Distinguishing cell types or populations based on the computational analysis of their infrared spectra

Salivary spectral signature using ATR-FTIR spectroscopy in different exercise protocols

The Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) has been applied to determine salivary biomarkers with high sensitivity and cost-effectiveness. Our study aimed to test the hypothesis that the spectral profile of saliva demonstrates distinct vibrational modes corresponding to different exercise protocols, thereby facilitating exercise monitoring. Saliva samples were collected from trained male subjects at three intervals: pre-exercise, post-exercise, and 3 h post-exercise. The protocols included acute sessions of continuous exercise (CE), high-intensity interval exercise (HIIE), and resistance exercise (RE). ATR-FTIR analysis revealed that salivary biochemical components changed uniquely with each exercise protocol. Specific spectral vibrational modes were identified as potential biomarkers for each exercise type. Notably, the salivary spectrum pattern of CE closely resembled that of HIIE, whereas RE showed minor alterations. Furthermore, we attempted to apply an algorithm capable of distinguishing the spectral range that differentiates the exercise modalities. This pioneering study is the first to compare changes in saliva spectra following different exercise protocols and to suggest spectrum peaks of vibrational modes as markers for specific types of exercises. We emphasize that the spectral wavenumbers identified by FTIR could serve as practical markers in distinguishing between different exercise modalities, with sensitivity, specificity, and accuracy correlating with the metabolic changes induced by exercise. Therefore, this study contributes a panel of ATR-FTIR spectral wavenumbers that can be referenced as a spectral signature capable of distinguishing between resistance and endurance exercises.

Optical photothermal infrared imaging using metabolic probes in biological systems

Infrared spectroscopy is a powerful tool for identifying biomolecules. In biological systems, infrared spectra provide information on structure, reaction mechanisms, and conformational change of biomolecules. However, the promise of applying infrared imaging to biological systems has been hampered by low spatial resolution and the overwhelming water background arising from the aqueous nature of in cell and in vivo work. Recently, optical photothermal infrared microscopy (OPTIR) has overcome these barriers and achieved both spatially and spectrally resolved images of live cells and organisms. Here, we determine the most effective modes of collection for work in biological samples. We examine three cell lines (Huh-7, differentiated 3T3-L1, and U2OS) and three organisms ( E. coli , tardigrades, and zebrafish). Our results suggest that the information provided by multifrequency imaging is comparable to hyperspectral imaging while reducing imaging times twenty-fold. We also explore the utility of IR active probes, including global and site-specific probes, for tracking metabolic pathways and protein localization, structure, and local environment. Our findings illustrate the versatility of OPTIR, and together, provide a direction for future dynamic imaging of living cells and organisms.

Salivary detection of Chikungunya virus infection using a portable and sustainable biophotonic platform coupled with artificial intelligence algorithms

The current detection method for Chikungunya Virus (CHIKV) involves an invasive and costly molecular biology procedure as the gold standard diagnostic method. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for the detection of CHIKV infection is imperative for public health. The portable Fourier-transform infrared coupled with Attenuated Total Reflection (ATR-FTIR) platform was applied to discriminate systemic diseases using saliva, however, the salivary diagnostic application in viral diseases is less explored. The study aimed to identify unique vibrational modes of salivary infrared profiles to detect CHIKV infection using chemometrics and artificial intelligence algorithms. Thus, we intradermally challenged interferon-gamma gene knockout C57/BL6 mice with CHIKV (20 µl, 1 X 105 PFU/ml, n = 6) or vehicle (20 µl, n = 7). Saliva and serum samples were collected on day 3 (due to the peak of viremia). CHIKV infection was confirmed by Real-time PCR in the serum of CHIKV-infected mice. The best pattern classification showed a sensitivity of 83%, specificity of 86%, and accuracy of 85% using support vector machine (SVM) algorithms. Our results suggest that the salivary ATR-FTIR platform can discriminate CHIKV infection with the potential to be applied as a non-invasive, sustainable, and cost-effective detection tool for this emerging disease.

Attenuated total reflection Fourier-transform infrared spectroscopy reveals environment specific phenotypes in clonal Japanese knotweed

BACKGROUND

Japanese knotweed (Reynoutria japonica var. japonica), a problematic invasive species, has a wide geographical distribution. We have previously shown the potential for attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy and chemometrics to segregate regional differentiation between Japanese knotweed plants. However, the contribution of environment to spectral differences remains unclear. Herein, the response of Japanese knotweed to varied environmental habitats has been studied. Eight unique growth environments were created by manipulation of the red: far-red light ratio (R: FR), water availability, nitrogen, and micronutrients. Their impacts on plant growth, photosynthetic parameters, and ATR-FTIR spectral profiles, were explored using chemometric techniques, including principal component analysis (PCA), linear discriminant analysis, support vector machines (SVM) and partial least squares regression. Key wavenumbers responsible for spectral differences were identified with PCA loadings, and molecular biomarkers were assigned. Partial least squared regression (PLSR) of spectral absorbance and root water potential (RWP) data was used to create a predictive model for RWP.

RESULTS

Spectra from plants grown in different environments were differentiated using ATR-FTIR spectroscopy coupled with SVM. Biomarkers highlighted through PCA loadings corresponded to several molecules, most commonly cell wall carbohydrates, suggesting that these wavenumbers could be consistent indicators of plant stress across species. R: FR most affected the ATR-FTIR spectra of intact dried leaf material. PLSR prediction of root water potential achieved an R2 of 0.8, supporting the potential use of ATR-FTIR spectrometers as sensors for prediction of plant physiological parameters.

CONCLUSIONS

Japanese knotweed exhibits environmentally induced phenotypes, indicated by measurable differences in their ATR-FTIR spectra. This high environmental plasticity reflected by key biomolecular changes may contribute to its success as an invasive species. Light quality (R: FR) appears critical in defining the growth and spectral response to environment. Cross-species conservation of biomarkers suggest that they could function as indicators of plant-environment interactions including abiotic stress responses and plant health.

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.

Attenuated total reflection Fourier-transform infrared spectroscopy for the prediction of hormone concentrations in plants

Plant hormones are important in the control of physiological and developmental processes including seed germination, senescence, flowering, stomatal aperture, and ultimately the overall growth and yield of plants. Many currently available methods to quantify such growth regulators quickly and accurately require extensive sample purification using complex analytic techniques. Herein we used ultra-performance liquid chromatography-high-resolution mass spectrometry (UHPLC-HRMS) to create and validate the prediction of hormone concentrations made using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectral profiles of both freeze-dried ground leaf tissue and extracted xylem sap of Japanese knotweed (Reynoutria japonica) plants grown under different environmental conditions. In addition to these predictions made with partial least squares regression, further analysis of spectral data was performed using chemometric techniques, including principal component analysis, linear discriminant analysis, and support vector machines (SVM). Plants grown in different environments had sufficiently different biochemical profiles, including plant hormonal compounds, to allow successful differentiation by ATR-FTIR spectroscopy coupled with SVM. ATR-FTIR spectral biomarkers highlighted a range of biomolecules responsible for the differing spectral signatures between growth environments, such as triacylglycerol, proteins and amino acids, tannins, pectin, polysaccharides such as starch and cellulose, DNA and RNA. Using partial least squares regression, we show the potential for accurate prediction of plant hormone concentrations from ATR-FTIR spectral profiles, calibrated with hormonal data quantified by UHPLC-HRMS. The application of ATR-FTIR spectroscopy and chemometrics offers accurate prediction of hormone concentrations in plant samples, with advantages over existing approaches.

