Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy最新文献

Parkinson's disease (PD) diagnosis faces substantial challenges due to the lack of reliable biomarkers and the limitations of existing detection techniques. Exosomes, which carry biomolecular cargo reflective of disease pathology, are increasingly recognized as promising biomarkers because of their stable presence in biofluids and accessibility through minimally invasive methods. Surface-enhanced Raman spectroscopy (SERS) provides high sensitivity and rapid molecular fingerprinting, but its clinical translation is hindered by spectral variability and sample heterogeneity. To address these limitations, we developed a novel diagnostic approach by integrating serum exosome SERS with support vector machine (SVM) classification. Systematic evaluation of 27 distinct data preprocessing strategies confirmed that data preprocessing critically influences classification performance, and the optimized model successfully differentiated PD patients from normal controls (NC), achieving an accuracy of 0.85 (95% confidence interval [CI], 0.75-1.00) and an area under the receiver operating characteristic curve (AUC) of 0.85 (95% CI, 0.67-1.00), which was statistically significant as validated by permutation testing (p < 0.05). Comparative analysis with the diagnostic criteria of the Movement Disorder Society (MDS) demonstrated that our model outperforms several conventional MDS methods. Furthermore, this study revealed that the "coffee-ring" effect, which introduces sample heterogeneity during SERS measurements, substantially compromised reproducibility and predictive accuracy. Several post-processing strategies were implemented to mitigate the coffee-ring effect, and notably, one of these strategies achieved results comparable to those of the optimal model (AUC = 0.85, accuracy = 0.85). This demonstrates that such post-processing approaches can effectively suppress the influence of the "coffee-ring" effect. In addition, linear SVM feature importance mapping identified potential exosomal biomarkers, including proteins (S-S stretching, tryptophan), nucleic acids (adenine vibrations, CH₃/CH₂ twisting), lipids, and saccharides. Collectively, these findings highlight a promising strategy for clinical PD diagnosis by combining exosome-based SERS and machine learning, with biomarker identification and heterogeneity analysis further advancing diagnostic reliability and paving the way for practical clinical translation.

The extraction of mycotoxins from complex food matrices for immunoassay often requires organic solvents, which can compromise antibody integrity and detection sensitivity. This study aimed to investigate the tolerance patterns of an ochratoxin a (OTA)-specific nanobody in organic solvents, elucidating the relationship between its structural stability and functional activity. Direct competitive enzyme-linked immunosorbent assay, fluorescence spectroscopy, ultraviolet-visible absorption spectroscopy, Fourier transform infrared spectroscopy, and molecular docking were employed to systematically analyze the tolerance thresholds, antibody activity retention rates, and structural changes of the OTA nanobody in eight organic solvents.The results showed that the OTA nanobody exhibited higher tolerance in methanol, ethylene glycol, glycerol, acetone and dimethyl sulfoxide (thresholds of 40%-60%), while lower tolerance was observed in acetonitrile and dimethylformamide (thresholds of 20% and 10%, respectively). Results of antibody activity retention rate and spectral analysis showed that methanol induced minimal structural and functional damage to OTA nanobodies; acetone severely impaired the nanobodies' structure and activity with significant destruction of their core domain; acetonitrile might affect antibody activity and tolerance via a non-structural mechanism; dimethylformamide exerted a drastic conformational impact, leading to complete structural disorder, acute activity loss, and the poorest tolerance. Molecular docking results indicated that organic solvents primarily interacted with framework residues via hydrogen bonds, without occupying the core antigen-binding region. This study elucidates the tolerance mechanism of the OTA nanobody in organic solvents, providing a theoretical basis for its application in complex sample detection and the design of solvent-resistant mutants.

Nitroreductase (NTR) plays a crucial role in the hypoxic metabolism of breast tumors and serves as an important indicator of tumor aggressiveness and therapeutic response. However, visualization of NTR activity remains challenging due to the limited photophysical properties of existing probes, particularly their short emission wavelengths and small Stokes shifts. Here, we report a near-infrared (NIR) activatable fluorescent probe, DHM-NO₂, which we constructed by coupling the large-Stokes-shift fluorophore DHM-OH with a nitrobenzyl recognition unit. DHM-NO₂ exhibited negligible background fluorescence; however, it could undergo NTR-mediated reduction that triggered self-immolative cleavage and the release of DHM-OH, which emitted fluorescence at 880 nm upon 590 nm excitation. The probe had a detection limit of 25.6 pg/mL, excellent selectivity, and could be rapidly activated. DHM-NO₂ could differentiate MCF-7 breast cancer cells from normal MCF-10 A cells, resist interference from ROS/RNS, and respond to pharmacological modulation of reductive metabolism. In an orthotopic breast cancer model, DHM-NO₂ could rapidly produce tumor-localized fluorescence with a markedly increased tumor-to-normal ratio. Inhibitor, hypoxia-enhancing, and oxygenation treatments further confirmed its NTR-dependent activation. Collectively, these results show that DHM-NO₂ is a sensitive NIR-I probe that can monitor NTR activity in breast cancer and is highly compatible with conventional fluorescence instrumentation.

