Integrating MALDI-TOF Mass Spectrometry with Machine Learning Techniques for Rapid Antimicrobial Resistance Screening of Foodborne Bacterial Pathogens.

Although MALDI-TOF mass spectrometry (MS) is considered as the gold standard for rapid and cost-effective identification of microorganisms in routine laboratory practices, its capability for antimicrobial resistance (AMR) detection has received limited focus. Nevertheless, recent studies explored the predictive performance of MALDI-TOF MS for detecting AMR in clinical pathogens when machine learning techniques are applied. This chapter describes a routine MALDI-TOF MS workflow for the rapid screening of AMR in foodborne pathogens, with Campylobacter spp. as a study model.

Comparison of Zybio Kit and saponin in-house method in rapid identification of bacteria from positive blood cultures by EXS2600 matrix-assisted laser desorption ionization time-of-flight mass spectrometry system.

We evaluated the performance of Zybio EXS2600 matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) (Zybio Inc., Chongqing, China) for the identification of bacteria from positive blood culture (BC) bottles using Blood Culture Positive Sample Pretreatment Kit (Zybio Inc., Chongqing, China) in comparison to an in-house saponin method. Following a positive signal by the BACTEC™ FX system, confirmation of identification was achieved using subcultured growing biomass used for MALDI-TOF MS analysis. A total of 94 positive BC bottles with 97 bacterial isolates were analyzed. The overall identification rates at the genus and species levels for the saponin method were 89.7% (87/97) and 74.2% (72/97), respectively. With the Zybio Kit, 88.7% (86/97) and 80.4% (78/97) of microorganisms were correctly identified to the genus and species levels, respectively. The saponin method identified 65.3% (32/49) of Gram-positive bacteria at the species level, whereas the Zybio Kit achieved a higher species-level identification rate of 79.6% (39/49) (p = 0.1153). The saponin method with additional on-plate formic acid extraction showed a significantly higher overall identification rate in comparison to the saponin method without that step for both genus (87.6% [85/97] vs. 70.1% [68/97], p = 0.0029) and species level (70.1% [68/97] vs. 46.4% [45/97], p = 0.0008). Identification rates of Gram-negative bacteria showed a higher identification rate, however, not statistically significant with additional Zybio Kit protocol step on both genus (85.4% [41/48] vs. 81.3% [39/48], p = 0.5858) and species level (77.1% [37/48] vs. 75% [36/48], p = 0.8120). Zybio Kit could offer an advantage in species-level identification, particularly for Gram-positive bacteria. The inclusion of on-plate formic acid extraction in the saponin method notably enhanced identification at both genus and species levels for Gram-positive bacteria. The extended protocol provided by the Zybio Kit could potentially offer an advantage in the identification of Gram-negative bacteria at both genus and species levels. Enhancements to the Zybio EXS2600 MALDI-TOF instrument software database are necessary.

Integration of MALDI-TOF MS and machine learning to classify enterococci: A comparative analysis of supervised learning algorithms for species prediction.

This research focused on distinguishing distinct matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) spectral signatures of three Enterococcus species. We evaluated and compared the predictive performance of four supervised machine learning algorithms, K-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF), to accurately classify Enterococcus species. This study involved a comprehensive dataset of 410 strains, generating 1640 individual spectra through on-plate and off-plate protein extraction methods. Although the commercial database correctly identified 76.9% of the strains, machine learning classifiers demonstrated superior performance (accuracy 0.991). In the RF model, top informative peaks played a significant role in the classification. Whole-genome sequencing showed that the most informative peaks are biomarkers connected to proteins, which are essential for understanding bacterial classification and evolution. The integration of MALDI-TOF MS and machine learning provides a rapid and accurate method for identifying Enterococcus species, improving healthcare and food safety.

Diversity in gut microbiota among colorectal cancer patients: findings from a case-control study conducted at a Tunisian University Hospital.

