Towards an understanding of physical activity-induced post-exertional malaise: Insights into microvascular alterations and immunometabolic interactions in post-COVID condition and myalgic encephalomyelitis/chronic fatigue syndrome.

BACKGROUND: A considerable number of patients who contracted SARS-CoV-2 are affected by persistent multi-systemic symptoms, referred to as Post-COVID Condition (PCC). Post-exertional malaise (PEM) has been recognized as one of the most frequent manifestations of PCC and is a diagnostic criterion of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). Yet, its underlying pathomechanisms remain poorly elucidated. PURPOSE AND METHODS: In this review, we describe current evidence indicating that key pathophysiological features of PCC and ME/CFS are involved in physical activity-induced PEM. RESULTS: Upon physical activity, affected patients exhibit a reduced systemic oxygen extraction and oxidative phosphorylation capacity. Accumulating evidence suggests that these are mediated by dysfunctions in mitochondrial capacities and microcirculation that are maintained by latent immune activation, conjointly impairing peripheral bioenergetics. Aggravating deficits in tissue perfusion and oxygen utilization during activities cause exertional intolerance that are frequently accompanied by tachycardia, dyspnea, early cessation of activity and elicit downstream metabolic effects. The accumulation of molecules such as lactate, reactive oxygen species or prostaglandins might trigger local and systemic immune activation. Subsequent intensification of bioenergetic inflexibilities, muscular ionic disturbances and modulation of central nervous system functions can lead to an exacerbation of existing pathologies and symptoms.

Spectral Zones-Based SHAP/LIME: Enhancing Interpretability in Spectral Deep Learning Models Through Grouped Feature Analysis.

Interpretability is just as important as accuracy when it comes to complex models, especially in the context of deep learning models. Explainable artificial intelligence (XAI) approaches have been developed to address this problem. The literature on XAI for spectroscopy mainly emphasizes independent feature analysis with limited application of zone analysis. Individual feature analysis methods, such as shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), have limitations due to their dependence on perturbations. These methods measure how AI models respond to sudden changes in the individual feature values. While they can help identify the most impactful features, the abrupt shifts introduced by replacing these values with zero or the expected ones may not accurately represent real-world scenarios. This can lead to mathematical and computational interpretations that are neither physically realistic nor intuitive to humans. Our proposed method does not rely on individual disturbances. Instead, it targets "spectral zones" to directly estimate the effect of group disturbances on a trained model. Consequently, factors such as sample size, hyperparameter selection, and other training-related considerations are not the primary focus of the XAI methods. To achieve this, we have developed a modified version of LIME and SHAP capable of performing group perturbations, enhancing explainability and realism while minimizing noise in the plots used for interpretability. Additionally, we employed an efficient approach to calculate spectral zones for complex spectra with indistinct spectral boundaries. Users can also define the zones themselves using their domain-specific knowledge.

Biomedical SERS - the current state and future trends.

Surface enhanced Raman spectroscopy (SERS) is meeting the requirements in biomedical science being a highly sensitive and specific analytical tool. By employing portable Raman systems in combination with customized sample pre-treatment, point-of-care-testing (POCT) becomes feasible. Powerful SERS-active sensing surfaces with high stability and modification layers if required are available for testing and application in complex biological matrices such as body fluids, cells or tissues. This review summarizes the current state in sample collection and pretreatment in SERS detection protocols, SERS detection schemes, i.e. direct and indirect SERS as well as targeted and non-targeted SERS, and SERS-active sensing surfaces. Moreover, the recent developments and advances of SERS in biomedical application scenarios, such as infectious diseases, cancer diagnostics and therapeutic drug monitoring is given, which enables the readers to identify the sample collection and preparation protocols, SERS substrates and detection strategies that are best-suited for their specific applications in biomedicine.

Imaging Diffractometric Biosensors for Label-Free, Multi-Molecular Interaction Analysis.

