Results 141 to 150 of about 47,748 (293)

Gingival Crevicular Fluid Peptidome Profiling in Healthy and in Periodontal Diseases [PDF]

open access: gold, 2020
Mariaimmacolata Preianò   +4 more
openalex   +1 more source

Stromal cell‐derived factor‐1 regulates expression of vascular endothelial growth factor and osteopontin after tooth extraction

open access: yesJournal of Periodontology, EarlyView.
Abstract Background The present study aimed to investigate the expression of stromal cell‐derived factor‐1 (SDF‐1) after tooth extraction in rats and its regulatory effect on the expression of osteopontin (OPN) and vascular endothelial growth factor (VEGF).
Jingjing Kong   +5 more
wiley   +1 more source

Transcriptome analysis of granulation tissue from periodontal osseous defects

open access: yesJournal of Periodontology, EarlyView.
Abstract Background Granulation tissue is routinely discarded in periodontal surgical procedures, but few studies have characterized it. The present study aimed to compare global gene expression in granulation tissue derived from different types of periodontal osseous defects.
Ye Han Sam   +5 more
wiley   +1 more source

Dose‐dependent association of systemic comorbidities with periodontitis severity: A large population cross‐sectional study

open access: yesJournal of Periodontology, EarlyView.
Abstract Background To examine whether the associations between periodontitis and multiple systemic conditions increase with increasing severity of periodontitis using a multi‐center electronic health record (EHR) repository. Methods A cross‐sectional analysis was conducted using EHR data from 9 dental schools in the United States.
Muhammad H. A. Saleh, Hamoun Sabri
wiley   +1 more source

Diversity and random forest models of oral microbiomes in periodontal health using publicly available data

open access: yesJournal of Periodontology, EarlyView.
Abstract Background Evidence on the 16S metabarcoding of supragingival, subgingival, and salivary microbiomes in periodontal health remains limited. We aimed to analyze the diversity and potential of machine‐learning models of supragingival, subgingival, and salivary microbiomes in periodontal health. Methods A total of 848 samples (supragingival = 210;
Alba Regueira‐Iglesias   +6 more
wiley   +1 more source

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