Construction and demolition waste (C&DW) accounts for nearly one-third of total waste generation in the European Union, representing a significant environmental challenge. Although recovery rates are high (∼89%), much of the recycled material is downcycled, hindering true circular economy goals. This study proposes an integrated analytical method combining portable X-ray fluorescence (XRF), near-infrared hyperspectral imaging (NIR-HSI), and Shallow Neural Networks (SNN) for fast, accurate classification of earthquake-related C&DW from central Italy. Thirty sample sets from the 2016-2017 earthquake zones in Abruzzo, Marche, and Emilia Romagna were analyzed using portable energy-dispersive XRF to define three recycling-oriented material classes: concrete-based (CON), ceramic-rich (CER), and natural aggregates (NAT). Statistical tests and principal component analysis (PCA) confirmed significant differences among classes. NIR-HSI spectra (1000-1700 nm) were processed to train an SNN with a single hidden layer. The classifier showed excellent precision, recall, specificity, and F1-scores (≥ 0.98) across classes, with misclassifications limited to borderline cases like glazed ceramics. The goal of this work is to evaluate the best achievable performance within a controlled feasibility framework, demonstrating that the coupling of NIR-HSI with SNN provides a rapid, robust, and transferable strategy for automated C&DW classification, thereby supporting circular economy goals through improved material recovery and recycling efficiency.

Hydrogen sulfide (H₂S) has attracted attention as a harmful substance causing ecological pollution. However, due to the lack of H₂S specific detection tools, the changes in endogenous hydrogen sulfide levels during the pathological progression of chronic liver disease are not fully understood. In this study, a novel aggregation induced emission (AIE) fluorescent bio-probe HBA was developed to facilitate high-selective detection of exogenous and endogenous H₂S. HBA showed strong water solubility, high selectivity, low detection limit (0.45 nM), rapid responsiveness, high fluorescence quantum yield (18.51%) and low cytotoxicity, and produces a strong fluorescence. Multiple mechanistic experiments demonstrated that the bio-probe undergoes cyclization reaction with H₂S, inducing fluorescence quenching followed by the appearance of a bright yellow color turning colorless. In this study, we leveraged this feature of HBA to detect the H₂S content in real water samples and food (fish, pork, and shrimp) during the spoilage process. In addition, HepG2 cells and zebrafish embryos were imaged, later Oil Red and H&E staining were performed on the constructed NAFLD mouse model. Furthermore, the biological imaging of mice was achieved, demonstrating that the bio-probe HBA may be a powerful tool for detecting endogenous H₂S in fatty liver in clinical diagnosis and environment detection.

The concentration of functional additives in lubricants is a critical parameter determining their tribological performance and service life. To achieve rapid and accurate detection of trace additive concentrations in lubricating oils, this study proposes a physics-informed Bias-Corrected Ensemble Model (BCE). By learning spectral features within complex mixtures, the method addresses the challenges in quantitative analysis caused by overlapping characteristic peaks of multiple additives and weak signals at low concentrations. Based on an extension of the Lambert-Beer law to derivative spectroscopy, an ensemble of learners was constructed to extract the intrinsic spectral features of additives. Subsequently, a meta-model was employed to systematically characterize and compensate for prediction biases induced by mixing interference. The learner ensemble and the meta-model together form the BCE, and accomplish the decomposition of overlapped spectral features in multi-component mixtures. Results demonstrate that the method enables accurate detection of specific additive content in complex simulated oil systems. The coefficient of determination (R²) for predicting the concentration of the target additive T321 reached 0.949. Furthermore, based on the model's predictions of additive concentration variations in oil samples, verification oil samples containing MoDTP were prepared according to the predicted concentrations and subjected to friction tests using a four-ball tribo-tester. The measured steady-state friction coefficient and wear scar diameter exhibited errors of less than 5.8% and 1%, respectively, compared to the results from the in-service oil samples.

