The delivery of PD-L1 siRNA by neutrophil-targeted lipid nanoparticles effectively ameliorates sepsis.

BACKGROUND: Sepsis, a complex and life-threatening disease, poses a significant global burden affecting over 48 million individuals. Recently, it has been reported that programmed death-ligand 1 (PD-L1) expressed on neutrophils is involved in both inflammatory organ dysfunction and immunoparalysis in sepsis. However, there is a dearth of strategies to specifically target PD-L1 in neutrophils in vivo. METHODS: We successfully developed two lipid nanoparticles (LNPs) specifically targeting neutrophils by delivering PD-L1 siRNA via neutrophil-specific antibodies and polypeptides. In vivo and in vitro experiments were performed to detect lipid nanoparticles into neutrophils. A mouse cecal ligation and puncture (CLP) model was used to detect neutrophil migration, neutrophil extracellular traps (NETs) level, and organ damage. RESULT: The PD-L1 siRNA-loaded LNPs that target neutrophils suppressed inflammation, reduced the release of NETs, and inhibited T-lymphocyte apoptosis. This approach could help maintain homeostasis of both the immune and inflammatory responses during sepsis. Furthermore, the PD-L1 siRNA-loaded LNPs targeting neutrophils have the potential to ameliorate the multi-organ damage and lethality resulting from CLP. CONCLUSIONS: Taken together, our data identify a previously unknown drug delivery strategy targeting neutrophils, which represents a novel, safe, and effective approach to sepsis therapy.

RegraphGAN: A graph generative adversarial network model for dynamic network anomaly detection.

Due to the wide application of dynamic graph anomaly detection in cybersecurity, social networks, e-commerce, etc., research in this area has received increasing attention. Graph generative adversarial networks can be used in dynamic graph anomaly detection due to their ability to model complex data, but the original graph generative adversarial networks do not have a method to learn reverse mapping and require an expensive process in recovering the potential representation of a given input. Therefore, this paper proposes a novel graph generative adversarial network by adding encoders to map real data to latent space to improve the training efficiency and stability of graph generative adversarial network models, which is named RegraphGAN in this paper. And this paper proposes a dynamic network anomaly edge detection method by combining RegraphGAN with spatiotemporal coding to solve the complex dynamic graph data and the problem of attribute-free node information coding challenges. Meanwhile, anomaly detection experiments are conducted on six real dynamic network datasets, and the results show that the dynamic network anomaly detection method proposed in this paper outperforms other existing methods.

The implication of targeting PD-1:PD-L1 pathway in treating sepsis through immunostimulatory and anti-inflammatory pathways.

Sepsis currently remains a major contributor to mortality in the intensive care unit (ICU), with 48.9 million cases reported globally and a mortality rate of 22.5% in 2017, accounting for almost 20% of all-cause mortality worldwide. This highlights the urgent need to improve the understanding and treatment of this condition. Sepsis is now recognized as a dysregulation of the host immune response to infection, characterized by an excessive inflammatory response and immune paralysis. This dysregulation leads to secondary infections, multiple organ dysfunction syndrome (MODS), and ultimately death. PD-L1, a co-inhibitory molecule expressed in immune cells, has emerged as a critical factor in sepsis. Numerous studies have found a significant association between the expression of PD-1/PD-L1 and sepsis, with a particular focus on PD-L1 expressed on neutrophils recently. This review explores the role of PD-1/PD-L1 in immunostimulatory and anti-inflammatory pathways, illustrates the intricate link between PD-1/PD-L1 and sepsis, and summarizes current therapeutic approaches against PD-1/PD-L1 in the treatment and prognosis of sepsis in preclinical and clinical studies.

Comparative analysis of intestinal bacteria among venom secretion and non-secrection snakes.

To further investigate the bacterial community and identify the bacterial biomarkers between venom secretion and non-venom secretion snakes, 50 intestinal samples (25 large intestine, 25 small intestine) were obtained from 29 snakes (13 gut samples from Deinagkistrodon, 26 from Naja and 11 from Ptyas mucosa). 16S rDNA high-throughput sequencing results showed that 29 bacterial phyla, 545 bacterial genera, and 1,725 OTUs (operational taxonomic units) were identified in these samples. OTU numbers and the Ace, Chao, Shannon, and Simpson indexes were very similar among the three breeds of snakes included in this study. The Bacteroidetes, Firmicutes, Fusobacteria and Proteobacteria were predominant bacterial phyla. The relative abundance at the phylum level among these samples was similar, and the difference between small and large intestinal samples was not obvious. However, at the genus level, venom secretion snakes Deinagkistrodon and Naja clustered together according to different breeds. 27, 24, and 16 genera were identified as core microbes for Deinagkistrodon, Naja, and Ptyas mucosa, respectively. Interestingly, the relative abundances of genera Hafnia_Obesumbacterium, Providencia, and Ureaplasma were found to be significantly higher in non-venom secretion snakes, and the genera Achromobacter, Cetobacterium, Clostridium innocuum group, Fusobacterium, Lachnoclostridium, Parabacteroides, and Romboutsia were only detected in venom secretion snakes. The function of these bacteria in venom secretion needs to be further studied, and these venom secretion related genera may be the promising target to improve venom production.