<abstract xmlns="http://www.w3.org/1999/xhtml">

In this paper, the Logistic regression model and χ2 automatic interaction test were used to study the distribution, number, and correlation of common pathogenic bacteria of periodontal origin with combined periodontal and endodontic lesions by sample training, extracting variable characteristics, and then these variables were modeled as characteristic variables of the regression model to test whether the independent variables were significantly correlated with the dependent variables. It is also necessary to perform χ2 automatic interaction test to compare with the normal distribution, and the results are merged into one group if they are the same and not incorporated into one group if they are different. After the merging was completed, the splitting was done. The study group had a detection rate of pathogenic bacteria of 50.70%, while the control group had a detection rate of 12.24%, according to the results. Campylobacter spp. was the main bacteria that infected periodontal pockets in the study group. And Clostridium spp. The root canal specimens were mainly from Corynebacterium spp. and Actinobacillus spp. There was a significant difference between the pathogenic species and the control group P&lt;0.05. Campylobacter spp. was also a risk factor for co-morbidities (P=0.031). Intra-root canal tissue with its subgingival plaque pathogenic bacteria <italic>Ef</italic>, <italic>Pe</italic> (Ρ&lt;0.05), <italic>Pg</italic>, <italic>Td</italic>, <italic>Tf</italic> (Ρ&lt;0.01) played a more important role in the development of pulpitis. Before and after clinical treatment, Pgi showed the most significant decrease in bacterial counts (t=2.759, P=0.022).
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In this paper, the Logistic regression model and χ2 automatic interaction test were used to study the distribution, number, and correlation of common pathogenic bacteria of periodontal origin with combined periodontal and endodontic lesions by sample training, extracting variable characteristics, and then these variables were modeled as characteristic variables of the regression model to test whether the independent variables were significantly correlated with the dependent variables. It is also necessary to perform χ2 automatic interaction test to compare with the normal distribution, and the results are merged into one group if they are the same and not incorporated into one group if they are different. After the merging was completed, the splitting was done. The study group had a detection rate of pathogenic bacteria of 50.70%, while the control group had a detection rate of 12.24%, according to the results. Campylobacter spp. was the main bacteria that infected periodontal pockets in the study group. And Clostridium spp. The root canal specimens were mainly from Corynebacterium spp. and Actinobacillus spp. There was a significant difference between the pathogenic species and the control group P<0.05. Campylobacter spp. was also a risk factor for co-morbidities (P=0.031). Intra-root canal tissue with its subgingival plaque pathogenic bacteria Ef, Pe (Ρ<0.05), Pg, Td, Tf (Ρ<0.01) played a more important role in the development of pulpitis. Before and after clinical treatment, Pgi showed the most significant decrease in bacterial counts (t=2.759, P=0.022).

Distribution and clinical characterization of pathogenic bacteria in combined periodontal and endodontic lesions of periodontal origin by digital technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

In this paper, the Logistic regression model and χ2 automatic interaction test were used to study the distribution, number, and correlation of common...

Distribution and clinical characterization of pathogenic bacteria in combined periodontal and endodontic lesions of periodontal origin by digital technology

Publicado en línea: 30 may 2024

Páginas: -

Recibido: 28 feb 2024

Aceptado: 01 may 2024

DOI: https://doi.org/10.2478/amns-2024-1258

Palabras clave
Logistic regression model, χ auto-interaction, Dependent variable, Pathogenic bacteria

© 2024 Zhixing Jing, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Distribution and clinical characterization of pathogenic bacteria in combined periodontal and endodontic lesions of periodontal origin by digital technology

Publicado en línea: 30 may 2024

Páginas: -

Recibido: 28 feb 2024

Aceptado: 01 may 2024

DOI: https://doi.org/10.2478/amns-2024-1258

Palabras claveLogistic regression model, χ auto-interaction, Dependent variable, Pathogenic bacteria

© 2024 Zhixing Jing, et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Logistic regression model, χ auto-interaction, Dependent variable, Pathogenic bacteria