OBJECTIVETo detect the heterogeneity of mitochondrial DNA (mtDNA) in black and white hair of patients with type 2 diabetes.

METHODSMtDNA was extracted from the hair shaft of the patients to amplify two target DNA fragment from mtDNA coding region and control region using PCR. The differences in the heterogeneity in the target DNA fragment was analyzed between diabetic patients and the control group with denaturing high-performance liquid chromatography (DHPLC).

RESULTSIn the control subjects and diabetic patients, the mtDNA heterogeneity in the black hair was 3% and 10% in 20-45 year-old groups and 9% and 17% in 45-70 year-old groups, as compared to 9%, 20%, 21%, and 40% in the white hair, respectively. The mtDNA heterogeneity in the black and white hair was both higher in the diabetic patients than in the control subjects of the same age group, and was also higher in older age subgroups in both control and diabetic groups (P<0.05). The white hair mtDNA showed a significantly higher heterogeneity than the black hair mtDNA in the two age groups of diabetic patients and in 45-70 year-old control group (P<0.05).

CONCLUSIONThe mtDNA heterogeneity in the hair increases in type 2 diabetic patients and show an association with aging.

Adult ; Age Factors ; Aging ; genetics ; Chromatography, High Pressure Liquid ; methods ; DNA, Mitochondrial ; genetics ; Diabetes Mellitus, Type 2 ; genetics ; metabolism ; Female ; Genetic Heterogeneity ; Hair ; metabolism ; Humans ; Male ; Middle Aged ; Young Adult

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.