Identification of medically relevant microorganisms by vibrational spectroscopy

Determination of Raman spectrum under different culture conditions: preliminary research on bacterial fish pathogens

The intensive labour and time required for conventional methods to identify bacterial fish pathogens have revealed the need to develop alternative methods. Raman spectroscopy has been used in the rapid optical identification of bacterial pathogens in recent years as an alternative method in microbiology. Strains of bacterial fish pathogens (Vibrio anguillarum, Lactococcus garvieae and Yersinia ruckeri) that often cause infectious diseases in fish were here identified and analyzed in terms of their biochemical structures in different media and at different incubation times, and the data were specified by using Raman spectroscopy. The results demonstrated that Raman spectroscopy presents species-specific Raman spectra of each disease-causing bacteria and that it would be more appropriate to choose general microbiological media over selective media for routine studies. Additionally, it was found that species-specific band regions did not differ in 24- and 48-hour cultures, but there could be a difference in peak intensity which may lead to difficult characterization of spectrum. The current study, conducted for the first time with bacterial fish pathogens under different incubation conditions, is believed to provide a basis for the routine use of Raman spectroscopy for quick pathogen identification and the precise determination of the methodology for further research.

Infrared spectroscopy-based machine learning algorithms for rapid detection of Klebsiella pneumoniae isolated directly from patients' urine and determining its susceptibility to antibiotics

Among the most prevalent and detrimental bacteria causing urinary tract infections (UTIs) is Klebsiella (K.) pneumoniae. A rapid determination of its antibiotic susceptibility can enhance patient treatment and mitigate the spread of resistant strains. In this study, we assessed the viability of using infrared spectroscopy-based machine learning as a rapid and precise approach for detecting K. pneumoniae bacteria and determining its susceptibility to various antibiotics directly from a patient's urine sample. In this study, 2333 bacterial samples, including 636 K. pneumoniae were investigated using infrared micro-spectroscopy. The obtained spectra (27996spectra) were analyzed with XGBoost classifier, achieving a success rate exceeding 95 % for identifying K. pneumoniae. Moreover, this method allows for the simultaneous determination of K. pneumoniae susceptibility to various antibiotics with sensitivities ranging between 74 % and 81 % within approximately 40 min after receiving the patient's urine sample.

Quantitative or qualitative biomolecular changes in blood serum composition induced by childhood obesity: A Fourier transform infrared examination

Childhood obesity (CO) negatively affects one in three children and stands as the fourth most common risk factor of health and well-being. Clarifying the molecular and structural modifications that transpire during the development of obesity is crucial for understanding its progression and devising effective therapies. The study was indeed conducted as part of an ongoing CO treatment trial, where data were collected from children diagnosed with CO before the initiation of non-drug treatment interventions. Our primary aim was to analyze the biochemical changes associated with childhood obesity, specifically focusing on concentrations of lipids, lipoproteins, insulin, and glucose. By comparing these parameters between the CO group (n = 60) and a control group of healthy children (n = 43), we sought to elucidate the metabolic differences present in individuals with CO. Our biochemical analyses unveiled lower LDL (low-density lipoproteins) levels and higher HDL (high-density lipoproteins), cholesterol, triglycerides, insulin, and glucose levels in CO individuals compared to controls. To scrutinize these changes in more detail, we employed Fourier transform infrared (FTIR) spectroscopy on the serum samples. Our results indicated elevated levels of lipids and proteins in the serum of CO, compared to controls. Additionally, we noted structural changes in the vibrations of glucose, β-sheet, and lipids in CO group. The FTIR technique, coupled with principal component analysis (PCA), demonstrated a marked differentiation between CO and controls, particularly in the FTIR region corresponding to amide and lipids. The Pearson test revealed a stronger correlation between biochemical data and FTIR spectra than between 2nd derivative FTIR spectra. Overall, our study provides valuable insights into the molecular and structural changes occurring in CO.

Isotope labeled 3D-Raman confocal imaging and atomic force microscopy study on epithelial cells interacting with the fungus Candida albicans

The human pathogenic fungus Candida albicans damages epithelial cells during superficial infections. Here we use three-dimensional-sequential-confocal Raman spectroscopic imaging and atomic force microscopy to investigate the interaction of C. albicans wild type cells, the secreted C. albicans peptide toxin candidalysin and mutant cells lacking candidalysin with epithelial cells. The candidalysin is responsible for epithelial cell damage and exhibits in its deuterated form an identifiable Raman signal in a frequency region distinct from the cellular frequency region. Vibration modes at 2100-2200 cm-1 attributed to carbon‑deuterium bending and at 477 cm-1, attributed to the nitrogen‑deuterium out-of-plane bending, found around the nucleus, can be assigned to deuterated candidalysin. Atomic force microscopy visualized 100 nm deep lesions on the cell and force-distance curves indicate the higher adhesion on pore surrounding after incubation with candidalysin. Candidalysin targets the plasma membrane, but is also found inside of the cytosol of epithelial cells during C. albicans infection.

Effects of p-coumaric acid on probiotic properties of Lactobacillus acidophilus LA-5 and lacticaseibacillus rhamnosus GG

Probiotics are defined as "live microorganisms that provide health benefits to the host when administered in adequate amounts." Probiotics have beneficial effects on human health, including antibacterial activity against intestinal pathogens, regulation of blood cholesterol levels, reduction of colitis and inflammation incidence, regulation of the immune system, and prevention of colon cancer. In addition to probiotic bacteria, some phenolic compounds found in foods we consume (both food and beverages) have positive effects on human health. p-coumaric acid (p-CA) is one of the most abundant phenolic compounds in nature and human diet. The interactions between these two different food components (phenolics and probiotics), resulting in more beneficial combinations called synbiotics, are not well understood in terms of how they will affect the gut microbiota by promoting the probiotic properties and growth of probiotic bacteria. Thus, this study aimed to investigate synbiotic relationship between p-CA and Lactobacillus acidophilus LA-5 (LA-5), Lacticaseibacillus rhamnosus GG (LGG). Probiotic bacteria were grown in the presence of p-CA at different concentrations, and the effects of p-CA on probiotic properties, as well as its in vitro effects on AChE and BChE activities, were investigated. Additionally, Surface analysis was conducted using FTIR. The results showed that treatment with p-CA at different concentrations did not exhibit any inhibitory effect on the growth kinetics of LA-5 and LGG probiotic bacteria. Additionally, both probiotic bacteria demonstrated high levels of antibacterial properties. It showed that it increased the auto-aggregation of both probiotics. While p-CA increased co-aggregation of LA-5 and LGG against Escherichia coli, it decreased co-aggregation against Staphylococcus aureus. Probiotics grown with p-CA were more resistant to pepsin. While p-CA increased the resistance of LA-5 to bile salt, it decreased the resistance of LGG. The combinations of bacteria and p-CA efficiently suppressed AChE and BChE with inhibition (%) 11.04-68.43 and 13.20-65.72, respectively. Furthermore, surface analysis was conducted using FTIR to investigate the interaction of p-coumaric acid with LA-5 and LGG, and changes in cell components on the bacterial surface were analyzed. The results, recorded in range of 4000 -600 cm-1 with resolution of 4 cm-1, demonstrated that p-CA significantly affected only the phosphate/CH ratio for both bacteria. These results indicate the addition of p-CA to the probiotic growth may enhance the probiotic properties of bacteria.

