There is an increasing interest in the concept that oxidant/antioxidant imbalance plays a role in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, most of the studies are concentrated on the local antioxidant/oxidant balance. In this study, we investigated the oxidant/antioxidant balance in systemic circulation of patients with COPD. Serum malonyldialdehyde (MDA), vitamin C and erythrocyte reduced glutathione (GSH) were determined in patients during acute exacerbation and during the stable phase of the disease, and compared with age- and sex-matched healthy controls. The levels of serum MDA, vitamin C and erythrocyte GSH were determined according to Yagi, Beutler and Bauer et al., respectively. Serum MDA levels were significantly higher in patients compared to controls, and during acute exacerbation compared to the stable phase. MDA levels in patients with acute exacerbation and in those in stable phase were also higher than in controls. We found significantly decreased levels of erythrocyte GSH and serum vitamin C in patients with acute exacerbation and stable COPD compared to controls. Although smoking caused an increase in oxidative stress in controls, the measured parameters were not affected by smoking in the patient group. In conclusion, there is a systemic oxidant/antioxidant imbalance in COPD, and this imbalance is probably independent of smoking.

Recent studies have shown that derangements in kynurenine pathway metabolite levels are associated with various pathologies such as neurodegenerative diseases, schizophrenia, depression, bipolar disorder, rheumatoid arthritis, and cancer. Therefore, reliable, accurate, fast, and multiplex measurement methods for kynurenines have become increasingly important. This study aimed to validate a new mass spectrometric method for analyzing tryptophan metabolites. A tandem mass spectrometric method, including protein precipitation and evaporation steps, was developed to measure serum levels of tryptophan, kynurenine, kynurenic acid, 3-hydroxykynurenine, and 3-hydroxyanthranilic acid. Samples were separated using a Phenomenex Luna C18 reversed-phase column. The kynurenine pathway metabolites were detected by tandem mass spectrometry. The developed method was validated according to Clinical & Laboratory Standards Institute (CLSI) guidelines and applied to hemodialysis samples. The developed method was linear at the concentrations of 48.8 – 25,000, 0.98 – 500, 1.2–5000, 1.2–5000, and 0.98–250 ng/mL for tryptophan, kynurenic acid, kynurenine, 3-hydroxyanthranilic acid, and 3-hydroxykynurenine, respectively. The imprecisions were less than 12 %. The median serum concentrations of tryptophan, kynurenine, kynurenic acid, 3-hydroxykynurenine, and 3-hydroxyanthranilic acid were 10530, 1100, 218, 17.6, and 25.4 ng/mL in pre-dialysis blood samples, respectively. They were 4560, 664, 135, 7.4, and 12.8 ng/mL in post-dialysis blood samples, respectively. A fast, simple, cost-effective, accurate, robust, and validated tandem mass spectrometric method was developed, and the method was successfully used for the quantitation of kynurenine pathway metabolite concentrations in hemodialysis patients.

Objectives: Data generation in clinical settings is ongoing and perpetually increasing. Artificial intelligence (AI) software may help detect data-related errors or facilitate process management. The aim of the present study was to test the extent to which the frequently encountered pre-analytical, analytical, and postanalytical errors in clinical laboratories, and likely clinical diagnoses can be detected through the use of a chatbot. Methods: A total of 20 case scenarios, 20 multiple-choice, and 20 direct questions related to errors observed in pre-analytical, analytical, and postanalytical processes were developed in English. Difficulty assessment was performed for the 60 questions. Responses by 4 chatbots to the questions were scored in a blinded manner by 3 independent laboratory experts for accuracy, usefulness, and completeness. Results: According to Chi-squared test, accuracy score of ChatGPT-3.5 (54.4 %) was significantly lower than CopyAI (86.7 %) (p=0.0269) and ChatGPT v4.0. (88.9 %) (p=0.0168), respectively in cases. In direct questions, there was no significant difference between ChatGPT-3.5 (67.8 %) and WriteSonic (69.4 %), ChatGPT v4.0. (78.9 %) and CopyAI (73.9 %) (p=0.914, p=0.433 and p=0.675, respectively) accuracy scores. CopyAI (90.6 %) presented significantly better performance compared to ChatGPT-3.5 (62.2 %) (p=0.036) in multiple choice questions. Conclusions: These applications presented considerable performance to find out the cases and reply to questions. In the future, the use of AI applications is likely to increase in clinical settings if trained and validated by technical and medical experts within a structural framework.