PURPOSE: Globally, colorectal cancer (CRC) is among the most prevalent cancers. One distinctive feature of colorectal cancer is its close relationship to the gut microbiota, which is a crucial component of the tumor microenvironment. Over the last ten years, research has demonstrated that colorectal cancer is accompanied with dysbiosis of gut bacteria, fungi, viruses, and Archaea, and that these alterations may be causal. OBJECTIVES: This study aimed to evaluate the disruption of the microorganism composition in the intestine, especially bacteria and to determine their relationship with colorectal cancer. METHODS: An evaluation system for determining colorectal cancer (CRC) risk and prognosis can be established more easily with the help of accurate gut microbiota profiling. Stool samples from 14 CRC patients and 13 controls were collected and the flora relative abundance was measured using targeted quantitative PCR (qPCR) assays to evaluate diagnostic potential of selected biomarkers: Streptococcus gallolyticus and Enterococcus faecalis. Culture and MALDI-TOF mass spectrometry were coupled to identify the gut microbiota in both colorectal cancer and control groups. RESULTS: Compared with controls, the gut microbiota of CRC patients showed an increase in the abundance of Enterococcus, Fusobacterium and Streptococcus. At the species level, the CRC enriched bacterium including Escherichia coli, Enterococcus faecalis, Fusobacterium nucleatum, Streptococcus gallolyticus, Flavoni fractorplautii and Eggerthella lenta acted as promising biomarkers for early detection of CRC. CONCLUSION: This study highlights the potential of gut microbiota biomarkers as a promising non-invasive tool for the accurate detection and distinction of individuals with CRC.

Performance of MALDI-TOF MS, real-time PCR, antigen detection, and automated biochemical testing for the identification of Burkholderia pseudomallei.

Burkholderia pseudomallei is the causative agent of melioidosis, a disease highly endemic to Southeast Asia and northern Australia, though the area of endemicity is expanding. Cases may occur in returning travelers or, rarely, from imported contaminated products. Identification of B. pseudomallei is challenging for laboratories that do not see this organism frequently, and misidentifications by matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and automated biochemical testing have been reported. The in vitro diagnostic database for use with the Vitek MS has recently been updated to include B. pseudomallei and we aimed to validate the performance for identification in comparison to automated biochemical testing with the Vitek 2 GN card, quantitative real-time polymerase chain reaction (qPCR) targeting the type III secretion system, and capsular polysaccharide antigen detection using a lateral flow immunoassay (LFA). We tested a "derivation" cohort including geographically diverse B. pseudomallei and a range of closely related Burkholderia species, and a prospective "validation" cohort of B. pseudomallei and B. cepacia complex clinical isolates. MALDI-TOF MS had a sensitivity of 1.0 and specificity of 1.0 for the identification and differentiation of B. pseudomallei from related Burkholderia species when a certainty cutoff of 99.9% was used. In contrast, automated biochemical testing for B. pseudomallei identification had a sensitivity of 0.83 and specificity of 0.88. Both qPCR and LFA correctly identified all B. pseudomallei isolates with no false positives. Due to the high level of accuracy, we have now incorporated MALDI-TOF MS into our laboratory's B. pseudomallei identification workflow.IMPORTANCEBurkholderia pseudomallei causes melioidosis, a disease associated with high morbidity and mortality that disproportionately affects rural areas in Southeast Asia and northern Australia. The known area of endemicity is expanding and now includes the continental United States. Laboratory identification can be challenging which may result in missed or delayed diagnoses and poor patient outcomes. In this study, we compared mass spectrometry using an updated spectral database with multiple other methods for B. pseudomallei identification and found mass spectrometry highly accurate. We have therefore incorporated this fast and cost-effective method into our laboratory's workflow for B. pseudomallei identification.

Accurate prediction of antimicrobial resistance and genetic marker of Staphylococcus aureus clinical isolates using MALDI-TOF MS and machine learning - Across DRIAMS and Taiwan database.

