Giliberti, Renato (2022) Oral plaque microbiome resilience under external dietary stimuli. [Tesi di dottorato]
Anteprima |
Testo
giliberti_renato_34.pdf Download (10MB) | Anteprima |
| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Oral plaque microbiome resilience under external dietary stimuli |
| Autori: | Autore Email Giliberti, Renato renato.gilberti@unina.it |
| Data: | 12 Dicembre 2022 |
| Numero di pagine: | 143 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Agraria |
| Dottorato: | Food Science |
| Ciclo di dottorato: | 34 |
| Coordinatore del Corso di dottorato: | nome email Barone, Amalia ambarone@unina.it |
| Tutor: | nome email Pasolli, Edoardo [non definito] Bolzan, Mattia [non definito] Georg, Zeller [non definito] |
| Data: | 12 Dicembre 2022 |
| Numero di pagine: | 143 |
| Parole chiave: | food science; oral microbiome; metagenomics; diet; peri-implantitis; oral diseases |
| Settori scientifico-disciplinari del MIUR: | Area 07 - Scienze agrarie e veterinarie > AGR/15 - Scienze e tecnologie alimentari Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica Area 01 - Scienze matematiche e informatiche > MAT/06 - Probabilità e statistica matematica |
| Depositato il: | 19 Gen 2023 14:08 |
| Ultima modifica: | 28 Feb 2024 11:11 |
| URI: | http://www.fedoa.unina.it/id/eprint/14355 |
Abstract
Current knowledge allows us to predict host phenotype based on the microbiome composition. Last studies have demonstrated how we can predict disease state starting from oral microbiome composition. Oral microbiome characterization has been made mainly using 16S sequencing techniques. Dietary habits influence on oral microbiome composition has been defined marginally and with obsolete techniques. The objective of this research project is to fulfil the lack in terms of dietary habits influence on oral microbiome using the latest sequencing technologies and using statistical, computational and machine learning approaches. The study was conducted on a cohort composed of 451 subjects with dental implants. Results showed that Random forest algorithm is the classifier able to maximize classification performances. We show weak correlations between dietary habits and implant disease state while multiple correlations between dietary habits and microbial species results as statistical significant more specifically between carbohydrates sources and some potentially pathogenic species. The extension of the analysis to metabolic potential allow us to identify the correlations between carbohydrates and fat sources consumption and different metabolic pathways involved in different pathogenic processes.
Downloads
Downloads per month over past year
Actions (login required)
 |
Modifica documento |
