Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring

Multi-spectroscopic characterization of organic salt components in medicinal plant

The characterization of structure of organic salts in complex mixtures has been a difficult problem in analytical chemistry. In the analysis of Scutellariae Radix (SR), the pharmacopoeia of many countries stipulates that the quality control component is baicalin (≥9% by high performance liquid chromatography (HPLC)). The component with highest response in SR was also baicalin detected by liquid chromatography-mass spectrometry (LC-MS). However, in the attenuated total reflection Fourier transform infrared spectroscopy, the carbonyl peak of glucuronic acid of baicalin did not appear in SR. The results of element analysis, time of flight secondary ion mass spectrometry, matrix assisted laser desorption ionization mass spectrometry and solid-state nuclear magnetic resonance all supported the existence of baicalin magnesium salt. Based on this, this study proposes an analysis strategy guided by infrared spectroscopy and combined with multi-spectroscopy techniques to analyze the structure of organic salt components in medicinal plant. It is meaningful for the research of mechanisms, development of new drugs, and quality control.

Unveiling brain disorders using liquid biopsy and Raman spectroscopy

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

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.

A novel interpretable machine learning model approach for the prediction of TiO2 photocatalytic degradation of air contaminants

Air contaminants lead to various environmental and health issues. Titanium dioxide (TiO2) features the benefits of autogenous photocatalytic degradation of air contaminants. To evaluate its performance, laboratory experiments are commonly used to determine the kinetics of the photocatalytic-degradation rate, which is labor intensive, time-consuming, and costly. In this study, Machine Learning (ML) models were developed to predict the photo-degradation rate constants of air-borne organic contaminants with TiO2 nanoparticles and ultraviolet irradiation. The hyperparameters of the ML models were optimized, which included Artificial Neural Network (ANN) with Bayesian optimization, gradient booster regressor (GBR) with Bayesian optimization, Extreme Gradient Boosting (XGBoost) with optimization using Hyperopt, and Catboost combined with Adaboost. The organic contaminant was encoded through Molecular fingerprints (MF). Imputation method was applied to deal with the missing data. A generative ML model Vanilla Gan was utilized to create synthetic data to further augment the size of available dataset and the SHapley Additive exPlanations (SHAP) was employed for ML model interpretability. The results indicated that data imputation allowed for the full utilization of the limited dataset, leading to good machine learning prediction performance and preventing common overfitting problems with small-sized data. Additionally, augmenting experimental data with synthetic data significantly improved prediction accuracy and considerably reduced overfitting issues. The results ranked the feature importance and assessed the impacts of different experimental variables on the rate of photo-degradation, which were consistent with physico-chemical laws.

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.

Probing Blood Plasma Protein Glycosylation with Infrared Spectroscopy

The health state of an individual is closely linked to the glycosylation patterns of his or her blood plasma proteins. However, obtaining this information requires cost- and time-efficient analytical methods. We put forward infrared spectroscopy, which allows label-free analysis of protein glycosylation but so far has only been applied to analysis of individual proteins. Although spectral information does not directly provide the molecular structure of the glycans, it is sensitive to changes therein and covers all types of glycosidic linkages. Combining single-step ion exchange chromatography with infrared spectroscopy, we developed a workflow that enables the separation and analysis of major protein classes in blood plasma. Our results demonstrate that infrared spectroscopy can identify different patterns and global levels of glycosylation of intact plasma proteins. To showcase the strengths and limitations of the proposed approach, we compare the glycoforms of human and bovine alpha-1-acid glycoproteins, which exhibit highly variable global levels of glycosylation. To independently evaluate our conclusions, the glycan moieties of human alpha-1-acid glycoprotein were further analyzed using an established glycomics workflow. Importantly, the chromatographic separation of blood plasma improves the detection of aberrant glycoforms of a given protein as compared to infrared spectroscopy of bulk plasma. The presented approach allows a time-efficient comparison of glycosylation patterns of multiple plasma proteins, opening new avenues for biomedical probing.

Antimicrobial susceptibility testing using infrared attenuated total reflection (IR-ATR) spectroscopy to monitor metabolic activity

Fast and accurate detection of antimicrobial resistance in pathogens remains a challenge, and with the increase in antimicrobial resistance due to mis- and overuse of antibiotics, it has become an urgent public health problem. We demonstrate how infrared attenuated total reflection (IR-ATR) can be used as a simple method for assessment of bacterial susceptibility to antibiotics. This is achieved by monitoring the metabolic activities of bacterial cells via nutrient consumption and using this as an indicator of bacterial viability. Principal component analysis of the obtained spectra provides a tool for fast and simple discrimination of antimicrobial resistance in the acquired data. We demonstrate this concept using four bacterial strains and four different antibiotics, showing that the change in glucose concentration in the growth medium after 2 h, as monitored by IR-ATR, can be used as a spectroscopic diagnostic technique, to reduce detection time and to improve quality in the assessment of antimicrobial resistance in pathogens.