Biosensors are used for the specific and sensitive detection of biomolecules. In conventional approaches, the suspected target molecules are bound to selected capture molecules and successful binding is indicated by additional labelling to enable optical readout. This labelling requires additional processing steps tailored to the application. While numerous label-free interaction assays exist, they often compromise on detection characteristics. In this context, we introduce a novel diffractometric biosensor, comprising a diffractive biosensor chip and an associated optical reader assembly. This innovative system can capture an entire assay, detecting various types of molecules in a label-free manner and present the results within in a single, comprehensive image. The applicability of the biosensor is assessed for the detection of viral DNA as well as proteins directly in human plasma, investigating different antigens. In our experiments, we achieve a detection limit of 4.2 pg/mm², which is comparable to other label-free optical biosensors. The simplicity and robustness of the method make it a compelling option for advancing biosensing technologies. This work contributes to the development of an imaging diffractometric biosensor with the potential for multiple applications in molecular interaction analysis.

Alkyne-tagged imidazolium-based membrane cholesterol analogs for Raman imaging applications.

Cholesterol is an important lipid playing a crucial role in mediating essential cellular processes as well as maintaining the basic structural integrity of biological membranes. Given its vast biological importance, there is an unabated need for sophisticated strategies to investigate cholesterol-mediated biological processes. Raman-tagged sterol analogs offer the advantage of being visualizable without the need for a bulky dye that potentially affects natural membrane integration and cellular interactions as it is the case for many conventionally used fluorescent analogs. Herein, we report a series of alkyne-tagged imidazolium-based cholesterol analogs (CHIMs) with large Raman scattering cross-sections that readily integrate into HEK cells and primary monocyte-derived macrophages and allow (multiplexed) cellular Raman imaging. We envision Raman-tagged CHIM analogs to be a powerful platform for the investigation of cholesterol-mediated cellular processes complementary to other established methods, such as the use of fluorescent analogs.

Estimation of biological variance in coherent Raman microscopy data of two cell lines using chemometrics.

Broadband Coherent Anti-Stokes Raman Scattering (BCARS) is a valuable spectroscopic imaging tool for visualizing cellular structures and lipid distributions in biomedical applications. However, the inevitable biological changes in the samples (cells/tissues/lipids) introduce spectral variations in BCARS data and make analysis challenging. In this work, we conducted a systematic study to estimate the biological variance in BCARS data of two commonly used cell lines (HEK293 and HepG2) in biomedical research. The BCARS data were acquired from two different experimental setups (Leibniz Institute of Photonics Technology (IPHT) in Jena and Politecnico di Milano (POLIMI) in Milano) to evaluate the reproducibility of results. Also, spontaneous Raman data were independently acquired at POLIMI to validate those results. First, Kramers-Kronig (KK) algorithm was utilized to retrieve Raman-like signals from the BCARS data, and a pre-processing pipeline was subsequently used to standardize the data. Principal component analysis - Linear discriminant analysis (PCA-LDA) was performed using two cross-validation (CV) methods: batch-out CV and 10-fold CV. Additionally, the analysis was repeated, considering different spectral regions of the data as input to the PCA-LDA. Finally, the classification accuracies of the two BCARS datasets were compared with the results of spontaneous Raman data. The results demonstrated that the CH band region (2770-3070 cm-1) and spectral data in the 1500-1800 cm-1 region have significantly contributed to the classification. A maximum of 100% balanced accuracies were obtained for the 10-fold CV for both BCARS setups. However, in the case of batch-out CV, it is 92.4% for the IPHT dataset and 98.8% for the POLIMI dataset. This study offers a comprehensive overview for estimating biological variance in biomedical applications. The insights gained from this analysis hold promise for improving the reliability of BCARS measurements in biomedical applications, paving the way for more accurate and meaningful spectroscopic analyses in the study of biological systems.

Raman Spectral Analysis in the CHx-Stretching Region as a Guiding Beacon for Non-Targeted, Disruption-Free Monitoring of Germination and Biofilm Formation in the Green Seaweed Ulva.

