Identification Of Pathogenic Bacteria Research Articles

Identification of pathogenic bacteria from eggshell of leatherback sea turtle (Dermochelys coriacea) in Lampuuk Beach, Aceh Besar using 16S rRNA gene

Currently, the leatherback turtle population in Indonesia tends to decline. One of the factors that cause turtles' existence is the turtle eggs that fail to hatch because bacteria contaminate them, 80% of the reasons turtle eggs fail to hatch are due to infection by microorganisms found on the eggshell. Turtle eggs have a soft structure and there are pores that function for gas exchange and water absorption. Microorganisms in sand can infect turtle eggs through the pores and cause hatching failure. This study aims to isolate and identify pathogenic bacteria found in leatherback turtle eggshell (Dermochelys coriacea) from Lampuuk Beach, Aceh Besar, based on the 16S rRNA gene analysis. Eight eggshell samples from leatherback turtle eggs that failed and hatched were cultured on Nutrient Agar (NA) medium. Then, the samples were inoculated on a blood agar medium for haemolysis test. Molecular identification of the 16S rRNA gene was also carried out to determine bacterial species. A total of five bacterial colonies from leatherback turtle eggshells were successfully isolated consisting of two Gram-negative bacteria (Penyu Belimbing Aceh (PBA) = PBA-1 and PBA-2 and three Gram-positive bacteria (PBA-3, PBA-4, and PBA-5). Based on the haemolysis test, the five bacterial isolates were unable to haemolyze blood. Bacterial DNA of PBA-1 and PBA-2 were successfully isolated and amplified using polymerase chain reaction (PCR) with a DNA target of ~1500 bp. Analysis using BLASTN and phylogenetic tree construction showed that PBA-1 isolate had 98.64% similarity with Acinetobacter baumannii, while PBA-2 isolate had 97.82% similarity with Enterobacter kobei. Both bacteria are members of the Enterobacteriaceae family. It can be concluded that, there were two pathogenic bacteria can potentially be opportunistic pathogens found in leatherback turtle eggshells that failed or succeeded in hatching, namely A. baumannii and E. Kobei

Rapid detection of causative bacteria including multiple infections of bovine respiratory disease using 16S rRNA amplicon-based nanopore sequencing.

Bovine respiratory disease (BRD) is a multifaceted condition that poses a primary challenge in calf rearing. Viruses and bacteria are etiological agents of BRD. Viral BRD is typically managed symptomatically, whereas bacterial BRD is predominantly managed through the empirical administration of antimicrobials. However, this empirical administration has raised concerns regarding the emergence of antimicrobial-resistant bacteria. Thus, rapid identification of pathogenic bacteria and judicious selection of antimicrobials are required. This study evaluated the usefulness of 16S rRNA analysis through nanopore sequencing for the rapid identification of BRD-causing bacteria. A comparative evaluation of nanopore sequencing and traditional culture method was performed on 100 calf samples detected with BRD. Nanopore sequencing facilitated the identification of bacteria at the species level in bovine nasal swabs, ear swabs, and lung tissue samples within approximately 6h. Of the 92 samples in which BRD-causing bacteria were identified via nanopore sequencing, 82 (89%) were concordant with the results of culture isolation. In addition, the occurrence of multiple infections exceeded that of singular infections. These results suggest that 16S rRNA sequencing via nanopore technology is effective in reducing analysis time and accurately identifying BRD-causing bacteria. This method is particularly advantageous for the initial detectable screening of BRD.

Metrological evaluation of DNA extraction method effects on the bacterial microbiome and resistome in sputum.

High-throughput sequencing (HTS) is one of the crucial new technologies that gives us insights into previously hidden parts of microbial communities. The DNA extraction method is an important step that can have a major impact on the results, and understanding this impact is of paramount importance for their reliable interpretation. Our results are of great value for the interpretation of sputum microbiome and resistome results obtained by targeted HTS. Our findings allow for a more rational design of future microbiome studies, which would lead to higher repeatability of results and easier comparison between different laboratories. This could also facilitate the introduction of targeted HTS in clinical microbiology for reliable identification of pathogenic bacteria and testing for antimicrobial resistance (AMR). As AMR is a major threat to public health, the improved methods for determining AMR would bring great benefits to both the healthcare system and society as a whole.

