SERS-based detection of the antibiotic ceftriaxone in spiked fresh plasma and microdialysate matrix by using silver-functionalized silicon nanowire substrates

Therapeutic drug monitoring (TDM) is an important tool in precision medicine as it allows estimating pharmacodynamic and pharmacokinetic effects of drugs in clinical settings. An accurate, fast and real-time determination of the drug concentrations in patients ensures fast decision-making processes at the bedside to optimize the clinical treatment. Surface-enhanced Raman spectroscopy (SERS), which is based on the application of metallic nanostructured substrates to amplify the inherent weak Raman signal, is a promising technique in medical research due to its molecular specificity and trace sensitivity accompanied with short detection times. Therefore, we developed a SERS-based detection scheme using silicon nanowires decorated with silver nanoparticles, fabricated by means of top-down etching combined with chemical deposition, to detect the antibiotic ceftriaxone (CRO) in spiked fresh plasma and microdialysis samples. We successfully detected CRO in both matrices with an LOD of 94 μM in protein-depleted fresh plasma and 1.4 μM in microdialysate.

Selection of ssDNA aptamers and construction of aptameric electrochemical biosensor for the detection of Giardia intestinalis trophozoite protein

Giardia intestinalis is one of the most widespread intestinal parasites and is considered a major cause of epidemic or sporadic diarrhea worldwide. In this study, we aimed to develop a rapid aptameric diagnostic technique for G. intestinalis infection. First, the SELEX (Systematic Evolution of Ligands by Exponential Enrichment) process generated DNA aptamers specific to a recombinant protein of the parasite's trophozoite. Ten selection rounds were performed; each round, the DNA library was incubated with the target protein conjugated to Sepharose beads. Then, the unbound sequences were removed by washing and the specific sequences were eluted and amplified by Polymerase Chain Reaction (PCR). Two aptamers were selected, and the dissociation constants (Kd), were determined as 2.45 and 16.95 nM, showed their high affinity for the G. intestinalis trophozoite protein. Subsequently, the aptamer sequence T1, which exhibited better affinity, was employed to develop a label-free electrochemical biosensor. A thiolated aptamer was covalently immobilized onto a gold screen-printed electrode (SPGE), and the binding of the targeted protein was monitored using square wave voltammetry (SWV). The developed aptasensor enabled accurate detection of the G. intestinalis recombinant protein within the range of 0.1 pg/mL to 100 ng/mL, with an excellent sensitivity (LOD of 0.35 pg/mL). Moreover, selectivity studies showed a negligible cross-reactivity toward other proteins such as bovine serum albumin, globulin, and G. intestinalis cyst protein.

Comprehensive multi-gas study by means of fiber-enhanced Raman spectroscopy for the investigation of nitrogen cycle processes

The extensive use of synthetic fertilizers has led to a considerable increase in reactive nitrogen input into agricultural and natural systems, resulting in negative effects in multiple ecosystems, the so-called nitrogen cascade. Since the global population relies on fertilization for food production, synthetic fertilizer use needs to be optimized by balancing crop yield and reactive nitrogen losses. Fiber-enhanced Raman spectroscopy (FERS) is introduced as a unique method for the simultaneous quantification of multiple gases to the study processes related to the nitrogen cycle. By monitoring changes in the headspace gas concentrations, processes such as denitrification, nitrification, respiration, and nitrogen fixation, as well as fertilizer addition were studied. The differences in concentration between the ambient and prepared process samples were evident in the Raman spectra, allowing for differentiation of process-specific spectra. Gas mixture concentrations were quantified within a range of low ppm to 100% for the gases N2, O2, CO2, N2O, and NH3. Compositional changes were attributed to processes of the nitrogen cycle. With help of multivariate curve resolution, it was possible to quantify N2O and CO2 simultaneously. The impact of fertilizers on N-cycle processes in soil was simulated and analyzed for identifying active processes. Thus, FERS was proven to be a suitable technique to optimize fertilizer composition and to quantify N2O and NH3 emissions, all with a single device and without further sample preparation.

Sophisticated Attenuated Total Reflection Correction Within Seconds for Unpolarized Incident Light at 45°

The most common mid-infrared (MIR) attenuated total reflection (ATR) accessory has a nominal angle of incidence of 45° and does not have a polarizer. A spectrum recorded with such an accessory does not hold enough information for the sophisticated ATR correction of MIR spectra with strong peaks, which are often strongly affected by refractive index changes due to anomalous dispersion. Here we show that a 45° ATR spectrum recorded without a polarizer and the polarization angle for the same ATR Fourier transform infrared spectroscopy system provide enough information to determine the ATR s-polarized spectrum. Further analysis with an improved non-iterative Kramers-Kronig analysis immediately yields the complex refractive index function. The analysis is about two orders of magnitude faster than iterative formalism and runs within seconds on a typical office PC. The effectiveness of our advanced ATR correction formalism is showcased through its application to water, employing diamond, ZnSe, and Ge ATR crystals, along with two distinct ATR accessories. Additionally, the formalism is applied to octadecane spectra. Potential sources of errors such as incidence angle spread, dispersion of the polarization angle, and the influence of reflection at the air/ATR crystal interface are investigated by simulations.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Siamese Networks for Clinically Relevant Bacteria Classification Based on Raman Spectroscopy

Identifying bacterial strains is essential in microbiology for various practical applications, such as disease diagnosis and quality monitoring of food and water. Classical machine learning algorithms have been utilized to identify bacteria based on their Raman spectra. However, convolutional neural networks (CNNs) offer higher classification accuracy, but they require extensive training sets and retraining of previous untrained class targets can be costly and time-consuming. Siamese networks have emerged as a promising solution. They are composed of two CNNs with the same structure and a final network that acts as a distance metric, converting the classification problem into a similarity problem. Classical machine learning approaches, shallow and deep CNNs, and two Siamese network variants were tailored and tested on Raman spectral datasets of bacteria. The methods were evaluated based on mean sensitivity, training time, prediction time, and the number of parameters. In this comparison, Siamese-model2 achieved the highest mean sensitivity of 83.61 ± 4.73 and demonstrated remarkable performance in handling unbalanced and limited data scenarios, achieving a prediction accuracy of 73%. Therefore, the choice of model depends on the specific trade-off between accuracy, (prediction/training) time, and resources for the particular application. Classical machine learning models and shallow CNN models may be more suitable if time and computational resources are a concern. Siamese networks are a good choice for small datasets and CNN for extensive data.