Continuous-Spectrum-Polarization Recombinant Optical Encryption with a Dielectric Metasurface

The Jones matrix, with eight degrees of freedom (DoFs), provides a general mathematical framework for the multifunctional design of metasurfaces. Theoretically, the maximum eight DoFs can be further extended in the spectrum dimension to endow unique encryption capabilities. However, the topology and intrinsic spectral responses of meta-atoms constrains the continuous engineering of polarization evolution over wavelength dimension. In this work, a forward evolution strategy to quickly establish the mapping relationships between the solutions of the dispersion Jones matrix and the spectral responses of meta-atoms is reported. Based on the eigenvector transformation method, arbitrary conjugate polarization channels over the continuous-spectrum dimension are successfully reconstructed. As a proof-of-concept, a silicon metadevice is demonstrated for optical information encryption transmission. Remarkably, the arbitrary combination forms of polarization and wavelength dimension increase the information capacity (210 ), and the measured polarization contrasts of the conjugate polarization conversion are >94% in the entire wavelength range (3-4 µm). It is believed that the proposed approach will benefit secure optical and quantum information technologies.

Research Progress in Surface-Enhanced Infrared Absorption Spectroscopy: From Performance Optimization, Sensing Applications, to System Integration

Infrared absorption spectroscopy is an effective tool for the detection and identification of molecules. However, its application is limited by the low infrared absorption cross-section of the molecule, resulting in low sensitivity and a poor signal-to-noise ratio. Surface-Enhanced Infrared Absorption (SEIRA) spectroscopy is a breakthrough technique that exploits the field-enhancing properties of periodic nanostructures to amplify the vibrational signals of trace molecules. The fascinating properties of SEIRA technology have aroused great interest, driving diverse sensing applications. In this review, we first discuss three ways for SEIRA performance optimization, including material selection, sensitivity enhancement, and bandwidth improvement. Subsequently, we discuss the potential applications of SEIRA technology in fields such as biomedicine and environmental monitoring. In recent years, we have ushered in a new era characterized by the Internet of Things, sensor networks, and wearable devices. These new demands spurred the pursuit of miniaturized and consolidated infrared spectroscopy systems and chips. In addition, the rise of machine learning has injected new vitality into SEIRA, bringing smart device design and data analysis to the foreground. The final section of this review explores the anticipated trajectory that SEIRA technology might take, highlighting future trends and possibilities.

Identification of acute myeloid leukemia by infrared difference spectrum of peripheral blood

Acute myeloid leukemia (AML) is a high mortality and recurrence rates hematologic malignancy. Thus, whatever early detection or subsequent visit are both of high significance. Traditional AML diagnosis is conducted via peripheral blood (PB) smear and bone marrow (BM) aspiration. But BM aspiration is a painful burden for patients especially in early detection or subsequent visit. Herein, the use of PB to evaluate and identify the leukemia characteristics will be an attractive alternative source for early detection or subsequent visit. Fourier transform infrared spectroscopy (FTIR) is a time- and cost-effective approach to reveal the disease-related molecular features and variations. However, to the best of our knowledge, there is no attempts using infrared spectroscopic signatures of PB to replace BM for identifying AML. In this work, we are the first to develop a rapid and minimally invasive method to identify AML by infrared difference spectrum (IDS) of PB with only 6 characteristic wavenumbers. We dissect the leukemia-related spectroscopic signatures of three subtypes of leukemia cells (U937, HL-60, THP-1) by IDS, revealing biochemical molecular information about leukemia for the first time. Furthermore, the novel study links cellular features to complex features of blood system which demonstrates the sensitivity and specificity with IDS method. On this basis, BM and PB of AML patients and healthy controls were provided to parallel comparison. The IDS of BM and PB combined with principal component analysis method revealing that the leukemic components in BM and PB can be described by IDS peaks of PCA loadings, respectively. It is demonstrated that the leukemic IDS signatures of BM can be replaced by the leukemic IDS signatures of PB. In addition, the IDS signatures of leukemia cells are reflected in PB of AML patients with peaks of 1629, 1610, 1604, 1536, 1528 and 1404 cm^-1 for the first time as well. To this end, we access the leukemic signatures of IDS peaks to compare the PB of AMLs and healthy controls. It is confirmed that the leukemic components can be detected from PB of AML and distinguished into positive (100%) and negative (100%) groups successfully by IDS classifier which is a novel and unique spectral classifier. This work demonstrates the potential use of IDS as a powerful tool to detect leukemia via PB which can release subjects' pain remarkably.

