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

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.

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.

One of the most commonly prescribed medications are antithrombotic agents which consisting of antiplatelet and anticoagulant medications. Currently, millions of patients rely on them to avoid blood-clot-related issues across various cardiovascular diseases. The combined administration of apixaban, aspirin and clopidogrel is an example of this therapy which can be used for the risk reduction in cardiovascular death. The importance of such medications encourages us to investigate novel analytical assay methods for determination of such drugs in dosage forms as well as in human plasma. Spectroscopic technique was the best choice to design new analytical methods due to its applicability and simplicity. Three spectroscopic methods were introduced for concurrent determination of apixaban, aspirin and clopidogrel. The designed methods were; A direct measurement for determination of apixaban without interference from the other two drugs (method I), Ratio spectra (method II) and first derivative ratio spectra (method III). The first method enables us to determine apixaban through direct measurement of its absorption spectrum at 310 nm with no reading from aspirin or clopidogrel. Ratio spectra method (method II) was performed at ΔP = 223.6-245.2 nm for aspirin, ΔP = 293.2-307.0 nm for apixaban and ΔP = 251.0-260.0 nm for clopidogrel. The third first derivative ratio spectra method was based on measuring apixaban at 255 nm, aspirin at 242 nm and clopidogrel at 260 nm using the other two analytes as a double divisor. The linearity of the designed methods was 0.5-18 μg/mL for apixaban by the three methods while were 2.0-28 μg/mL for both aspirin and clopidogrel by method II & III. These developed approaches were effectively applied for estimation of the three studied drugs in their raw materials, synthetic mixtures and dosage forms simultaneously. The co-administration of these treatments enables us to extend the application for determination of them in spiked human plasma without complicated procedures. The applied methods were validated following the ICH Q2(R1) guidelines. The greenness of the designed methods was evaluated using six different tools including; Analytical Eco-scale, GAPI, AGREE metrics, NEMI, whiteness and blueness assessment.

In recent years, Raman spectroscopy analysis of hematological diseases is increasingly applied in research, but its application in serum analysis of myeloid neoplastic diseases represented by myeloproliferative neoplasms (MPN), myelodysplastic/myeloproliferative neoplasms (MDS/MPN), and acute myeloid leukemia (AML) has not been fully tested. To establish an oversimplified non-invasive serum test approach for MPN, MDS/MPN and AML, we systematically examined peripheral blood serum samples from 8 patients diagnosed with MPN, 4 patients with MDS/MPN, 3 patients with AML, and 9 control participants. A laser Raman spectroscopy was utilized together with orthogonal partial least squares discriminant analysis (OPLS-DA). Next, a differentiation model for MPN, MDS/MPN, AML, and the control was constructed. Compared with the healthy participants, the serum spectral data of patients with myeloid tumors were specific, and the intensities of Raman peaks representing nucleic acids (786, 1579 cm^-1), proteins (643, 759, 1031, 1260, 1603, 1616 cm^-1), lipids (1437, 1443, 1446 cm^-1), and β-carotene (957 cm^-1) were significantly decreased, while the intensity of the Raman peak representing collagen (1345 cm^-1) was significantly increased. Metabolic serum marker analysis revealed consistent patterns across MPN, MDS/MPN, and AML patients: adenosine deaminase (ADA) levels were significantly elevated, while both total protein and low-density lipoprotein concentrations showed marked reductions compared to controls. This provides spectroscopic evidence that will guide early differentiation of massive serum test data of patients with MPN, MDS/MPN and AML, and simultaneously uncovers crucial details for rapid and rudimentary differentiating them. This exploratory study show that the Raman spectroscopy analysis is an innovative non-invasive clinical instrument for the detection of MPN, MDS/MPN and AML.

Raman spectroscopy can play a crucial role in coal rank identification by providing direct insights into the structural evolution of carbon during coalification. Unlike any traditional method such as vitrinite reflectance, which rely on specific macerals and often face limitations in low-vitrinite or compositionally altered samples, Raman offers a non-destructive and comprehensive assessment of all organic matter types. It detects changes in chemical bonding, aromaticity, and the degree of structural order-key indicators of coal maturity-by analyzing vibrational energy levels. Its ability to differentiate between sp²- and sp³-hybridized carbon and monitor the transition from amorphous to graphitic structures makes it especially valuable for evaluating thermal evolution. This research investigates the evolution of coal's chemical composition and microstructure during maturation using Raman spectroscopy, supported by Fourier Transform Infrared (FT-IR) and X-ray photoelectron spectroscopy (XPS). The transformation of peat into coal, driven by microbial activity, thermochemical degradation and burial, increases carbon concentration and releases volatiles. Traditional methods such as vitrinite reflectance suffer from limitations, particularly in samples with low or absent vitrinite content, or when vitrinite is suppressed by bitumen or enhanced by recycled particles. Measurement errors may arise from polishing issues, anisotropy or instrument calibration, but Raman spectroscopy offers more profound insights into carbon structure changes during coalification and graphitization. Initially, coal and lignite samples of varying ranks (from lignite to semi-anthracite) were analyzed using traditional methods such as proximate analysis and vitrinite reflectance to determine coal rank. Subsequently, spectroscopic techniques were employed to evaluate carbon structure, functional groups, and sp²/sp³ carbon ratios. FT-IR identified functional groups, while XPS examined surface elements. Raman spectra revealed a clear relationship between coal rank and carbon structure, showing higher-rank coals with greater sp² carbon ordering, resembling graphite. Gaussian fitting confirmed that sp² content increases while sp³ content decreases with rank, consistent with XPS and IR findings. The G-band shift and broadening indicated increased nanocrystalline graphite from sub-bituminous to anthracite, while lignite displayed more sp³ content and amorphous carbon. XPS confirmed that higher-rank coals have more CC bonds, while lignite contains CO and COOH groups. The study provides a novel framework for assessing coal rank and maturation, enhancing understanding of coal's metamorphic history, and offering insights for optimizing coal utilization and expanding geological knowledge of organic sediment metamorphism.