Salmonella-mediated blood-brain barrier penetration,tumor homing and tumor microenvironment regulation for enhanced chemo/bacterial glioma therapy

Chemotherapy is an important adjuvant treatment of glioma,while the efficacy is far from satisfactory,due not only to the biological barriers of blood-brain barrier(BBB)and blood-tumor barrier(BTB)but also to the intrinsic resistance of glioma cells via multiple survival mechanisms such as upregulation of P-glycoprotein(P-gp).To address these limitations,we report a bacteria-based drug delivery strategy for BBB/BTB transportation,glioma targeting,and chemo-sensitization.Bacteria selectively colonized into hypoxic tumor region and modulated tumor microenvironment,including macrophages repolarization and neutrophils infiltration.Specifically,tumor migration of neutrophils was employed as hitchhiking delivery of doxorubicin(DOX)-loaded bacterial outer membrane vesicles(OMVs/DOX).By virtue of the surface pathogen-associated molecular patterns derived from native bacteria,OMVs/DOX could be selectively recognized by neutrophils,thus facilitating glioma targeted delivery of drug with significantly enhanced tumor accumulation by 18-fold as compared to the classical passive targeting effect.Moreover,the P-gp expression on tumor cells was silenced by bacteria typeⅢsecretion effector to sensitize the efficacy of DOX,resulting in complete tumor eradication with 100%survival of all treated mice.In addition,the colonized bacteria were finally cleared by anti-bacterial activity of DOX to minimize the potential infection risk,and cardiotoxicity of DOX was also avoided,achieving excellent compatibility.This work provides an efficient trans-BBB/BTB drug delivery strategy via cell hitchhiking for enhanced glioma therapy.

Extracellular Polysaccharide from Rhizopus nigricans Inhibits Hepatocellular Carcinoma via miR-494-3p/TRIM36 Axis and Cyclin E Ubiquitination

Background and Aims:This study was designed to uncov-er the mechanism for extracellular polysaccharide(EPS1-1)-mediated effects on hepatocellular carcinoma(HCC)devel-opment.Methods:HCC cells were treated with EPS1-1,miR-494-3p mimic,sh-TRIM36,and pcDNA3.1-TRIM36.The levels of miR-494-3p and TRIM36 were measured in nor-mal hepatocytes,THLE-2,and HepG2 and HuH7HCC cell lines,along with the protein expression of cyclin D/E and p21.The proliferation,cell cycle,and apoptosis of HCC cells were assayed.The interactions between miR-494-3p and TRIM36,and between TRIM36 and cyclin E were assessed.Finally,the expression and localization of TRIM36 and cyclin E were monitored,and tumor apoptosis was detected,in tumor xenograft model.Results:EPS1-1 suppressed HCC cell proliferation and cyclin D/E expression and promoted apoptosis and p21 expression.miR-494-3p was upregulated and TRIM36 was downregulated in HCC cells.Transfection with miR-494-3p mimic or sh-TRIM36 facilitated HCC cell proliferation and the expression of cyclin D/E protein but they inhibited apoptosis and p21 expression in the pres-ence of EPS1-1.Overexpression of TRIM36 further con-solidated EPS1-1-mediated inhibition of HCC proliferation,cyclin D/E,and the promotion of apoptosis and p21 expres-sion.Those effects were reversed by miR-494-3p overex-pression.TRIM36 was a target gene of miR-494-3p,and TRIM36 induced cyclin E ubiquitination.EPS1-1 suppressed cyclin E expression,promoted TRIM36 expression and tu-mor apoptosis,all of which were abrogated by increasing the expression of miR-494-3p in vivo.Conclusions:EPS1-1 protected against HCC by limiting its proliferation and sur-vival through the miR-494-3p/TRIM36 axis and by inducing cyclin E ubiquitination.

Study design of deep learning based automatic detection of cerebrovascular diseases on medical imaging: a position paper from Chinese Association of Radiologists

In recent years,with the development of artificial intelligence,especially deep learning technology,researches on automatic detection of cerebrovascular diseases on medical images have made tremendous progress and these models are gradually entering into clinical practice.However,because of the complexity and flexibility of the deep learning algorithms,these researches have great variability on model building,validation process,performance description and results interpretation.The lack of a reliable,consistent,standardized design protocol has,to a certain extent,affected the progress of clinical translation and technology development of computer aided detection systems.After reviewing a large number of literatures and extensive discussion with domestic experts,this position paper put forward recommendations of standardized design on the key steps of deep learning-based automatic image detection models for cerebrovascular diseases.With further research and application expansion,this position paper would continue to be updated and gradually extended to evaluate the generalizability and clinical application efficacy of such tools.