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

Nitric oxide (NO) is a key signaling molecule that regulates energy metabolism, apoptosis, and antioxidant balance within mitochondria. It is closely associated with the development of cardiovascular diseases, neurodegenerative diseases, and cancer. Therefore, developing fluorescent probes capable of accurately detecting NO levels in mitochondria is essential for understanding disease mechanisms and clinical diagnostics. In this study, we developed a novel fluorescent probe based on the isophorone fluorophore. This probe achieves high sensitivity and specific ratiometric detection of NO in mitochondria by regulating the intramolecular charge transfer (ICT) effect. The probe emits red fluorescence before reacting with NO, and the addition of NO triggers an amine-NO addition reaction that inhibits the ICT effect, resulting in a color change to yellow-green fluorescence. This ratiometric fluorescence response provides a new method for quantitatively detecting NO. Additionally, the probe has a significant Stokes shift and good ratiometric wavelength separation, enhancing detection accuracy. It localizes explicitly to mitochondria, directly reflecting changes in mitochondrial NO concentration. Experiments in HeLa cells and zebrafish models have demonstrated the potential application of the probe in diagnosing and studying NO-related diseases. This provides new strategies and tools for researching the biological functions of NO and the early diagnosis of related diseases.

Psychological stress is a major contributor to individual health disparities. Accurate and quantitative detection of stress markers is crucial preventing mental health related problems. Supramolecular chemistry is widely used in drug delivery, catalysis, sensors and other applications. However, due to the difficulty of host functionalization such as cyclodextrins and solid-state pillar[n], it is still a challenge to directly realize the detection of guests through host-guest recognition behavior. Here, we reported an atom transfer radical polymerization (ATRP) fluorescent biosensor for direct and selective detection of guest molecule stress marker cortisol, translating molecular recognition behavior into quantifiable detection signals. Realizes quantitative chemical detection and builds a portable and affordable sensing platform for quantitative detection of target molecules without complex cross-linking steps. Overcomes the disadvantages of traditional methods that require the use of antibodies or are difficult to functionalize during the host-guest recognition process. This ATRP fluorescent biosensor was fabricated by employing zinc phthalocyanine (ZnPc) as a photocatalyst under 630 nm wavelength radiation, β-CD-Br₁₅ as a macromolecular initiator, and fluorescein O-methacrylate (FMA-O) as a monomer for polymerization. The system provides ultra-high sensitivity for the detection of cortisol (limit of detection 0.47 ng/mL) and specificity for the detection of cortisol in the presence of interfering substances such as progesterone and urea. Selective and real sample experiments confirm the specificity and scalability of this mechanism can also be customized by the rational design of the host-guest complex to quantitatively detect various molecules. This study confirms the feasibility of using a cyclodextrin-centered macromolecular initiator as a capture and label-free fluorescent biosensor for cortisol, a stress biomarker.

Culture media are widely used for biological research and production. It is essential for the growth of microorganisms, cells, or tissues. It includes complex components like carbohydrates, proteins, vitamins, and minerals. The media's consistency is key for predictable outcomes in biology applications. However, traditional methods of analyzing media are costly and time-consuming by using chromatography or mass spectrometry. This study introduces an innovative approach using optimized convolutional neural networks (CNN) combined with Raman spectroscopy to identify culture media. Samples of culture media from different models and batches are prepared for identification experiment. Raman spectra of each culture media samples are captured with unique molecular vibrations and rotations by Raman spectrometer rapidly. After preprocessing of sample data, Raman spectra are input to CNN for identification training and validation. An optimized CNN with more layers is designed to enhance the identify ability for Raman spectra. In experiment, it compared the performance of PCA-SVM, the original CNN, and an optimized CNN for media identification. The PCA-SVM achieved high accuracy and precision rates of 99.19% and 98.39% respectively. The original CNN achieved an accuracy of 71.89% due to limited training dataset. The optimized CNN model achieved a perfect accuracy rate of 100% in identifying different culture media. To avoid overfitting risk, additional external test is performed with optimized CNN. The result confirmed that optimized CNN offering effectiveness in identifying media from different models and batches, with strong generalization ability. The findings in study may offer an efficient and cost-effective method for pharmaceutical companies, to ensure the consistency of culture media.

