Linear Mixed-Effects Models in chemistry: A tutorial.

A common goal in chemistry is to study the relationship between a measured signal and the variability of certain factors. To this end, researchers often use Design of Experiment to decide which experiments to conduct and (Multiple) Linear Regression, and/or Analysis of Variance to analyze the collected data. Among the assumptions to the very foundation of this strategy, all the experiments are independent, conditional on the settings of the factors. Unfortunately, due to the presence of uncontrollable factors, real-life experiments often deviate from this assumption, making the data analysis results unreliable. In these cases, Mixed-Effects modeling, despite not being widely used in chemometrics, represents a solid data analysis framework to obtain reliable results. Here we provide a tutorial for Linear Mixed-Effects models. We gently introduce the reader to these models by showing some motivating examples. Then, we discuss the theory behind Linear Mixed-Effect models, and we show how to fit these models by making use of real-life data obtained from an exposome study. Throughout the paper we provide R code so that each researcher is able to implement these useful model themselves.

A multivariate curve resolution analysis of multicenter proton spectroscopic imaging of the prostate for cancer localization and assessment of aggressiveness.

In this study, we investigated the potential of the multivariate curve resolution alternating least squares (MCR-ALS) algorithm for analyzing three-dimensional (3D) 1 H-MRSI data of the prostate in prostate cancer (PCa) patients. MCR-ALS generates relative intensities of components representing spectral profiles derived from a large training set of patients, providing an interpretable model. Our objectives were to classify magnetic resonance (MR) spectra, differentiating tumor lesions from benign tissue, and to assess PCa aggressiveness. We included multicenter 3D 1 H-MRSI data from 106 PCa patients across eight centers. The patient cohort was divided into a training set (N = 63) and an independent test set (N = 43). Singular value decomposition determined that MR spectra were optimally represented by five components. The profiles of these components were extracted from the training set by MCR-ALS and assigned to specific tissue types. Using these components, MCR-ALS was applied to the test set for a quantitative analysis to discriminate tumor lesions from benign tissue and to assess tumor aggressiveness. Relative intensity maps of the components were reconstructed and compared with histopathology reports. The quantitative analysis demonstrated a significant separation between tumor and benign voxels (t-test, p < 0.001). This result was achieved including voxels with low-quality MR spectra. A receiver operating characteristic analysis of the relative intensity of the tumor component revealed that low- and high-risk tumor lesions could be distinguished with an area under the curve of 0.88. Maps of this component properly identified the extent of tumor lesions. Our study demonstrated that MCR-ALS analysis of 1 H-MRSI of the prostate can reliably identify tumor lesions and assess their aggressiveness. It handled multicenter data with minimal preprocessing and without using prior knowledge or quality control. These findings indicate that MCR-ALS can serve as an automated tool to assess the presence, extent, and aggressiveness of tumor lesions in the prostate, enhancing diagnostic capabilities and treatment planning of PCa patients.

Systematic reduction of hyperspectral images for high-throughput plastic characterization.

Hyperspectral Imaging (HSI) combines microscopy and spectroscopy to assess the spatial distribution of spectroscopically active compounds in objects, and has diverse applications in food quality control, pharmaceutical processes, and waste sorting. However, due to the large size of HSI datasets, it can be challenging to analyze and store them within a reasonable digital infrastructure, especially in waste sorting where speed and data storage resources are limited. Additionally, as with most spectroscopic data, there is significant redundancy, making pixel and variable selection crucial for retaining chemical information. Recent high-tech developments in chemometrics enable automated and evidence-based data reduction, which can substantially enhance the speed and performance of Non-Negative Matrix Factorization (NMF), a widely used algorithm for chemical resolution of HSI data. By recovering the pure contribution maps and spectral profiles of distributed compounds, NMF can provide evidence-based sorting decisions for efficient waste management. To improve the quality and efficiency of data analysis on hyperspectral imaging (HSI) data, we apply a convex-hull method to select essential pixels and wavelengths and remove uninformative and redundant information. This process minimizes computational strain and effectively eliminates highly mixed pixels. By reducing data redundancy, data investigation and analysis become more straightforward, as demonstrated in both simulated and real HSI data for plastic sorting.

