Using Fourier transform IR spectroscopy to analyze biological materials

Vibrational microspectroscopy as a tool to unveil new chemotherapeutic strategies against osteosarcoma

Over the years, osteosarcoma therapy has had a significative improvement with the use of a multidrug regime strategy, increasing the survival rates from less than 20 % to circa 70 %. Different types of development of new antineoplastic agents are critical to achieve irreversible damage to cancer cells, while preserving the integrity of their healthy counterparts. In the present study, complexes with two and three Pd(II) centres linked by the biogenic polyamines: spermine (Pd2SpmCl4) and spermidine (Pd3Spd2Cl6) were tested against non-malignant (osteoblasts, HOb) and cancer (osteosarcoma, MG-63) human cell lines. Either alone or in combination according to the EURAMOS-1 protocol, they were used versus cisplatin as a drug reference. By evaluating the cytotoxic effects of both therapeutic approaches (single and drug combination) in HOb and MG-63 cell lines, the selective anti-tumoral potential is assessed. To understand the different treatments at a molecular level, Synchrotron Radiation Fourier Transform Infrared and Raman microspectroscopies were applied. Principal component analysis and hierarchical cluster analysis are applied to the vibrational data, revealing the major metabolic changes caused by each drug, which were found to rely on DNA, lipids, and proteins, acting as biomarkers of drug-to-cell impact. The main changes were observed for the B-DNA native conformation to either Z-DNA (higher in the presence of polynuclear complexes) or A-DNA (preferably after cisplatin exposure). Additionally, a higher effect upon variation in proteins content was detected in drug combination when compared to single drug administration proving the efficacy of the EURAMOS-1 protocol with the new drugs tested.

Vibrational spectroscopy coupled with machine learning sheds light on the cellular effects induced by rationally designed TLR4 agonists

In this work, we present the potential of Fourier transform infrared (FTIR) microspectroscopy to compare on whole cells, in an unbiased and untargeted way, the capacity of bacterial lipopolysaccharide (LPS) and two rationally designed molecules (FP20 and FP20Rha) to activate molecular circuits of innate immunity. These compounds are important drug hits in the development of vaccine adjuvants and tumor immunotherapeutics. The biological assays indicated that FP20Rha was more potent than FP20 in inducing cytokine production in cells and in stimulating IgG antibody production post-vaccination in mice. Accordingly, the overall significant IR spectral changes induced by the treatment with LPS and FP20Rha were similar, lipids and glycans signals being the most diagnostic, while the effect of the less potent molecule FP20 on cells resulted to be closer to control untreated cells. We propose here the use of FTIR spectroscopy supported by artificial intelligence (AI) to achieve a more holistic understanding of the cell response to new drug candidates while screening them in cells.

Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening

Infrared spectroscopy is a powerful technique for probing the molecular profiles of complex biofluids, offering a promising avenue for high-throughput in vitro diagnostics. While several studies showcased its potential in detecting health conditions, a large-scale analysis of a naturally heterogeneous potential patient population has not been attempted. Using a population-based cohort, here we analyze 5,184 blood plasma samples from 3,169 individuals using Fourier transform infrared (FTIR) spectroscopy. Applying a multi-task classification to distinguish between dyslipidemia, hypertension, prediabetes, type 2 diabetes, and healthy states, we find that the approach can accurately single out healthy individuals and characterize chronic multimorbid states. We further identify the capacity to forecast the development of metabolic syndrome years in advance of onset. Dataset-independent testing confirms the robustness of infrared signatures against variations in sample handling, storage time, and measurement regimes. This study provides the framework that establishes infrared molecular fingerprinting as an efficient modality for populational health diagnostics.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

Structural changes in amide I and amide II regions of PCOS women analyzed by ATR-FTIR spectroscopy

The etiology of PCOS is complex and frequently mis or undiagnosed, which may enhance morbidity and reduce the quality of life. Attenuated total reflection- Fourier transform infrared (ATR-FTIR) spectroscopy examines the structural fingerprints of the biochemical compounds and can provide distinct FTIR spectra of the PCOS cases and controls. The present study recruited 61 PCOS cases and 38 control women. The student's t-test was used to compare BMI, WHR, and lipid profile. The FTIR spectral region was compared among both groups using the Mann-Whitney U test and multivariate analysis involved principal component analysis (PCA) and hierarchical cluster analysis (HCA). FTIR spectra of different phenotypes of PCOS were also analyzed using multivariate analysis. In univariate analysis, PCOS women had significantly higher WHR (p = 0.007), BMI (p = 0.04), triglycerides (p = 0.04), and VLDL (p = 0.02) than the controls. The spectral regions of amide I (1700-1600 cm-1) and amide II (1580-1480 cm-1), were significantly greater in the PCOS group than in the controls (p < 0.01 and p < 0.001, respectively). The PCA and HCA revealed a distinct molecular fingerprint for phenotype A (PCOM + OA + HA) and phenotype B (HA + OA). Our study postulated that the spectral regions of amide I and amide II can distinguish between PCOS cases and control women and it may be used for the diagnosis of cases.

Fcg-Former: Identification of Functional Groups in FTIR Spectra Using Enhanced Transformer-Based Model

Deep learning (DL) is becoming more popular as a useful tool in various scientific domains, especially in chemistry applications. In the infrared spectroscopy field, where identifying functional groups in unknown compounds poses a significant challenge, there is a growing need for innovative approaches to streamline and enhance analysis processes. This study introduces a transformative approach leveraging a DL methodology based on transformer attention models. With a data set containing approximately 8677 spectra, our model utilizes self-attention mechanisms to capture complex spectral features and precisely predict 17 functional groups, outperforming conventional architectures in both functional group prediction accuracy and compound-level precision. The success of our approach underscores the potential of transformer-based methodologies in enhancing spectral analysis techniques.

Monolithic DNApatite: An Elastic Apatite with Sub-Nanometer Scale Organo-Inorganic Structures

Hydroxyapatite (HA) exhibits outstanding biocompatibility, bioactivity, osteoconductivity, and natural anti-inflammatory properties. Pure HA, ion-doped HA, and HA-polymer composites are investigated, but critical limitations such as brittleness remain; numerous efforts are being made to address them. Herein, the novel self-crystallization of a polymeric single-stranded deoxyribonucleic acid (ssDNA) without additional phosphate ions for synthesizing deoxyribonucleic apatite (DNApatite) is presented. The synthesized DNApatite, DNA1Ca2.2(PO4)1.3OH2.1, has a repetitive dual phase of inorganic HA crystals and amorphous organic ssDNA at the sub-nm scale, forming nanorods. Its mechanical properties, including toughness and elasticity, are significantly enhanced compared with those of HA nanorod, with a Young's modulus similar to that of natural bone.

Proteome profiling, biochemical and histological analysis of diclofenac-induced liver toxicity in Yersinia enterocolitica and Lactobacillus fermentum fed rat model: a comparative analysis

Diclofenac is a hepatotoxic non-steroidal anti-inflammatory drug (NSAID) that affects liver histology and its protein expression levels. Here, we studied the effect of diclofenac on rat liver when co-administrated with either Yersinia enterocolitica strain 8081 serotype O:8 biovar 1B (D*Y) or Lactobacillus fermentum strain 9338 (D*L). Spectroscopic analysis of stool samples showed biotransformation of diclofenac. When compared with each other, D*Y rats lack peaks at 1709 and 1198 cm-1, while D*L rats lack peaks at 1411 cm-1. However, when compared to control, both groups lack peaks at 1379 and 1170 cm-1. Assessment of serum biomarkers of hepatotoxicity indicated significantly altered activities of AST (D*Y: 185.65 ± 8.575 vs Control: 61.9 ± 2.607, D*L: 247.5 ± 5.717 vs Control: 61.9 ± 2.607), ALT (D*Y: 229.8 ± 6.920 vs Control: 70.7 ± 3.109, D*L: 123.75 ± 6.068 vs Control: 70.7 ± 3.109), and ALP (D*Y: 276.4 ± 18.154 vs Control: 320.6 ± 9.829, D*L: 298.5 ± 12.336 vs Control: 320.6 ± 9.829) in IU/L. The analysis of histological alterations showed hepatic sinusoidal dilation with vein congestion and cell infiltration exclusively in D*Y rats along with other histological changes that are common to both test groups, thereby suggesting more pronounced alterations in D*Y rats. Further, LC-MS/MS based label-free quantitation of proteins from liver tissues revealed 74.75% up-regulated, 25.25% down-regulated in D*Y rats and 51.16% up-regulated, 48.84% down-regulated in D*L experiments. The proteomics-identified proteins majorly belonged to metabolism, apoptosis, stress response and redox homeostasis, and detoxification and antioxidant defence that demonstrated the potential damage of rat liver, more pronounced in D*Y rats. Altogether the results are in favor that the administration of lactobacilli somewhat protected the rat hepatic cells against the diclofenac-induced toxicity.