A new methodology for a rapid and high-throughput comparison of molecular profiles and biological activity of phytoextracts

To robustly discover and explore phytocompounds, it is necessary to evaluate the interrelationships between the plant species, plant tissue, and the extraction process on the extract composition and to predict its cytotoxicity. The present work evaluated how Fourier Transform InfraRed spectroscopy can acquire the molecular profile of aqueous and ethanol-based extracts obtained from leaves, seeds, and flowers of Cynara Cardunculus, and ethanol-based extracts from Matricaria chamomilla flowers, as well the impact of these extracts on the viability of mammalian cells. The extract molecular profile enabled to predict the extraction yield, and how the plant species, plant tissue, and extraction process affected the extract's relative composition. The molecular profile obtained from the culture media of cells exposed to extracts enabled to capture its impact on cells metabolism, at a higher sensitivity than the conventional assay used to determine the cell viability. Furthermore, it was possible to detect specific impacts on the cell's metabolism according to plant species, plant tissue, and extraction process. Since spectra were acquired on small volumes of samples (25 µL), after a simple dehydration step, and based on a plate with 96 wells, the method can be applied in a rapid, simple, high-throughput, and economic mode, consequently promoting the discovery of phytocompounds.

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.

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.

Effects of Benzo (a) Pyrene and 2,2',4,4'-Tetrabromodiphenyl Ether Exposure on the Thyroid Gland in Rats by Attenuated Total Reflection Fourier-Transform Infrared Spectroscopy

Benzo[a]pyrene (B[a]P) and 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) are widespread environmental pollutants and can destroy thyroid function. We assessed the biochemical changes in the thyroid tissue of rats exposed to B[a]P and BDE-47 using attenuated total reflection Fourier-transform infrared spectroscopy combined with support vector machine(SVM). After B[a]P and BDE-47 treatment in rats, the structure of thyroid follicles was destroyed and epithelial cells were necrotic, indicating that B[a]P and BDE-47 may lead to changes of the thyroid morphology of the rats. These damages are mainly related to C=O stretch vibrations of lipids (1743 cm-1), as well as the secondary structure of proteins [amide I (1645 cm-1) and amide II (1550 cm-1)], and carbohydrates [C-OH (1138 cm-1), C-O (1106 cm-1, 1049 cm-1, 991 cm-1), C-C (1106 cm-1) stretching] and collagen (phosphodiester stretching at 922 cm-1) vibration modes. When SVM was used for classification, there was a substantial separation between the control and the exposure groups (accuracy = 96%; sensitivity = 98%; specificity = 87%), and there was also a major separation between the exposed groups (accuracy = 93%; sensitivity = 94%; and specificity = 92%).

Estimation of time since deposition of semen stain on different fabric types using ATR-FTIR spectroscopy and chemometrics

Various body fluids such as blood, semen, vaginal secretions, and saliva are frequently encountered at crime scene. In cases of sexual assault, semen stains are one of the most reliable evidence of biological origin. In this study, our objective was to develop a method for estimating the time since deposition of semen stains on five different fabric types using Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) Spectroscopy, with a focus on a time frame of up to 8 weeks. Semen samples from six different volunteers were dripped onto five distinct fabric materials, and ATR-FTIR measurements were obtained at 17 different time points. Principal component analysis (PCA) and partial least squares (PLS) methods were employed to differentiate semen stains on various fabric samples and estimate the age of semen stains. Models constructed using PCA and PLSR achieved high R2 values and low root-mean-square error (RMSE). While the performance varies depending on fabric types, it was observed that age estimation of semen stains can be made within following intervals: 0.39-0.76 days for 0-7 day range, 2.59-3.38 days for the 1-8 week range, and 3.98-8.1 days for the 0-56 day range. This study demonstrates the effectiveness of using ATR-FTIR spectroscopy in combination with chemometrics to estimate the age of human semen stains on various fabric types based on time-dependent spectral changes.

Synergistic/antagonistic toxicity characterization and source-apportionment of heavy metals and organophosphorus pesticides by the biospectroscopy-bioreporter-coupling approach

Many anthropogenic chemicals are manufactured and eventually enter the surrounding environment, threatening food security and human health. Considering the additive or synergistic effects of pollutant mixtures, there is an expanding need for rapid, cost-effective and field-portable screening methods in environmental monitoring. This study used a recently developed biospectroscopy-bioreporter-coupling (BBC) approach to investigate the binary toxicity of Ag(I), Cr(VI) and four organophosphorus pesticides (dichlorvos, parathion, omethoate and monocrotophos). Ag(I) and Cr(VI) altered the toxicity mechanisms of pesticides, explained by the synergistic or antagonistic effect of Ag/Cr-induced cytotoxicity and pesticide-induced genotoxicity. The discriminating Raman spectral peaks associated with organophosphorus pesticides were 1585 and 1682 cm-1, but 750, 1004, 1306 and 1131 cm-1 were found in heavy metal and pesticide mixtures. More spectral alterations were related to pesticides rather than Ag(I) or Cr(VI), hinting at the dominant toxicity mechanisms of pesticides in mixtures. Ag(I) supplement significantly increased the levels of reactive oxygen species induced by organophosphorus pesticides, attributing to the increased permeability of cell membrane and entrance of toxic substances into the cells by the oligodynamic actions. This study lends deeper insights into the interactions between microbes and pollutant mixtures, offering clues to assess the cocktail effects of multiple pollutants comprehensively.

Quantification of skin penetration of caffeine and propylene glycol applied topically in a mixture by tailored multivariate curve resolution-alternating least squares of depth-resolved Raman spectra

The quantitative determination of topically applied substances in the skin is severely limited and represents a challenging task. The porcine skin ex vivo was topically treated with a gel containing caffeine (CF) and propylene glycol (PG), and depth-resolved Raman spectra were recorded with two confocal Raman microscopes. We applied a novel tailored multivariate curve resolution-alternating least squares method to the selected spectral regions (512-604 and 778-1148 cm-1 ) of gel-treated skin and quantitatively determined the concentrations of CF and PG in the stratum corneum (SC). The highest concentration of CF (181 mg/cm3 ) was found at the surface, while PG (384 mg/cm3 ) was found at 10% SC depth, indicating the formation of a reservoir at the superficial SC. The concentrations of CF and PG decreased monotonically and reached the detection limit at ≈60% and ≈80% SC depth, respectively, indicating that neither permeate the SC.