BACKGROUND: The use of matrix-assisted laser desorption/ionization-time-of-flight mass spectra (MALDI-TOF MS) with machine learning (ML) has been explored for predicting antimicrobial resistance. This study evaluates the effectiveness of MALDI-TOF MS paired with various ML classifiers and establishes optimal models for predicting antimicrobial resistance and mecA gene existence among Staphylococcus aureus. MATERIALS AND METHODS: The antimicrobial resistance against tier 1 antibiotics and MALDI-TOF MS of S. aureus were analyzed using data from the Database of Resistance against Antimicrobials with MALDI-TOF Mass Spectrometry (DRIAMS) and one medical center (CS database). Five ML classifiers were used to analyze performance metrics. The Shapley value quantified the predictive contribution of individual feature. RESULTS: The LightGBM demonstrated superior performance in predicting antimicrobial resistance for most tier 1 antibiotics among oxacillin-resistant S. aureus (ORSA) than all and oxacillin-susceptible S. aureus (OSSA) in both databases. In DRIAMS, MLP encompassed excellent predictive performance, expressed as accuracy/AUROC/AUPR, for clindamycin (0.74/0.81/0.90), tetracycline (0.86/0.87/0.94), and trimethoprim-sulfamethoxazole (0.95/0.72/0.97). In CS database, Ada and LightGBM retained excellent performance for erythromycin (0.97/0.92/0.86) and tetracycline (0.68/0.79/0.86), respectively. Mass-to-charge ratio (m/z) features of 2,411-2,414 and 2,429-2,432 correlated with clindamycin resistance, while 5,033-5,036 was linked to erythromycin resistance in DRIAMS. In CS database, overlapping features of 2,423-2,426, 4,496-4,499, and 3,764-3,767 simultaneously predicted mecA existence and oxacillin resistance. CONCLUSION: The predictive performance of antimicrobial resistance against S. aureus using MALDI-TOF MS depends on database characteristics and ML algorithm selected. Specific and overlapping MS features are excellent predictive markers for mecA and specific antimicrobial resistance.

Identification of N-linked glycans recognized by WGA in saliva from patients with non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer-related deaths. Saliva diagnosis is an essential approach for clinical applications owing to its noninvasive and material-rich features. The purpose of this study was to investigate differences in wheat germ agglutinin (WGA)-based recognition of salivary protein N-linked glycan profiles to distinguish non-small cell lung cancer (NSCLC) patients from controls. We used WGA-magnetic particle conjugates to isolate glycoproteins in the pooled saliva of healthy volunteers (HV, n = 35), patients with benign pulmonary disease (BPD, n = 35), lung adenocarcinoma (ADC, n = 35), and squamous cell carcinoma (SCC, n = 35), following to release the N-linked glycans from the isolated proteins with PNGase F, and further identified and annotated the released glycans by MALDI-TOF/TOF-MS, respectively. The results showed that 34, 35, 39, and 44 N-glycans recognized by WGA were identified and annotated from pooled saliva samples of HV, BPD, ADC, and SCC, respectively. Furthermore, the proportion of N-glycans recognized by WGA in BPD (81.2 %), ADC (90.1 %), and SCC (88.7 %), increased compared to HV (71.9 %). Two N-glycan peaks (m/z 2286.799, and 3399.211) specifically recognized by WGA were present only in NSCLC. These findings suggest that altered salivary glycopatterns such as sialic acids and GlcNAc containing N-glycans recognized by WGA might serve as potential personalized biomarkers for the diagnosis of NSCLC patients.

Evidence for the occurrence of Acinetobacter faecalis in cattle feces and its emended description.

This study provides an emended description of Acinetobacter faecalis, a species previously described based on a single isolate (YIM 103518T) from elephant feces in China. Our emended description is based on 15 novel isolates conspecific with the A. faecalis type strain, obtained from eight cattle farms in the Czech Republic. The A. faecalis strains have relatively small genomes (≈2.5-2.7 Mbp), with a GC content of 36.3-36.7 mol%. Core genome-based phylogenetic analysis showed that the 15 strains, together with the type strain of A. faecalis, form a distinct and internally coherent phylogroup within the genus. Pairwise genomic ANIb values for the 16 A. faecalis strains were 97.32-99.04 %, while ANIb values between the genomes of the 16 strains and those of the other Acinetobacter spp. were ≤ 86.2 %. Analysis of whole-cell MALDI-TOF mass spectra supported the distinctness and cohesiveness of the taxon. The A. faecalis strains could be differentiated from the other validly named Acinetobacter spp. by the absence of hemolytic activity along with their ability to grow at 37 °C and on L-aspartate, ethanol, and L-glutamate but not at 41 °C or on adipate or 2,3-butanediol. Reduced susceptibility to sulfamethoxazole, trimethoprim and/or streptomycin was shown in eight strains, along with the presence of corresponding antibiotic resistance genes. In conclusion, this study provides a comprehensive description of A. faecalis and demonstrates its occurrence in cattle feces. Though the ecological role of A. faecalis remains unknown, our results show its ability to acquire antibiotic resistance genes, likely as an adaptation to antibiotic selection pressure in livestock farms.