Chemical sensing of common microorganisms found in biopharmaceutical industries using MIR laser spectroscopy and multivariate analysis

Mid-infrared laser spectroscopy was used to investigate common bacteria encountered in biopharmaceutical industries. The study involved the detection of bacteria using quantum cascade laser spectroscopy coupled to a grazing angle probe (QCL-GAP). Substrates similar to surfaces commonly used in biopharmaceutical industries were used as support media for the samples. Reflectance measurements were assisted by Multivariate Analysis (MVA) to assemble a powerful spectroscopic technique with classification and identification resources. The species analyzed, Staphylococcus aureus, Staphylococcus epidermidis, and Micrococcus luteus, were used to challenge the technique's capability to discriminate from microorganisms of the same family. Principal Components Analysis and Partial Least Squares-Discriminant Analysis differentiated between the bacterial species, using QCL-GAP-MVA as the reference. Spectral differences in the bacterial membrane were used to determine if these microorganisms were present in the samples analyzed. Results herein provided effective discrimination for the bacteria under study with high sensitivity and specificity.

Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%-88%. The six remaining species were correctly predicted by only 0%-49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.

Correlation analysis between Raman spectral signature and transcriptomic features of carbapenem-resistant Klebsiella pneumoniae

The Raman microspectroscopy technology has been successfully applied to evaluate the molecular composition of living cells for identifying cell types and states, but the rationale behind it was not well investigated. In this study, we acquired single-cell Raman spectra (SCRS) of three Klebsiella pneumoniae (K. pneumoniae) strains with different Carbapenem resistant mechanisms and analyzed them with machine learning algorithm. Two carbapenem resistant Klebsiella pneumoniae (CRKP) strains can be successfully distinguished from susceptible strain and CRKP with KPC or IMP carbapenemases can be classified with an overall accuracy achieving 100 %. Furthermore, we performed a correlation analysis between transcriptome and Raman spectra, and found that Raman shifts such as 752 and 1039 cm-1 highly correlated with drug resistance genes expression and could be regarded as Raman biomarkers for CRKP with different mechanisms. The findings of the study provide a theoretical basis for identifying the relationship between Raman spectra and transcriptome of bacteria, as well as a novel method for rapid identification of CRKP and their carbapenemases types.

Use of in vivo Raman spectroscopy and cryoablation for diagnosis and treatment of bladder cancer

Transurethral resection of bladder tumor (TURBT) is the first-line treatment option for non-muscle invasive bladder cancer (NMIBC), but residual tumor often remains after TURBT, thereby leading to cancer recurrence. Here, we introduce combined use of in vivo Raman spectroscopy and in vivo cryoablation as a new approach to detect and remove residual bladder tumor during TURBT. Bladder cancer (BCa) patients treated with TURBT at our urological department between Dec 2019 and Jan 2021 were collected. First, Raman signals were collected from 74 BCa patients to build reference spectra of normal bladder tissue and of bladder cancers of different pathological types. Then, another 53 BCa patients were randomly categorized into two groups, 26 patients accepted traditional TURBT, 27 patients accepted TURBT followed by Raman scanning and cryoablation if Raman detected existence of residual tumor. The recurrence rates of the two groups until Oct 2022 were compared. Raman was capable of discriminating normal bladder tissue and BCa with a sensitivity and specificity of 90.5% and 80.8 %; and discriminating invasive (T1, T2) and noninvasive (Ta) BCa with a sensitivity and specificity of 83.3 % and 87.3 %. During follow-up, 2 in 27 patients had cancer recurrence in Raman-Cryoablation group, while 8 in 26 patients had cancer recurrence in traditional TURBT group. Combined use of Raman and cryoablation significantly reduced cancer recurrence (p = 0.0394). Raman and cryoablation can serve as an adjuvant therapy to TURBT to improve therapeutic effects and reduce recurrence rate.

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.

Explorative characterization and taxonomy-aligned comparison of alterations in lipids and other biomolecules in Antarctic bacteria grown at different temperatures

Temperature significantly impacts bacterial physiology, metabolism and cell chemistry. In this study, we analysed lipids and the total cellular biochemical profile of 74 fast-growing Antarctic bacteria grown at different temperatures. Fatty acid diversity and temperature-induced alterations aligned with bacterial classification-Gram-groups, phylum, genus and species. Total lipid content, varied from 4% to 19% of cell dry weight, was genus- and species-specific. Most bacteria increased lipid content at lower temperatures. The effect of temperature on the profile was complex and more species-specific, while some common for all bacteria responses were recorded. Gram-negative bacteria adjusted unsaturation and acyl chain length. Gram-positive bacteria adjusted methyl branching (anteiso-/iso-), chain length and unsaturation. Fourier transform infrared spectroscopy analysis revealed Gram-, genus- and species-specific changes in the total cellular biochemical profile triggered by temperature fluctuations. The most significant temperature-related alterations detected on all taxonomy levels were recorded for mixed region 1500-900 cm-1 , specifically the band at 1083 cm-1 related to phosphodiester groups mainly from phospholipids (for Gram-negative bacteria) and teichoic/lipoteichoic acids (for Gram-positive bacteria). Some changes in protein region were detected for a few genera, while the lipid region remained relatively stable despite the temperature fluctuations.

Synergistic effects of trace sulfadiazine and corrosion scales on disinfection by-product formation in bulk water of cast iron pipe

The effects of trace sulfadiazine (SDZ) and cast-iron corrosion scales on the disinfection by-product (DBP) formation in drinking water distribution systems (DWDSs) were investigated. The results show that under the synergistic effect of trace SDZ (10 μg/L) and magnetite (Fe3O4), higher DBP concentration occurred in the bulk water with the transmission and distribution of the drinking water. Microbial metabolism-related substances, one of the important DBP precursors, increased under the SDZ/Fe3O4 condition. It was found that Fe3O4 induced a faster microbial extracellular electron transport (EET) pathway, resulting in a higher microbial regrowth activity. On the other hand, the rate of chlorine consumption was quite high, and the enhanced microbial EET based on Fe3O4 eliminated the need for microorganisms to secrete excessive extracellular polymeric substances (EPS). More importantly, EPS could be continuously secreted due to the higher microbial activity. Finally, high reactivity between EPS and chlorine disinfectant resulted in the continuous formation of DBPs, higher chlorine consumption, and lower EPS content. Therefore, more attention should be paid to the trace antibiotics polluted water sources and cast-iron corrosion scale composition in the future. This study reveals the synergistic effects of trace antibiotics and corrosion scales on the DBP formation in DWDSs, which has important theoretical significance for the DBP control of tap water.