Cysteamine Nanoemulsion Delivery by Inhalation to Attenuate Adverse Effects of Exposure to Cigarette Smoke: A Metabolomics Study in Wistar Rats

There is a pressing need for novel pharmacological interventions and drug delivery innovations to attenuate the cigarette smoke-associated oxidative stress and lung disease. We report here on the attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) and metabolomics of Wistar rats exposed to cigarette smoke for 28 days. The animals were treated for 15 days with plain cysteamine given orally or cysteamine as nanoemulsion given orally or via inhalation. The study design also included two control groups as follows: rats exposed to cigarette smoke but did not receive a treatment (diseased control group) and rats neither exposed to cigarette smoke nor a treatment (normal control group). The targeted metabolomics using Parallel Reaction Monitoring showed that in the diseased control group, ornithine, nicotinamide, xanthine, hypoxanthine, and caprolactam were increased compared with the normal control group. In addition, (±)8(9)-DiHET, which was initially downregulated in the diseased control group, exhibited a reversal of this trend with cysteamine nanoemulsion given via inhalation. The cysteamine nanoemulsion delivered by inhalation highlighted the importance of the route of drug administration for targeting the lungs. To the best of our knowledge, this is the first work to use ATR-FTIR and metabolomics in Wistar rat lung tissues, suggesting how cysteamine nanoemulsion can potentially reduce cigarette smoke-induced oxidative damage. The metabolites reported herein have potential implications for discovery of novel theranostics and, thus, to cultivate diagnostic and therapeutic innovation for early prevention and treatment of cigarette smoke-associated lung diseases.

Imidazolium-based ionic liquids disrupt saccharomyces cerevisiae cell membrane integrity

Ionic liquids (ILs) are interesting chemical compounds that have a wide range of industrial and scientific applications. They have extraordinary properties, such as the tunability of many of their physical properties and, accordingly, their activities; and the ease of synthesis methods. Hence, they became important building blocks in catalysis, extraction, electrochemistry, analytics, biotechnology, etc. This study determined antifungal activities of various imidazolium-based ionic liquids against yeast Saccharomyces cerevisiae via minimum inhibitory concentration (MIC) estimation method. Increasing the length of the alkyl group attached to the imidazolium cation, enhanced the antifungal activity of the ILs, as well as their ability of the disruption of the cell membrane integrity. FTIR studies performed on the S. cerevisiae cells treated with the ILs revealed alterations in the biochemical composition of these cells. Interestingly, the alterations in fatty acid content occurred in parallel with the increase in the activity of the molecules upon the increase in the length of the attached alkyl group. This trend was confirmed by statistical analysis and machine learning methodology. The classification of antifungal activities based on FTIR spectra of S. cerevisiae cells yielded a prediction accuracy of 83%, indicating the pharmacy and medicine industries could benefit from machine learning methodology. Furthermore, synthesized ionic compounds exhibit significant potential for pharmaceutical and medical applications.

Infrared microspectroscopy to elucidate the underlying biomolecular mechanisms of FLASH radiotherapy

BACKGROUND

FLASH-radiotherapy (FLASH-RT) is an emerging modality that uses ultra-high dose rates of radiation to enable curative doses to the tumor while preserving normal tissue. The biological studies showed the potential of FLASH-RT to revolutionize radiotherapy cancer treatments. However, the complex biological basis of FLASH-RT is not fully known yet.

AIM

Within this context, our aim is to get deeper insights into the biomolecular mechanisms underlying FLASH-RT through Fourier Transform Infrared Microspectroscopy (FTIRM).

METHODS

C57Bl/6J female mice were whole brain irradiated at 10 Gy with the eRT6-Oriatron system. 10 Gy FLASH-RT was delivered in 1 pulse of 1.8μs and conventional irradiations at 0.1 Gy/s. Brains were sampled and prepared for analysis 24 h post-RT. FTIRM was performed at the MIRAS beamline of ALBA Synchrotron. Infrared raster scanning maps of the whole mice brain sections were collected for each sample condition. Hyperspectral imaging and Principal Component Analysis (PCA) were performed in several regions of the brain.

RESULTS

PCA results evidenced a clear separation between conventional and FLASH irradiations in the 1800-950 cm-1 region, with a significant overlap between FLASH and Control groups. An analysis of the loading plots revealed that most of the variance accounting for the separation between groups was associated to modifications in the protein backbone (Amide I). This protein degradation and/or conformational rearrangement was concomitant with nucleic acid fragmentation/condensation. Cluster separation between FLASH and conventional groups was also present in the 3000-2800 cm-1 region, being correlated with changes in the methylene and methyl group concentrations and in the lipid chain length. Specific vibrational features were detected as a function of the brain region.

CONCLUSION

This work provided new insights into the biomolecular effects involved in FLASH-RT through FTIRM. Our results showed that beyond nucleic acid investigations, one should take into account other dose-rate responsive molecules such as proteins, as they might be key to understand FLASH effect.

Inspiring a convergent engineering approach to measure and model the tissue microenvironment

Understanding the molecular and physical complexity of the tissue microenvironment (TiME) in the context of its spatiotemporal organization has remained an enduring challenge. Recent advances in engineering and data science are now promising the ability to study the structure, functions, and dynamics of the TiME in unprecedented detail; however, many advances still occur in silos that rarely integrate information to study the TiME in its full detail. This review provides an integrative overview of the engineering principles underlying chemical, optical, electrical, mechanical, and computational science to probe, sense, model, and fabricate the TiME. In individual sections, we first summarize the underlying principles, capabilities, and scope of emerging technologies, the breakthrough discoveries enabled by each technology and recent, promising innovations. We provide perspectives on the potential of these advances in answering critical questions about the TiME and its role in various disease and developmental processes. Finally, we present an integrative view that appreciates the major scientific and educational aspects in the study of the TiME.

Label-free investigation of infected acute pyelonephritis tissue by FTIR microspectroscopy with unsupervised and supervised analytical methods

Acute pyelonephritis (AP) is a severe urinary tract infection (UTI) syndrome with a large population of patients worldwide. Current approaches to confirming AP are limited to urinalysis, radiological imaging methods and histological assessment. Fourier transform infrared (FTIR) microspectroscopy is a promising label-free modality that can offer information about both morphological and molecular pathologic alterations from biological tissues. Here, FTIR microspectroscopy serves to investigate renal biological histology of a rat model with AP and classify normal cortex, normal medulla and infected acute pyelonephritis tissues. The spectra were experimentally collected by FTIR with an infrared Globar source through raster scanning procedure. Unsupervised analysis methods, including integrating, clustering and principal component analysis (PCA) were performed on such spectra data to form infrared histological maps of entire kidney section. In comparison to Hematoxylin & Eosin-stained results of the adjacent tissue sections, these infrared maps were proved to enable the differentiation of the renal tissue types. The results of both integration and clustering indicated that the concentration of amide II decreases in the infected acute pyelonephritis tissues, with an increased presence of nucleic acids and lipids. By means of PCA, the infected tissue was linearly separated from normal ones by plotting confident ellipses with the score values of the first and second principal components. Moreover, supervised analysis was performed based on the supported vector machines (SVM). Normal cortex, normal medulla and infected acute pyelonephritis tissues were classified by SVM models with the best accuracy of 96.11% in testing dataset. In addition, these analytical methods were further employed on synchrotron-based FTIR spectra data and successfully form high-resolution infrared histological maps of glomerulus and necrotic cell mass. This work demonstrates that FTIR microspectroscopy will be a powerful manner to investigate AP tissue and differentiate infected tissue from normal tissue in a renal infected model system.

