The survival rate, p , of a measured inoculum of Staph. aureus in a standard volume of denbrinated blood, is a reliable quantitative measure of the bactericidal power of blood. The number of viable organisms in the inoculum and in the blood-bacterium mixture may be estimated with the necessary accuracy by counts of colonies developing from measured volumes of the fluids let fall on to the surface of solid media. Fildes' agar was the most suitable medium for this surface-viable count, and was selected on the basis of four criteria; of the media tested it yielded the highest counts, and the counts conformed most closely to a Poisson series; and on it the mean colony size was maximum, and the coefficient of variation of colony size was minimum. On this medium, the close conformity of the separate count values to a Poisson series enabled the standard error of the survival rate to be determined from a simplification of the general expression for the standard error of a ratio. The number of colonies growing from a sample of a blood-bacterium mixture may be reduced, not by killing of the individual cocci, but as a result of their aggregation either by agglutinins in the blood, or in the cytoplasm of leucocytes that are phagocytic but not bactericidal. It appears that these mechanisms are unlikely to operate in blood-bacterium mixture containing relatively few organisms; in such mixtures the survival rate is a reflexion of the killing power only. The immunological significance of p has not been investigated, but the range of values for healthy human adults differs significantly from that for sufferers from chronic staphylococcal infection. Moreover, by the technique employed differences may be detected between individual values of p that cannot reasonably be attributed to technical or sampling errors.

The Estimation of the Bactericidal Power of the Blood. With a Note by J. O. IRWIN.

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

https://zenodo.org/record/6651711/files/2022%20Plou%20acsphotonics.pdf

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Classification of skin cancer using convolutional neural networks analysis of Raman spectra

With the low-cost, label-free, and nondestructive features, Raman spectroscopy is becoming an attractive technique with great potential to discriminate bacterial infections. In this pilot study, we analyzed surfaced-enhanced Raman spectroscopy (SERS) spectra via supervised machine learning algorithms, through which we confirmed the application potentials of the SERS technique in rapid and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles. ABSTRACT In clinical settings, rapid and accurate diagnosis of antibiotic resistance is essential for the efficient treatment of bacterial infections. Conventional methods for antibiotic resistance testing are time consuming, while molecular methods such as PCR-based testing might not accurately reflect phenotypic resistance. Thus, fast and accurate methods for the analysis of bacterial antibiotic resistance are in high demand for clinical applications. In this pilot study, we isolated 7 carbapenem-sensitive Klebsiella pneumoniae (CSKP) strains and 8 carbapenem-resistant Klebsiella pneumoniae (CRKP) strains from clinical samples. Surface-enhanced Raman spectroscopy (SERS) as a label-free and noninvasive method was employed for discriminating CSKP strains from CRKP strains through computational analysis. Eight supervised machine learning algorithms were applied for sample analysis. According to the results, all supervised machine learning methods could successfully predict carbapenem sensitivity and resistance in K. pneumoniae, with a convolutional neural network (CNN) algorithm on top of all other methods. Taken together, this pilot study confirmed the application potentials of surface-enhanced Raman spectroscopy in fast and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles. IMPORTANCE With the low-cost, label-free, and nondestructive features, Raman spectroscopy is becoming an attractive technique with great potential to discriminate bacterial infections. In this pilot study, we analyzed surfaced-enhanced Raman spectroscopy (SERS) spectra via supervised machine learning algorithms, through which we confirmed the application potentials of the SERS technique in rapid and accurate discrimination of Klebsiella pneumoniae strains with different antibiotic resistance profiles.

https://doi.org/10.1128/spectrum.02409-21

Discrimination between Carbapenem-Resistant and Carbapenem-Sensitive Klebsiella pneumoniae Strains through Computational Analysis of Surface-Enhanced Raman Spectra: a Pilot Study