In Vivo Near-Infrared Fluorescence Resonance Energy Transfer (NIR-FRET) Imaging of MMP-2 in ALI/ARDS in LPS-Treated Mice

Matrix metalloproteinases (MMPs) are zinc-dependent proteinases that are capable of cleavage of extracellular matrix (ECM) proteins and enzymes and play an important role in lung dysfunction. Specifically, MMP-2 is produced in the lung by alveolar epithelial and endothelial cells and other immune cells, such as macrophages. MMP-2 regulatory pathway is initiated in alveolar macrophages during acute lung injury (ALI), which may increase pulmonary inflammation. Therefore, there is a critical need for fast and reliable techniques to track the acute respiratory distress syndrome (ARDS). Here, we describe near-infrared fluorescence resonance energy transfer (NI-FRET) MMP-2-based probe for the in vivo detection of ALI induced by lipopolysaccharides (LPS). LPS-induced MMP-2 was measured using near-infrared (NIR) imaging after 1, 2, 4, 5, and 24 h of LPS exposure. Our results were compared with the data obtained from ELISA and Western blotting, demonstrating that MMP-2 fluorescence probe provide a promising in vivo diagnostic tool for ALI/ARDS in infected mice.

Quantitative evaluation of IR and corresponding VCD spectra

Classical electromagnetic theory applied to infrared (IR) and vibrational circular dichroism (VCD) spectra of chiral compounds can provide useful insights, such as the fact that the area of all bands of wavenumber-normalized absorbance above zero must be the same as the area below zero. Additionally, dispersion analysis based on wave optics and dispersion theory, which was extended by Born and Kuhn to include chiral substances, can be used to quantitatively describe the dielectric function and the chiral admittance functions that shape IR and VCD spectra. For dispersion analysis, pairs of coupled oscillators, with five different kinds of parameters, namely oscillator strength, damping, oscillator position, vertical distance between coupled oscillators, and the coupling constant are used to model the dielectric functions and chiral admittance functions. We report the results of such an analysis for α-Pinene and Propylene oxide. For most bands, the oscillator model using two coupled oscillators is sufficient to achieve a good correspondence between experimental and modelled data.

Simulating a reference medium for determining bacterial growth in hospital wastewater for Raman spectroscopic investigation

Wastewater is a very complex and diverse medium, which despite low nutrient density still harbors bacteria. Especially the wastewater from hospitals contains a high germ load. However, wastewater is also very variable and differs not only from day to day, but also from house to house. Since wastewater is always changing and medium has an impact on Raman spectra of bacteria, it is necessary to find a surrogate material in which bacteria can be cultured to mimic a real hospital wastewater sample. In this study, we investigate two different artificial wastewaters for their abilities as a good alternative to real wastewater from the Jena University Hospital and to serve as a reference material for bacterial cultivation with subsequent Raman measurement. Each of the artificial wastewater on its own was not suitable to be used as a reference medium. Only the combination of the two simulated wastewaters achieved satisfactory results in the Raman spectroscopic identification of bacteria from real wastewater. These results could be used later in new experiments as a reference dataset to identify bacteria from real hospital wastewater samples.

The footprint of linear dichroism in infrared 2D-Correlation spectra

On the level of the Bouguer-Beer-Lambert approximation, the effects introduced by linear dichroism into absorbance spectra can be simulated by classic linear dichroism theory. If wave optics and dispersion theory are employed, linear dichroism can be modelled with a 4x4 matrix formalism. For linear dichroism theory, the angle between polarization direction and transition moment can be seen as a perturbation which allows to calculate corresponding infrared 2D correlation spectra. Similarly, with help of an orientation representation based on Euler's angles, varying the latter allows the same if electromagnetic theory is employed. Correspondingly, we compare the substantially different footprints of linear dichroism according to both theories in infrared 2D correlation spectra and show that only those based on wave optics and dispersion theory are in accordance with experimental results. Accordingly, in particular asynchronous 2D correlation spectra allow to detect orientation with a sensitivity that is unparalleled in case of conventional spectra, even if they are recorded with help of a polarizer and an analyzer.

Prussian blue (PB) modified gold nanoparticles as a SERS-based sensing platform for capturing and detection of pyrazinoic acid (POA)

Pyrazinoic acid (POA) is a metabolite of the anti-tuberculosis drug pyrazinamide (PZA), and its detection can be used to assess the resistance of Mycobacterium tuberculosis in cultures, as only sensitive strains of the bacteria can metabolize PZA into POA. Prussian blue is a well-known metal-organic framework compound widely used in various sensing platforms such as electrochemical, photochemical, and magnetic sensors. In this study, we present a novel sensing platform based on Prussian blue-modified gold nanoparticles (AuNPs) designed to enhance the affinity of POA towards the sensing surface and to capture POA molecules from aqueous solutions. This SERS-based method allows for the selective enrichment of POA, which can be detected in both pure aqueous solution and in the presence of its pro-drug PZA. The limit of detection (LOD) for POA was estimated to be 1.08 μM in pure aqueous solution and 0.18 mM in the presence of PZA. Furthermore, the precision of the SERS method was verified by the relative standard deviation (RSD) of 3.34-12.02% for three parallel samples using different matrices, i.e. aqueous solution, spiked river water and spiked simulated saliva. The recoveries of the samples ranged from 92.65 to 118.51%. These all demonstrate the potential application of the proposed detection scheme in medical research.

Unveiling chiral optical constants of α-pinene and propylene oxide through ATR and VCD spectroscopy in the mid-infrared range

Optical constants functions of analytes are indispensable for the effective design of plasmonic sensors. Such sensors are potentially able to enhance the sensitivity by several order of magnitudes which can greatly facilitate the determination of the generally weak spectral signals caused by vibrational circular dichroism. Accordingly, to demonstrate how to obtain these functions, we have determined the dielectric and chirality admittance functions of α-Pinene and Propylene oxide in the mid-infrared spectral range using attenuated total reflection and vibrational circular dichroism spectroscopy. Our iterative formalism starts with an estimation of the absorption index function, followed by the calculation of the refractive index function using the Kramers-Kronig relation and a modelled spectrum based on Fresnel's equations. By comparing the experimental and modelled spectra, we improve the absorption index function. To determine the chirality admittance function, we use the same iterative formalism, but with a modified 4x4 matrix formalism formulated by Berreman. Our results show that the experimental absorbance difference is independent of the dielectric function of the chiral substance and depends linearly on the cuvette thickness. Additionally, we provide a sum rule that can be used to assess the quality of VCD spectra and determine the position of the baseline. Our findings provide crucial insights into the optical properties of chiral substances in the mid-infrared spectral range, which have important implications for a range of applications in fields such as analytical chemistry and materials science.

