Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

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.

Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy: a comparative machine learning analysis.

Bacterial species within the Acinetobacter baumannii-calcoaceticus (Acb) complex are very similar and are difficult to discriminate. Misidentification of these species in human infection may lead to severe consequences in clinical settings. Therefore, it is important to accurately discriminate these pathogens within the Acb complex. Raman spectroscopy is a simple method that has been widely studied for bacterial identification with high similarities. In this study, we combined surfaced-enhanced Raman spectroscopy (SERS) with a set of machine learning algorithms for identifying species within the Acb complex. According to the results, the support vector machine (SVM) model achieved the best prediction accuracy at 98.33% with a fivefold cross-validation rate of 96.73%. Taken together, this study confirms that the SERS-SVM method provides a convenient way to discriminate between A. baumannii, Acinetobacter pittii, and Acinetobacter nosocomialis in the Acb complex, which shows an application potential for species identification of Acinetobacter baumannii-calcoaceticus complex in clinical settings in near future.

The combination of Schisandrin C and Luteolin synergistically attenuates hepatitis B virus infection via repressing HBV replication and promoting cGAS-STING pathway activation in macrophages.

BACKGROUND: HBV infection can result in severe liver diseases and is one of the primary causes of liver cell carcinoma-related mortality. Liuwei Wuling tablet (LWWL) is a traditional Chinese medicine formula, with a protecting liver and decreasing enzyme activity, usually used to treat chronic hepatitis B with NAs in clinic. However, its main active ingredients and mechanism of action have not been fully investigated. Hence, we aimed to screen the active ingredient and effective ingredient combinations from Liuwei Wuling tablet to explore the anti-herpatitis B virus activity and mechanism. METHODS: Analysis and screening of effective antiviral components in LWWL by network pharmacology, luteolin (Lut) may be a compound with significant antiviral activity. The mechanism of antiviral action of Lut was also found by real-time PCR detection and western blotting. Meanwhile, we established a co-culture model to investigate the antiviral mechanism of Schisandrin C (SC), one of the main active components of Schisandra chinensis fructus (the sovereign drug of LWWL). Next, HBV-infected mice were established by tail vein injection of pAAV-HBV1.2 plasmid and administered continuously for 20 days. And their antiviral capacity was evaluated by checking serum levels of HBsAg, HBeAg, levels of HBV DNA, and liver levels of HBcAg. RESULTS: In this study, we conducted network pharmacology analysis on LWWL, and through in vitro experimental validation and data analysis, we found that luteolin (Lut) possessed obviously anti-HBV activity, inhibiting HBV replication by downregulating hepatocyte nuclear factor 4α (HNF4α) via the ERK pathway. Additionally, we established a co-culture system and proved that SC promoted activation of cGAS-STINIG pathway and IFN-ß production in THP-1 cells to inhibit HBV replication in HepG2.2.15 cells. Moreover, we found the combination of SC and Lut shows a greater effect in inhibiting HBV compared to SC or Lut alone in HBV-infected mice. CONCLUSION: Taken together, our study suggests that combination of SC and Lut may be potential candidate drug for the prevention and treatment of chronic hepatitis B.

Tunicates as Sources of High-Quality Nutrients and Bioactive Compounds for Food/Feed and Pharmaceutical Applications: A Review.

Tunicates are widely distributed worldwide and are recognized as abundant marine bioresources with many potential applications. In this review, state-of-the-art studies on chemical composition analyses of various tunicate species were summarized; these studies confirmed that tunicates contain nutrients similar to fish (such as abundant cellulose, protein, and ω-3 fatty acid (FA)-rich lipids), indicating their practical and feasible uses for food or animal feed exploration. However, the presence of certain toxic elements should be evaluated in terms of safety. Moreover, recent studies on bioactive substances extracted from tunicates (such as toxins, sphingomyelins, and tunichromes) were analyzed, and their biological properties were comprehensively reviewed, including antimicrobial, anticancer, antioxidant, antidiabetic, and anti-inflammatory activities. In addition, some insights and prospects for the future exploration of tunicates are provided which are expected to guide their further application in the food, animal feed, and pharmaceutical industries. This review is critical to providing a new pathway for converting the common pollution issues of hydroponic nutrients into valuable marine bioresources.