Rapid discrimination of Lentilactobacillus parabuchneri biofilms via in situ infrared spectroscopy

Microbial contamination in food industry is a source of foodborne illnesses and biofilm-related diseases. In particular, biogenic amines (BAs) accumulated in fermented foods via lactic acid bacterial activity exert toxic effects on human health. Among these, biofilms of histamine-producer Lentilactobacillus parabuchneri strains adherent at food processing equipment surfaces can cause food spoilage and poisoning. Understanding the chain of contamination is closely related to elucidating molecular mechanisms of biofilm formation. In the present study, an innovative approach using integrated chemical sensing technologies is demonstrated to fundamentally understand the temporal behavior of biofilms at the molecular level by combining mid-infrared (MIR) spectroscopy and fluorescence sensing strategies. Using these concepts, the biofilm forming capacity of six cheese-isolated L. parabuchneri strains (IPLA 11151, 11150, 11129, 11125, 11122 and 11117) was examined. The cut-off values for the biofilm production ability of each strain were quantified using crystal violet (CV) assays. Real-time infrared attenuated total reflection spectroscopy (IR-ATR) combined with fluorescence quenching oxygen sensing provides insight into distinct molecular mechanisms for each strain. IR spectra showed significant changes in characteristic bands of amides, lactate, nucleic acids, and extracellular polymeric substances (i.e., lipopolysaccharides, phospholipids, phosphodiester, peptidoglycan, etc.), which are major contributors to biofilm maturation involved in the initial adhesion processes. Chemometric methods including principal component analysis and partial least square-discriminant analysis facilitated the rapid determination and classification of cheese isolated L. parabuchneri strains unambiguously differentiating the IR signatures based on their ability to produce biofilm. All biofilms were morphologically characterized by confocal laser scanning microscopy on relevant industrial equipment surfaces. In summary, this innovative approach combining MIR spectroscopy with luminescence sensing enables real-time insight into the molecular composition and formation of L. parabuchneri biofilms.

Single-cell phenotypes revealed as a key biomarker in bacterial-fungal interactions: a case study of Staphylococcus and Malassezia

IMPORTANCE

Evaluating bacterial-fungal interactions is important for understanding ecological functions in a natural habitat. Many studies have defined bacterial-fungal interactions according to changes in growth rates when co-cultivated. However, the current literature lacks detailed studies on phenotypic changes in single cells associated with transcriptomic profiles to understand the bacterial-fungal interactions. In our study, we measured the single-cell phenotypes of bacteria co-cultivated with fungi using Raman spectroscopy with its transcriptomic profiles and determined the consequence of these interactions in detail. This rapid and reliable phenotyping approach has the potential to provide new insights regarding bacterial-fungal interactions.

A Multi-Fluorophore Staining Scheme for Identification and Quantification of Vomocytosis

Vomocytosis is a process by which fungal pathogens, for instance, Cryptococcus neoformans (CN), escape from the digestive phagolysosome of phagocytic cells after ingestion. Interestingly, this expulsion leaves both the pathogen and phagocyte unharmed, and is believed to be an important mechanism by which CNs disseminate throughout infected hosts. This phenomenon was discovered in 2006, and research to date has relied almost entirely on quantification via manual counting of vomocytosis events in time-lapse microscopy videos. This archaic method has the significant disadvantages of requiring excessive labor in manual analysis, limited throughput capabilities, and low accuracy due to subjectivity. Here, we present an alternative method to measure vomocytosis rates using a multi-fluorophore reporter system comprised of two in situ staining steps during infection and a flow cytometry readout. This approach overcomes the limitations of conventional time lapse microscopy methods, with key advantages of high throughput capability, simple procedural steps, and accurate objective readouts. This study rigorously characterizes this vomocytosis reporter system in CN-infected MΦ and DC cultures via fluorescence microscopy, confocal microscopy, and flow cytometry. Here, this fluorescent tool is used to observe differences in expulsion rates after phagosome-modifying drug treatments and additionally utilized to distinguish differences in biochemical compositions among fluorescence-activated cell sorted fungal populations via Raman spectroscopy. Furthermore, this reporter scheme is demonstrated to be adaptable for use in measuring potential biomaterial particle expulsion events. Ultimately, the fluorescent reporter system presented here provides a universal tool for vomocytosis rate measurement of phagocytosed material. This facile approach opens the door to previously unfeasible types of vomocytosis-related studies such as high throughput treatment mechanistic screening and downstream characterization of expelled material.

Bactericidal Efficacy and Mechanisms of Non-Electrolytic Slightly Acidic Hypochlorous Water on Pseudomonas fragi and Pseudomonas fluorescens

Chilled pork is frequently contaminated with Pseudomonas fragi and Pseudomonas fluorescens. In this study, the bactericidal efficacy and mechanisms of non-electrolytic slightly acidic hypochlorous water (NE-SAHW) against two strains of these two species were evaluated. The results showed that the antibacterial efficacy of NE-SAHW was positively correlated with the concentration level of NE-SAHW and negatively correlated with the initial populations of the strains. The strains of small populations were completely inhibited when provided with each level of NE-SAHW. The killed cells of P. fragi were 0.94, 1.39, 4.02, and 5.60 log10 CFU/mL, respectively, and of P. fluorescens they were 1.21, 1.52, 4.14, and 5.74 log10 CFU/mL, respectively, when the initial populations of the strains were at high levels (about 7 log10 CFU/mL). Both strains were completely killed within 12 s with the available chlorine concentration (ACC) of 50 mg/L of NE-SAHW. Morphological changes in both cells were observed by using a Scanning Electron Microscope (SEM) and it was discovered that the cell membranes were damaged, which led to the leakage of the intracellular substances, including K+, nucleic acid, and protein. In terms of the Fourier Transform Infrared Spectroscopy (FTIR) results, NE-SAHW destroyed the structures of membrane proteins and cell structure proteins, and influenced the composition of polysaccharides. The bacteria were definitely dead after treatment by NE-SAHW compared to the control according to the results of flow cytometry. These results demonstrated the potential bactericidal property of NE-SAHW when applied to the meat and other food sterilization industries.

