Citrus essential oils (EOs) require accurate identification and assessment to ensure authenticity and consistency. However, conventional techniques such as gas chromatography (GC) and mass spectrometry (MS) are time-consuming and expensive, highlighting the need for novel analytical methods. This study proposes an approach for EOs detection using Raman spectroscopy (RS) combined with machine learning (ML) algorithms. Six citrus EOs underwent an evaporation experiment, with Raman spectra collected at five time points and GC-MS was used to analyze compositional changes at the starting and ending points of evaporation as a standard reference. Five ML algorithms were developed to identify differences among EOs and monitor their temporal changes. The predictive performance was evaluated using multiple quantitative metrics. The results show that the support vector machine (SVM) consistently achieves the best performance across all prediction tasks, and the interpretability algorithm identified key components in the Raman spectra of EOs. Taken together, RS-SVM is proven to be an accurate and cost-effective analytical technique for the rapid identification and quality assessment of citrus EOs.

Ophiocordyceps sinensis (Berk.) is a functional food with health. O. sinensis quality varies by geographical origins, and current identification methods are sophisticated and time-consuming. This study aims to develop a rapid and straightforward method for accurately identifying O. sinensis geographic origins. Surface-enhanced Raman spectroscopy (SERS) was applied to analyze O. sinensis from four major production areas in China. Liquid chromatography-mass spectrometry (LC-MS) was used as a reference method to characterize compositional differences among samples and to verify the geographical authenticity of O. sinensis from the four production areas. Six machine learning (ML) algorithms were introduced for predicting geographical origins, and evaluation metrics were used to assess model performance. According to the comparative analysis, the Support Vector Machine (SVM) model performed best with the highest discrimination accuracy. A feature importance map was constructed to understand further how the model makes predictive decisions, revealing the significant Raman shifts in classifying O. sinensis from different geographical origins. The SERS-SVM method developed in this study contributes to the authenticity identification of O. sinensis geographical origins. It shows the potential to serve as an effective quality control method for the valuable TCM (traditional Chinese medicine).

Due to their high economic value, essential oils (EOs) are increasingly subject to adulteration, posing significant challenges for quality assessment and control. Owing to its excellent molecular specificity, Raman spectroscopy has been widely employed for the analysis and evaluation of EO products. In this study, we collected a total of 2700 Raman spectra comprising rose essential oil (REO, n = 900), geranium essential oil (GEO, n = 900), and their mixtures (n = 900). We first constructed a conventional convolutional neural network to distinguish spectra corresponding to varying EO mixing ratios. Interpretability analysis revealed that spectral peaks at 800, 1000, 1002, 1028, 1200, 1378, and 1668 cm-1, which show notable intensity variations in the averaged spectra of REO and GEO, also played a critical role in model decision-making, indicating that these spectral features can serve as discriminative markers for different EO ratios. Subsequently, we developed a channel attention residual feature extraction network (CARFENet), which employs spectral capturing and spectral separation modules to deconstruct mixed EO spectra into their constituent pure components. CARFENet demonstrated robust performance on the validation set and yielded predicted spectra that closely resembled the true spectra in an external test set, with similarity indices exceeding 0.99. These findings indicate that CARFENet enables the effective quantitative analysis of pure EO components within mixed Raman spectra.

Thyroid nodules are a very common entity. The overall prevalence in the populace is estimated to be around 65-68%, among which a small portion (less than 5%) is malignant (cancerous). Therefore, it is important to discriminate benign thyroid nodules from malignant thyroid nodules. In this study, an equal number of participants with benign and malignant thyroid nodules (N = 10/group) were recruited. Saliva samples were collected from each participant, and SERS spectra were acquired, followed by validation using a metabolomics approach. An additional equal number of patients (N = 40/group) were recruited to construct diagnostic models. The performance of various machine learning (ML) algorithms was assessed using multiple evaluation metrics. Finally, the reliability of the optimal model was tested using blind test data (N = 10/group for benign and malignant thyroid nodules). The results showed a consistent trend between the SERS metabolic profile and the metabolites identified through MS analysis. The Multi-ResNet algorithm was optimal, achieving a 95% accuracy in sample discrimination. Additionally, blind test data sets yielded an overall accuracy of 83%. In summary, the deep-learning-guided SERS technique holds great potential in the accurate discrimination of benign and malignant thyroid nodules via human saliva samples, which facilitates the noninvasive diagnosis of malignant thyroid nodules in clinical settings.