ENOVAT: the European Network for Optimization of Veterinary Antimicrobial Treatment.

The global antimicrobial resistance crisis has been the driver of several international strategies on antimicrobial stewardship. For their implementation on field level, the veterinary sector encounters several specific challenges and in particular: (i) a shortage of experts in key disciplines related to antimicrobial stewardship, (ii) a lack of evidence-based antimicrobial treatment guidelines, and (iii) inferior diagnostic tests available compared to human medicine. The present white paper describes how the COST Action ENOVAT (the European Network for Optimization of Veterinary Antimicrobial Treatment, CA18217), comprising 332 persons from 51 countries, worked towards solutions to these challenges. Initially, surveys were conducted to explore the present state in Europe in terms of existing antimicrobial use guidelines and microbiology practices performed. Concurrently, various research activities were launched to optimize diagnostics, including development of epidemiological cut-offs, clinical breakpoints and matrix-assisted laser desorption ionization time of flight mass spectrometry interpretive criteria. Also, guidelines drafting groups working towards evidence-based antimicrobial treatment guidelines for six conditions in food-producing and companion animals were established. The processes and outcomes, also in terms of capacity building, are summarized in this white paper where emphasis is placed on sustainability of the activities. Although several ENOVAT initiatives and spin-off projects will continue beyond the Action, we recommend that a new European veterinary research agenda is launched focusing on research and funding leading to long-term impacts on veterinary antimicrobial use.

Antimicrobial resistance is an urgent global public health threat that is amplified by over- and misuse of antimicrobials. As a result of antimicrobial resistance, antibiotics and other antimicrobial medicines become ineffective and infections become difficult or impossible to treat. This goes for human infections, but also for infections in animals. In a recently finished European project called ENOVAT we tried to tackle the problem of antimicrobial resistance in animals. We focused on two topics. First we optimized and harmonized diagnostics of bacterial infections in the laboratory, and second we developed evidence-based treatment guidelines to support veterinary practitioners on how and when to use antibiotics in the best way. Improved diagnostics and new treatment guidelines can help veterinary practitioners to a more sensible antibiotic choice and with that less over- and misuse of antimicrobials. This article summarizes the process and progress of the work done in the ENOVAT project. Emphasis is also put on how the project benefitted from a unique consortium encompassing 332 professionals with diverse backgrounds, from 51 countries.

A Case Report of a Rare Pulmonary Opportunistic Infection in an Infant Caused by Tsukamurella tyrosinosolvens.

This case report describes the case of a female infant hospitalized for severe pneumonia. During the treatment process, various antibiotics are used to treat and prevent further infection. The child had a weak physical condition, combined with neuroblastoma, paraneoplastic syndrome, and low immune function, leading to Tsukamurella tyrosinosolvens infection. The treatment was eventually abandoned owing to poor prognosis. This study aims to through the medium, dyeing, electron microscope observation, 16s rRNA and high-throughput sequencing investigated the morphological characteristics, staining properties, electron microscope morphology, antibiotic resistance, and genomic characteristics of Tsukamurella tyrosinosolvens. The aim of the study is to provide data reference for clinical laboratory staff in bacteria identification research, and to provide relevant help for clinicians in diagnosis and treatment.

The first case of urosepsis caused by Corynebacterium aurimucosum in an immunocompetent patient.

Non-diphtheroid Corynebacterium sepsis is rare and has affected only immunocompromised or particularly predisposed patients so far. We present the first case of urosepsis caused by Corynebacterium aurimucosum in a 67-year-old woman, without any known immunodeficiencies and in absence of any immunosuppressive therapy, admitted to the hospital for fever and acute dyspnea. This work suggests a new approach in evaluating the isolation of Corynebacteria, especially if isolated from blood. In particular, it highlights the potential infectious role of C. aurimucosum (often considered a contaminant and only rarely identified as an etiological agent of infections) and its clinical consequences, detailing also interesting aspects about its microbiological diagnosis and relative therapy and clarifying contrasting data of literature.