Systematic investigation of simultaneous copper biosorption and nitrogen removal from wastewater by an aerobic denitrifying bacterium of auto-aggregation

Finding effective methods for simultaneous removal of eutrophic nutrients and heavy metals has attracted increasing concerns for the environmental remediation. Herein, a novel auto-aggregating aerobic denitrifying strain (Aeromonas veronii YL-41) was isolated with capacities for copper tolerance and biosorption. The denitrification efficiency and nitrogen removal pathway of the strain were investigated by nitrogen balance analysis and amplification of key denitrification functional genes. Moreover, the changes in the auto-aggregation properties of the strain caused by extracellular polymeric substances (EPS) production were focused on. The biosorption capacity and mechanisms of copper tolerance during denitrification were further explored by measuring changes in copper tolerance and adsorption indices, as well as by variations in extracellular functional groups. The strain showed extremely strong total nitrogen removal ability, with 67.5%, 82.08% and 78.48% of total nitrogen removal when NH4+-N, NO2--N, and NO3--N were used as the only initial nitrogen source, respectively. The successful amplification of napA, nirK, norR, and nosZ genes further demonstrated that the strain accomplished nitrate removal through a complete aerobic denitrification pathway. The production of protein-rich EPS of up to 23.31 mg/g and an auto-aggregation index of up to 76.42% may confer a strong biofilm-forming potential to the strain. Under the stress of 20 mg/L copper ions, the removal of nitrate-nitrogen was still as high as 71.4%. In addition, the strain could achieve an efficient removal of 96.9% of copper ions at an initial concentration of 80 mg/L. Scanning electron microscopy and deconvolution analysis of characteristic peaks confirmed that the strains encapsulate heavy metals by secreting EPS and, meanwhile, form strong hydrogen bonding structures to enhance intermolecular forces to resist copper ion stress. This study provides an innovative and effective biological approach for the synergistic bioaugmentation removal of eutrophic substances and heavy metals from aquatic environments.

Multi-resistant diarrheagenic Escherichia coli identified by FTIR and machine learning: a feasible strategy to improve the group classification

The identification of multidrug-resistant strains from E. coli species responsible for diarrhea in calves still faces many laboratory limitations and is necessary for adequately monitoring the microorganism spread and control. Then, there is a need to develop a screening tool for bacterial strain identification in microbiology laboratories, which must show easy implementation, fast response, and accurate results. The use of FTIR spectroscopy to identify microorganisms has been successfully demonstrated in the literature, including many bacterial strains; here, we explored the FTIR potential for multi-resistant E. coli identification. First, we applied principal component analysis to observe the group formation tendency; the first results showed no clustering tendency with a messy sample score distribution; then, we improved these results by adequately selecting the main principal components which most contribute to group separation. Finally, using machine learning algorithms, a predicting model showed 75% overall accuracy, demonstrating the method's viability as a screaming test for microorganism identification.

Detection of extended-spectrum β-lactamase-producing bacteria isolated directly from urine by infrared spectroscopy and machine learning

Resistant bacteria have become one of the leading health threats in the last decades. Extended-spectrum β-lactamase (ESBL) producing bacteria, including Escherichia (E.) coli and Klebsiella (K.) pneumoniae (the most frequent ones), are a significant class out of all resistant infecting bacteria. Due to the widespread and ongoing development of ESBL-producing (ESBL⁺) resistant bacteria, many routinely used antibiotics are no longer effective against them. However, an early and reliable ESBL⁺ bacteria detection method will improve the efficiency of treatment and limit their spread. In this work, we investigated the capability of infrared (IR) spectroscopy based machine learning tools [principal component analysis (PCA) and Random Forest (RF) classifier] for the rapid detection of ESBL⁺ bacteria isolated directly from patients' urine. For that, we examined 1881 E. coli samples (416 ESBL⁺ and 1465 ESBL^-) and 609 K. pneumoniae samples (237 ESBL⁺ and 372 ESBL^-). All samples were isolated directly from the urine of midstream patients. This study revealed that within 40 min of receiving the patient urine it is possible to determine the infecting bacterium as E. coli or K. pneumoniae with 95% success rate while it was possible to determine the ESBL⁺E. coli and ESBL⁺K. pneumoniae with 83% and 78% accuracy rates, respectively.

Raman spectral analysis of microbial pigment compositions in vegetative cells and heterocysts of multicellular cyanobacterium

The one-dimensional multicellular cyanobacterium, Anabaena sp. PCC 7120, exhibits a simple topology consisting of two types of cells under the nitrogen-depleted conditions. Although the differentiated (heterocyst) and undifferentiated cells (vegetative cells) were distinguished by their cellular shapes, we found that their internal states, that is, microbial pigment compositions, were distinguished by using a Raman microscope. Almost of Raman bands of the cellular components were assigned to vibrations of the pigments; chlorophyll a, β-carotene, phycocyanin, and allophycocyanin. We found that the Raman spectral measurement can detect the decomposition of both phycocyanin and allophycocyanin, which are components of the light-harvesting phycobilisome complex in the photosystem II. We observed that the Raman bands of phycocyanin and allophycocyanin exhibited more remarkable decrease in the heterocysts when compared to those of chlorophyll a and β-carotene. This result indicated the prior decomposition of phycobilisome in the heterocysts. We show that the Raman measurement is useful to detect the change of pigment composition in the cell differentiation.

Study on the distribution characteristics and metabolic mechanism of chlorine-resistant bacteria in indoor water supply networks

The presence and attachment of chlorine-resistant bacteria on the surface of water distribution network will deteriorate water quality and threaten human health. Chlorination is critical in drinking water treatment to ensure the biosafety of drinking water. However, how disinfectants affect the structures of dominant flora during biofilm development and whether the changes are consistent with the free flora remain unclear. Therefore, we investigated changes in species diversity and relative abundance of different bacterial communities in planktonic and biofilm samples at different chlorine residual concentrations (blank, 0.3 mg/L, 0.8 mg/L, 2.0 mg/L and 4.0 mg/L), and the main reasons for the development of chlorine resistance in bacteria was also discussed. The results showed that the richness of microbial species in the biofilm was higher than that in planktonic microbial samples. In the planktonic samples, Proteobacteria and Actinobacteria were the dominant groups regardless of the chlorine residual concentration. For biofilm samples, the dominant position of Proteobacteria bacteria was gradually replaced by actinobacteria bacteria with the increase of chlorine residual concentration. In addition, at higher chlorine residual concentration, Gram-positive bacteria were more concentrated to form biofilms. There are three main reasons for the generation of chlorine resistance of bacteria: enhanced function of efflux system, activated bacterial self-repair system, and enhanced nutrient uptake capacity.

