The aim of this study is to evaluate the ability of infrared wavenumber of calculus to predict postoperative infection in patients with upper urinary tract calculus (UUTC), and to establish a predictive model based on this. From March 2018 to March 2023, 480 UUTC patients from Fujian Provincial Hospital were included in this study. The infrared-wavenumbers related infection score (IR-infection score) was constructed by univariate analysis, multicollinearity screening, and Lasso analysis to predict postoperative infection. Continually, the Delong test was used to compare the predictive power between the IR-infection score and traditional indicators. Afterward, we performed urine metagene sequencing and stone culture to prove the correlation between calculus toxicity and IR-infection score. Finally, logistic regression was used to build a nomogram. IR-infection score composed of four independent wavenumbers could precisely predict postoperative infection (AUCvalidation cohort = 0.707) and sepsis (AUCvalidation cohort = 0.824). IR-infection score had better predictive ability than commonly used clinical indicators. Moreover, metagenomics sequencing and calculus culture confirmed the correlation between IR-infection score and calculus toxicity (all P < 0.05). The nomogram based on the IR-infection score had high predictive power (all AUCs > 0.803). Our study first developed a novel infrared spectroscopy marker and nomogram, which can help urologists better predict postoperative infection in UUTC patients.

Myasthenia Gravis (MG) is a rare neurological disease. Although there are intensive efforts, the underlying mechanism of MG still has not been fully elucidated, and early diagnosis is still a question mark. Diagnostic paraclinical tests are also time-consuming, burden patients financially, and sometimes all test results can be negative. Therefore, rapid, cost-effective novel methods are essential for the early accurate diagnosis of MG. Here, we aimed to determine MG-induced spectral biomarkers from blood serum using infrared spectroscopy. Furthermore, infrared spectroscopy coupled with multivariate analysis methods e.g., principal component analysis (PCA), support vector machine (SVM), discriminant analysis and Neural Network Classifier were used for rapid MG diagnosis. The detailed spectral characterization studies revealed significant increases in lipid peroxidation; saturated lipid, protein, and DNA concentrations; protein phosphorylation; PO2-asym + sym /protein and PO2-sym/lipid ratios; as well as structural changes in protein with a significant decrease in lipid dynamics. All these spectral parameters can be used as biomarkers for MG diagnosis and also in MG therapy. Furthermore, MG was diagnosed with 100% accuracy, sensitivity and specificity values by infrared spectroscopy coupled with multivariate analysis methods. In conclusion, FTIR spectroscopy coupled with machine learning technology is advancing towards clinical translation as a rapid, low-cost, sensitive novel approach for MG diagnosis.

Electron spin resonance coupled with uranium-series dating (ESR/U-series) of carbonate hydroxyapatite in tooth enamel is the main technique used to obtain age determinations from Pleistocene fossils beyond the range of radiocarbon dating. This chronological information allows to better understand diachronic change in the palaeontological record, especially with regard to the evolution of the genus Homo. Given the relative paucity of human teeth at palaeontological and archaeological localities, ESR/U-series is widely applied to the teeth of ungulate species. However, the accuracy of ESR/U-series ages is greatly affected by the incorporation of uranium in the enamel during burial in sediments. It has been shown that uranium content is positively correlated with an increased degree of atomic order in carbonate hydroxyapatite crystals, the latter determined using infrared spectroscopy. Here we present a reference infrared spectral library of tooth enamel from African ungulates, based on the grinding curve method, which serves as baseline to track the diagenetic history of carbonate hydroxyapatite in different species and thus select the best-preserved specimens for dating.

