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烧伤并发腹腔间隙综合征二例

【摘要】　目的　总结重度以上烧伤患者合并腹腔间隙综合征（abdominal compartment syndrome，ACS）的诊治方法。　方法　回顾性分析2009年11月-2011年6月2例确诊为重度以上烧伤且合并ACS的患儿临床诊治资料，总结烧伤合并ACS的临床诊治方法。　结果　重度以上烧伤合并ACS的患者可能出现临床表现:心率增快、中心静脉压升高、心输出量降低、低血压、腹胀、呼吸困难、低氧血症和高碳酸血症、少尿或无尿、膀胱压增高等。腹腔减压术后，患儿心率增快、低血压、腹胀、少尿、呼吸困难等症状得到缓解。　结论　重度及以上烧伤可能合并ACS，烧伤患者中以小儿多见，尚无一致的诊断标准，表现具有一定的隐蔽性、多样化特点，腹腔减压是干预ACS最为有效的治疗措施。

半腱肌肌瓣加阔筋膜张肌肌皮瓣修复坐骨结节褥疮

A study of invasive lung adenocarcinoma different-grade pathological subtypes’genes and construction of machine learning-based prognostic prediction models

Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which was obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), GO analysis, KEGG analysis, and GSEA, to analyze the data were employed. Our objective was to establish a five-gene panel risk assessment model using Cox regression and LASSO regression. Based on this model, we constructed a Nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, ROC curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as Metabolism of xenobiotics by cytochrome P450, Natural killer cell-mediated cytotoxicity, Antigen processing and presentation, and Regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the ROC curve (AUC) of 0.675, as well as in time-dependent ROC curves. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a Nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.