Database-assisted global metabolomics profiling of pleural effusion induced by tuberculosis and malignancy

Database-assisted global metabolomics has received growing attention due to its capability for unbiased identification of metabolites in various biological samples. Herein, we established a mass spectrometry (MS)-based database-assisted global metabolomics method and investigated metabolic distance between pleural effusion induced by tuberculosis and malignancy, which are difficult to be distinguished due to their similar clinical symptoms. The present method utilized a liquid chromatography (LC) system coupled with high resolution mass spectrometry (MS) working on full scan and data dependent mode for data acquisition. Unbiased identification of metabolites was performed through mass spectral searching and then confirmed by using authentic standards. As a result, a total of 194 endogenous metabolites were identified and 33 metabolites were found to be differentiated between tuberculous and malignant pleural effusions. These metabolites involved in tryptophan catabolism, bile acid biosynthesis, and β-oxidation of fatty acids, provided non-invasive biomarkers for differentiation of the pleural effusion samples with high sensitivity and specificity.     

火焰原子吸收法测定人胸水中铜,锌,铁含量的研究

本文用火焰原子吸收光谱法研究测定了人（肺结核、肺癌）胸水中铜、锌、铁。测定肺结核胸水２１例，该胸水中铜、锌、铁含量平均值分别为０．７１±０．０８ｍｇ／Ｌ，４．７８±０．１８ｍｇ／Ｌ，３．２２±０．１５ｍｇ／Ｌ。肺癌胸水２１例，其胸水铜、锌、铁含量为０．９８±０．０９ｍｇ／Ｌ，３．８０±０．１６ｍｇ／Ｌ，５．３５±０．２１ｍｇ／Ｌ。该方法的回收率为９５．２１％－１０５．５４％。相对标准偏差小于４．８４％。     

人胸膜间皮瘤与结核型胸膜炎的傅里叶红外光谱分析

研究以上皮型胸膜间皮瘤,纤维型胸膜间皮瘤,结核型胸膜炎与正常胸膜组织为材料, 通过傅里叶变换红外光谱分析组织中生物大分子的结构及含量的改变。研究发现,四种胸膜组织的傅里叶红外光谱较为相似,但存在明显区别,四种胸膜组织红外光谱数据方差差异极显著(sig.<0.001), 说明胸膜间皮瘤中生物大分子的含量及结构发生了明显变化,主要包括: (1)胸膜间皮瘤蛋白质酰胺Ⅰ带及酰胺Ⅱ带,核酸1 232 cm-1峰强,脂类物质2 922 cm-1峰强均显著高于正常人胸膜组织,与正常胸膜组织存在显著区别;纤维型胸膜间皮瘤中,蛋白质酰胺Ⅰ带及酰胺Ⅱ带峰强,与核酸密切相关的1 078 cm-1峰强,以及与脂类物质相关的2 922和2 854 cm-1峰强均显著高于上皮型胸膜间皮瘤(p<0.05);结核型胸膜炎蛋白质酰胺Ⅰ带及酰胺Ⅱ、核酸1 232和1 078 cm-1峰强略有增加,但与正常胸膜组织差异不显著(p>0.05),与脂类物质含量有关2 922 cm-1峰强、2 854 cm-1峰强,极显著地高于正常胸膜组织(p<0.01),显著高于胸膜间皮瘤(p<0.05)。(2)蛋白质、核酸、脂类物质的相对峰强I_{1 641}/I_{2 922}, I_{1 641}/I_{1 232}, I_{1 232}/I_{1 078}, I_{1 078}/I_{1 546}, I_{1 078}/I_{2 854}, I_{2 922}/I_{1 232}, I_{1 458}/I_{1 400}能有效放大四类胸膜组织间的差异,其效果优于峰强效果,可作为胸膜间皮瘤诊断的优化指标。(3)上皮型胸膜间皮瘤中指示核酸分子中磷酸二酯键的C-C/C-O的1 078 cm-1峰强以及指示脂类物质的2 854 cm-1峰强显著低于纤维型胸膜间皮瘤和正常胸膜组织(p<0.05),表明上皮型胸膜间皮瘤中磷酸二酯键断裂程度较高,DNA受损严重,膜脂过氧化降解明显。说明上皮型胸膜间皮瘤恶化程度高于纤维型胸膜间皮瘤。(4)胸膜间皮瘤蛋白质酰胺Ⅰ带、Ⅱ带谱带、核酸1 232 cm-1峰、脂类物质1 458 cm-1处CH₂振动及1 400 cm-1处CH₃振动红移,说明蛋白质分子间的氢键受到破坏,核酸分子的氢键结合力减弱,核酸分子的双链结构受到一定程度的破坏,肿瘤组织中膜脂的亚甲基链趋向无序。(5)傅里叶红外光谱能有效区分纤维型胸膜间皮瘤、上皮型胸膜间皮瘤、结核型胸膜炎、正常胸膜组织,为胸膜间皮瘤与结核型胸膜炎的早期、快速诊断提供了可靠的数据。     

