No abstract

A grade 3 burn is a nonstatic fatal injury, which can lead to complete damage to the skin structure, accompanied by a series of symptoms such as persistent inflammation, pain, pruritus, ulcer, and peripheral neuropathy. Although the primary clinical burn treatment is skin grafting, it cannot comprehensively solve burn symptoms. Here, a multifunctional DNA hydrogel integrated system is conveniently obtained through dynamic cross-linking of the DNA unit, polyacrylamide, and l-ascorbate 2-phosphate (l-A2P) formation of dense hydrogen bonds. The DNA hydrogel is doped with borneol for pain and itch relief. The obtained DNA hydrogel provides a hotbed similar to the extracellular matrix structure in vivo for the growth and development of stem cells, which can regulate cell proliferation, maintain cell viability, and achieve perfect release in a suitable environment. Additionally, the pharmacological wound dressing shell features excellent mechanical behavior, tissue adhesion, and antibacterial properties. Beyond that, the multifunctional DNA hydrogel integrated system can promote macrophage transformation, angiogenesis, and neurogenesis. Notably, the system activates the phosphatidylinositol 3′-kinase (PI3K)-Akt signaling pathway, which helps to promote tissue regeneration. Therefore, the DNA hydrogel integrated system opens the cascade mode of effective integrated treatment of burn wounds, further driving the in-depth study of clinical transformation mechanisms in the future.

No abstract

In gene expression analysis, sample differences and experimental operation differences are common, but sometimes, these differences will cause serious errors to the results or even make the results meaningless. Finding suitable internal reference genes efficiently to eliminate errors is a challenge. Aside from the need for high efficiency, there is no package for screening endogenous genes available in Python. Here, we introduce ERgene, a Python library for screening endogenous reference genes. It has extremely high computational efficiency and simple operation steps. The principle is based on the inverse process of the internal reference method, and the robust matrix block operation makes the selection of internal reference genes faster than any other method.

No abstract

COVID-19 has become one of the worst epidemic in the world, currently already more than four million people have been infected, which probably co-exist with human beings, and has a significant impact on the global economy and political order. In the process of fighting against the epidemic in China, the clinical value of a variety of herbal medicines has been recognized and written into the clinical application guide. However, their effective molecular mechanism and potential targets are still not clear. Pathology and pharmacology research will gradually attract attention in the post-epidemic outbreak term. Here, we constructed a COVID-19 protein microarray of potential therapy targets, which contains the main drug targets to the SARS-CoV-2 virus and the anti-virus, anti-inflammatory cellar targets of the host. Series of quality controls test has been carried out, which showed that it could be applied for drug target screening of bio-active natural products. The establishment of this microarray will provide a useful tool for the study of the molecular pharmacology of natural products.

An outbreak of new SARS-like viral in Wuhan, China has been named 2019-nCoV. The current state of the epidemic is increasingly serious, and there has been the urgent necessity to develop an effective new drug. In previous studies, it was found that the conformation change in CTD1 was the region where SARS-CoV bound to human ACE2. Although there are mutations of the 2019-nCoV, the binding energy of ACE2 remains high. The surface glycoprotein of 2019-nCoV was coincident with the CTD1 region of the S-protein by comparing the I-TASSER prediction model with the actual SARS model, which suggests that 2019-nCoV may bind to the ACE2 receptor through conformational changes. Furthermore, site prediction on the surface glycoprotein of 2019-nCoV suggests some core amino acid area may be a novel drug target against 2019-nCoV.

Single-cell sequencing often results in cellular "interruptions" due to limitations in sequencing throughput, while bulk RNA-seq may contain these potential "interrupted" cells. Here, we propose the BulkTrajBlend algorithm in OmicVerse that combines Beta-Variational AutoEncoder for deconvolution and graph neural networks for overlapping community discovery to effectively interpolate and restore the continuity of "interrupted" cells in the original scRNA-seq data. Additionally, OmicVerse serves as a comprehensive tool, providing standardized and consistent access to a wide range of methods for bulk and single cell RNA-seq analysis, enhancing computational efficiency and enabling beautiful visualization, and helping researchers to explore the transcriptomic universe, unravel novel biological insights, and propel scientific discoveries.