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Background: Exosomes, one of small extracellular vesicles, play a vital role in cell to cell communication and contribute to the advancement of tumors through their cargo molecules. Exosomal circRNAs have emerged as significant players in various types of tumors. Thus, this study aimed to investigate how exosomal circRNAs are involved in the diagnosis and progression of gastric cancer (GC). Methods: Serum exosomes were characterized using transmission electron microscopy, nanoparticle tracking analysis and Western blot. CCK-8, colony formation and transwell assays were conducted to study the function of hsa_circ_0050547 (named as circ50547). qRT-PCR was used to quantify the expression of circ50547 in GC tissues and serum exosomes. Fluorescence in situ hybridization was applied to detect the cellular distribution of circ50547. Stemness and drug-resistance were detected by sphere formation, WB, flow cytometry and half-maximal inhibitory concentration analyses. Bioinformatic analyses, luciferase experiments, qRT-PCR and WB were used to investigate molecular mechanisms. Results: We discovered for the first time a new type of GC-derived exosomal circRNA, circ50547. We found that circ50547 is highly expressed in both GC tissues and serum exosomes. Interestingly, we observed that the diagnostic value of exosomal circ50547 is superior to that of serum circ50547. Circ50547 overexpression enhanced the proliferation, migration, invasion, stemness and drug resistance of GC cells, while knockdown of circ50547 showed the opposite effect. Mechanistically, circ50547 acted as a sponge for miR-217 to regulate the expression of HNF1B, which promoted gastric cancer progression. Conclusion: Exosomal circ50547 may be a promising marker for the diagnosis and prognosis prediction of GC. These findings suggest that it plays an oncogenic role through miR-217/HNF1B signaling pathway in GC.

The development of messenger RNA (mRNA) vaccines and therapeutics necessitates the production of high-quality in vitro-transcribed mRNA drug substance with specific critical quality attributes (CQAs), which are closely tied to the uniformity of linear DNA template. The supercoiled plasmid DNA is the precursor to the linear DNA template, and the supercoiled DNA percentage is commonly regarded as a key in-process control (IPC) during the manufacturing of linear DNA template. In this study, we investigate the influence of supercoiled DNA percentage on key mRNA CQAs, including purity, capping efficiency, double-stranded RNA (dsRNA), and distribution of poly(A) tail. Our findings reveal a significant impact of supercoiled DNA percentage on mRNA purity and in vitro transcription yield. Notably, we observe that the impact on mRNA purity can be mitigated through oligo-dT chromatography, alleviating the tight range of DNA supercoiled percentage to some extent. Overall, this study provides valuable insights into IPC strategies for DNA template chemistry, manufacturing, and controls (CMC) and process development for mRNA drug substance.

The continuous increase in the number of transactions is exerting significant pressure on the storage capacity of blockchains. By storing data on multiple blockchains and interacting with each other with cross-chain technology, the storage pressure can be relieved to a large degree. Nevertheless, the feature of immutability may impede the further development of blockchains, e.g., some expired identity information cannot be updated due to immutability. The decentralized chameleon hash function can resolve the issue while ensuring the feature of decentralization. Unfortunately, how to ensure the consistency of dynamic data updating in cross-chain interaction is still a question worth considering. In this paper, we propose a dynamic cross-chain decentralized data consistency verification (DCCV) model, in which we adopt an audit chain and design a dynamic Merkle hash tree to guarantee the consistency of dynamic data updating between the source chain and the target chain. In addition, in order to enhance the relevance of calculations across different smart contracts and improve audit efficiency, we propose a way to develop cross-chain smart contracts collaboratively. Moreover, Cosi protocol and multi-signcryption are utilized to ensure the security and privacy of cross-chain data transmission. Finally, theoretical and experimental analysis demonstrates that DCCV can achieve dynamic data consistency verification in the process of cross-chain interaction.

Medical images are usually degraded by numerous noises during acquisition or transmission, which often causes low contrast leading to deterioration of image quality. As such, medical image denoising and enhancement has become a paramount routine task. To overcome this problem, we propose a cutting-edge joint statistical and morphological model for the denoising and enhancement operation. Firstly, we propose a statistical model in formulating the marginal distribution of the wavelet coefficients. This model is integrated into a Bayesian inference framework to develop a maximum a posterior (MAP) estimator of the noise-free coefficient. Based on the statistical model, we eliminate the need for noise level estimation, and allows the model to automatically adapts to the observed image data. Secondly, we propose an adjustable morphological reconstruction model to eliminate known and unknown noises associated with medical images, while preserving the image details. After these operations, the image is decomposed into several wavelet subbands to extract the illumination and detail components. The image is then reconstructed based on the inverse wavelet to generate the enhanced noise-free image.Experimental results show that the proposed framework obtained high EME values of 41.04, 48.81, 47.81, and 45.75 for OCTA, FFA, CT, and X-ray imaging modalities, and performs better than the state-of-the-art methods. The proposed algorithm can effectively and efficiently enhance medical images, which will assist the clinicians in disease diagnosis, monitoring, and treatment.