Sensible Functional Linear Discriminant Analysis Effectively Discriminates Enhanced Raman Spectra of Mycobacterium Species

Shenling Baizhu Decoction treats ulcerative colitis of spleen-deficiency and dampness obstruction types by targeting 'gut microbiota and galactose metabolism-bone marrow' axis

ETHNOPHARMACOLOGICAL RELEVANCE

Shenlin Baizhu Decoction (SLBZD), which comes from 'Taiping Huimin Heji Ju Fang', belongs to a classical prescription for treating spleen deficiency and dampness obstruction (SQDDS)-type ulcerative colitis (UC) in traditional Chinese medicine. However, the mechanism of SLBZD in treating UC with SQDDS remains unclear.

AIM OF THE STUDY

This study aims to investigate the mechanism of SLBZD against SQDDS-type UC of based on the "gut microbiota and metabolism - bone marrow" axis to induce endogenous bone marrow mesenchymal stem cells (BMSCs) homing.

MATERIALS AND METHODS

Ultra-performance liquid chromatography-mass spectrometry was used to analysis of SLBZD qualitatively. The efficacy of SLBZD in SQDDS-type UC was evaluated based on the following indicators: the body weight, colon length, disease activity index (DAI) score, Haemotoxylin and Eosin (H&E) pathological sections, and intestinal permeability proteins (occluding and ZO-1). 16S rRNA gene sequencing and non-target metabolomics were performed to identify gut microbiota changes and its metabolites in feces, respectively. BMSCs in each group was collected, cultured, and analyzed. Optimal passaged BMSCs were injected by tail vein into UC rats of SQDDS types. BMSCs homing to the colonic mucosal tissue was observed by immunofluorescent. Finally, the repairing effect of BMSCs homing to the colonic mucosal tissue after SLBZD treatment was analyzed by transmission electron microscopy, qRT-PCR, and immunohistochemistry.

RESULTS

SLBZD effectively improved the colonic length and the body weight, reduced DAI and H&E scores, and increased the expression of the intestinal permeability proteins, including occluding and ZO-1, to treat SQDDS-type UC. After SLBZD treatment, the α-diversity and β-diversity of the gut microbiota were improved. The differential microbiota was screened as Aeromonadaceae, Lactobacillaceae, and Clostridiaceae at the family level, and Aeromonas, Lactobacillus, Clostridium_sensu_stricto_1 at the genus level. Meanwhile, the main metabolic pathway was the galactose metabolism pathway. SLBZD treatment timely corrected the aberrant levels of β-galactose in peripheral blood and bone marrow, senescence-associate-β-galactosidase in BMSCs, and galactose kinase-2, galactose mutase, and galactosidase beta-1 in peripheral blood to further elevate the expression levels of senescence-associated (SA) proteins (p16, p53, p21, and p27) in BMSCs. The Spearman's correlation analysis demonstrated the relationship between microbiota and metabolism, and the relationship between the galactose metabolism pathway and SA proteins. After BMSCs in each group injection via the tail vein, the pharmacodynamic effects were consistent with those of SLBZD in SQDDS-type UC rats. Furthermore, BMSCs have been homing to colonic mucosal tissue. BMSCs from the SLBZD treatment group had stronger restorative effects on intestinal permeability function due to increasing protein and mRNA expressions of occludin and ZO-1, and decreasing the proteins and mRNA expressions of SDF-1 and CXCR4 in colon.

CONCLUSIONS

SLBZD alleviated the damaged structure of gut microbiota and regulated their metabolism, specifically the galactose metabolism, to treat UC of SDDOS types. SLBZD treatment promotes endogenous BMSCs homing to colonic mucosal tissue to repaire the intestinal permeability. The current exploration revealed an underlying mechanism wherein SLBZD activates endogenous BMSCs by targeting 'the gut microbiota and its metabolism-bone marrow' axis and repairs colonic mucosal damage to treat SDDOS-type UC.

Unraveling RNA contribution to the molecular origins of bacterial surface-enhanced Raman spectroscopy (SERS) signals

Surface-enhanced Raman spectroscopy (SERS) is widely utilized in bacterial analyses, with the dominant SERS peaks attributed to purine metabolites released during sample preparation. Although adenosine triphosphate (ATP) and nucleic acids are potential molecular origins of these metabolites, research on their exact contributions remains limited. This study explored purine metabolite release from E. coli and RNA integrity following various sample preparation methods. Standard water washing generated dominant SERS signals within 10 s, a duration shorter than the anticipated RNA half-lives under starvation. Evaluating RNA integrity indicated that the most abundant ribosomal RNA species remained intact for hours post-washing, whereas messenger RNA and transfer RNA species degraded gradually. This suggests that bacterial SERS signatures observed after the typical washing step could originate from only a small fraction of endogenous purine-containing molecules. In contrast, acid depurination led to degradation of most RNA species, releasing about 40 times more purine derivatives than water washing. Mild heating also instigated the RNA degradation and released more purine derivatives than water washing. Notably, differences were also evident in the dominant SERS signals following these treatments. This work provides insights into SERS-based studies of purine metabolites released by bacteria and future development of methodologies.

