N6-methyladenosine (m 6 A) messenger RNA methylation play important role in cell proliferation and tumorigenicity of endometrial cancer, but the key mechanism is not fully clear. Here, we found that RNA demethylase ALKBH5 expression was significantly upregulated in endometrial cancer, ALKBH5 was then identified to positively regulate proliferation and invasion of endometrial cancer. Mechanistically, the m 6 A eraser ALKBH5 demethylated target transcripts IGF1R and enhanced IGF1R mRNA stability, consequently promoting IGF1R translation and activating IGF1R signaling pathway. Thus, we demonstrated that ALKBH5 promoted proliferation and invasion of endometrial cancer via erasing IGF1R m 6 A-modifications, which suggests a potential therapeutic target for endometrial cancer.

Polycystic ovary syndrome (PCOS) is a common endocrine disease regulated by metabolic disorders, the effective intervention of which depends on diverse phenotypes (e.g., insulin resistance). Serum metabolic fingerprint (SMF) holds promise in characterizing the pathogenesis stress related to diseases; yet, PCOS diagnosis and phenotyping are time‐consuming and challenging due to the lack of an integrated metabolic tool. Here, a nanoparticle‐enhanced laser desorption/ionization mass spectrometry platform is introduced for one‐time serum metabolic fingerprinting and to identify the metabolic heterogeneity associated with obesity in PCOS patients. A decision tree based on the acquired SMFs is constructed, and real‐world simulations on independent internal and external cohorts are performed. The decision tree yields the area under the receiver operating characteristic curves (AUC) of 0.967 for PCOS diagnosis and AUC of 0.898 for phenotyping, respectively. The technical robustness of the “one‐stop shop” decision tree across laboratories is validated for clinical utility. The decision tree aims to improve PCOS management in comparison to clinical assessment, leading to a potential reduction in multiple blood tests and physician workload.

Minor allele of rs1800795 and rs2069837 and its interaction were associated with increased cervical cancer risk.

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Background: Colposcopy is indispensable for the diagnosis of cervical lesions.However, its diagnosis accuracy for high-grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of colposcopists. The advancement in computational power made it possible for the application of artificial intelligence (AI) to clinical problems. Here, we explored the feasibility and accuracy of the application of AI on precancerous and cancerous cervical colposcopic image recognition and classification. Methods:The images were collected from 6002 colposcopy examinations of normal control, low-grade squamous intraepithelial lesion (LSIL), and HSIL. For each patient, the original, Schiller test, and acetic-acid images were all collected.We built a new neural network classification model based on the hybrid algorithm. EfficientNet-b0 was used as the backbone network for the image feature extraction, and GRU(Gate Recurrent Unit)was applied for feature fusion of the three modes examinations (original, acetic acid, and Schiller test). Results:The connected network classifier achieved an accuracy of 90.61% in distinguishing HSIL from normal and LSIL. Furthermore, the model was applied to "Trichotomy", which reached an accuracy of 91.18% in distinguishing the HSIL, LSIL and normal control at the same time. Conclusion:Our results revealed that as shown by the high accuracy of AI in the classification of colposcopic images, AI exhibited great potential to be an effective tool for the accurate diagnosis of cervical disease and for early therapeutic intervention in cervical precancer.