Scattering correction for samples with cylindrical domains measured with polarized infrared spectroscopy.

Scattering artifacts are one of the most common effects distorting transmission spectra in Fourier-Transform Infrared spectroscopy. Their increased impact, strongly diminishing the quantitative and qualitative power of IR spectroscopy, is especially observed for structures with a size comparable to the radiation wavelength. To tackle this problem, a wide range of preprocessing techniques based on the Extended Multiplicative Scattering Correction method was developed, using physical properties to remove scattering presence in the spectra. However, until recently those algorithms were mostly focused on spherically shaped samples, for example, cells. Here, an algorithm for samples with cylindrical domains is described, with additional implementation of a linearly polarized light case, which is crucial for the growing field of polarized IR imaging and spectroscopy. An open-source code with GPU based implementation is provided, with a calculation time of several seconds per spectrum. Optimizations done to improve the throughput of this algorithm allow the application of this method into the standard preprocessing pipeline of small datasets.

Comprehensive Histopathology Imaging in Pancreatic Biopsies: High Definition Infrared Imaging with Machine Learning Approach.

Infrared (IR) based histopathology offers a new paradigm in looking at tissues and can provide a complimentary information source for more classical histopathology, which makes it a noteworthy tool given possible clinical application. This study aims to build a robust, pixel level machine learning model for pancreatic cancer detection using IR imaging. In this article, we report a pancreatic cancer classification model based on data from over 600 biopsies (coming from 250 patients) imaged with IR diffraction-limited spatial resolution. To fully research model's classification ability, we measured tissues using two optical setups, resulting in Standard and High Definitions data. This forms one of the largest IR datasets analyzed up to now, with almost 700 million spectra of different tissue types. The first six-class model created for comprehensive histopathology achieved pixel (tissue) level AUC values above 0.95, giving a successful technique for digital staining with biochemical information extracted from IR spectra.

Relationship between GABA-Ergic System and the Expression of Mephedrone-Induced Reward in Rats-Behavioral, Chromatographic and In Vivo Imaging Study.

Mephedrone is a psychoactive drug that increases dopamine, serotonin and noradrenaline levels in the central nervous system via interaction with transporters or monoamines. The aim of the presented study was to assess the role of the GABA-ergic system in the expression of mephedrone-induced reward. For this purpose, we conducted (a) a behavioral evaluation of the impact of baclofen (a GABAB receptors agonist) and GS39783 (a positive allosteric modulator of GABAB receptors) on the expression of mephedrone-induced conditioned place preference (CPP) in rats, (b) an ex vivo chromatographic determination of the GABA level in the hippocampi of rats subchronically treated with mephedrone and (c) an in vivo evaluation of GABA hippocampal concentration in rats subchronically administered with mephedrone using magnetic resonance spectroscopy (MRS). The results show that GS39783 (but not baclofen) blocked the expression of CPP induced by (20 mg/kg of) mephedrone. The behavioral effect was consistent with chromatographic analysis, which showed that mephedrone (5 and 20 mg/kg) led to a decrease in GABA hippocampal concentration. Altogether, the presented study provides a new insight into the involvement of the GABA-ergic system in the rewarding effects of mephedrone, implying that those effects are at least partially mediated through GABAB receptors, which suggests their potential role as new targets for the pharmacological management of mephedrone use disorder.

Super-Resolved 3D Mapping of Molecular Orientation Using Vibrational Techniques.

When a sample has an anisotropic structure, it is possible to obtain additional information controlling the polarization of incident light. With their straightforward instrumentation approaches, infrared (IR) and Raman spectroscopies are widely popular in this area. Single-band-based determination of molecular in-plane orientation, typically used in materials science, is here extended by the concurrent use of two vibration bands, revealing the orientational ordering in three dimension. The concurrent analysis was applied to IR spectromicroscopic data to obtain orientation angles of a model polycaprolactone spherulite sample. The applicability of this method spans from high-resolution, diffraction-limited Fourier transform infrared (FT-IR) and Raman imaging to super-resolved optical photothermal infrared (O-PTIR) imaging. Due to the nontomographic experimental approach, no image distortion is visible and nanometer scale orientation domains can be observed. Three-dimensional (3D) bond orientation maps enable in-depth characterization and consequently precise control of the sample's physicochemical properties and functions.