Raman spectroscopy was used to study the complex interactions and morphogenesis of the green seaweed Ulva (Chlorophyta) and its associated bacteria under controlled conditions in a reductionist model system. Integrating multiple imaging techniques contributes to a more comprehensive understanding of these biological processes. Therefore, Raman spectroscopy was introduced as a non-invasive, label-free tool for examining chemical information of the tripartite community Ulva mutabilis-Roseovarius sp.-Maribacter sp. The study explored cell differentiation, cell wall protrusion, and bacterial-macroalgae interactions of intact algal thalli. Using Raman spectroscopy, the analysis of the CHx-stretching wavenumber region distinguished spatial regions in Ulva germination and cellular malformations under axenic conditions and upon inoculation with a specific bacterium in bipartite communities. The spectral information was used to guide in-depth analyses within the fingerprint region and to identify substance classes such as proteins, lipids, and polysaccharides, including evidence for ulvan found in cell wall protrusions.

A Data Augmentation Methodology to Reduce the Class Imbalance in Histopathology Images.

Deep learning techniques have recently yielded remarkable results across various fields. However, the quality of these results depends heavily on the quality and quantity of data used during the training phase. One common issue in multi-class and multi-label classification is class imbalance, where one or several classes make up a substantial portion of the total instances. This imbalance causes the neural network to prioritize features of the majority classes during training, as their detection leads to higher scores. In the context of object detection, two types of imbalance can be identified: (1) an imbalance between the space occupied by the foreground and background and (2) an imbalance in the number of instances for each class. This paper aims to address the second type of imbalance without exacerbating the first. To achieve this, we propose a modification of the copy-paste data augmentation technique, combined with weight-balancing methods in the loss function. This strategy was specifically tailored to improve the performance in datasets with a high instance density, where instance overlap could be detrimental. To validate our methodology, we applied it to a highly unbalanced dataset focused on nuclei detection. The results show that this hybrid approach improves the classification of minority classes without significantly compromising the performance of majority classes.

MMIR: an open-source software for the registration of multimodal histological images.

BACKGROUND: Multimodal histology image registration is a process that transforms into a common coordinate system two or more images obtained from different microscopy modalities. The combination of information from various modalities can contribute to a comprehensive understanding of tissue specimens, aiding in more accurate diagnoses, and improved research insights. Multimodal image registration in histology samples presents a significant challenge due to the inherent differences in characteristics and the need for tailored optimization algorithms for each modality. RESULTS: We developed MMIR a cloud-based system for multimodal histological image registration, which consists of three main modules: a project manager, an algorithm manager, and an image visualization system. CONCLUSION: Our software solution aims to simplify image registration tasks with a user-friendly approach. It facilitates effective algorithm management, responsive web interfaces, supports multi-resolution images, and facilitates batch image registration. Moreover, its adaptable architecture allows for the integration of custom algorithms, ensuring that it aligns with the specific requirements of each modality combination. Beyond image registration, our software enables the conversion of segmented annotations from one modality to another.

Label-Free SERS and MD Analysis of Biomarkers for Rapid Point-of-Care Sensors Detecting Head and Neck Cancer and Infections.

For the progress of point-of-care medicine, where individual health status can be easily and quickly monitored using a handheld sensor, saliva serves as one of the best-suited body fluids thanks to its availability and abundance of physiological indicators. Salivary biomarkers, combined with rapid and highly sensitive detection tools, may pave the way to new real-time health monitoring and personalized preventative therapy branches using saliva as a target matrix. Saliva is increasing in importance in liquid biopsy, a non-invasive approach that helps physicians diagnose and characterize specific diseases in patients. Here, we propose a proof-of-concept study combining the unique specificity in biomolecular recognition provided by surface-enhanced Raman spectroscopy (SERS) in combination with molecular dynamics (MD) simulations, which give leave to explore the biomolecular absorption mechanism on nanoparticle surfaces, in order to verify the traceability of two validated salivary indicators, i.e., interleukin-8 (IL-8) and lysozyme (LYZ), implicated in oropharyngeal squamous cell carcinoma (OSCC) and oral infection. This strategy simultaneously assures the detection and interpretation of protein biomarkers in saliva, ultimately opening a new route for the evolution of fast and accurate point-of-care SERS-based sensors of interest in precision medicine diagnostics.

