Detection and characterization of pancreatic and biliary tract cancers using cell-free DNA fragmentomics.

BACKGROUND: Plasma cell-free DNA (cfDNA) fragmentomics has demonstrated significant differentiation power between cancer patients and healthy individuals, but little is known in pancreatic and biliary tract cancers. The aim of this study is to characterize the cfDNA fragmentomics in biliopancreatic cancers and develop an accurate method for cancer detection. METHODS: One hundred forty-seven patients with biliopancreatic cancers and 71 non-cancer volunteers were enrolled, including 55 patients with cholangiocarcinoma, 30 with gallbladder cancer, and 62 with pancreatic cancer. Low-coverage whole-genome sequencing (median coverage: 2.9 ×) was performed on plasma cfDNA. Three cfDNA fragmentomic features, including fragment size, end motif and nucleosome footprint, were subjected to construct a stacked machine learning model for cancer detection. Integration of carbohydrate antigen 19-9 (CA19-9) was explored to improve model performance. RESULTS: The stacked model presented robust performance for cancer detection (area under curve (AUC) of 0.978 in the training cohort, and AUC of 0.941 in the validation cohort), and remained consistent even when using extremely low-coverage sequencing depth of 0.5 × (AUC: 0.905). Besides, our method could also help differentiate biliopancreatic cancer subtypes. By integrating the stacked model and CA19-9 to generate the final detection model, a high accuracy in distinguishing biliopancreatic cancers from non-cancer samples with an AUC of 0.995 was achieved. CONCLUSIONS: Our model demonstrated ultrasensitivity of plasma cfDNA fragementomics in detecting biliopancreatic cancers, fulfilling the unmet accuracy of widely-used serum biomarker CA19-9, and provided an affordable way for accurate noninvasive biliopancreatic cancer screening in clinical practice.

Exploring Embodied Intelligence in Soft Robotics: A Review.

Soft robotics is closely related to embodied intelligence in the joint exploration of the means to achieve more natural and effective robotic behaviors via physical forms and intelligent interactions. Embodied intelligence emphasizes that intelligence is affected by the synergy of the brain, body, and environment, focusing on the interaction between agents and the environment. Under this framework, the design and control strategies of soft robotics depend on their physical forms and material properties, as well as algorithms and data processing, which enable them to interact with the environment in a natural and adaptable manner. At present, embodied intelligence has comprehensively integrated related research results on the evolution, learning, perception, decision making in the field of intelligent algorithms, as well as on the behaviors and controls in the field of robotics. From this perspective, the relevant branches of the embodied intelligence in the context of soft robotics were studied, covering the computation of embodied morphology; the evolution of embodied AI; and the perception, control, and decision making of soft robotics. Moreover, on this basis, important research progress was summarized, and related scientific problems were discussed. This study can provide a reference for the research of embodied intelligence in the context of soft robotics.

Tumorigenicity risk of iPSCs in vivo: nip it in the bud.

In 2006, Takahashi and Yamanaka first created induced pluripotent stem cells from mouse fibroblasts via the retroviral introduction of genes encoding the transcription factors Oct3/4, Sox2, Klf44, and c-Myc. Since then, the future clinical application of somatic cell reprogramming technology has become an attractive research topic in the field of regenerative medicine. Of note, considerable interest has been placed in circumventing ethical issues linked to embryonic stem cell research. However, tumorigenicity, immunogenicity, and heterogeneity may hamper attempts to deploy this technology therapeutically. This review highlights the progress aimed at reducing induced pluripotent stem cells tumorigenicity risk and how to assess the safety of induced pluripotent stem cells cell therapy products.

An Efficient Fine-to-Coarse Wayfinding Strategy for Robot Navigation in Regionalized Environments.

This paper proposes an efficient wayfinding strategy for robot navigation in regionalized environments by designing a regionalized spatial knowledge model (RSK model) and a region-based wayfinding algorithm, i.e., a fine-to-coarse A* (FTC-A*) search algorithm. First, the RSK model, which imitates the representation of environments in the human brain, is presented to describe the search environments. The environments that are divided into regions are represented by a hierarchical nested structure where small regions are grouped together to form superordinate regions. Second, on the basis of the RSK model, an FTC-A* search algorithm is developed to plan the fine-to-coarse route. By making a fine planning to robot surroundings in vicinity, but a coarse planning to that at the distance, the FTC-A* algorithm can effectively reduce computational complexity, so as to enhance the efficiency of route search, and meanwhile makes robots to react quickly to user's commands, especially in large-scale environments. Finally, four exhaustive simulations and a physical experiment have been carried out to illustrate the feasibility and effectiveness of the proposed wayfinding strategy.