Optimization of mid-infrared noninvasive blood-glucose prediction model by support vector regression coupled with different spectral features

Mid-infrared spectral analysis of glucose in subcutaneous interstitial fluid has been widely employed as a noninvasive alternative to the standard blood-glucose detection requiring blood-sampling via skin-puncturing, but improving the confidence level of such a replacement remains highly desirable. Here, we show that with an innovative metric of attributes in measurements and data-management, a high accuracy in correlating the test results of our improved spectral analysis to those of the standard detection is accomplished. First, our comparative laser speckle contrast imaging of subcutaneous interstitial fluid in fingertips, thenar and hypothenar reveal that spectral measurements from hypothenar, with an attenuated total reflection Fourier transform infrared spectrometer, give much stronger signals than the stereotype measurements from fingertips. Second, we demonstrate that discriminative selection of the spectral locations and ranges, to minimize spectral interference and maximize signal-to-noise, are critically important. The optimal band is pinned at that between 1000 ± 3 cm-1 and1040 ± 3 cm-1. Third, we propose an individual exclusive prediction model by adopting the support vector regression analysis of the spectral data from four subjects. The average predicted coefficient of determination, root mean square error and mean absolute error of four subjects are 0.97, 0.21 mmol/L, 0.17 mmol/L, respectively, and the average probability of being in Zone A of the Clark error grid is 100.00 %. Additionally, we demonstrate with the Bland and Altman plot that our proposed model has the highest consistency with portable blood glucose meter detection method.

Comparative profiling of serum biomarkers and ATR-FTIR spectroscopy for differential diagnosis of patients with rheumatoid and psoriatic arthritis - a pilot study

BACKGROUND

Rheumatoid arthritis (RA) and psoriatic arthritis (PsA) are chronic inflammatory diseases in which innate and adaptive responses of the immune system are induced. RA and PsA have complex signaling pathways. Despite the differences in their clinical presentation, there is a great demand for fast and accurate diagnosis of diseases to implement treatment and plan an individual therapeutic strategy quickly. In this report, we present the results of differential diagnosis of patients with RA and PsA and healthy subjects (C, control group), allowing for reliable differentiation of groups of rheumatoid patients based on biochemical parameters, attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectra, and combined data sets.

MATERIALS AND METHODS

Biochemical analyses, ELISA (enzyme-linked immunosorbent assays), and multiplex assays were conducted for blood sera from patients with RA (n = 32), patients with PsA (n = 28), and the control group (n = 18). ATR-FTIR spectra were collected for lyophilized sera.

RESULTS

The combination of six biochemical parameters (WBC, ESR, RF, CRP, HCC-4/CCL16, and HMGB1/HMGB) allowed the development of the partial least squares discriminant analysis (PLS-DA) model with an overall accuracy (OA) of 80% for test samples. The best separation between RA, PsA, and the control group was obtained utilizing spectral data. Using the interval PLS algorithm (iPLS) specific spectral ranges were selected and a classifier characterized by OA value for test set equal to 88% was obtained. This parameter, for the hybrid PLS-DA model constructed using selected biochemical parameters and a significantly reduced number of spectral variables, reached the level of 84%.

CONCLUSIONS

PLS-DA models developed on the basis of spectral data enable effective differentiation of patients with RA, patients with PsA, and healthy subjects. They appeared to be insensitive to existing inflammation processes which opens interesting perspectives for new diagnostic tests and algorithms for identification of patients with RA and PsA.

Structural Mapping of Protein Aggregates in Live Cells Modeling Huntington's Disease

While protein aggregation is a hallmark of many neurodegenerative diseases, acquiring structural information on protein aggregates inside live cells remains challenging. Traditional microscopy does not provide structural information on protein systems. Routinely used fluorescent protein tags, such as Green Fluorescent Protein (GFP), might perturb native structures. Here, we report a counter-propagating mid-infrared photothermal imaging approach enabling mapping of secondary structure of protein aggregates in live cells modeling Huntington's disease. By comparing mid-infrared photothermal spectra of label-free and GFP-tagged huntingtin inclusions, we demonstrate that GFP fusions indeed perturb the secondary structure of aggregates. By implementing spectra with small spatial step for dissecting spectral features within sub-micrometer distances, we reveal that huntingtin inclusions partition into a β-sheet-rich core and a ɑ-helix-rich shell. We further demonstrate that this structural partition exists only in cells with the [RNQ+] prion state, while [rnq-] cells only carry smaller β-rich non-toxic aggregates. Collectively, our methodology has the potential to unveil detailed structural information on protein assemblies in live cells, enabling high-throughput structural screenings of macromolecular assemblies.

Colorimetric sensing for translational applications: from colorants to mechanisms

Colorimetric sensing offers instant reporting via visible signals. Versus labor-intensive and instrument-dependent detection methods, colorimetric sensors present advantages including short acquisition time, high throughput screening, low cost, portability, and a user-friendly approach. These advantages have driven substantial growth in colorimetric sensors, particularly in point-of-care (POC) diagnostics. Rapid progress in nanotechnology, materials science, microfluidics technology, biomarker discovery, digital technology, and signal pattern analysis has led to a variety of colorimetric reagents and detection mechanisms, which are fundamental to advance colorimetric sensing applications. This review first summarizes the basic components (e.g., color reagents, recognition interactions, and sampling procedures) in the design of a colorimetric sensing system. It then presents the rationale design and typical examples of POC devices, e.g., lateral flow devices, microfluidic paper-based analytical devices, and wearable sensing devices. Two highlighted colorimetric formats are discussed: combinational and activatable systems based on the sensor-array and lock-and-key mechanisms, respectively. Case discussions in colorimetric assays are organized by the analyte identities. Finally, the review presents challenges and perspectives for the design and development of colorimetric detection schemes as well as applications. The goal of this review is to provide a foundational resource for developing colorimetric systems and underscoring the colorants and mechanisms that facilitate the continuing evolution of POC sensors.

Infrared spectroscopy-based machine learning algorithms for rapid detection of Klebsiella pneumoniae isolated directly from patients' urine and determining its susceptibility to antibiotics

Among the most prevalent and detrimental bacteria causing urinary tract infections (UTIs) is Klebsiella (K.) pneumoniae. A rapid determination of its antibiotic susceptibility can enhance patient treatment and mitigate the spread of resistant strains. In this study, we assessed the viability of using infrared spectroscopy-based machine learning as a rapid and precise approach for detecting K. pneumoniae bacteria and determining its susceptibility to various antibiotics directly from a patient's urine sample. In this study, 2333 bacterial samples, including 636 K. pneumoniae were investigated using infrared micro-spectroscopy. The obtained spectra (27996spectra) were analyzed with XGBoost classifier, achieving a success rate exceeding 95 % for identifying K. pneumoniae. Moreover, this method allows for the simultaneous determination of K. pneumoniae susceptibility to various antibiotics with sensitivities ranging between 74 % and 81 % within approximately 40 min after receiving the patient's urine sample.

Sensitive Discrete Frequency Mid-Infrared Absorption Spectroscopy Using Digitally Referenced Detection

Broadly tunable mid-infrared (IR) lasers, including quantum cascade lasers (QCL), are an asset for vibrational spectroscopy wherein high-intensity, coherent illumination can target specific spectral bands for rapid, direct chemical detection with microscopic localization. These emerging spectrometers are capable of high measurement throughputs with large detector signals from the high-intensity lasers and fast detection speeds as short as a single laser pulse, challenging the decades old benchmarks of Fourier transform infrared spectroscopy. However, noise in QCL emissions limits the feasible acquisition time for high signal-to-noise ratio (SNR) data. Here, we present an implementation that is broadly compatible with many laser-based spectrometer and microscope designs to address these limitations by leveraging high-speed digitizers and dual detectors to digitally reference each pulse individually. Digitally referenced detection (DRD) is shown to improve measurement sensitivity, with broad spectral indifference, regardless of imbalance due to dissimilarities among system designs or component manufacturers. We incorporated DRD into existing instruments and demonstrated its generalizability: a spectrometer with a 10-fold reduction in spectral noise, a microscope with reduced pixel dwell times to as low as 1 pulse while maintaining SNR normally achieved when operating 8-fold slower, and finally, a spectrometer to measure vibrational circular dichroism (VCD) with a ∼ 4-fold reduction in scan times. The approach not only proves versatile and effective but can also be tailored for specific applications with minimal hardware changes, positioning it as a simple and promising module for spectrometer designs using lasers.