Visual Tract Degradation in Bilateral Normal-Tension Glaucoma-Cortical Thickness Maps and Volumetric Study of Visual Pathway Areas.

The aim of the study was to evaluate changes in the central visual pathways during the early and advanced stages of bilateral normal-tension glaucoma (NTG). METHODS: The studied groups constituted patients with bilateral normal-tension glaucoma of the same stage (n = 45) and age-matched healthy volunteers (n = 17). All patients underwent ophthalmic examination and examination on a 1.5 Tesla Magnetic Resonance Scanner (Optima 360, GE Healthcare). Volume and cortical thickness analyses were performed using the open-source automated software package FreeSurfer. RESULTS: There was a significant difference in lateral geniculate nuclei volume between the control and advanced glaucoma groups in the right hemisphere (p = 0.03) and in the left hemisphere between the early and advanced glaucoma patients (p = 0.026). The optic chiasm volume differed significantly between the control and advanced NTG groups (p = 0.0003) and between early and advanced glaucoma patients (p = 0.004). Mean cortical thickness analysis revealed a significant increase in values in the advanced glaucoma group in the right Brodmann area 17 (BA17) (p = 0.007) and right BA18 (p = 0.049) as compared to early NTG. In the left BA18 area, the mean thickness of the cortex in the early glaucoma group was significantly lower than in the control group (p = 0.03). CONCLUSIONS: The increase in the grey matter thickness in the V1 region with more-advanced glaucoma stages may reflect compensatory hypertrophy. Additionally, the regions of the brain early affected during glaucoma with reduced thickness were the right lateral occipital gyrus and left lingual gyrus. The most prominent change during the course of glaucoma was the increase in grey matter thickness in the right cuneus.

Insight into Glutamatergic Involvement in Rewarding Effects of Mephedrone in Rats: In Vivo and Ex Vivo Study.

Mephedrone is a widely used drug of abuse, exerting its effects by interacting with monoamine transporters. Although this mechanism has been widely studied heretofore, little is known about the involvement of glutamatergic transmission in mephedrone effects. In this study, we comprehensively evaluated glutamatergic involvement in rewarding effects of mephedrone using an interdisciplinary approach including (1) behavioural study on effects of memantine (non-selective NMDA antagonist) on expression of mephedrone-induced conditioned place preference (CPP) in rats; (2) evaluation of glutamate concentrations in the hippocampus of rats following 6 days of mephedrone administration, using in vivo magnetic resonance spectroscopy (MRS); and (3) determination of glutamate levels in the hippocampus of rats treated with mephedrone and subjected to MRS, using ion-exchange chromatography. In the presented research, we confirmed priorly reported mephedrone-induced rewarding effects in the CPP paradigm and showed that memantine (5 mg/kg) was able to reverse the expression of this effect. MRS study showed that subchronic mephedrone administration increased glutamate level in the hippocampus when measured in vivo 24 h (5 mg/kg, 10 mg/kg and 20 mg/kg) and 2 weeks (5 mg/kg and 20 mg/kg) after last injection. Ex vivo chromatographic analysis did not show significant changes in hippocampal glutamate concentrations; however, it showed similar results as obtained in the MRS study proving its validity. Taken together, the presented study provides new insight into glutamatergic involvement in rewarding properties of mephedrone.

The Impact of Preprocessing Methods for a Successful Prostate Cell Lines Discrimination Using Partial Least Squares Regression and Discriminant Analysis Based on Fourier Transform Infrared Imaging.

