Transgenic expression of Cre recombinase driven by a specific promoter is normally used to conditionally knockout a gene in a tissue- or cell-type-specific manner. In αMHC-Cre transgenic mouse model, expression of Cre recombinase is controlled by the myocardial-specific α-myosin heavy chain (αMHC) promoter, which is commonly used to edit myocardial-specific genes. Toxic effects of Cre expression have been reported, including intro-chromosome rearrangements, micronuclei formation and other forms of DNA damage, and cardiomyopathy was observed in cardiac-specific Cre transgenic mice. However, mechanisms associated with Cardiotoxicity of Cre remain poorly understood. In our study, our data unveiled that αMHC-Cre mice developed arrhythmias and died after six months progressively, and none of them survived more than one year. Histopathological examination showed that αMHC-Cre mice had aberrant proliferation of tumor-like tissue in the atrial chamber extended from and vacuolation of ventricular myocytes. Furthermore, the αMHC-Cre mice developed severe cardiac interstitial and perivascular fibrosis, accompanied by significant increase of expression levels of MMP-2 and MMP-9 in the cardiac atrium and ventricular. Moreover, cardiac-specific expression of Cre led to disintegration of the intercalated disc, along with altered proteins expression of the disc and calcium-handling abnormality. Comprehensively, we identified that the ferroptosis signaling pathway is involved in heart failure caused by cardiac-specific expression of Cre, on which oxidative stress results in cytoplasmic vacuole accumulation of lipid peroxidation on the myocardial cell membrane. Taken together, these results revealed that cardiac-specific expression of Cre recombinase can lead to atrial mesenchymal tumor-like growth in the mice, which causes cardiac dysfunction, including cardiac fibrosis, reduction of the intercalated disc and cardiomyocytes ferroptosis at the age older than six months in mice. Our study suggests that αMHC-Cre mouse models are effective in young mice, but not in old mice. Researchers need to be particularly careful when using αMHC-Cre mouse model to interpret those phenotypic impacts of gene responses. As the Cre-associated cardiac pathology matched mostly to that of the patients, the model could also be employed for investigating age-related cardiac dysfunction.

Liver hepatocellular carcinoma (LIHC) remains a global health challenge with poor prognosis and high mortality. FKBP1A was first discovered as a receptor for the immunosuppressant drug FK506 in immune cells and is critical for various tumors and cancers. However, the relationships between FKBP1A expression, cellular distribution, tumor immunity, and prognosis in LIHC remain unclear. Here, we investigated the expression level of FKBP1A and its prognostic value in LIHC via multiple datasets including ONCOMINE, TIMER, GEPIA, UALCAN, HCCDB, Kaplan–Meier plotter, LinkedOmics, and STRING. Human liver tissue microarray was employed to analyze the characteristics of FKBP1A protein including the expression level and pathological alteration in cellular distribution. FKBP1A expression was significantly higher in LIHC and correlated with tumor stage, grade and metastasis. The expression level of the FKBP1A protein was also increased in LIHC patients along with its accumulation in endoplasmic reticulum (ER). High FKBP1A expression was correlated with a poor survival rate in LIHC patients. The analysis of gene co-expression and the regulatory pathway network suggested that FKBP1A is mainly involved in protein synthesis, metabolism and the immune-related pathway. FKBP1A expression had a significantly positive association with the infiltration of hematopoietic immune cells including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. Moreover, M2 macrophage infiltration was especially associated with a poor survival prognosis in LIHC. Furthermore, FKBP1A expression was significantly positively correlated with the expression of markers of M2 macrophages and immune checkpoint proteins such as PD-L1, CTLA-4, LAG3 and HAVCR2. Our study demonstrated that FKBP1A could be a potential prognostic target involved in tumor immune cell infiltration in LIHC.

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Methanation is an effective way to efficiently utilize product gas generated from the pyrolysis and gasification of organic solid wastes. To deeply study the heat transfer and mass transfer mechanisms in the reactor, a successful three-dimensional comprehensive model has been established. Multiphase flow behavior and heat transfer mechanisms were investigated under reference working conditions. Temperature is determined by the heat release of the reaction and the heat transfer of the gas-solid flow. The maximum temperature can reach 951 K where the catalyst gathers. In the simulation, changes in the gas inlet velocity and catalyst flow rate were made to explore their effects on CO conversion rate and temperature for optimization purposes. As the inlet gas velocity increases from 2.78 to 4.79 m/s, the CO conversion rate decreases from 81.6% to 72.4%. However, more heat is removed from the reactor, and the temperature rise increases from 78.03 to 113.49 K. When the catalyst flow rate is increased from 7.18 to 17.96 kg/(m 2 •s), the mass of the catalyst in the reactor is increased from 0.0019 to 0.0042 kg, and the CO conversion rate is increased from 66.8% to 81.5%. However, this increases the maximum temperature in the reactor from 940.0 to 966.4 K.

In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the research in this paper is based on the bottom-up model in the multiperson pose estimation method; firstly, all the human key points in the image are detected by feature extraction through the convolutional neural network, and then the accurate labelling of human key points is achieved by using the heat map and offset fusion optimization method in the feature point confidence map prediction, and finally, the human body detection results are obtained. In the study of the correlation algorithm, this paper combines the HOG feature extraction of the KCF algorithm and the scale filter of the DSST algorithm to form a fusion correlation filter based on the principle study of the MOSSE correlation filter. The algorithm solves the problems of lack of scale estimation of KCF algorithm and low real-time rate of DSST algorithm and improves the tracking accuracy while ensuring the real-time performance of the algorithm.