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

Near infrared spectroscopy (NIRS) and machine learning as a promising tandem for fast viral detection in serum microsamples: A preclinical proof of concept

Fast detection of viral infections is a key factor in the strategy for the prevention of epidemics expansion and follow-up. Hepatitis C is paradigmatic within viral infectious diseases and major challenges to elimination still remain. Near infrared spectroscopy (NIRS) is an inexpensive, clean, safe method for quickly detecting viral infection in transmission vectors, aiding epidemic prevention. Our objective is to evaluate the combined potential of machine learning and NIRS global molecular fingerprint (GMF) from biobank sera as an efficient method for HCV activity discrimination in serum. GMF of 151 serum biobank microsamples from hepatitis C patients were obtained with a FT-NIR spectrophotometer in reflectance mode. Multiple scatter correction, smoothing and Saviztsky-Golay second derivative were applied. Spectral analysis included Principal Component Analysis (PCA), Bootstrap and L1-penalized classification. Microsamples of 70 µl were sufficient for GMF acquisition. Bootstrap evidenced significant difference between HCV PCR positive and negative sera. PCA renders a neat discrimination between HCV PCR-positive and negative samples. PCA loadings together with L1-penalized classification allow the identification of discriminative bands. Active virus positive sera are associated to free molecular water, whereas water in solvation shells is associated to HCV negative samples. Divergences in the water matrix structure and the lipidome between HCV negative and positive sera, as well as the relevance of prooxidants and glucose metabolism are reported as potential biomarkers of viral activity. Our proof of concept demonstrates that NIRS GMF of hepatitis C patients' sera aided by machine learning allows for efficient discrimination of viral presence and simultaneous potential biomarker identification.

Capillary electrophoresis with capacitively coupled contactless conductivity detection (C4 D) for rapid and simple determination of lactate in sweat

An analytical method based on capillary electrophoresis (CE) using capacitively coupled contactless conductivity detection (C4 D) was developed and validated for fast, straightforward, and reliable determination of lactate in artificial and human sweat samples. The background electrolyte was composed of equimolar concentrations (10 mmol/L) of 2-(N-morpholino)ethanesulfonic acid and histidine, with 0.2 mmol/L of cetyltrimethylammonium bromide as electroosmotic flow inverter. The limit of detection and quantification were 3.1 and 10.3 µmol/L, respectively. Recoveries in the 97 to 118% range were obtained using sweat samples spiked with lactate at three concentration levels, indicating an acceptable accuracy. The intraday and interday precisions were 1.49 and 7.08%, respectively. The proposed CE-C4 D method can be a starting point for monitoring lactate concentrations in sweat samples for diagnostics, physiological studies, and sports performance assessment applications.

Nondestructive assessment of tissue engineered cartilage based on biochemical markers in cell culture media: application of attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy

ATR spectral data obtained from cell culture medium discards can be used to assess glucose and lactate content, which are shown here to be a surrogate for matrix development in tissue engineered cartilage.

Recent Advances in Bedside Device-Based Early Detection of Sepsis

Early detection of sepsis is challenging to achieve with current diagnostic methods, leading to expenditures of $27 billion annually in the United States with significant associated mortality. Various scoring systems have been proposed such as the sequential organ failure assessment (SOFA) and systemic inflammatory response syndrome (SIRS) criteria for identification of sepsis, but their sensitivities range from 60% to 70% when used in the emergency department triage. Other methods for the recognition of sepsis may rely on laboratory work, in addition to vitals monitoring, and are often outpaced by the development of sepsis. Automated alerts have not shown any reduction in mortality thus far. New technology may fill a critical gap in the early detection of sepsis. The ideal bedside screening device for would demonstrate rapid time to result, high portability, and high sensitivity to not miss cases, but also reasonable specificity to prevent provider fatigue from excessive false alerts. Non-invasive end-organ perfusion devices analyzing lactate and capillary refill time (CRT) tend to perform well in speed and portability, but may be less sensitive. Biomarker devices demonstrate a wider array of performance metrics. Those analyzing a single biomarker tend to be more sensitive but are less specific to the diagnosis of sepsis than technologies that assess multiple biomarkers, which in turn have lower sensitivity. Additionally, biomarker devices are generally invasive requiring blood samples, which may or may not be feasible in all patients especially when serial draws are needed. Sepsis is a complex disease process and most likely will require a combination of improved technology in addition to vital signs and high-risk patient history for better recognition. This review examines recent advances in the device-based early detection of sepsis between 2017 and 2020 with emphasis on bedside diagnostics, divided into markers of perfusion and biomarkers commonly implicated in sepsis.

Monte Carlo Characterization of Short-Wave Infrared Optical Wavelengths for Biosensing Applications

Short-wave infrared (SWIR) spectroscopy has shown great promise in probing the composition of biological tissues. Currently there exists an enormous drive amongst researchers to design and develop SWIR-based optical sensors that can predict the concentration of various biomarkers non-invasively. However, there is limited knowledge regarding the interaction of SWIR light with vascular tissue, especially in terms of parameters like the optimal source-detector separation, light penetration depth, optical pathlength, etc., all of which are essential components in designing optical sensors. With the aim to determine these parameters, Monte Carlo simulations were carried out to examine the interaction of SWIR light with vascular skin. SWIR photons were found to penetrated only 1.3 mm into the hypodermal fat layer. The highest optical pathlength and penetration depths were seen at 1mm source-detector separation, and the lowest being 0.7mm. Although the optical pathlength varied significantly with increasing source-detector separation at SWIR wavelengths, penetration depth remained constant. This may explain why collecting optical spectra from depth of tissue at SWIR wavelengths is more challenging than collecting optical spectra from near-infrared wavelengths, where both the optical pathlength and penetration depth change rapidly with source-detector separation.