Investigating the effect of different pre-treatment methods on Raman spectra recorded with different excitation wavelengths

Raman reference libraries can be used for identification of components in unknown samples as Raman spectroscopy offers fingerprint information of the measured samples. Since Raman libraries often contain many different and/or highly similar spectra, it is important that the spectra are a reliable fingerprint for each compound. However, Raman spectra are highly sensitive to the experimental conditions, and the Raman spectra will change in different conditions even though the same sample is measured. Raman data pre-treatment minimizes the differences between Raman spectra arising from different experimental conditions. In this study, different combinations of pre-treatment methods are used to quantify the effect of each pre-treatment step in minimizing the differences between Raman spectra of the same sample in different experimental conditions, e.g., different excitation wavelengths. These different pre-treatment processes are evaluated for six solvents. The spectra differences between spectra recorded with three excitation wavelengths (532 nm, 633 nm, and 830 nm) are evaluated by angular difference index and the influence on a classification model is tested. The angular difference index of each spectrum after every data pre-treatment step shows a decreasing behavior. It could be demonstrated that wavenumber calibration has the largest effect on the differences between the Raman spectra. However, ω4 correction doesn't have a significate effect in this dataset. The classification results show that the prediction accuracy is improving by doing data pre-treatment. In the dataset obtained in 633 nm a lower amount of pre-treatment steps is needed but in the dataset 830 nm more pre-treatment steps are needed for a high accuracy. The result shows that the choice of an optimal pre-treatment method or combination of methods strongly influences the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis.

Label-free biomedical optical imaging

Label-free optical imaging employs natural and nondestructive approaches for the visualisation of biomedical samples for both biological assays and clinical diagnosis. Currently, this field revolves around multiple broad technology-oriented communities, each with a specific focus on a particular modality despite the existence of shared challenges and applications. As a result, biologists or clinical researchers who require label-free imaging are often not aware of the most appropriate modality to use. This manuscript presents a comprehensive review of and comparison among different label-free imaging modalities and discusses common challenges and applications. We expect this review to facilitate collaborative interactions between imaging communities, push the field forward and foster technological advancements, biophysical discoveries, as well as clinical detection, diagnosis, and monitoring of disease.

Turning on hotspots: supracolloidal SERS probes made brilliant by an external activation mechanism

We achieved external activation of local hot-spot sites in supracolloidal assembly structures. The concept was demonstrated by boosting surface-enhanced Raman scattering (SERS) efficiency by one order of magnitude through a heating-induced process. Our approach involves assembling gold nanoparticles with distinct dimensions, i.e. 16 and 80 nm, into well-defined planet-satellite-type arrangement structures using thermoresponsive (poly(N-isopropylacrylamide)) star polymer linkers. Insights into the assembly process were obtained by calculations within the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory framework. We observe one order of magnitude increase in SERS enhancement by a heating-induced volume-phase transition. This magnification aligns with simulations run using the finite-difference time-domain (FDTD) method. The implications of this adaptive supracolloidal concept are twofold: Firstly, our approach bypasses limitations of existing systems that are associated with the limited accessibility of electromagnetic hot-spot sites in strongly coupled, static assemblies of plasmonic nanoparticles, by providing the capability of dynamic hot-spot re-configuration. Second, these externally activated probes offer promising opportunities for the development of messenger materials and associated sensing strategies.

Optical photothermal infrared spectroscopy and discrete wavenumber imaging for high content screening of single cells

Major drawbacks of direct mid-infrared spectroscopic imaging of single cells in an aqueous buffer are strong water absorption, low resolution typically above 10 μm, and Mie scattering effects. This study demonstrates how an indirect detection principle can overcome these drawbacks using the optical photothermal infrared (O-PTIR) technique for high-resolution discrete wavenumber imaging and fingerprint spectroscopy of cultivated cells as a model system in a simple liquid sample chamber. The O-PTIR spectra of six leukemia- and cancer-derived cell lines showed main IR bands near 1648, 1547, 1447, 1400, 1220, and 1088 cm-1. Five spectra of approximately 260 single cells per cell type were averaged, the O-PTIR data set was divided into leukemia-derived cells (THP-1, HL 60, Jurkat, and Raji) and cancer cells (HeLa and HepaRG), and partial least squares linear discriminant analysis (PLS-LDA) was applied in the spectral range 800-1800 cm-1 to train three classification models. A leukemia versus cancer cell model showed an accuracy of 90.0%, the HeLa versus HepaRG cell model had an accuracy of 95.4%, and the model for the distinction of leukemia cells had an accuracy of 75.4%. IR bands in linear discriminants (LDs) of the models were correlated with second derivative spectra that resolved more than 25 subbands. The IR and second derivative spectra of proteins, DNA, RNA and lipids were collected as references to confirm band assignments. O-PTIR images of single cells at a 200 nm step size were acquired at 1086, 1548, and 1746 cm-1 to visualize the nucleic acid, protein, and lipid distribution, respectively. Variations in subcellular features and in the lipid-to-protein and nucleic acid-to-protein ratios were identified that were consistent with biomolecular information in LDs. In conclusion, O-PTIR can provide high-quality spectra and images with submicron resolution of single cells in aqueous buffers that offer prospects in high-content screening applications.

Morpho-mechanical mapping of human dura mater microstructure

The human dura mater is known to impact vastly traumatic brain injury mechanopathology. In spite of this involvement, dura mater is typically neglected in computational and physical human head models. The lack of location-dependent microstructural and related mechanical data of dura mater may be considered a rationale behind this simplification. The anisotropic nature of dura mater under various loading conditions so far remains unelucidated. Furthermore, principal collagen fiber orientation is yet to be quantified for a morpho-mechanically-informed material model on the dura mater. This study aims to assess how location-dependent mechanical anisotropy is linked to principal collagen fiber orientation. Uniaxial extension tests were performed in a heated tissue bath for 60 samples from six individuals and correlated to the three-dimensional collagen structure in four individuals using second-harmonic generation (SHG) imaging. Failure stress and stretch at failure, elastic modulus, and a microstructurally motivated material model were integrated to examine local differences in dura mater morpho-mechanics. The quantitative observation of collagen fiber orientation and dispersion confirmed that collagen is highly aligned in the human dura mater and that both fiber orientation and dispersion differ depending on the location investigated. This observation provides a possible explanation for the previously observed isotropic mechanical behavior, as the main collagen fiber direction is not oriented along the anterior-posterior or medial-lateral direction at most of the mapped locations. Additionally, these site-dependent structural properties have implications for the mechanical load response and therefore potentially for the regional functions dura mater has to fulfill. The here chosen non-symmetrical fiber dispersion material model fits the data well and provides a comprehensive parameter base for further studies and future finite element models. STATEMENT OF SIGNIFICANCE: The human dura mater greatly affects traumatic brain injury mechanisms, but it is often ignored in computational and physical head models. This is because there is a lack of detailed microstructural and mechanical data specific to the dura mater. Its anisotropic nature and collagen fiber orientation have not been fully understood, hindering the development of an accurate material model. Hence, this study combines morphological data on collagen fiber orientation and dispersion at multiple locations of human cranial dura mater, and links microstructure to location-specific load-displacement behavior. It provides microstructurally informed mechanical information towards realistic head models for predicting location-dependent tissue behavior and failure for assessing brain injury and graft material development.