Fourier transform infrared spectroscopy (FT-IR) is widely used in the analysis of the chemical composition of biological materials and has the potential to reveal new aspects of the molecular basis of diseases, including different types of cancer. The potential of FT-IR in cancer research lies in its capability of monitoring the biochemical status of cells, which undergo malignant transformation and further examination of spectral features that differentiate normal and cancerous ones using proper mathematical approaches. Such examination can be performed with the use of chemometric tools, such as partial least squares discriminant analysis (PLS-DA) classification and partial least squares regression (PLSR), and proper application of preprocessing methods and their correct sequence is crucial for success. Here, we performed a comparison of several state-of-the-art methods commonly used in infrared biospectroscopy (denoising, baseline correction, and normalization) with the addition of methods not previously used in infrared biospectroscopy classification problems: Mie extinction extended multiplicative signal correction, Eiler's smoothing, and probabilistic quotient normalization. We compared all of these approaches and their effect on the data structure, classification, and regression capability on experimental FT-IR spectra collected from five different prostate normal and cancerous cell lines. Additionally, we tested the influence of added spectral noise. Overall, we concluded that in the case of the data analyzed here, the biggest impact on data structure and performance of PLS-DA and PLSR was caused by the baseline correction; therefore, much attention should be given, especially to this step of data preprocessing.

Influence of interference effects on the spectral quality and histological classification by FT-IR imaging in transflection geometry.

Infrared (IR) imaging can be used for fast, accurate and non-destructive pathology recognition of biopsies when supported by machine learning algorithms. Transflection mode of measurements has the potential to be translated into the clinic due to economic reasons of large-scale imaging with the need for inexpensive substrates. Unfortunately, in this mode spectral distortions originating from light interference appear. Due to this fact transmission measurement mode is more frequently used in pathology recognition. Nevertheless, this measurement mode also is not devoid of spectral distortion effects like scattering. However, this effect is better understood and there are preprocessing algorithms to minimize it. In this work, we investigated the influence of interference effects on spectral quality of pancreatic tissues measured in transmission and transflection mode with Fourier tranform IR (FT-IR) microscopy using samples embedded with and without paraffin. The removal of paraffin leads to an altered magnitude of interference in transflection and provides a platform for a detailed analysis of its effect on the spectra of biological material, since the same sample is measured with different interference conditions. Moreover, the potential of transflection mode measurements in histological classification of analyzed samples was investigated and compared with classification results for transmission mode.

Dietary supplementation with Lactobacillus rhamnosus JB-1 restores brain neurochemical balance and mitigates the progression of mood disorder in a rat model of chronic unpredictable mild stress.

Major depressive disorder is a stress-related disease associated with brain metabolic dysregulation in the glutamine-glutamate/γ-aminobutyric acid (Gln-Glu/GABA) cycle. Recent studies have demonstrated that microbiome-gut-brain interactions have the potential to influence mental health. The hypothesis of this study was that Lactobacillus rhamnosus JB-1 (LR-JB1™) dietary supplementation has a positive impact on neuro-metabolism which can be quantified in vivo using magnetic resonance spectroscopy (MRS). A rat model of depressive-like disorder, chronic unpredictable mild stress (CUMS), was used. Baseline comparisons of MRS and behavior were obtained in a control group and in a stressed group subjected to CUMS. Of the 22 metabolites measured using MRS, stressed rats had significantly lower concentrations of GABA, glutamate, glutamine + glutathione, glutamate + glutamine, total creatine, and total N-acetylaspartate (tNAA). Stressed rats were then separated into 2 groups and supplemented with either LR-JB1™ or placebo and re-evaluated after 4 weeks of continued CUMS. The LR-JB1™ microbiotic diet restored these metabolites to levels previously observed in controls, while the placebo diet resulted in further significant decrease of glutamate, total choline, and tNAA. LR-JB1™ treated animals also exhibited calmer and more relaxed behavior, as compared with placebo treated animals. In summary, significant cerebral biochemical downregulation of major brain metabolites following prolonged stress were measured in vivo using MRS, and these decreases were reversed using a microbiotic dietary supplement of LR-JB1™, even in the presence of continued stress, which also resulted in a reduction of stress-induced behavior in a rat model of depressive-like disorder.

