Fast label-free detection of Legionella spp. in biofilms by applying immunomagnetic beads and Raman spectroscopy

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Vibrational spectroscopy for decoding cancer microbiota interactions: Current evidence and future perspective

The role of human microbiota in cancer initiation and progression is recognized in recent years. In order to investigate the interactions between cancer cells and microbes, a systematic analysis using various emerging techniques is required. Owing to the label-free, non-invasive and molecular fingerprinting characteristics, vibrational spectroscopy is uniquely suited to decode and understand the relationship and interactions between cancer and the microbiota at the molecular level. In this review, we first provide a quick overview of the fundamentals of vibrational spectroscopic techniques, namely Raman and infrared spectroscopy. Next, we discuss the emerging evidence underscoring utilities of these spectroscopic techniques to study cancer or microbes separately, and share our perspective on how vibrational spectroscopy can be employed at the intersection of the two fields. Finally, we envision the potential opportunities in exploiting vibrational spectroscopy not only in basic cancer-microbiome research but also in its clinical translation, and discuss the challenges in the bench to bedside translation.

Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy

In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health.

Artificial Intelligence in Chemistry: Current Trends and Future Directions

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions.

Identification of resistance in Escherichia coli and Klebsiella pneumoniae using excitation-emission matrix fluorescence spectroscopy and multivariate analysis

Klebsiella pneumoniae and Escherichia coli are part of the Enterobacteriaceae family, being common sources of community and hospital infections and having high antimicrobial resistance. This resistance profile has become the main problem of public health infections. Determining whether a bacterium has resistance is critical to the correct treatment of the patient. Currently the method for determination of bacterial resistance used in laboratory routine is the antibiogram, whose time to obtain the results can vary from 1 to 3 days. An alternative method to perform this determination faster is excitation-emission matrix (EEM) fluorescence spectroscopy combined with multivariate classification methods. In this paper, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Support Vector Machines (SVM), coupled with dimensionality reduction and variable selection algorithms: Principal Component Analysis (PCA), Genetic Algorithm (GA), and the Successive Projections Algorithm (SPA) were used. The most satisfactory models achieved sensitivity and specificity rates of 100% for all classes, both for E. coli and for K. pneumoniae. This finding demonstrates that the proposed methodology has promising potential in routine analyzes, streamlining the results and increasing the chances of treatment efficiency.

Intercepting signalling mechanism to control environmental biofouling

Biofouling in environmental systems employs bacterial quorum sensing signals (autoinducers) and extracellular polymeric substances to onset the event. The present review has highlighted on the fundamental mechanisms behind biofilm formation over broad spectrum environmental niches especially membrane biofouling in water systems and consequent chances of pathogenic contamination leading to global economic loss. It has broadly discussed on bioelectrical signal (via, potassium gradient) and molecular signal (via, AHLs) mediated quorum sensing which help to propagate biofilm formation. The review has illustrated the potential of genomic intervention towards biofouled membrane microbial community and has uncovered possible features of biofilm microenvironment like quorum quenching bacteria, bioelectrical waves capture, siderophores arrest and surface modifications. Based on information, the concept of interception of quorum signals (AHLs) and bioelectrical signals (K⁺) by employing electro-modified (negative charges) membrane surface have been hypothesized in the present review to favour anti-biofouling.

Raman and Surface-Enhanced Raman Scattering for Biofilm Characterization

Biofilms are a communal way of living for microorganisms in which microorganism cells are surrounded by extracellular polymeric substances (EPS). Most microorganisms can live in biofilm form. Since microorganisms are everywhere, understanding biofilm structure and composition is crucial for making the world a better place to live, not only for humans but also for other living creatures. Raman spectroscopy is a nondestructive technique and provides fingerprint information about an analyte of interest. Surface-enhanced Raman spectroscopy is a form of this technique and provides enhanced scattering of the analyte that is in close vicinity of a nanostructured noble metal surface such as silver or gold. In this review, the applications of both techniques and their combination with other biofilm analysis techniques for characterization of composition and structure of biofilms are discussed.