Analytical Sciences最新文献

One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited availability of open-source and large Raman spectra dataset. Therefore we compared typical ML partial least square-discriminant analysis (PLS-DA) and DL one dimensional convolution neural network (1D-CNN) based pathogenic microbe identification on 12,000 Raman spectra from six species of microbe (i.e., K. aerogenes (Klebsiella aerogenes), C. albicans (Candida albicans), C. glabrata (Candida glabrata), Group A Strep. (Group A Streptococcus), E. coli1 (Escherichia coli1), E. coli2 (Escherichia coli2)) when 100%, 75%, 50% and 25% of the 12,000 Raman spectra were retained. The total Raman dataset was analyzed with 80% split for training and 20% for testing. The 100% retained testing dataset accuracy, area under curve (AUC) of the receiver operating characteristic (ROC) curve were 95.25% and 0.997 for 1D-CNN, which are higher than those (89.42% and 0.979) of PLS-DA. Yet, PLS-DA outperforms 1D-CNN for 75%, 50% and 25% retained testing dataset. The resultant accuracies and AUCs demonstrated the performance reliance of PLS-DA and 1D-CNN on Raman spectra number. Besides, both loadings on the latent variables of PLS-DA and the saliency maps of 1D-CNN largely captured Raman peaks arising from DNA and proteins with comparable interpretability. The results of the current work indicated that both ML and DL algorithms should be explored for application-wise Raman spectra identification to select whichever with higher accuracies and AUCs.

A straightforward label-free colorimetric aptasensor utilizing the aptamer-enhanced peroxidase-like activity of cobalt oxyhydroxide (CoOOH) nanosheets has been established for kanamycin detection. In the kanamycin-free state, aptamers adsorb onto the CoOOH surface through electrostatic forces, enhancing the peroxidase-like activity of CoOOH and thereby resulting in a strong absorption signal and a yellow hue in 3,3',5,5'-tetramethylbenzidine (TMB) upon termination of the reaction with a stop solution. Conversely, upon the introduction of kanamycin, aptamers and CoOOH nanosheets compete for binding to kanamycin, resulting in a significant decrease in the number of aptamers bound to CoOOH. As a result, the activity of CoOOH diminishes, leading to a corresponding reduction in coloration and absorbance of the solution. Hence, the quantitative determination of kanamycin could be realized by analyzing the absorbance variations. Under optimal conditions, the aptasensor demonstrated high sensitivity and specificity, with a linear detection range from 500 nM to 5 µM and a detection limit as low as 54.6 nM. Moreover, the aptasensor effectively identified kanamycin in river water samples, achieving a recovery rate between 91.7% and 102.1%. This approach offers good practicability and provides a novel platform for kanamycin detection in environmental samples.

The ability to sensitively and quantitatively detect uranyl in complex samples plays a vital role in environmental monitoring. In this work, an MOF-coated gold (Au) nanohybrid was synthesized for uranyl detection by surface-enhanced Raman scattering (SERS) technology. The MOF shell not only prevents the Au nanoparticles from rapid aggregation, but also effectively enhances the Raman signal of uranyl. A detection limit of as low as 0.5 μM could be achieved in solution, which could be comparable to the previously reported ones from SERS-based approaches. Moreover, the prepared SERS-active substrate was also applied to uranyl detection in real samples.

In our previous study, the combination of two on-line sample preconcentration techniques, large-volume sample stacking with an electroosmotic flow (EOF) pump (LVSEP) and transient isotachophoresis (tITP), in microchip electrophoresis (MCE) was developed, which was named large-volume dual preconcentration by isotachophoresis and stacking (LDIS). LDIS was apparently effective for improving the sensitivity and the peak shape. In LDIS, however, there was a limit to the improvement of the sensitivity enhancement factor (SEF) since the amount of analytes to be concentrated was limited to the channel volume. To overcome this issue, in the present article, LDIS was coupled with field-amplified sample injection (FASI) technique on Y-shaped channel microchips. The use of a Y-channel in LDIS-FASI allowed consecutive LVSEP, FASI and tITP enrichments with a simple voltage control. In conventional LVSEP and LDIS analyses of a standard analyte, the SEFs were evaluated to be 2630 and 13,100, respectively, whereas in LDIS-FASI that was increased to 27,900 even at the FASI injection time of 0 s. To achieve higher SEFs, furthermore, the FASI injection time was increased to 150 s, resulting in the best SEF of 58,500. It should be emphasized that the peak width in LDIS-FASI was quite narrow, only 0.3-3.1 s, while in normal LVSEP that was 13 s. Furthermore, the LDIS-FASI technique was applied to the analysis of oligosaccharide mixture. Due to the focusing effect by LDIS-FASI, the resolutions were improved from 0.97-1.57 to 2.08-2.73.

Methamphetamine (METH) abuse poses a serious risk to human health and social stability. It is critical to develop sensitive and selective methods for detecting METH. Here, we develop a fluorescence aptamer sensor to detect METH based on DNA exonuclease III (Exo III), graphene oxide (GO), and FAM-labeled aptamer. First, the sensor used GO's strong binding capacity to adsorb and quench the fluorescence of the aptamer attached to GO surface. When METH was added to the system, the formation of stable complex for aptamer and METH dissociated from the surface of GO, leading to a fluorescence restoration. Then, the fluorescence signal was further amplified by using Exo III to liberate target METH for cyclic hybridization. And the gel electrophoresis experiment further verified the reliability of this strategy. This aptamer sensor exhibited a low detection limit (0.52 nM) and excellent selectivity under optimal conditions. Notably, this sensor has been successfully validated in the detection of METH in urine and saliva samples, exhibited commendable recovery (94.00-104.65%). Its benefits include facile, sensitive, and rapid. Expected to be used in practical METH detection.