Mass spectrometry and machine learning for the accurate diagnosis of benzylpenicillin and multidrug resistance of Staphylococcus aureus in bovine mastitis

Machine learning and matrix-assisted laser desorption/ionization time-of-flight mass spectra for antimicrobial resistance prediction: A systematic review of recent advancements and future development

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.

Machine learning-based early prediction of growth and morphological traits at yearling age in pure and hybrid goat offspring

The purpose of this study was to evaluate the performance of various prediction models in estimating the growth and morphological traits of pure Hair, Alpine × Hair F1 (AHF1), and Saanen × Hair F1 (SHF1) hybrid offspring at yearling age by employing early body measurement records from birth till 9th month combined with meteorological data, in an extensive natural pasture-based system. The study also included other factors such as sex, farm, doe and buck IDs, birth type, gestation length, age of the doe at birth etc. For this purpose, seven different machine learning algorithms-linear regression, artificial neural network (ANN), support vector machines (SVM), decision tree, random forest, extra gradient boosting (XGB) and ExtraTree - were applied to the data coming from 1530 goat offspring in Türkiye. Early predictions of growth and morphological traits at yearling age; such as live weight (LW), body length (BL), wither height (WH), rump height (RH), rump width (RW), leg circumference (LC), shinbone girth (SG), chest width (CW), chest girth (CG) and chest depth (CD) were performed by using birth date measurements only, up to month-3, month-6 and month-9 records. Satisfactory predictive performances were achieved once the records after 6th month were used. In extensive natural pasture-based systems, this approach may serve as an effective indirect selection method for breeders. Using month-9 records, the predictions were improved, where LW and BL were found with the highest performance in terms of coefficient of determination (R2 score of 0.81 ± 0.00) by ExtraTree. As one of the rarely applied machine learning models in animal studies, we have shown the capacity of this algorithm. Overall, the current study offers utilization of the meteorological data combined with animal records by machine learning models as an alternative decision-making tool for goat farming.

Predicting Pseudomonas aeruginosa drug resistance using artificial intelligence and clinical MALDI-TOF mass spectra

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 β-lactam/β-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.

The Use of Machine Learning to Predict Prevalence of Subclinical Mastitis in Dairy Sheep Farms

The objective of the study was to develop a computational model with which predictions regarding the level of prevalence of mastitis in dairy sheep farms could be performed. Data for the construction of the model were obtained from a large Greece-wide field study with 111 farms. Unsupervised learning methodology was applied for clustering data into two clusters based on 18 variables (17 independent variables related to health management practices applied in farms, climatological data at the locations of the farms, and the level of prevalence of subclinical mastitis as the target value). The K-means tool showed the highest significance for the classification of farms into two clusters for the construction of the computational model: median (interquartile range) prevalence of subclinical mastitis among farms was 20.0% (interquartile range: 15.8%) and 30.0% (16.0%) (p = 0.002). Supervised learning tools were subsequently used to predict the level of prevalence of the infection: decision trees, k-NN, neural networks, and Support vector machines. For each of these, combinations of hyperparameters were employed; 83 models were produced, and 4150 assessments were made in total. A computational model obtained by means of Support vector machines (kernel: 'linear', regularization parameter C = 3) was selected. Thereafter, the model was assessed through the results of the prevalence of subclinical mastitis in 373 records from sheep flocks unrelated to the ones employed for the selection of the model; the model was used for evaluation of the correct classification of the data in each of 373 sets, each of which included a test (prediction) subset with one record that referred to the farm under assessment. The median prevalence of the infection in farms classified by the model in each of the two categories was 10.4% (5.5%) and 36.3% (9.7%) (p < 0.0001). The overall accuracy of the model for the results presented by the K-means tool was 94.1%; for the estimation of the level of prevalence (<25.0%/≥25.0%) in the farms, it was 96.3%. The findings of this study indicate that machine learning algorithms can be usefully employed in predicting the level of subclinical mastitis in dairy sheep farms. This can facilitate setting up appropriate health management measures for interventions in the farms.

