Analytical Methods最新文献

Quantitative nuclear magnetic resonance (qNMR) has a potential risk of inaccurate quantification of complex organic compounds with low purity due to incomplete separation of the impurity signals and the target component signals. The high performance liquid chromatography-qNMR (HPLC-qNMR) method removes impurities from the sample by HPLC and accurately determines the purity of the sample by qNMR, avoiding the laborious, time-consuming, and costly step of qualitative and quantitative determination of impurities in conventional mass balance methods. An improved method, named post-collection purity correction for internal standard correction-HPLC-qNMR (ISC-HPLC-qNMR), was developed and demonstrated on a complex compound oxytetracycline with low purity. In this method, a correction factor was introduced to compensate for the inability to achieve 100% purity through the HPLC purification procedure. The purity value with standard deviation of the oxytetracycline study material using this method was 82.00% ± 0.82%, while that obtained from the conventional qNMR with deconvolution was 81.70% ± 0.35%. The consistency of these results demonstrated that the improved method extends the applicability to the samples where HPLC is not capable of purifying complex compounds with low purity to near 100%, especially containing highly similar structural-related impurities. Furthermore, this method allows purification and quantification without the need to identify impurities in the sample, resulting in significant savings of time and cost. Additionally, it effectively compensates for the limitations of qNMR deconvolution in handling peak overlap in the sample.

Esophageal cancer is a common cancer with high morbidity and mortality that severely threatens the safety and quality of human life. The strong metastatic nature of esophageal cancer enables it to metastasize more quickly and covertly, making it difficult for current diagnostic and treatment methods to achieve efficient early screening, as well as timely and effective treatment. As a promising solution, nucleic acid aptamers, a kind of special single-stranded DNA or RNA oligonucleotide selected by the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) technology, can specifically bind with different molecular targets. In this paper, random DNA single-stranded oligonucleotides were used as the initial library. Using TE-1 cells and HEEC cells as targets, specific binding sequences were selected by 15 rounds of the cell-SELEX method, and the aptamer sequence that binds to TE-1 cells with the most specificity was obtained and named Te4. The Te4 aptamer was further validated for binding specificity, binding affinity, type of target, in vitro cytotoxicity when conjugated with DOX(Te4-DOX), and in vivo distribution. Results of in vitro validation showed that Te4 has outstanding binding specificity with a K_d value of 51.16 ± 5.52 nM, and the target type of Te4 was preliminarily identified as a membrane protein. Furthermore, the cytotoxicity experiment showed that Te4-DOX has specific cytotoxicity towards cultured TE-1 cells. Finally, the results of the in vivo distribution experiment showed that the Te4 aptamer is able to specifically target tumor regions in nude mice, showing great potential to be applied in future diagnosis and targeted therapy of esophageal cancer.

To assess the quality of apple samples during storage, this study proposes a spoilage benchmark based on hyperspectral data feature indicators and the Mahalanobis Distance (MD). Additionally, a quality assessment model was developed utilizing LIB Support Vector Machine (LIBSVM). Initially, a spoilage benchmark for apple samples was preliminarily established using hyperspectral data feature indicators, including the color feature, texture feature of sample hyperspectral images, and wavelet packet energy (WPE) of sample spectral information. Secondly, this study utilized the successive projection algorithm (SPA) to extract three wavelength sets sensitive to changes in the three indicators. This process resulted in the identification of 20 feature wavelengths based on the three sets. Subsequently, the spoilage benchmark for apple samples was verified using MD based on the spectral information of feature wavelengths. Ultimately, utilizing pre-processed spectral information enhanced by the sliding window algorithm and spoilage benchmark, the LIBSVM quality assessment model was developed, achieving a training set accuracy of 99.94% and a test set accuracy of 99.66%. Moreover, to assess the strength and applicability of the model, a verification experiment was conducted using a different set of apple samples. The training set accuracy was 100% and the test set accuracy was 99.83%. These findings indicate that the model can effectively indicate the level of spoilage in each sample during long-term storage. This also serves to demonstrate the robustness of the model and the effectiveness of the spoilage benchmark determination method during apple storage.

