Introduction: Hydrogen sulfide (H2S) is a lethal environmental and industrial poison. The mortality rate of occupational acute H2S poisoning reported in China is 23.1% ~ 50%. Due to the huge amount of information on metabolomics changes after body poisoning, it is important to use intelligent algorithms to mine multivariate interactions. Methods: This paper first uses GC-MS metabolomics to detect changes in the urine components of the poisoned group and control rats to form a metabolic data set, and then uses the SVM classification algorithm in machine learning to train the hydrogen sulfide poisoning training data set to obtain a classification recognition model. A batch of rats (n = 15) was randomly selected and exposed to 20 ppm H2S gas for 40 days (twice morning and evening, 1 hour each exposure) to prepare a chronic H2S rat poisoning model. The other rats (n = 15) were exposed to the same volume of air and 0 ppm hydrogen sulfide gas as the control group. The treated urine samples were tested using a GC-MS. Results: The method locates the optimal parameters of SVM, which improves the accuracy of SVM classification to 100%. This paper uses the information gain attribute evaluation method to screen out the top 6 biomarkers that contribute to the predicted category (Glycerol,β-Hydroxybutyric acid, arabinofuranose,Pentitol,L-Tyrosine,L-Proline). Conclusion: The SVM diagnostic model of hydrogen sulfide poisoning constructed in this work has training time and prediction accuracy; it has achieved excellent results and provided an intelligent decision-making method for the diagnosis of hydrogen sulfide poisoning.
Background: Carbamazepine has been used in the treatment of bipolar disorder, both in acute mania and maintenance therapy, particularly in developing countries. Not only its interaction with various drugs and auto-inducer na-ture, but the narrow therapeutic range of carbamazepine also makes monitoring necessary to guarantee the adequacy of its safety and therapeutic concentration. To date, the most common biological specimen used for therapeutic drug monitoring (TDM) purposes is still plasma, but saliva can become an alternative biological matrix since its level in saliva strongly correlates with carbamazepine plasma concentration. Objective: This study validated the bioanalytical method parameters used for carbamazepine in spiked-saliva in accord-ance with the Food and Drug Administration (FDA) criteria in the Guidance for Industry Bioanalytical Method Valida-tion. Method: HPLC - UV detector was employed at 285 nm λ with methanol: water: glacial acetic acid (65:34:1) as the mobile phase and C8 as the stationary phase (4.6x150 mm; 5 μm). Results: The linearity test in a range of 0.0 - 5 μg/mL carbamazepine concentration resulted in a correlation coefficient of 0.999 with 0.20 μg/mL LoD, 0.30 μg/mL LLoQ, and 0.61 μg/mL LoQ. The coefficient of variation and 0iff in the selec-tivity, accuracy, and precision parameters remained below 20%, indicating fulfillment of the criteria for a bioanalytical method, while the average % recovery was more than 90%. Conclusion: The currently-developed bioanalytical method has fulfilled the stipulated validation criteria to be used for de-termining carbamazepine concentration in spiked-saliva as an alternative method for relative bioequivalence studies or TDM application in a clinical setting
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