Advancements in medical research: Exploring Fourier Transform Infrared (FTIR) spectroscopy for tissue, cell, and hair sample analysis

BACKGROUND

Fourier Transform Infrared (FTIR) spectroscopy has emerged as a powerful analytical tool in medical research, offering non-invasive and precise examination of the molecular composition of biological samples. The primary objective of this review is to underscore the benefits of FTIR spectroscopy in medicinal research, emphasizing its ability to delineate molecular fingerprints and assist in the identification of biochemical structures and key peaks in biological samples.

METHODS

This review comprehensively explores the diverse applications of FTIR spectroscopy in medical investigations, with a specific focus on its utility in analyzing tissue, cells, and hair samples. Various sources, including Google Scholar, PubMed, WorledCat and Scopus, were utilized to conduct this comprehensive literature review.

RESULTS

Recent advancements showcase the versatility of FTIR spectroscopy in elucidating cellular and molecular processes, facilitating disease diagnostics, and enabling treatment monitoring. Notably, FTIR spectroscopy has found significant utility in clinical assessment, particularly in screening counterfeit medicines, owing to its user-friendly operation and minimal sample preparation requirements. Furthermore, customs officials can leverage this technique for preliminary analysis of suspicious samples.

CONCLUSION

This review aims to bridge a gap in the literature and serve as a valuable resource for future research endeavors in FTIR spectroscopy within the medical domain. Additionally, it presents fundamental concepts of FTIR spectroscopy and spectral data interpretation, highlighting its utility as a tool for molecular analysis using Mid-Infrared (MIR) radiation.

INSTRAS: INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation

Infrared (IR) spectroscopic imaging is of potentially wide use in medical imaging applications due to its ability to capture both chemical and spatial information. This complexity of the data both necessitates using machine intelligence as well as presents an opportunity to harness a high-dimensionality data set that offers far more information than today's manually-interpreted images. While convolutional neural networks (CNNs), including the well-known U-Net model, have demonstrated impressive performance in image segmentation, the inherent locality of convolution limits the effectiveness of these models for encoding IR data, resulting in suboptimal performance. In this work, we propose an INfrared Spectroscopic imaging-based TRAnsformers for medical image Segmentation (INSTRAS). This novel model leverages the strength of the transformer encoders to segment IR breast images effectively. Incorporating skip-connection and transformer encoders, INSTRAS overcomes the issue of pure convolution models, such as the difficulty of capturing long-range dependencies. To evaluate the performance of our model and existing convolutional models, we conducted training on various encoder-decoder models using a breast dataset of IR images. INSTRAS, utilizing 9 spectral bands for segmentation, achieved a remarkable AUC score of 0.9788, underscoring its superior capabilities compared to purely convolutional models. These experimental results attest to INSTRAS's advanced and improved segmentation abilities for IR imaging.

Portable spectroscopy, digital imaging colorimetry and multivariate statistical tools in contaminant identification: A case study of mint (Mentha) and basil (Ocimum basilicum)

The advent of portable Fourier-Transform Infrared (FTIR) and Raman spectrometers has revolutionized analysis capabilities, presenting the possibility of on-site contaminant identification without the need for specialized laboratory settings. Compared to laboratory instrumentation, portable spectroscopy is more prone to noise, and appropriate spectral processing procedures need to be established. This paper introduces a comprehensive methodology that integrates acquisition techniques, spectral analysis, and mathematical tools necessary for utilizing handheld spectrometers to diagnose plant contamination. It focuses on determining the efficacy of handheld FTIR, Raman spectroscopy, and digital imaging for detecting contaminants in two food plants, Basil (Ocimum basilicum) and Mint (Mentha). The study examines the impact of three pollutants: iron (II) sulphate (F e S O 4 ), zinc (II) sulphate (Z n S O 4 ), and copper (II) sulphate (C u S O 4 ), on these plants, but also the necessary amount of measurements to spot the pollutants' effects. Measurements were conducted at the start, after 24 hours, and after 48 hours of exposure, on both fresh and dried plant leaves, as well as in solution. Spectral effects of each of the pollutants were identified with the use of multivariate statistical process control techniques. With the help of the developed methodologies, researchers can identify in-situ contaminant effects, exposure times and run diagnostics directly on the leaf both in alive and dried plants.

Machine learning assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues

Significance

Label-free multimodal imaging methods that can provide complementary structural and chemical information from the same sample are critical for comprehensive tissue analyses. These methods are specifically needed to study the complex tumor-microenvironment where fibrillar collagen's architectural changes are associated with cancer progression. To address this need, we present a multimodal computational imaging method where mid-infrared spectral imaging (MIRSI) is employed with second harmonic generation (SHG) microscopy to identify fibrillar collagen in biological tissues.

Aim

To demonstrate a multimodal approach where a morphology-specific contrast mechanism guides a mid-infrared spectral imaging method to detect fibrillar collagen based on its chemical signatures.

Approach

We trained a supervised machine learning (ML) model using SHG images as ground truth collagen labels to classify fibrillar collagen in biological tissues based on their mid-infrared hyperspectral images. Five human pancreatic tissue samples (sizes are in the order of millimeters) were imaged by both MIRSI and SHG microscopes. In total, 2.8 million MIRSI spectra were used to train a random forest (RF) model. The remaining 68 million spectra were used to validate the collagen images generated by the RF-MIRSI model in terms of collagen segmentation, orientation, and alignment.

Results

Compared to the SHG ground truth, the generated MIRSI collagen images achieved a high average boundary F-score (0.8 at 4 pixels threshold) in the collagen distribution, high correlation (Pearson's R 0.82) in the collagen orientation, and similarly high correlation (Pearson's R 0.66) in the collagen alignment.

Conclusions

We showed the potential of ML-aided label-free mid-infrared hyperspectral imaging for collagen fiber and tumor microenvironment analysis in tumor pathology samples.

Discovery of Delirium Biomarkers through Minimally Invasive Serum Molecular Fingerprinting

Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.

Investigating the biochemical response of proton minibeam radiation therapy by means of synchrotron-based infrared microspectroscopy

The biology underlying proton minibeam radiation therapy (pMBRT) is not fully understood. Here we aim to elucidate the biological effects of pMBRT using Fourier Transform Infrared Microspectroscopy (FTIRM). In vitro (CTX-TNA2 astrocytes and F98 glioma rat cell lines) and in vivo (healthy and F98-bearing Fischer rats) irradiations were conducted, with conventional proton radiotherapy and pMBRT. FTIRM measurements were performed at ALBA Synchrotron, and multivariate data analysis methods were employed to assess spectral differences between irradiation configurations and doses. For astrocytes, the spectral regions related to proteins and nucleic acids were highly affected by conventional irradiations and the high-dose regions of pMBRT, suggesting important modifications on these biomolecules. For glioma, pMBRT had a great effect on the nucleic acids and carbohydrates. In animals, conventional radiotherapy had a remarkable impact on the proteins and nucleic acids of healthy rats; analysis of tumour regions in glioma-bearing rats suggested major nucleic acid modifications due to pMBRT.

A novel functionalized CuTi hybrid nanocomposites: facile one-pot mycosynthesis, characterization, antimicrobial, antibiofilm, antifouling and wastewater disinfection performance

BACKGROUND

The continuous progress in nanotechnology is rapid and extensive with overwhelming futuristic aspects. Through modernizing inventive synthesis protocols, a paradigm leapfrogging in novelties and findings are channeled toward fostering human health and sustaining the surrounding environment. Owing to the overpricing and jeopardy of physicochemical synthesizing approaches, the quest for ecologically adequate schemes is incontestable. By developing environmentally friendly strategies, mycosynthesis of nanocomposites has been alluring.

RESULTS

Herein, a novel architecture of binary CuO and TiO2 in nanocomposites form was fabricated using bionanofactory Candida sp., for the first time. For accentuating the structural properties of CuTi nanocomposites (CuTiNCs), various characterization techniques were employed. UV-Vis spectroscopy detected SPR at 350 nm, and XRD ascertained the crystalline nature of a hybrid system. However, absorption peaks at 8, 4.5, and 0.5 keV confirmed the presence of Cu, Ti and oxygen, respectively, in an undefined assemblage of polygonal-spheres of 15-75 nm aggregated in the fungal matrix of biomolecules as revealed by EDX, SEM and TEM. However, FTIR, ζ-potential and TGA reflected long-term stability (- 27.7 mV) of self-functionalized CuTiNCs. Interestingly, a considerable and significant biocide performance was detected at 50 µg/mL of CuTiNCs against some human and plant pathogens, compared to monometallic counterparts. Further, CuTiNCs (200 µg/mL) ceased significantly the development of Staphylococcus aureus, Pseudomonas aeruginosa and Candida albicans biofilms by 80.3 ± 1.4, 68.7 ± 3.0 and 55.7 ± 3.0%, respectively. Whereas, 64.63 ± 3.5 and 89.82 ± 4.3% antimicrofouling potentiality was recorded for 100 and 200 µg/ml of CuTiNCs, respectively; highlighting their destructive effect against marine microfoulers cells and decaying of their extracellular polymeric skeleton as visualized by SEM. Moreover, CuTiNCs (100 and 200 µg/ml) exerted significantly outstanding disinfection potency within 2 h by reducing the microbial load (i.e., total plate count, mold & yeast, total coliforms and faecal Streptococcus) in domestic and agricultural effluents reached >50%.