Application of spectrochemical analysis with chemometrics to profile biochemical alterations in nanoplastic-exposed HepG2 cells

Humans are routinely exposed to nanoplastics (NPs) in various ways, and this exposure presents a significant health risk. Nevertheless, there remain gaps in our knowledge, particularly in the mechanisms of toxicity of NPs with different surface charges at very low environmental concentrations. Herein, a spectrochemical approach was used to profile the cytotoxicity of NPs with different surface charges in HepG2 cells. It was found that all three NPs can cause some biomolecular alterations in cells, affecting cellular lipids, proteins, amino acids, and genetic material. Of these, PS and PS-COOH led to a non-linear dose-response, which may be related to a biphasic dose-response, whereas PS-NH2 led to a linear dose-response with a gradual increase in toxicity with increasing exposure concentration. In addition, the spectroscopic results showed that surface modifications led to cellular biochemical changes and caused adverse biological effects, with PS-NH2 exhibiting higher toxicity compared to PS or PS-COOH along with an inhibition of cell proliferation. Surprisingly PS-COOH, although considered the least toxic NP, appears to cause DNA damage. Overall, the toxic effects of different surface-modified NPs in cells were detected for the first time by applying spectrochemical techniques, and these findings provide important data towards understanding the emerging widespread environmental pollution of NPs and their effects on humans.

Point-of-Care Disease Screening in Primary Care Using Saliva: A Biospectroscopy Approach for Lung Cancer and Prostate Cancer

Saliva is a largely unexplored liquid biopsy that can be readily obtained noninvasively. Not dissimilar to blood plasma or serum, it contains a vast array of bioconstituents that may be associated with the absence or presence of a disease condition. Given its ease of access, the use of saliva is potentially ideal in a point-of-care screening or diagnostic test. Herein, we developed a swab "dip" test in saliva obtained from consenting patients participating in a lung cancer-screening programme being undertaken in north-west England. A total of 998 saliva samples (31 designated as lung-cancer positive and 17 as prostate-cancer positive) were taken in the order in which they entered the clinic (i.e., there was no selection of participants) during the course of this prospective screening programme. Samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. In addition to unsupervised classification on resultant infrared (IR) spectra using principal component analysis (PCA), a range of feature selection/extraction algorithms were tested. Following preprocessing, the data were split between training (70% of samples, 22 lung-cancer positive versus 664 other) and test (30% of samples, 9 lung-cancer positive versus 284 other) sets. The training set was used for model construction and the test set was used for validation. The best model was the PCA-quadratic discriminant analysis (QDA) algorithm. This PCA-QDA model was built using 8 PCs (90.4% of explained variance) and resulted in 93% accuracy for training and 91% for testing, with clinical sensitivity at 100% and specificity at 91%. Additionally, for prostate cancer patients amongst the male cohort (n = 585), following preprocessing, the data were split between training (70% of samples, 12 prostate-cancer positive versus 399 other) and test (30% of samples, 5 prostate-cancer positive versus 171 other) sets. A PCA-QDA model, again the best model, was built using 5 PCs (84.2% of explained variance) and resulted in 97% accuracy for training and 93% for testing, with clinical sensitivity at 100% and specificity at 92%. These results point to a powerful new approach towards the capability to screen large cohorts of individuals in primary care settings for underlying malignant disease.

Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics

As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [...].

Evaluation of the toxicity of clozapine on the freshwater diatom Navicula sp. using the FTIR spectroscopy

Clozapine is an often prescribed neuroactive pharmaceutical and frequently detected in the aquatic environments. However, its toxicity on low trophic level species (i.e., diatoms) and associated mechanisms are seldom reported. In this study, the toxicity of clozapine on a widely distributed freshwater diatom Navicula sp. was evaluated using the FTIR spectroscopy along with biochemical analyses. The diatoms were exposed to various concentrations of clozapine (0, 0.01, 0.05, 0.10, 0.50, 1.00, 2.00, 5.00 mg/L) for 96 h. The results revealed that clozapine reached up to 392.8 μg/g in the cell wall and 550.4 μg/g within the cells at 5.00 mg/L, suggesting that clozapine could be adsorbed extracellularly and accumulated intracellularly in diatoms. In addition, hormetic effects were displayed on the growth and photosynthetic pigments (chlorophyll a and carotenoid) of Navicula sp., with a promotive effect at concentrations less than 1.00 mg/L while an inhibited effect at concentrations over 2 mg/L. Clozapine induced oxidative stress in Navicula sp., accompanied by decreased levels of total antioxidant capacity (T-AOC) (>0.05 mg/L), in which, the activity of superoxide dismutase (SOD) (at 5.00 mg/L) was increased whereas the activity of catalase (CAT) (>0.05 mg/L) was decreased. Furthermore, FTIR spectroscopic analysis showed that exposure to clozapine resulted in accumulation of lipid peroxidation products, increased sparse β-sheet structures, and altered DNA structures in Navicula sp. This study can facilitate the ecological risk assessment of clozapine in the aquatic ecosystems.

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: high cost, incompatibility with standard cell culture workflows, limited spectral range, and small penetration depth into the analyte. In this work, we introduce an alternative live cell biosensing platform based on metallic nanogratings fabricated atop 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). We demonstrate the analytic power of ITS by measuring protein concentrations in solution. The ability of ITS to interrogate live cells that naturally wrap themselves around the grating is also exploited to characterize their adhesion kinetics.

Elemental and molecular characterization of degrading blood pools

Blood is a commonly encountered type of biological evidence and can provide critical information about the crime that occurred. The ability to accurately and precisely determine the time since deposition (TSD) of a bloodstain is highly sought after in the field of forensic science. Current spectral methods for determining TSD are typically developed using small volume bloodstains, we investigate the applicability to larger volume blood pools where drying and degradation mechanics are different. We explored the differences that exist between the surface and bulk of dried segments from fragments collected from 15 mL dried blood pools and identified heterogeneity using RGB colour analysis and hierarchical cluster analysis (HCA). The physical, molecular, and atomic differences between the layers were further investigated using scanning electron microscopy (SEM), X-Ray photoelectron spectroscopy (XPS), attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy, and Raman spectroscopy. SEM identified different morphology on the surface and the bulk indicative of density-dependant cellular settling. XPS revealed that iron was not present on the surface but rather was present in the bulk where the red blood cells had settled. The oxidation state of the iron was quantified over three weeks in which it transitioned from entirely Fe2+ to primarily Fe3+, as expected for ex vivo degradation of hemoglobin. Further, indications of amide saponification occurring at the blood-air interface were identified in the increased quantity of the C-O moiety relative to CO, and the formation of free amines and OC-ONa groups over time. ATR-FTIR and Raman spectroscopy provided insights into differences in the molecular composition of the layers, suggesting that the surface consists of more nucleic acids, lipids, and glycoproteins than the bulk, which was dominated by proteins (p < 0.001% using principal component analysis (PCA)). Additionally, spectral band trends previously reported to have applicability to the estimation of TSD were observed for the bulk portion of the blood pool as the Hb underwent predictable time dependant changes from oxyHb to metHb. PCA was performed based on all spectral data which demonstrated statistically significant differences between the surface and bulk, as well as proof-of-concept for linear TSD estimation models.