The development of new methods of identification of active pharmaceutical ingredients (API) is a subject of paramount importance for research centers, the pharmaceutical industry, and law enforcement agencies. Here, a system for identifying and classifying pharmaceutical tablets containing acetaminophen (AAP) by brand has been developed. In total, 15 tablets of 11 brands for a total of 165 samples were analyzed. Mid-infrared vibrational spectroscopy with multivariate analysis was employed. Quantum cascade lasers (QCLs) were used as mid-infrared sources. IR spectra in the spectral range 980-1600 cm-1 were recorded. Five different classification methods were used. First, a spectral search through correlation indices. Second, machine learning algorithms such as principal component analysis (PCA), support vector classification (SVC), decision tree classifier (DTC), and artificial neural network (ANN) were employed to classify tablets by brands. SNV and first derivative were used as preprocessing to improve the spectral information. Precision, recall, specificity, F1-score, and accuracy were used as criteria to evaluate the best SVC, DEE, and ANN classification models obtained. The IR spectra of the tablets show characteristic vibrational signals of AAP and other APIs present. Spectral classification by spectral search and PCA showed limitations in differentiating between brands, particularly for tablets containing AAP as the only API. Machine learning models, specifically SVC, achieved high accuracy in classifying AAP tablets according to their brand, even for brands containing only AAP.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common form of liver cancer, with cirrhosis being a major risk factor. Traditional blood markers like alpha-fetoprotein (AFP) demonstrate limited efficacy in distinguishing between HCC and cirrhosis, underscoring the need for more effective diagnostic methodologies. In this context, extracellular vesicles (EVs) have emerged as promising candidates; however, their practical diagnostic application is restricted by the current lack of label-free methods to accurately profile their molecular content. To address this gap, our study explores the potential of mid-infrared (mid-IR) spectroscopy, both alone and in combination with plasmonic nanostructures, to detect and characterize circulating EVs.
RESULTS: EVs were extracted from HCC and cirrhotic patients. Mid-IR spectroscopy in the Attenuated Total Reflection (ATR) mode was utilized to identify potential signatures for patient classification, highlighting significant changes in the Amide I-II region (1475-1700 cm-1). This signature demonstrated diagnostic performance comparable to AFP and surpassed it when the two markers were combined. Further investigations utilized a plasmonic metasurface suitable for ultrasensitive spectroscopy within this spectral range. This device consists of two sets of parallel rod-shaped gold nanoantennas (NAs); the longer NAs produced an intense near-field amplification in the Amide I-II bands, while the shorter NAs were utilized to provide a sharp reflectivity edge at 1800-2200 cm-1 for EV mass-sensing. A clinically relevant subpopulation of EVs was targeted by conjugating NAs with an antibody specific to Epithelial Cell Adhesion Molecule (EpCAM). This methodology enabled the detection of variations in the quantity of EpCAM-presenting EVs and revealed changes in the Amide I-II lineshape.
CONCLUSIONS: The presented results can positively impact the development of novel laboratory methods for the label-free characterization of EVs, based on the combination between mid-IR spectroscopy and plasmonics. Additionally, data obtained by using HCC and cirrhotic subjects as a model system, suggest that this approach could be adapted for monitoring these conditions.

Yeast phenylalanine tRNA (tRNAphe) is a paradigmatic model in structural biology. In this work, we combine molecular dynamics simulations and spectroscopy modeling to establish a direct link between its structure, conformational dynamics, and infrared (IR) spectra. Employing recently developed vibrational frequency maps and coupling models, we apply a mixed quantum/classical treatment of the line shape theory to simulate the IR spectra of tRNAphe in the 1600-1800 cm-1 region across its folded and unfolded conformations and under varying concentrations of Mg2+ ions. The predicted IR spectra of folded and unfolded tRNAphe are in good agreement with experimental measurements, validating our theoretical framework. We then elucidate how the characteristic L-shaped tertiary structure of the tRNA and its modulation in response to diverse chemical environments give rise to distinct IR absorption peaks and line shapes. These calculations effectively bridge IR spectroscopy experiments and atomistic molecular simulations, unraveling the molecular origins of the observed IR spectra of tRNAphe. This work presents a robust theoretical protocol for modeling the IR spectroscopy of nucleic acids, which will facilitate its application as a sensitive probe for detecting the fluctuating secondary and tertiary structures of these essential biological macromolecules.

The dynamics of lysozyme is probed by attaching -SCN to all alanine residues. The one-dimensional infrared spectra exhibit frequency shifts in the position of the maximum absorption of 4 cm-1, which is consistent with experiments in different solvents and indicates moderately strong interactions of the vibrational probe with its environment. Isotopic substitution 12C → 13C leads to a redshift by -47 cm-1, which agrees quantitatively with experiments for CN-substituted copper complexes in solution. The low-frequency, far-infrared part of the protein spectra contains label-specific information in the difference spectra when compared with the wild type protein. Depending on the position of the labels, local structural changes are observed. For example, introducing the -SCN label at Ala129 leads to breaking of the α-helical structure with concomitant change in the far-infrared spectrum. Finally, changes in the local hydration of SCN-labeled alanine residues as a function of time can be related to the reorientation of the label. It is concluded that -SCN is potentially useful for probing protein dynamics, both in the high-frequency part (CN-stretch) and in the far-infrared part of the spectrum.