Investigating biochemical and structural changes of glycated collagen using multimodal multiphoton imaging, Raman spectroscopy, and atomic force microscopy

Advanced glycation end products (AGEs) form extracellular crosslinking with collagenous proteins, which contributes to the development of diabetic complications. In this study, AGEs-related pentosidine (PENT) crosslinks-induced structural and biochemical changes are studied using multimodal multiphoton imaging, Raman spectroscopy and atomic force microscopy (AFM). Decellularized equine pericardium (EP) was glycated with four ribose concentrations ranging between 5 and 200 mM and monitored for up to 30 days. Two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) microscopic imaging probed elastin and collagen fibers, respectively. The glycated EP showed a decrease in the SHG intensities associated with loss of non-centrosymmetry of collagen and an increase of TPEF intensities associated with PENT crosslinks upon glycation. TPEF signals from elastin fibers were unaffected. A three-dimensional reconstruction with SHG + TPEF z-stack images visualized the distribution of collagen and elastin within the EP volume matrix. In addition, Raman spectroscopy (RS) detected changes in collagen-related bands and discriminated glycated from untreated EP. Furthermore, AFM scans showed that the roughness increases and the D-unit structure of fibers remained unchanged during glycation. The PENT crosslinked-induced changes are discussed in the context of previous studies of glutaraldehyde- and genipin-induced crosslinking and collagenase-induced digestion of collagen. We conclude that TPEF, SHG, RS, and AFM are effective, label-free, and non-destructive methods to investigate glycated tissues, differentiate crosslinking processes, and characterize general collagen-associated and disease-related changes, in particular by their RS fingerprints.

Paper-Based Biosensor for the Detection of Sepsis Using MMP-9 Biomarker in FIP Mice Model

Sepsis is an immune response to a microbial invasion that causes organ injury and dysfunction due to a systemic inflammatory response. Sepsis is a serious, life-threatening condition and a widely recognized global health challenge. Given its high death rate, it is critical to diagnose sepsis and start treatment as early as possible. There is an urgent need for a sensitive and rapid screening method for detecting sepsis. In this study, we investigated the use of MMP-9 as a biomarker for sepsis. A colorimetric paper-based biosensor was used for the detection of MMP-9 utilizing peptide-magnetic nanoparticle conjugates. The method is based on the cleavage of the MMP-9-specific peptide by the protease leading to the detaching of the magnetic beads from the sensor surface and changing of color. A fecal intraperitoneal (FIP) challenge was used to induce sepsis in mice, and an MMP-9 secretion was measured by taking blood and Bronchoalveolar Lavage (BAL) fluid samples at 1 h, 2 h, 4 h, and 20 h (early sepsis) post-challenge intervals. The results of the paper-based sensor for the detection of MMP-9 levels in blood samples and BAL samples were compared with ELISA and Western Blot. We found that both blood and BAL levels of MMP-9 increased immediately and could be detected as early as 1 h in FIP mice post-challenge. Our work adds evidence to the assertion that MMP-9 is a reliable biomarker for the detection of sepsis at early stages.

Identification of bacteria in mixed infection from urinary tract of patient's samples using Raman analysis of dried droplets

Urinary tract infections (UTI) are among the most frequent nosocomial infections. A fast identification of the pathogen and assignment of Gram type could help to prescribe most suitable treatments. Raman spectroscopy holds high potential for fast and reliable bacterial pathogens identification. While most studies so far have focused on individual pathogens or artificial mixtures, this contribution aims to translate the analysis to primary urine samples from patients with suspected UTIs. For this, we have included 59 primary urine samples out of which 29 were diagnosed as mixed infections. For Raman analysis, we first trained two classification models based on principal component analysis - linear discriminant analysis (PCA-LDA) with more than 3500 Raman spectra of 85 clinical isolates from 23 species in order to (1) identify the Gram type of the bacteria and (2) assign family membership to one of the six most abundant bacterial families in urinary tract infections (Enterobacteriaceae, Morganellaceae, Pseudomonadaceae, Enterococcaceae, Staphylococcaceae and Streptococcaceae). The classification models were applied to artificial mixtures of Gram positive and Gram negative bacteria to correctly predict mixed infections with an accuracy of 75%. Raman scans of dried droplets did not yet yield optimal classification results on family level. When translating the method to primary urine samples, we observed a strong bias towards Gram negative bacteria, on family level towards Morganellaceae, which reduced prediction accuracy. Spectral differences were observed between isolates grown on standard growth medium and bacteria of the same strain when characterized directly from the patient. Thus, improvement of the classification accuracy is expected with a larger data base containing also bacteria measured directly from the urine sample.

Molecular in situ monitoring of the pH-triggered response in adaptive polymers by two-dimensional Raman micro-correlation-spectroscopy

Stimuli-responsive polymers can switch specific physical properties in response to a change of the environmental conditions. This behavior offers unique advantages in applications where adaptive materials are needed. To tune the properties of stimuli-responsive polymers, a detailed understanding of the relationship between the applied stimulus and changes in molecular structure as well as the relationship between the latter and macroscopic properties is required, which until now has required laborious methods. Here, we present a straightforward way to investigate the progressing trigger, the change of the chemical composition of the polymer and the macroscopic properties simultaneously. Thereby, the response behavior of the reversible polymer is studied in situ with molecular sensitivity and spatial as well as temporal resolution utilizing Raman micro-spectroscopy. Combined with two-dimensional correlation analysis (2DCOS), this method reveals the stimuli-response on a molecular level and determines the sequence of changes and the diffusion rate inside the polymer. Due to the label-free and non-invasive approach, it is furthermore possible to combine this method with the investigation of macroscopic properties revealing the response of the polymer to the external stimulus on both the molecular and the macroscopic level.

Comparison of denoising tools for the reconstruction of nonlinear multimodal images

Biophotonic multimodal imaging techniques provide deep insights into biological samples such as cells or tissues. However, the measurement time increases dramatically when high-resolution multimodal images (MM) are required. To address this challenge, mathematical methods can be used to shorten the acquisition time for such high-quality images. In this research, we compared standard methods, e.g., the median filter method and the phase retrieval method via the Gerchberg-Saxton algorithm with artificial intelligence (AI) based methods using MM images of head and neck tissues. The AI methods include two approaches: the first one is a transfer learning-based technique that uses the pre-trained network DnCNN. The second approach is the training of networks using augmented head and neck MM images. In this manner, we compared the Noise2Noise network, the MIRNet network, and our deep learning network namely incSRCNN, which is derived from the super-resolution convolutional neural network and inspired by the inception network. These methods reconstruct improved images using measured low-quality (LQ) images, which were measured in approximately 2 seconds. The evaluation was performed on artificial LQ images generated by degrading high-quality (HQ) images measured in 8 seconds using Poisson noise. The results showed the potential of using deep learning on these multimodal images to improve the data quality and reduce the acquisition time. Our proposed network has the advantage of having a simple architecture compared with similar-performing but highly parametrized networks DnCNN, MIRNet, and Noise2Noise.