Biomacromolecular and Toxicity Responses of Bacteria upon the Nano-Bio Interfacial Interactions with Ti3C2Tx Nanosheets

The biomolecular responses of bacteria to 2D nanosheets that result from nano-bio interfacial interactions remain to be thoroughly examined. Herein, Fourier transform infrared (FTIR) multivariate and 2D correlation analyses were performed to assess the composition and conformational changes in bacterial biomacromolecules (lipids, polysaccharides, and carbohydrates) upon exposure to Ti3C2Tx nanosheets. General toxicity assays, 3D excitation-emission matrix fluorescence analyses, extended Derjaguin-Landau-Verwey-Overbeek theory interaction calculations, and isothermal titration calorimetry were also performed. Our results demonstrate that Ti3C2Tx nanosheets considerably impact Gram-positive bacteria (Bacillus subtilis), causing oxidative damage and inactivation by preferentially interacting with and disrupting the cell walls. The bilayer membrane structure of Gram-negative bacteria (Escherichia coli) endows them with increased resistance to Ti3C2Tx nanosheets. The unmodified nanosheets had a higher affinity to bacterial protein components with lower toxicity due to their susceptibility to oxidation. Surface modification with KOH or hydrazine (HMH), particularly HMH, induced stronger dispersion, antioxidation, and affinity to bacterial phospholipids, which resulted in severe cell membrane lipid peroxidation and bacterial inactivation. These findings provide valuable insight into nano-bio interfacial interactions, which can facilitate the development of antimicrobial and antifouling surfaces and contribute to the evaluation of the environmental risks of nanomaterials.

ATR-FTIR Spectroscopy with Chemometrics for Analysis of Saliva Samples Obtained in a Lung-Cancer-Screening Programme: Application of Swabs as a Paradigm for High Throughput in a Clinical Setting

There is an increasing need for inexpensive and rapid screening tests in point-of-care clinical oncology settings. Herein, we develop a swab "dip" test in saliva obtained from consenting patients participating in a lung-cancer-screening programme being undertaken in North West England. In a pilot study, a total of 211 saliva samples (n = 170 benign, 41 designated cancer-positive) were randomly taken during the course of this prospective lung-cancer-screening programme. The samples (sterile Copan blue rayon swabs dipped in saliva) were analysed using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. An exploratory analysis using principal component analysis (PCA,) with or without linear discriminant analysis (LDA), was then undertaken. Three pairwise comparisons were undertaken including: (1) benign vs. cancer following swab analysis; (2) benign vs. cancer following swab analysis with the subtraction of dry swab spectra; and (3) benign vs. cancer following swab analysis with the subtraction of wet swab spectra. Consistent and remarkably similar patterns of clustering for the benign control vs. cancer categories, irrespective of whether the swab plus saliva sample was analysed or whether there was a subtraction of wet or dry swab spectra, was observed. In each case, MANOVA demonstrated that this segregation of categories is highly significant. A k-NN (using three nearest neighbours) machine-learning algorithm also showed that the specificity (90%) and sensitivity (75%) are consistent for each pairwise comparison. In detailed analyses, the swab as a substrate did not alter the level of spectral discrimination between benign control vs. cancer saliva samples. These results demonstrate a novel swab "dip" test using saliva as a biofluid that is highly applicable to be rolled out into a larger lung-cancer-screening programme.

Spectrochemical analysis of blood combined with chemometric techniques for detecting osteosarcopenia

Among several complications related to physiotherapy, osteosarcopenia is one of the most frequent in elderly patients. This condition is limiting and quite harmful to the patient's health by disabling several basic musculoskeletal activities. Currently, the test to identify this health condition is complex. In this study, we use mid-infrared spectroscopy combined with chemometric techniques to identify osteosarcopenia based on blood serum samples. The purpose of this study was to evaluate the mid-infrared spectroscopy power to detect osteosarcopenia in community-dwelling older women (n = 62, 30 from patients with osteosarcopenia and 32 healthy controls). Feature reduction and selection techniques were employed in conjunction with discriminant analysis, where a principal component analysis with support vector machines (PCA-SVM) model achieved 89% accuracy to distinguish the samples from patients with osteosarcopenia. This study shows the potential of using infrared spectroscopy of blood samples to identify osteosarcopenia in a simple, fast and objective way.

Cuticles and postharvest life of tomato fruit: A rigid cover for aerial epidermis or a multifaceted guard of freshness?

Fruit cuticle is a specialized cell wall hydrophobic architecture covering the aerial surfaces of fruit, which forms the interface between the fruit and its environment. As a specialized seed-bearing organ, fruit utilize cuticles as physical barriers, water permeation regulator and resistance to pathogens, thus appealing extensive research interests for its potential values in developing postharvest freshness-keeping strategies. Here, we provide an overview for the composition and functions of fruit cuticles, mainly focusing on its functions in mechanical support, water permeability barrier and protection over pathogens, further introduce key mechanisms implicated in fruit cuticle biosynthesis. Moreover, currently available state-of-art techniques for examining compositional diversity and architecture of fruit are also compared.

Metasurface-enhanced infrared spectroscopy in multiwell format for real-time assaying of live cells

Fourier transform infrared (FTIR) spectroscopy is a popular technique for the analysis of biological samples, yet its application in characterizing live cells is limited due to the strong attenuation of mid-IR light in water. Special thin flow cells and attenuated total reflection (ATR) FTIR spectroscopy have been used to mitigate this problem, but these techniques are difficult to integrate into a standard cell culture workflow. In this work, we demonstrate that the use of a plasmonic metasurface fabricated on planar substrates and the probing of cellular IR spectra through metasurface-enhanced infrared spectroscopy (MEIRS) can be an effective technique to characterize the IR spectra of live cells in a high-throughput manner. Cells are cultured on metasurfaces integrated with multiwell cell culture chambers and are probed from the bottom using an inverted FTIR micro-spectrometer. To demonstrate the use of MEIRS as a cellular assay, cellular adhesion on metasurfaces with different surface coatings and cellular response to the activation of the protease-activated receptor (PAR) signaling pathway were characterized through the changes in cellular IR spectra.

Protocol for bacterial typing using Fourier transform infrared spectroscopy

The Fourier transform infrared (FT-IR) signals obtained from bacterial samples are specific and reproducible, making FT-IR an efficient tool for bacterial typing at a subspecies level. However, the typing accuracy could be affected by many factors, including sample preparation and spectral acquisition. Here, we present a unified protocol for bacterial typing based on FT-IR spectroscopy. We describe sample preparation from bacterial culture and FT-IR spectrum collection. We then detail FT-IR spectrum preprocessing and multivariate analysis of spectral data for bacterial typing.