Magnetic Fe3O4@MOF@aptamer-mediated entropy-driven fluorescence biosensor for multiplexed and DNA extraction- and amplification-free detection of pathogenic bacteria

Rapid and early identification of pathogenic bacteria in food and clinical samples is critical for infectious disease control and precision medicine. In this study, we present a novel Magnetic Fe3O4@metal–organic framework (MOF)@Aptamer-based Entropy-driven fluorescence biosensor combined with mesophilic Clostridium butyricumArgonaute (CbAgo) for multiplexed and DNA extraction- and amplification-free detection of pathogenic bacteria, termed the MAEA biosensor. In this study, magnetic zirconium-based MOF nanoparticles were modified with a phosphorylated aptamer as an efficient signal converter, which recognized the pathogens and released the complementary DNA to activate the entropy-driven circuit reaction. The highly efficient catalytic DNA circuit and isothermal enzyme-free format produce large amounts of guide DNA. CbAgo exerts precise and highly efficient endonuclease activity under the guidance of guide DNA, which makes the MAEA biosensor useful for multiplexed and DNA extraction- and amplification-free detection of pathogenic bacteria with high sensitivity (<102 CFU/mL). This study demonstrates a highly selective and reliable magnetic Fe3O4@MOF@aptamer-based biosensor using CbAgo for simultaneously detecting multiple pathogenic bacteria, providing a potential tool for other infectious disease pathogens with convenient operation and high sensitivity.

Engineering the bacteriophage 80 alpha endolysin as a fast and ultrasensitive detection toolbox against Staphylococcus aureus

The isolation and identification of pathogenic bacteria from a variety of samples are critical for controlling bacterial infection-related health problems. The conventional methods, such as plate counting and polymerase chain reaction-based approaches, tend to be time-consuming and reliant on specific instruments, severely limiting the effective identification of these pathogens. In this study, we employed the specificity of the cell wall-binding (CBD) domain of the Staphylococcus aureus bacteriophage 80 alpha (80α) endolysin towards the host bacteria for isolation. Amidase 3-CBD conjugated magnetic beads successfully isolated as few as 1 × 102 CFU/mL of S. aureus cells from milk, blood, and saliva. The cell wall hydrolyzing activity of 80α endolysin promoted the genomic DNA extraction efficiency by 12.7 folds on average, compared to the commercial bacterial genomic DNA extraction kit. Then, recombinase polymerase amplification (RPA) was exploited to amplify the nuc gene of S. aureus from the extracted DNA at 37 °C for 30 min. The RPA product activated Cas12a endonuclease activity to cleave fluorescently labeled ssDNA probes. We then converted the generated signal into a fluorescent readout, detectable by either the naked eye or a portable, self-assembled instrument with ultrasensitivity. The entire procedure, from isolation to identification, can be completed within 2 h. The simplicity and sensitivity of the method developed in this study make it of great application value in S. aureus detection, especially in areas with limited resource supply.

Isolation and Identification of Pathogenic Bacteria from Ready to Eat Fast Foods from Kothanur, Bangalore.

Isolation and Identification of Pathogenic Bacteria from Ready to Eat Fast Foods from Kothanur, Bangalore.

ITO-Coated Vertical Nanowires-Based Biosensors for the Label-Free Detection of Escherichia coli

A major concern in public health is the direct in-situ detection of pathogenic bacteria in food, beverages, and the air [1]. This study utilizes ITO-coated vertical nanowires (ITO-VNWs) as sensing substrates for the detection of Escherichia coli (E. coli). The innovation in this research lies in the direct detection of various concentrations of E. coli using boronic acid (BA)-modified ITO-VNWs connected to a benchtop E4980A precision LCR meter. Impedance spectra were expressed in terms of real and imaginary parts, corresponding to resistance (R) and reactance (X). Serial dilutions of E. coli were prepared in saline for bacterial enumeration and tested using the standard plate count method. Subsequently, 100 μl of each bacterial dilutions (107, 106, 105, 104, 103, 102, and 101 CFU/ml) were applied to BA-modified ITO-VNWs. In summary, BA-modified ITO-VNWs demonstrated accurate and distinct detection of varying concentrations of E. coli. This accomplishment highlights the promising potential of our BA-modified ITO-VNW biosensor for precise bacterial detection across diverse applications. Further exploration and optimization of this platform could lead to advancements in the rapid and specific identification of pathogenic bacteria, making it an invaluable tool in the realm of public health and environmental safety. REFERENCES [1] A. Ahmed, J.V. Rushworth, N.A. Hirst, P.A. Millner, Biosensors for whole-cell bacterial detection, Clin Microbiol Rev 27(3) (2014) 631-46. Figure 1

Rapid identification of bloodstream infection pathogens and drug resistance using Raman spectroscopy enhanced by convolutional neural networks.

