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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.

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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.

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Objective: MALDI-TOF-MS facilitates the identification of microorganisms from positive cultures in a timely and accurate manner. It eliminates the necessity for the application of biochemicals and operates on the principle of proteomics. It decreases the time required to report culture results. Prompt detection and notification of the pathogen, prior to the disclosure of antimicrobial susceptibilities, could potentially shorten the duration until the initial antibiotic adjustment is necessary, thereby influencing patients' clinical prognoses. Methodology: Fifty patients in the conventional arm and one hundred patients in the interventional arm were compared in a pre and post quasi-experimental study conducted at a tertiary care centre in North India. Patients with positive cultures from medical wards and intensive care units were included. Comparing the time to first antibiotic modification after culture positivity, MALDI-TOF-MS-based identification, and clinical outcomes in both arms was the primary objective. Antibiotic modifications, escalation, and de-escalation were all recorded. Results: The intervention arm exhibited a substantially shorter median time to first antibiotic modification (2010 mins vs 2905 mins, p=0.002) than the conventional arm. In the interventional group, a total of 44 out of 100 antibiotic modifications were implemented. Of these, 19 (43.3%) were determined solely by the MALDI report, without the anticipation of susceptibility assessments. De-escalation of antibiotics constituted the pre-dominant form of modification (47.4%). The difference between the 27% and 32% mortality rates in the intervention arm and the conventional arm was not statistically significant (p=0.52). Conclusion: MALDI-TOF-MS facilitates the modification of antibiotics early on. The primary benefit lies in the reduction of superfluous antibiotic usage. Early organism identification and reporting prior to the availability of susceptibility results did not result in any mortality benefit. This strategy, when combined with a strong antimicrobial stewardship programme, can aid in the reduction of antibiotic use.

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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.

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In DNA, methylation at the fifth position of cytosine (5mC) by DNA methyltransferases is essential for eukaryotic gene regulation. Methylation patterns are dynamically controlled by epigenetic machinery. Erasure of 5mC by Fe2+ and 2-ketoglutarate (2KG) dependent dioxygenases in the ten-eleven translocation family (TET1-3), plays a key role in nuclear processes. Through the event of active demethylation, TET proteins iteratively oxidize 5mC to 5-hydroxymethyl cytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxycytosine (5caC), each of which has been implicated in numerous diseases when aberrantly generated. A wide range of biochemical assays have been developed to characterize TET activity, many of which require multi-step processing to detect and quantify the 5mC oxidized products. Herein, we describe the development and optimization of a sensitive MALDI mass spectrometry-based technique that directly measures TET activity and eliminates tedious processing steps. Employing optimized assay conditions, we report the steady-state activity of wild type TET2 enzymes to furnish 5hmC, 5fC and 5caC. We next determine IC50 values of several small-molecule inhibitors of TETs. The utility of this assay is further demonstrated by analyzing the activity of V1395A which is an activating mutant of TET2 that primarily generates 5caC. Lastly, we describe the development of a secondary assay that utilizes bisulfite chemistry to further examine the activity of wildtype TET2 and V1395A in a base-resolution manner. The combined results demonstrate that the activity of TET proteins can be gauged, and their products accurately quantified using our methods.

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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.

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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.

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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.

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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.

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The venom of the Odontobuthus doriae scorpion, prevalent in East Asia and Iran, has not been fully characterized. This study provides the first proteomic profile of O. doriae venom to explore its potential as a medical. 2D-PAGE analysis revealed 96 protein spots with isoelectric points from 3 to 9 and molecular weights between 6.6 to 205 kDa. Fourteen toxin fractions were isolated via HPLC, and SDS-PAGE showed seven protein bands ranging from 3.8 to 182 kDa. MALDI-TOF MS identified Peptide 1 and Peptide 2, resembling Hemoglobin beta-2 chain and Chaperonin HSP60 and suggest potential therapeutic applications for P1 and P2.

