Rapid discrimination and ratio quantification of mixed antibiotics in aqueous solution through integrative analysis of SERS spectra via CNN combined with NN-EN model.

INTRODUCTION: Abusing antibiotic residues in the natural environment has become a severe public health and ecological environmental problem. The side effects of its biochemical and physiological consequences are severe. To avoid antibiotic contamination in water, implementing universal and rapid antibiotic residue detection technology is critical to maintaining antibiotic safety in aquatic environments. Surface-enhanced Raman spectroscopy (SERS) provides a powerful tool for identifying small molecular components with high sensitivity and selectivity. However, it remains a challenge to identify pure antibiotics from SERS spectra due to coexisting components in the mixture. OBJECTIVES: In this study, an intelligent analysis model for the SERS spectrum based on a deep learning algorithm was proposed for rapid identification of the antibiotic components in the mixture and quantitative determination of the ratios of these components. METHODS: We established a water environment system containing three antibiotic residues of ciprofloxacin, doxycycline, and levofloxacin. To facilitate qualitative and quantitative analysis of the SERS spectra antibiotic mixture datasets, we developed a computational framework integrating a convolutional neural network (CNN) and a non-negative elastic network (NN-EN) method. RESULTS: The experimental results demonstrate that the CNN model has a recognition accuracy of 98.68%, and the interpretation analysis of Shapley Additive exPlanations (SHAP) shows that our model can specifically focus on the characteristic peak distribution. In contrast, the NN-EN model can accurately quantify each component's ratio in the mixture. CONCLUSION: Integrating the SERS technique assisted by the CNN combined with the NN-EN model exhibits great potential for rapid identification and high-precision quantification of antibiotic residues in aquatic environments.

Structural abnormality of hepatic glycogen in rat liver with diethylnitrosamine-induced carcinogenic injury.

Growing evidence confirms associations between glycogen metabolic re-wiring and the development of liver cancer. Previous studies showed that glycogen structure changes abnormally in liver diseases such as cystic fibrosis, diabetes, etc. However, few studies focus on glycogen molecular structural characteristics during liver cancer development, which is worthy of further exploration. In this study, a rat model with carcinogenic liver injury induced by diethylnitrosamine (DEN) was successfully constructed, and hepatic glycogen structure was characterized. Compared with glycogen structure in the healthy rat liver, glycogen chain length distribution (CLD) shifts towards a short region. In contrast, glycogen particles were mainly present in small-sized ß particles in DEN-damaged carcinogenic rat liver. Comparative transcriptomic analysis revealed significant expression changes of genes and pathways involved in carcinogenic liver injury. A combination of transcriptomic analysis, RT-qPCR, and western blot showed that the two genes, Gsy1 encoding glycogen synthase and Gbe1 encoding glycogen branching enzyme, were significantly altered and might be responsible for the structural abnormality of hepatic glycogen in carcinogenic liver injury. Taken together, this study confirmed that carcinogenic liver injury led to structural abnormality of hepatic glycogen, which provided clues to the future development of novel drug targets for potential therapeutics of carcinogenic liver injury.

Molecular mechanisms of glycogen particle assembly in Escherichia coli.

It has been reported that glycogen in Escherichia coli has two structural states, that is, fragility and stability, which alters dynamically. However, molecular mechanisms behind the structural alterations are not fully understood. In this study, we focused on the potential roles of two important glycogen degradation enzymes, glycogen phosphorylase (glgP) and glycogen debranching enzyme (glgX), in glycogen structural alterations. The fine molecular structure of glycogen particles in Escherichia coli and three mutants (ΔglgP, ΔglgX and ΔglgP/ΔglgX) were examined, which showed that glycogen in E. coli ΔglgP and E. coli ΔglgP/ΔglgX were consistently fragile while being consistently stable in E. coli ΔglgX, indicating the dominant role of GP in glycogen structural stability control. In sum, our study concludes that glycogen phosphorylase is essential in glycogen structural stability, leading to molecular insights into structural assembly of glycogen particles in E. coli.

Identification of geographic origins of Morus alba Linn. through surfaced enhanced Raman spectrometry and machine learning algorithms.

The leaves of Morus alba Linn., which is also known as white mulberry, have been commonly used in many of traditional systems of medicine for centuries. In traditional Chinese medicine (TCM), mulberry leaf is mainly used for anti-diabetic purpose due to its enrichment in bioactive compounds such as alkaloids, flavonoids and polysaccharides. However, these components are variable due to the different habitats of the mulberry plant. Therefore, geographic origin is an important feature because it is closely associated with bioactive ingredient composition that further influences medicinal qualities and effects. As a low-cost and non-invasive method, surface enhanced Raman spectrometry (SERS) is able to generate the overall fingerprints of chemical compounds in medicinal plants, which holds the potential for the rapid identification of their geographic origins. In this study, we collected mulberry leaves from five representative provinces in China, namely, Anhui, Guangdong, Hebei, Henan and Jiangsu. SERS spectrometry was applied to characterize the fingerprints of both ethanol and water extracts of mulberry leaves, respectively. Through the combination of SERS spectra and machine learning algorithms, mulberry leaves were well discriminated with high accuracies in terms of their geographic origins, among which the deep learning algorithm convolutional neural network (CNN) showed the best performance. Taken together, our study established a novel method for predicting the geographic origins of mulberry leaves through the combination of SERS spectra with machine learning algorithms, which strengthened the application potential of the method in the quality evaluation, control and assurance of mulberry leaves.