Spectral narrowing and broadening of Cr:ZnS/Se laser oscillation due to mode competition and spatial hole burning in the gain element

In this paper, we demonstrate the laser characterization of Cr:ZnS/Se polycrystalline gain media in non-selective unpolarized, linearly polarized, and twisted mode cavities. Lasers were based on post-growth diffusion-doped, commercially available antireflective-coated Cr:ZnSe and Cr:ZnS polycrystals with a length of 9 mm. The spectral output of lasers based on these gain elements in non-selective unpolarized and linearly polarized cavities was measured to be broadened to ∼20-50 nm due to the spatial hole burning (SHB) effect. SHB alleviation in the same crystals was realized in the "twisted mode" cavity, with linewidth narrowing to ∼80-90 pm. Both broadened and narrow-line oscillations were captured by adjusting the orientation of intracavity waveplates with respect to facilitated polarization.

In Situ Spatiotemporal SERS Measurements and Multivariate Analysis of Virally Infected Bacterial Biofilms Using Nanolaminated Plasmonic Crystals

In situ spatiotemporal biochemical characterization of the activity of living multicellular biofilms under external stimuli remains a significant challenge. Surface-enhanced Raman spectroscopy (SERS), combining the molecular fingerprint specificity of vibrational spectroscopy with the hotspot sensitivity of plasmonic nanostructures, has emerged as a promising noninvasive bioanalysis technique for living systems. However, most SERS devices do not allow reliable long-term spatiotemporal SERS measurements of multicellular systems because of challenges in producing spatially uniform and mechanically stable SERS hotspot arrays to interface with large cellular networks. Furthermore, very few studies have been conducted for multivariable analysis of spatiotemporal SERS datasets to extract spatially and temporally correlated biological information from multicellular systems. Here, we demonstrate in situ label-free spatiotemporal SERS measurements and multivariate analysis of Pseudomonas syringae biofilms during development and upon infection by bacteriophage virus Phi6 by employing nanolaminate plasmonic crystal SERS devices to interface mechanically stable, uniform, and spatially dense hotspot arrays with the P. syringae biofilms. We exploited unsupervised multivariate machine learning methods, including principal component analysis (PCA) and hierarchical cluster analysis (HCA), to resolve the spatiotemporal evolution and Phi6 dose-dependent changes of major Raman peaks originating from biochemical components in P. syringae biofilms, including cellular components, extracellular polymeric substances (EPS), metabolite molecules, and cell lysate-enriched extracellular media. We then employed supervised multivariate analysis using linear discriminant analysis (LDA) for the multiclass classification of Phi6 dose-dependent biofilm responses, demonstrating the potential for viral infection diagnosis. We envision extending the in situ spatiotemporal SERS method to monitor dynamic, heterogeneous interactions between viruses and bacterial networks for applications such as phage-based anti-biofilm therapy development and continuous pathogenic virus detection.

ATR-FTIR spectrum analysis of plasma samples for rapid identification of recovered COVID-19 individuals

The development of fast, cheap and reliable methods to determine seroconversion against infectious agents is of great practical importance. In the context of the COVID-19 pandemic, an important issue is to study the rate of formation of the immune layer in the population of different regions, as well as the study of the formation of post-vaccination immunity in individuals after vaccination. Currently, the main method for this kind of research is enzyme immunoassay (ELISA, enzyme-linked immunosorbent assay). This technique is sufficiently sensitive and specific, but it requires significant time and material costs. We investigated the applicability of attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy associated with machine learning in blood plasma to detect seroconversion against SARS-CoV-2. The study included samples of 60 patients. Clear spectral differences in plasma samples from recovered COVID-19 patients and conditionally healthy donors were identified using multivariate and statistical analysis. The results showed that ATR-FTIR spectroscopy, combined with principal components analysis (PCA) and linear discriminant analysis (LDA) or artificial neural network (ANN), made it possible to efficiently identify specimens from recovered COVID-19 patients. We built classification models based on PCA associated with LDA and ANN. Our analysis led to 87% accuracy for PCA-LDA model and 91% accuracy for ANN, respectively. Based on this proof-of-concept study, we believe this method could offer a simple, label-free, cost-effective tool for detecting seroconversion against SARS-CoV-2. This approach could be used as an alternative to ELISA.

Fourier Transform Infrared (FTIR) Spectroscopic Study of Biofilms Formed by the Rhizobacterium Azospirillum baldaniorum Sp245: Aspects of Methodology and Matrix Composition

Biofilms represent the main mode of existence of bacteria and play very significant roles in many industrial, medical and agricultural fields. Analysis of biofilms is a challenging task owing to their sophisticated composition, heterogeneity and variability. In this study, biofilms formed by the rhizobacterium Azospirillum baldaniorum (strain Sp245), isolated biofilm matrix and its macrocomponents have for the first time been studied in detail, using Fourier transform infrared (FTIR) spectroscopy, with a special emphasis on the methodology. The accompanying novel data of comparative chemical analyses of the biofilm matrix, its fractions and lipopolysaccharide isolated from the outer membrane of the cells of this strain, as well as their electrophoretic analyses (SDS-PAGE) have been found to be in good agreement with the FTIR spectroscopic results.

Dydrogesterone disrupts lipid metabolism in zebrafish brain: A study based on metabolomics and Fourier transform infrared spectroscopy

Brain is a potential target for neuroprogestogens and/or peripheral progestogens. Previous studies reported that expression of genes about steroidogenesis, reproduction, cell cycle, and circadian rhythm in zebrafish brain could be affected by progestogens. However, there are limited information from metabolites or biomacromolecules aspects, leaving an enormous gap in understanding toxic effects of progestogens on fish brain. In this study, we exposed zebrafish embryos to 2.8, 27.6, and 289.8 ng/L dydrogesterone (DDG, a synthetic progestogen) until sexual maturity (140 days). LC-MS and GC-MS based untargeted metabolomics and Fourier-transform infrared (FTIR) spectroscopy were then performed to investigate the metabolic profiles and macromolecular changes of brain of these zebrafish. The results from multivariate statistical analysis of metabolite features showed a clear separation between different treatment groups of both female and male zebrafish brains. DDG exposure increased the levels of cholesterol, saturated fatty acids, and nucleoside monophosphates, but decreased the contents of polyunsaturated fatty acids (PUFAs), lysophosphatides, and nucleosides in dose-dependent manner. FTIR results indicated that DDG exposure led to accumulation of saturated lipids, reduction of nucleic acids and carbohydrates, and alteration of protein secondary structures. The findings from this study demonstrated that DDG could affect contents of metabolites and biomacromolecules of zebrafish brain, which may finally lead to brain dysfunctions.

Screening of structural and functional alterations in duckweed (Lemna minor) induced by per- and polyfluoroalkyl substances (PFASs) with FTIR spectroscopy

As a class of common emerging pollutants, per- and polyfluoroalkyl substances (PFASs) and their alternatives have been widely detected in various environmental matrices, exhibiting a great threat to the ecological environment and human health. Nevertheless, changes in biomolecular structure and function of duckweed caused by PFASs and their alternatives remain unknown thus far. Herein, the effects of four PFASs, including two common legacy PFASs (perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA)) and two PFASs alternatives (perfluorobutane sulfonic acid (PFBS) and 1H,1H,2H, 2H-perfluorooctane sulfonic acid (6:2 FTS)) on duckweed (Lemna minor) at biochemical level were investigated with Fourier transform infrared spectroscopy (FTIR). Although no obvious inhibitions were observed in the growth of L. minor with PFASs exposure at three levels of 1 μg L^-1, 100 μg L^-1, and 10 mg L^-1, significant structural and functional alterations were induced at the biochemical level. In response to PFASs exposure, lipid peroxidation, proteins aggregation and α-helix to β-sheet transformation of the protein conformation, as well as changes of DNA conformations were detected. Moreover, alterations in lipid, protein, and DNA were proved to be concentration-related and compound-specific. Compared to the two legacy PFASs (PFOS and PFOA), alternative ones exhibited greater effects on the biological macromolecules of L. minor. The findings of this study firstly reveal structural and functional alterations in L. minor induced by PFASs exposure, providing further understanding of their toxicity effects.