Distinguishing Oligosaccharide Isomers Using Far-Infrared Ion Spectroscopy: Identification of Biomarkers for Inborn Errors of Metabolism.

Distinguishing isomeric saccharides poses a major challenge for analytical workflows based on (liquid chromatography) mass spectrometry (LC-MS). In recent years, many studies have proposed infrared ion spectroscopy as a possible solution as the orthogonal, spectroscopic characterization of mass-selected ions can often distinguish isomeric species that remain unresolved using conventional MS. However, the high conformational flexibility and extensive hydrogen bonding in saccharides cause their room-temperature fingerprint infrared spectra to have broad features that often lack diagnostic value. Here, we show that room-temperature infrared spectra of ion-complexed saccharides recorded in the previously unexplored far-infrared wavelength range (300-1000 cm-1) provide well-resolved and highly diagnostic features. We show that this enables distinction of isomeric saccharides that differ either by their composition of monosaccharide units and/or the orientation of their glycosidic linkages. We demonstrate the utility of this approach from single monosaccharides up to isomeric tetrasaccharides differing only by the configuration of a single glycosidic linkage. Furthermore, through hyphenation with hydrophilic interaction liquid chromatography, we identify oligosaccharide biomarkers in patient body fluid samples, demonstrating a generalized and highly sensitive MS-based method for the identification of saccharides found in complex sample matrices.

Deciphering interactions between the marine dinoflagellate Prorocentrum lima and the fungus Aspergillus pseudoglaucus.

The comprehension of microbial interactions is one of the key challenges in marine microbial ecology. This study focused on exploring chemical interactions between the toxic dinoflagellate Prorocentrum lima and a filamentous fungal species, Aspergillus pseudoglaucus, which has been isolated from the microalgal culture. Such interspecies interactions are expected to occur even though they were rarely studied. Here, a co-culture system was designed in a dedicated microscale marine-like condition. This system allowed to explore microalgal-fungal physical and metabolic interactions in presence and absence of the bacterial consortium. Microscopic observation showed an unusual physical contact between the fungal mycelium and dinoflagellate cells. To delineate specialized metabolome alterations during microalgal-fungal co-culture metabolomes were monitored by high-performance liquid chromatography coupled to high-resolution mass spectrometry. In-depth multivariate statistical analysis using dedicated approaches highlighted (1) the metabolic alterations associated with microalgal-fungal co-culture, and (2) the impact of associated bacteria in microalgal metabolome response to fungal interaction. Unfortunately, only a very low number of highlighted features were fully characterized. However, an up-regulation of the dinoflagellate toxins okadaic acid and dinophysistoxin 1 was observed during co-culture in supernatants. Such results highlight the importance to consider microalgal-fungal interactions in the study of parameters regulating toxin production.

Water quality monitoring based on chemometric analysis of high-resolution phytoplankton data measured with flow cytometry.