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Enhanced diagnosis of pulmonary tuberculosis through nucleotide MALDI-TOF MS analysis of BALF: a retrospective clinical study.

To evaluate the diagnostic accuracy of matrix-assisted laser desorption ionization time-of-flight mass spectrometry based on nucleotide (nucleotide MALDI-TOF MS) on bronchoalveolar lavage fluid (BALF) from suspected pulmonary tuberculosis (PTB) patients. A retrospective study was conducted on suspected PTB patients (total of 960) admitted to Chongqing Public Health Medical Center between May 2021 and January 2022. The sensitivity, specificity, positive predictive value, negative predictive value (NPV) and area under the curve values of nucleotide MALDI-TOF MS as well as smear microscopy, Mycobacterium Growth Indicator Tube 960 culture (MGIT culture), and Xpert MTB/RIF were calculated and compared. Total of 343 presumed PTB cases were enrolled. Overall, using the clinical diagnosis as reference, the sensitivity and NPV of nucleotide MALDI-TOF MS was 71.5% and 43.1%, respectively, significantly higher than smear microscopy (22.6%, 23.2%), MGIT culture (40.6%, 18.9%), Xpert MTB/RIF (40.8%, 27.9%). Furthermore, nucleotide MALDI-TOF MS also outperformed over Xpert MTB/RIF and MGIT culture on smear-negative BALFs. Approximately 50% and 30% of patients benefited from nucleotide MALDI-TOF MS compared with smear and MGIT culture or Xpert MTB/RIF, respectively. This study demonstrated that the analysis of BALF with nucleotide MALDI-TOF MS provided an accurate and promising tool for the early diagnosis of PTB.

Modelling the rapid detection of Carbapenemase-resistant Klebsiella pneumoniae based on machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

In this study, 80 carbapenem-resistant Klebsiella pneumoniae (CR-KP) and 160 carbapenem-susceptible Klebsiella pneumoniae (CS-KP) strains detected in the clinic were selected and their matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) peaks were collected. K-means clustering was performed on the MS peak data to obtain the best "feature peaks", and four different machine learning models were built to compare the area under the ROC curve, specificity, sensitivity, test set score, and ten-fold cross-validation score of the models. By adjusting the model parameters, the test efficacy of the model is increased on the basis of reducing model overfitting. The area under the ROC curve of the Random Forest, Support Vector Machine, Logistic Regression, and Xgboost models used in this study are 0.99, 0.97, 0.96, and 0.97, respectively; the model scores on the test set are 0.94, 0.91, 0.90, and 0.93, respectively; and the results of the ten-fold cross-validation are 0.84, 0.81, 0.81, and 0.85, respectively. Based on the machine learning algorithms and MALDI-TOF MS assay data can realize rapid detection of CR-KP, shorten the in-laboratory reporting time, and provide fast and reliable identification results of CR-KP and CS-KP.

Comparison of Vaginal microbiota in HPV-negative and HPV-positive pregnant women using a culture-based approach.

The purpose of this study was to conduct a comparative analysis of the composition of the dominant groups of vaginal microorganisms in healthy pregnant women and pregnant women infected with HPV using a microbiological culture-based method. The MALDI TOF MS method and 16S rRNA gene fragment sequencing were used to identify microorganisms isolated from healthy pregnant women (n=32) and pregnant women infected with HPV (n=24). It was found that vaginal secretion samples from both groups contained bacteria of 4 phyla: Bacillota, Actinomycetota, Pseudomonadota, Bacteroidota, and Ascomycota fungi. The most common microbial community in healthy pregnant women being CST I (p=0.0007), and CST V in pregnant women infected with HPV (p=0.0001). At the genus level, a total of 25 taxa were found in all samples, with Lactobacillus being the dominant genus overall. Escherichia (p<0.0001) and Prevotella (p=0.0001) concentrations were higher in HPV infected patients. When calculating the Pearson correlation coefficient for the phyla, it was found that Bacillota correlated negatively with HPV genotypes 16 and 51 (p≤0.05), but positively with HPV genotype 59 (p≤0.05), just like Actinomycetota (p≤0.05). Bacteroidota correlated positively with HPV genotype 56 (0.001

Flavonifractor plautii Bacteremia With Generalized Peritonitis: A Case Report and Literature Review.