Molecular Evaluation of the mRNA Expression of the ERG11, ERG3, CgCDR1, and CgSNQ2 Genes Linked to Fluconazole Resistance in Candida glabrata in a Colombian Population

INTRODUCTION

The study of Candida glabrata genes associated with fluconazole resistance, from a molecular perspective, increases the understanding of the phenomenon with a view to its clinical applicability.

OBJECTIVE

We sought to establish the predictive molecular profile of fluconazole resistance in Candida glabrata by analyzing the ERG11, ERG3, CgCDR1, and CgSNQ2 genes.

METHOD

Expression was quantified using RT-qPCR. Metrics were obtained through molecular docking and Fisher discriminant functions. Additionally, a predictive classification was made against the susceptibility of C. glabrata to fluconazole.

RESULTS

The relative expression of the ERG3, CgCDR1, and CgSNQ2 genes was higher in the fluconazole-resistant strains than in the fluconazole-susceptible, dose-dependent strains. The gene with the highest relative expression in the fluconazole-exposed strains was CgCDR1, and in both the resistant and susceptible, dose-dependent strains exposed to fluconazole, this was also the case. The molecular docking model generated a median number of contacts between fluconazole and ERG11 that was lower than the median number of contacts between fluconazole and ERG3, -CgCDR1, and -CgSNQ2. The predicted classification through the multivariate model for fluconazole susceptibility achieved an accuracy of 73.5%.

CONCLUSION

The resistant strains had significant expression levels of genes encoding efflux pumps and the ERG3 gene. Molecular analysis makes the identification of a low affinity between fluconazole and its pharmacological target possible, which may explain the lower intrinsic susceptibility of the fungus to fluconazole.

Rapid quality evaluation and geographical origin recognition of ginger powder by portable NIRS in tandem with chemometrics

Ginger powder is an important spice that is susceptible to improper sales such as adulteration or geographical fraud. In this study, a portable near infrared spectroscopy was used to quantitatively predict the 6-gingerol content, an important quality index of ginger, as well as to identify the gingers from three origins in China. Specifically, the optimal preprocessing method was first investigated by comparing the predictions of models. Then three feature variable selection methods including PCA, CARS, and RFrog, on the quantitative analysis of 6-gingerol were also compared, respectively. After comparison, the PLS model established on the S-G combined with SNV preprocessing outperformed the others. The PLS regression of 6-gingerol with variables selected by RFrog possessed the Rc2 of 0.9463, Rp2 of 0.9497, and the RPD of 4.2257, respectively. Moreover, the results further verified that the LDA model by SPA variables extraction successfully identify gingers from different origins with 100 % accuracy.

Key steps for improving bacterial SERS signals in complex samples: Separation, recognition, detection, and analysis

Rapid and reliable detection of pathogenic bacteria is absolutely essential for research in environmental science, food quality, and medical diagnostics. Surface-enhanced Raman spectroscopy (SERS), as an emerging spectroscopic technique, has the advantages of high sensitivity, good selectivity, rapid detection speed, and portable operation, which has been broadly used in the detection of pathogenic bacteria in different kinds of complex samples. However, the SERS detection method is also challenging in dealing with the detection difficulties of bacterial samples in complex matrices, such as interference from complex matrices, confusion of similar bacteria, and complexity of data processing. Therefore, researchers have developed some technologies to assist in SERS detection of bacteria, including both the front-end process of obtaining bacterial sample data and the back-end data processing process. The review summarizes the key steps for improving bacterial SERS signals in complex samples: separation, recognition, detection, and analysis, highlighting the principles of each step and the key roles for SERS pathogenic bacteria analysis, and the interconnectivity between each step. In addition, the current challenges in the practical application of SERS technology and the development trends are discussed. The purpose of this review is to deepen researchers' understanding of the various stages of using SERS technology to detect bacteria in complex sample matrices, and help them find new breakthroughs in different stages to facilitate the detection and control of bacteria in complex samples.

SERS-based rapid susceptibility testing of commonly administered antibiotics on clinically important bacteria species directly from blood culture of bacteremia patients

Bloodstream infections are a growing public health concern due to emerging pathogens and increasing antimicrobial resistance. Rapid antibiotic susceptibility testing (AST) is urgently needed for timely and optimized choice of antibiotics, but current methods require days to obtain results. Here, we present a general AST protocol based on surface-enhanced Raman scattering (SERS-AST) for bacteremia caused by eight clinically relevant Gram-positive and Gram-negative pathogens treated with seven commonly administered antibiotics. Our results show that the SERS-AST protocol achieves a high level of agreement (96% for Gram-positive and 97% for Gram-negative bacteria) with the widely deployed VITEK 2 diagnostic system. The protocol requires only five hours to complete per blood-culture sample, making it a rapid and effective alternative to conventional methods. Our findings provide a solid foundation for the SERS-AST protocol as a promising approach to optimize the choice of antibiotics for specific bacteremia patients. This novel protocol has the potential to improve patient outcomes and reduce the spread of antibiotic resistance.