CONCLUSION

The synergistic efficiency provided by CuNPs and TiNPs in mycofunctionalized CuTiNCs boosted its recruitment as antiphytopathogenic, antibiofilm, antimicrofouling and disinfectant agent in various realms.

Understanding the transition to viable but non-culturable state in Escherichia coli W3110: a comprehensive analysis of potential spectrochemical biomarkers

The viable but non-culturable (VBNC) state is considered a survival strategy employed by bacteria to endure stressful conditions, allowing them to stay alive. Bacteria in this state remain unnoticed in live cell counts as they cannot proliferate in standard culture media. VBNC cells pose a significant health risk because they retain their virulence and can revive when conditions normalize. Hence, it is crucial to develop fast, reliable, and cost-effective methods to detect bacteria in the VBNC state, particularly in the context of public health, food safety, and microbial control assessments. This research examined the biomolecular changes in Escherichia coli W3110 induced into the VBNC state in artificial seawater under three different stress conditions (temperature, metal, and antibiotic). Initially, confirmation of VBNC cells under various stresses was done using fluorescence microscopy and plate counts. Subsequently, lipid peroxidation was assessed through the TBARS assay, revealing a notable increase in peroxidation end-products in VBNC cells compared to controls. ATR-FTIR spectroscopy and chemomometrics were employed to analyze biomolecular changes, uncovering significant spectral differences in RNA, protein, and nucleic acid concentrations in VBNC cells compared to controls. Notably, RNA levels increased, while protein and nucleic acid amounts decreased. ROC analyses identified the 995 cm- 1 RNA band as a consistent marker across all studied stress conditions, suggesting its potential as a robust biomarker for detecting cells induced into the VBNC state under various stressors.

Human erythrocytes under stress. Spectroscopic fingerprints of known oxidative mechanisms and beyond

In this work, we investigated the oxidative stress-related biochemical alterations in red blood cells (RBCs) and their membranes with the use of spectroscopic techniques. We aimed to show their great advantage for the in situ detection of lipid classes and secondary structures of proteins without the need for their extraction in the cellular environment. The exposition of the cells to peroxides, t-butyl hydroperoxide (tBOOH) or hydrogen peroxide (H2O2) led to different degradation processes encompassing the changes in the composition of membranes and structural modifications of hemoglobin (Hb). Our results indicated that tBOOH is generally a stronger oxidizing agent than H2O2 and this observation was congruent with the activity of superoxide and glutathione peroxidase. ATR-FTIR and Raman spectroscopies of membranes revealed that tBOOH caused primarily the partial loss and peroxidation of the lipids resulting in loss of the integrity of membranes. In turn, both peroxides induced several kinds of damage in the protein layer, including the partial decrease of their content and irreversible aggregation of spectrin, ankyrin, and membrane-bound globin. These changes were especially pronounced on the membrane surface where stress conditions induced the formation of β-sheets and intramolecular aggregates, particularly for tBOOH. Interestingly, nano-FTIR spectroscopy revealed the lipid peroxidative damage on the membrane surface in both cases. As far as hemoglobin was concerned, tBOOH and H2O2 caused the increase of the oxyhemoglobin species and conformational alterations of its polypeptide chain into β-sheets. Our findings confirm that applied spectroscopies effectively track the oxidative changes occurring in the structural components of red blood cells and the simplicity of conducting measurements and sample preparation can be readily applied to pharmacological and clinical studies.

Quantitative or qualitative biomolecular changes in blood serum composition induced by childhood obesity: A Fourier transform infrared examination

Childhood obesity (CO) negatively affects one in three children and stands as the fourth most common risk factor of health and well-being. Clarifying the molecular and structural modifications that transpire during the development of obesity is crucial for understanding its progression and devising effective therapies. The study was indeed conducted as part of an ongoing CO treatment trial, where data were collected from children diagnosed with CO before the initiation of non-drug treatment interventions. Our primary aim was to analyze the biochemical changes associated with childhood obesity, specifically focusing on concentrations of lipids, lipoproteins, insulin, and glucose. By comparing these parameters between the CO group (n = 60) and a control group of healthy children (n = 43), we sought to elucidate the metabolic differences present in individuals with CO. Our biochemical analyses unveiled lower LDL (low-density lipoproteins) levels and higher HDL (high-density lipoproteins), cholesterol, triglycerides, insulin, and glucose levels in CO individuals compared to controls. To scrutinize these changes in more detail, we employed Fourier transform infrared (FTIR) spectroscopy on the serum samples. Our results indicated elevated levels of lipids and proteins in the serum of CO, compared to controls. Additionally, we noted structural changes in the vibrations of glucose, β-sheet, and lipids in CO group. The FTIR technique, coupled with principal component analysis (PCA), demonstrated a marked differentiation between CO and controls, particularly in the FTIR region corresponding to amide and lipids. The Pearson test revealed a stronger correlation between biochemical data and FTIR spectra than between 2nd derivative FTIR spectra. Overall, our study provides valuable insights into the molecular and structural changes occurring in CO.

Identifying hypothermia death in a mouse model by ATR-FTIR

The identification of hypothermia death (HD) is difficult for cadavers, especially the distinction from death due to alternative causes. A large number of studies have shown that brown adipose tissue (BAT) plays critical roles in thermoregulation of mammals. In this study, BAT of mice was used for the discrimination of HD using attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). A modified mouse HD model conducted by Feeney DM was used in this study to obtain infrared spectra of BAT. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to establish discrimination models. The PLS-DA and OPLS-DA models exhibit prominent discriminative efficiency, and the accuracy of HD identification using fingerprint regions and ratios of absorption intensity is near 100% in both the calibration and validation sets. Our preliminary study suggests that BAT may be an extremely effective target tissue for identification of cadavers of HD, and ATR-FTIR spectra combined with chemometrics have also shown potential for cadaver identification in forensic practice in a fast and accurate manner.

Caenorhabditis elegans endorse bacterial nanocellulose fibers as functional dietary Fiber reducing lipid markers

Bacterial nanocellulose (BNC) is a promising dietary fiber with potential as a functional food additive. We evaluated BNC fibers (BNCf) in the Caenorhabditis elegans model to obtain insight into the BNCf's biointeraction with its gastrointestinal tract while reducing the variables of higher complex animals. BNCf were uptaken and excreted by worms without crossing the intestinal barrier, confirming its biosafety regarding survival rate, reproduction, and aging for concentrations up to 34 μg/ml BNCf. However, a slight decrease in the worms' length was detected. A possible nutrient shortage or stress produced by BNCf was discarded by measuring stress and chemotactic response pathways. Besides, we detected a lipid-lowering effect of BNCf in N2 C. elegans in normal and high-caloric diets. Oxidative damage was computed in N2 worms and Rac1/ced-10 mutants. The GTPase Rac1 is involved in neurological diseases, where its dysregulation enhances ROS production and neuronal damage. BNCf reduced the lipid oxidative markers produced by ROS species in this worm strain. Finally, we detected that BNCf activated the genetic expression of the immunological response and lipid catabolic process. These results strengthen the use of BNCf as a functional dietary fiber and encourage the potential treatment of neurological disease by modulating diet.

