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

Alpha-fetoprotein (AFP) is a crucial cancer biomarker for the early screening and diagnosis of hepatocellular carcinoma. Here, we report the discovery of a phenolic compound, 5,5'- (anthracene- 9,10-diyl)bis(benzene-1,3-diol) (ABOL), exhibiting typical aggregation-induced emission enhancement (AIEE) effect. ABOL can bind to the nucleobases of the AFP-specific aptamer (AFP-Apt) through hydrogen bonding, π-π stacking, and hydrophobic interactions, forming an ABOL/AFP-Apt complex. After complexation, the free rotation of ABOL's three aromatic rings is hindered, inducing the AIEE effect and subsequently enhancing ABOL's fluorescent intensity. Upon introduction of AFP into the system, the significantly higher binding affinity between AFP-Apt and AFP (compared to that between AFP-Apt and ABOL) causes the dissociation of ABOL from the complex. The return of released ABOL molecules to the solution state leads to the loss of the AIEE effect and reduced fluorescence. Notably, the degree of fluorescence reduction shows a proportional relationship with AFP concentration, enabling both qualitative and quantitative determination of AFP. Experimental results demonstrate a linear correlation between fluorescence reduction and AFP level across a wide range of 6.6-50,000 pg/mL, with an exceptionally low limit of detection (2 pg/mL). The fluorescence probe exhibits high specificity and performs well in AFP detection within human serum samples.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with growing incidence among younger populations, highlighting the urgent need for early and accurate detection of AD-related biomarkers. Excessive production of reactive oxygen species (ROS), particularly hydrogen peroxide (H₂O₂), induces oxidative stress, leading to neuronal damage and death, which implicates H₂O₂ as a potential biomarker for AD. In this study, leveraging the advantages of fluorescence imaging, we developed a novel near-infrared (NIR) fluorescent probe, LDMD-B, capable of selectively detecting H₂O₂ through a boronate-cleavage mechanism that releases the fluorophore LDMD-OH. Upon reaction with H₂O₂, LDMD-B exhibits a strong "turn-on" fluorescence response at 785 nm (λ_ex = 680 nm) with a large Stokes shift (105 nm) and a low detection limit (LOD) of 24.4 nmol/L. The probe successfully visualized endogenous and exogenous H₂O₂ in SH-SY5Y cells, zebrafish, and an APP/PS1 transgenic AD mouse model, effectively distinguishing between wild-type and AD mice. These findings demonstrate that LDMD-B is a promising tool for imaging H₂O₂ in AD contexts and may facilitate early diagnosis and pathological investigation of AD.

Reducing phytic acid content in crop seeds through breeding is an important strategy to enhance nutritional quality. Twenty-eight crosses were generated from eight parent using half-diallel mating design. Both quantitative and qualitative estimation of oil was carried out for eight parents and their derived twenty-eight crosses. F₂ population were raised from the cross, VRI-1 × Prachi and 100 plants were randomly selected for estimation of oil percentage. Based on high oil percentage, 25 individual plants of F₂ seeds were analyzed using FT-IR (Fourier transform infrared spectroscopy) for screening of promising lines for low phytic acid content. The FT-IR spectra were used to assess the presence or absence of phytic acid based on absorption peaks in the 3350 cm^-1 to 889 cm^-1 range. Significant variation in phytic acid content was observed among all the individual plant genotype. Plant numbers 12, and 23 exhibited absence of band at 3100-3600 cm^-1 (3350 cm^-1) and were shortlisted as potential candidates for further evaluation.