Raman Spectroscopy Profiling of Splenic T-Cells in Sepsis and Endotoxemia in Mice.

Sepsis is a life-threatening condition that results from an overwhelming and disproportionate host response to an infection. Currently, the quality and extent of the immune response are evaluated based on clinical symptoms and the concentration of inflammatory biomarkers released or expressed by the immune cells. However, the host response toward sepsis is heterogeneous, and the roles of the individual immune cell types have not been fully conceptualized. During sepsis, the spleen plays a vital role in pathogen clearance, such as bacteria by an antibody response, macrophage bactericidal capacity, and bacterial endotoxin detoxification. This study uses Raman spectroscopy to understand the splenic T-lymphocyte compartment profile changes during bona fide bacterial sepsis versus hyperinflammatory endotoxemia. The Raman spectral analysis showed marked changes in splenocytes of mice subjected to septic peritonitis principally in the DNA region, with minor changes in the amino acids and lipoprotein areas, indicating significant transcriptomic activity during sepsis. Furthermore, splenocytes from mice exposed to endotoxic shock by injection of a high dose of lipopolysaccharide showed significant changes in the protein and lipid profiles, albeit with interindividual variations in inflammation severity. In summary, this study provided experimental evidence for the applicability and informative value of Raman spectroscopy for profiling the immune response in a complex, systemic infection scenario. Importantly, changes within the acute phase of inflammation onset (24 h) were reliably detected, lending support to the concept of early treatment and severity control by extracorporeal Raman profiling of immunocyte signatures.

Feasibility studies of multimodal nonlinear endoscopy using multicore fiber bundles for remote scanning from tissue sections to bulk organs.

Here, we report on the development and application of a compact multi-core fiber optical probe for multimodal non-linear imaging, combining the label-free modalities of Coherent Anti-Stokes Raman Scattering, Second Harmonic Generation, and Two-Photon Excited Fluorescence. Probes of this multi-core fiber design avoid moving and voltage-carrying parts at the distal end, thus providing promising improved compatibility with clinical requirements over competing implementations. The performance characteristics of the probe are established using thin cryo-sections and artificial targets before the applicability to clinically relevant samples is evaluated using ex vivo bulk human and porcine intestine tissues. After image reconstruction to counteract the data's inherently pixelated nature, the recorded images show high image quality and morpho-chemical conformity on the tissue level compared to multimodal non-linear images obtained with a laser-scanning microscope using a standard microscope objective. Furthermore, a simple yet effective reconstruction procedure is presented and demonstrated to yield satisfactory results. Finally, a clear pathway for further developments to facilitate a translation of the multimodal fiber probe into real-world clinical evaluation and application is outlined.

Towards therapeutic drug monitoring of antibiotic levels - analyzing the pharmacokinetics of levofloxacin using DUV-resonance Raman spectroscopy.

Therapeutic drug monitoring (TDM) plays an important role in clinical practice. Here, pharmacokinetics has a decisive influence on the effective antibiotic concentration during treatment. Moreover, different kinetics exist for different administration forms. Accordingly, adjusting the correct concentration depends, in addition to gender, age, weight, clinical picture, etc., on the dosage form of the antibiotic. This study investigates the capability of deep UV resonance Raman spectroscopy (DUV-RRS) to simulate the pharmacokinetics of fluoroquinolone levofloxacin after two different administration forms (intravenous and oral). Three different pre-processing methods were applied, and the best agreement with the simulation was achieved using the extended multiplicative scatter correction. The resulting spectra were used for partial least squares (PLS) regression and ordinary least squares (OLS) regression. The kinetic parameters were compared with the simulated data, with PLS showing the best performance for intravenous administration and a comparable result to OLS for oral administration, while the errors are smaller. The acquired results show the potential of DUV-RRS in combination with PLS regression as a promising supportive method for TDM.

