Gastric cancer (GC) is a major global cancer burden, and only HER2-targeted therapies have been approved in first line clinical therapy. CLDN18.2 has been regarded as a potential therapeutic target for gastrointestinal tumors, and global clinical trials have been in process. Hence, the precise, efficient, and noninvasive detection of CLDN18.2 expression is important for the effective application of this attractive target. A high similarity of protein sequence between CLDN18.1 and -18.2 made RNA become more suitable for the detection of CLDN18.2 expression. In this study, CLDN18.2 molecular beacon (MB) with a stem-loop hairpin structure was optimized by phosphorothioate and 2′-O-methyl for stability and efficiency. The MB could recognize CLDN18.2 RNA rapidly. Its resolution and selectivity has been verified in several model cells, demonstrating that MB can distinguish CLDN18.2 expression in several model cells. Furthermore, it was applied successfully to the circulating tumor cell (CTC) assay. The concordance in the expression of CLDN18.2 between CTCs and tissue biopsy is 100% (negative: 3 vs 3; positive: 7 vs 7), indicating that CLDN18.2 RNA detection in CTCs based on a MB will be a promising approach for searching potential patients to CLDN 18.2 targeted drug.

Type 2 diabetes (T2D) accounts for approximately 90% of diabetes worldwide and has become a global public health problem. Generally, individuals go to hospitals and get healthcare only when they have obvious T2D symptoms. While the underlying cause and mechanism of the disease are usually not well understood, treatment is for the symptoms, but not for the disease cause, and patients often continue to progress with more symptoms. Prediabetes is the early stage of diabetes and provides a good time window for intervention and prevention. However, with few symptoms, prediabetes is usually ignored without any treatment. Obviously, it is far from ideal to rely on the traditional medical system for diabetes healthcare. As a result, the medical system must be transformed from a reactive approach to a proactive approach. Root cause analysis and personalized intervention should be conducted for patients with prediabetes. Based on systems medicine, also known as P4 medicine, with a predictive, preventive, personalized, and participatory approach, new medical system is expected to significantly promote the prevention and treatment of chronic diseases such as prediabetes and diabetes. Many studies have shown that the occurrence and development of diabetes is closely related to gut microbiota. However, the relationship between diabetes and gut microbiota has not been fully elucidated. This review describes the research on the relationship between gut microbiota and diabetes and some exploratory trials on the interventions of prediabetes based on P4 medicine model. Furthermore, we also discussed how these findings might influence the diagnosis, prevention and treatment of diabetes in the future, thereby to improve the wellness of human beings.

Purpose: Inflammatory bowel disease (IBD) is difficult to diagnose and classify. The purpose of this study is to establish an artificial intelligence model based on fecal multiomics data for multi-classification diagnosis of IBD and its subtypes. Materials and Methods: A total of 299 clinical cohort studies were included in this study, including 86 healthy people, 140 CD patients and 73 UC patients. Based on the idea of hierarchical modeling for different groups, we model the total population and the groups with self-evaluation of "very well" and "slightly below par", respectively. The original total features were fecal multi-omics data, including metagenomics, metatranscriptomics, proteomics, metabolomics, viromics, faecal calprotectin. The importance, collinearity and other feature engineering methods were used to evaluate the features. Finally, three individualized diagnosis models with less features and high accuracy were obtained. Results: First, we screened 111 features to form the optimal feature set for the total population and established a three-classification individual diagnosis model with AUC of 0.83, which can simultaneously diagnose health, CD and UC. Secondly, according to the hierarchical modeling of the total population, we established two models for population with different self-evaluation. For "very well" population, we screened 59 features and established a three-classification diagnostic model with AUC of 0.85. For the self-evaluation population with "slightly below par", we finally included 22 features and established a threeclassification diagnostic model with AUC of 0.84. Only metabolomics and metatranscriptomics features were included in the optimal feature sets. Conclusion:This study provides a valuable method for high accuracy, noninvasive diagnosis and subtype identification of IBD patients. Researchers can choose biomarkers in different models according to different self-evaluation of patients. Simple noninvasive fecal sampling can be used to detect metabolomics and metatranscriptomics data, thus replacing the tedious and painful clinical colonoscopy and biopsy procedures.

Objective: To find out whether the prediction model using a machine learning approach can have comparable accuracy with the current state-of-the-art trisomy detection methods in extremely low-depth sequencing data. Verify the practical feasibility of being used for clinical auxiliary screening of fetal trisomy. Design: A public dataset with 144 samples is divided into training/validation/test (testA) set. A dataset with 270 sequencing samples was used for independent testing. Setting: Samples are from Hong Kong, China; London, England; Amsterdam, the Netherlands; and Beijing, China. Population: 414 maternal blood samples were analyzed for this study. Methods: The machine learning method for low-depth short sequencing data from maternal blood. Main Outcome Measures: Fetal karyotype was analyzed by interventional prenatal diagnosis or obtaining cord blood after birth. Results: We demonstrate the predictive ability of our method by testing on data from different sources. The final best model achieved an AUC of 99.85% in predicting T21 using chr21 features which are the DNA fragment concentrations. The AUC is 99.50%, and 97.70% in predicting T18 and T13 with all features from 24 chromosomes. PPV was 91.67%, 93.33%, and 83.33% in predicting T21, T18, and T13, respectively. The NPV to identify T21, T18, and T13 were 100%, 99.33%, and 98.70%, respectively. Our approach does not need to calculate fetal fraction (FF) and can handle samples from a wide range of gestational ages (GA), twin pregnancies and fetal mosaicism. We achieved high PPV with low-depth sequencing and robust performance in an independent dataset. Conclusion: Our approach can achieve comparable accuracy with the current best methods. Our pipeline can be an important aid for the detection of fetal trisomy in clinical NIPT.