Upconversion time-stretch infrared spectroscopy

High-speed measurement confronts the extreme speed limit when the signal becomes comparable to the noise level. In the context of broadband mid-infrared spectroscopy, state-of-the-art ultrafast Fourier-transform infrared spectrometers, in particular dual-comb spectrometers, have improved the measurement rate up to a few MSpectra s^-1, which is limited by the signal-to-noise ratio. Time-stretch infrared spectroscopy, an emerging ultrafast frequency-swept mid-infrared spectroscopy technique, has shown a record-high rate of 80 MSpectra s^-1 with an intrinsically higher signal-to-noise ratio than Fourier-transform spectroscopy by more than the square-root of the number of spectral elements. However, it can measure no more than ~30 spectral elements with a low resolution of several cm^-1. Here, we significantly increase the measurable number of spectral elements to more than 1000 by incorporating a nonlinear upconversion process. The one-to-one mapping of a broadband spectrum from the mid-infrared to the near-infrared telecommunication region enables low-loss time-stretching with a single-mode optical fiber and low-noise signal detection with a high-bandwidth photoreceiver. We demonstrate high-resolution mid-infrared spectroscopy of gas-phase methane molecules with a high resolution of 0.017 cm^-1. This unprecedentedly high-speed vibrational spectroscopy technique would satisfy various unmet needs in experimental molecular science, e.g., measuring ultrafast dynamics of irreversible phenomena, statistically analyzing a large amount of heterogeneous spectral data, or taking broadband hyperspectral images at a high frame rate.

Rapid Biomarker-Based Diagnosis of Fibromyalgia Syndrome and Related Rheumatologic Disorders by Portable FT-IR Spectroscopic Techniques

Fibromyalgia syndrome (FM), one of the most common illnesses that cause chronic widespread pain, continues to present significant diagnostic challenges. The objective of this study was to develop a rapid vibrational biomarker-based method for diagnosing fibromyalgia syndrome and related rheumatologic disorders (systemic lupus erythematosus (SLE), osteoarthritis (OA) and rheumatoid arthritis (RA)) through portable FT-IR techniques. Bloodspot samples were collected from patients diagnosed with FM (n = 122) and related rheumatologic disorders (n = 70), including SLE (n = 17), RA (n = 43), and OA (n = 10), and stored in conventional protein saver bloodspot cards. The blood samples were prepared by four different methods (blood aliquots, protein-precipitated extraction, and non-washed and water-washed semi-permeable membrane filtration extractions), and spectral data were collected with a portable FT-IR spectrometer. Pattern recognition analysis, OPLS-DA, was able to identify the signature profile and classify the spectra into corresponding classes (Rcv > 0.93) with excellent sensitivity and specificity. Peptide backbones and aromatic amino acids were predominant for the differentiation and might serve as candidate biomarkers for syndromes such as FM. This research evaluated the feasibility of portable FT-IR combined with chemometrics as an accurate and high-throughput tool for distinct spectral signatures of biomarkers related to the human syndrome (FM), which could allow for real-time and in-clinic diagnostics of FM.

Study of the suitable climate factors and geographical origins traceability of Panax notoginseng based on correlation analysis and spectral images combined with machine learning

INTRODUCTION

The cultivation and sale of medicinal plants are some of the main ways to meet the increased market demand for plant-based drugs. Panax notoginseng is a widely used Chinese medicinal material. The growth and accumulation of bioactive constituents mainly depend on a satisfactory growing environment. Additionally, the occurrence of market fraud means that care should be taken when purchasing.

METHODS

In this study, we report the correlation between saponins and climate factors based on high performance liquid chromatography (HPLC), and evaluate the influence of climate factors on the quality of P. notoginseng. In addition, the synchronous two-dimensional correlation spectroscopy (2D-COS) images of near infrared (NIR) data combined with the deep learning model were applied to traceability of geographic origins of P. notoginseng at two different levels (district and town levels).