Ultrafast Spectral Tuning of a Fiber Laser for Time-Encoded Multiplex Coherent Raman Scattering Microscopy.

Coherent Raman scattering microscopy utilizing bioorthogonal tagging approaches like isotope or alkyne labeling allows for a targeted monitoring of spatial distribution and dynamics of small molecules of interest in cells, tissues, and other complex biological matrices. To fully exploit this approach in terms of real-time monitoring of several Raman tags, e.g., to study drug uptake dynamics, extremely fast tunable lasers are needed. Here, we present a laser concept without moving parts and fully electronically controlled for the quasi-simultaneous acquisition of coherent anti-Stokes Raman scattering images at multiple Raman resonances. The laser concept is based on the combination of a low noise and spectrally narrow Fourier domain mode-locked laser seeding a compact four wave mixing-based high-power fiber-based optical parametric amplifier.

Software tools and platforms in Digital Pathology: a review for clinicians and computer scientists.

At the end of the twentieth century, a new technology was developed that allowed an entire tissue section to be scanned on an objective slide. Originally called virtual microscopy, this technology is now known as Whole Slide Imaging (WSI). WSI presents new challenges for reading, visualization, storage, and analysis. For this reason, several technologies have been developed to facilitate the handling of these images. In this paper, we analyze the most widely used technologies in the field of digital pathology, ranging from specialized libraries for the reading of these images to complete platforms that allow reading, visualization, and analysis. Our aim is to provide the reader, whether a pathologist or a computational scientist, with the knowledge to choose the technologies to use for new studies, development, or research.

[Multimodal spectroscopic imaging : A new, powerful tool for intraoperative tumor diagnostics]. / Multimodale spektroskopische Bildgebung : Ein neues leistungsstarkes Werkzeug für die intraoperative Tumordiagnostik.

BACKGROUND: The increasing number of cancer cases requires new imaging approaches for intraoperative tumor characterization. OBJECTIVE: Utilization of new optical/photonic methods in combination with artificial intelligence (AI) approaches to address urgent challenges in clinical pathology in terms of intraoperative computational spectral histopathology. METHODS: Multimodal nonlinear imaging by combining the spectroscopic methods coherent anti-Stokes Raman scattering (CARS), two-photon excited autofluorescence (TPEF), fluorescence lifetime imaging microscopy (FLIM), and second harmonic generation (SHG). RESULTS: By using multimodal spectroscopic imaging, tissue morphochemistry, i.e., its morphology and molecular structure can be visualized in a label-free manner. The multimodal images can be automatically analyzed using AI-based image analysis approaches. For clinical application in terms of frozen section diagnostics or in vivo use, the presented multimodal imaging approach can be translated into a compact microscope or endoscopic probe concepts. CONCLUSIONS: The synergistic combination of spectroscopic imaging modalities in combination with automated data analysis has great potential for fast and precise tumor diagnostics e.g., in terms of precise surgical guidance in laser or robotic surgery. Overall, intraoperative multimodal spectroscopic imaging may represent an innovative advancement for tumor diagnostics in the future, directly leading to improved patient care and significant cost savings.

Identification of inflammatory markers in eosinophilic cells of the immune system: fluorescence, Raman and CARS imaging can recognize markers but differently.