Application of MALDI-TOF MS to Identify and Detect Antimicrobial-Resistant Streptococcus uberis Associated with Bovine Mastitis

Streptococcus uberis is a common bovine mastitis pathogen in dairy cattle. The rapid identification and characterization of antimicrobial resistance (AMR) in S. uberis plays an important role in its diagnosis, treatment, and prevention. In this study, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used to identify S. uberis and screen for potential AMR biomarkers. Streptococcus uberis strains (n = 220) associated with bovine mastitis in northern Thailand were identified using the conventional microbiological methods and compared with the results obtained from MALDI-TOF MS. Streptococcus uberis isolates were also examined for antimicrobial susceptibility using a microdilution method. Principal component analysis (PCA) and the Mann-Whitney U test were used to analyze the MALDI-TOF mass spectrum of S. uberis and determine the difference between antimicrobial-resistant and -susceptible strains. Using MALDI-TOF MS, 73.18% (161/220) of the sampled isolates were identified as S. uberis, which conformed to the identifications obtained using conventional microbiological methods and PCR. Using PCR, antimicrobial-resistant strains could not be distinguished from antimicrobial-susceptible strains for all three antimicrobial agents, i.e., tetracycline, ceftiofur, and erythromycin. The detection of spectral peaks at 7531.20 m/z and 6804.74 m/z was statistically different between tetracycline- and erythromycin-resistant and susceptible strains, respectively. This study demonstrates a proteomic approach for the diagnosis of bovine mastitis and potentially for the surveillance of AMR among bovine mastitis pathogens.

Pharmacological composition based on bacteriocinnisin in experiments in vitro and in vivo

Background

Antibiotic resistance is a global health problem related to the transmission of bacteria and genes between humans and animals. The development of new drugs with antimicrobial activity research is an urgent task of modern science.

Aim

The article presents data of in vitro and in vivo experiments on new pharmaceutical composition based on nisin.

Methods

The antimicrobial activity was studied on the mastitis pathogens. To identify microorganisms the Matrix-Assisted Lazer Desorption/Ionization Time-of-Flight (MALDI-TOF) (mass spectrometry) method was performed using. To determine sensitivity, the serial dilution method and the diffusion method were used. On laboratory animals, biochemical, hematological, and histological research methods were used. Female nonlinear white laboratory rats were used, which were divided into one control group and three experimental ones.

Results

"Duration" factor was statistically significant for the following indicators: hemoglobin, hematocrit, leukocytes, lymphocytes, erythrocyte sedimentation rate, and eosinophils. The "Dose" factor did not show significance for any indicator, which means that the effect was similar regardless of the dose chosen. When analyzing the biochemical indicators, significant differences were found in the "Duration" and "Dose" factors, in the direction of a decrease in the indicators of total protein, globulins, urea, and an increase in the concentration of alkaline phosphatase. When conducting histological studies in the first experimental group, it was established that there were no changes in the structural and functional units of the organs. In animals of the second experimental group, the presence of reversible pathological processes of a compensatory nature was noted. More profound changes in the structure of the studied organs were recorded in the third experimental group.

Conclusion

An in vitro study on cell cultures showed that the pharmacological composition has high antimicrobial activity against isolates from the mammary gland secretion of cows with mastitis. An in vivo study on laboratory animals showed that the developed composition belongs to the IV class of substances "low-hazard substances". Histological examination made it possible to select the safest dose of the pharmacological composition of no more than 500 mg/kg.

Tackling the Antimicrobial Resistance "Pandemic" with Machine Learning Tools: A Summary of Available Evidence

Antimicrobial resistance is recognised as one of the top threats healthcare is bound to face in the future. There have been various attempts to preserve the efficacy of existing antimicrobials, develop new and efficient antimicrobials, manage infections with multi-drug resistant strains, and improve patient outcomes, resulting in a growing mass of routinely available data, including electronic health records and microbiological information that can be employed to develop individualised antimicrobial stewardship. Machine learning methods have been developed to predict antimicrobial resistance from whole-genome sequencing data, forecast medication susceptibility, recognise epidemic patterns for surveillance purposes, or propose new antibacterial treatments and accelerate scientific discovery. Unfortunately, there is an evident gap between the number of machine learning applications in science and the effective implementation of these systems. This narrative review highlights some of the outstanding opportunities that machine learning offers when applied in research related to antimicrobial resistance. In the future, machine learning tools may prove to be superbugs' kryptonite. This review aims to provide an overview of available publications to aid researchers that are looking to expand their work with new approaches and to acquaint them with the current application of machine learning techniques in this field.