Biosensing Platform for the Detection of Biomarkers for ALI/ARDS in Bronchoalveolar Lavage Fluid of LPS Mice Model

Acute respiratory distress syndrome (ARDS) is a worldwide health concern. The pathophysiological features of ALI/ARDS include a pulmonary immunological response. The development of a rapid and low-cost biosensing platform for the detection of ARDS is urgently needed. In this study, we report the development of a paper-based multiplexed sensing platform to detect human NE, PR3 and MMP-2 proteases. Through monitoring the three proteases in infected mice after the intra-nasal administration of LPS, we showed that these proteases played an essential role in ALI/ARDS. The paper-based sensor utilized a colorimetric detection approach based on the cleavage of peptide-magnetic nanoparticle conjugates, which led to a change in the gold nanoparticle-modified paper sensor. The multiplexing of human NE, PR3 and MMP-2 proteases was tested and compared after 30 min, 2 h, 4 h and 24 h of LPS administration. The multiplexing platform of the three analytes led to relatively marked peptide cleavage occurring only after 30 min and 24 h. The results demonstrated that MMP-2, PR3 and human NE can provide a promising biosensing platform for ALI/ARDS in infected mice at different stages. MMP-2 was detected at all stages (30 min-24 h); however, the detection of human NE and PR3 can be useful for early- (30 min) and late-stage (24 h) detection of ALI/ARDS. Further studies are necessary to apply these potential diagnostic biosensing platforms to detect ARDS in patients.

Design and test of a rigid endomicroscopic system for multimodal imaging and femtosecond laser ablation

Significance

Conventional diagnosis of laryngeal cancer is normally made by a combination of endoscopic examination, a subsequent biopsy, and histopathology, but this requires several days and unnecessary biopsies can increase pathologist workload. Nonlinear imaging implemented through endoscopy can shorten this diagnosis time, and localize the margin of the cancerous area with high resolution.

Aim

Develop a rigid endomicroscope for the head and neck region, aiming for in-vivo multimodal imaging with a large field of view (FOV) and tissue ablation.

Approach

Three nonlinear imaging modalities, which are coherent anti-Stokes Raman scattering, two-photon excitation fluorescence, and second harmonic generation, as well as the single photon fluorescence of indocyanine green, are applied for multimodal endomicroscopic imaging. High-energy femtosecond laser pulses are transmitted for tissue ablation.

Results

This endomicroscopic system consists of two major parts, one is the rigid endomicroscopic tube 250 mm in length and 6 mm in diameter, and the other is the scan-head (10 × 12 × 6    cm 3 in size) for quasi-static scanning imaging. The final multimodal image accomplishes a maximum FOV up to 650    μ m , and a resolution of 1    μ m is achieved over 560    μ m FOV. The optics can easily guide sub-picosecond pulses for ablation.

Conclusions

The system exhibits large potential for helping real-time tissue diagnosis in surgery, by providing histological tissue information with a large FOV and high resolution, label-free. By guiding high-energy fs laser pulses, the system is even able to remove suspicious tissue areas, as has been shown for thin tissue sections in this study.

Simple, Fast and Convenient Magnetic Bead-Based Sample Preparation for Detecting Viruses via Raman-Spectroscopy

We introduce a magnetic bead-based sample preparation scheme for enabling the Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2)-positive and -negative samples. The beads were functionalized with the angiotensin-converting enzyme 2 (ACE2) receptor protein, which is used as a recognition element to selectively enrich SARS-CoV-2 on the surface of the magnetic beads. The subsequent Raman measurements directly enable discriminating SARS-CoV-2-positive and -negative samples. The proposed approach is also applicable for other virus species when the specific recognition element is exchanged. A series of Raman spectra were measured on three types of samples, namely SARS-CoV-2, Influenza A H1N1 virus and a negative control. For each sample type, eight independent replicates were considered. All of the spectra are dominated by the magnetic bead substrate and no obvious differences between the sample types are apparent. In order to address the subtle differences in the spectra, we calculated different correlation coefficients, namely the Pearson coefficient and the Normalized cross correlation coefficient. By comparing the correlation with the negative control, differentiating between SARS-CoV-2 and Influenza A virus is possible. This study provides a first step towards the detection and potential classification of different viruses with the use of conventional Raman spectroscopy.

Raman Spectroscopic Imaging of Human Bladder Resectates towards Intraoperative Cancer Assessment

Raman spectroscopy offers label-free assessment of bladder tissue for in vivo and ex vivo intraoperative applications. In a retrospective study, control and cancer specimens were prepared from ten human bladder resectates. Raman microspectroscopic images were collected from whole tissue samples in a closed chamber at 785 nm laser excitation using a 20× objective lens and 250 µm step size. Without further preprocessing, Raman images were decomposed by the hyperspectral unmixing algorithm vertex component analysis into endmember spectra and their abundancies. Hierarchical cluster analysis distinguished endmember Raman spectra that were assigned to normal bladder, bladder cancer, necrosis, epithelium and lipid inclusions. Interestingly, Raman spectra of microplastic particles, pigments or carotenoids were detected in 13 out of 20 specimens inside tissue and near tissue margins and their identity was confirmed by spectral library surveys. Hypotheses about the origin of these foreign materials are discussed. In conclusion, our Raman workflow and data processing protocol with minimal user interference offers advantages for future clinical translation such as intraoperative tumor detection and label-free material identification in complex matrices.

High-throughput Raman spectroscopy allows ex vivo characterization of murine small intestinal intra-epithelial lymphocytes (IEL)

T cells are considered to be critical drivers of intestinal inflammation in mice and people. The so called intra-epithelial lymphocyte (IEL) compartment largely consist of T cells. Interestingly, the specific regulation and contribution of IELs in the context of inflammatory bowel disease remains poorly understood, in part due to the lack of appropriate analysis tools. Powerful, label-free methods could ultimately provide access to this cell population and hence give valuable insight into IEL biology and even more to their disease-related functionalities. Raman spectroscopy has demonstrated over the last few years its potential for reliable cell characterization and differentiation, but its utility in regard to IEL exploration remains unknown. To address this question experimentally, we utilized a murine, T cell-driven experimental model system which is accepted to model human gut inflammation. Here, we repopulated the small intestinal IEL compartment (SI IELs) of Rag1-deficient mice endogenously lacking T cells by transferring naïve CD4⁺ T helper cells intraperitoneally. Using multivariate statistical analysis, high-throughput Raman spectroscopy managed to define a cell subpopulation ex vivo within the SI IEL pool of mice previously receiving T cells in vivo that displayed characteristic spectral features of lymphocytes. Raman data sets matched flow cytometry analyses with the latter identifying T cell receptor (TCR)αβ⁺ CD4⁺ T cell population in SI IELs from T cell-transferred mice, but not from control mice, in an abundance comparable to the one detected by Raman spectroscopy. Hence, in this study, we provide experimental evidence for high-throughput Raman spectroscopy to be a novel, future tool to reliably identify and potentially further characterize the T cell pool of small intestinal IELs ex vivo.

