Dataset Information

Tregs biomimetic nanoparticle to reprogram inflammatory and redox microenvironment in infarct tissue to treat myocardial ischemia reperfusion injury in mice.

ABSTRACT:

Background

At present, patients with myocardial infarction remain an increased risk for myocardial ischemia/reperfusion injury (MI/RI). There lacks effectively method to treat MI/RI in clinic. For the treatment of MI/RI, it is still a bottleneck to effectively deliver drug to ischemic myocardium. In this paper, a regulatory T cells (Tregs) biomimetic nanoparticle (CsA@PPTK) was prepared by camouflaging nanoparticle with platelet membrane.

Results

CsA@PPTK actively accumulated in ischemic myocardium of MI/RI mice. CsA@PPTK significantly scavenged reactive oxygen species (ROS) and increased the generation of Tregs and the ratio of M2 type macrophage to M1 type macrophage in ischemic myocardium. Moreover, CsA@PPTK significantly attenuated apoptosis of cardiomyocytes and reduced the infarct size and fibrosis area in ischemic myocardium. CsA@PPTK markedly decreased the protein expression of MMP-9 and increased the protein expression of CX43 in ischemic myocardium tissue. Subsequently, the remodeling of the left ventricle was significant alleviated, and heart function of MI/RI mice was markedly improved.

Conclusion

CsA@PPTK showed significant therapeutic effect on MI/RI, and it has great potential application in the treatment of MI/RI.

SUBMITTER: Li F

PROVIDER: S-EPMC9164893 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Publications

Tregs biomimetic nanoparticle to reprogram inflammatory and redox microenvironment in infarct tissue to treat myocardial ischemia reperfusion injury in mice.

Li Fangyuan F Liu Daozhou D Liu Miao M Ji Qifeng Q Zhang Bangle B Mei Qibing Q Cheng Ying Y Zhou Siyuan S

Journal of nanobiotechnology 20220603 1

<h4>Background</h4>At present, patients with myocardial infarction remain an increased risk for myocardial ischemia/reperfusion injury (MI/RI). There lacks effectively method to treat MI/RI in clinic. For the treatment of MI/RI, it is still a bottleneck to effectively deliver drug to ischemic myocardium. In this paper, a regulatory T cells (Tregs) biomimetic nanoparticle (CsA@PPTK) was prepared by camouflaging nanoparticle with platelet membrane.<h4>Results</h4>CsA@PPTK actively accumulated in i ...[more]

PMID: 35659239

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Project description:Infarct size (IS) is the most robust end point for evaluating the success of preclinical studies on cardioprotection. The gold standard for IS quantification in ischemia/reperfusion (I/R) experiments is triphenyl tetrazolium chloride (TTC) staining, typically done manually. This study aimed to determine if automation through deep learning segmentation is a time-saving and valid alternative to standard IS quantification. High-resolution images from TTC-stained, macroscopic heart slices were retrospectively collected from pig experiments (n = 390) with I/R without/with cardioprotection to cover a wide IS range. Existing IS data from pig experiments, quantified using a standard method of manual and subsequent digital labeling of film-scan annotations, were used as reference. To automate the evaluation process with the aim to be more objective and save time, a deep learning pipeline was implemented; the collected images (n = 3869) were pre-processed by cropping and labeled (image annotations). To ensure their usability as training data for a deep learning segmentation model, IS was quantified from image annotations and compared to IS quantified using the existing film-scan annotations. A supervised deep learning segmentation model based on dynamic U-Net architecture was developed and trained. The evaluation of the trained model was performed by fivefold cross-validation (n = 220 experiments) and testing on an independent test set (n = 170 experiments). Performance metrics (Dice similarity coefficient [DSC], pixel accuracy [ACC], average precision [mAP]) were calculated. IS was then quantified from predictions and compared to IS quantified from image annotations (linear regression, Pearson's r; analysis of covariance; Bland-Altman plots). Performance metrics near 1 indicated a strong model performance on cross-validated data (DSC: 0.90, ACC: 0.98, mAP: 0.90) and on the test set data (DSC: 0.89, ACC: 0.98, mAP: 0.93). IS quantified from predictions correlated well with IS quantified from image annotations in all data sets (cross-validation: r = 0.98; test data set: r = 0.95) and analysis of covariance identified no significant differences. The model reduced the IS quantification time per experiment from approximately 90 min to 20 s. The model was further tested on a preliminary test set from experiments in isolated, saline-perfused rat hearts with regional I/R without/with cardioprotection (n = 27). There was also no significant difference in IS between image annotations and predictions, but the performance on the test set data from rat hearts was lower (DSC: 0.66, ACC: 0.91, mAP: 0.65). IS quantification using a deep learning segmentation model is a valid and time-efficient alternative to manual and subsequent digital labeling.

Dataset Information

Tregs biomimetic nanoparticle to reprogram inflammatory and redox microenvironment in infarct tissue to treat myocardial ischemia reperfusion injury in mice.

Background

Results

Conclusion

Publications

Tregs biomimetic nanoparticle to reprogram inflammatory and redox microenvironment in infarct tissue to treat myocardial ischemia reperfusion injury in mice.

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