Various metal ions exist in nature and human beings and play limitless vital roles in both the atmosphere and biology. A fundamental and useful aspect is the qualitative and quantitative assessment of Zn(II) at concentration levels as low as parts per billion (ppb). Thus, the design and development of novel fluorescent turn-on receptors have gained significant interest because of their potential for use in live cell imaging to detect biologically relevant metal ions with high selectivity and sensitivity. The present research illustrates the design and synthesis of a novel fluorescent sensor [(1,3,5-triazine-2,4,6-triyl)tris(hydrazine-2-yl-1-ylidene)tris(methaneylylidene)]tris(2,4-di-tert-butylphenol) (THDBP) for the selective and sensitive probing of Zn(II). The sensor exhibited a fluorescence turn-on mechanism upon treatment with Zn(II) ions at λ_emi. 503 nm in aq. acetonitrile. The formation of a 1 : 3 complex between THDBP and Zn(II) is confirmed from the Job plot and ESI-MS spectrum. The evaluated limit of detection (LOD) and association constant (K_a) of the sensor THDBP for Zn(II) were found to be 1.03 × 10^-10 M and 2.33 × 10⁸ M^-1, respectively. Further research demonstrates the practical application of the sensor for the detection of Zn(II) ions in live cells. The sensing ability of the sensor THDBP was also explored through inexpensive test strips and TLC sheets.

Peroxide-mediated oxidation of drug molecules is a known challenge faced throughout the pharmaceutical development pathway-from early-stage stability studies to manufacturing processes. During the initial development stage, the major source of peroxide is the formulation excipients, whether they are pre-loaded or generated in situ due to slow degradation, and in the late phase, peroxides can be introduced during sanitization processes or generated via cavitation. In essence, a control strategy for peroxide mitigation often becomes a critical quality attribute for successful drug development. To this end, quantitation of peroxide is essential to monitor the peroxide level to ensure product quality and proposed shelf-life. However, methods for reliable and robust quantitation to detect trace levels of peroxide in a complex drug product matrix become increasingly challenging. This article discusses three high-throughput assays based on absorbance, fluorescence and chemiluminescence measurements to detect peroxide at a low level and compares the methods through validation studies in water. Selected methods have also been tested to understand the forced degradation of model peptide drug products with spiked hydrogen peroxide. Peptide degradation profiles and residual peroxide levels are presented to provide an understanding of the suitability of the quantitation methods and their performance.

In order to develop a highly efficient H₂S gas sensor at low working temperature, in this work, a kind of novel Ce-doped ZnCo₂O₄ hollow microspheres (Ce/ZnCo₂O₄ HMSs) were successfully synthesized using a template-free one-pot method, showing a sensitive response toward H₂S. The microstructure and morphology of the material were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The gas-sensing performance of the composite was investigated, showing that the ZnCo₂O₄ doped with 6 mol% Ce had the highest response to 20 ppm H₂S at a low operating temperature of 160 °C with a response value of 67.42, which was about 2 times higher than that of original ZnCo₂O₄. The prepared Ce/ZnCo₂O₄ HMS sensor in response to H₂S exhibited a linear range of 0.1-200 ppm with a low detection limit of 0.1 ppm under the conditions of ambient humidity of 45% and ambient temperature of 20 °C. Meanwhile, it also possessed good selectivity, repeatability and reproducibility. The response value of the sensor decreased by 5.32% after 7 months of continuous monitoring of H₂S in an atmospheric environment of a pig farm, indicating that the sensor had a long-term stability and continuous service life with important application prospects.