SERS-based detection of 5-S-cysteinyl-dopamine as a novel biomarker of Parkinson's disease in artificial biofluids

The early detection of Parkinson's disease (PD) can significantly improve treatment and quality of life in patients. 5-S-Cysteinyl-dopamine (CDA) is a key metabolite of high relevance for the early detection of PD. Therefore, its sensitive detection with fast and robust methods can improve its use as a biomarker. In this work we show the potentialities of label-free SERS spectroscopy in detecting CDA in aqueous solutions and artificial biofluids, with a simple, fast and sensitive approach. We present a detailed experimental SERS band assignment of CDA employing silver nanoparticle (AgNP) substrates in aqueous media, which was supported by theoretical calculations and simulated Raman and SERS spectra. The tentative orientation of CDA over the AgNP was also studied, indicating that catechol and carboxylic acid play a key role in the metallic surface adsorption. Moreover, we showed that SERS can allow us to identify CDA in aqueous media at low concentration, leading to the identification of some of its characteristic bands in pure water and in synthetic cerebrospinal fluid (SCSF) below 1 × 10^-8 M, while its band identification in simulated urine (SUR) can be reached at 1 × 10^-7 M. In conclusion, we show that CDA can be suitably detected by means of label-free SERS spectroscopy, which can significantly improve its sensitive detection for further analytical studies as a novel biomarker and further clinical diagnosis in PD patients.

Dispersion related coupling effects in IR spectra on the example of water and Amide I bands

We discuss coupling effects in infrared spectra which are caused by dispersion and local field effects. The first effect is instigated by changes of the refractive index due to absorption which have an impact on the strength of adjacent absorptions. The second effect is a consequence of the light-induced polarization of one molecule affecting neighboring ones. These coupling effects do not only effect band positions, but also influence relative intensities. They are particularly strong in case of overlapping bands and complicate their deconvolution by band fitting. We investigated the corresponding challenges for the HO-stretching vibrations in water and the Amide I band in proteins. Our findings show that the effects are significant and of high interest for protein and water structure determination. Especially, for the water stretching vibrations we conclude that it is of utmost importance to consider such coupling effects in quantum mechanical calculations of water spectra. Otherwise, progress in understanding band positions and profiles is likely to be hampered. Also, in case of the Amide I band we found a distinct impact of such coupling effects. Accordingly, we strongly recommend consideration of dispersion and local field effects to ensure the possibility of an accurate, quantitative determination of α-helix and β-sheet structures.

Tuning the corona-core ratio of polyplex micelles for selective oligonucleotide delivery to hepatocytes or hepatic immune cells

Targeted delivery of oligonucleotides or small molecular drugs to hepatocytes, the liver's parenchymal cells, is challenging without targeting moiety due to the highly efficient mononuclear phagocyte system (MPS) of the liver. The MPS comprises Kupffer cells and specialized sinusoidal endothelial cells, efficiently clearing nanocarriers regardless of their size and surface properties. Physiologically, this non-parenchymal shield protects hepatocytes; however, these local barriers must be overcome for drug delivery. Nanocarrier structural properties strongly influence tissue penetration, in vivo pharmacokinetics, and biodistribution profile. Here we demonstrate the in vivo biodistribution of polyplex micelles formed by polyion complexation of short interfering (si)RNA with modified poly(ethylene glycol)-block-poly(allyl glycidyl ether) (PEG-b-PAGE) diblock copolymer that carries amino moieties in the side chain. The ratio between PEG corona and siRNA complexed PAGE core of polyplex micelles was chemically varied by altering the degree of polymerization of PAGE. Applying Raman-spectroscopy and dynamic in silico modeling on the polyplex micelles, we determined the corona-core ratio (CCR) and visualized the possible micellar structure with varying CCR. The results for this model system reveal that polyplex micelles with higher CCR, i.e., better PEG coverage, exclusively accumulate and thus allow passive cell-type-specific targeting towards hepatocytes, overcoming the macrophage-rich reticuloendothelial barrier of the liver.

Use of polymers as wavenumber calibration standards in deep-UVRR

Deep-UV resonance Raman spectroscopy (UVRR) allows the classification of bacterial species with high accuracy and is a promising tool to be developed for clinical application. For this attempt, the optimization of the wavenumber calibration is required to correct the overtime changes of the Raman setup. In the present study, different polymers were investigated as potential calibration agents. The ones with many sharp bands within the spectral range 400-1900 cm^-1 were selected and used for wavenumber calibration of bacterial spectra. Classification models were built using a training cross-validation dataset that was then evaluated with an independent test dataset obtained after 4 months. Without calibration, the training cross-validation dataset provided an accuracy for differentiation above 99 % that dropped to 51.2 % after test evaluation. Applying the test evaluation with PET and Teflon calibration allowed correct assignment of all spectra of Gram-positive isolates. Calibration with PS and PEI leads to misclassifications that could be overcome with majority voting. Concerning the very closely related and similar in genome and cell biochemistry Enterobacteriaceae species, all spectra of the training cross-validation dataset were correctly classified but were misclassified in test evaluation. These results show the importance of selecting the most suitable calibration agent in the classification of bacterial species and help in the optimization of the deep-UVRR technique.

Intestinal epithelial barrier integrity investigated by label-free techniques in ulcerative colitis patients

The intestinal epithelial barrier, among other compartments such as the mucosal immune system, contributes to the maintenance of intestinal homeostasis. Therefore, any disturbance within the epithelial layer could lead to intestinal permeability and promote mucosal inflammation. Considering that disintegration of the intestinal epithelial barrier is a key element in the etiology of ulcerative colitis, further assessment of barrier integrity could contribute to a better understanding of the role of epithelial barrier defects in ulcerative colitis (UC), one major form of chronic inflammatory bowel disease. Herein, we employ fast, non-destructive, and label-free non-linear methods, namely coherent anti-Stokes Raman scattering (CARS), second harmonic generation (SHG), two-photon excited fluorescence (TPEF), and two-photon fluorescence lifetime imaging (2P-FLIM), to assess the morpho-chemical contributions leading to the dysfunction of the epithelial barrier. For the first time, the formation of epithelial barrier gaps was directly visualized, without sophisticated data analysis procedures, by the 3D analysis of the colonic mucosa from severely inflamed UC patients. The results were compared with histopathological and immunofluorescence images and validated using transmission electron microscopy (TEM) to indicate structural alterations of the apical junction complex as the underlying cause for the formation of the epithelial barrier gaps. Our findings suggest the potential advantage of non-linear multimodal imaging is to give precise, detailed, and direct visualization of the epithelial barrier in the gastrointestinal tract, which can be combined with a fiber probe for future endomicroscopy measurements during real-time in vivo imaging.

