Membrane inlet mass spectrometry method for food intake impact assessment on specific volatile organic compounds in exhaled breath.

This research work describes the development of a novel bioanalytical method for the assessment of food impact on selected exhaled breath volatile organic compounds (VOCs) using a fast and portable screening VOC prototype sensor based on membrane inlet mass spectrometry (MIMS). Method and sensor prototype functionality was verified by obtaining good response times, linearity in the examined concentration ranges, and sensitivity and repeatability for several breath VOCs-acetone, ethanol, n-pentane, and isoprene. A new VOC sensor prototype was also proven to be sensitive enough for selected breath VOC quantification with limits of detection at low part per billion (ppb) levels-5 ppb for n-pentane, 10 ppb for acetone and ethanol, and 25 ppb for isoprene. Food impact assessment was accomplished by tracking the levels of acetone, ethanol, n-pentane, and isoprene in exhaled breath samples collected from 50 healthy participants before the meal and 60 min and 120 min after the meal. For acetone, isoprene, and n-pentane, a larger impact was noticed 120 min after the meal, while for ethanol, it was after 60 min. Obtained VOC levels were in the expected concentration ranges. Mean values at all time points were ~ 500-900 ppb for acetone and ~ 400-600 ppb for ethanol. Most of the results for n-pentane were below 5 ppb, but the mean value for those which were detected was ~ 30 ppb. Along with samples, data about participants' lifestyle were collected via a short questionnaire, which were compared against obtained VOC levels in order to reveal some significant correlations between habits of participants and their breath VOC levels. Portable MS: monitoring of food impact on the levels of selected VOCs from exhaled breath.

Prognostic Role of Monocyte Distribution Width, CRP, Procalcitonin and Lactate as Sepsis Biomarkers in Critically Ill COVID-19 Patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a global pandemic and one group of patients has developed a severe form of COVID-19 pneumonia with an urgent need for hospitalization and intensive care unit (ICU) admission. The aim of our study was to evaluate the prognostic role of MDW, CRP, procalcitonin (PCT), and lactate in critically ill COVID-19 patients. The primary outcome of interest is the 28 day mortality of ICU patients with confirmed SARS-CoV-2 infection and sepsis (according to Sepsis 3 criteria with acute change in SOFA score ≥ 2 points). Patients were divided into two groups according to survival on the 28th day after admission to the ICU. Every group was divided into two subgroups (women and men). Nonparametric tests (Mann-Whitney) for variables age, PCT, lactate, and MDW were lower than alpha p < 0.05, so there was a significant difference between survived and deceased patients. The Chi-square test confirmed statistically significant higher values of MDW and lactate in the non-survivor group. We found a significant association between MDW, lactate, procalcitonin, and fatal outcome, higher values were reported in the deceased group.

Prognostic Value of Routine Biomarkers in the Early Stage of COVID-19.

Various biomarkers like certain complete blood cell count parameters and the derived ratios including neutrophil-lymphocyte ratio are commonly used to evaluate disease severity. Our study aimed to establish if baseline levels of complete blood cell count-derived biomarkers and CRP, measured before any treatment which can interfere with their values, could serve as a predictor of development of pneumonia and the need for hospitalization requiring oxygen therapy. We retrospectively analyzed the laboratory data of 200 consecutive patients without comorbidities, who denied usage of medications prior to blood analysis and visited a COVID-19 ambulance between October and December 2021. Multivariate regression analysis extracted older age, elevated CRP and lower eosinophil count as significant independent predictors of pneumonia (p = 0.003, p = 0.000, p = 0.046, respectively). Independent predictors of hospitalization were higher CRP (p = 0.000) and lower platelet count (p = 0.005). There was no significant difference in the neutrophil-lymphocyte and platelet-lymphocyte ratios between examined groups. Individual biomarkers such as platelet and eosinophil count might be better in predicting the severity of COVID-19 than the neutrophil-lymphocyte and platelet-lymphocyte ratios.

Machine Learning Based Metagenomic Prediction of Inflammatory Bowel Disease.

In this study, we investigate faecal microbiota composition, in an attempt to evaluate performance of classification algorithms in identifying Inflammatory Bowel Disease (IBD) and its two types: Crohn's disease (CD) and ulcerative colitis (UC). From many investigated algorithms, a random forest (RF) classifier was selected for detailed evaluation in three-class (CD versus UC versus nonIBD) classification task and two binary (nonIBD versus IBD and CD versus UC) classification tasks. We dealt with class imbalance, performed extensive parameter search, dimensionality reduction and two-level classification. In three-class classification, our best model reaches F1 score of 91% in average, which confirms the strong connection of IBD and gastrointestinal microbiome. Among most important features in three-class classification are species Staphylococcus hominis, Porphyromonas endodontalis, Slackia piriformis and genus Bacteroidetes.