Nowadays, developing new and natural compounds with antibacterial activities from plants has become a promising approach to solve antibiotic resistance of pathogenic bacteria. Chlorogenic acid (CA), as a kind of phenolic acid existing in many plants, has been found to process multifunctional activities including antibacterial activity. Herein, the antibacterial and antibiofilm activities of CA against Yersinia enterocolitica (Y. enterocolitica) were tested for the first time, and its mechanism of action was investigated. It was demonstrated that CA could exert outstanding antibacterial activity against Y. enterocolitica. Biofilm susceptibility assays further indicated that CA could inhibit biofilm formation and decrease the established biofilm biomass of Y. enterocolitica. It was deduced that through binding to Y. enterocolitica, CA destroyed the cell membrane, increased the membrane permeability, and led to bacterial cell damage. In addition, the transcriptomic analysis revealed that CA could disorder many physiological pathways, mainly including the ones of antagonizing biofilms and increasing cell membrane permeability. Finally, the spiked assay showed that the growth of Y. enterocolitica in milk was significantly inhibited by CA. Taken together, CA, as an effective bactericidal effector with application potential, exerts antagonistic activity against Y. enterocolitica by mainly intervening biofilm formation and membrane permeability-related physiological pathways.

Antibacterial and Antibiofilm Activities of Chlorogenic Acid Against Yersinia enterocolitica

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

/pdf/comparative-analysis-of-machine-learning-algorithms-on-1jys14cjug.pdf

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species.

Glycogen is a highly branched polysaccharide that is widely present in all life domains. It has been identified in many bacterial species and functions as an important energy storage compound. In addition, it plays important roles in bacterial transmission, pathogenicity, and environmental viability. There are five essential enzymes (coding genes) directly involved in bacterial glycogen metabolism, which forms a single operon glgBXCAP with a suboperonic promoter in glgC gene in Escherichia coli. Currently, there is no comparative study of how the disruptions of the five glycogen metabolism genes influence bacterial phenotypes, such as growth rate, biofilm formation, and environmental survival, etc. In this study, we systematically and comparatively studied five E. coli single-gene mutants (ΔglgC, ΔglgA, ΔglgB, ΔglgP, ΔglgX) in terms of glycogen metabolism and explored their phenotype changes with a focus on environmental stress endurance, such as nutrient deprivation, low temperature, desiccation, and oxidation, etc. Biofilm formation in wild-type and mutant strains was also compared. E. coli wild-type stores the highest glycogen content after around 20-h culture while disruption of degradation genes (glgP, glgX) leads to continuous accumulation of glycogen. However, glycogen primary structure was abnormally changed in ΔglgP and ΔglgX. Meanwhile, increased accumulation of glycogen facilitates the growth of E. coli mutants but reduces glucose consumption in liquid culture and vice versa. Glycogen metabolism disruption also significantly and consistently increases biofilm formation in all the mutants. As for environmental stress endurance, glycogen over-accumulating mutants have enhanced starvation viability and reduced desiccation viability while all mutants showed decreased survival rate at low temperature. No consistent results were found for oxidative stress resistance in terms of glycogen metabolism disruptions, though ΔglgA shows highest resistance toward oxidation with unknown mechanisms. In sum, single gene disruptions in glgBXCAP operon significantly influence bacterial growth and glucose consumption during culture. Accumulation and structure of intracellular glycogen were also significantly altered. In addition, we observed significant changes in E. coli environmental viabilities due to the deletions of certain genes in the operon. Further investigations shall be focused on the molecular mechanisms behind these phenotype changes.

/pdf/glycogen-metabolism-impairment-via-single-gene-mutation-in-b9yo1i7jxh.pdf

Glycogen Metabolism Impairment via Single Gene Mutation in the glgBXCAP Operon Alters the Survival Rate of Escherichia coli Under Various Environmental Stresses.

Xing-Xing Kang

Papers

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species.

Glycogen Metabolism Impairment via Single Gene Mutation in the glgBXCAP Operon Alters the Survival Rate of Escherichia coli Under Various Environmental Stresses.