Exploring dynamic changes of fungal cellular components during nanoemulsion treatment by multivariate microRaman imaging

Recently, essential oils (EO) have gained a lot of interest for use as antifungal agent in food and agricultural industry and extensive research is ongoing to understand their mode of action. However, the exact mechanism is not yet elucidated. Here, we integrated spectral unmixing and Raman microspectroscopy imaging to unveil the antifungal mechanism of green tea EO based nanoemulsion (NE) against Magnaporthe oryzae. The dramatic change in protein, lipid, adenine, and guanine bands indicate that NE has a significant impact on the protein, lipid and metabolic processes of purine. The results also demonstrated that the NE treatment caused damage to fungal hyphae by inducing a physical injury leading to cell wall damage and loss of integrity. Our study shows that MCR-ALS (Multivariate Curve Resolution-Alternating Least Squares) and N-FINDR (N-finder algorithm) Raman imaging could serve as a suitable complementary package to the traditional methods, for revealing the antifungal mechanism of action of EO/NE.

Quorum sensing responses of activated sludge to free nitrous acid: Zoogloea deformation, AHL redistribution, and microbiota acclimatization

Free nitrous acid (FNA) has been widely employed for improvement of wastewater management by altering sludge characteristic and function based on its polymer lysing and biocidal capacity. Sludge characteristic and function are commonly considered as the joint consequence of microbial individual behaviors and quorum sensing (QS) involved collective behaviours, but the role of the latter in FNA treatment was still as-yet-unidentified and addressed in this research. The results of sludge morphology and component characterized FNA-induced zoogloea deformation, including inner cell exposure, half of extracellular polymeric substances (EPS) reduction and adsorption site depletion. During zoogloea deformation, four acyl-homoserine lactones (AHLs), including C4-HSL, C8-HSL, C10-HSL and C12-HSL, transferred inward of microbiota, and their total contents reduced by 66% because of depressed signal production, augmented decomposer and recognition. Transcriptome analysis revealed that differentially expressed QS driven by AHL redistribution facilitated microbiota acclimatization including cellular motility and hydrolase synthesis for EPS consumption. Boosted motility may favor escaping from stress spot and moderating intercellular acidity based on cell motility test. Feasible EPS consumption provided nutrition for heterotrophic metabolisms testified by pure culture with EPS as sole nutrition. Our work thus comprehensively revealed QS behaviours responding to FNA and deepened the understanding to FNA treatment performance in wastewater management.

Structural Rearrangements of Polylactide/Natural Rubber Composites during Hydro- and Biotic Degradation

In the work, the impact of the biological medium and water on structural rearrangements in pure polylactide and polylactide/natural rubber film composites was studied. Polylactide/natural rubber films with a rubber content of 5, 10, and 15 wt.% were obtained by the solution method. Biotic degradation was carried out according to the Sturm method at a temperature of 22 ± 2 °C. Hydrolytic degradation was studied at the same temperature in distilled water. The structural characteristics were controlled by thermophysical, optical, spectral, and diffraction methods. Optical microscopy revealed the surface erosion of all samples after exposure to microbiota and water. Differential scanning calorimetry showed a decrease in the degree of crystallinity of polylactide by 2-4% after the Sturm test, and a tendency to an increase in the degree of crystallinity after the action of water was noted. Changes in the chemical structure were shown in the spectra recorded by infrared spectroscopy. Due to degradation, significant changes in the intensities of the bands in the regions of 3500-2900 and 1700-1500 cm^-1 were shown. The X-ray diffraction method established differences in diffraction patterns in very defective and less damaged regions of polylactide composites. It was determined that pure polylactide hydrolyzed more readily under the action of distilled water than polylactide/natural rubber composites. Film composites were more rapidly subjected to biotic degradation. The degree of biodegradation of polylactide/natural rubber composites increased with the rise in the content of natural rubber in the compositions.

Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.

Instant detection of extended-spectrum β-lactamase-producing bacteria from the urine of patients using infrared spectroscopy combined with machine learning

Antibiotics are considered the most effective treatment against bacterial infections. However, most bacteria have already developed resistance to a broad spectrum of commonly used antibiotics, mainly due to their uncontrolled use. Extended-spectrum beta-lactamase (ESBL)-producing bacteria are an essential class of multidrug-resistant (MDR) bacteria. It is of extreme urgency to develop a method that can detect ESBL-producing bacteria rapidly for the effective treatment of patients with bacterial infectious diseases. Fourier transform infrared (FTIR) microscopy is a sensitive method that can rapidly detect cellular molecular changes. In this study, we examined the potential of FTIR spectroscopy-based machine learning algorithms for the rapid detection of ESBL-producing bacteria obtained directly from a patient's urine. Using 591 ESBL-producing and 1658 non-ESBL-producing samples of Escherichia coli (E. coli) and Klebsiella pneumoniae, our results show that the FTIR spectroscopy-based machine learning approach can identify ESBL-producing bacteria within 40 minutes from receiving a patient's urine sample, with a success rate of 80%.

SERS-Tags: Selective Immobilization and Detection of Bacteria by Strain-Specific Antibodies and Surface-Enhanced Raman Scattering

Efficient separation and sensitive identification of pathogenic bacterial strains is essential for a prosperous modern society, with direct applications in medical diagnostics, drug discovery, biodefense, and food safety. We developed a fast and reliable method for antibody-based selective immobilization of bacteria from suspension onto a gold-plated glass surface, followed by detection using strain-specific antibodies linked to gold nanoparticles decorated with a reporter molecule. The reporter molecules are subsequently detected by surface-enhanced Raman spectroscopy (SERS). Such a multi-functionalized nanoparticle is called a SERS-tag. The presented procedure uses widely accessible and cheap materials for manufacturing and functionalization of the nanoparticles and the immobilization surfaces. Here, we exemplify the use of the produced SERS-tags for sensitive single-cell detection of opportunistic pathogen Escherichia coli, and we demonstrate the selectivity of our method using two other bacterial strains, Staphylococcus aureus and Serratia marcescens, as negative controls. We believe that the described approach has a potential to inspire the development of novel medical diagnostic tools for rapid identification of bacterial pathogens.

Preparation of Laser-Ablated Ag Nanoparticle-MMT Clay-Based Beeswax Antibiofilm Coating

Unlike other antimicrobial agents, Ag-based composites are stable and currently widely used as broad spectral additives, fighting microbial biofilms and other biological threats. The goal of the present study is to develop a green, multifunctional, and robust antibiofilm water-insoluble coating, inhibiting histamine-producing Lentilactobacillus parabuchneri biofilms. Herein, laser-ablated Ag NPs (L-Ag NPs) were incorporated into and onto a montmorillonite (MMT) surface layer with a simple wet chemical method, provided that the electrostatic interaction between L-Ag NPs and MMT clay led to the formation of L-Ag/MMT nanoantimicrobials (NAMs). The use of MMT support can facilitate handling Ag NPs in industrial applications. The Ag/MMT composite was characterized with transmission electron microscopy (TEM) and scanning electron microscopy (SEM), which confirmed the entrapment of L-Ag NPs into MMT clay. The surface chemical composition was assessed with X-ray photoelectron spectroscopy, proving that Ag NPs were in contact with and deposited onto the surface of MMT. The characteristic L-Ag/MMT band was investigated with UV-vis spectroscopy. Following that, the L-Ag/MMT composite was embedded into a biosafe water-insoluble beeswax agent with a spin coating technique. The antimicrobial ion release kinetic profile of the L-Ag/MMT/beeswax coating through an electrothermal atomic absorption spectroscopy (ETAAS) study supported the controlled release of Ag ions, reaching a plateau at 420 ± 80 nM, which is safe from the point of view of Ag toxicity. Microbial biofilm growth inhibition was assessed with real-time in situ Fourier transform infrared attenuated total reflection spectroscopy (FTIR-ATR) in a flow cell assembly over 32 h. The study was further supported by optical density (OD) measurements and SEM on bacteria incubated in the presence of the L-Ag/MMT/beeswax coating.