Diagnostic Screening of Bovine Mastitis Using MALDI-TOF MS Direct-Spotting of Milk and Machine Learning

Novel strategies for diagnostic screening of animal and herd health are crucial to contain disease outbreaks, maintain animal health, and maximize production efficiency. Mastitis is an inflammation of the mammary gland in dairy cows, often resulting from infection from a microorganism. Mastitis outbreaks result in loss of production, degradation of milk quality, and the need to isolate and treat affected animals. In this work, we evaluate MALDI-TOF mass spectrometry as a diagnostic for the culture-less screening of mastitis state from raw milk samples collected from regional dairies. Since sample preparation requires only minutes per sample using microvolumes of reagents and no cell culture, the technique is promising for rapid sample turnaround and low-cost diagnosis. Machine learning algorithms have been used to detect patterns embedded within MALDI-TOF spectra using a training set of 226 raw milk samples. A separate scoring set of 100 raw milk samples has been used to assess the specificity (spc) and sensitivity (sens) of the approach. Of machine learning models tested, the gradient-boosted tree model gave global optimal results, with the Youden index of J = 0.7, sens = 0.89, and spc = 0.81 achieved for the given set of conditions. Random forest models also performed well, achieving J > 0.63, with sens = 0.83 and spc = 0.81. Naïve Bayes, generalized linear, fast large-margin, and deep learning models failed to produce diagnostic results that were as favorable. We conclude that MALDI-TOF MS combined with machine learning is an alternative diagnostic tool for detection of high somatic cell count (SCC) and subclinical mastitis in dairy herds.

Current molecular approach for diagnosis of MRSA: a meta-narrative review

Introduction:

Detection and diagnosis of methicillin-resistant Staphylococcus aureus (MRSA) are important in ensuring a correct and effective treatment, further reducing its spread. A wide range of molecular approaches has been used for the diagnosis of antimicrobial resistance (AMR) in MRSA. This review aims to study and appraise widely used molecular diagnostic methods for detecting MRSA.

Methods:

This meta-narrative review was performed by searching PubMed using the following search terms: (molecular diagnosis) AND (antimicrobial resistance) AND (methicillin-resistant Staphylococcus aureus). Studies using molecular diagnostic techniques for the detection of MRSA were included, while non-English language, duplicates and non-article studies were excluded. After reviewing the libraries and a further manual search, 20 studies were included in this article. RAMESES publication standard for narrative reviews was used for this synthesis.

Results:

A total of 20 full papers were reviewed and appraised in this synthesis, consisting of PCR technique (n = 7), deoxyribonucleic acid (DNA) Microarray (n = 1), DNA sequencing (n = 2), Xpert MRSA/SA BC assay (n = 2), matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) (n = 2), MLST (n = 4), SCCmec typing (n = 1) and GENECUBE (n = 1).

Discussion:

Different diagnostic methods used to diagnose MRSA have been studied in this review. This study concludes that PCR has been extensively used due to its higher sensitivity and cost-effectiveness in the past five years

Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry in veterinary medicine: Recent advances (2019-present)

Matrix-assisted laser desorption ionization-time-of-flight (MALDI-TOF) mass spectrometry (MS) has become a valuable laboratory tool for rapid diagnostics, research, and exploration in veterinary medicine. While instrument acquisition costs are high for the technology, cost per sample is very low, the method requires minimal sample preparation, and analysis is easily conducted by end-users requiring minimal training. Matrix-assisted laser desorption ionization-time-of-flight MS has found widespread application for the rapid identification of microorganisms, diagnosis of dermatophytes and parasites, protein/lipid profiling, molecular diagnostics, and the technique demonstrates significant promise for 2D chemical mapping of tissue sections collected postmortem. In this review, an overview of the MALDI-TOF technique will be reported and manuscripts outlining current uses of the technology for veterinary science since 2019 will be summarized. The article concludes by discussing gaps in knowledge and areas of future growth.

Precision Medicine Approaches with Metabolomics and Artificial Intelligence

Recent technological innovations in the field of mass spectrometry have supported the use of metabolomics analysis for precision medicine. This growth has been allowed also by the application of algorithms to data analysis, including multivariate and machine learning methods, which are fundamental to managing large number of variables and samples. In the present review, we reported and discussed the application of artificial intelligence (AI) strategies for metabolomics data analysis. Particularly, we focused on widely used non-linear machine learning classifiers, such as ANN, random forest, and support vector machine (SVM) algorithms. A discussion of recent studies and research focused on disease classification, biomarker identification and early diagnosis is presented. Challenges in the implementation of metabolomics-AI systems, limitations thereof and recent tools were also discussed.