As a common sulfate mineral on Martian surface, calcium sulfate hydrate experiences wide temperature variations. However, the permittivity properties of calcium sulfate hydrate as a function of temperature remains underexplored. In this study, this gap has been addressed by systematically investigating the complex permittivity of calcium sulfate dihydrate (CaSO₄·2H₂O) in THz frequency band using terahertz time-domain spectroscopy over a temperature range from 100 K to 320 K. Base on the effective medium theory of Landau-Lifshitz-Looyenga (LLL), the permittivity has been extracted from the matrix and compared with that of the calcium sulfate (CaSO₄). It is found that as the temperature increases from 100 K to 320 K, the real part and the imaginary part of the permittivity for CaSO₄·2H₂O increases from 5.3 to 5.8, and 0.25 to 0.32 at 1.0 THz, respectively. For CaSO₄, the corresponding values change from 5.2 to 5.3 and 0.28 to 0.4 respectively. The difference in the permittivity properties is mainly attributed to the temperature-dependent changes in crystal-water molecular polarizability, as well as its frequency-dependent response. Finally, to investigate the effect of solar-wind on these properties, calcium sulfate dihydrate irradiated by proton with a fluence of 2 × 10¹⁰ protons/cm² has also been measured and discussed. Following proton irradiation, at 220 K the real part of the permittivity increases approximately 0.3, while the imaginary part of the permittivity decreases about 0.2. These findings provide valuable insights into the temperature-sensitive permittivity behavior of hydrated minerals as well as to quantitatively identify minerals on the Mars.

In this study, Surface-enhanced Raman Spectroscopy (SERS) and Fourier Transform Infrared Spectroscopy (FTIR) were employed to investigate the molecular changes in Escherichia coli (E. coli) induced by exposure to ampicillin (AMP), enrofloxacin (ENR), ciprofloxacin (CIP), and norfloxacin (NFX) over time. The optimal concentration of E. coli for SERS analysis was determined to be 50 μL of bacterial suspension, diluted six times to achieve an OD600 ≈ 0.1. The primary changes in the SERS spectra were observed at 1267 cm^-1, corresponding to the amide III band in proteins, while the FTIR spectra revealed significant changes in the 1200-900 cm^-1 range, associated with carbohydrates, under AMP treatment. ENR, CIP, and NFX, which are quinolone antibiotics, act as inhibitors of DNA synthesis. The main changes in the SERS spectra for antibiotic-resistant E. coli were observed at 760 cm^-1 (attributed to cytosine and uracil), 960 cm^-1 (CN stretching and CC deformation), and 1140 cm^-1 (COC stretching and ring breathing). In the FTIR spectra, significant changes were detected at 1655 cm^-1, 1544 cm^-1, and 1239 cm^-1, corresponding to the amide I, amide II, and amide III bands, respectively. The combination of SERS and FTIR with principal component analysis (PCA) enabled the detection of molecular modifications in antibiotic-resistant E. coli exposed to different classes of antibiotics. These findings enhance our understanding of the mechanisms of action of antibiotics in bacteria.

This paper discusses about the dielectric studies of binary mixtures of paracetamol (PCM) and Diethylamine (DEA). Parallel resistance (R_p) and Parallel capacitance (C_p) measured using a precision LCR meter over a frequency range of 20 Hz-2 MHz at four distinct temperatures, starting from 293.15 K and increasing by 10 K for each subsequent measurement. These experimental parameters were used to compute the complex dielectric function, from which electrical properties like complex conductivity, complex impedance and complex electrical modulus-were derived. In addition to conventional analysis, machine learning (ML) models were implemented to predict dielectric constant (ε') and dielectric loss (ε″) values based experimental inputs, with their predictive performance significantly enhanced through Bayesian hyperparameter optimization. This dual approach of combining experimental data with ML modelling offers a novel methodology for efficient and accurate characterization of dielectric systems. The added value of this study lies in its ability to bridge physical measurements with computational predictions, reducing experimental workloads and improving generalization in similar systems. The findings have potential applications in material science, pharmaceuticals, and electronic device modelling. This study demonstrates that ML assisted dielectric analysis can serve as a powerful tool in predictive material characterization.

Pitch canker, caused by Fusarium circinatum, poses a major threat to Pinus radiata plantations, resulting in substantial economic and ecological losses. Early detection of this pathogen is crucial, as conventional methods rely on late-stage visual symptoms. This study explores the potential of visible-near-infrared hyperspectral imaging (VIS-NIR HSI) combined with multivariate techniques for the early detection of F. circinatum infection in P. radiata cuttings before symptom onset. The infection process was monitored over 57 days in two P. radiata genotypes through hyperspectral image acquisition in the 400-1000 nm range. Fast Principal Component Analysis (Fast-PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were applied to identify key spectral variations and classify samples as infected or healthy, respectively. The results demonstrate that early differentiation between infected and healthy cuttings is possible, achieving high classification accuracy at 27 days post-inoculation (dpi) in predictive model validation. Additionally, phenotypic differences between genotypes were observed, with genotype A exhibiting earlier and more pronounced spectral changes between infected and control samples than genotype B, suggesting varying resistance levels of genotypes. These findings underscore the potential of VIS-NIR HSI for both early disease detection and the assessment of genetic susceptibility, providing valuable insights for breeding programs aimed at enhancing P. radiata resistance while establishing HSI as a powerful, non-invasive, and high-throughput phenotyping tool with applications in precision forestry and large-scale disease monitoring.