RESULTS

The results indicated that the contents of saponins in P. notoginseng are negatively related to the annual mean temperature and the temperature annual range. A lower annual mean temperature and temperature annual range are favorable for the content accumulation of saponins. Additionally, high annual precipitation and high humidity are conducive to the content accumulation of Notoginsenoside R1 (NG-R1), Ginsenosides Rg1 (G-Rg1), and Ginsenosides Rb1 (G-Rb1), while Ginsenosides Rd (G-Rd), this is not the case. Regarding geographic origins, classifications at two different levels could be successfully distinguished through synchronous 2D-COS images combined with the residual convolutional neural network (ResNet) model. The model accuracy of the training set, test set, and external validation is achieved at 100%, and the cross-entropy loss function curves are lower. This demonstrated the potential feasibility of the proposed method for P. notoginseng geographic origin traceability, even if the distance between sampling points is small.

DISCUSSION

The findings of this study could improve the quality of P. notoginseng, provide a reference for cultivating P. notoginseng in the future and alleviate the occurrence of market fraud.

Ultra-broadband all-optical sampling of optical waveforms

Optical-field sampling techniques provide direct access to the electric field of visible and near-infrared light. The existing methods achieve the necessary bandwidth using highly nonlinear light-matter interaction that involves ionization of atoms or generation of charge carriers in solids. We demonstrate an alternative, all-optical approach for measuring electric fields of broadband laser pulses, which offers an advantage in terms of sensitivity and signal-to-noise ratio and extends the detection bandwidth of optical methods to the petahertzdomain.

Fast and Deep Diagnosis Using Blood-Based ATR-FTIR Spectroscopy for Digestive Tract Cancers

Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) of liquid biofluids enables the probing of biomolecular markers for disease diagnosis, characterized as a time and cost-effective approach. It remains poorly understood for fast and deep diagnosis of digestive tract cancers (DTC) to detect abundant changes and select specific markers in a broad spectrum of molecular species. Here, we present a diagnostic protocol of DTC in which the in-situ blood-based ATR-FTIR spectroscopic data mining pathway was designed for the identification of DTC triages in 252 blood serum samples, divided into the following groups: liver cancer (LC), gastric cancer (GC), colorectal cancer (CC), and their different three stages respectively. The infrared molecular fingerprints (IMFs) of DTC were measured and used to build a 2-dimensional second derivative spectrum (2D-SD-IR) feature dataset for classification, including absorbance and wavenumber shifts of FTIR vibration peaks. By comparison, the Partial Least-Squares Discriminant Analysis (PLS-DA) and backpropagation (BP) neural networks are suitable to differentiate DTCs and pathological stages with a high sensitivity and specificity of 100% and averaged more than 95%. Furthermore, the measured IMF data was mutually validated via clinical blood biochemistry testing, which indicated that the proposed 2D-SD-IR-based machine learning protocol greatly improved DTC classification performance.

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.

Wavelength-multiplexed hook nanoantennas for machine learning enabled mid-infrared spectroscopy

Infrared (IR) plasmonic nanoantennas (PNAs) are powerful tools to identify molecules by the IR fingerprint absorption from plasmon-molecules interaction. However, the sensitivity and bandwidth of PNAs are limited by the small overlap between molecules and sensing hotspots and the sharp plasmonic resonance peaks. In addition to intuitive methods like enhancement of electric field of PNAs and enrichment of molecules on PNAs surfaces, we propose a loss engineering method to optimize damping rate by reducing radiative loss using hook nanoantennas (HNAs). Furthermore, with the spectral multiplexing of the HNAs from gradient dimension, the wavelength-multiplexed HNAs (WMHNAs) serve as ultrasensitive vibrational probes in a continuous ultra-broadband region (wavelengths from 6 μm to 9 μm). Leveraging the multi-dimensional features captured by WMHNA, we develop a machine learning method to extract complementary physical and chemical information from molecules. The proof-of-concept demonstration of molecular recognition from mixed alcohols (methanol, ethanol, and isopropanol) shows 100% identification accuracy from the microfluidic integrated WMHNAs. Our work brings another degree of freedom to optimize PNAs towards small-volume, real-time, label-free molecular recognition from various species in low concentrations for chemical and biological diagnostics.

Infrared spectroscopy with plasmonic nanoantennas is limited by small overlap between molecules and hot spots, and sharp resonance peaks. The authors demonstrate spectral multiplexing of hook nanoantennas with gradient dimensions as ultrasensitive vibrational probes in a continuous ultra-broadband region and utilize machine learning for enhanced sensing performance.