River water is an important source of Dutch drinking water. For this reason, continuous monitoring of river water quality is needed. However, comprehensive chemical analyses with high-resolution gas chromatography [GC]-mass spectrometry [MS]/liquid chromatography [LC]-MS are quite tedious and time consuming; this makes them poorly fit for routine water quality monitoring and, therefore, many pollution events are missed. Phytoplankton are highly sensitive and responsive to toxicity, which makes them highly usable for effect-based water quality monitoring. Flow cytometry can measure the optical properties of phytoplankton every hour, generating a large amount of information-rich data in one year. However, this requires chemometrics, as the resulting fingerprints need to be processed into information about abnormal phytoplankton behaviour. We developed Discriminant Analysis of Multi-Aspect CYtometry (DAMACY) to model the "normal condition" of the phytoplankton community imposed by diurnal, meteorological, and other exogenous influences. DAMACY first describes the cellular variability and distribution of phytoplankton in each measurement using principal component analysis, and then aims to find subtle differences in these phytoplankton distributions that predict normal environmental conditions. Deviations from these normal environmental conditions indicated abnormal phytoplankton behaviour that happened alongside pollution events measured with the GC/MS and LC/MS systems. Thus, our results demonstrate that flow cytometry in combination with chemometrics may be used for an automated hourly assessment of river water quality and as a near real-time early warning for detecting harmful known or unknown contaminants. Finally, both the flow cytometer and the DAMACY algorithm run completely autonomous and only requires maintenance once or twice per year. The warning system results may be uploaded automatically, so that drinking water companies may temporary stop pumping water whenever abnormal phytoplankton behaviour is detected. In the case of prolonged abnormal phytoplankton behaviour, comprehensive analysis may still be used to identify the chemical compound, its origin, and toxicity.

Influence of measurement procedure on the use of a handheld NIR spectrophotometer.

The development of portable NIR instruments facilitates widespread use among non-specialists. However, untrained operators may follow non-optimal measurement procedures. This work investigates how different factors in the measurement procedure influence the spectra of pig feed samples produced by SCiO, a handheld NIR. Measurement conditions were studied by means of Design of Experiments and evaluated with analysis of variance - simultaneous component analysis (ANOVA-SCA or ASCA). We quantified and visualized how measurement distance, angle, background lighting, the use of plastic lids and different devices interactively affect the resulting spectra. The samples could be distinguished with 100% accuracy with Partial Least Squares-Discriminant Analysis (PLS-DA) a scanning distance of 0.5 cm. Replication of the experiment with special attention to reproducing the conditions still lead to some differences, which highlights both the challenges in controlling conditions and the importance of considering them. Based on the results, generalizable guidelines for acceptance of spectra were proposed for this case study. Of main importance are performing measurements at distances of 0.5 cm or at least in an environment without background lighting. Overall, the provided guidelines for measurement conditions and a methodology to investigate this for other devices are a key enabler to spreading handheld spectrometry to a non-expert audience.

Evaluation and comparison of unsupervised methods for the extraction of spatial patterns from mass spectrometry imaging data (MSI).

For the extraction of spatially important regions from mass spectrometry imaging (MSI) data, different clustering methods have been proposed. These clustering methods are based on certain assumptions and use different criteria to assign pixels into different classes. For high-dimensional MSI data, the curse of dimensionality also limits the performance of clustering methods which are usually overcome by pre-processing the data using dimension reduction techniques. In summary, the extraction of spatial patterns from MSI data can be done using different unsupervised methods, but the robust evaluation of clustering results is what is still missing. In this study, we have performed multiple simulations on synthetic and real MSI data to validate the performance of unsupervised methods. The synthetic data were simulated mimicking important spatial and statistical properties of real MSI data. Our simulation results confirmed that K-means clustering with correlation distance and Gaussian Mixture Modeling clustering methods give optimal performance in most of the scenarios. The clustering methods give efficient results together with dimension reduction techniques. From all the dimension techniques considered here, the best results were obtained with the minimum noise fraction (MNF) transform. The results were confirmed on both synthetic and real MSI data. However, for successful implementation of MNF transform the MSI data requires to be of limited dimensions.

Convolutional neural networks to predict brain tumor grades and Alzheimer's disease with MR spectroscopic imaging data.