Label-free detection and discrimination of respiratory pathogens based on electrochemical synthesis of biomaterials-mediated plasmonic composites and machine learning analysis

Seasonal outbreaks of respiratory viral infections remain a global concern, with increasing morbidity and mortality rates recorded annually. Timely and false responses contribute to the widespread of respiratory pathogenic diseases owing to similar symptoms at an early stage and subclinical infection. The prevention of emerging novel viruses and variants is also a big challenge. Reliable point-of-care diagnostic assays for early infection diagnosis play a critical role in the response to threats of epidemics or pandemics. We developed a facile method for specifically identifying different viruses based on surface-enhanced Raman spectroscopy (SERS) with pathogen-mediated composite materials on Au nanodimple electrodes and machine learning (ML) analyses. Virus particles were trapped in three-dimensional plasmonic concave spaces of the electrode via electrokinetic preconcentration, and Au films were simultaneously electrodeposited, leading to the acquisition of intense and in-situ SERS signals from the Au-virus composites for ultrasensitive SERS detection. The method was useful for rapid detection analysis (<15 min), and the ML analysis for specific identification of eight virus species, including human influenza A viruses (i.e., H1N1 and H3N2 strains), human rhinovirus, and human coronavirus, was conducted. The highly accurate classification was achieved using the principal component analysis-support vector machine (98.9%) and convolutional neural network (93.5%) models. This ML-associated SERS technique demonstrated high feasibility for direct multiplex detection of different virus species for on-site applications.

Mycobacteriophage Derived Lipoarabinomannan Binding Protein for Recognizing Non-Tuberculosis Mycobacteria

Non-tuberculosis mycobacteria (NTM) is one family of pathogens usually leading to nosocomial infections. Exploration of high-performance biological recognition agent plays a pivotal role for the development of point-of-care testing device and kit for detecting NTM. Mycobacterium smegmatis (M. smegmatis) is a NTM which has been frequently applied as an alternative model for highly pathogenic mycobacteria. Herein, a recombinant tail protein derived from mycobacteriophage SWU1 infecting M. smegmatis was expressed with Escherichia coli system and noted as GP89. It shows a fist-like structure according to the results of homology modeling and ab initio modeling. It is confirmed as a lipoarabinomannan (LAM) binding protein, which can recognize studied NTM genus since abundant LAM constructed with d-mannan and d-arabinan is distributed over the mycobacterial surface. Meanwhile an enhanced green fluorescent protein (eGFP)-fused GP89 protein was acquired with a fusion expression technique. Then GP89 and eGFP-fused GP89 were applied to establish a sensitive and rapid method for fluorescent detection of M. smegmatis with a broad linear range of 1.0 × 10² to 1.0 × 10⁶ CFU mL^-1 and a low detection limit of 69 CFU mL^-1. Rapid and reliable testing of antimicrobial susceptibility was achieved by the GP89-based fluorescent method. The present work provides a promising recognition agent for studied NTM and opens an avenue for clinical diagnosis of NTM-induced infections.

Predicting functional outcome in acute ischemic stroke patients after endovascular treatment by machine learning

Background

Endovascular therapy (EVT) was the standard treatment for acute ischemic stroke with large vessel occlusion. Prognosis after EVT is always a major concern. Here, we aimed to explore a predictive model for patients after EVT.

Method

A total of 156 patients were retrospectively enrolled. The primary outcome was functional dependence (defined as a 90-day modified Rankin Scale score ≤ 2). Least absolute shrinkage and selection operator and univariate logistic regression were used to select predictive factors. Various machine learning algorithms, including multivariate logistic regression, linear discriminant analysis, support vector machine, k-nearest neighbors, and decision tree algorithms, were applied to construct prognostic models.

Result

Six predictive factors were selected, namely, age, baseline National Institute of Health Stroke Scale (NIHSS) score, Alberta Stroke Program Early CT (ASPECT) score, modified thrombolysis in cerebral infarction score, symptomatic intracerebral hemorrhage (sICH), and complications (pulmonary infection, gastrointestinal bleeding, and cardiovascular events). Based on these variables, various models were constructed and showed good discrimination. Finally, a nomogram was constructed by multivariate logistic regression and showed a good performance.

Conclusion

Our nomogram, which was composed of age, baseline NIHSS score, ASPECT score, recanalization status, sICH, and complications, showed a very good performance in predicting outcome after EVT.