Bloodstream infections (BSIs) are a critical medical concern, characterized by elevated morbidity, mortality, extended hospital stays, substantial healthcare costs, and diagnostic challenges. The clinical outcomes for patients with BSI can be markedly improved through the prompt identification of the causative pathogens and their susceptibility to antibiotics and antimicrobial agents. Traditional BSI diagnosis via blood culture is often hindered by its lengthy incubation period and its limitations in detecting pathogenic bacteria and their resistance profiles. Surface-enhanced Raman scattering (SERS) has recently gained prominence as a rapid and effective technique for identifying pathogenic bacteria and assessing drug resistance. This method offers molecular fingerprinting with benefits such as rapidity, sensitivity, and non-destructiveness. The objective of this study was to integrate deep learning (DL) with SERS for the rapid identification of common pathogens and their resistance to drugs in BSIs. To assess the feasibility of combining DL with SERS for direct detection, erythrocyte lysis and differential centrifugation were employed to isolate bacteria from blood samples with positive blood cultures. A total of 12,046 and 11,968 SERS spectra were collected from the two methods using Raman spectroscopy and subsequently analyzed using DL algorithms. The findings reveal that convolutional neural networks (CNNs) exhibit considerable potential in identifying prevalent pathogens and their drug-resistant strains. The differential centrifugation technique outperformed erythrocyte lysis in bacterial isolation from blood, achieving a detection accuracy of 98.68% for pathogenic bacteria and an impressive 99.85% accuracy in identifying carbapenem-resistant Klebsiella pneumoniae. In summary, this research successfully developed an innovative approach by combining DL with SERS for the swift identification of pathogenic bacteria and their drug resistance in BSIs. This novel method holds the promise of significantly improving patient prognoses and optimizing healthcare efficiency. Its potential impact could be profound, potentially transforming the diagnostic and therapeutic landscape of BSIs.

Identification of Pathogenic Bacteria in Fresh and Smoked Fish of the Tuna (Thunnus sp.) with SNI 01-2332.2-2006

Identification of Pathogenic Bacteria in Fresh and Smoked Fish of the Tuna (Thunnus sp.) with SNI 01-2332.2-2006

Isolation and Identification of Pathogenic Bacteria from Patients with Dental Plaque in Sana’a City, Yemen

Isolation and Identification of Pathogenic Bacteria from Patients with Dental Plaque in Sana’a City, Yemen

Isolation and Identification of Pathogenic Bacteria in Pasteurized Powdered Milk Sold in Ogun, Lagos and Oyo, Southwest, Nigeria

Study’s Novelty/Excerpt This study provides novel insights into the bacteriological quality of powdered milk sold in Ogun, Lagos, and Oyo, Southwest Nigeria, by isolating and identifying bacterial species through 16S rRNA gene sequencing and evaluating their antibiotic resistance profiles. The research highlights the detection of potentially harmful bacteria, including Pseudomonas aeruginosa and Bacillus cereus, indicating issues related to inadequate storage or sanitation practices in the milk supply chain. These findings underscore the importance of rigorous microbial monitoring and improved hygiene standards to ensure the safety and extended shelf life of powdered milk products. Full Abstract Milk is a nutritious beverage enjoyed by people of all ages, but it also provides an ideal environment for the growth of various microorganisms. This study aimed to isolate and identify the bacteriological quality of powdered milk sold in Ogun, Lagos, and Oyo, Southwest Nigeria. Three samples from each of five different brands were analyzed by homogenizing 1g of each milk sample with 10ml of nutritional broth, followed by overnight incubation at 37°C. Subsequently, the bacteria were plated on nutrient agar, Mac-Conkey, and EMB, with further characterization through partial sequencing of 16S rRNA genes. The detected species were then evaluated against antibiotics including Acyclovir, Cefotaxime, Augmentin, Cefuroxime, Gentamicin, Erythromycin, Ofloxacin, Nitrofurantoin, Ciprofloxacin, Nalidixic acid, and Azithromycin. The molecular testing revealed the presence of Pseudomonas aeruginosa, Bacillus cereus, Brevibacillus agri, Lysinibacillus sphaericus, and Bacillus thuringiensis as the identified bacterial species. Pseudomonas aeruginosa exhibited sensitivity to Gentamicin and Cefuroxime, with Nitrofurantoin showing the highest sensitivity at 80.0%. Brevibacillus agri (LATC3) demonstrated sensitivity to Cefuroxime and Gentamicin, with a higher sensitivity of 55.00% to Gentamicin. Bacillus cereus (LAKG2 and LAC2) showed higher responsiveness to Ceftriaxone compared to Ofloxacin, Cefotaxime, Augmentin, Acyclovir, and Levofloxacin. These findings indicate the presence of potentially harmful organisms in the microbial populations of pasteurized milk, suggesting inadequate storage practices or insufficient sanitation of facilities and equipment post-production and packaging. Therefore, monitoring the microbial quality of pasteurized milk is essential to ensure safety and extend its shelf life.

