The effect of sampling procedures on the urate and lactate concentration in oral fluid

Lomonaco, T; Ghimenti, Silvia; Biagini, Denise; Bramanti, Emilia; Onor, Massimo; Bellagambi, Francesca G.; Fuoco, Roger; Francesco, Fabio Di

doi:10.1016/j.microc.2017.02.032

Cited by 40 publications

(34 citation statements)

References 44 publications

(52 reference statements)

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“…It has to be noted that the salivary flow rate and pH can affect concentration of biomarkers in saliva [13,42,43]. In our study, we were not able to assess the salivary flow rate because study participants collected samples at home.…”

Section: Discussionmentioning

confidence: 84%

Saliva Samples as A Tool to Study the Effect of Meal Timing on Metabolic And Inflammatory Biomarkers

et al. 2020

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Meal timing affects metabolic regulation in humans. Most studies use blood samples for their investigations. Saliva, although easily available and non-invasive, seems to be rarely used for chrononutritional studies. In this pilot study, we tested if saliva samples could be used to study the effect of timing of carbohydrate and fat intake on metabolic rhythms. In this cross-over trial, 29 nonobese men were randomized to two isocaloric 4-week diets: (1) carbohydrate-rich meals until 13:30 and high-fat meals between 16:30 and 22:00 or (2) the inverse order of meals. Stimulated saliva samples were collected every 4 h for 24 h at the end of each intervention, and levels of hormones and inflammatory biomarkers were assessed in saliva and blood. Cortisol, melatonin, resistin, adiponectin, interleukin-6 and MCP-1 demonstrated distinct diurnal variations, mirroring daytime reports in blood and showing significant correlations with blood levels. The rhythm patterns were similar for both diets, indicating that timing of carbohydrate and fat intake has a minimal effect on metabolic and inflammatory biomarkers in saliva. Our study revealed that saliva is a promising tool for the non-invasive assessment of metabolic rhythms in chrononutritional studies, but standardisation of sample collection is needed in out-of-lab studies.

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Section: Discussionmentioning

confidence: 84%

Saliva Samples as A Tool to Study the Effect of Meal Timing on Metabolic And Inflammatory Biomarkers

et al. 2020

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“…As it is seen from the outlined data (Table 3), no statistically reliable differences were found in the values of inclination angles and the contents of the surfactants between the studied groups. However, it was established that, when simultaneously taking into the account of age and gender of the studied persons (Table 2), the surfactant content is maximum for the younger age group (30)(31)(32)(33)(34)(35)(36)(37)(38)(39), both for females and for males. Subsequently, it decreases at transition to the middle group (−11.2% and −50.5% for males and females correspondingly) and further it insignificantly increases (+8.0% for males and +3.3% for females).…”

Section: The Influence Of Gender and Age Characteristics On Morphologmentioning

confidence: 99%

Morphology of Dried Drop Patterns of Saliva from a Healthy Individual Depending on the Dynamics of Its Surface Tension

2019

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Background: The study of processes in the drying drops of biological fluids (dried drop patterns) and the method of dynamic surface tensiometry can be attributed to integral methods of assessing the state of the organism. Research objective: to establish the relationship between the type of crystallization patterns and the surface tension of human saliva in normal conditions. Methods: 100 volunteers (40 males, 60 females) that were aged 30–59 participated in the study. In all saliva samples, the parameters of dynamic tensiometry, types of crystallization patterns and 11 biochemical parameters were determined. Results: No statistically significant differences in the saliva crystallization patterns were observed, depending on the age and gender characteristics of the volunteers. A negative correlation of the area of the crystallization zone and the surface tension of saliva is shown. When considering the crystallization patterns, their considerable variability was noted; on this basis, the entire studied sample was divided into four clusters by surface tension. Conclusion: In general, the crystallization patterns that are inside the selected groups remain quite heterogeneous. This increases the likelihood of making an incorrect diagnosis when using visual methods to evaluate the crystallization patterns, which significantly limits the use of such diagnostic methods in clinical practice.

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“…socio-demographic, clinical examination, medical condition, lab tests, medication intake, phenotypic data, sensor data) along with machine learning techniques are presented in [24,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]49]. Recent studies have identified certain biomarkers which strongly correlate with the HF severity, progression and mortality [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79][80][81], while the progress in analytical chemistry and biosensor development allowed their detection in saliva and breath [82][83][84][85] with prominent merits due to the easy and non-invasive sample collection. The Event prediction module aims to inform the experts about the possible presence of adverse events (relapses and mortality): (i) by introducing saliva and breath biomarkers into the adverse event prediction process, (ii) based on a machine le...…”

Section: Event Prediction Modulementioning

confidence: 99%

HEARTEN KMS – A knowledge management system targeting the management of patients with heart failure

Tripoliti

Karanasiou

Kalatzis

et al. 2019

Journal of Biomedical Informatics

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The aim of this work is to present the HEARTEN Knowledge Management System, one of the core modules of the HEARTEN platform. The HEARTEN platform is an mHealth collaborative environment enabling the Heart Failure patients to self-manage the disease and remain adherent, while allowing the other ecosystem actors (healthcare professionals, caregivers, nutritionists, physical activity experts, psychologists) to monitor the patient's health progress and offer personalized, predictive and preventive disease management. The HEARTEN Knowledge Management System is a tool which provides multiple functionalities to the ecosystem actors for the assessment of the patient's condition, the estimation of the patient's adherence, the prediction of potential adverse events, the calculation of Heart Failure related scores, the extraction of statistics, the association of patient clinical and non-clinical data and the provision of alerts and suggestions. The innovation of this tool lays in the analysis of multiparametric personal data coming from different sources, including for the first time breath and saliva biomarkers, and the use of machine learning techniques. The HEARTEN Knowledge Management System consists of nine modules. The accuracy of the KMS modules ranges from 78-95% depending on the module/functionality.

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The effect of sampling procedures on the urate and lactate concentration in oral fluid

Cited by 40 publications

References 44 publications

Saliva Samples as A Tool to Study the Effect of Meal Timing on Metabolic And Inflammatory Biomarkers

Saliva Samples as A Tool to Study the Effect of Meal Timing on Metabolic And Inflammatory Biomarkers

Morphology of Dried Drop Patterns of Saliva from a Healthy Individual Depending on the Dynamics of Its Surface Tension

HEARTEN KMS – A knowledge management system targeting the management of patients with heart failure

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