In recent years, the misuse of antibiotics has led to severe pollution in water environments, with excessive residues in lake water damaging ecosystems and promoting the emergence of antibiotic-resistant bacteria. Therefore, rapid detection of antibiotic residues in the environment is crucial. This study introduces a novel method for the simultaneous quantification of mixed antibiotics in lake water using Surface-Enhanced Raman Scattering (SERS) combined with deep learning methods. To demonstrate the accuracy of our experiments, we tested four lake water samples collected from four distinct sampling points of an artificial lake in a municipal city in China. We independently analyzed each sample mixed with commonly used antibiotics, including ciprofloxacin, doxycycline, and levofloxacin. A non-negative elastic network was then employed to predict concentration ratios of mixed antibiotics in the lake water samples. The results showed that the established method can accurately quantify the ratios of individual antibiotics in mixed solutions at all four lake water sampling points. This approach facilitates the identification and quantification of antibiotics in lake water with simplicity and rapidity, exhibiting potential application for real-world monitoring of fluctuations of antibiotic residues in natural water systems.

Polygonatum odoratum (P. odoratum) oligosaccharides (POO) are bioactive compounds with complex structures. Since the precise structure of POO remains undefined, their pharmacological development and clinical application have been hindered. This study developed a universal strategy to elucidate oligosaccharide structures of P. odoratum by combining Orbitrap high-resolution mass spectrometry (Orbitrap-HRMS) and enzymatic hydrolysis. Initially, monosaccharide constituents of free oligosaccharides (POO) and polysaccharides (POP) in P. odoratum were quantitatively analyzed. The accurate mass values, retention time, and sequence-specific fragment information facilitated initial characterization by comparing with the self-established oligosaccharide library based on these monosaccharides. Due to the limitations of MS in precisely identifying isomer structures, enzymatic hydrolysis was further employed to distinguish the glycosidic linkage and oligosaccharide type. Fructanase mixture, endo-1,4-β-mannanase, exo-1,3-β-D-glucanase, α-galactosidase and amyloglucosidase were used to identify hexose-based isomers, while α-L-arabinofuranosidase and endo-1,4-β-xylanase were used for pentose-based isomers. The study revealed that POO primarily consisted of fructooligosaccharides, and its isomers, such as 1-kestose, 6-kestose, and neokestose, were clarified. POP mainly comprised galactomannan, with the branching site at α-(1 → 6)-galactoside in POPO determined for the first time in P. odoratum. This strategy provides a straightforward methodology for analyzing plant oligosaccharides, establishing a structure analytical foundation for further pharmacological research and potential clinical applications.

Glycogen structure is closely associated with its physiological functions. Previous studies confirmed that liver glycogen structure had two dominant states: mainly stable during the day and largely fragile at night. However, the diurnal change of glycogen structure is impaired, with dominant fragility in diseased conditions such as diabetes mellitus and liver fibrosis. Therefore, the persistent structural fragility of glycogen particles could be a potential molecular-level pathological biomarker for early screening of certain liver diseases. However, the current method for identifying glycogen structural stability and fragility suffers from sophisticated procedures and reliance on expensive instruments, which demands developing novel methods for rapidly discriminating the two types of glycogen particles. This study applied surface-enhanced Raman spectroscopy (SERS) to generate SERS spectra of glycogen samples, revealing distinct structural differences between fragile and stable glycogen particles. Machine learning models were then constructed to predict the structural states of unknown glycogen samples via SERS spectra, according to which the convolutional neural network (CNN) model achieved the best discrimination capacity. Taken together, the SERS technique coupled with the CNN model can identify stable and fragile liver glycogen samples, facilitating the application of glycogen structural fragility as a biomarker in diagnosing liver diseases.

