Centering around the concept that metabolites from the gut commensals can exert metabolic health benefits along the gut–liver axis, we tested whether the cell-free global metabolome of probiotic bacteria can exert hepatoprotective benefits against H2O2-induced oxidative stress. Cell-free global metabolites of Lactobacillus plantarum (LPM) were isolated and untargeted metabolomics was performed. The free radical scavenging potentials of LPM were measured. The cytoprotective effects of LPM were tested on HepG2 cells. A total of 66 diverse metabolites were identified in LPM, among which saturated fatty acids, amino acids and dicarboxylic acids were highly enriched. LPM attenuated cell damage, lipid peroxidation and the levels of intracellular cytoprotective enzymes in H2O2-treated cells. LPM also attenuated H2O2-induced increased expressions of TNF-α and IL-6. However, the cytoprotective effects of LPM were diminished in cells that were pretreated with a pharmacological inhibitor of Nrf2. Our data collectively indicate that LPM can significantly attenuate oxidative damage to HepG2 cells. However, the cytoprotective effects of LPM likely depend on an Nrf2-dependent mechanism.
Background and Objectives: Monitoring laboratory performance continuously is crucial for recognizing errors and fostering further improvements in laboratory medicine. This study aimed to review the quality indicators (QIs) and describe the laboratory errors in the preanalytical phase of hematology testing in a clinical laboratory. Methods: All samples received in the Hematology Laboratory of the Maternity and Pediatric Hospital in Hail for 3 years were retrospectively reviewed and evaluated for preanalytical issues using a set of QIs. The rate of each QI was compared to the quality specifications cited in the literature. Results: A total of 95002 blood samples were collected for analysis in the hematology laboratory from January 2017 through December 2019. Overall, 8852 (9.3%) were considered to show preanalytical errors. The most common were “clotted specimen” (3.6%) and “samples not received” (3.5%). Based on the quality specifications, the preanalytical QIs were classified generally as low and medium level of performance. In contrast, the sigma-based performance level indicates acceptable performance on all the key processes. Further analysis of the study showed a decreasing rate of preanalytical errors from 11.6% to 6.5%. Conclusions: Preanalytical errors remain a challenge to hematology laboratories. The errors in this case were predominantly related to specimen collection procedures that compromised the specimen quality. Quality indicators are a valuable instrument in the preanalytical phase that allows an opportunity to improve and explore clinical laboratory process performance and progress. Continual monitoring and management of QI data are critical to ensure ongoing satisfactory performance and to enhance the quality in the preanalytical phase.
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