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.

Breast-cancer detection using blood-based infrared molecular fingerprints

BACKGROUND

Breast cancer screening is currently predominantly based on mammography, tainted with the occurrence of both false positivity and false negativity, urging for innovative strategies, as effective detection of early-stage breast cancer bears the potential to reduce mortality. Here we report the results of a prospective pilot study on breast cancer detection using blood plasma analyzed by Fourier-transform infrared (FTIR) spectroscopy - a rapid, cost-effective technique with minimal sample volume requirements and potential to aid biomedical diagnostics. FTIR has the capacity to probe health phenotypes via the investigation of the full repertoire of molecular species within a sample at once, within a single measurement in a high-throughput manner. In this study, we take advantage of cross-molecular fingerprinting to probe for breast cancer detection.

METHODS

We compare two groups: 26 patients diagnosed with breast cancer to a same-sized group of age-matched healthy, asymptomatic female participants. Training with support-vector machines (SVM), we derive classification models that we test in a repeated 10-fold cross-validation over 10 times. In addition, we investigate spectral information responsible for BC identification using statistical significance testing.

RESULTS

Our models to detect breast cancer achieve an average overall performance of 0.79 in terms of area under the curve (AUC) of the receiver operating characteristic (ROC). In addition, we uncover a relationship between the effect size of the measured infrared fingerprints and the tumor progression.

CONCLUSION

This pilot study provides the foundation for further extending and evaluating blood-based infrared probing approach as a possible cross-molecular fingerprinting modality to tackle breast cancer detection and thus possibly contribute to the future of cancer screening.

Enhanced Molecular Infrared Spectroscopy Employing Bilayer Graphene Acoustic Plasmon Resonator

Graphene plasmon resonators with the ability to support plasmonic resonances in the infrared region make them a promising platform for plasmon-enhanced spectroscopy techniques. Here we propose a resonant graphene plasmonic system for infrared spectroscopy sensing that consists of continuous graphene and graphene ribbons separated by a nanometric gap. Such a bilayer graphene resonator can support acoustic graphene plasmons (AGPs) that provide ultraconfined electromagnetic fields and strong field enhancement inside the nano-gap. This allows us to selectively enhance the infrared absorption of protein molecules and precisely resolve the molecular structural information by sweeping graphene Fermi energy. Compared to the conventional graphene plasmonic sensors, the proposed bilayer AGP sensor provides better sensitivity and improvement of molecular vibrational fingerprints of nanoscale analyte samples. Our work provides a novel avenue for enhanced infrared spectroscopy sensing with ultrasmall volumes of molecules.

Infrared molecular fingerprinting of blood-based liquid biopsies for the detection of cancer

Recent omics analyses of human biofluids provide opportunities to probe selected species of biomolecules for disease diagnostics. Fourier-transform infrared (FTIR) spectroscopy investigates the full repertoire of molecular species within a sample at once. Here, we present a multi-institutional study in which we analysed infrared fingerprints of plasma and serum samples from 1639 individuals with different solid tumours and carefully matched symptomatic and non-symptomatic reference individuals. Focusing on breast, bladder, prostate, and lung cancer, we find that infrared molecular fingerprinting is capable of detecting cancer: training a support vector machine algorithm allowed us to obtain binary classification performance in the range of 0.78-0.89 (area under the receiver operating characteristic curve [AUC]), with a clear correlation between AUC and tumour load. Intriguingly, we find that the spectral signatures differ between different cancer types. This study lays the foundation for high-throughput onco-IR-phenotyping of four common cancers, providing a cost-effective, complementary analytical tool for disease recognition.

Molecular Origin of Blood-Based Infrared Spectroscopic Fingerprints*

Infrared spectroscopy of liquid biopsies is a time- and cost-effective approach that may advance biomedical diagnostics. However, the molecular nature of disease-related changes of infrared molecular fingerprints (IMFs) remains poorly understood, impeding the method's applicability. Here we probe 148 human blood sera and reveal the origin of the variations in their IMFs. To that end, we supplemented infrared spectroscopy with biochemical fractionation and proteomic profiling, providing molecular information about serum composition. Using lung cancer as an example of a medical condition, we demonstrate that the disease-related differences in IMFs are dominated by contributions from twelve highly abundant proteins-that, if used as a pattern, may be instrumental for detecting malignancy. Tying proteomic to spectral information and machine learning advances our understanding of the infrared spectra of liquid biopsies, a framework that could be applied to probing of any disease.