Discrimination of Human Pathogen Clostridium Species Especially of the Heterogeneous C. sporogenes and C. botulinum by MALDI-TOF Mass Spectrometry

Schaumann, Reiner; Dallacker-Losensky, Kevin; Rosenkranz, Christiane; Genzel, Gelimer H.; Stîngu, Cătălina Suzana; Schellenberger, Wolfgang; Schulz-Stübner, Sebastian; Rodloff, Arne C.; Eschrich, Klaus

doi:10.1007/s00284-018-1552-7

Cited by 15 publications

(10 citation statements)

References 45 publications

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“…botulinum 'training database') [61]. The same method was even able to differentiate the isolates belonging to various metabolic groups of C. botulinum [61].…”

Section: Anaerobic Bacteriamentioning

confidence: 89%

“…As an example, Clostridium sporogenes and Clostridium botulinum, usually important but difficult to differentiate, can be correctly identified to species level by mass spectrum analysis in combination with methods of multivariate statistical analysis (developing the C. sporogenes/C. botulinum 'training database') [61]. The same method was even able to differentiate the isolates belonging to various metabolic groups of C. botulinum [61].…”

Section: Anaerobic Bacteriamentioning

confidence: 99%

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How MALDI-TOF mass spectrometry can aid the diagnosis of hard-to-identify pathogenic bacteria – the rare and the unknown

Kostrzewa

Nagy

Schröttner

et al. 2019

Expert Review of Molecular Diagnostics

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Introduction: Ten years after its introduction into clinical microbiology, MALDI-TOF mass spectrometry has become the standard routine identification tool for bacteria in most laboratories. The technology has accelerated analyses and improved the quality of results. The greatest significance has been observed for bacteria that were challenging to be identified by traditional methods. Areas covered: We searched in existing literature (Pubmed) for reports how MALDI-TOF MS has contributed to identification of rare and unknown bacteria from different groups. We describe how this has improved the diagnostics in different groups of bacteria. Reference patterns for strains which yet cannot be assigned to a known species even enable the search for related bacteria in studies as well as in routine diagnostics. MALDI-TOF MS can help to discover and investigate new species and their clinical relevance. It is a powerful tool in the elucidation of the bacterial composition of complex microbiota in culturomics studies. Expert opinion: MALDI-TOF MS has improved the diagnosis of bacterial infections. It also enables knowledge generation for prospective diagnostics. The term 'hard-to-identify' might only be rarely attributed to bacteria in the future. Novel applications are being developed, e.g. subspecies differentiation, typing, and antibiotic resistance testing which may further contribute to improved microbial diagnostics.

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“…botulinum 'training database') [61]. The same method was even able to differentiate the isolates belonging to various metabolic groups of C. botulinum [61].…”

Section: Anaerobic Bacteriamentioning

confidence: 89%

Section: Anaerobic Bacteriamentioning

confidence: 99%

How MALDI-TOF mass spectrometry can aid the diagnosis of hard-to-identify pathogenic bacteria – the rare and the unknown

Kostrzewa

Nagy

Schröttner

et al. 2019

Expert Review of Molecular Diagnostics

View full text Add to dashboard Cite

show abstract

“…While this approach is common practice in the routine clinical microbiology lab, a lot of useful information remains, unfortunately, unexploited [89]. Therefore, several research groups have been applying ML algorithms to fully exploit the valuable information contained in MALDI-TOF MS spectra [89][90][91][92][93][94][95][96]. As an example, ML models (e.g.…”

Section: Detection and Identification Of Microorganismsmentioning

confidence: 99%

“…As an example, ML models (e.g. SVM, RF, and ANN) have been recently developed to allow rapid classification of group B Streptococcus serotypes [90], to differentiate between Escherichia coli and Shigella species [91], to strain type Staphylococcus haemolyticus [93], to discriminate between Clostridium species [94], and to differentiate between different Klebsiella species [95]. Furthermore, Desaire and Hua [92] adapted an existing ML tool, originally developed for classifying glycomics and glycoproteomics data, to accurately distinguish between highly similar bacteria analyzed by MALDI-TOF MS.…”

Section: Detection and Identification Of Microorganismsmentioning

confidence: 99%

Recent evolutions of machine learning applications in clinical laboratory medicine

Bruyne

Speeckaert

Biesen

et al. 2020

Critical Reviews in Clinical Laboratory Sciences

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Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

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“…A multiplex PCR-technológia alkalmas a toxinok génjeinek egyidejű vizsgálatára [43]. Biztatónak tűnik a MALDI-TOF MS, amely lehetővé teszi az eltérő toxintermelő törzsek azonosítását [44].…”