Exposure to environmental pharmaceuticals affects the macromolecular composition of mussels digestive glands

Human pharmaceuticals represent a major challenge in natural environment. A better knowledge on their mechanisms of action and adverse effects on cellular pathways is fundamental to predict long-term consequences for marine wildlife. The FTIRI Imaging (FTIRI) spectroscopy represents a vibrational technique allowing to map specific areas of non-homogeneous biological samples, providing a unique biochemical and ultrastructural fingerprint of the tissue. In this study, FTIRI technique has been applied, for the first time, to characterize (i) the chemical building blocks of digestive glands of Mytilus galloprovincialis, (ii) alterations and (iii) resilience of macromolecular composition, after a 14-days exposure to 0.5 µg/L of carbamazepine (CBZ), valsartan (VAL) and their mixture, followed by a 14-days recovery period. Spectral features of mussels digestive glands provided insights on composition and topographical distribution of main groups of biological macromolecules, such as proteins, lipids, and glycosylated compounds. Pharmaceuticals caused an increase in the total amount of protein and a significant decrease of lipids levels. Changes in macromolecular features reflected the modulation of specific molecular and biochemical pathways thus supporting our knowledge on mechanisms of action of such emerging pollutants. Overall, the applied approach could represent an added value within integrated strategies for the effects-based evaluation of environmental contaminants.

Biochemical analysis of Hyalomma dromedarii salivary glands and gut tissues using SR-FTIR micro-spectroscopy

Ticks are obligatory voracious blood feeders infesting diverse vertebrate hosts, that have a crucial role in the transmission of diverse pathogens that threaten human and animal health. The continuous emergence of tick-borne diseases due to combined worldwide climatic changes, human activities, and acaricide-resistant tick strains, necessitates the development of novel ameliorative tick control strategies such as vaccines. The synchrotron-based Fourier transform infrared micro-spectroscopy (SR-FTIR) is a bioanalytical microprobe capable of exploring the molecular chemistry within microstructures at a cellular or subcellular level and is considered as a nondestructive analytical approach for biological specimens. In this study, SR-FTIR analysis was able to explore a qualitative and semi-quantitative biochemical composition of gut and salivary glands of Hyalomma dromedarii (H. dromedarii) tick detecting differences in the biochemical composition of both tissues. A notable observation regarding Amide I secondary structure protein profile was the higher ratio of aggregated strands in salivary gland and beta turns in gut tissues. Regarding the lipid profile, there was a higher intensity of lipid regions in gut tissue when compared to salivary glands. This detailed information on the biochemical compositions of tick tissues could assist in selecting vaccine and/or control candidates. Altogether, these findings confirmed SR-FTIR spectroscopy as a tool for detecting differences in the biochemical composition of H. dromedarii salivary glands and gut tissues. This approach could potentially be extended to the analysis of other ticks that are vectors of important diseases such as babesiosis and theileriosis.

Structural characterization of amyloid aggregates with spatially resolved infrared spectroscopy

The self-assembly of proteins and peptides into ordered structures called amyloid fibrils is a hallmark of numerous diseases, impacting the brain, heart, and other organs. The structure of amyloid aggregates is central to their function and thus has been extensively studied. However, the structural heterogeneities between aggregates as they evolve throughout the aggregation pathway are still not well understood. Conventional biophysical spectroscopic methods are bulk techniques and only report on the average structural parameters. Understanding the structure of individual aggregate species in a heterogeneous ensemble necessitates spatial resolution on the length scale of the aggregates. Recent technological advances have led to augmentation of infrared (IR) spectroscopy with imaging modalities, wherein the photothermal response of the sample upon vibrational excitation is leveraged to provide spatial resolution beyond the diffraction limit. These combined approaches are ideally suited to map out the structural heterogeneity of amyloid ensembles. AFM-IR, which integrates IR spectroscopy with atomic force microscopy enables identification of the structural facets the oligomers and fibrils at individual aggregate level with nanoscale resolution. These capabilities can be extended to chemical mapping in diseased tissue specimens with submicron resolution using optical photothermal microscopy, which combines IR spectroscopy with optical imaging. This book chapter provides the basic premise of these novel techniques and provides the typical methodology for using these approaches for amyloid structure determination. Detailed procedures pertaining to sample preparation and data acquisition and analysis are discussed and the aggregation of the amyloid β peptide is provided as a case study to provide the reader the experimental parameters necessary to use these techniques to complement their research efforts.

Monitoring the efficacy of antibiotic therapy in febrile pediatric oncology patients with bacteremia using infrared spectroscopy of white blood cells-based machine learning

Bacteremia refers to the presence of bacteria in the bloodstream, which can lead to a serious and potentially life-threatening condition. In oncology patients, individuals undergoing cancer treatment have a higher risk of developing bacteremia due to a weakened immune system resulting from the disease itself or the treatments they receive. Prompt and accurate detection of bacterial infections and monitoring the effectiveness of antibiotic therapy are essential for enhancing patient outcomes and preventing the development and dissemination of multidrug-resistant bacteria. Traditional methods of infection monitoring, such as blood cultures and clinical observations, are time-consuming, labor-intensive, and often subject to limitations. This manuscript presents an innovative application of infrared spectroscopy of leucocytes of pediatric oncology patients with bacteremia combined with machine learning to diagnose the etiology of infection as bacterial and simultaneously monitor the efficacy of the antibiotic therapy in febrile pediatric oncology patients with bacteremia infections. Through the implementation of effective monitoring, it becomes possible to promptly identify any indications of treatment failure. This, in turn, indirectly serves to limit the progression of antibiotic resistance. The logistic regression (LR) classifier was able to differentiate the samples as bacterial or control within an hour, after receiving the blood samples with a success rate of over 95 %. Additionally, initial findings indicate that employing infrared spectroscopy of white blood cells (WBCs) along with machine learning is viable for monitoring the success of antibiotic therapy. Our follow up results demonstrate an accuracy of 87.5 % in assessing the effectiveness of the antibiotic treatment.

Decoding chronic rhinosinusitis: A metabolomics-based approach

BACKGROUND

Chronic rhinosinusitis (CRS) is a common and intractable disease in otorhinolaryngology, laying a heavy burden on healthcare systems. The worldwide researchers are making efforts to find solutions to this disease. Metabolomics has recently gained more and more traction, and might become a promising tool to unravel the complexity of CRS. This paper provides an overview of current studies on the metabolomics of various CRS subtypes.

METHODS

We conducted a comprehensive literature search in PubMed, Web of Science, EMBASE, Google Scholar, and Cochrane Library, up to May 25, 2023. Search strategies incorporated key terms such as "chronic rhinosinusitis" and "metabolomics" with relevant synonyms and MeSH terms. Titles and abstracts of 86 screened articles were assessed for relevance to CRS and metabolomics. Methodological robustness, data reliability, and relevance were considered for shortlisted articles.

RESULTS

After the refined process, a total of 26 articles were included in this study and sorted out by research themes, methodology and pivotal discoveries. These included studies identified the metabolic pathways and markers related to the pathophysiology in each subtype of CRS.

CONCLUSIONS

Metabolomics helps to shed light on the complexity of CRS. The mentioned findings highlight the importance of specific metabolic pathways and markers in understanding the pathophysiology of CRS. Despite that, challenges and future directions in metabolomics research for CRS would be worth being further explored.

Point-of-care diagnosis of tissue fibrosis: a review of advances in vibrational spectroscopy with machine learning

Histopathology is the gold standard for diagnosing fibrosis, but its routine use is constrained by the need for additional stains, time, personnel and resources. Vibrational spectroscopy is a novel technique that offers an alternative atraumatic approach, with short scan times, while providing metabolic and morphological data. This review evaluates vibrational spectroscopy for the assessment of fibrosis, with a focus on point-of-care capabilities. OVID Medline, Embase and Cochrane databases were systematically searched using PRISMA guidelines for search terms including vibrational spectroscopy, human tissue and fibrosis. Studies were stratified based on imaging modality and tissue type. Outcomes recorded included tissue type, machine learning technique, metrics for accuracy and author conclusions. Systematic review yielded 420 articles, of which 14 were relevant. Ten of these articles considered mid-infrared spectroscopy, three dealt with Raman spectroscopy and one with near-infrared spectroscopy. The metrics for detecting fibrosis were Pearson correlation coefficients ranging from 0.65-0.98; sensitivity from 76-100%; specificity from 90-99%; area under receiver operator curves from 0.83-0.98; and accuracy of 86-99%. Vibrational spectroscopy identified fibrosis in myeloproliferative neoplasms in bone, cirrhotic and hepatocellular carcinoma in liver, end-stage heart failure in cardiac tissue and following laser ablation for acne in skin. It also identified interstitial fibrosis as a predictor of early renal transplant rejection in renal tissue. Vibrational spectroscopic techniques can therefore accurately identify fibrosis in a range of human tissues. Emerging data show that it can be used to quantify, classify and provide data about the nature of fibrosis with a high degree of accuracy with potential scope for point-of-care use.