In-silico investigation towards the non-invasive optical detection of blood lactate

This paper uses Monte Carlo simulations to investigate the interaction of short-wave infrared (SWIR) light with vascular tissue as a step toward the development of a non-invasive optical sensor for measuring blood lactate in humans. The primary focus of this work was to determine the optimal source-detector separation, penetration depth of light at SWIR wavelengths in tissue, and the optimal light power required for reliable detection of lactate. The investigation also focused on determining the non-linear variations in absorbance of lactate at a few select SWIR wavelengths. SWIR photons only penetrated 1.3 mm and did not travel beyond the hypodermal fat layer. The maximum output power was only 2.51% of the input power, demonstrating the need for a highly sensitive detection system. Simulations optimized a source-detector separation of 1 mm at 1684 nm for accurate measurement of lactate in blood.

An empirical investigation of deviations from the Beer-Lambert law in optical estimation of lactate

The linear relationship between optical absorbance and the concentration of analytes-as postulated by the Beer-Lambert law-is one of the fundamental assumptions that much of the optical spectroscopy literature is explicitly or implicitly based upon. The common use of linear regression models such as principal component regression and partial least squares exemplifies how the linearity assumption is upheld in practical applications. However, the literature also establishes that deviations from the Beer-Lambert law can be expected when (a) the light source is far from monochromatic, (b) the concentrations of analytes are very high and (c) the medium is highly scattering. The lack of a quantitative understanding of when such nonlinearities can become predominant, along with the mainstream use of nonlinear machine learning models in different fields, have given rise to the use of methods such as random forests, support vector regression, and neural networks in spectroscopic applications. This raises the question that, given the small number of samples and the high number of variables in many spectroscopic datasets, are nonlinear effects significant enough to justify the additional model complexity? In the present study, we empirically investigate this question in relation to lactate, an important biomarker. Particularly, to analyze the effects of scattering matrices, three datasets were generated by varying the concentration of lactate in phosphate buffer solution, human serum, and sheep blood. Additionally, the fourth dataset pertained to invivo, transcutaneous spectra obtained from healthy volunteers in an exercise study. Linear and nonlinear models were fitted to each dataset and measures of model performance were compared to attest the assumption of linearity. To isolate the effects of high concentrations, the phosphate buffer solution dataset was augmented with six samples with very high concentrations of lactate between (100-600 mmol/L). Subsequently, three partly overlapping datasets were extracted with lactate concentrations varying between 0-11, 0-20 and 0-600 mmol/L. Similarly, the performance of linear and nonlinear models were compared in each dataset. This analysis did not provide any evidence of substantial nonlinearities due high concentrations. However, the results suggest that nonlinearities may be present in scattering media, justifying the use of complex, nonlinear models.

Comparison of Dual Beam Dispersive and FTNIR Spectroscopy for Lactate Detection

Near Infrared (800–2500 nm) spectroscopy has been extensively used in biomedical applications, as it offers rapid, in vivo, bed-side monitoring of important haemodynamic parameters, which is especially important in critical care settings. However, the choice of NIR spectrometer needs to be investigated for biomedical applications, as both the dual beam dispersive spectrophotomer and the FTNIR spectrometer have their own advantages and disadvantages. In this study, predictive analysis of lactate concentrations in whole blood were undertaken using multivariate techniques on spectra obtained from the two spectrometer types simultaneously and results were compared. Results showed significant improvement in predicting analyte concentration when analysis was performed on full range spectral data. This is in comparison to analysis of limited spectral regions or lactate signature peaks, which yielded poorer prediction models. Furthermore, for the same region, FTNIR showed 10% better predictive capability than the dual beam dispersive NIR spectrometer.

A Graphene-Based Enzymatic Biosensor Using a Common-Gate Field-Effect Transistor for L-Lactic Acid Detection in Blood Plasma Samples

Lactate is an important organic molecule that is produced in excess during anaerobic metabolism when oxygen is absent in the human organism. The concentration of this substance in the body can be related to several medical conditions, such as hemorrhage, respiratory failure, and ischemia. Herein, we describe a graphene-based lactate biosensor to detect the concentrations of L-lactic acid in different fluids (buffer solution and plasma). The active surface (graphene) of the device was functionalized with lactate dehydrogenase enzyme using different substances (Nafion, chitosan, and glutaraldehyde) to guarantee stability and increase selectivity. The devices presented linear responses for the concentration ranges tested in the different fluids. An interference study was performed using ascorbic acid, uric acid, and glucose, and there was a minimum variation in the Dirac point voltage during detection of lactate in any of the samples. The stability of the devices was verified at up to 50 days while kept in a dry box at room temperature, and device operation was stable until 12 days. This study demonstrated graphene performance to monitor L-lactic acid production in human samples, indicating that this material can be implemented in more simple and low-cost devices, such as flexible sensors, for point-of-care applications.