Flavonifractor plautii is an obligate anaerobic rod bacterium that is part of the human gut microbiota. We describe a case of bacteremia caused by F. plautii in a mildly immunocompromised patient with acute generalized peritonitis. The patient is an 83-year-old male, with a history of stage III hepatocellular carcinoma 11 months prior, stage I gastric cancer, and cerebral infarction three months prior. He visited the emergency room of our hospital with a chief complaint of right-sided abdominal pain. A partial resection of the colon was performed due to stenosis of the transverse colon. Due to increasing abdominal pain, the patient underwent surgery for acute generalized peritonitis on the 11th postoperative day. F. plautii was detected in blood cultures collected prior to surgery, and the patient was treated with piperacillin/tazobactam 2.25 g four times a day for 11 days. The patient resumed eating and was discharged with no recurrence. This species may also stain gram-negative, and caution should be exercised in reporting results due to the potential impact on initial antimicrobial therapy. Gram staining showed variation in the length of the bacterium, which is considered a characteristic of this species. Appropriate antimicrobial therapy for F. plautii has yet to be established, and further accumulation of cases is needed to understand the resistance mechanism and confirm the effectiveness of different antimicrobials.

MALDI-TOF mass spectrometry from nucleic acid: development and evaluation of a novel platform for identification of mycobacteria and detection of genetic markers of resistance.

Complete identification methods are critical for evaluating nontuberculous mycobacteria (NTM). Here, we describe a novel diagnostic method for identification of eight NTM, Mycobacterium tuberculosis complex, and three drug resistance markers using PCR/matrix-assisted, laser-desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) from cultured organisms. With this technology, a multiplex end-point PCR is performed for targets of interest. Detection probes that are extended in the presence of a target are added. The extended probes have greater molecular weight and can be detected by MALDI-TOF MS. An AFB Primary Panel was designed to differentiate Mycobacterium avium; Mycobacterium intracellulare subsp. chimaera; Mycobacterium avium complex (other); Mycobacterium abscessus subsp. abscessus, bolletii, and massiliense; Mycobacterium kansasii, and M. tuberculosis complex. This design should cover 90% (3,483/3,691) of mycobacteria seen onsite. A development set of unblinded isolates (n = 217) was used to develop PCR primers, detection probes, and probe barcodes. It demonstrated 99.1% (215/217) agreement with reference methods. An evaluation set using blinded isolates (n = 320) showed an overall sensitivity of 94.3% (range by target: 90.0-100%). Overall specificity from negative media, non-target mycobacteria, and bacteria was 99.1% (108/109; range by target: 94.4-100%). Three drug resistance markers erm (41), rrl, and rrs demonstrated 100%, 91%, and 100% sensitivity, respectively, and >99% specificity. Limit of detection per target ranged from 2.2 × 103 to 9.9 × 106 CFU/mL. The AFB Primary Panel allows for mycobacterial speciation, subspeciation, and resistance mutation detection, which is essential for diagnosis, appropriate therapy, identifying outbreaks, and managing treatment-refractory disease. It can perform with high-throughput and high specificity and sensitivity from isolates.IMPORTANCEEven closely related mycobacteria can have unique treatment patterns, but differentiating these organisms is a challenge. Here, we tested an innovative platform that combines two commonly used technologies and creates something new: matrix-assisted, laser-desorption ionization time-of flight mass spectrometry was performed on PCR amplicons instead of on proteins. This created a robust system with the advantages of PCR (high discriminatory power, high throughput, detection of resistance) with the advantages of mass spectrometry (more targets, lower operational cost) in order to identify closely related mycobacterial organisms.

Screening and identification of microbes from polluted environment for azodye (Turquoise blue) decolorization.