Fast identification and susceptibility determination of E. coli isolated directly from patients' urine using infrared-spectroscopy and machine learning

For effective treatment, it is crucial to identify the infecting bacterium at the species level and to determine its antimicrobial susceptibility. This is especially true now, when numerous bacteria have developed multidrug resistance to most commonly used antibiotics. Currently used methods need ∼ 48 h to identify a bacterium and determine its susceptibility to specific antibiotics. This study reports the potential of using infrared spectroscopy with machine learning algorithms to identify E. coli isolated directly from patients' urine while simultaneously determining its susceptibility to antibiotics within ∼ 40 min after receiving the patient's urine sample. For this goal, 1,765 E. coli isolates purified directly from urine samples were collected from patients with urinary tract infections (UTIs). After collection, the samples were tested by infrared microscopy and analyzed by machine learning. We achieved success rates of ∼ 96% in isolate level identification and ∼ 84% in susceptibility determination.

Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.

Dynamic monitoring of bacteriostatic process by SERS analysis based on a simple but effective detection strategy

Investigating antibacterial process at a molecular level is helpful to fully understand the mechanism of bacteriostasis and develop new antimicrobial agents. Herein, a simple but effective sensor strategy of antibacterial nanocomposite combined with surface-enhanced Raman scattering (SERS) substrate was applied for the robust detection of bacteriostatic process. The synergistic SERS effect of nanocomposite and Ag nanoparticles (NPs) substrate was confirmed by finite difference time domain (FDTD) solutions. A curcumin liposome@Au NPs nanocomposite was designed and prepared as a kind of bacteriostatic agent and SERS material as well. By means of electrostatic attraction between the nanocomposite and bacteria (methicillin resistant staphylococcus aureus, MRSA), specific detection of MRSA and monitoring of the molecular structure changes after bacteriostaticeffect were realized by SERS. Important intermediates produced in the bacteriostatic process were also measured at the same time. The relationship between the relative peak intensities and the structure of MRSA were thus established. The results were verified by high performance liquid chromatography-mass spectrometry (HPLC-MS), reactive oxygen species (ROS) kit, and flow cytometry. The detection strategy we proposed could not only be used for real-time detection of bacteriostatic processes with a high efficiency, but also a powerful tool for analyzing the mechanism in biochemical processes.

Phenotypic convergence of bacterial adaption to sub-lethal antibiotic treatment

Microorganisms can adapt quickly to changes in their environment, leading to various phenotypes. The dynamic for phenotypic plasticity caused by environmental variations has not yet been fully investigated. In this study, we analyzed the time-series of phenotypic changes in Staphylococcus cells during adaptive process to antibiotics stresses using flow cytometry and Raman spectroscopy. The nine antibiotics with four different mode of actions were treated in bacterial cells at a sub-lethal concentration to give adaptable stress. Although the growth rate initially varied depending on the type of antibiotic, most samples reached the maximum growth comparable to the control through the short-term adaptation after 24 h. The phenotypic diversity, which showed remarkable changes depending on antibiotic treatment, converged identical to the control over time. In addition, the phenotype with cellular biomolecules converted into a bacterial cell that enhance tolerance to antibiotic stress with increases in cytochrome and lipid. Our findings demonstrated that the convergence into the phenotypes that enhance antibiotic tolerance in a short period when treated with sub-lethal concentrations, and highlight the feasibility of phenotypic approaches in the advanced antibiotic treatment.

Impact of Fe2+ and Shear Stress on the Development and Mesoscopic Structure of Biofilms-A Bacillus subtilis Case Study

Bivalent cations are known to affect the structural and mechanical properties of biofilms. In order to reveal the impact of Fe²⁺ ions within the cultivation medium on biofilm development, structure and stability, Bacillus subtilis biofilms were cultivated in mini-fluidic flow cells. Two different Fe²⁺ inflow concentrations (0.25 and 2.5 mg/L, respectively) and wall shear stress levels (0.05 and 0.27 Pa, respectively) were tested. Mesoscopic biofilm structure was determined daily in situ and non-invasively by means of optical coherence tomography. A set of ten structural parameters was used to quantify biofilm structure, its development and change. The study focused on characterizing biofilm structure and development at the mesoscale (mm-range). Therefore, biofilm replicates (n = 10) were cultivated and analyzed. Three hypotheses were defined in order to estimate the effect of Fe²⁺ inflow concentration and/or wall shear stress on biofilm development and structure, respectively. It was not the intention to investigate and describe the underlying mechanisms of iron incorporation as this would require a different set of tools applied at microscopic levels as well as the use of, i.e., omic approaches. Fe²⁺ addition influenced biofilm development (e.g., biofilm accumulation) and structure markedly. Experiments revealed the accumulation of FeO(OH) within the biofilm matrix and a positive correlation of Fe²⁺ inflow concentration and biofilm accumulation. In more detail, independent of the wall shear stress applied during cultivation, biofilms grew approximately four times thicker at 2.5 mg Fe²⁺/L (44.8 µmol/L; high inflow concentration) compared to the low Fe²⁺ inflow concentration of 0.25 mg Fe²⁺/L (4.48 µmol/L). This finding was statistically verified (Scheirer-Ray-Hare test, ANOVA) and hints at a higher stability of Bacillus subtilis biofilms (e.g., elevated cohesive and adhesive strength) when grown at elevated Fe²⁺ inflow concentrations.

Surface-enhanced Raman spectroscopy combined with stable isotope probing to assess the metabolic activity of Escherichia coli cells in chicken carcass wash water

Rapid evaluation of the metabolic activity of microorganisms is crucial in the assessment of the disinfection ability of various antimicrobial agents in the food industry. In this study, surface-enhanced Raman spectroscopy combined with isotope probing was employed for the analysis of the disinfection of single bacterial cells in the chicken carcass wash water. The Raman signals from single Escherichia coli O157:H7 cells were enhanced by in situ synthesis of silver nanoparticles. The ΔCD of the cells grown in presence of 0.5% hydrogen peroxide and 50 ppm chlorine was 5.86 ± 1.86% and 5.1 ± 2.3%, respectively, which showed significant reduction compared with cells grown in the absence of disinfecting agents (19.86 ± 2.51%) after 2 h of incubation. The study proved that the proposed method had the potential to assess the metabolic activity of microorganisms in other food products and optimize the disinfection process.