Enhancing Open-World Bacterial Raman Spectra Identification by Feature Regularization for Improved Resilience against Unknown Classes.

The combination of deep learning techniques and Raman spectroscopy shows great potential offering precise and prompt identification of pathogenic bacteria in clinical settings. However, the traditional closed-set classification approaches assume that all test samples belong to one of the known pathogens, and their applicability is limited since the clinical environment is inherently unpredictable and dynamic, unknown, or emerging pathogens may not be included in the available catalogs. We demonstrate that the current state-of-the-art neural networks identifying pathogens through Raman spectra are vulnerable to unknown inputs, resulting in an uncontrollable false positive rate. To address this issue, first we developed an ensemble of ResNet architectures combined with the attention mechanism that achieves a 30-isolate accuracy of 87.8 ± 0.1%. Second, through the integration of feature regularization by the Objectosphere loss function, our model both achieves high accuracy in identifying known pathogens from the catalog and effectively separates unknown samples drastically reducing the false positive rate. Finally, the proposed feature regularization method during training significantly enhances the performance of out-of-distribution detectors during the inference phase improving the reliability of the detection of unknown classes. Our algorithm for Raman spectroscopy empowers the identification of previously unknown, uncataloged, and emerging pathogens ensuring adaptability to future pathogens that may surface. Moreover, it can be extended to enhance open-set medical image classification, bolstering its reliability in dynamic operational settings.

Specific discrimination of pathogenic bacteria causing septic arthritis using Raman spectroscopy: In-vitro study

In this study we performed preliminary experiments using Raman spectroscopy as an evolving technology in biofluid and microbial characterization, to explore its potential for rapid diagnosis of pathogenic bacteria in an in-vitro synovial fluid infection model. Normal human synovial fluids samples were collected from patients undergoing knee surgery and the three most common pathogenic bacteria introduced in-vitro into the samples. The bacterial growth was systematically monitored using a Raman spectroscopy. Multivariate regression analysis of acquired spectra showed bacterial characteristic Raman bands related to bacterial cell membranes and DNA structures to increase continuously as the incubation period was increased. Spectra signature recorded from cultured synovial fluid samples showed a significant loss in synovial quality and protein morphology over time compared to control samples. In this study, Raman spectroscopy shows promise for rapid pathogenic bacteria identification in synovial fluid. Marker peaks distinguished inoculated bacteria, while chemical changes reveal infection dynamics.

Rapid Detection of Broad-Spectrum Pathogenic Bacteria Based on Highly Sensitive Proton Response of the Nucleic Acid Amplification SPQC Platform.

Pathogenic bacteria significantly contribute to elevated morbidity and mortality rates, highlighting the urgent need for early and precise detection. Currently, there is a paucity of effective broad-spectrum methods for detecting pathogenic bacteria. We have developed an innovative proton-responsive series piezoelectric quartz crystal (PR-SPQC) platform for the broad-spectrum identification of pathogenic bacteria. This was achieved by retrieving and aligning sequences from the NCBI GenBank database to identify and validate 16S rRNA oligonucleotide sequences that are signatures of pathogenic bacteria but absent in humans or fungi. The hyperbranched rolling circle amplification, activated exclusively by the screened target, exponentially generates protons that are detected by SPQC through a 2D polyaniline (PANI) film. The PR-SPQC platform demonstrates broad-spectrum capabilities in detecting pathogenic bacteria, with a detection limit of 2 CFU/mL within 90 min. Clinical testing of blood samples yielded satisfactory results. With its advantages in miniaturization, cost efficiency, and suitability for point-of-care testing, PR-SPQC has the potential to be extensively used for the rapid identification of diverse pathogenic bacteria within clinical practice and public health sectors.