Identification of motives and barriers to physical activity of polish young mothers.

BACKGROUND: The purpose of the study was to determine the level of physical activity of young mothers living in rural and urban areas and their free time budget. The article also aims to indicate motivating factors and barriers encountered during physical activity. METHODS: A diagnostic survey method, including two survey IPAQ questionnaires - the long version and author's questionnaire, has been applied in the study. A representative sample consisted of 1064 young mothers who gave birth in 2017. Student T test and discriminate function analysis have been used in statistical analysis. RESULTS: No significant difference appeared between young mothers from rural areas and urban areas in the level of physical activity. At the same time, the authors of the study identified several smaller differences when analyzing particular aspects of physical activity. Young mothers from rural areas more often declared their participation in physical activity to improve their physical condition and reduce pain complaints. On the other hand, young mothers from urban areas emphasized that physical activity helps them to strengthen self-confidence and improve their position in the family. In case of the main barriers to physical activity, both group of respondents pointed to the lack of money and interest in occasional events. CONCLUSIONS: The main conclusion is that more efforts are needed is to strengthen family support and material status of young families. It is also important to eliminate infrastructure barriers, mostly in rural areas and raise awareness on the importance of health education.

Macromolecular Orientation in Biological Tissues Using a Four-Polarization Method in FT-IR Imaging.

Fourier transform infrared spectroscopy has emerged as a powerful tool for tissue specimen investigation. Its nondestructive and label-free character enables direct determination of biochemical composition of samples. Furthermore, the introduction of polarization enriches this technique by the possibility of molecular orientation study apart from purely quantitative analysis. Most of the molecular orientation studies focused on polymer samples with a well-defined molecular axis. Here, a four-polarization approach for Herman's in-plane orientation function and azimuthal angle determination was applied to a human tissue sample investigation for the first time. Attention was focused on fibrous tissues rich in collagen because of their cylindrical shape and established amide bond vibrations. Despite the fact that the tissue specimen contains a variety of molecules, the presented results of molecular ordering and orientation agree with the theoretical prediction based on sample composition and vibration directions.

Volume of Lateral Geniculate Nucleus in Patients with Glaucoma in 7Tesla MRI.

The aim of the study was to assess the volume of the lateral geniculate nucleus (LGN) in patients with open-angle glaucoma in 7Tesla MRI and to evaluate its relation to RNFL thickness and VF indices. MATERIAL AND METHODS: The studied group consisted of 20 open-angle glaucoma patients with bilaterally the same stage of glaucoma (11 with early glaucoma and nine with advanced glaucoma) and nine healthy volunteers from the Department of Diagnostics and Microsurgery of Glaucoma, Medical University of Lublin, Poland. Circumpapillary RNFL-thickness measurements were performed using OCT in all patients and visual fields were performed in the glaucoma group. A 7Tesla MRI was performed to assess the volume of both lateral geniculate bodies. RESULTS: The LGN volume varied significantly between groups from 122.1 ± 14.4 mm3 (right LGN) and 101.6 ± 13.3 mm3 (left LGN) in the control group to 80.2 ± 17.7 mm3 (right LGN) and 71.8 ± 14.2 mm3 (left LGN) in the advanced glaucoma group (right LGN p = 0.003, left LGN p = 0.018). However, volume values from early glaucoma: right LGN = 120.2 ± 26.5 mm3 and left LGN = 103.2 ± 28.0 mm3 differed significantly only from values from the advanced group (right LGN p = 0.006, left LGN p = 0.012), but not from controls (right LGN p = 0.998, left LGN p = 0.986). There were no significant correlations between visual field indices (MD (mean deviation) and VFI (visual field index)) and LGN volumes in both glaucoma groups. Significant correlations between mean RNFL (retinal nerve fiber layers) thickness and corresponding and contralateral LGN were observed for the control group (corresponding LGN: p = 0.064; contralateral LGN: p = 0.031) and early glaucoma (corresponding LGN: p = 0.017; contralateral LGN: p = 0.008), but not advanced glaucoma (corresponding LGN: p = 0.496; contralateral LGN: p = 0.258). CONCLUSIONS: The LGN volume decreases in the course of glaucoma. These changes are correlated with RNFL thickness in early stages of glaucoma and are not correlated with visual field indices.