Turquoise blue dye is frequently used for industrial dyeing applications. But the release of untreated colored wastewater became an environmental and public health hazard. Microbial remediation of Azodye is environmentally safe and an alternative to a physicochemical approach. The aim of this research is to isolate and characterize turquoise blue dye degrading microbes from polluted environment. Microbial isolation and purification from soil and effluent sample was done on PDA and NA. Turquoise blue dye degrading test was investigated under optimized conditions using -the definitive screening design method. UV-Vis spectrophotometer used to measure the degradation percentage at 620 nm and 25 °C. The results revealed that 24 fungi and 6 bacterial species were identified from the contaminated site using Biolog Microstation and MALDI-TOF. Among all identified microbial species Pencilium citrinum Thom BCA & Penicillium heriquei show the highest percentage decolorization of turquoise blue dye up to 300 ppm with 90 % removal at pH4 and 87 % at pH 7 up to 400 ppm respectively. The azodye degradation ability of these fungi species used in the development of mycoremediation technologies provide an alternative option for Azodye removal after HPLC analysis, molecular characterization, and toxic analysis.

Performance of common primary and chromogenic culture media for MALDI-TOF MS identification of clinically relevant yeasts.

Timely and accurate identification of yeasts is essential for adequate treatment, considering the increase in antifungal resistance of some species, particularly for C. auris. Current matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) manufacturer's protocol for identification of yeasts requires 24- to 72-h cultivation on Sabouraud dextrose media (SAB), but not some of the mainstay primary culture media used in mycology such as inhibitory mold agar (IMA), Mycosel, CHROMagar Candida Plus, and CHROMagar Candida. As culture media can influence MALDI-TOF MS identification results, this study evaluated the accuracy and performance of identification of clinically relevant yeasts on these first-line media using the VITEK-MS MALDI-TOF MS system.IMPORTANCEIn this study, a panel of 140 strains (21 species) was used to assess the performance of the selected media. Although not in the manufacturer's list of accepted media, IMA and chromogenic media are suitable for the identification of yeasts on the VITEK-MS systems. CHROMagar Candida Plus allowed the identification of 135/140 isolates tested after 24-h incubation similar to SAB reference media (137/140). Yeast isolates that grew on Mycosel selective media were also reliably identified by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. VITEK-MS system with IVD database V3.2 correctly identified C. auris strains to the species level on CHROMagar Candida Plus alleviating the need for subcultivation and reduced turnaround time (24-72 h) to identification for patient screening.

Classification of Latilactobacillus sakei subspecies based on MALDI-TOF MS protein profiles using machine learning models.

Latilactobacillus sakei is an important bacterial species used as a starter culture for fermented foods; however, two subspecies within this species exhibit different properties in the foods. Matrix-assisted laser desorption/ionization-time of flight mass spectrometer (MALDI-TOF MS) is the gold standard for microbial fingerprinting. However, the resolution power is down to the species level. This study was to combine MALDI-TOF mass spectra and machine learning to develop a new method to identify two L. sakei subspecies (L. sakei subsp. sakei and L. sakei subsp. carnosus) and non-L. sakei species. Totally, 227 strains were collected, with 908 spectra obtained via on- and off-plate protein extraction. Only 68.7% of strains were correctly identified at the subspecies level in the Biotyper database; however, a high level of performance was observed from the machine learning models. Partial least squares-discriminant analysis (PLS-DA), principal component analysis-K-nearest neighbor (PCA-KNN), and support vector machine (SVM) demonstrated 0.823, 0.914, and 0.903 accuracies, respectively, whereas the random forest (RF) achieved an accuracy of 0.954, with an area under the receiver operating characteristic (AUROC) curve of 0.99, outperforming the other algorithms in distinguishing the subspecies. The machine learning proved to be a promising technique for the rapid and high-resolution classification of L. sakei subspecies using MALDI-TOF MS. IMPORTANCE: Latilactobacillus sakei plays a significant role in the realm of food bacteria. One particular subspecies of L. sakei is employed as a protective agent during food fermentation, whereas another strain is responsible for food spoilage. Hence, it is crucial to precisely differentiate between the two subspecies of L. sakei. In this study, machine learning models based on protein mass peaks were developed for the first time to distinguish L. sakei subspecies. Furthermore, the efficacy of three commonly used machine learning algorithms for microbial classification was evaluated. Our results provide the foundation for future research on developing machine learning models for the classification of microbial species or subspecies. In addition, the developed model can be used in the food industry to monitor L. sakei subspecies in fermented foods in a time- and cost-effective method for food quality and safety.