PURPOSE: To evaluate the value of convolutional neural network (CNN) in the diagnosis of human brain tumor or Alzheimer's disease by MR spectroscopic imaging (MRSI) and to compare its Matthews correlation coefficient (MCC) score against that of other machine learning methods and previous evaluation of the same data. We address two challenges: 1) limited number of cases in MRSI datasets and 2) interpretability of results in the form of relevant spectral regions. METHODS: A shallow CNN with only one hidden layer and an ad-hoc loss function was constructed involving two branches for processing spectral and image features of a brain voxel respectively. Each branch consists of a single convolutional hidden layer. The output of the two convolutional layers is merged and fed to a classification layer that outputs class predictions for the given brain voxel. RESULTS: Our CNN method separated glioma grades 3 and 4 and identified Alzheimer's disease patients using MRSI and complementary MRI data with high MCC score (Area Under the Curve were 0.87 and 0.91 respectively). The results demonstrated superior effectiveness over other popular methods as Partial Least Squares or Support Vector Machines. Also, our method automatically identified the spectral regions most important in the diagnosis process and we show that these are in good agreement with existing biomarkers from the literature. CONCLUSION: Shallow CNNs models integrating image and spectral features improved quantitative and exploration and diagnosis of brain diseases for research and clinical purposes. Software is available at https://bitbucket.org/TeslaH2O/cnn_mrsi.

Preanalytical Pitfalls in Untargeted Plasma Nuclear Magnetic Resonance Metabolomics of Endocrine Hypertension.

Despite considerable morbidity and mortality, numerous cases of endocrine hypertension (EHT) forms, including primary aldosteronism (PA), pheochromocytoma and functional paraganglioma (PPGL), and Cushing's syndrome (CS), remain undetected. We aimed to establish signatures for the different forms of EHT, investigate potentially confounding effects and establish unbiased disease biomarkers. Plasma samples were obtained from 13 biobanks across seven countries and analyzed using untargeted NMR metabolomics. We compared unstratified samples of 106 PHT patients to 231 EHT patients, including 104 PA, 94 PPGL and 33 CS patients. Spectra were subjected to a multivariate statistical comparison of PHT to EHT forms and the associated signatures were obtained. Three approaches were applied to investigate and correct confounding effects. Though we found signatures that could separate PHT from EHT forms, there were also key similarities with the signatures of sample center of origin and sample age. The study design restricted the applicability of the corrections employed. With the samples that were available, no biomarkers for PHT vs. EHT could be identified. The complexity of the confounding effects, evidenced by their robustness to correction approaches, highlighted the need for a consensus on how to deal with variabilities probably attributed to preanalytical factors in retrospective, multicenter metabolomics studies.

Monitoring Reaction Intermediates to Predict Enantioselectivity Using Mass Spectrometry.

Enantioselective reactions are at the core of chemical synthesis. Their development mostly relies on prior knowledge, laborious product analysis and post-rationalization by theoretical methods. Here, we introduce a simple and fast method to determine enantioselectivities based on mass spectrometry. The method is based on ion mobility separation of diastereomeric intermediates, formed from a chiral catalyst and prochiral reactants, and delayed reactant labeling experiments to link the mass spectra with the reaction kinetics in solution. The data provide rate constants along the reaction paths for the individual diastereomeric intermediates, revealing the origins of enantioselectivity. Using the derived kinetics, the enantioselectivity of the overall reaction can be predicted. Hence, this method can offer a rapid discovery and optimization of enantioselective reactions in the future. We illustrate the method for the addition of cyclopentadiene (CP) to an α,ß-unsaturated aldehyde catalyzed by a diarylprolinol silyl ether.

Non-Invasive Monitoring of Inflammation in Inflammatory Bowel Disease Patients during Prolonged Exercise via Exhaled Breath Volatile Organic Compounds.

The aim of this study was to investigate volatile organic compounds (VOCs) in exhaled breath as possible non-invasive markers to monitor the inflammatory response in inflammatory bowel disease (IBD) patients as a result of repeated and prolonged moderate-intensity exercise. We included 18 IBD patients and 19 non-IBD individuals who each completed a 30, 40, or 50 km walking exercise over three consecutive days. Breath and blood samples were taken before the start of the exercise event and every day post-exercise to assess changes in the VOC profiles and cytokine concentrations. Proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) was used to measure exhaled breath VOCs. Multivariate analysis, particularly ANOVA-simultaneous component analysis (ASCA), was employed to extract relevant ions related to exercise and IBD. Prolonged exercise induces a similar response in breath butanoic acid and plasma cytokines for participants with or without IBD. Butanoic acid showed a significant correlation with the cytokine IL-6, indicating that butanoic acid could be a potential non-invasive marker for exercise-induced inflammation. The findings are relevant in monitoring personalized IBD management.