Distinguishing Asphyxia from Sudden Cardiac Death as the Cause of Death from the Lung Tissues of Rats and Humans Using Fourier Transform Infrared Spectroscopy

The ability to determine asphyxia as a cause of death is important in forensic practice and helps us to judge whether a case is criminal. However, in some cases where the deceased has underlying heart disease, death by asphyxia cannot be determined by traditional autopsy and morphological observation under a microscope because there are no specific morphological features for either asphyxia or sudden cardiac death (SCD). Here, Fourier transform infrared (FTIR) spectroscopy was employed to distinguish asphyxia from SCD. A total of 40 lung tissues (collected at 0 h and 24 h postmortem) from 20 rats (10 died from asphyxia and 10 died from SCD) and 16 human lung tissues from 16 real cases were used for spectral data acquisition. After data preprocessing, 2675 spectra from rat lung tissues and 1526 spectra from human lung tissues were obtained for subsequent analysis. First, we found that there were biochemical differences in the rat lung tissues between the two causes of death by principal component analysis and partial least-squares discriminant analysis (PLS-DA), which were related to alterations in lipids, proteins, and nucleic acids. In addition, a PLS-DA classification model can be built to distinguish asphyxia from SCD. Second, based on the spectral data obtained from lung tissues allowed to decompose for 24 h, we could still distinguish asphyxia from SCD even when decomposition occurred in animal models. Nine important spectral features that contributed to the discrimination in the animal experiment were selected and further analyzed. Third, 7 of the 9 differential spectral features were also found to be significantly different in human lung tissues from 16 real cases. A support vector machine model was finally built by using the seven variables to distinguish asphyxia from SCD in the human samples. Compared with the linear PLS-DA model, its accuracy was significantly improved to 0.798, and the correct rate of determining the cause of death was 100%. This study shows the application potential of FTIR spectroscopy for exploring the subtle biochemical differences resulting from different death processes and determining the cause of death even after decomposition.

Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing

The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting.

Integrated System for On-Site Rapid and Safe Screening of COVID-19

Since the outbreak of coronavirus disease 2019 (COVID-19), the epidemic has been spreading around the world for more than 2 years. Rapid, safe, and on-site detection methods of COVID-19 are in urgent demand for the control of the epidemic. Here, we established an integrated system, which incorporates a machine-learning-based Fourier transform infrared spectroscopy technique for rapid COVID-19 screening and air-plasma-based disinfection modules to prevent potential secondary infections. A partial least-squares discrimination analysis and a convolutional neural network model were built using the collected infrared spectral dataset containing 857 training serum samples. Furthermore, the sensitivity, specificity, and prediction accuracy could all reach over 94% from the results of the field test regarding 968 blind testing samples. Additionally, the disinfection modules achieved an inactivation efficiency of 99.9% for surface and airborne tested bacteria. The proposed system is conducive and promising for point-of-care and on-site COVID-19 screening in the mass population.

Fingerprinting of skin cells by live cell Raman spectroscopy reveals melanoma cell heterogeneity and cell-type-specific responses to UVR

Raman spectroscopy is an emerging dermatological technique with the potential to discriminate biochemically between cell types in a label-free and non-invasive manner. Here, we use live single-cell Raman spectroscopy and principal component analysis (PCA) to fingerprint mouse melanoblasts, melanocytes, keratinocytes and melanoma cells. We show the differences in their spectra are attributable to biomarkers in the melanin biosynthesis pathway and that melanoma cells are a heterogeneous population that sit on a trajectory between undifferentiated melanoblasts and differentiated melanocytes. We demonstrate the utility of Raman spectroscopy as a highly sensitive tool to probe the melanin biosynthesis pathway and its immediate response to ultraviolet (UV) irradiation revealing previously undescribed opposing responses to UVA and UVB irradiation in melanocytes. Finally, we identify melanocyte-specific accumulation of β-carotene correlated with a stabilisation of the UVR response in lipids and proteins consistent with a β-carotene-mediated photoprotective mechanism. In summary, our data show that Raman spectroscopy can be used to determine the differentiation status of cells of the melanocyte lineage and describe the immediate and temporal biochemical changes associated with UV exposure which differ depending on cell type, differentiation status and competence to synthesise melanin. Our work uniquely applies Raman spectroscopy to discriminate between cell types by biological function and differentiation status while they are growing in culture. In doing so, we demonstrate for the first time its utility as a tool with which to probe the melanin biosynthesis pathway.

Intracellular gold nanoparticles influence light scattering and facilitate amplified spontaneous emission generation

Generation of amplified stimulated emission inside mammalian cells has paved the way for a novel bioimaging and cell sensing approach. Single cells carrying gain media (e.g., fluorescent molecules) are placed inside an optical cavity, allowing the production of intracellular laser emission upon sufficient optical pumping. Here, we investigate the possibility to trigger another amplified emission phenomenon (i.e., amplified spontaneous emission or ASE) inside two different cell types, namely macrophage and epithelial cells from different species and tissues, in the presence of a poorly reflecting cavity. Furthermore, the resulting ASE properties can be enhanced by introducing plasmonic nanoparticles. The presence of gold nanoparticles (AuNPs) in rhodamine 6G-labeled A549 epithelial cells results in higher intensity and lowered ASE threshold in comparison to cells without nanoparticles, due to the effect of plasmonic field enhancement. An increase in intracellular concentration of AuNPs in rhodamine 6G-labeled macrophages is, however, responsible for the twofold increase in the ASE threshold and a reduction in the ASE intensity, dominantly due to a suppressed in and out-coupling of light at high nanoparticle concentrations.

Potential of ATR-FTIR-Chemometrics in Covid-19: Disease Recognition

The COVID-19 pandemic has caused major disturbances to human health and economy on a global scale. Although vaccination campaigns and important advances in treatments have been developed, an early diagnosis is still crucial. While PCR is the golden standard for diagnosing SARS-CoV-2 infection, rapid and low-cost techniques such as ATR-FTIR followed by multivariate analyses, where dimensions are reduced for obtaining valuable information from highly complex data sets, have been investigated. Most dimensionality reduction techniques attempt to discriminate and create new combinations of attributes prior to the classification stage; thus, the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. In this work, we developed a method for evaluating SARS-CoV-2 infection and COVID-19 disease severity on infrared spectra of sera, based on a rather simple feature selection technique (correlation-based feature subset selection). Dengue infection was also evaluated for assessing whether selectivity toward a different virus was possible with the same algorithm, although independent models were built for both viruses. High sensitivity (94.55%) and high specificity (98.44%) were obtained for assessing SARS-CoV-2 infection with our model; for severe COVID-19 disease classification, sensitivity is 70.97% and specificity is 94.95%; for mild disease classification, sensitivity is 33.33% and specificity is 94.64%; and for dengue infection assessment, sensitivity is 84.27% and specificity is 94.64%.