Human Angiotensin I-Converting Enzyme Produced by Different Cells: Classification of the SERS Spectra with Linear Discriminant Analysis

Angiotensin I-converting enzyme (ACE) is a peptidase widely presented in human tissues and biological fluids. ACE is a glycoprotein containing 17 potential N-glycosylation sites which can be glycosylated in different ways due to post-translational modification of the protein in different cells. For the first time, surface-enhanced Raman scattering (SERS) spectra of human ACE from lungs, mainly produced by endothelial cells, ACE from heart, produced by endothelial heart cells and miofibroblasts, and ACE from seminal fluid, produced by epithelial cells, have been compared with full assignment. The ability to separate ACEs’ SERS spectra was demonstrated using the linear discriminant analysis (LDA) method with high accuracy. The intervals in the spectra with maximum contributions of the spectral features were determined and their contribution to the spectrum of each separate ACE was evaluated. Near 25 spectral features forming three intervals were enough for successful separation of the spectra of different ACEs. However, more spectral information could be obtained from analysis of 50 spectral features. Band assignment showed that several features did not correlate with band assignments to amino acids or peptides, which indicated the carbohydrate contribution to the final spectra. Analysis of SERS spectra could be beneficial for the detection of tissue-specific ACEs.

Dual-Approach Electrochemical Surface-Enhanced Raman Scattering Detection of Mycobacterium tuberculosis in Patient-Derived Biological Specimens: Proof of Concept for a Generalizable Method to Detect and Identify Bacterial Pathogens

The recent surge in infectious disease-causing pathogens, resulting in global catastrophe, has merited a pivotal quest toward point-of-care (POC) diagnostics. Mycobacterium tuberculosis (MTB) is still the top bacterium-based infectious disease-causing pathogen worldwide. In a concerted effort toward simplifying and decentralizing the discriminatory screening of MTB causing pathogens, electrochemical surface-enhanced Raman scattering (EC-SERS) was adopted to create a customized screening tool. The development strategy combined five key factors, including (i) a simplified Tollens'-based chemical synthesis method for bulk supply of silver nanoparticles, (ii) the deliberate surface modification of nanoparticles with carefully selected polyelectrolytes to resemble the conditioning layer usually found on a natural substratum, (iii) uniform SERS-active films formed through simple unprogrammed assembly, (iv) the controlled manipulation of the local electric field through applied voltage using a technique that does not conform to the limitations of classical EC-SERS, and (v) the inherent specificity of the target-specific SERS vibrational signature. The EC-SERS platform was able to discriminatively detect and identify TB-derived mycobacteria, including three clinically relevant MTB strains, TB-H37Rv, TB-HN878, and TB-CDC1551. Moreover, a customized voltage stepping protocol, compatible with either the inclusion of a short preincubation step or with in situ EC-SERS is illustrated. From the obtained SERS vibrational signatures, a band indicating a mode unique to TB-derived/TB-affiliated mycobacteria and thus not observed for other bacterial types used in this study was illustrated. Furthermore, provisional investigation, done as prelude for assessing the potential for translational adaptability of the EC-SERS technique toward POC clinical settings for sputum and urine specimens, was carried out.

Recent advances in antibiotic resistance diagnosis using SERS: focus on the “Big 5” challenges

SERS for antibiotic resistance diagnosis.

Weak Reaction Scatterometry of Plasmonic Resonance Light Scattering with Machine Learning

Weak reactions are usually overlooked due to weak detectable features and susceptibility to interference from noise signals. Strategies for detecting weak reactions are essential for exploring reaction mechanisms and exploiting potential applications. Machine learning has recently been successfully used to identify patterns and trends in the data. Here, it is demonstrated that machine learning-based techniques can offer accurate local surface plasmon resonance (LSPR) scatterometry by improving the precision of the plasmonic scattering imaging in weak chemical reactions. Dark-field microscopy (DFM) imaging technique is the most effective method for high-sensitivity plasmonic nanoparticles LSPR scatterometry. Unfortunately, deviations caused by the instrument and operating errors are inevitable, and it is difficult to effectively detect the presence of weak reactions. Thus, introducing a machine learning calibration model to automatically calibrate the scattering signal of the nanoprobe in the reaction process can greatly improve the confidence of LSPR scatterometry under DFM imaging and allow DFM imaging to effectively monitor unobvious or weak reactions. By this approach, the weak oxidation of silver nanoparticles (AgNPs) in water by dissolved oxygen was successfully monitored. Moreover, a trivial reaction between AgNPs and mercury ions was detected in a dilute mercury solution with a concentration greater than 1.0 × 10^-10 mol/L. These results suggest the great potential of the combination of LSPR scatterometry and machine learning as a method for imaging analysis and intelligent sensing.