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Staphylococcus species are widespread in poultry environments and can cause various infections, often when the host's defences are compromised. This manuscript reports on a co-infection of chickens with Staphylococcus lentus and Staphylococcus aureus associated with an outbreak of arthritis, synovitis, and osteomyelitis in an organic broiler breeder flock in Austria. Clinically, the affected flock showed weakness, lethargy, lameness, and increased mortality. Post-mortem examinations identified purulent arthritis and femoral head necrosis. Bacteriological analysis using MALDI-TOF MS identified both S. aureus and S. lentus in the affected joints. Antibiotic resistance testing revealed significant resistance, particularly in S. lentus. Histological analysis showed severe inflammation and bacterial colonies in the joints. While S. aureus is a common pathogen in poultry, S. lentus is less frequently reported. This study emphasises the need for detailed bacterial characterisation in outbreaks to better understand the role of less common pathogens like S. lentus. Further research is necessary to elucidate the impact of S. lentus on poultry health and its role in causing arthritis and synovitis, highlighting the importance of comprehensive investigation in such outbreaks.

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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.

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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.

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BACKGROUND: Effective and rapid diagnostic strategies are required to manage antibiotic resistance in Klebsiella pneumonia (KP). This study aimed to design an artificial intelligence-clinical decision support system (AI-CDSS) using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and machine learning for the rapid detection of ceftazidime-avibactam (CZA) resistance in KP to improve clinical decision-making processes. METHODS: Out of 107,721 bacterial samples, 675 specimens of KP with suspected multi-drug resistance were selected. These specimens were collected from a tertiary hospital and four secondary hospitals between 2022 and 2023 to evaluate CZA resistance. We used MALDI-TOF MS and machine learning to develop an AI-CDSS with enhanced speed of resistance detection. RESULTS: Machine learning models, especially light gradient boosting machines (LGBM), exhibited an area under the curve (AUC) of 0.95, indicating high accuracy. The predictive models formed the core of our newly developed AI-CDSS, enabling clinical decisions quicker than traditional methods using culture and antibiotic susceptibility testing by a day. CONCLUSIONS: The study confirms that MALDI-TOF MS, integrated with machine learning, can swiftly detect CZA resistance. Incorporating this insight into an AI-CDSS could transform clinical workflows, giving healthcare professionals immediate, crucial insights for shaping treatment plans. This approach promises to be a template for future anti-resistance strategies, emphasizing the vital importance of advanced diagnostics in enhancing public health outcomes.

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Background: Detecting monoclonal protein (M-protein), a hallmark of plasma cell disorders, traditionally relies on methods such as protein electrophoresis, immune-electrophoresis, and immunofixation electrophoresis (IFE). Mass spectrometry (MS)-based methods, such as matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) and electrospray ionization-quadrupole time-of-flight (ESI-qTOF) MS, have emerged as sensitive methods. We explored the M-protein-detection efficacies of different MS techniques. Methods: To isolate immunoglobulin and light chain proteins, six types of beads (IgG, IgA, IgM, kappa, lambda, and mixed kappa and lambda) were used to prepare samples along with CaptureSelect nanobody affinity beads (NBs). After purification, both MALDI-TOF MS and liquid chromatography coupled with Synapt G2 ESI-qTOF high-resolution MS analysis were performed. We purified 25 normal and 25 abnormal IFE samples using NBs and MALDI-TOF MS (NB-MALDI-TOF). Results: Abnormal samples showed monoclonal peaks, whereas normal samples showed polyclonal peaks. The IgG and mixed kappa and lambda beads showed monoclonal peaks following the use of daratumumab (an IgG/kappa type of monoclonal antibody) with both MALDI-TOF and ESI-qTOF MS analysis. The limits of detection for MALDI-TOF MS and ESI-qTOF MS were established as 0.1 g/dL and 0.025 g/dL, respectively. NB-MALDI-TOF and IFE exhibited comparable sensitivity and specificity (92% and 92%, respectively). Conclusions: NBs for M-protein detection, particularly with mixed kappa-lambda beads, identified monoclonal peaks with both MALDI-TOF and ESI-qTOF analyses. Qualitative analysis using MALDI-TOF yielded results comparable with that of IFE. NB-MALDI-TOF might be used as an alternative method to replace conventional tests (such as IFE) to detect M-protein with high sensitivity.

