Induction and elimination of viable but non-culturable Campylobacter jejuni in agri-food systems
Notice bibliographique
Résumé
Numerous bacterial species possess the ability to enter a state known as viable but non-culturable (VBNC) in response to adverse environmental conditions.Although pathogenic bacteria are unlikely to cause diseases in the VBNC state, they can regain virulence upon resuscitation under favorable conditions, thereby posing a substantial threat to food safety and public health.Moreover, the inability to detect VBNC bacteria using conventional microbiological culture-based methods further accentuates the risk.Therefore, it is imperative to inactivate pathogenic bacteria in the VBNC state before they regain culturability.Due to the high antimicrobial tolerance exhibited by VBNC bacterial pathogens, current inactivation methods may be ineffective, necessitating the development of novel strategies to control these pathogens in the VBNC state.Campylobacter jejuni is a leading cause of human gastroenteritis worldwide and is commonly found in both food production and environmental settings.However, the mechanisms by which this microaerophilic microbe survives in the aerobic environment and disseminates throughout the food supply chain remain incompletely understood.The factors involved in inducing C. jejuni to enter the VBNC state during food processing and the persistence of VBNC C. jejuni to antimicrobials are largely unknown and require further investigation.This dissertation aims to address the aforementioned knowledge gaps and enhance our understanding of the persistence of C. jejuni in the agri-food system, with the ultimate goal of developing more effective intervention strategies.C. jejuni encounters various stressors that can trigger the induction of the VBNC state during food processing and within food products.Notably, when exposed to chlorine, C. jejuni completely lost VI its ability to form colonies, but a fraction of the bacteria (1-10%) maintained their viability.In contrast, under aerobic and low temperatures conditions, ~10% of C. jejuni entered the VBNC state after 24 h and 20 days, respectively.Furthermore, the strain C. jejuni ATCC 33560 showed a higher propensity for entering the VBNC state in both refrigerated UHT and pasteurized milk compared to other strains such as F38011, NCTC 11168, and 81116.These observations of heterogeneous behavior among different strains suggest the existence of strain-specific variations in response to stressors.By conducting the time-kill assay, notable variations were observed in the response of VBNC C. jejuni towards plant-based antimicrobials and metal oxide nanoparticles (NPs).Specifically, the bacterium exhibited a remarkable persistence against carvacrol or diallyl sulfide, whereas it displayed susceptibility to aluminum oxide NPs.Moreover, the interactions among these antimicrobials were thoroughly investigated.The combination of carvacrol and diallyl sulfide resulted in an additive antimicrobial effect.Furthermore, synergistic effects were observed when either carvacrol or diallyl sulfide was combined with Al2O3 NPs.Remarkably, the ternary combination of carvacrol, diallyl sulfide, and Al2O3 NPs demonstrated a synergistic effect, enabling reduced concentrations of these antimicrobials to achieve effective inactivation.Subsequently, these antimicrobial treatments were employed to inactivate VBNC C. jejuni under poultry processing conditions.However, relatively lower effectiveness was observed, possibly attributed to the presence of lipids and proteins in chicken juice.These components have the potential to interact with antimicrobials, resulting in reduced antimicrobial availability and compromised efficacy.The interactions among the antimicrobials were first assessed using the time-kill assay, revealing additive effects in all combinations tested.To ensure accurate assessment without overestimation or underestimation, a novel mathematical model was developed to further investigate these antimicrobial interactions.The application of this computational approach
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».