RamanCluster: A deep clustering-based framework for unsupervised Raman spectral identification of pathogenic bacteria

Raman spectroscopy serves as a powerful and reliable tool for the characterization of pathogenic bacteria. The integration of Raman spectroscopy with artificial intelligence techniques to rapidly identify pathogenic bacteria has become paramount for expediting disease diagnosis. However, the development of prevailing supervised artificial intelligence algorithms is still constrained by costly and limited well-annotated Raman spectroscopy datasets. Furthermore, tackling various high-dimensional and intricate Raman spectra of pathogenic bacteria in the absence of annotations remains a formidable challenge. In this paper, we propose a concise and efficient deep clustering-based framework (RamanCluster) to achieve accurate and robust unsupervised Raman spectral identification of pathogenic bacteria without the need for any annotated data. RamanCluster is composed of a novel representation learning module and a machine learning-based clustering module, systematically enabling the extraction of robust discriminative representations and unsupervised Raman spectral identification of pathogenic bacteria. The extensive experimental results show that RamanCluster has achieved high accuracy on both Bacteria-4 and Bacteria-6, with ACC values of 77 % and 74.1 %, NMI values of 75 % and 73 %, as well as AMI values of 74.6 % and 72.6 %, respectively. Furthermore, compared with other state-of-the-art methods, RamanCluster exhibits the superior accuracy on handling various complicated pathogenic bacterial Raman spectroscopy datasets, including situations with strong noise and a wide variety of pathogenic bacterial species. Additionally, RamanCluster also demonstrates commendable robustness in these challenging scenarios. In short, RamanCluster has a promising prospect in accelerating the development of low-cost and widely applicable disease diagnosis in clinical medicine.

Exonuclease-III Assisted the Target Recycling Coupling with Hybridization Chain Reaction for Sensitive mecAGene Analysis by Using PGM.

In the field of neonatal infections nursing, methicillin-resistant Staphylococcus aureus (MRSA) is a major bacterial pathogen. Here, we present a portable biosensor for MRSA detection that is both highly sensitive and portable, owing to its implementation on the personal glucose meter (PGM) platform. The H probe was fixed on the magnetic bead for mecA gene analysis. A blunt 3' terminus appeared in the MBs-H probe when the mecA gene was present. Exonuclease-III (Exo-III) recognized the blunt terminus and cleaved it, freeing the mecA gene and so facilitating target recycling. In the meantime, the remaining H probe-initiated hybridization chain reaction (HCR) led to the desired signal amplification. Portable quantitative detection of mecA gene is possible because PGM can read the quantity of invertase tagged on HCR product. After optimizing several experimental parameters, such as the concentration of Exo-III and incubation time, the constructed sensor is extremely sensitive, with a detection limit of 2 CFU/mL. The results from this sensitive PGM-based sensor are in agreement with those obtained from plate counting methods, suggesting that it can be used to accurately assess the MRSA content in artificial clinical samples. In addition, the PGM sensor can significantly cut down on time spent compared to plate counting techniques. The manufactured sensor provides a promising option for accurate identification of pathogenic bacteria.

Isolation and Identification of Pathogenic Bacteria from Hospital Door Handles/knobs

Background and objectives. Contracting infectious diseases from microbial contaminated door handles is a potential threat to health and safety. Therefore, we performed microbiological screening of door handles at the door handles of Zawia Education Hospital, Zawia, Libya, to estimate the presence of potentially pathogenic organisms. Methodology. A total of 125 samples were collected aseptically by using a moistened sterile bacteriologic swab on different days by scrubbing the selected handles and immersing them in the sterile broth to be transported to the microbiology laboratory, within no more than 30 minutes after collection. All the bacterial isolates were characterized and identified by standard diagnostic microbiological procedures and criteria, according to the Clinical and Laboratory Standards Institute. Data were presented as counts and percentages using Excel spread data sheath. Results: There was a total of 147 isolates obtained from the 12 sampled departments. The frequency of isolation of these organisms from these samples was as follows: coagulase-negative staphylococcus (16.80%) in 21 samples, Staphylococcus aureus (10.40%) in 13 samples, Diphtheroid (76.00%) in 95 samples, non-lactose fermenting gram negative bacilli (12.80%) in 16 samples, streptococcus pyogenes (0.80%) in one sample, and Klebsiella pneumoniae (0.80%) in one sample. Conclusion. Staphylococcus aureus, coagulase negative staphylococcus, streptococcus pyogenes, klebsiella pneumonae, Diphtheroid, and non-lactose fermenting gram-negative bacilli were the main bacteria isolated in this research study. The results of the present study demonstrated that contact surfaces such as door handles are often colonized by several bacteria and serve as a potential source of infections.