Fractional extraction and structural characterization of glycogen particles from the whole cultivated caterpillar fungus Ophiocordyceps sinensis.

Ophiocordyceps sinensis (syn. Cordyceps sinensis) is a valuable medicinal fungus in traditional Chinese medicine, and one or more polysaccharides are the key constituents with important medical effects. Glycogen as a functional polysaccharide is widely identified in eukaryotes including fungi. However, there is no definitive report of glycogen presence in O. sinensis. In this study, we carefully fractionated polysaccharides from cultivated caterpillar fungus O. sinensis, which were then characterized via methods for glycogen analysis. According to the results, 1.03 ± 0.43 % of polysaccharides were quantified via amyloglucosidase digestion in the whole cultivated caterpillar fungus, which had a typical spherical shape under transmission electron microscope with an average peak radius of 37.63 ± 0.57 nm via size exclusion chromatography and an average chain length of 12.47 ± 0.94 degree of polymerization via fluorophore-assisted capillary electrophoresis. Taken together, this study confirmed that the polysaccharides extracted form O. sinensis were mostly glycogen.

Machine learning analysis of SERS fingerprinting for the rapid determination of Mycobacterium tuberculosis infection and drug resistance.

Over the past decades, conventional methods and molecular assays have been developed for the detection of tuberculosis (TB). However, these techniques suffer limitations in the identification of Mycobacterium tuberculosis (Mtb), such as long turnaround time and low detection sensitivity, etc., not even mentioning the difficulty in discriminating antibiotics-resistant Mtb strains that cause great challenges in TB treatment and prevention. Thus, techniques with easy implementation for rapid diagnosis of Mtb infection are in high demand for routine TB diagnosis. Due to the label-free, low-cost and non-invasive features, surface enhanced Raman spectroscopy (SERS) has been extensively investigated for its potential in bacterial pathogen identification. However, at current stage, few studies have recruited handheld Raman spectrometer to discriminate sputum samples with or without Mtb, separate pulmonary Mtb strains from extra-pulmonary Mtb strains, or profile Mtb strains with different antibiotic resistance characteristics. In this study, we recruited a set of supervised machine learning algorithms to dissect different SERS spectra generated via a handheld Raman spectrometer with a focus on deep learning algorithms, through which sputum samples with or without Mtb strains were successfully differentiated (5-fold cross-validation accuracy = 94.32%). Meanwhile, Mtb strains isolated from pulmonary and extra-pulmonary samples were effectively separated (5-fold cross-validation accuracy = 99.86%). Moreover, Mtb strains with different drug-resistant profiles were also competently distinguished (5-fold cross-validation accuracy = 99.59%). Taken together, we concluded that, with the assistance of deep learning algorithms, handheld Raman spectrometer has a high application potential for rapid point-of-care diagnosis of Mtb infections in future.

Comparative transcriptome analysis of diurnal alterations of liver glycogen structure: A pilot study.

Molecular mechanisms behind structural alterations between fragile and stable glycogen α particles in liver are not clear yet. In this pilot study, we re-examined the diurnal alterations of glycogen structure from the perspective of liver tissue transcriptome. By comparing the structures of liver glycogen from mice at 12 am, 8 am, 12 pm, and 8 pm (light-on: 6 am; light-off: 6 pm), we re-confirmed that the liver glycogen was fragile at 12 am and 8 am and stable at 12 pm and 8 pm as previously reported. The structural differences of glycogen particles at 12 am and 12 pm were thoroughly compared via transcriptomics. Differentially expressed genes (DEGs) with statistical significance were identified, while expression level of the gene ppp1r3g (log2Fold_Change = -6.368, P-value = 2.89E-04) that encoded PPP1R3G with glycogen binding domain was most significantly changed, which provided preliminary clues to the structural alterations of glycogen α particles during the diurnal cycle.

A Non-Degradative Extraction Method for Molecular Structure Characterization of Bacterial Glycogen Particles.

Currently, there exist a variety of glycogen extraction methods, which either damage glycogen spatial structure or only partially extract glycogen, leading to the biased characterization of glycogen fine molecular structure. To understand the dynamic changes of glycogen structures and the versatile functions of glycogen particles in bacteria, it is essential to isolate glycogen with minimal degradation. In this study, a mild glycogen isolation method is demonstrated by using cold-water (CW) precipitation via sugar density gradient ultra-centrifugation (SDGU-CW). The traditional trichloroacetic acid (TCA) method and potassium hydroxide (KOH) method were also performed for comparison. A commonly used lab strain, Escherichia coli BL21(DE3), was used as a model organism in this study for demonstration purposes. After extracting glycogen particles using different methods, their structures were analyzed and compared through size exclusion chromatography (SEC) for particle size distribution and fluorophore-assisted capillary electrophoresis (FACE) for linear chain length distributions. The analysis confirmed that glycogen extracted via SDGU-CW had minimal degradation.