Deep Learning-Enabled Raman Spectroscopic Identification of Pathogen-Derived Extracellular Vesicles and the Biogenesis Process

Pathogenic bacterial infections, exacerbated by increasing antimicrobial resistance, pose a major threat to human health worldwide. Extracellular vesicles (EVs), secreted by bacteria and acting as their "long-distance weapons", play an important role in the occurrence and development of infectious diseases. However, no efficient methods to rapidly detect and identify EVs of different bacterial origins are available. Here, label-free Raman spectroscopy in combination with a new deep learning model of the attentional neural network (aNN) was developed to identify pathogen-derived EVs at Gram^±, species, strain, and even down to physiological levels. By training the aNN model with a large Raman data set from six typical pathogen-derived EVs, we achieved the identification of EVs with high accuracies at all levels: exceeding 96% at the Gram and species levels, 93% at the antibiotic-resistant and sensitive strain levels, and still above 87% at the physiological level. aNN enabled Raman spectroscopy to interrogate the bacterial origin of EVs to a much higher level than previous methods. Moreover, spectral markers underpinning EV discrimination were uncovered from subtly different EV spectra via an interpretation algorithm of the integrated gradient. A further comparative analysis of the rich Raman biochemical signatures of EVs and parental pathogens clearly revealed the biogenesis process of EVs, including the selective encapsulation of biocomponents and distinct membrane compositions from the original bacteria. This developed platform provides an accurate and versatile means to identify pathogen-derived EVs, spectral markers, and the biogenesis process. It will promote rapid diagnosis and allow the timely treatment of bacterial infections.

Contrasting behaviors of pre-ozonation on ceramic membrane biofouling: Early stage vs late stage

Pre-ozonation coupled with ceramic membrane filtration has been widely used to alleviate membrane fouling. However, information on the efficiency and underlying mechanism of pre-ozonation in the evolution of ceramic membrane biofouling is limited. Herein, filtration experiments with a synthesis wastewater containing activated sludge were conducted in a cross-flow system to evaluate the effects of pre-ozonation on ceramic membrane biofouling. Results of flux tests show that pre-ozonation aggravated biofouling at the early stage, but alleviated the biofouling at the late stage. In situ FTIR spectra show that the aggravated biofouling with pre-ozonation was mainly caused by the enhanced complexation between phosphate group from DNA and Al₂O₃ surface and the increased rigid of proteins' structure. At the early stage, more severe pore blockage further substantiated the higher permeate resistance. By contrast, more dead cells were observed on membrane surface at the late stage, indicating the prevention of biofouling development after long-term pre-ozonation. Additionally, the structures and compositions of cake layers at the early and late stages exhibited considerable differences accompanied by the variation in microbial community with the evolution of biofouling. Therefore, this work demonstrates the effectiveness of pre-ozonation in biofouling in long-term operation and provides mechanistic insights into the evolution of biofouling on ceramic membrane.

Identifying traumatic brain injury (TBI) by ATR-FTIR spectroscopy in a mouse model

Traumatic brain injury (TBI) is one of the most common mechanical injuries and plays a significant role in forensic practice. For cadavers, however, accurate diagnosis of TBI becomes a more and more challenging task as the level of decomposition increases. Our main purpose was to investigate whether TBI in putrefied mouse cadavers can be identified by Fourier Transform Infrared (FT-IR). The method proposed by Feeney et al. was used to establish the mouse TBI model. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) modeling were used to distinguish fresh and putrefied brain tissues. Then, we established two PLS-DA models to identify injured area samples in fresh and putrefied brain tissue samples. The accuracy of the two models were 100% and 92.5%. Our preliminary research has proved that the use of FT-IR spectroscopy combined with chemometrics can identify TBI more quickly and accurately in cadavers, providing crucial evidence for judicial proceedings.

Non/mini-invasive monitoring of diabetes-induced myocardial damage by Fourier transform infrared spectroscopy: Evidence from biofluids

Early identification of diabetic cardiomyopathy (DCM) can help clinicians develop targeted treatment plans and forensic pathologists make accurate postmortem diagnoses. In the present study, diabetes-induced metabolic abnormalities in the myocardium and biofluids (plasma, urine, and saliva) of db/db mice of various ages (7, 12, and 21 weeks) were investigated by attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy. The results indicated that the diabetic and control groups had significantly different changes in the function groups of lipids, phosphate macromolecules (mostly nucleic acids), protein compositions and conformations, and carbohydrates (primarily glucose) in the myocardium and biofluids. The prediction model for quantifying DCM severity was developed on db/db mice's myocardial spectra using a genetic algorithm (GA)-partial least squares (PLS) regression method. Following that, the linear correlations between the predicted values for DCM severity and spectra for db/db biofluids were evaluated using the GA-PLS regression algorithm. The results showed there were good linear correlations between the predicted values for DCM severity and spectra for plasma (R² = 0.929), saliva (R² = 0.967), urine (R² = 0.954), and combination of plasma and saliva (R² = 0.980). This study provides a novel perspective on detecting diabetes-related biofluid and cardiac metabolic abnormalities and demonstrates the potential of biofluid infrared spectro-diagnostic models for non/mini-invasive assessment of DCM.

Artificial neural network in the discrimination of lung cancer based on infrared spectroscopy

Given the increasing prevalence of lung cancer worldwide, an auxiliary diagnostic method is needed alongside the microscopic examination of biopsy samples, which is dependent on the skills and experience of pathologists. Thus, this study aimed to advance lung cancer diagnosis by developing five (5) artificial neural network (NN) models that can discriminate malignant from benign samples based on infrared spectral data of lung tumors (n = 122; 56 malignant, 66 benign). NNs were benchmarked with classical machine learning (CML) models. Stratified 10-fold cross-validation was performed to evaluate the NN models, and the performance metrics-area under the curve (AUC), accuracy (ACC) positive predictive value (PPV), negative predictive value (NPV), specificity rate (SR), and recall rate (RR)-were averaged for comparison. All NNs were able to outperform the CML models, however, support vector machine is relatively comparable to NNs. Among the NNs, CNN performed best with an AUC of 92.28% ± 7.36%, ACC of 98.45% ± 1.72%, PPV of 96.62% ± 2.30%, NPV of 90.50% ± 11.92%, SR of 96.01% ± 3.09%, and RR of 89.21% ± 12.93%. In conclusion, NNs can be potentially used as a computational tool in lung cancer diagnosis based on infrared spectroscopy of lung tissues.

