688 research outputs found

    Comparison of a Genetic Algorithm Variable Selection and Interval Partial Least Squares for quantitative analysis of lactate in PBS

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    Blood lactate is an important biomarker that has been linked to morbidity and mortality of critically ill patients, acute ischemic stroke, septic shock, lung injuries, insulin resistance in diabetic patients, and cancer. Currently, the clinical measurement of blood lactate is done by collecting intermittent blood samples. Therefore, noninvasive, optical measurement of this significant biomarker would lead to a big leap in healthcare. This study, presents a quantitative analysis of the optical properties of lactate. The benefits of wavelength selection for the development of accurate, robust, and interpretable predictive models have been highlighted in the literature. Additionally, there is an obvious, time- and cost-saving benefit to focusing on narrower segments of the electromagnetic spectrum in practical applications. To this end, a dataset consisting of 47 spectra of Na-lactate and Phosphate Buffer Solution (PBS) was produced using a Fourier transform infrared spectrometer, and subsequently, a comparative study of the application of a genetic algorithm-based wavelength selection and two interval selection methods was carried out. The high accuracy of predictions using the developed models underlines the potential for optical measurement of lactate. Moreover, an interesting finding is the emergence of local features in the proposed genetic algorithm, while, unlike the investigated interval selection methods, no explicit constraints on the locality of features was imposed. Finally, the proposed genetic algorithm suggests the formation of α-hydroxy-esters methyl lactate in the solutions while the other investigated methods fail to indicate this

    Identification and Quantitative Determination of Lactate Using Optical Spectroscopy—Towards a Noninvasive Tool for Early Recognition of Sepsis

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    Uninterrupted monitoring of serum lactate levels is a prerequisite in the critical care of patients prone to sepsis, cardiogenic shock, cardiac arrest, or severe lung disease. Yet there exists no device to continuously measure blood lactate in clinical practice. Optical spectroscopy together with multivariate analysis is proposed as a viable noninvasive tool for estimation of lactate in blood. As an initial step towards this goal, we inspected the plausibility of predicting the concentration of sodium lactate (NaLac) from the UV/visible, near-infrared (NIR), and mid-infrared (MIR) spectra of 37 isotonic phosphate-buffered saline (PBS) samples containing NaLac ranging from 0 to 20 mmol/L. UV/visible (300–800 nm) and NIR (800–2600 nm) spectra of PBS samples were collected using the PerkinElmer Lambda 1050 dual-beam spectrophotometer, while MIR (4000–500 cm−1) spectra were collected using the Spectrum two FTIR spectrometer. Absorption bands in the spectra of all three regions were identified and functional groups were assigned. The concentration of lactate in samples was predicted using the Partial Least-Squares (PLS) regression analysis and leave-one-out cross-validation. The regression analysis showed a correlation coefficient (R2) of 0.926, 0.977, and 0.992 for UV/visible, NIR, and MIR spectra, respectively, between the predicted and reference samples. The RMSECV of UV/visible, NIR, and MIR spectra was 1.59, 0.89, and 0.49 mmol/L, respectively. The results indicate that optical spectroscopy together with multivariate models can achieve a superior technique in assessing lactate concentrations

    Monitoring of lactate in interstitial fluid, saliva and sweat by electrochemical biosensor: the uncertainties of biological interpretation

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    Lactate electrochemical biosensors were fabricated using Pediococcus sp lactate oxidase (E.C. 1.1.3.2), an external polyurethane membrane laminate diffusion barrier and an internal ionomeric polymer barrier (sulphonated polyether ether sulphone polyether sulphone, SPEES PES). In a needle embodiment, a Pt wire working electrode was retained within stainless steel tubing serving as pseudoreference. The construct gave linearity to at least 25 mM lactate with 0.17 nA/mM lactate sensitivity. A low permeability inner membrane was also unexpectedly able to increase linearity. Responses were oxygen dependent at pO2 < 70 mmHg, irrespective of the inclusion of an external diffusion barrier membrane. Subcutaneous tissue was monitored in Sprague Dawley rats, and saliva and sweat during exercise in human subjects. The tissue sensors registered no response to intravenous Na lactate, indicating a blood-tissue lactate barrier. Salivary lactate allowed tracking of blood lactate during exercise, but lactate levels were substantially lower than those in blood (0–3.5 mM vs. 1.6–12.1 mM), with variable degrees of lactate partitioning from blood, evident both between subjects and at different exercise time points. Sweat lactate during exercise measured up to 23 mM but showed highly inconsistent change as exercise progressed. We conclude that neither tissue interstitial fluid nor sweat are usable as surrogates for blood lactate, and that major reappraisal of lactate sensor use is indicated for any extravascular monitoring strategy for lactate

    Multi-parameter phenotyping of platelets and characterisation of the effects of agonists using machine learning

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    Platelet function is driven by the expression of specialised surface markers. The concept of distinct circulating sub-populations of platelets has emerged in recent years, but their exact nature remains debatable. Objective To design a spectral flow cytometry-based phenotyping workflow to provide a more comprehensive characterisation, at a global and individual level, of surface markers in resting and activated healthy platelets. Secondly, to apply this workflow to investigate how responses differ according to platelet age. Methods A 14-marker flow cytometry panel was developed and applied to vehicle- or agonist-stimulated platelet-rich plasma and whole blood samples obtained from healthy volunteers, or to platelets sorted according to SYTO-13 staining intensity as an indicator of platelet age. Data were analysed using both user-led and independent approaches incorporating novel machine learning-based algorithms. Results The assay detected differences in marker expression in healthy platelets, at rest and on agonist activation, in both platelet rich plasma and whole blood samples, that are consistent with the literature. Machine learning identified stimulated populations of platelets with high accuracy (>80%). Similarly, machine learning differentiation between young and old platelet populations achieved 76% accuracy, primarily weighted by FSC-A, CD41, SSC-A, GPVI, CD61, and CD42b expression patterns. Conclusions Our approach provides a powerful phenotypic assay coupled with robust bioinformatic and machine learning workflows for deep analysis of platelet sub-populations. Cleave-able receptors, GPVI and CD42b, contribute to defining shared and unique sub-populations. This adoptable, low-volume approach will be valuable in deep characterisation of platelets in disease
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