The unique fatty acid composition of BSF larvae oil makes it suitable for various applications, including use in animal feed, aquaculture, biodiesel production, biomaterials, and the food industry. Determination of BSF larvae composition usually requires analytical methods with chemicals, thus needing emerging techniques for fast characterization of its composition. In this study, Near Infrared Hyperspectral Imaging (NIR-HSI) (928 - 2524 nm) coupled with chemometrics was applied to predict the lipid content and fatty acid composition in intact black soldier fly (BSF) larvae. Partial Least Squares Regression (PLSR) and Support Vectors Machine Regression (SVMR) models, combined with two variable selection methods, Interval Partial Least Squares (iPLS) and Bootstrapping Soft Shrinkage (BOSS), were compared. PLSR reached a good performance to predict myristic acid with Root Mean Square Error in prediction (RMSEP) = 0.45 %, while SVMR reached values of Ratio to Prediction Deviation (RPD) > 3 to predict total lipid content, lauric acid, myristic acid, palmitic acid and oleic acid. In addition, selecting wavelength by BOSS improved PLSR models (6 - 15 % increases in RPD), while iPLS improved SVMR model to predict palmitic acid (16 % increases in RPD). The study emphasizes the advantages of NIR-HSI as a non-invasive, rapid method for lipid and fatty acid quantification, which can be highly valuable for industrial applications such as monitoring BSF larvae feeding systems to ensure high-quality oil production.

Water is a greatly convenient solvent in Raman spectroscopy. However, non-additive effects sometimes make its signal difficult to subtract. To understand these effects, spectra for clusters of model ions, including transition metal complexes and water molecules, were simulated and analyzed. A combined molecular mechanics/quantum mechanics approach was taken to reveal how relative Raman scattering intensities depend on the distance from the solute and the excitation wavelength. The computations indicate a big effect of solute charge; for example, the sodium cation affects Raman scattering by water to a lesser extent than the chlorine anion. The modeling was able to qualitatively reproduce the experimental observation that a solution of a simple salt may work as a baseline better than pure water in many Raman experiments. For absorbing species, an additional scattering boost occurs due to the resonance effect. Simulations thus provide useful insight into solute-solvent interactions and their effects on measured spectra.

The main objective of this study was to evaluate the potential of near infrared (NIR) spectroscopy and machine learning in detecting microplastics (MPs) in chicken feed. The application of machine learning techniques in building optimal classification models for MPs-contaminated chicken feeds was explored. 80 chicken feed samples with non-contaminated and 240 MPs-contaminated chicken feed samples including polypropylene (PP), polyvinyl chloride (PVC), and polyethylene terephthalate (PET) were prepared, and the NIR diffuse reflectance spectra of all the samples were collected. NIR spectral properties of chicken feeds, three MPs of PP, PVC and PET, MPs-contaminated chicken feeds were firstly investigated, and principal component analysis was carried out to reveal the effect of MPs on spectra of chicken feed. Moreover, the raw spectral data were pre-processed by multiplicative scattering correction (MSC) and standard normal variate (SNV), and the characteristic variables were selected using the competitive adaptive re-weighted sampling (CARS) algorithm and the successive projections algorithm (SPA), respectively. On this basis, four machine learning methods, namely partial least squares discriminant analysis (PLSDA), back propagation neural network (BPNN), support vector machine (SVM) and random forest (RF), were used to establish discriminant models for MPs-contaminated chicken feed, respectively. The overall results indicated that SPA was a powerful tool to select the characteristic wavelength. SPA-SVM model was proved to be optimal in all constructed models, with a classification accuracy of 96.26% for unknow samples in test set. The results show that it is not only feasible to combine NIR spectroscopy with machine learning for rapid detection of microplastics in chicken feed, but also achieves excellent analysis results.

Excessive plastic consumption can pose potential risks to the human respiratory and circulatory systems, leading to various diseases. Therefore, the sensitive detection of plastics holds significant implications for ensuring food safety, environmental protection, and human health. Conducting tests on rivers and drinking water can ensure their compliance with relevant safety standards, thereby mitigating the potential environmental and health risks associated with plastic pollution. In this experiment, we prepared a roseate petal homochiral nanogold (Au RHNs) as a surface-enhanced Raman scattering (SERS) substrate for detecting plastics in the water. Due to the intricate rose petal-like surface and structures with symmetry breaking, which result in a large surface area, the mean enhancement factor (EF) of the Au RHNs was determined to be 8.4696 × 10⁵. The Au RHNs as the SERS substrate were used to test the plastic polyethylene (PE) and polyvinyl chloride (PVC), with the detection limits of 0.0986 mg/mL and 0.0975 mg/mL, respectively. Moreover, the prepared Au RHNs substrate were successfully applied for ananlyzing analyze actual samples (tap water, mineral water, river water), yielding a satisfactory recovery rate. The exceptional performance of Au RHNs as a SERS detection substrate indicated its promising potential for practical detection of plastic samples.