Perfluorooctanoic acid (PFOA)-induced alterations of biomolecules in the wetland plant Alismaorientale

Perfluoroalkyl substances (PFASs) have been widely studied by researchers due to their environmental persistence, chemical stability and potential toxicity. Some researchers have reported the physiological and biochemical toxicity of PFASs on plants through traditional and innovative methods; however, the changes in biological macromolecules caused by PFASs are rarely studied. Here, Fourier transform infrared spectroscopy (FTIR) was used to study how exposure to perfluorooctanoic acid (PFOA) alters the structure and function of biomolecules of the wetland plant Alisma orientale. Biomass results showed that PFOA had negative effects on plant growth. FTIR results showed that PFOA could result in changes in the structures, compositions, and functions of lipids, proteins and DNA in plant cells. In the treatment groups, the ratios of CH₃ to lipids and carbonyl esters to lipids increased compared with the control, while the ratios of CH₂ to lipids and olefinicCH to lipids decreased, which indicated lipid peroxidation caused by PFOA exposure. Changes in the compositions and secondary structures of proteins were also found, which were indicated by the decreased ratio of amide I to amide II and the increased ratio of β-sheet to α-helix in the treatment groups compared to the control. Moreover, PFOA affected the composition of DNA by promoting the B- to A-DNA transition. These results showed that the mechanism of PFOA toxicity toward plants at the biochemical level could be illustrated by FTIR.

Probing the Drug Dynamics of Chemotherapeutics Using Metasurface-Enhanced Infrared Reflection Spectroscopy of Live Cells

Infrared spectroscopy has drawn considerable interest in biological applications, but the measurement of live cells is impeded by the attenuation of infrared light in water. Metasurface-enhanced infrared reflection spectroscopy (MEIRS) had been shown to mitigate the problem, enhance the cellular infrared signal through surface-enhanced infrared absorption, and encode the cellular vibrational signatures in the reflectance spectrum at the same time. In this study, we used MEIRS to study the dynamic response of live cancer cells to a newly developed chemotherapeutic metal complex with distinct modes of action (MoAs): tricarbonyl rhenium isonitrile polypyridyl (TRIP). MEIRS measurements demonstrated that administering TRIP resulted in long-term (several hours) reduction in protein, lipid, and overall refractive index signals, and in short-term (tens of minutes) increase in these signals, consistent with the induction of endoplasmic reticulum stress. The unique tricarbonyl IR signature of TRIP in the bioorthogonal spectral window was monitored in real time, and was used as an infrared tag to detect the precise drug delivery time that was shown to be closely correlated with the onset of the phenotypic response. These results demonstrate that MEIRS is an effective label-free real-time cellular assay capable of detecting and interpreting the early phenotypic responses of cells to IR-tagged chemotherapeutics.

Multi- and transgenerational biochemical effects of low-dose exposure to bisphenol A and 4-nonylphenol on testicular interstitial (Leydig) cells

Bisphenol A (BPA) and 4-nonylphenol (NP) are well-known endocrine-disrupting chemicals (EDCs) that have been proven to affect Leydig cell (LC) functions and testosterone production, but whether BPA and NP have multi- and transgenerational biochemical effects on Leydig cells (LCs) is unknown. Fourier transform infrared (FTIR) spectroscopy is a powerful analytical technique that enables label-free and non-destructive analysis of the tissue specimen. Herein we employed FTIR coupled with chemometrics analysis to identify biomolecular changes in testicular interstitial (Leydig) cells of rats after chronic exposure to low doses of BPA and NP. Cluster segregations between exposed and control groups were observed based on the fingerprint region of 1800-900 cm^-1 in all generations. The main biochemical alterations for segregation were amide I, amide II and nucleic acids. BPA and NP single and co-exposure induced significant differences in the ratio of amide I to amide II compared to the corresponding control group in all generations. BPA exposure resulted in remarkable changes of cellular gene transcription and DNA oxidative damage across all generations. Direct exposure to BPA, NP, and BPA&NP of F0 and F1 generations could significantly decrease lipid accumulation in LCs in the F2 and F3 generations. The overall findings revealed that single or co-exposure to BPA and NP at environmental concentrations affects the biochemical structures and properties of LCs.

Microporous Multiresonant Plasmonic Meshes by Hierarchical Micro-Nanoimprinting for Bio-Interfaced SERS Imaging and Nonlinear Nano-Optics

Microporous mesh plasmonic devices have the potential to combine the biocompatibility of microporous polymeric meshes with the capabilities of plasmonic nanostructures to enhance nanoscale light-matter interactions for bio-interfaced optical sensing and actuation. However, scalable integration of dense and uniformly structured plasmonic hotspot arrays with microporous polymeric meshes remains challenging due to the processing incompatibility of conventional nanofabrication methods with flexible microporous substrates. Here, scalable nanofabrication of microporous multiresonant plasmonic meshes (MMPMs) is achieved via a hierarchical micro-/nanoimprint lithography approach using dissolvable polymeric templates. It is demonstrated that MMPMs can serve as broadband nonlinear nanoplasmonic devices to generate second-harmonic generation, third-harmonic generation, and upconversion photoluminescence signals with multiresonant plasmonic enhancement under fs pulse excitation. Moreover, MMPMs are employed and explored as bio-interfaced surface-enhanced Raman spectroscopy mesh sensors to enable in situ spatiotemporal molecular profiling of bacterial biofilm activity. Microporous mesh plasmonic devices open exciting avenues for bio-interfaced optical sensing and actuation applications, such as inflammation-free epidermal sensors in conformal contact with skin, combined tissue-engineering and biosensing scaffolds for in vitro 3D cell culture models, and minimally invasive implantable probes for long-term disease diagnostics and therapeutics.

Noninvasive Diagnostic for COVID-19 from Saliva Biofluid via FTIR Spectroscopy and Multivariate Analysis

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the worst global health crisis in living memory. The reverse transcription polymerase chain reaction (RT-qPCR) is considered the gold standard diagnostic method, but it exhibits limitations in the face of enormous demands. We evaluated a mid-infrared (MIR) data set of 237 saliva samples obtained from symptomatic patients (138 COVID-19 infections diagnosed via RT-qPCR). MIR spectra were evaluated via unsupervised random forest (URF) and classification models. Linear discriminant analysis (LDA) was applied following the genetic algorithm (GA-LDA), successive projection algorithm (SPA-LDA), partial least squares (PLS-DA), and a combination of dimension reduction and variable selection methods by particle swarm optimization (PSO-PLS-DA). Additionally, a consensus class was used. URF models can identify structures even in highly complex data. Individual models performed well, but the consensus class improved the validation performance to 85% accuracy, 93% sensitivity, 83% specificity, and a Matthew's correlation coefficient value of 0.69, with information at different spectral regions. Therefore, through this unsupervised and supervised framework methodology, it is possible to better highlight the spectral regions associated with positive samples, including lipid (∼1700 cm-1), protein (∼1400 cm-1), and nucleic acid (∼1200-950 cm-1) regions. This methodology presents an important tool for a fast, noninvasive diagnostic technique, reducing costs and allowing for risk reduction strategies.