Ophiocordyceps sinensis is a genus of ascomycete fungi that has been widely used as a valuable tonic or medicine. However, due to over-exploitation and the destruction of natural ecosystems, the shortage of wild O. sinensis resources has led to an increase in artificially cultivated O. sinensis. To rapidly and accurately identify the molecular differences between cultivated and wild O. sinensis, this study employs surface-enhanced Raman spectroscopy (SERS) combined with machine learning algorithms to distinguish the two O. sinensis categories. Specifically, we collected SERS spectra for wild and cultivated O. sinensis and validated the metabolic profiles of SERS spectra using Ultra-Performance Liquid Chromatography coupled with Orbitrap High-Resolution Mass Spectrometry (UPLC-Orbitrap-HRMS). Subsequently, we constructed machine learning classifiers to mine potential information from the spectral data, and the spectral feature importance map is determined through an optimized algorithm. The results indicate that the representative characteristic peaks in the SERS spectra are consistent with the metabolites identified through metabolomics analysis, confirming the feasibility of the SERS method. The optimized support vector machine (SVM) model achieved the most accurate and efficient capacity in discriminating between wild and cultivated O. sinensis (accuracy = 98.95%, 5-fold cross-validation = 98.38%, time = 0.89s). The spectral feature importance map revealed subtle compositional differences between wild and cultivated O. sinensis. Taken together, these results are expected to enable the application of SERS in the quality control of O. sinensis raw materials, providing a foundation for the efficient and rapid identification of their quality and origin.

Ochratoxin A (OTA) is a toxic pollutant in foods, and its actual detection is crucial. A novel and sensitive dual signaling ratiometric electrochemical aptasensor based on nanoporous gold (NPG) was proposed to determine OTA. NPG, with high specific surface area and conductivity, improved the sensitivity by immobilizing more aptamers. Meanwhile, the dual signaling ratiometric strategy improved the detection reproducibility through self-referencing and built-in correction. NPG and ratiometric strategy multi-amplified the dual signal changes. The sensitivity of OTA was evaluated by the ratio of methylene to ferrocene current values. Under the optimal conditions, the NPG-based aptasensor demonstrated excellent sensitivity with a wide linear range of 1 pg/mL to 2 ng/mL and the limit of detection (LOD) of 0.4 pg/mL for OTA. This developed aptasensor also effectively detected OTA in spiked Cordyceps sinensis and grape juice samples, with recovery values falling in the 98.49-108.0% range.

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 β 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.

The structure of glycogen alpha particles in healthy mouse liver has two states: stability and fragility. In contrast, glycogen alpha particles in diabetic liver present consistent fragility, which may exacerbate hyperglycemia. Currently, the molecular mechanism behind glycogen structural alteration is still unclear. In this study, we characterized the fine molecular structure of liver glycogen alpha particles in healthy mice under time-restricted feeding (TRF) mode during a 24-h cycle. Then, differentially expressed genes (DEGs) in the liver during daytime and nighttime were revealed via transcriptomics, which identified that the key downregulated DEGs were mainly related to insulin secretion in daytime. Furthermore, GO annotation and KEGG pathway enrichment found that negative regulation of the glycogen catabolic process and insulin secretion process were significantly downregulated in the daytime. Therefore, transcriptomic analyses indicated that the structural stability of glycogen alpha particles might be correlated with the glycogen degradation process via insulin secretion downregulation. Further molecular experiments confirmed the significant upregulation of glycogen phosphorylase (PYGL), phosphorylated PYGL (p-PYGL), and glycogen debranching enzyme (AGL) at the protein level during the daytime. Overall, we concluded that the downregulation of insulin secretion in the daytime under TRF mode facilitated glycogenolysis, contributing to the structural stability of glycogen alpha-particles.