Translation of an esophagus histopathological FT-IR imaging model to a fast quantum cascade laser modality.

The technical progress in fast quantum cascade laser (QCL) microscopy offers a platform where chemical imaging becomes feasible for clinical diagnostics. QCL systems allow the integration of previously developed FT-IR-based pathology recognition models in a faster workflow. The translation of such models requires a systematic approach, focusing only on the spectral frequencies that carry crucial information for discrimination of pathologic features. In this study, we optimize an FT-IR-based histopathological method for esophageal cancer detection to work with a QCL system. We explore whether the classifier's performance is affected by paraffin presence from tissue blocks compared to removing it chemically. Working with paraffin-embedded samples reduces preprocessing time in the lab and allows samples to be archived after analysis. Moreover, we test, whether the creation of a QCL model requires a preestablished FTIR model or can be optimized using solely QCL measurements.

Author Correction: Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Noise-free simulation of an FT-IR imaging hyperspectral dataset of pancreatic biopsy core bound by experiment.

A noise-free hyperspectral FT-IR imaging dataset of a pancreatic tissue core was simulated based on experimental data that allows to test the performance of various data analysis and processing algorithms. A set of experimental noise levels was also added and used for denoising approaches comparison, which due to the noise-free reference signal enables to truly observe signal distortion caused by different approaches.

Influence of denoising on classification results in the context of hyperspectral data: High Definition FT-IR imaging.

Owing to the high information content about the biochemical composition of the sample, the implementation of Fourier-Transform Infrared Spectroscopy (FT-IR) in the clinic is currently under investigation by many researchers. Cancer biology with the use of histopathological models is one of the most explored application areas. Most of the publications show sensitivity of the method to be above 90%, however, it is still often not enough for clinical standards. Robust denoising techniques with an optimized classification model allow to shorten the experimental acquisition times which still are a bottleneck for FT-IR translation into the clinic. The main premise of this work is to evaluate denoising impact on classification results using spectral techniques: Savitzky Golay (SG), Wavelets (WV), Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF); and spatial techniques: Deep Neural Network (DNN), Median Filter. Using denoising methods, especially MNF and PCA, gave significant improvement of the classification and prediction results. Moreover, the increase in pixel level accuracy for High Definition data (1.1 µm projected pixel size) was found to be dependent on the complexity of the histopathological class and reached even 43-44% level, while core level increase reached around 28%. Moreover, we investigated the impact of denoising methods on the spectral input to better understand the mechanism of such large improvement. The results presented here highlight the benefits and the importance of proper denoising for classification purposes of FT-IR imaging data.

General technical remarks on 1HMRS translational research in 7T.

PURPOSE: The aim of the work was to share the practical experience of preclinical and clinical proton magnetic resonance spectroscopy (1HMRS) studies conducted using a 7-Tesla magnetic field strength scanner, taking into account the specificity of both settings in the context of translational research. MATERIAL AND METHODS: 1HMRS volunteer studies conducted using a Discovery 950 GE 7T scanner, were carried out with PRESS sequence, and a VOI measuring 2.0 × 2.0 × 2.0 cm3 placed in the white matter at the parietal occipital lobe. Rodent spectra obtained using a 7T Bruker were measured with PRESS, with a VOI 2.0 × 2.0 × 5.5 mm3 placed over the hippocampus. RESULTS: 1HMRS data from humans and rats show that the brain spectra obtained in the same field are characterised by a similar neurochemical structure and spectral resolution. Spectra obtained from rats demonstrate the following metabolites: NAA, Glu, Gln, Ins, Cho, Cr, PCr, Tau, GABA, Lac, NAAG, and Asp. In turn, spectra from humans allowed estimation of the following metabolites: Ala, NAA, Glu, Gln, Ins, Cho, Cr, PCr, Tau, GABA, Lac, NAAG, and Asp. Signals from Gln, Glu with chemical shift around 2.4 ppm, from Cr, PCr, and GABA at 3 ppm, and signals from Cho and Tau at approximately 3.2 ppm, can be properly separated and estimated both in humans and in rats. CONCLUSIONS: These results are promising in terms of broadening the knowledge of many neurological diseases by inducing them on animal models and then transferring this knowledge to clinical practice. In spite of this, important distinctions in the technical aspects and methodological differences of high-field 1HMRS in both preclinical and clinical conditions should be taken into account.