Pre- versus post-operative untargeted plasma nuclear magnetic resonance spectroscopy metabolomics of pheochromocytoma and paraganglioma.

PURPOSE: Pheochromocytomas and Paragangliomas (PPGL) result in chronic catecholamine excess and serious health complications. A recent study obtained a metabolic signature in plasma from PPGL patients; however, its targeted nature may have generated an incomplete picture and a broader approach could provide additional insights. We aimed to characterize the plasma metabolome of PPGL patients before and after surgery, using an untargeted approach, and to broaden the scope of the investigated metabolic impact of these tumors. DESIGN: A cohort of 36 PPGL patients was investigated. Blood plasma samples were collected before and after surgical tumor removal, in association with clinical and tumor characteristics. METHODS: Plasma samples were analyzed using untargeted nuclear magnetic resonance (NMR) spectroscopy metabolomics. The data were evaluated using a combination of uni- and multi-variate statistical methods. RESULTS: Before surgery, patients with a nonadrenergic tumor could be distinguished from those with an adrenergic tumor based on their metabolic profiles. Tyrosine levels were significantly higher in patients with high compared to those with low BMI. Comparing subgroups of pre-operative samples with their post-operative counterparts, we found a metabolic signature that included ketone bodies, glucose, organic acids, methanol, dimethyl sulfone and amino acids. Three signals with unclear identities were found to be affected. CONCLUSIONS: Our study suggests that the pathways of glucose and ketone body homeostasis are affected in PPGL patients. BMI-related metabolite levels were also found to be altered, potentially linking muscle atrophy to PPGL. At baseline, patient metabolomes could be discriminated based on their catecholamine phenotype.

High-throughput single cell data analysis - A tutorial.

White blood cells protect the body against disease but may also cause chronic inflammation, auto-immune diseases or leukemia. There are many different white blood cell types whose identity and function can be studied by measuring their protein expression. Therefore, high-throughput analytical instruments were developed to measure multiple proteins on millions of single cells. The information-rich biochemistry information may only be fully extracted using multivariate statistics. Here we show an overview of the most essential steps for multivariate data analysis of single cell data. We used white blood cells (immunology) as a case study, but a similar approach may be used in environment or biotech research. The first step is analyzing the study design and subsequently formulating a research question. The three main designs are immunophenotyping (finding different cell types), cell activation and rare cell discovery. When preparing the data it is essential to consider the design and focus on the cell type of interest by removing all unwanted events. After pre-processing, the ten-thousands to millions of single cells per sample need to be converted into a cellular distribution. For immunophenotyping a clustering method such as Self-Organizing Maps is useful and for cell activation a model that describes the covariance such as Principal Component Analysis is useful. In rare cell discovery it is useful to first model all common cells and remove them to find the rare cells. Finally discriminant analysis based on the cellular distribution may highlight which cell (sub)types are different between groups.

Cost-benefit analysis of calibration model maintenance strategies for process monitoring.