Glycogen, a complex branched glucose polymer, is responsible for sugar storage in blood glucose homeostasis. It comprises small β particles bound together into composite α particles. In diabetic livers, α particles are fragile, breaking apart into smaller particles in dimethyl sulfoxide, DMSO; they are however stable in glycogen from healthy animals. We postulate that the bond between β particles in α particles involves hydrogen bonding. Liver-glycogen fragility in normal and db/db mice (an animal model for diabetes) is compared using various hydrogen-bond breakers (DMSO, guanidine and urea) at different temperatures. The results showed different degrees of α-particle disruption. Disrupted glycogen showed changes in the mid-infra-red spectrum that are related to hydrogen bonds. While glycogen α-particles are only fragile under harsh, non-physiological conditions, these results nevertheless imply that the bonding between β particles in α particles is different in diabetic livers compared to healthy, and is probably associated with hydrogen bonding.

Human milk stands as the optimal natural source of nutrition for infants, harboring a plethora of complex nutrients including water, carbohydrates, lipids, minerals, proteins, and vitamins, as well as a diverse array of bioactive molecules such as oligosaccharides and growth factors, etc. These components facilitate and safeguard the growth and development of infants. Furthermore, the composition and concentration of human milk subtly evolve across different stages of breastfeeding, adapting to the specific requirements of infants. Therefore, attaining a more precise and comprehensive understanding of human milk composition holds significant value. In this study, a succinct overview of the primary constituents of human milk and their fluctuations during various lactation periods was provided. Then, existing research is critically examined concerning the existence of free glycogen in raw human milk because previous studies indicate the presence of glycogen in human milk but do not determine whether glycogen exists in free form. Glycogen within human milk may exert influence on the growth, development, and establishment of the immune system in infants, but precise mechanisms must be determined. Elucidation of the existing form of glycogen particles in human milk will not only update our understanding of human milk composition, but will also strengthen the roles of glycogen in infant development. Through our exploration, the presence of free glycogen in human milk appears plausible. However, further experiments are needed to confirm the claim via solid evidence.

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.

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.

Background: The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential.Aim of Review: Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings.Key Scientific Concepts of Review: The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed. Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.& COPY; 2023 The Authors. Published by Elsevier B.V. on behalf of Cairo University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Polygonatum odoratum has been known as having therapeutic effects on diabetes. However, due to its complex composition, it is difficult to elucidate the molecular mechanisms of its functions. In this study, we investigated the material basis and molecular mechanisms underlying the antidiabetic activities of methanol extract of P. odoratum (Mill.) Druce by ultra-performance liquid chromatography orbitrap mass spectrometry (UPLC-Orbitrap-MS) and network pharmacology. In specificity, we first constructed a compound-protein interaction network for type 2 diabetes mellitus (T2DM) to identify potential drug targets. Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were then conducted to discover the pathways involved in the antidiabetic activities of P. odoratum. Among the 334 compounds detected in P. odoratum, 277 active constituents and 897 corresponding targets were identified to be associated with antidiabetic activities. Five compounds and five targets were consequently obtained by analyzing the compound-target-pathway network. The five compounds were 9-Aminocamptothecin, 9-Methoxycamptothecin, 5-Hydroxy-1-tetralone, 5,7,3′-Trihydroxy-6,4′,5′-trimethoxyflavone, and Diacerein, while the five targets were ACTB, JUN, STAT3, HIF1A, and MMP9. In addition, 30 antidiabetic-related pathways were recognized by using KEGG pathway analysis. Moreover, molecular docking was performed with the five essential compounds and the top five targets based on degree ranking. Our study confirms that active components in P. odoratum can exert antidiabetic activities via multi-component, multi-target, and multi-pathway mechanisms of action, which serves as a practical guidance for further experimental investigations into the biological functions of the methanol extract of P. odoratum in the treatment of T2DM.