Inverted Transflection Spectroscopy of Live Cells Using Metallic Grating on Elevated Nanopillars

Water absorption of mid-infrared (MIR) radiation severely limits the options for vibrational spectroscopy of the analytes-including live biological cells-that must be probed in aqueous environments. While internal reflection elements, such as attenuated total reflection prisms and metasurfaces, partially overcome this limitation, such devices have their own limitations: ATR prisms are difficult to integrate with multiwell cell culture workflows, while metasurfaces suffer from a limited spectral range and small penetration depth into analytes. In this work, we introduce an alternative live cell biosensing platform based on metallic nanogratings fabricated on top of elevated dielectric pillars. For the MIR wavelengths that are significantly longer than the grating period, reflection-based spectroscopy enables broadband sensing of the analytes inside the trenches separating the dielectric pillars. Because the depth of the analyte twice-traversed by the MIR light excludes the highly absorbing thick water layer above the grating, we refer to the technique as inverted transflection spectroscopy (ITS). The analytic power of ITS is established by measuring a wide range of protein concentrations in solution, with the limit of detection in the single-digit mg mL-1. The ability of ITS to interrogate live cells that naturally wrap themselves around the grating is used to characterize their adhesion kinetic.

Comparative analysis of biofilm characterization of probiotic Escherichia coli

Biofilms are thought to play a vital role in the beneficial effects of probiotic bacteria. However, the structure and function of probiotic biofilms are poorly understood. In this work, biofilms of Escherichia coli (E. coli) Nissle 1917 were investigated and compared with those of pathogenic and opportunistic strains (E. coli MG1655, O157:H7) using crystal violet assay, confocal laser scanning microscopy, scanning electron microscopy and FTIR microspectroscopy. The study revealed significant differences in the morphological structure, chemical composition, and spatial heterogeneity of the biofilm formed by the probiotic E. coli strain. In particular, the probiotic biofilm can secrete unique phospholipid components into the extracellular matrix. These findings provide new information on the morphology, architecture and chemical heterogeneity of probiotic biofilms. This information may help us to understand the beneficial effects of probiotics for various applications.

Fast cancer imaging in pancreatic biopsies using infrared imaging

Pancreatic cancer, particularly Pancreatic ductal adenocarcinoma, remains a highly lethal form of cancer with limited early diagnosis and treatment options. Infrared (IR) spectroscopy, combined with machine learning, has demonstrated great potential in detecting various cancers. This study explores the translation of a diagnostic model from Fourier Transform Infrared to Quantum Cascade Laser (QCL) microscopy for pancreatic cancer classification. Furthermore, QCL microscopy offers faster measurements with selected frequencies, improving clinical feasibility. Thus, the goals of the study include establishing a QCL-based model for pancreatic cancer classification and creating a fast surgical margin detection model using reduced spectral information. The research involves preprocessing QCL data, training Random Forest (RF) classifiers, and optimizing the selection of spectral features for the models. Results demonstrate successful translation of the diagnostic model to QCL microscopy, achieving high predictive power (AUC = 98%) in detecting cancerous tissues. Moreover, a model for rapid surgical margin recognition, based on only a few spectral frequencies, is developed with promising differentiation between benign and cancerous regions. The findings highlight the potential of QCL microscopy for efficient pancreatic cancer diagnosis and surgical margin detection within clinical timeframes of minutes per surgical resection tissue.

Biospectroscopic fingerprinting phytotoxicity towards environmental monitoring for food security and contaminated site remediation

Human activities have resulted in severe environmental pollution since the industrial revolution. Phytotoxicity-based environmental monitoring is well known due to its sedentary nature, abundance, and sensitivity to environmental changes, which are essential preconditions to avoiding potential environmental and ecological risks. However, conventional morphological and physiological methods for phytotoxicity assessment mainly focus on descriptive determination rather than mechanism analysis and face challenges of labour and time-consumption, lack of standardized protocol and difficulties in data interpretation. Molecular-based tests could reveal the toxicity mechanisms but fail in real-time and in-situ monitoring because of their endpoint manner and destructive operation in collecting cellular components. Herein, we systematically propose and lay out a biospectroscopic tool (e.g., infrared and Raman spectroscopy) coupled with multivariate data analysis as a relatively non-destructive and high-throughput approach to quantitatively measure phytotoxicity levels and qualitatively profile phytotoxicity mechanisms by classifying spectral fingerprints of biomolecules in plant tissues in response to environmental stresses. With established databases and multivariate analysis, this biospectroscopic fingerprinting approach allows ultrafast, in situ and on-site diagnosis of phytotoxicity. Overall, the proposed protocol and validation of biospectroscopic fingerprinting phytotoxicity can distinguish the representative biomarkers and interrogate the relevant mechanisms to quantify the stresses of interest, e.g., environmental pollutants. This state-of-the-art concept and design broaden the knowledge of phytotoxicity assessment, advance novel implementations of phytotoxicity assay, and offer vast potential for long-term field phytotoxicity monitoring trials in situ.

A review of concepts and methods for FTIR imaging of biomarker changes in the post-stroke brain

Stroke represents a core area of study in neurosciences and public health due to its global contribution toward mortality and disability. The intricate pathophysiology of stroke, including ischemic and hemorrhagic events, involves the interruption in oxygen and nutrient delivery to the brain. Disruption of these crucial processes in the central nervous system leads to metabolic dysregulation and cell death. Fourier transform infrared (FTIR) spectroscopy can simultaneously measure total protein and lipid content along with a number of key biomarkers within brain tissue that cannot be observed using conventional techniques. FTIR imaging provides the opportunity to visualize this information in tissue which has not been chemically treated prior to analysis, thus retaining the spatial distribution and in situ chemical information. Here we present a review of FTIR imaging methods for investigating the biomarker responses in the post-stroke brain.

Supplementing probiotics during intermittent fasting proves more effective in restoring ileum and colon tissues in aged rats

This study aimed to explore the impact of SCD Probiotics supplementation on biomolecule profiles and histopathology of ileum and colon tissues during a 30-day intermittent fasting (IF) program. Male Sprague-Dawley rats, aged 24 months, underwent 18-h daily fasting and received 3 mL (1 × 108 CFU) of SCD Probiotics. The differences in biomolecule profiles were determined using FTIR Spectroscopy and two machine learning techniques, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM), which showed significant differences with high accuracy rates. Spectrochemical bands indicating alterations in lipid, protein and nucleic acid profiles in both tissues. The most notable changes were observed in the group subjected to both IF and SCD Probiotics, particularly in the colon. Both interventions, individually and in combination, decreased protein carbonylation levels. SCD Probiotics exerted a more substantial impact on membrane dynamics than IF alone. Additionally, both IF and SCD Probiotics were found to have protective effects on intestinal structure and stability by reducing mast cell density and levels of TNF-α and NF-κB expression in ileum and colon tissues, thus potentially mitigating age-related intestinal damage and inflammation. Furthermore, our results illustrated that while IF and SCD Probiotics individually instigate unique changes in ileum and colon tissues, their combined application yielded more substantial benefits. This study provides evidence for the synergistic potential of IF and SCD Probiotics in combating age-related intestinal alterations.

Fast screening using attenuated total reflectance- fourier transform infrared (ATR-FTIR) spectroscopy of patients based on D-dimer threshold value

Attenuated Total Reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy is an emerging technology in the medical field. Blood D-dimer was initially studied as a marker of the activation of coagulation and fibrinolysis. It is mainly used as a potential diagnosis screening test for pulmonary embolism or deep vein thrombosis but was recently associated with COVID-19 severity. This study aimed to evaluate the use of ATR-FTIR spectroscopy with machine learning to classify plasma D-dimer concentrations. The plasma ATR-FTIR spectra from 100 patients were studied through principal component analysis (PCA) and two supervised approaches: genetic algorithm with linear discriminant analysis (GA-LDA) and partial least squares with linear discriminant (PLS-DA). The spectra were truncated to the fingerprint region (1800-1000 cm-1). The GA-LDA method effectively classified patients according to D-dimer cutoff (≤0.5 μg/mL and >0.5 μg/mL) with 87.5 % specificity and 100 % sensitivity on the training set, and 85.7 % specificity, and 95.6 % sensitivity on the test set. Thus, we demonstrate that ATR-FTIR spectroscopy might be an important additional tool for classifying patients according to D-dimer values. ATR-FTIR spectral analyses associated with clinical evidence can contribute to a faster and more accurate medical diagnosis, reduce patient morbidity, and save resources and demand for professionals.