Eosinophils (Eos) play an important role in the immune system's response releasing several inflammatory factors and contributing to allergic rhinitis, asthma, or atopic dermatitis. Since Eos have a relatively short lifetime after isolation from blood, usually eosinophilic cell line (EoL-1) is used to study mechanisms of their activation and to test therapies. In particular, EoL-1 cells are examined in terms of signalling pathways of the inflammatory response manifested by the presence of lipid bodies (LBs). Here we examined the differences in response to inflammation modelled by various factors, between isolated human eosinophils and EoL-1 cells, as manifested in the number and chemical composition of LBs. The analysis was performed using fluorescence, Raman, and coherent anti-Stokes Raman scattering (CARS) microscopy, which recognised the inflammatory process in the cells, but it is manifested slightly differently depending on the method used. We showed that unstimulated EoL-1 cells, compared to isolated eosinophils, contained more LBs, displayed different nucleus morphology and did not have eosinophilic peroxidase (EPO). In EoL-1 cells stimulated with various proinflammatory agents, including butyric acid (BA), liposaccharide (LPS), or cytokines (IL-1ß, TNF-α), an increased production of LBs with a various degree of lipid unsaturation was observed in spontaneous Raman spectra. Furthermore, stimulation of EoL-1 cells resulted in alterations of the LBs morphology. In conclusion, a level of lipid unsaturation and eosinophilic peroxidase as well as LBs distribution among cell population mainly accounted for the biochemistry of eosinophils upon inflammation.

Multimodal nonlinear endomicroscopic imaging probe using a double-core double-clad fiber and focus-combining micro-optical concept.

Multimodal non-linear microscopy combining coherent anti-Stokes Raman scattering, second harmonic generation, and two-photon excited fluorescence has proved to be a versatile and powerful tool enabling the label-free investigation of tissue structure, molecular composition, and correlation with function and disease status. For a routine medical application, the implementation of this approach into an in vivo imaging endoscope is required. However, this is a difficult task due to the requirements of a multicolour ultrashort laser delivery from a compact and robust laser source through a fiber with low losses and temporal synchronization, the efficient signal collection in epi-direction, the need for small-diameter but highly corrected endomicroobjectives of high numerical aperture and compact scanners. Here, we introduce an ultra-compact fiber-scanning endoscope platform for multimodal non-linear endomicroscopy in combination with a compact four-wave mixing based fiber laser. The heart of this fiber-scanning endoscope is an in-house custom-designed, single mode, double clad, double core pure silica fiber in combination with a 2.4 mm diameter NIR-dual-waveband corrected endomicroscopic objective of 0.55 numerical aperture and 180 µm field of view for non-linear imaging, allowing a background free, low-loss, high peak power laser delivery, and an efficient signal collection in backward direction. A linear diffractive optical grating overlays pump and Stokes laser foci across the full field of view, such that diffraction-limited performance is demonstrated for tissue imaging at one frame per second with sub-micron spatial resolution and at a high transmission of 65% from the laser to the specimen using a distal resonant fiber scanner.

Hydrogen and C2-C6 Alkane Sensing in Complex Fuel Gas Mixtures with Fiber-Enhanced Raman Spectroscopy.

Power-to-gas is a heavily discussed option to store surplus electricity from renewable sources. Part of the generated hydrogen could be fed into the gas grid and lead to fluctuations in the composition of the fuel gas. Consequently, both operators of transmission networks and end users would need to frequently monitor the gas to ensure safety as well as optimal and stable operation. Currently, gas chromatography-based analysis methods are the state of the art. However, these methods have several downsides for time-resolved and distributed application and Raman gas spectroscopy is favorable for future point-of-use monitoring. Here, we demonstrate that fiber-enhanced Raman gas spectroscopy (FERS) enables the simultaneous detection of all relevant gases, from major (methane, CH4; hydrogen, H2) to minor (C2-C6 alkanes) fuel gas components. The characteristic peaks of H2 (585 cm-1), CH4 (2917 cm-1), isopentane (765 cm-1), i-butane (798 cm-1), n-butane (830 cm-1), n-pentane (840 cm-1), propane (869 cm-1), ethane (993 cm-1), and n-hexane (1038 cm-1) are well resolved in the broadband spectra acquired with a compact spectrometer. The fiber enhancement achieved in a hollow-core antiresonant fiber enables highly sensitive measurements with limits of detection between 90 and 180 ppm for different hydrocarbons. Both methane and hydrogen were quantified with high accuracy with average relative errors of 1.1% for CH4 and 1.5% for H2 over a wide concentration range. These results show that FERS is ideally suited for comprehensive fuel gas analysis in a future, where regenerative sources lead to fluctuations in the composition of gas.