The long-term prediction performance of spectroscopic calibration models is a critical factor to monitor or control many production processes. Over time, new variations may emerge that deteriorate prediction performance. Therefore, models have to be maintained to retain or improve their prediction performance through time, requiring considerable resources and data. Maintenance should improve relevant predictions but also needs to be resource and cost efficient. Current approaches do not consider these trade-offs. We propose a new method to quantify the effectiveness and cost of model maintenance strategies based on historical data. Model performance over time for past, imminent and future samples is evaluated as these may react differently to maintenance. The model performance and required updating resources are translated into relative cost and benefit to compare strategies and determine optimal maintenance parameters. We used this method to evaluate a maintenance strategy that combines adding incoming samples to the calibration data with re-optimization of spectral preprocessing and modelling parameters. Continuously adding samples to the calibration data is shown to improve prediction performance and leads to more robust and generic models for emerging variations in all investigated data streams. Selectively adding incoming sample variations showed a reduced prediction performance but saves considerably in resources. Comparing model performance on the different sampling windows can also be used to determine an optimal updating frequency. This novel strategy to evaluate the expected performance and determine an optimal maintenance strategy is generally applicable and should lead to robust and consistently high prospective and/or retrospective model performance through time, which can be crucial for optimal operation and fault detection in industrial processes.

Quantitative vibrational spectroscopy on liquid mixtures: concentration units matter.

Quantitative vibrational absorption spectroscopies rely on Beer's law relating spectroscopic intensities in a linear fashion to chemical concentrations. To address and clarify contrasting results in the literature about the difference between volume- and mass-based concentrations units used for quantitative spectroscopy on liquid solutions, we performed near-infrared, mid-infrared, and Raman spectroscopy measurements on four different binary solvent mixtures. Using classical least squares (CLS) and partial least squares (PLS) as multivariate analysis methods, we demonstrate that spectroscopic intensities are linearly related to volume-based concentration units rather than more widely used mass-based concentration units such as weight percent. The CLS results show that the difference in root mean square error of prediction (RMSEP) values between CLS models based on mass and volume fractions correlates strongly with the density difference between the two solvents in each binary mixture. This is explained by the fact that density differences are the source of non-linearity between mass and volume fractions in such mixtures. We also show that PLS calibration handles the non-linearity in mass-based models by the inclusion of additional latent variables that describe residual spectroscopic variation beyond the first latent variable (e.g., due to small peak shifts), as observed in the experimental data of all binary solvent mixtures. Using simulation studies, we have quantified the relative errors (up to 10-15%) that are made in PLS modeling when using mass fractions instead of volume fractions. Overall, our results provide conclusive evidence that concentration units based on volume should be preferred for optimal spectroscopic calibration results in academic and industrial practice.

Exhaled Breath Reflects Prolonged Exercise and Statin Use during a Field Campaign.

Volatile organic compounds (VOCs) in exhaled breath provide insights into various metabolic processes and can be used to monitor physiological response to exercise and medication. We integrated and validated in situ a sampling and analysis protocol using proton transfer reaction time-of-flight mass spectrometry (PTR-ToF-MS) for exhaled breath research. The approach was demonstrated on a participant cohort comprising users of the cholesterol-lowering drug statins and non-statin users during a field campaign of three days of prolonged and repeated exercise, with no restrictions on food or drink consumption. The effect of prolonged exercise was reflected in the exhaled breath of participants, and relevant VOCs were identified. Most of the VOCs, such as acetone, showed an increase in concentration after the first day of walking and subsequent decrease towards baseline levels prior to walking on the second day. A cluster of short-chain fatty acids including acetic acid, butanoic acid, and propionic acid were identified in exhaled breath as potential indicators of gut microbiota activity relating to exercise and drug use. We have provided novel information regarding the use of breathomics for non-invasive monitoring of changes in human metabolism and especially for the gut microbiome activity in relation to exercise and the use of medication, such as statins.

Refractory neutrophils and monocytes in patients with inflammatory bowel disease after repeated bouts of prolonged exercise.