Vibrational spectroscopy of protein structure often utilizes 13C18O-labeling of backbone carbonyls to further increase structural resolution. However, sidechains such as arginine, aspartate, and glutamate absorb within the same spectral region, complicating the analysis of isotope-labeled peaks. In this study, we report that the waiting time between pump and probe pulses in two-dimensional infrared spectroscopy can be used to suppress sidechain modes in favor of backbone amide I\' modes based on differences in vibrational lifetimes. Furthermore, differences in the lifetimes of 13C18O-amide I\' modes can aid in the assignment of secondary structure for labeled residues. Using model disordered and β-sheet peptides, it was determined that while β-sheets exhibit a longer lifetime than disordered structures, amide I\' modes in both secondary structures exhibit longer lifetimes than sidechain modes. Overall, this work demonstrates that collecting 2D IR data at delayed waiting times, based on differences in vibrational lifetime between modes, can be used to effectively suppress interfering sidechain modes and further identify secondary structures.

ANOVA-simultaneous component analysis (ASCA) was applied to short-wave infrared spectral fingerprints of 5 malting barley varieties collected using a hyperspectral imaging system to determine the effect of germination, the influence of time and the influence of barley by means of a full factorial experimental design. ASCA indicated that there was a significant (p < 0.0001) effect of the germination status, the germination time and interaction on the spectral data for all varieties. The biochemical and physiological modification of the samples were characterised by visualisation of the longitudinal scores obtained from simultaneous component analysis for the germination time factor. This resulted in the visualisation and explanation of biochemical change over the course of barley germination as a factor of time. The relevant loadings indicated a significant change to the proteome, lipid and starch structure as driven by the uptake of water over time. The ASCA model were extrapolated to include the effect of barley variety to the already mentioned germination status and germination time factors, resulting once again in all the effects being significant (p < 0.0001). Here it was shown that all the barley varieties are significantly different from one another pre- and post-modification, based on the molecular vibrations observed in the short wave-infrared (SWIR) spectra, suggesting that the detection of biotic stress factors, such as pre-harvest germination, also differ for each variety, by indicating that the germination profile of each barley variety varies as a function of germination time. Thus, also the malting performance, germinative energy and chemical profile of each barley variety tested will vary before, during and after imbibition and germination - indicating the importance of malting commercial barley malt true to variety. These results indicate that (SWIR) spectral imaging instrumentation can possibly be used to monitor controlled germination of barley grain. Due to the shown ability of SWIR spectral imaging to detect small biochemical changes over time of barley grain during germination.

Glycans are oligosaccharides attached to proteins or lipids and affect their functions, such as drug efficacy, structural contribution, metabolism, immunogenicity, and molecular recognition. Conventional glycosylation analysis has relied on destructive, slow, system-sensitive methods, including enzymatic reactions, chromatography, fluorescence labeling, and mass spectrometry. Herein, we propose quantum cascade laser (QCL) infrared (IR) spectroscopy as a rapid, nondestructive method to quantify glycans and their monosaccharide composition. Previously, we demonstrated high-sensitivity IR spectroscopy of protein solution using solvent absorption compensation (SAC) and double-beam modulation (DBM) techniques. However, the SAC-DBM approach suffered a limited frequency scanning range (<400 cm-1) due to the light dispersion by acousto-optic modulators (AOMs). Here, we implemented a mirror-based double-pass AOM in the SAC-DBM scheme and successfully extended the frequency range to (970 to 1840 cm-1), which encompasses the vibrational fingerprint of biomolecules. The extended frequency range allowed the simultaneous observation of monosaccharide ring bands (1000 to 1200 cm-1) and protein amide bands (1500 to 1700 cm-1). We compared the IR spectra of six glycoproteins and two nonglycosylated proteins with the results from intact mass spectrometry. The IR absorbance ratios of the ring band to the amide band of glycoproteins in solutions showed a linear correlation with the ratios of glycan to protein backbone masses. Furthermore, a multivariate analysis produced monosaccharide compositions consistent with the reported database for the glycoproteins, and the monosaccharide compositions were used to improve the predictability of the glycan-protein mass ratio from the IR-absorbance ratio. This nondestructive, high-sensitivity QCL-IR spectroscopy could be used as a standard method to monitor batch-to-batch comparability during drug manufacturing and quantify the glycosylation and monosaccharide composition of new glycoproteins and other glycosylated biosystems.