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Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used in clinical microbiology laboratories for bacterial identification but its use for detection of antimicrobial resistance (AMR) remains limited. Here, we used MALDI-TOF MS with artificial intelligence (AI) approaches to successfully predict AMR in Pseudomonas aeruginosa, a priority pathogen with complex AMR mechanisms. The highest performance was achieved for modern ß-lactam/ß-lactamase inhibitor drugs, namely, ceftazidime/avibactam and ceftolozane/tazobactam. For these drugs, the model demonstrated area under the receiver operating characteristic curve (AUROC) of 0.869 and 0.856, specificity of 0.925 and 0.897, and sensitivity of 0.731 and 0.714, respectively. As part of this work, we developed dynamic binning, a feature engineering technique that effectively reduces the high-dimensional feature set and has wide-ranging applicability to MALDI-TOF MS data. Compared to conventional feature engineering approaches, the dynamic binning method yielded highest performance in 7 of 10 antimicrobials. Moreover, we showcased the efficacy of transfer learning in enhancing the AUROC performance for 8 of 11 antimicrobials. By assessing the contribution of features to the model's prediction, we identified proteins that may contribute to AMR mechanisms. Our findings demonstrate the potential of combining AI with MALDI-TOF MS as a rapid AMR diagnostic tool for Pseudomonas aeruginosa.IMPORTANCEPseudomonas aeruginosa is a key bacterial pathogen that causes significant global morbidity and mortality. Antimicrobial resistance (AMR) emerges rapidly in P. aeruginosa and is driven by complex mechanisms. Drug-resistant P. aeruginosa is a major challenge in clinical settings due to limited treatment options. Early detection of AMR can guide antibiotic choices, improve patient outcomes, and avoid unnecessary antibiotic use. Matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) is widely used for rapid species identification in clinical microbiology. In this study, we repurposed mass spectra generated by MALDI-TOF and used them as inputs for artificial intelligence approaches to successfully predict AMR in P. aeruginosa for multiple key antibiotic classes. This work represents an important advance toward using MALDI-TOF as a rapid AMR diagnostic for P. aeruginosa in clinical settings.

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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.

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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.

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BACKGROUND: The use of matrix-assisted laser desorption/ionization time-of-flight mass spectra (MALDI-TOF MS) combined with machine learning techniques has recently emerged as a method to address the public health crisis of antimicrobial resistance. This systematic review, conducted following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, aims to evaluate the current state of the art in using machine learning for the detection and classification of antimicrobial resistance from MALDI-TOF mass spectrometry data. METHODS: A comprehensive review of the literature on machine learning applications for antimicrobial resistance detection was performed using databases such as Web Of Science, Scopus, ScienceDirect, IEEE Xplore, and PubMed. Only original articles in English were included. Studies applying machine learning without using MALDI-TOF mass spectra were excluded. RESULTS: Forty studies met the inclusion criteria. Staphylococcus aureus, Klebsiella pneumoniae and Escherichia coli were the most frequently cited bacteria. The antibiotics resistance most studied corresponds to methicillin for S. aureus, cephalosporins for K. pneumoniae, and aminoglycosides for E. coli. Random forest, support vector machine and logistic regression were the most employed algorithms to predict antimicrobial resistance. Additionally, seven studies reported using artificial neural networks. Most studies reported metrics such as accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (AUROC) above 0.80. CONCLUSIONS: Our study indicates that random forest, support vector machine, and logistic regression are effective for predicting antimicrobial resistance using MALDI-TOF MS data. Recent studies also highlight the potential of deep learning techniques in this area. We recommend further exploration of deep learning and multi-label supervised learning for comprehensive antibiotic resistance prediction in clinical practice.

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