Direct Bactericidal Comparison of Metal Nanoparticles and Their Salts against S. aureus Culture by TEM and FT-IR Spectroscopy

We report the bactericidal effect of Ag and Cu NPs with different concentrations on methicillin-resistant S. aureus strain in comparison to the effect of AgNO₃ and CuCl₂ solutions, characterized by microbiological tests, TEM and Fourier-transform infrared spectroscopy. NPs were produced by nanosecond laser ablation in distilled water and characterized by scanning electron microscopy, UV-vis, energy dispersive X-ray, FT-IR spectroscopy, as well as X-ray diffraction, dynamic light scattering size and zeta-potential measurements. Microbiological tests showed antibacterial activity of NPs and metal ion-containing salts. Comparative FT-IR spectroscopy of bacteria, treated with metal NPs and salts, showed the broadening of amide I and II bands, a CH₂-related peak and its frequency decrease, indicating the increase of membrane fluidity. The main mechanisms of the antibacterial effect were proposed: Ag and Cu NPs release ions and ROS, which result in lipid peroxidation; AgNO₃ forms precipitates on the cell surface, which lead to the mechanical rupture of the membrane and subsequent possible penetration of the precipitates in the emerged damaged spots, complete destruction of the membrane and bacterial death; Cu ions from the CuCl₂ solution cause damage to phosphorus- and sulfur-containing biomolecules, which leads to disruption of intracellular biochemical processes. The theories were confirmed by FT-IR spectroscopy and TEM.

Stain-free Gram staining classification of pathogens via single-cell Raman spectroscopy combined with machine learning

Gram staining (GS) is one of the routine microbiological operations to classify bacteria based on the cell wall structure. Accurate GS classification of pathogens is of great significance since it helps correct administration of antimicrobial treatment. The laborious procedure and low sensitivity results related to conventional GS have resulted in reluctance among clinicians. In this study, we integrate confocal Raman spectroscopy and machine learning techniques to distinguish Gram-negative (GN) or Gram-positive (GP) bacteria. A single-cell Raman database including seven most common clinical pathogens (three GP strains and four GN strains) was constructed. Machine learning algorithms including the support-vector machine (SVM), k-nearest neighbors' algorithm (k-NN), gradient boosting machine (GBM), linear discriminant analysis (LDA), and t-distributed stochastic neighbor embedding (t-SNE) were trained to achieve the binary classification for GS. With such a relatively small database, the SVM model achieved the highest accuracy of 98.1%. The molecular signatures of GN and GP embedded in their Raman fingerprints were identified with hierarchical cluster analysis (HCA). The results indicated that Raman peaks for peptidoglycan and teichoic acid were the most significant factors that contributed to accurate classification. The Raman machine learning approach could greatly enhance the diagnosis of pathogenic infections.

Surface-enhanced Raman spectroscopy (SERS) for monitoring colistin-resistant and susceptible E. coli strains

The emergence of drug-resistant bacteria is a precarious global health concern. In this study, surface-enhanced Raman spectroscopy (SERS) is used to characterize colistin-resistant and susceptible E. coli strains based on their distinguished SERS spectral features for the development of rapid and cost-effective detection and differentiation methods. For this purpose, three colistin-resistant and three colistin susceptible E. coli strains were analyzed by comparing their SERS spectral signatures. Moreover, multivariate data analysis techniques including Principal component analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were used to examine the SERS spectral data of colistin-resistant and susceptible strains. PCA technique was employed for differentiating colistin susceptible and resistant E.coli strains due to alteration in biochemical compositions of the bacterial cell. PLS-DA is employed on SERS spectral data sets for discrimination of these resistant and susceptible E. coli strains with 100% specificity, 100% accuracy, 99.8% sensitivity, and 86% area under receiver operating characteristics (AUROC) curve.

Interactive effect of urbanization and flood in modulating microplastic pollution in rivers

Freshwater ecosystems play an important role in transporting and accumulating microplastics. Spatial and temporal variability in microplastic pollution can create critical spots and moments of elevated pollution, however, the consequences of their interaction are still poorly understood. This study aimed to assess the interaction between urbanization and flood episodes on river microplastic pollution. The water surface was sampled in two sites of the Garonne River, upstream and downstream a large urban area, during two flood episodes. Samples were chemically digested to facilitate particles isolation, and microplastics (700 μm-5 mm) were characterized through infrared spectroscopy (ATR-FTIR). Microplastic concentration increased by 5-8 fold during flood episodes, driven by river discharge. This increase was more significant in the downstream site. During the flood, there was an overall increase of larger particles on water surface, but only in the downstream site microplastic colours and polymeric compositions significantly varied. Principal component analysis of infrared spectra from polyethylene microplastics revealed that the main variance in the spectral region corresponded to hydroxyl and carbonyl groups. The carbonyl content in microplastics was significantly higher for particles collected during the flood, likely indicating a higher level of degradation. Urbanization modulates freshwater microplastic pollution during floods, and changes in microplastic physicochemical profile should be further integrated within toxicity studies to evaluate risks potentially elevated to animal and human health.

Papain-Decorated Mucopenetrating SEDDS: A Tentative Approach to Combat Absorption Issues of Acyclovir via the Oral Route

The aim of the current study was to enhance the oral bioavailability of Acyclovir (ACV) based on the papain-functionalized self-emulsifying drug delivery systems (SEDDS). The optimum control SEDDS formulation comprised of kolliphore (40%), transcutol (30%), propylene glycol (20%) and oleoyl chloride (10%). However, in the targeted SEDDS formulation, oleoyl chloride was replaced with oleoyl chloride-papain (OC-PAP) conjugate that was synthesized via an amide bond formation between the acyl halide groups of oleoyl chloride and the amino group of papain. Prior to adding in the SEDDS formulation, the newly synthesized conjugate was evaluated quantitatively by a Bradford assay that demonstrated 45 µg of papain contents per mg of the conjugate. Moreover, the conjugate formation was qualitatively confirmed through FTIR analysis and thin layer chromatography. ACV (a BCS class III drug) was incorporated into the SEDDS formulations after being hydrophobically ion paired with sodium deoxycholate, thereby making it lipophilic. The drug-loaded formulations were emulsified in the 0.1 M phosphate buffer (pH 6.8) and evaluated in vitro with respect to drug release and rabbit mucosal permeation studies. Both the formulations illustrated a very comparable drug release over a period of 4 h, afterwards, the OC-PAP-based formulation demonstrated a more sustaining effect. The extent of mucus diffusion evaluated via the silicon tube method demonstrated a 4.92-fold and a 1.46-fold higher penetration of the drug, a 3.21-fold and a 1.56-fold higher permeation through the rabbit intestinal mucus layer, and a 22.94-fold and a 2.27-fold higher retention of the drug over the intact mucosa of rabbit intestine, illustrated by OC-PAP-based nanoemulsions compared to the drug-free solution and controlled nanoemulsion, respectively. According to these in vitro results, papain-functionalized SEDDS is a promising approach for the oral delivery of ACV and many other drugs with oral bioavailability issues, however, in vivo studies in this respect have to be employed before making a comprehensive conclusion.

Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates

Streptococcus pneumoniae, commonly referred to as pneumococci, can cause severe and invasive infections, which are major causes of communicable disease morbidity and mortality in Europe and globally. The differentiation of S. pneumoniae from other Streptococcus species, especially from other oral streptococci, has proved to be particularly difficult and tedious. In this work, we evaluate if Raman spectroscopy holds potential for a reliable differentiation of S. pneumoniae from other streptococci. Raman spectra of eight different S. pneumoniae strains and four other Streptococcus species (S. sanguinis, S. thermophilus, S. dysgalactiae, S. pyogenes) were recorded and their spectral features analyzed. Together with Raman spectra of 59 Streptococcus patient isolates, they were used to train and optimize binary classification models (PLS-DA). The effect of normalization on the model accuracy was compared, as one example for optimization potential for future modelling. Optimized models were used to identify S. pneumoniae from other streptococci in an independent, previously unknown data set of 28 patient isolates. For this small data set balanced accuracy of around 70% could be achieved. Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.

Raman Microscopic Analysis of Dry-Surface Biofilms on Clinically Relevant Materials

Moist/hydrated biofilms have been well-studied in the medical area, and their association with infections is widely recognized. In contrast, dry-surface biofilms (DSBs) on environmental surfaces in healthcare settings have received less attention. DSBs have been shown to be widespread on commonly used items in hospitals and to harbor bacterial pathogens that are known to cause healthcare-acquired infections (HAI). DSBs cannot be detected by routine surface swabbing or contact plates, and studies have shown DSBs to be less susceptible to cleaning/disinfection products. As DSBs are increasingly reported in the medical field, and there is a likelihood they also occur in food production and manufacturing areas, there is a growing demand for the rapid in situ detection of DSBs and the identification of pathogens within DSBs. Raman microspectroscopy allows users to obtain spatially resolved information about the chemical composition of biofilms, and to identify microbial species. In this study, we investigated Staphylococcus aureus mono-species DSB on polyvinylchloride blanks and stainless steel coupons, and dual-species (S. aureus/Bacillus licheniformis) DSB on steel coupons. We demonstrated that Raman microspectroscopy is not only suitable for identifying specific species, but it also enables the differentiation of vegetative cells from their sporulated form. Our findings provide the first step towards the rapid identification and characterization of the distribution and composition of DSBs on different surface areas.

Bacterial Resuscitation from Starvation-Induced Dormancy Results in Phenotypic Diversity Coupled with Translational Activity Depending on Carbon Substrate Availability

Dormancy is a survival strategy of stressed bacteria inhabiting a various environment. Frequent dormant-active transitions owing to environmental changes play an important role in functional redundancy. However, a proper understanding of the phenotypic changes in bacteria during these transitions remains to be clarified. In this study, orthogonal approaches, such as electron microscopy, flow cytometry, and Raman spectroscopy, which can evaluate phenotypic heterogeneity at the single-cell level, were used to observe morphological and molecular phenotypic changes in resuscitated cells, and RNA sequencing (RNASeq) was used to determine the genetic characteristics associated with phenotypes. Within 12 h of the resuscitation process, morphological (cell size and shape) and physiological (growth and viability) characteristics as well as molecular phenotypes (cellular components) were found to be recovered to the extent that they were similar to those in active cells. The recovery rate and detailed phenotypic properties of the resuscitated cells differed significantly depending on the type or concentration of carbon sources. RNASeq analysis revealed that genes related to translation were significantly upregulated under all resuscitation conditions. The simpler the carbon source (e.g., glucose), the higher the expression of genes involved in cellular repair, and the more complex the carbon source (e.g., beef extract), the higher the expression of genes associated with increased energy production associated with cellular aerobic respiration. This study of phenotypic plasticity of resuscitated cells provides fundamental insight into understanding the adaptive fine-tuning of the microbiome in response to environmental changes and the functional redundancy resulting from phenotype heterogeneity.

Label-free differentiation of clinical E. coli and Klebsiella isolates with Raman spectroscopy

Raman spectroscopy is a promising spectroscopic technique for microbiological diagnostics. In routine diagnostic, the differentiation of pathogens of the Enterobacteriaceae family remain challenging. In this study, Raman spectroscopy was applied for the differentiation of 24 clinical E. coli, Klebsiella pneumoniae and Klebsiella oxytoca isolates. Spectra were collected with two spectroscopic approaches: UV-Resonance Raman spectroscopy (UVRR) and single-cell Raman microspectroscopy with 532 nm excitation. A description of the different biochemical profiles provided by the different excitation wavelengths was performed followed by machine-learning models for the classification at the genus and species levels. UVRR was shown to outperform 532 nm excitation, enabling correct classification at the genus level of 23/24 isolates. Furthermore, for the first time, Klebsiella species were correctly classified at the species level with 92% accuracy, classifying all three K. oxytoca isolates correctly. These findings should guide future applicative studies, increasing the scope of Raman spectroscopy's suitability for clinical applications.

In situ identification of environmental microorganisms with Raman spectroscopy

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

Phenotype Identification of HeLa Cells Knockout CDK6 Gene Based on Label-Free Raman Imaging

Identifying cell phenotypes is essential for understanding the function of biological macromolecules and molecular biology. We developed a noninvasive, label-free, single-cell Raman imaging analysis platform to distinguish between the cell phenotypes of the HeLa cell wild type (WT) and cyclin-dependent kinase 6 (CDK6) gene knockout (KO) type. Via large-scale Raman spectral and imaging analysis, two phenotypes of the HeLa cells were distinguished by their intrinsic biochemical profiles. A significant difference was found between the two cell lines: large lipid droplets formed in the knockout HeLa cells but were not observed in the WT cells, which was confirmed by Oil Red O staining. The band ratio of the Raman spectrum of saturated/unsaturated fatty acids was identified as the Raman spectral marker for HeLa cell WT or gene knockout type differentiation. The interaction between organelles involved in lipid metabolism was revealed by Raman imaging and Lorentz fitting, where the distribution intensity of the mitochondria and the endoplasmic reticulum membrane decreased. At the same time, lysosomes increased after the CDK6 gene knockout. The parameters obtained from Raman spectroscopy are based on hierarchical cluster analysis and one-way ANOVA, enabling highly accurate cell classification.