Pathogen identification with laser-induced breakdown spectroscopy: the effect of bacterial and biofluid specimen contamination
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Notice bibliographique
Résumé
In this paper, the potential use of laser-induced breakdown spectroscopy (LIBS) for the rapid discrimination and identification of bacterial pathogens in realistic clinical specimens is investigated. Specifically, the common problem of sample contamination was studied by creating mixed samples to investigate the effect that the presence of a second contaminant bacterium in the specimen had on the LIBS-based identification of the primary pathogen. Two closely related bacterial specimens, Escherichia coli strain ATCC 25922 and Enterobacter cloacae strain ATCC 13047, were mixed together in mixing fractions of 10:1, 100:1, and 1000:1. LIBS spectra from the three mixtures were reliably classified as the correct E. coli strain with 98.5% accuracy when all the mixtures were withheld from the training model and classified against spectra from pure specimens. To simulate a rapid test for the presence of urinary tract infection pathogens, LIBS spectra were obtained from specimens of Staphylococcus epidermidis obtained from distilled water and sterile urine. LIBS spectra from the urine-harvested bacteria were classified as S. epidermidis with 100% accuracy when classified using a model containing only spectra from other Staphylococci species and with 88.5% accuracy when a model containing five genera of bacteria was utilized. Bacterial specimens comprising five different genera and 13 classifiable taxonomic groups of species and strains were compiled in a library that was tested using external validation techniques. The importance of utilizing external validation techniques where the library is tested with data withheld from all previous testing and training of the model was revealed by comparing the results against "leave-one-out" cross-validation results. Last, the effect of using sequential models for the classification of a single unknown spectrum was investigated by comparing the misclassification of two closely related bacteria, E. coli and E. cloacae, when the classification was first performed using the five-genus bacterial library and then with a smaller model consisting only of E. coli and E. cloacae specimens. This result shows the utility of using successively more targeted analyses and models that use preliminary classifications from more general models as input.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle