Shuangdan Jiedu Decoction improved LPS-induced acute lung injury by regulating both cGAS-STING pathway and inflammasome.

ETHNOPHARMACOLOGICAL RELEVANCE: Shuangdan Jiedu Decoction (SJD) is a formula composed of six Chinese herbs with heat-removing and detoxifying, antibacterial, and anti-inflammatory effects, which is clinically used in the therapy of various inflammatory diseases of the lungs including COVID-19, but the therapeutic material basis of its action as well as its molecular mechanism are still unclear. AIM OF THE STUDY: The study attempted to determine the therapeutic effect of SJD on LPS-induced acute lung injury (ALI), as well as to investigate its mechanism of action and assess its therapeutic potential for the cure of inflammation-related diseases in the clinical setting. MATERIALS AND METHODS: We established an ALI model by tracheal drip LPS, and after the administration of SJD, we collected the bronchoalveolar lavage fluid (BALF) and lung tissues of mice and examined the expression of inflammatory factors in them. In addition, we evaluated the effects of SJD on the cyclic guanosine monophosphate-adenosine monophosphate synthase -stimulator of interferon genes (cGAS-STING) and inflammasome by immunoblotting and real-time quantitative polymerase chain reaction (RT-qPCR). RESULTS: We demonstrated that SJD was effective in alleviating LPS-induced ALI by suppressing the levels of pro-inflammatory cytokines in the BALF, improving the level of lung histopathology and the number of neutrophils, as well as decreasing the inflammatory factor-associated gene expression. Importantly, we found that SJD could inhibit multiple stimulus-driven activation of cGAS-STING and inflammasome. Further studies showed that the Chinese herbal medicines in SJD had no influence on the cGAS-STING pathway and inflammasome alone at the formulated dose. By increasing the concentration of these herbs, we observed inhibitory effects on the cGAS-STING pathway and inflammasome, and the effect exerted was maximal when the six herbs were combined, indicating that the synergistic effects among these herbs plays a crucial role in the anti-inflammatory effects of SJD. CONCLUSIONS: Our research demonstrated that SJD has a favorable protective effect against ALI, and its mechanism of effect may be associated with the synergistic effect exerted between six Chinese medicines to inhibit the cGAS-STING and inflammasome abnormal activation. These results are favorable for the wide application of SJD in the clinic as well as for the development of drugs for ALI from herbal formulas.

Rapid discrimination between wild and cultivated Ophiocordyceps sinensis through comparative analysis of label-free SERS technique and mass spectrometry.

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.

Total tanshinones ameliorates cGAS-STING-mediated inflammatory and autoimmune diseases by affecting STING-IRF3 binding.

BACKGROUND: An important signaling pathway connecting illness and natural immunity is the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway, but aberrant activation of this pathway is associated with the development of autoimmune and inflammatory diseases. Hence, targeted inhibition of the activation of the cGAS-STING pathway is potentially valuable in the treatment of disease. The primary active component of Salvia miltiorrhiza is total tanshinone (TTN). Research has indicated that TTN possesses noteworthy anti-inflammatory properties. However, the protective mechanism of TTN against acute liver injury (ALI) and autoimmune diseases is unknown. METHODS: A model of aberrant activation of the cGAS-STING pathway was established in various cells and treated with TTN, and the expression of cGAS-STING pathway-related proteins, type I interferon, interferon stimulated genes and inflammatory factors was assessed by western blotting, real-time qPCR. Immunofluorescence analysis of the effect of TTN on the entry of associated proteins into the nucleus following aberrant activation of the cGAS-STING pathway. The effect of TTN on STING oligomerisation was investigated using 2'-3'-cyclic GMP-AMP (2',3'-cGAMP) to induce STING oligomerisation. Western blotting was used to examine the impact of TTN on the interactions of STING, tank-binding kinase 1 (TBK1), and interferon regulatory factor 3 (IRF3) after HA or Flag-labelled plasmids were transfected into HEK-293 T cells. A dimethylxanthenone-4-acetic acid (DMXAA) -induced activation model of the cGAS-STING pathway in mice was established to study the effect of TTN on aberrant activation of the cGAS-STING pathway in vivo. On the other hand, an animal model of lipopolysaccharide/D-galactosamine (LPS/D-GaIN)-induced ALI and an autoimmune disease model induced by trex1 knockout were established to study the effects of TTN on inflammatory and autoimmune diseases mediated by the cGAS-STING pathway in vivo. RESULTS: In several models of aberrant activation of the cGAS-STING pathway, TTN significantly inhibited the phosphorylation of STING and IRF3, thereby suppressing the expression of type I interferon, interferon-stimulated genes and inflammatory factors. Additionally, TTN prevented P65 and IRF3 from entering the nucleus after the cGAS-STING signalling pathway was abnormally activated. Subsequent research indicated that TTN was not involved in the oligomerization of STING or the integration of STING-TBK1 and TBK1-IRF3. However, TTN was found to have a substantial effect on the binding process between STING and IRF3. On the other hand, DMXAA-induced STING activation and activation of downstream signalling in vivo are inhibited by TTN. Furthermore, TTN exhibits positive treatment effects on autoimmune diseases caused by deficiency of trex1 and LPS/D-GaIN-induced ALI. CONCLUSION: Our research indicates that TTN effectively treats ALI and autoimmune illnesses mediated by the cGAS-STING pathway by inhibiting the abnormal activation of this pathway.

Pseudo-Siamese network combined with label-free Raman spectroscopy for the quantification of mixed trace amounts of antibiotics in human milk: A feasibility study.

The utilization of antibiotics is prevalent among lactating mothers. Hence, the rapid determination of trace amounts of antibiotics in human milk is crucial for ensuring the healthy development of infants. In this study, we constructed a human milk system containing residual doxycycline (DXC) and/or tetracycline (TC). Machine learning models and clustering algorithms were applied to classify and predict deficient concentrations of single and mixed antibiotics via label-free SERS spectra. The experimental results demonstrate that the CNN model has a recognition accuracy of 98.85% across optimal hyperparameter combinations. Furthermore, we employed Independent Component Analysis (ICA) and the pseudo-Siamese Convolutional Neural Network (pSCNN) to quantify the ratios of individual antibiotics in mixed human milk samples. Integrating the SERS technique with machine learning algorithms shows significant potential for rapid discrimination and precise quantification of single and mixed antibiotics at deficient concentrations in human milk.

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.

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.

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.

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.