Conventional near-infrared (NIR) spectral modeling typically follows a stepwise optimization pipeline, which suffers from fragmented stages, heavy reliance on manual expertise, and an inability to capture interactive effects among modeling components. These limitations often lead to suboptimal predictive performance and poor generalization. To address these issues, this study proposes a differential evolution (DE)-based full-process joint optimization method that integrates the entire NIR modeling workflow. Specifically, outlier removal, spectral preprocessing, feature wavelength selection, and the type and hyperparameters of the regression algorithm are jointly encoded into a mixed-type decision vector. The method performs global co-optimization within a cross-validation framework by minimizing the root mean square error (RMSE) on the validation set. A simple ensemble strategy, based on averaging predictions from ten independently optimized sub-models, is employed to enhance robustness. Experiments on three forest leaf litter datasets and one public corn dataset demonstrate that the proposed method achieves high prediction accuracy using no more than 10 selected wavelengths. Moreover, it exhibits chemical interpretability and consistent generalization across different datasets and instrument platforms. This method provides a practical pathway for developing low-cost, task-specific, lightweight NIR sensors, thereby offering new insights for advancing NIR analysis toward intelligent and application-oriented deployment.

To ensure food and fruit safety, it is essential to use accurate and appropriate techniques that allow irradiated products to be identified and labelled, thereby preventing their adulteration and that of their derivatives. In this context, in this study, we evaluated the physicochemical properties of γ-irradiated red pitaya peel flour from lab-prepared (PT) and commercial (PC) samples, using X-ray fluorescence (XRF), X-ray diffraction (XRD), Fourier transform infrared (FT-IR), Raman, and electron paramagnetic resonance (EPR) spectroscopic techniques, as well as thermal analysis (TGA/DSC). The moisture content ranged from 8.01% to 16.38%, with the highest values being 16.38% for the PC sample at 3.5 kGy and 10.56% for the PT sample at 7.5 kGy. FT-IR and Raman spectroscopy revealed differences in betalain concentrations, indicating that irradiation did not significantly alter the spectral profiles. However, chemical changes were detected in the PC sample compared to the PT sample. In the PT sample, at least three distinct paramagnetic centers were identified. A six-line EPR spectrum was assigned to Mn²⁺ ions (center I). Center II arises from at least two radical species, with the dominant radical exhibiting hyperfine interaction with one α-type and two nearly equivalent β-type protons and characterized by a g-value of 2.0023. The cellulosic component of the pitaya contributes to the formation of center III, which exhibits a g-value of 2.0027. In the PC sample, only center III was detected, with no evidence of Mn²⁺ signals.

To address the low accuracy of traditional methods for animal-derived products due to spatial heterogeneity and limited portable tools resolution. This study built a data-driven rapid non-destructive framework integrating portable near infrared, multi-location spectral fusion, and machine learning. It applied low-level (raw spectral stitching) and mid-level (feature stitching) fusion for Calculus bovis heterogeneity, combined with variable selection. Qualitatively, the accuracy of the optimized linear model achieved 96.70%. Quantitatively, the mid-level model demonstrated superior performance compared to the other models, achieving the R² value of 0.9450 and the RPD value of 2.89. This framework meets the demands of analytical chemistry for on-site efficacy and qualitative/quantitative precision and provides a transferable paradigm for complex natural products.

The energies of stationary torsional states of methanethiol (methyl mercaptan; CH₃SH) and several of its isotopic analogs (CH₃SD, CD₃SH, CD₃SD, ¹³CH₃SH, and CH₃³⁴SH), which are of considerable interest in astronomical studies, were calculated using several levels of theory, including extrapolation to the complete basis set (CBS) limit and explicitly correlated methods. The potential energy function and kinetic coefficients were computed at the CCSD(T)-F12-RI/Aug-cc-pVTZ-F12, MP2/CBS(Aug-ano-VDZ, Aug-ano-VTZ, Aug-ano-VQZ), and MP2/CBS(dAug-cc-pVDZ, dAug-cc-pVTZ, dAug-cc-pVQZ) levels of theory. Analysis of the calculation results indicates that incorporating corrections to the initial potential energy functions for relativistic effects and zero-point vibrational energy (ZPVE) is essential. The magnitude of the additional potential barrier arising from ZPVE was determined directly from anharmonic calculations, while the functional dependence of this correction on the torsional coordinate was obtained by scaling the corresponding function calculated in the harmonic approximation. Consideration of a parameter χ₀ that is independent of the vibrational quantum numbers but depends on the molecular structural parameters also proved to be important. The calculated energies of stationary torsional states of the CH₃SH molecule at certain levels of theory and for relatively low torsional energies appeared to be in good agreement with experiment. Even better agreement between calculated and experimental data, extending over a wider energy range, was obtained for CH₃SD. The computed values of tunneling splitting of the ground vibrational states and the energies of torsional level of CD₃SH, CD₃SD, ¹³CH₃SH, and CH₃³⁴SH may be useful for analyzing their torsional-rotational spectra.

