highly expressed in IUA and its expression was inversely correlated with miR-223-3p expression in IUA tissue samples. Additionally, upregulation of SP3 reversed the influence of miR-223-3p on the phenotypes of EECs. In conclusion, miR-223-3p alleviates TGF-β-induced cell migration, invasion, EMT process and ECM deposition in EECs by targeting SP3.