Section: öSszefoglaló Közleményunclassified

Élelmiszer eredetű bakteriális megbetegedések patogenezise, klinikai jellegzetességei, diagnosztikája és kezelése

Müller

Rozgonyi

2020

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Összefoglaló. Az élelmiszer-eredetű megbetegedések igen gyakoriak, bár pontos adatok nem állnak rendelkezésre, mivel az enyhe, gyorsan múló gastrointestinalis tünetekkel a betegek nem fordulnak orvoshoz, vagy nem történik diagnosztikus vizsgálat. Az amerikai Járványügyi és Betegségmegelőzési Központ (CDC) adatai szerint az USA-ban évente 6 lakosból 1 esik át élelmiszer okozta tüneteken. Az ételintoxikációk során a baktérium által termelt toxinok okozzák a tüneteket, közülük a leggyakoribb a Clostridium perfringens, a Staphylococcus aureus és a Bacillus cereus okozta, élelmiszer-eredetű intoxikáció. A nem megfelelően tárolt vagy hőkezelt élelmiszerekben – beleértve a S. aureus által szennyezett anyatejet – ezen baktériumok életképesek maradnak, elszaporodnak, és toxint termelhetnek, illetve toxinjaik megőrzik megbetegítőképességüket. Az étel elfogyasztása után 3–12 órával hányást, hasmenést okoznak. A tünetek többnyire 24 órán belül megszűnnek. A Clostridium botulinum súlyos neurológiai tünetei miatt emelkedik ki a többi toxikoinfekció sorából. C. botulinum okozta tünetekre felnőtteknél házi készítésű konzervek és húskészítmények elfogyasztása után jelentkező gastrointestinalis vagy neurológiai tünetek esetén kell gondolnunk. A Clostridioides difficile szintén a toxinjai révén okoz súlyos, életveszélyes megbetegedést, továbbá az esetek 20–30%-ában számolnunk kell az infekció relapsusával. Növekvő gyakorisága miatt ismernünk érdemes a laboratóriumi és klinikai diagnosztika részleteit és a legmodernebb kezelési lehetőségeket, úgymint megfelelő mintavétel, mintatárolás és -szállítás, tenyésztés, toxinkimutatás, helyes tüneti kezelés, antibiotikumkombinációk, széklettranszplantáció és monoklonálisantitest-kezelés. Orv Hetil. 2020; 161(48): 2019–2028. Summary. Foodborne diseases are quite common, however, accurate data are not available because patients do not visit doctors with mild, rapidly resolving symptoms and diagnostic tests are not performed. The Centers of Disease Control and Prevention (CDC) estimates that, in the USA, 1 in 6 citizens gets food poisoning yearly. Symptoms of intoxication are due to the toxins produced by bacteria, mostly by Clostridium perfringens, Staphylococcus aureus and Bacillus cereus. These bacteria can survive in not properly stored or heated food, including S. aureus contaminated breastmilk. They can multiply and produce toxins causing intoxications. The gastrointestinal symptoms start 3–12 hours after consumption of the contaminated food and resolve in 24 hours. Clostridium botulinum causes severe neurological symptoms that should be suspected after consumption of home-made cans, smoked hams and sausages. The disease caused by Clostridioides difficile is not a foodborne one, but C. difficile causes severe infection via its toxins. Another problem is that C. difficile infection recurs in 20–30% of cases. Due to the increasing incidence of foodborne diseases, it is worth to learn the precise clinical and laboratory diagnostic algorithms including sampling, storage and transportation of samples, cultivation of bacteria and differential diagnosis of these diseases, furthermore the most up-to-date symptomatic and causative treatment options like antibiotic combinations, stool transplantation and monoclonal antibodies. Orv Hetil. 2020; 161(48): 2019–2028.

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Discrimination of Human Pathogen Clostridium Species Especially of the Heterogeneous C. sporogenes and C. botulinum by MALDI-TOF Mass Spectrometry

Cited by 15 publications

References 45 publications

How MALDI-TOF mass spectrometry can aid the diagnosis of hard-to-identify pathogenic bacteria – the rare and the unknown

How MALDI-TOF mass spectrometry can aid the diagnosis of hard-to-identify pathogenic bacteria – the rare and the unknown

Recent evolutions of machine learning applications in clinical laboratory medicine

Élelmiszer eredetű bakteriális megbetegedések patogenezise, klinikai jellegzetességei, diagnosztikája és kezelése

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