Chemical exchange saturation transfer (CEST) as a new method of signal obtainment in magnetic resonance molecular imaging in clinical and research practice.

The work describes the physical basis of the chemical exchange saturation transfer (CEST) technique; it presents the beginnings of the implementation of the method and its possible applications. The principles of correct data acquisition and possible solutions used during the design of the CEST sequence are shown. The main problems related to data analysis are indicated, and an example Z-spectrum from in vivo study of the rat brain is introduced. Furthermore, the parameters related to spectrum analyses such as magnetisation transfer asymmetry (MTRasym) and amide proton transfer asymmetry (APTasym) are presented. In the following part, different types of the CEST method often mentioned in the literature are discussed. Subsequently, the possible applications of the CEST method in both clinical and experimental practice are described.

Denoising influence on discrete frequency classification results for quantum cascade laser based infrared microscopy.

Currently, there is great interest in bringing the application of IR spectroscopy into the clinic. This however will require a significant reduction in measurement time as Fourier Transform Infrared (FT-IR) imaging takes hours to days to scan a clinically relevant specimen. A potential remedy for this issue is the use of Quantum Cascade Laser Infrared (QCL IR) microscopy performed in Discrete Frequency (DF) mode for maximum speed gain. This gain could be furthermore improved by applying a proper denoising algorithm that takes into account the specific data structure. We have recently compared spectral and spatial denoising techniques in the context of Fourier Transform IR (FT-IR) imaging and showed that the optimal methods depend heavily on the exact data structure. In general multivariate denoising methods such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) are the most effective for a dataset containing multiple bands. Histologic classification of QCL IR images of pancreatic tissue using Random Forest was therefore performed to investigate which denoising schemes are the most optimal for such experimental data structure. This work is the first to show the effects of denoising on classification accuracy of QCL data and is likely to be transferable to other QCL microscopes and other modalities using DF imaging, e.g. AFM-IR or CARS/SRS imaging.

Comparison of spectral and spatial denoising techniques in the context of High Definition FT-IR imaging hyperspectral data.

The recent emergence of High Definition (HD) FT-IR and Quantum Cascade Laser (QCL) Microscopes elevated the IR imaging field very close to clinical timescales. However, the speed of acquisition and data quality are still the critical factors in reaching the clinic. Denoising offers aide in both aspects if performed properly. However, there is a lack of a direct comparison of the efficiency of denoising techniques in IR imaging in general. To achieve such comparison within a rigorous framework and obtaining the critical information about signal loss, a simulated dataset strongly bound by experimental parameters was created. Using experimental structural and spectral information and experimental noise levels data as an input for the simulation, a direct comparison of spatial (Fourier transform, Mean Filter, Weighted Mean Filter, Gauss Filter, Median Filter, spatial Wavelets and Deep Neural Networks) and spectral (Savitzky-Golay, Fourier transform, Principal Component Analysis, Minimum Noise Fraction and spectral Wavelets) denoising schemes was enabled. All of these techniques were compared on the simulated dataset, taking into account SNR gain, signal distortion and sensitivity to tuning parameters as comparison metrics. Later, the best techniques were applied to experimental data for validation. The results presented here clearly show the benefit of using hyperspectral denoising schemes such as PCA and MNF which outperform other methods.