Line image sensor-based colony fingerprinting system for rapid pathogenic bacteria identification

The rapid identification of pathogenic bacteria is crucial across various industries, including food or beverage manufacturing. Bacterial microcolony image-based classification has emerged as a promising approach to expedite identification, automate inspections, and reduce costs. However, conventional imaging methods have significant practical limitations, namely low throughput caused by the limited imaging range and slow imaging speed. To address these challenges, we developed an imaging system based on a line image sensor for rapid and wide-field imaging compared to existing colony imaging methods. This system can image a standard Petri dish (92 mm in diameter) completely within 22 s, successfully acquiring bacterial microcolony images. This process yielded a set of discrimination parameters termed as colony fingerprints, which were employed for machine learning. We demonstrated the performance of our system by identifying Staphylococcus aureus in food products using a machine learning model trained on a colony fingerprint dataset of 15 species from 9 genera, including foodborne pathogens. While conventional mass spectrometry-based methods require 24 h of incubation, our colony fingerprinting approach achieved 96% accuracy in just 10 h of incubation. Line image sensor offer high imaging speeds and scalability, allowing for swift and straightforward microbiological testing, eliminating the need for specialized expertise and overcoming the limitations of conventional methods. This innovation marks a transformative shift in industrial applications.

Isolation and Identification of Pathogenic Bacteria from Commercially Marketed Fish in Wukari, North-Eastern Nigeria

Abstract Pathogenic bacteria that are part of the natural microflora can be found in aquaculture products. Microbial pathogens associated with fish can be transmitted to humans who consume or handle the fish. This study aimed to isolate human pathogenic bacteria present in the gills, intestine and skin of apparently healthy and spoil fishes. Tilapia (Oreochromisniloticus) and Cat fish (Clariaslazera) were collected from Wukari old market and Yam market of Wukari Nigeria. Bacteria were isolated from the fish gill, intestine and skin. The growth properties of distinct isolates on specific culture media were used to differentiate and characterize them. Pathogenic bacteria were recovered from various fish species that appeared with different features. The following human pathogenic bacteria were isolated; Escherichia coli and Staphylococcus aureus. The total bacteria count value ranges between 0.12 x 105 to 3.52 x 105 for the tilapia fish and 0.12 x 105 to 3.96 x 105 cfu/ml for cat fish. The identification revealed that Staphylococcus aureus and Escherichia coli was the most abundant pathogenic bacteria and also present in the initial water samples collected. Presence of Staphylococcus aureus and Eshcherichia coli is attributed to contamination of the fish samples by man through handling and processing. These enteric bacteria in fish serve as indicator organisms of faecal contamination and or water pollution. Their presence also represents a potential hazard to humans’ life. The isolates were higher from Yam market samples compared to Wukari old market.

Fast and accurate identification of pathogenic bacteria using excitation-emission spectroscopy and machine learning.

Fast and reliable identification of pathogenic bacteria is of upmost importance to human health and safety. Methods that are currently used in clinical practice are often time consuming, require expensive equipment, trained personnel, and therefore have limited applications in low resource environments. Molecular identification methods address some of these shortcomings. At the same time, they often use antibodies, their fragments, or other biomolecules as recognition units, which makes such tests specific to a particular target. In contrast, array-based methods use a combination of reporters that are not specific to a single pathogen. These methods provide a more data-rich and universal response that can be used for identification of a variety of bacteria of interest. In this report, we demonstrate the application of the excitation-emission spectroscopy of an environmentally sensitive fluorescent dye for identification of pathogenic bacterial species. 2-(4'-Dimethylamino)-3-hydroxyflavone (DMAF) interacts with the bacterial cell envelope resulting in a distinct spectral response that is unique to each bacterial species. The dynamics of dye-bacteria interaction were thoroughly investigated, and the limits of detection and identification were determined. Neural network classification algorithm was used for pattern recognition analysis and classification of spectral data. The sensor successfully discriminated between eight representative pathogenic bacteria, achieving a classification accuracy of 85.8% at the species level and 98.3% at the Gram status level. The proposed method based on excitation-emission spectroscopy of an environmentally sensitive fluorescent dye is a powerful and versatile diagnostic tool with high accuracy in identification of bacterial pathogens.