BACKGROUND: Neutrophils and monocytes are key immune effector cells in inflammatory bowel disease (IBD) that is associated with chronic inflammation in the gut. Patients with stable IBD who perform exercise have significantly fewer flare-ups of the disease, but no underlying mechanism has been identified. Therefore, the aim of this study was to compare the responsiveness/refractoriness of these innate immune cells after repeated bouts of prolonged exercise in IBD patients and controls. METHODS: Patients with IBD and age- and gender-matched healthy controls were recruited from a cohort of walkers participating in a 4-day walking event. Blood analysis was performed at baseline and after 3 days of walking. Responsiveness to the bacterial/mitochondrial-stimulus N-Formylmethionine-leucyl-phenylalanine (fMLF) was tested in granulocytes and monocytes by measuring the expression of activation markers after adding this stimulus to whole blood. RESULTS: In total 38 participants (54 ± 12 years) were included in this study: 19 walkers with and 19 walkers without IBD. After 3 days of prolonged exercise, a significant increase in responsiveness to fMLF was observed in all participants irrespective of disease. However, IBD patients showed significantly less responsiveness in neutrophils and monocytes, compared with non-IBD walkers. CONCLUSIONS: Increased responsiveness of neutrophils and monocyte to fMLF was demonstrated after repetitive bouts of prolonged exercise. Interestingly, this exercise was associated with relative refractoriness of both neutrophils and monocytes in IBD patients. These refractory cells might create a lower inflammatory state in the intestine providing a putative mechanism for the decrease in flare-ups in IBD patients after repeated exercise.

Flow cytometric evaluation of the neutrophil compartment in COVID-19 at hospital presentation: A normal response to an abnormal situation.

Coronavirus disease 2019 (COVID-19) is a rapidly emerging pandemic disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Critical COVID-19 is thought to be associated with a hyper-inflammatory process that can develop into acute respiratory distress syndrome, a critical disease normally mediated by dysfunctional neutrophils. This study tested the hypothesis whether the neutrophil compartment displays characteristics of hyperinflammation in COVID-19 patients. Therefore, a prospective study was performed on all patients with suspected COVID-19 presenting at the emergency room of a large academic hospital. Blood drawn within 2 d after hospital presentation was analyzed by point-of-care automated flow cytometry and compared with blood samples collected at later time points. COVID-19 patients did not exhibit neutrophilia or eosinopenia. Unexpectedly neutrophil activation markers (CD11b, CD16, CD10, and CD62L) did not differ between COVID-19-positive patients and COVID-19-negative patients diagnosed with other bacterial/viral infections, or between COVID-19 severity groups. In all patients, a decrease was found in the neutrophil maturation markers indicating an inflammation-induced left shift of the neutrophil compartment. In COVID-19 this was associated with disease severity.

Analysis of human neutrophil phenotypes as biomarker to monitor exercise-induced immune changes.

The amplitude of the innate immune response reflects the degree of physiological stress imposed by exercise load. An optimal balance of exercise intensity and duration is essential for a balanced immune system and reduces the risk of dysfunction of the immune system. Therefore, it is hypothesized that neutrophils, as key players in the innate immune system, can be used as biomarker in detecting overtraining. The aim was to monitor the state of the innate immune system by phenotyping neutrophils during consecutive bouts of prolonged exercise. Study subjects were recruited from a cohort of walkers participating in a walking event on 3 consecutive days. Participants with immune deficiencies were excluded. Questionnaires to determine the physiological status of the participants were completed. Analysis of neutrophil receptor expression was done by a point-of-care fully automated flow cytometer. A total of 45 participants were recruited, of whom 39 participants were included for data analysis. Study participants had a median age of 64 (58-70) years. The absolute numbers CD16dim /CD62Lbright and CD16bright /CD62Ldim neutrophils were increased after the first 2 days of exercise followed by an adaptation/normalization after the third day. Participants with activated neutrophils (high CD11b expression) had an impaired physical feeling indicated by the participant on a lower visual analog scale compared to participants who did not have activated neutrophils (P = 0.017, P = 0.022). Consecutive days of prolonged exercise results in an initial systemic innate immune response, followed by normalization/adaptation. Increased neutrophil activation was associated with impaired physical feeling measured by a validated VAS score indicated by the participant. Fully automated point-of-care flow cytometry analysis of neutrophil phenotypes in a field laboratory might be a useful tool to monitor relevant differences in the systemic innate immune response in response to exercise.