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Summary: Autologous fat injection is becoming a more and more widely accepted procedure in plastic surgery nowadays. Concomitantly, there are many complications. The most serious one is arterial fat embolism, for which there is only poor prognosis and no effective treatment. Here, we report the first case of successful treatment of cerebral fat embolization with the help of mechanical lipectomy. Our patient was found unconscious with left-sided hemiparesis after a facial fat injection surgery 4 hours before. Cerebral artery computed tomography angiography indicated it was multiple fat embolism. Then Solitaire stent (4 × 20 mm) and Solumbra (continuously negative pressure attraction) were utilized for the mechanical extraction of fat emboli. The patient recovered from left-side hemiparesis to muscle strength of Medical Research Council scale grade 4, and National Institutes of Health Stroke Scale score was 0 after 3 months visit. In the cerebral infarction after fat transplantation, mechanical lipectomy can be a novel and significant treatment.

Background Hepatocellular carcinoma (HCC) is a common digestive tumor with great heterogeneity and different overall survival (OS) time, causing stern problems for selecting optimal treatment. Here we aim to establish a nomogram to predict the OS in HCC patients. Methods International Cancer Genome Consortium (ICGC) database was searched for the target information in our study. Lasso regression, univariate and multivariate cox analysis were applied during the analysis process. And a nomogram integrating model scoring and clinical characteristic was drawn. Results Six mRNAs were screened out by Lasso regression to make a model for predicting the OS of HCC patients. And this model was proved to be an independent prognostic model predicting OS in HCC patients. The area under the ROC curve (AUC) of this model was 0.803. TCGA database validated the significant value of this 6-mRNA model. Eventually a nomogram including 6-mRNA risk score, gender, age, tumor stage and prior malignancy was set up to predict the OS in HCC patients. Conclusions We established an independent prognostic model of predicting OS for 1–3 years in HCC patients, which is available to all populations. And we developed a nomogram on the basis of this model, which could be of great help to precisely individual treatment measures.

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MicroRNA is a small noncoding RNA that plays a role in regulating gene expression. miR-1271 is a tumor suppressor microRNA, which is related to the biological changes of many cancers. miR-1271 is considered a biomarker with a potential prognosis and high therapeutic value in tumors. Besides, the expression of miR-1271 is also regulated by many factors. In this study, we summarize the role of miR-1271 in tumors, focusing on the molecular mechanisms of the target genes of miR-1271. Our review will provide a comprehensive understanding of miR-1271 in tumors, as well as ideas for subsequent tumor research related to miR-1271.

Background Hypoxia plays a crucial role in immunotherapy of hepatocellular carcinoma (HCC) by changing the tumor microenvironment. Until now the association between hypoxia genes and prognosis of HCC remains obscure. We attempt to construct a hypoxia model to predict the prognosis in HCC. Results We screened out 3 hypoxia genes (ENO1, UGP2, TPI1) to make the model, which can predict prognosis in HCC. And this model emerges as an independent prognostic factor for HCC. A Nomogram was drawn to evaluate the overall survival in a more accurate way. Furthermore, immune infiltration state and immunosuppressive microenvironment of the tumor were detected in high-risk patients. Conclusion We establish and validate a risk prognostic model developed by 3 hypoxia genes, which could effectively evaluate the prognosis of HCC patients. This prognostic model can be used as a guidance for hypoxia modification in HCC patients undergoing immunotherapy.

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ObjectiveTo search for significant biomarkers associated with sudden death (SD).MethodsDifferential genes were screened by comparing the whole blood samples from 15 cases of accidental death (AD) and 88 cases of SD. The protein-protein interaction (PPI) network selects core genes that interact most frequently. Machine learning is applied to find characteristic genes related to SD. The CIBERSORT method was used to explore the immune-microenvironment changes.ResultsA total of 10 core genes (MYL1, TNNC2, TNNT3, TCAP, TNNC1, TPM2, MYL2, TNNI1, ACTA1, CKM) were obtained and they were mainly related to myocarditis, hypertrophic myocarditis and dilated cardiomyopathy (DCM). Characteristic genes of MYL2 and TNNT3 associated with SD were established by machine learning. There was no significant change in the immune-microenvironment before and after SD.ConclusionDetecting characteristic genes is helpful to identify patients at high risk of SD and speculate the cause of death.