Glioblastoma multiforme (GBM) is the most lethal intracranial tumor with a median survival of approximately 15 months. Due to its highly invasive properties, it is particularly difficult to accurately identify the tumor margins intraoperatively. The current gold standard for diagnosing GBM during surgery is pathology, but it is time-consuming. Under these circumstances, we developed a method combining Raman spectroscopy (RS) with convolutional neural networks (CNN) to distinguish GBM. Analysis of the spectra of normal brain samples (478 spectra) and GBM samples (462 spectra) from 29 in situ intracranial tumor-bearing mice showed that this method identified GBM tissue with 96.8 % accuracy. Subsequently, spectral analysis of 23 normal human brain tissues (223 spectra) versus 21 tissues from patients with pathologically diagnosed GBM (267 spectra) revealed that the accuracy of this method was 93.9 %. Most importantly, for the difference peaks in the spectra of GBM and normal brain tissue, the common difference peaks in the mouse and human spectra were at 750 cm^-1, 1440 cm^-1, and 1586 cm^-1, which emphasized the differences in cytochrome C and lipids between GBM samples and normal brain samples in both mice and human. The preliminary results showed that CNN-assisted RS is simple to operate and can rapidly and accurately identify whether it is GBM tissue or normal brain tissue.

In this work, the interaction behaviour of gold nanoparticles (AuNPs) with o-phenylenediamine (OPD) was studied to ascertain the nanozyme-substrate interaction. The UV-Vis absorption, high-resolution transmission electron microscopy and zeta potential analysis revealed that the electron-rich nitrogen atoms in OPD showed a stronger affinity toward electron-deficient surface, indicating a stronger interaction between nanozyme and substrate molecules. Subsequently, under optimum conditions, AuNPs are used as nanozyme to catalyze the oxidation of OPD in the presence of H₂O₂. The catalyzed product (2,3-diaminophenazine, (DAP)) generated visible colorimetric readout (yellow color) and showed yellow fluorescence upon excitation at 450 nm. The nanozyme-based oxidation reaction of OPD was then applied to detect glutathione (GSH) by colorimetric and fluorometric techniques. The detection principle is based on the fact that GSH being a thiol-containing moiety can readily interact with AuNPs and considerably decrease the catalytic activity of nanoparticles. In the presence of varying concentrations (1-15 µM) of GSH, the formation of DAP is significantly decreased leading to a decrease in the absorbance and fluorescence intensity at 450 nm and 540 nm, respectively. The colorimetric and fluorescence assay for GSH exhibited a limit of detection of 3.42 and 2.01 µM, respectively. Kinetic studies were conducted to elucidate the inhibition mechanism of GSH on the catalytic function of AuNPs. To demonstrate the practical applicability of the nanozyme-based assay, GSH detection in artificial urine samples were carried out.

This study explores the nonlinear optical (NLO) and photophysical properties of newly designed naphthyridine derivatives by density functional theory (DFT). The first hyperpolarizability (β_tot), a key indicator of NLO activity, varies significantly depending on the substituent groups. N-substituted compounds (IUB-N series) generally show lower β_tot values, while compounds with electron donor/acceptor groups (IUB-P series) demonstrate a broader range, with IUB-A-02 achieving the highest β_tot value of 16,362 a.u. due to the presence of two -NH₂ groups. TD-DFT analysis confirms key electronic transitions, mostly from HOMO to LUMO, with absorption wavelengths (λmax) ranging from 349.596 to 440.692 nm for the IUB-P series. The introduction of electron-donor groups considerably boosts absorption, particularly in IUB-P-06, with highest λ_max and oscillator strength (f_o) signifying excellent light absorption capabilities. The calculated light harvesting efficiency (LHE) correlates strongly with f_o values, IUB-N-01 to IUB-N-05 exhibiting higher LHE than the unsubstituted IUB. Additionally, lower radiative lifetimes (τ) for the modified compounds indicate faster decay, useful for applications in photodynamic therapy and fluorescence imaging. Lower transition energy (ΔE) and higher f_o values contributed to greater first hyperpolarizability (β_o). IUB-P-06, with two -NH₂ donor groups, shows the lowest ΔE (2.81 eV) and a correspondingly high β_o (60218.89 a.u.). Whereas IUB-A-02 exhibits the highest β_o (68907.84 a.u.) due to its large dipole moment change (Δμ = -6.37 D). Among N-substituted compounds, IUB-N-01 exhibits the highest charge density. IUB-P-06 has the highest charge density and electron-hole separation due to electron donor/acceptor groups, indicating a higher degree of internal atomic localization. This enhanced charge separation further confirms the superior performance of these compounds in NLO applications. In conclusion, this comprehensive analysis spanning ESP, TD-DFT, TLM, LHE, and TDM demonstrates that the studied naphthyridine derivatives possess promising NLO properties and exhibit strong potential for use in optoelectronics, photovoltaics, photodynamic therapy, and other advanced optical technologies.