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The immune-excluded tumors (IETs) show limited response to current immunotherapy due to intrinsic and adaptive immune resistance. In this study, it is identified that inhibition of transforming growth factor-β (TGF-β) receptor 1 can relieve tumor fibrosis, thus facilitating the recruitment of tumor-infiltrating T lymphocytes. Subsequently, a nanovesicle is constructed for tumor-specific co-delivery of a TGF-β inhibitor (LY2157299, LY) and the photosensitizer pyropheophorbide a (PPa). The LY-loaded nanovesicles suppress tumor fibrosis to promote intratumoral infiltration of T lymphocytes. Furthermore, PPa chelated with gadolinium ion is capable of fluorescence, photoacoustic and magnetic resonance triple-modal imaging-guided photodynamic therapy, to induce immunogenic death of tumor cells and elicit antitumor immunity in preclinical cancer models in female mice. These nanovesicles are further armored with a lipophilic prodrug of the bromodomain-containing protein 4 inhibitor (i.e., JQ1) to abolish programmed death ligand 1 expression of tumor cells and overcome adaptive immune resistance. This study may pave the way for nanomedicine-based immunotherapy of the IETs. Targeting the TGF-β signaling pathway has been exploited to relieve immunosuppression in the tumor microenvironment. Here the authors describe the design of a nanoplatform integrating the TGF-β receptor 1 inhibitor LY2157299 and the ROS-responsive JQ1 pro-drug, promoting anti-tumor immune responses in preclinical cancer models. [ABSTRACT FROM AUTHOR]

Objective To analyze the genetic evolution characteristics and the amino acid sites variations of influenza A virus (H3N2) based on the neuraminidase genes in Hainan during 2019-2020. Methods 16 strains of H3N2 virus isolated by Hainan Province Influenza Surveillance Network Laboratory were collected and the genomes were sequenced. The DNA Star 7.0.1 software were applied to analyze the neuraminidase gene similarities. The phylogenetic tree were constructed by MEGA 10.8.1 and sequence alignment were performed to analyze the drug resistant sites and antigenic determinants variants. Results The NA gene similarity analysis and phylogenetic tree showed that all the analyzed H3N2 strains belonged to the 3c. 2a1 clade. The Nucleotide and amino acid similarities between the 16 strains of H3N2 viruses and vaccine strains A / Singapore/INFIMH-16-0019/2016 (2018-2019) were 98.5%-99.1%, 98.3%-98.7% respectively. Antigenic sites variations of NA occurred at N329S in 14 strains, and E344K in 11 strains. No amino acid substitutions on the drug resistance sites were detected. Conclusion The H3N2 subtype virus prevailed in Hainan during 2019-2020 belonged to 3c. 2a1 clade. The genetic relationship was close to the 2018-2019 vaccine strains. The antigenic determinants in NA gene get changed but not obviously. The drug resistance sites variation did not occur. We should further strengthen the surveillance analysis of influenza virus and pay more attention to the the drug resistance gene variations to provide scientific evidence for the influenza virus prevention and control. [ABSTRACT FROM AUTHOR]

Due to the complex routes and the dynamic changing factors in transportation, precise traffic speed prediction is very difficult. Traditional prediction methods only focus on a single monitoring site, without establishing a relationship between different sites, so the precision is poor. The deep learning method can model traffic networks well, but suffers from information loss and the disadvantage of single input data. A multisource spatio-temporal hybrid dilated graph convolutional network (GCN) for forecasting traffic speed is proposed in this paper. A GCN based on hybrid dilated convolution can extract the influence of adjacent information and capture dynamic spatial and non-linear temporal correlations. Considering multisource data will increase the forecasting precision and improve the generalisation ability. Using a real-world data set, the performance of the proposed model was validated against other baselines (a fully connected neural network, convolutional neural network and spatio-temporal GCN). The proposed model was found to be superior to other models as it considers proximity information, which is often overlooked, and multifactorial influence. [ABSTRACT FROM AUTHOR]

Achieving selective and durable inhibition of programmed death ligand 1 (PD-L1) in tumors for T cell activation remains a major challenge in immune checkpoint blockade therapy. We herein presented a set of clickable inhibitors for spatially confined PD-L1 degradation and radioimmunotherapy of cancer. Using metabolic glycan engineering click bioorthogonal chemistry, PD-L1 expressed on tumor cell membranes was labeled with highly active azide groups. This enables covalently binding of the clickable inhibitor with PD-L1 and subsequent PD-L1 degradation. A pH-activatable nanoparticle responding to extracellular acidic pH of tumor was subsequently used to deliver the clickable PD-L1 inhibitor into extracellular tumor microenvironment for depleting PD-L1 on the surface of tumor cell and macrophage membranes in vivo. We further demonstrated that a combination of the clickable PD-L1 inhibitor with radiotherapy (RT) eradicated the established tumor by inhibiting RT-up-regulated PD-L1 in the tumor tissue. Therefore, selective PD-L1 blockade in tumors via the clickable PD-L1 inhibitor offers a versatile approach to promote cancer immunotherapy.