Isotope labeled 3D-Raman confocal imaging and atomic force microscopy study on epithelial cells interacting with the fungus Candida albicans.

The human pathogenic fungus Candida albicans damages epithelial cells during superficial infections. Here we use three-dimensional-sequential-confocal Raman spectroscopic imaging and atomic force microscopy to investigate the interaction of C. albicans wild type cells, the secreted C. albicans peptide toxin candidalysin and mutant cells lacking candidalysin with epithelial cells. The candidalysin responsible for epithelial cell damage and exhibits in its deuterated form an identifiable Raman signal in a frequency region distinct from the cellular frequency region. Vibration modes at 2100-2200 cm-1 attributed to carbon­deuterium bending and at 477 cm-1, attributed to the nitrogen­deuterium out-of-plane bending, found around the nucleus, can be assigned to deuterated candidalysin. Atomic force microscopy visualized 100 nm deep lesions on the cell and force-distance curves indicate the higher adhesion on pore surrounding after incubation with candidalysin. Candidalysin targets the plasma membrane, but is also found inside of the cytosol of epithelial cells during C. albicans infection.

Dispersion Analysis of Perpendicular Modes Using a Hybrid Two-Trace Two-Dimensional (2T2D) Smart Error Sum.

Using linear dichroism theory, one would assume that a z-cut of a uniaxial crystal is equivalent to an x-cut to determine the perpendicular component of the dielectric tensor and the corresponding oscillator parameters. However, Fresnel's equations show that the effect of interfaces in the form of the continuity relations of the different components of the electric field must be considered. A consequence of the continuity relations is that perpendicular modes increase less significantly in strength with increasing angle of incidence than expected. This is a consequence of the fact that it is the inverse of the perpendicular component of the dielectric function that increasingly becomes important with a growing angle of incidence. An inverse dielectric function, however, has typically much smaller values than the dielectric function. An additional consequence is that perpendicular modes are blueshifted and coupled in such a way that oscillator strength is transferred to the higher wavenumber mode. Thus, the spectral signatures of perpendicular modes are often weak and masked by the parallel modes when two modes overlap. Accordingly, to enable dispersion analysis, it is suggested to use a hybrid of the conventional residual sum of squares and the two-trace two-dimensional (2T2D) smart error sum, which can correct systematic multiplicable errors in the experimental spectrum. As demonstrated for fresnoite (Ba2TiSi2O8), this is an important step toward determining the perpendicular component of the dielectric tensor and the corresponding oscillator parameters using dispersion analysis, since asynchronous 2T2D correlation spectra are, in particular, sensitive to perpendicular modes.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.