Gynecological tumors pose a serious risk to female health, and traditional therapies have many limitations. Here, we developed a near-infrared light-triggered nanoplatform (UCNPs@SiO₂-MB@PDA) for synergistic photodynamic therapy (PDT) and photothermal therapy (PTT) of deep tumors. The nanoplatform takes the UCNPs as the core, wraps the silica shell loaded with the photosensitizer MB via the inverse microemulsion method, and finally modifies the surface of the PDA. Under 980 nm excitation, the red light emitted by the UCNPs can effectively activate MB to produce singlet oxygen through luminescence resonance energy transfer, achieving PDT. At the same time, the PDA shell can efficiently absorb the multiband emitted light of the UCNPs and convert it into thermal energy, achieving PTT with a photothermal conversion efficiency of 35.6%. In vitro cell experiments have shown that the material has good biocompatibility and can be effectively internalized by tumor cells. Under near-infrared light irradiation, its killing effect on HeLa cells was considerably better than that of monotherapy, and the cell survival rate decreased to 18.9% when the cells were exposed to a concentration of 200 μg/mL. The results of the live/dead cell staining further confirmed the excellent synergistic antitumor performance of these materials. This work offers a promising strategy for the precise and effective treatment of deep tumors.

Chronic Obstructive Pulmonary Disease (COPD) is a major global respiratory illness causing death and disability. Traditional methods lack consistent standards, often miss diagnoses, and cannot explore diseases' molecular relationships. Thus, there is a need for a diagnostic method that is both efficient and convenient. This study aimed to evaluate the potential of diagnosing COPD, Non-COPD (Pulmonary infection), and Healthy Group using serum fluorescence, Raman, and surface-enhanced Raman spectra (FS, RS, and SERS) algorithms combined with eight machine learning algorithms. The experiment reveals variations at each peak by examining the serum FS, RS, and SERS of COPD patients compared to the control group. The combination of serum RS or SERS with machine learning algorithms provides superior classification results compared to serum FS. Serum SERS and machine learning algorithms classify COPD and healthy individuals with over 0.98 accuracy. Serum SERS combined with the synthetic minority over-sampling technique (SMOTE) -gradient boosting (GB) algorithm achieves a three-classification accuracy of 0.84. In summary, the integration of serum SERS with SMOTE-GB machine learning techniques showed significant promise for COPD detection.

In our previous work, the region of 2500-4000 cm^-1 of non-polarized Raman spectrum was used to evaluate the relative contents of the components of EG aqueous solutions at room temperature. In this short communication, we trace for the first time the evolution of polarized Raman spectra of EG in this spectral region in the temperature range of 30-100 °C. We concluded that use of the Raman method, proposed for the room-temperature measurements, requires slight modification for exploitation at higher temperatures. In particular, it is necessary to account for change of the peak position and shape of the OH stretching band. Also, for analysis of the EG solution composition, we propose to use the polarized Raman spectra instead of the non-polarized ones. We showed that the polarized spectra weakly depend on used Raman setup and, thus, ensure analysis that is more reliable and unambiguous. The results of this work can be in demand for development and testing antifreezes, heat-transfer agents and other EG-containing products, which are used at high temperatures.