Correcting systematic errors by hybrid 2D correlation loss functions in nonlinear inverse modelling

Recently a new family of loss functions called smart error sums has been suggested. These loss functions account for correlations within experimental data and force modeled data to obey these correlations. As a result, multiplicative systematic errors of experimental data can be revealed and corrected. The smart error sums are based on 2D correlation analysis which is a comparably recent methodology for analyzing spectroscopic data that has found broad application. In this contribution we mathematically generalize and break down this methodology and the smart error sums to uncover the mathematic roots and simplify it to craft a general tool beyond spectroscopic modelling. This reduction also allows a simplified discussion about limits and prospects of this new method including one of its potential future uses as a sophisticated loss function in deep learning. To support its deployment, the work includes computer code to allow reproduction of the basic results.

Nonlinear Optical Signal Generation Mediated by a Plasmonic Azimuthally Chirped Grating

Plasmonic gratings are simple and effective platforms for nonlinear signal generation since they provide a well-defined momentum for photon-plasmon coupling and local hot spots for frequency conversion. Here, a plasmonic azimuthally chirped grating (ACG), which provides spatially resolved broadband momentum for photon-plasmon coupling, was exploited to investigate the plasmonic enhancement effect in two nonlinear optical processes, namely two-photon photoluminescence (TPPL) and second harmonic generation (SHG). The spatial distributions of the nonlinear signals were determined experimentally by hyperspectral mapping with ultrashort pulsed excitation. The experimental spatial distributions of nonlinear signals agree very well with the analytical prediction based on photon-plasmon coupling with the momentum of the ACG, revealing the "antenna" function of the grating in plasmonic nonlinear signal generation. This work highlights the importance of the antenna effect of the gratings for nonlinear signal generation and provides insight into the enhancement mechanism of plasmonic gratings in addition to local hot spot engineering.

Photophysics of Anionic Bis(4H-imidazolato)CuI Complexes

In this paper, the photophysical behavior of four panchromatically absorbing, homoleptic bis(4H-imidazolato)Cu^I complexes, with a systematic variation in the electron-withdrawing properties of the imidazolate ligand, were studied by wavelength-dependent time-resolved femtosecond transient absorption spectroscopy. Excitation at 400, 480, and 630 nm populates metal-to-ligand charge transfer, intraligand charge transfer, and mixed-character singlet states. The pump wavelength-dependent transient absorption data were analyzed by a recently established 2D correlation approach. Data analysis revealed that all excitation conditions yield similar excited-state dynamics. Key to the excited-state relaxation is fast, sub-picosecond pseudo-Jahn-Teller distortion, which is accompanied by the relocalization of electron density onto a single ligand from the initially delocalized state at Franck-Condon geometry. Subsequent intersystem crossing to the triplet manifold is followed by a sub-100 ps decay to the ground state. The fast, nonradiative decay is rationalized by the low triplet-state energy as found by DFT calculations, which suggest perspective treatment at the strong coupling limit of the energy gap law.

Label-free multimodal imaging of infected Galleria mellonella larvae

Non-linear imaging modalities have enabled us to obtain unique morpho-chemical insights into the tissue architecture of various biological model organisms in a label-free manner. However, these imaging techniques have so far not been applied to analyze the Galleria mellonella infection model. This study utilizes for the first time the strength of multimodal imaging techniques to explore infection-related changes in the Galleria mellonella larvae due to massive E. faecalis bacterial infection. Multimodal imaging techniques such as fluorescent lifetime imaging (FLIM), coherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF), and second harmonic generation (SHG) were implemented in conjunction with histological HE images to analyze infection-associated tissue damage. The changes in the larvae in response to the infection, such as melanization, vacuolization, nodule formation, and hemocyte infiltration as a defense mechanism of insects against microbial pathogens, were visualized after Enterococcus faecalis was administered. Furthermore, multimodal imaging served for the analysis of implant-associated biofilm infections by visualizing biofilm adherence on medical stainless steel and ePTFE implants within the larvae. Our results suggest that infection-related changes as well as the integrity of the tissue of G. mellonella larvae can be studied with high morphological and chemical contrast in a label-free manner.

Optical photothermal infrared spectroscopy with simultaneously acquired Raman spectroscopy for two-dimensional microplastic identification

In recent years, vibrational spectroscopic techniques based on Fourier transform infrared (FTIR) or Raman microspectroscopy have been suggested to fulfill the unmet need for microplastic particle detection and identification. Inter-system comparison of spectra from reference polymers enables assessing the reproducibility between instruments and advantages of emerging quantum cascade laser-based optical photothermal infrared (O-PTIR) spectroscopy. In our work, IR and Raman spectra of nine plastics, namely polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, polycarbonate, polystyrene, silicone, polylactide acid and polymethylmethacrylate were simultaneously acquired using an O-PTIR microscope in non-contact, reflection mode. Comprehensive band assignments were presented. We determined the agreement of O-PTIR with standalone attenuated total reflection FTIR and Raman spectrometers based on the hit quality index (HQI) and introduced a two-dimensional identification (2D-HQI) approach using both Raman- and IR-HQIs. Finally, microplastic particles were prepared as test samples from known materials by wet grinding, O-PTIR data were collected and subjected to the 2D-HQI identification approach. We concluded that this framework offers improved material identification of microplastic particles in environmental, nutritious and biological matrices.

Infrared spectroscopy of quasi-ideal binary liquid mixtures: The challenges of conventional chemometric regression

We have recorded ATR-IR spectra of binary mixtures in the (quasi-)ideal systems Benzene-Toluene, Benzene-Carbon tetrachloride and Benzene-Cyclohexane and performed classical least squares, inverse least squares and principal component regression on the resulting spectra. In contrast to the general expectation, the spectra of ideal mixtures follow only roughly Beer's approximation, in particular stronger bands show shifts and increased intensities for intermediary compositions since the polarization of matter by light cannot be neglected. As a consequence, these conventional regression techniques lead to principle and unavoidable errors, even though some of the classical regression techniques are assumed to be able to cope with nonlinearities. In particular in the system Benzene-Carbon tetrachloride large errors result and the relative average error of the volume fraction determination is about 10 % for all three methods. Especially remarkable is that the multivariate regression methods do not perform better than the classical univariate calibration if for the latter a peak due to an oscillator with comparably low oscillator strength is selected, since for such bands polarization effects are weak and Beer's approximation holds comparably well.

Efficient Gene Delivery of Tailored Amphiphilic Polypeptides by Polyplex Surfing

Within this study, an amphiphilic and potentially biodegradable polypeptide library based on poly[(4-aminobutyl)-l-glutamine-stat-hexyl-l-glutamine] [P(AB-l-Gln-stat-Hex-l-Gln)] was investigated for gene delivery. The influence of varying proportions of aliphatic and cationic side chains affecting the physicochemical properties of the polypeptides on transfection efficiency was investigated. A composition of 40 mol% Hex-l-Gln and 60 mol % AB-l-Gln (P3) was identified as best performer over polypeptides with higher proportions of protonatable monomers. Detailed studies of the transfection mechanism revealed the strongest interaction of P3 with cell membranes, promoting efficient endocytic cell uptake and high endosomal release. Spectrally, time-, and z-resolved fluorescence microscopy further revealed the crucial role of filopodia surfing in polyplex-cell interaction and particle internalization in lamellipodia regions, followed by rapid particle transport into cells. This study demonstrates the great potential of polypeptides for gene delivery. The amphiphilic character improves performance over cationic homopolypeptides, and the potential biodegradability is advantageous toward other synthetic polymeric delivery systems.

Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates

Streptococcus pneumoniae, commonly referred to as pneumococci, can cause severe and invasive infections, which are major causes of communicable disease morbidity and mortality in Europe and globally. The differentiation of S. pneumoniae from other Streptococcus species, especially from other oral streptococci, has proved to be particularly difficult and tedious. In this work, we evaluate if Raman spectroscopy holds potential for a reliable differentiation of S. pneumoniae from other streptococci. Raman spectra of eight different S. pneumoniae strains and four other Streptococcus species (S. sanguinis, S. thermophilus, S. dysgalactiae, S. pyogenes) were recorded and their spectral features analyzed. Together with Raman spectra of 59 Streptococcus patient isolates, they were used to train and optimize binary classification models (PLS-DA). The effect of normalization on the model accuracy was compared, as one example for optimization potential for future modelling. Optimized models were used to identify S. pneumoniae from other streptococci in an independent, previously unknown data set of 28 patient isolates. For this small data set balanced accuracy of around 70% could be achieved. Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.

Label-free differentiation of clinical E. coli and Klebsiella isolates with Raman spectroscopy

Raman spectroscopy is a promising spectroscopic technique for microbiological diagnostics. In routine diagnostic, the differentiation of pathogens of the Enterobacteriaceae family remain challenging. In this study, Raman spectroscopy was applied for the differentiation of 24 clinical E. coli, Klebsiella pneumoniae and Klebsiella oxytoca isolates. Spectra were collected with two spectroscopic approaches: UV-Resonance Raman spectroscopy (UVRR) and single-cell Raman microspectroscopy with 532 nm excitation. A description of the different biochemical profiles provided by the different excitation wavelengths was performed followed by machine-learning models for the classification at the genus and species levels. UVRR was shown to outperform 532 nm excitation, enabling correct classification at the genus level of 23/24 isolates. Furthermore, for the first time, Klebsiella species were correctly classified at the species level with 92% accuracy, classifying all three K. oxytoca isolates correctly. These findings should guide future applicative studies, increasing the scope of Raman spectroscopy's suitability for clinical applications.

Boosting Efficiency in Light-Driven Water Splitting by Dynamic Irradiation through Synchronizing Reaction and Transport Processes

This work elaborates the effect of dynamic irradiation on light-driven molecular water oxidation to counteract deactivation. It highlights the importance of overall reaction engineering to overcome limiting factors in artificial photosynthesis reactions. Systematic investigation of a homogeneous three-component ruthenium-based water oxidation system revealed significant potential to enhance the overall catalytic efficiency by synchronizing the timescales of photoreaction and mass transport in a capillary flow reactor. The overall activity could be improved by a factor of more than 10 with respect to the turnover number and a factor of 31 referring to the external energy efficiency by controlling the local availability of photons. Detailed insights into the mechanism of light driven water oxidation could be obtained using complementary methods of investigation like Raman, IR, and UV/Vis/emission spectroscopy, unraveling the importance of avoiding high concentrations of excited photosensitizers.

Phenotypic Differentiation of Autotrophic and Heterotrophic Bacterial Cells Using Raman-D2O Labeling

Carbon cycling is one of the major biogeochemical processes driven by bacteria. Autotrophic bacteria convert carbon dioxide (CO₂) into organic compounds that are used by heterotrophs. Mixotrophic bacteria can employ both autotrophy and heterotrophy for growth. The characterization of the lifestyle of individual cells is essential to understand the microbial activity and thus reveal the implication of bacteria in the carbon flux. In this study, we used groundwater bacteria to investigate the potential of Raman-D₂O labeling in combination with chemometrics to identify the carbon assimilation strategies of bacteria. Classification models were built using principal component analysis (PCA) followed by linear discriminant analysis (LDA). Autotrophs assimilated a significantly higher amount (mean C-D ratio between 16.63 and 21.69%) of deuterium than heterotrophs. The C-D signal only provides information about the activity since it appears in the Raman-silent region, where no interference with the taxonomic information is expected. The classification between autotrophs and heterotrophs achieved an overall accuracy of 96.3%. In the validation step with an independent dataset containing species not included in the model, the PCA-LDA model achieved 100% accuracy. This demonstrated that the C-D signal contributed to the identification of autotrophic and heterotrophic bacterial cells. This work reports a robust, rapid, and nondestructive approach for the identification of single cells based on their carbon acquisition strategies. The present study foresees the potential of Raman-D₂O labeling as a promising method for automated discrimination of in situ functional activities of bacteria in environmental systems.

To generate a photonic nanojet outside a high refractive index microsphere illuminated by a Gaussian beam

A non-resonant, concentrated, narrow beam of light emerging from an illuminated microlens is called a photonic nanojet (PNJ). According to currently prevailing opinion, microspheres and microcylinders are only able to generate a PNJ in their exterior when their refractive index ns (or refractive index contrast) is less than 2. In this Letter we demonstrate that a PNJ can emerge from a microsphere even when ns > 2: first by employing the laws of geometrical optics for a divergent light source; then, by using ray transfer matrix analysis, a mathematical condition for the Gaussian beam (GB) outside the high ns microsphere is derived. The PNJ outside the microsphere with ns = 2.5 is simulated using Generalized Lorenz-Mie theory (GLMT), by using a front focused GB source. The simulated difference between front and back focusing on the dependence of ns is confirmed experimentally by Raman imaging. By opening the PNJ field for high refractive index materials, we believe this work will be a nucleus for new ideas in the field and enable new PNJ applications.

Revealing the Chemical Composition of Birch Pollen Grains by Raman Spectroscopic Imaging

The investigation of the biochemical composition of pollen grains is of the utmost interest for several environmental aspects, such as their allergenic potential and their changes in growth conditions due to climatic factors. In order to fully understand the composition of pollen grains, not only is an in-depth analysis of their molecular components necessary but also spatial information of, e.g., the thickness of the outer shell, should be recorded. However, there is a lack of studies using molecular imaging methods for a spatially resolved biochemical composition on a single-grain level. In this study, Raman spectroscopy was implemented as an analytical tool to investigate birch pollen by imaging single pollen grains and analyzing their spectral profiles. The imaging modality allowed us to reveal the layered structure of pollen grains based on the biochemical information of the recorded Raman spectra. Seven different birch pollen species collected at two different locations in Germany were investigated and compared. Using chemometric algorithms such as hierarchical cluster analysis and multiple-curve resolution, several components of the grain wall, such as sporopollenin, as well as the inner core presenting high starch concentrations, were identified and quantified. Differences in the concentrations of, e.g., sporopollenin, lipids and proteins in the pollen species at the two different collection sites were found, and are discussed in connection with germination and other growth processes.