Rapid hyperspectral photothermal mid-infrared spectroscopic imaging from sparse data for gynecologic cancer tissue subtyping

Ovarian cancer detection has traditionally relied on a multi-step process that includes biopsy, tissue staining, and morphological analysis by experienced pathologists. While widely practiced, this conventional approach suffers from several drawbacks: it is qualitative, time-intensive, and heavily dependent on the quality of staining. Mid-infrared (MIR) hyperspectral photothermal imaging is a label-free, biochemically quantitative technology that, when combined with machine learning algorithms, can eliminate the need for staining and provide quantitative results comparable to traditional histology. However, this technology is slow. This work presents a novel approach to MIR photothermal imaging that enhances its speed by an order of magnitude. Our method significantly accelerates data collection by capturing a combination of highresolution and interleaved, lower-resolution infrared band images and applying computational techniques for data interpolation. We effectively minimize data collection requirements by leveraging sparse data acquisition and employing curvelet-based reconstruction algorithms. This approach enhances imaging speed without compromising image quality and ensures robust tissue segmentation. This method resolves the longstanding trade-off between imaging resolution and data collection speed, enabling the reconstruction of high-quality, high-resolution images from undersampled datasets and achieving a 10X improvement in data acquisition time. We assessed the performance of our sparse imaging methodology using a variety of quantitative metrics, including mean squared error (MSE), structural similarity index (SSIM), and tissue subtype classification accuracies, employing both random forest and convolutional neural network (CNN) models, accompanied by Receiver Operating Characteristic (ROC) curves. Our statistically robust analysis, based on data from 100 ovarian cancer patient samples and over 65 million data points, demonstrates the method's capability to produce superior image quality and accurately distinguish between different gynecological tissue types with segmentation accuracy exceeding 95%. Our work demonstrates the feasibility of integrating rapid MIR hyperspectral photothermal imaging with machine learning in enhancing ovarian cancer tissue characterization, paving the way for quantitative, label-free, automated histopathology. It represents a significant leap forward from traditional histopathological methods, offering profound implications for cancer diagnostics and treatment decision-making.

Wide-field mid-infrared hyperspectral imaging beyond video rate

Mid-infrared hyperspectral imaging has become an indispensable tool to spatially resolve chemical information in a wide variety of samples. However, acquiring three-dimensional data cubes is typically time-consuming due to the limited speed of raster scanning or wavelength tuning, which impedes real-time visualization with high spatial definition across broad spectral bands. Here, we devise and implement a high-speed, wide-field mid-infrared hyperspectral imaging system relying on broadband parametric upconversion of high-brightness supercontinuum illumination at the Fourier plane. The upconverted replica is spectrally decomposed by a rapid acousto-optic tunable filter, which records high-definition monochromatic images at a frame rate of 10 kHz based on a megapixel silicon camera. Consequently, the hyperspectral imager allows us to acquire 100 spectral bands over 2600-4085 cm-1 in 10 ms, corresponding to a refreshing rate of 100 Hz. Moreover, the angular dependence of phase matching in the image upconversion is leveraged to realize snapshot operation with spatial multiplexing for multiple spectral channels, which may further boost the spectral imaging rate. The high acquisition rate, wide-field operation, and broadband spectral coverage could open new possibilities for high-throughput characterization of transient processes in material and life sciences.

Aberrant ATM signaling and homology-directed DNA repair as a vulnerability of p53-mutant GBM to AZD1390-mediated radiosensitization

ATM is a key mediator of radiation response, and pharmacological inhibition of ATM is a rational strategy to radiosensitize tumors. AZD1390 is a brain-penetrant ATM inhibitor and a potent radiosensitizer. This study evaluated the spectrum of radiosensitizing effects and the impact of TP53 mutation status in a panel of IDH1 wild-type (WT) glioblastoma (GBM) patient-derived xenografts (PDXs). AZD1390 suppressed radiation-induced ATM signaling, abrogated G0-G1 arrest, and promoted a proapoptotic response specifically in p53-mutant GBM in vitro. In a preclinical trial using 10 orthotopic GBM models, AZD1390/RT afforded benefit in a cohort of TP53-mutant tumors but not in TP53-WT PDXs. In mechanistic studies, increased endogenous DNA damage and constitutive ATM signaling were observed in TP53-mutant, but not in TP53-WT, PDXs. In plasmid-based reporter assays, GBM43 (TP53-mutant) showed elevated DNA repair capacity compared with that in GBM14 (p53-WT), whereas treatment with AZD1390 specifically suppressed homologous recombination (HR) efficiency, in part, by stalling RAD51 unloading. Furthermore, overexpression of a dominant-negative TP53 (p53DD) construct resulted in enhanced basal ATM signaling, HR activity, and AZD1390-mediated radiosensitization in GBM14. Analyzing RNA-seq data from TCGA showed up-regulation of HR pathway genes in TP53-mutant human GBM. Together, our results imply that increased basal ATM signaling and enhanced dependence on HR represent a unique susceptibility of TP53-mutant cells to ATM inhibitor-mediated radiosensitization.

The Use of Data Mining for Obtaining Deeper Insights into the Fabrication of Prednisolone-Loaded Chitosan Nanoparticles

The present work explores a data mining approach to study the fabrication of prednisolone-loaded chitosan nanoparticles and their properties. Eight PLC formulations were prepared using an automated adaptation of the antisolvent precipitation method. The PLCs were characterized using dynamic light scattering, infrared spectroscopy, and drug release studies. Results showed that that the effective diameter, loading capacity, encapsulation efficiency, zeta potential, and polydispersity of the PLCs were influenced by the concentration and molecular weight of chitosan. The drug release studies showed that PLCs exhibited significant dissolution enhancement compared to pure prednisolone crystals. Principal components analysis and partial least squares regression were applied to the infrared spectra and the DLS data to extract higher-order interactions and correlations between the critical quality attributes and the diameter of the PLCs. Principal components revealed that the spectra clustered according to the type of material, with PLCs forming a separate cluster from the raw materials and the physical mix. PLS was successful in predicting the ED of the PLCs from the FTIR spectra with R2 = 0.98 and RMSE = 27.18. The present work demonstrates that data mining techniques can be useful tools for obtaining deeper insights into the fabrication and properties of PLCs, and for optimizing their quality and performance. It also suggests that FTIR spectroscopy can be a rapid and non-destructive method for predicting the ED of PLCs.

Rapid Classification of Serum from Patients with Paracoccidioidomycosis Using Infrared Spectroscopy, Univariate Statistics, and Linear Discriminant Analysis (LDA)

Paracoccidioidomycosis (PCM) is a systemic mycosis that is diagnosed by visualizing the fungus in clinical samples or by other methods, like serological techniques. However, all PCM diagnostic methods have limitations. The aim of this study was to develop a diagnostic tool for PCM based on Fourier transform infrared (FTIR) spectroscopy. A total of 224 serum samples were included: 132 from PCM patients and 92 constituting the control group (50 from healthy blood donors and 42 from patients with other systemic mycoses). Samples were analyzed by attenuated total reflection (ATR) and a t-test was performed to find differences in the spectra of the two groups. The wavenumbers that had p < 0.05 had their diagnostic potential evaluated using receiver operating characteristic (ROC) curves. The spectral region with the lowest p value was used for variable selection through principal component analysis (PCA). The selected variables were used in a linear discriminant analysis (LDA). In univariate analysis, the ROC curves with the best performance were obtained in the region 1551-1095 cm-1. The wavenumber that had the highest AUC value was 1264 cm-1, achieving a sensitivity of 97.73%, specificity of 76.01%, and accuracy of 94.22%. The total separation of groups was obtained in the PCA performed with a spectral range of 1551-1095 cm-1. LDA performed with the eight wavenumbers with the greatest weight from the group discrimination in the PCA obtained 100% accuracy. The methodology proposed here is simple, fast, and highly accurate, proving its potential to be applied in the diagnosis of PCM. The proposed method is more accurate than the currently known diagnostic methods, which is particularly relevant for a neglected tropical mycosis such as paracoccidioidomycosis.