High-frequency pulse lasers, applied in the form of rapid scanning, act upon the surface of aircraft skin paint layers, thereby removing the paint layers, exhibiting characteristics of efficiency and eco-friendliness. Real-time monitoring of the paint removal effect and prevention of substrate damage necessitates the continuous monitoring of paint removal thickness. Combining Laser-Induced Breakdown Spectroscopy (LIBS) online monitoring technology enables laser-controlled paint removal under multiple effects coupling, meeting the requirements of airworthiness maintenance. This paper, based on a high-frequency nanosecond infrared pulse laser paint removal LIBS monitoring platform, conducts research on laser paint removal thickness LIBS online monitoring of aluminum alloy plates coated with dual-layer paint. Spectra corresponding to the removal thickness of each group are collected and, respectively, paint removal thickness monitoring models based on LIBS spectra are established using the standard curve method and Principal Component Analysis-Support Vector Regression (PCA-SVR) algorithm. When monitoring paint removal thickness using the standard curve method, the intensity of five Ti element characteristic spectral lines selected is correlated with the paint removal thickness, and segmented curve fitting according to the paint layer structure satisfies the segmented curve fitting of topcoat and topcoat + primer. Among them, the average coefficient of the curve fitting of the Ti II 589.088 nm characteristic spectral line is 0.89, and the root mean square error (RMSE) is 12.28 μm. Its performance is superior in the five standard curves; thus, its fitting equation is used as the criterion for paint removal thickness monitoring. To further improve monitoring accuracy, research on paint removal thickness monitoring models based on PCA-SVR is conducted. Compared to the traditional univariate standard curve method, the PCA-SVR model does not require segmented monitoring. After parameter optimization, the average fitting coefficient reaches 0.97, and the RMSE is 2.92 μm. The results indicate that the PCA-SVR-based paint removal thickness monitoring model has higher accuracy, thereby forming the basis for paint removal thickness monitoring. Through comparative research on paint removal thickness monitoring models, two types of paint removal thickness monitoring criteria are obtained, providing model solutions for high-precision monitoring and automation of aircraft skin laser paint removal thickness.

This work deals with the rapid and simple determination of the probable carcinogen ethyl carbamate (EC), which is naturally present in fermented food products. An undemanding, robust, and rapid pre-column derivatization utilizing a 9-xanthydrol reagent has been developed. The resulting derivative was subsequently analysed by reversed-phase high-performance liquid chromatography coupled with fluorescence detection. As a result of the thorough optimisation of the chromatographic conditions, the run was completed in just 5 minutes, considerably speeding up the usual time of EC separation (30-60 min). Thanks to the fast separation, satisfactory yields (around 90%), negligible matrix effects, no interfering peaks, very low detection limit, and simple sample pre-treatment (for the very first time, the derivatization was performed in the presence of light and without any extraction step), the proposed method represents a significant improvement of the EC determination protocol used so far. After method validation, a total of fifty food samples were subjected to analysis without any additional sample pre-treatment despite their diverse matrix. Due to its robustness, simplicity, and low time, cost, and manual demands, this method is suitable for rapid screening of EC in both final food products and during their production.

Influenza A virus (IAV), a common respiratory infectious pathogen, poses a significant risk to personal health and public health safety due to rapid mutation and wide host range. To better prevent and treat IAV, comprehensive measures are needed for early and rapid screening and detection of IAV. Although traditional laboratory-based techniques are accurate, they are often time-consuming and not always feasible in emergency or resource-limited areas. In contrast, emerging point-of-care strategies provide faster results but may compromise sensitivity and specificity. Here, this review critically evaluates various detection methods for IAV from established laboratory-based procedures to innovative rapid diagnosis. By analyzing the recent research progress, we aim to address significant gaps in understanding the effectiveness, practicality, and applicability of these methods in different scenarios, which could provide information for healthcare strategies, guide public health response measures, and ultimately strengthen patient care in the face of the ongoing threat of IAV. Through a detailed comparison of diagnostic models, this review can provide a reliable reference for rapid, accurate and efficient detection of IAV, and to contribute to the diagnosis, treatment, prevention, and control of IAV.

It has been well-elaborated that KIN17 protein is closely related to the expression, development and prognosis of liver cancer; however, till date, there has been no study about detecting the KIN17 protein in serum, which is important to developing clinical applications. The objective of this work is to detect serum KIN17 protein by the ELISA method and to explore the diagnostic significance of the KIN17 protein in liver cancer. First, we verified the ELISA method for serum KIN17 measurement according to five aspects: accuracy, precision, specificity, stability and detection limit. Results illustrate that the recovery rate of the ELISA method can be controlled between 90% and 110%, the variation coefficient of intra-assay can be controlled within 16%, and the variation coefficient of inter-assay can be controlled within 10%. There is no non-specific reaction with common tumor markers, and the detection limit can reach 0.125 ng mL^-1. The results show that the KIN17 protein can be detected by ELISA, and there is a significant rise in KIN17 concentration in a liver cancer group compared with a healthy group, whose average concentrations are 1.730 ng mL^-1 and 0.3897 ng mL^-1, respectively. On this basis, we hypothesize that the serum KIN17 protein can serve as a potential biomarker of liver cancer and be measurable with the verified ELISA system after specific ultrafiltration and centrifugation, which is of great significance for the diagnosis and treatment of liver cancer.