ObjectiveTo investigate the characteristic volatile organic compounds (VOCs) in exhaled breath and their diagnostic value in patients with early stage lung cancer.MethodsSolid-phase micro-extraction combined with gas chromatography mass spectrometry was used to analyze exhaled breath VOCs of 117 patients with early stage lung cancer (54 males and 63 females, with an average age of 61.9±6.8 years) and 130 healthy subjects (79 males and 51 females, with an average age of 63.3±6.6 years. The characteristic VOCs of early stage lung cancer were identified, and a diagnostic model was established.ResultsTen characteristic VOCs of early stage lung cancer were identified, including acetic acid, n-butanol, dimethylsilanol, toluene, 2,3,4-trimethylheptane, 3,4-dimethylbenzoic acid, 5-methyl-3-hexene-2-ketone, n-hexanol, methyl 2-oxoglutarate and 4-methoxyphenol. Gender and the 10 characteristic VOCs were included in the diagnostic model, with a sensitivity of 83.8% and a specificity of 96.2%.ConclusionAnalysis of exhaled breath VOCs is expected to be one of the potential methods used for early stage lung cancer diagnosis.
Objective To detect the bile acid profile in serum based on liquid chromatography-tandem mass spectrometry, and construct a combined biomarker diagnostic model for differentiating acute myocardial infarction (AMI) from unstable angina (UA). Methods A total of 180 patients with acute coronary syndrome who visited Huludao Central Hospital between August 2023 and February 2024 were randomly selected, and there were 117 patients with UA and 63 patients with AMI. Using liquid chromatography-tandem mass spectrometry, 15 bile acid subtypes in serum were detected. Orthogonal partial least squares discriminant analysis was used to compare the serum bile acid metabolic profiles of the subjects. Differences in metabolites were screened based on a significance level of P<0.05 and variable importance in projection (VIP)>1. Multiple logistic regression analysis was performed to construct a diagnostic model for differentiating AMI from UA, and the diagnostic performance of the model was evaluated using receiver operating characteristic (ROC) curve and other statistical methods. Results The differential bile acid biomarkers in the serum of UA and AMI patients included glycodeoxycholic acid, glycochenodeoxycholic acid (GCDCA), deoxycholic acid (DCA), glycocholic acid, and aurodeoxycholic acid (TDCA) (P<0.05, VIP>1). A binary logistic stepwise regression analysis showed that three bile acid biomarkers (GCDCA, DCA, and TDCA) and three common biochemical indicators (aspartate aminotransferase, creatine kinase, and total bile acid) were factors differentiating AMI from UA (P<0.05). The area under the ROC curve of the model was 0.986 [95% confidence interval (0.973, 0.999), P<0.001], demonstrating a good diagnostic performance. Conclusions GCDCA, DCA, and TDCA can serve as potential biomarkers for distinguishing AMI from UA. The model combining these three bile acids with aspartate aminotransferase, creatine kinase, and total bile acid can effectively identify AMI.
Objective To construct and verify the diagnostic model of preoperative malignant risk of ovarian tumors, so as to improve the diagnostic efficiency of existing test indexes. Methods The related serological indicators and clinical data of patients with ovarian tumors confirmed by pathology who were treated in the Affiliated Hospital of Southwest Medical University between January 2019 and September 2023 were retrospectively collected, and the patients were randomly divided into a training set and a verification set at a 7∶3 ratio. Logistic regression was used to construct a diagnostic model in the training set, and the diagnostic efficacy of the model was verified through discrimination, calibration, clinical benefit, and clinical applicability evaluation. Results A total of 929 patients with ovarian tumors were included, including 318 cases of malignant ovarian tumors and 611 cases of benign ovarian tumors. The patients were randomly divided into a training set of 658 cases and a validation set of 271 cases. A diagnostic model was constructed using logistic regression in the training set, containing 5 factors namely age, percentage of neutrophil (NEU%), fibrinogen to albumin ratio (FAR), carbohydrate antigen 125 (CA125), and human epididymis protein 4 (HE4): modelUAM=−3.211+0.667×age+2.966×CA125+0.792×FAR+1.637×HE4+0.533×NEU%, with a Hosmer-Lemeshow test P-value of 0.21. The area under the receiver operating characteristic (ROC) curve measured in the training set was 0.927 [95% confidence interval (0.903, 0.951)], the sensitivity was 0.947, and the specificity was 0.780. The area under the ROC curve of the validation set was 0.888 [95% confidence interval (0.840, 0.930)], the sensitivity was 0.744, and the specificity was 0.901. Conclusion A new quantitative tool based on age, NEU%, FAR, CA125 and HE4 can be used for the clinical diagnosis of ovarian malignant tumors, and it is helpful to improve the diagnostic efficiency and is worth popularizing.