Exploring the value of pleural fluid biomarkers for complementary pleural effusion disease examination

ObjectivePleural fluid biomarkers are beneficial for the complementary diagnosis of pleural effusion etiologies. This study focuses on the multidimensional evaluation of deep learning to investigate the pleural effusion biomarkers value and the diagnostic utility of combining these markers, in distinguishing pleural effusion etiologies.MethodsPleural effusion were divided into three groups according to the diagnosis and treatment guidelines: malignant pleural effusion (MPE), parapneumonic effusion (PPE), and congestive heart failure (CHF). First, the value of the biomarker was analyzed by a receiver operating characteristic (ROC) curve. Then by utilizing deep learning and entropy weight method (EWM), the clinical value of biomarkers was computed multidimensionally for complementary diagnosis of pleural effusion diseases.ResultsThere were significant differences in the six biomarkers, TP, ADA, CEA, CYFRA211, NSE, MNC% (p < 0.05) and no significant differences in three physical characteristics including color, transparency, specific gravity and six other biomarkers such as WBC, PNC%, MTC%, pH level, GLU, LDH (p > 0.05) among the three pleural effusion groups. The comprehensive test of pleural fluid biomarkers based on deep learning is of high accuracy. The clinical value of cytomorphology biomarkers WBC, MNC %, PNC %, MTC % was higher among pleural fluid biomarkers.ConclusionThe clinical value of multi-dimensional analysis of biomarkers by deep learning and entropy weight method is different from the ROC curve analysis. It is suggested that during the clinical examination process, more attention should be paid to the cell morphology biomarkers, but the physical properties of the pleural fluid are less clinical significance.     

Analysis of volatile organic compounds in pleural effusions by headspace solid‐phase microextraction coupled with cryotrap gas chromatography and mass spectrometry

Headspace solid‐phase microextraction coupled with cryotrap gas chromatography and mass spectrometry was applied to the analysis of volatile organic compounds in pleural effusions. The highly volatile organic compounds were separated successfully with high sensitivity by the employment of a cryotrap device, with the construction of a cold column head by freezing a segment of metal capillary with liquid nitrogen. A total of 76 volatile organic compounds were identified in 50 pleural effusion samples (20 malignant effusions and 30 benign effusions). Among them, 34 more volatile organic compounds were detected with the retention time less than 8 min, by comparing with the normal headspace solid‐phase microextraction coupled with gas chromatography and mass spectrometry method. Furthermore, 24 volatile organic compounds with high occurrence frequency in pleural effusion samples, 18 of which with the retention time less than 8 min, were selected for the comparative analysis. The results of average peak area comparison and box‐plot analysis showed that except for cyclohexanone, 2‐ethyl‐1‐hexanol, and tetramethylbenzene, which have been reported as potential cancer biomarkers, cyclohexanol, dichloromethane, ethyl acetate, n‐heptane, ethylbenzene, and xylene also had differential expression between malignant and benign effusions. Therefore, the proposed approach was valuable for the comprehensive characterization of volatile organic compounds in pleural effusions.