scholarly journals Subgingival Microbiota and Cytokines Profile Changes in Patients with Periodontitis: A Pilot Study Comparing Healthy and Diseased Sites in the Same Oral Cavities

2021 ◽  
Vol 9 (11) ◽  
pp. 2364
Author(s):  
Pauline Esparbès ◽  
Arnaud Legrand ◽  
Octave Nadile Bandiaky ◽  
Marjorie Chéraud-Carpentier ◽  
Hamida Martin ◽  
...  
Keyword(s):  
Pilot Study ◽  
Alveolar Bone ◽  
Interleukin 10 ◽  
Interleukin 4 ◽  
Tnf Alpha ◽  
Common Condition ◽  
Tissue Destruction ◽  
Interleukin 17A ◽  
Profile Changes ◽  
Subgingival Microbiota

Periodontitis is a common condition characterized by an exacerbated pro-inflammatory response, which leads to tissue destruction and, ultimately, alveolar bone loss. In this pilot study, we assess the microbiota composition and cytokine profile changes in patients with stage III/IV, grade B/C periodontitis, specifically by comparing healthy and diseased sites in the same oral cavity. Overall, we found that microbiota architecture was significantly disrupted between diseased and healthy sites, and that the clustering was driven, in part, by the increased relative abundances of Synergistetes in diseased sites, as well as the increased abundances of Firmicutes in healthy sites. We also observed that diseased sites were enriched in Synergistetes, TM7, SR1, Spirochaetes, Bacteroidetes and Fusobacteria, and depleted in Firmicutes, Proteobacteria, Tenericutes and Actinobacteria compared to healthy sites. We found that Interleukin-1b, Interleukin-4, Interleukin-10, and Interleukin-17A were significantly overexpressed in diseased sites, whereas Interleukin-6 and TNF-alpha do not differ significantly between healthy and diseased sites. Here, we observed concomitant changes in the subgingival plaque microbiota and cytokines profile, suggesting that this combined alteration could contribute to the pathobiology of periodontitis.

Differential biomarker signatures in unipolar and bipolar depression: A machine learning approach

2019 ◽  
Vol 54 (4) ◽  
pp. 393-401 ◽  
Author(s):  
Bianca Wollenhaupt-Aguiar ◽  
Diego Librenza-Garcia ◽  
Giovana Bristot ◽  
Laura Przybylski ◽  
Laura Stertz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Interleukin 6 ◽  
Bipolar Depression ◽  
Thiobarbituric Acid ◽  
Unipolar Depression ◽  
Interleukin 10 ◽  
Interleukin 4 ◽  
Healthy Controls ◽  
Interleukin 17A ◽  
Factor Α

Objective: This study used machine learning techniques combined with peripheral biomarker measurements to build signatures to help differentiating (1) patients with bipolar depression from patients with unipolar depression, and (2) patients with bipolar depression or unipolar depression from healthy controls. Methods: We assessed serum levels of interleukin-2, interleukin-4, interleukin-6, interleukin-10, tumor necrosis factor-α, interferon-γ, interleukin-17A, brain-derived neurotrophic factor, lipid peroxidation and oxidative protein damage in 54 outpatients with bipolar depression, 54 outpatients with unipolar depression and 54 healthy controls, matched by sex and age. Depressive symptoms were assessed using the Hamilton Depression Rating Scale. Variable selection was performed with recursive feature elimination with a linear support vector machine kernel, and the leave-one-out cross-validation method was used to test and validate our model. Results: Bipolar vs unipolar depression classification achieved an area under the receiver operating characteristics (ROC) curve (AUC) of 0.69, with 0.62 sensitivity and 0.66 specificity using three selected biomarkers (interleukin-4, thiobarbituric acid reactive substances and interleukin-10). For the comparison of bipolar depression vs healthy controls, the model retained five variables (interleukin-6, interleukin-4, thiobarbituric acid reactive substances, carbonyl and interleukin-17A), with an AUC of 0.70, 0.62 sensitivity and 0.7 specificity. Finally, unipolar depression vs healthy controls comparison retained seven variables (interleukin-6, Carbonyl, brain-derived neurotrophic factor, interleukin-10, interleukin-17A, interleukin-4 and tumor necrosis factor-α), with an AUC of 0.74, a sensitivity of 0.68 and 0.70 specificity. Conclusion: Our findings show the potential of machine learning models to aid in clinical practice, leading to more objective assessment. Future studies will examine the possibility of combining peripheral blood biomarker data with other biological data to develop more accurate signatures.

scholarly journals Molecular Mechanisms of Periodontal Disease

2021 ◽  
Vol 22 (2) ◽  
pp. 930
Author(s):  
Mikihito Kajiya ◽  
Hidemi Kurihara
Keyword(s):  
Infectious Diseases ◽  
Periodontal Disease ◽  
Molecular Mechanisms ◽  
Alveolar Bone ◽  
Tissue Destruction ◽  
Connective Tissues ◽  
Inflammatory Tissue

Periodontal disease, one of the most prevalent human infectious diseases, is characterized by chronic inflammatory tissue destruction of the alveolar bone and the connective tissues supporting the tooth [...]

Subcutaneous interleukin-10 plus corticosteroids in the treatment of severe alcoholic hepatitis. Results of a pilot study

2003 ◽  
Vol 38 ◽  
pp. 202
Author(s):  
J. Taieb ◽  
S. Chollet Martin ◽  
C. Delarche ◽  
M. Cohard ◽  
A. LeBeaut ◽  
...  
Keyword(s):  
Pilot Study ◽  
Alcoholic Hepatitis ◽  
Interleukin 10 ◽  
Severe Alcoholic Hepatitis

scholarly journals Interleukin 4 induces selective production of interleukin 6 from normal human B lymphocytes.

1989 ◽  
Vol 170 (4) ◽  
pp. 1463-1468 ◽  
Author(s):  
E B Smeland ◽  
H K Blomhoff ◽  
S Funderud ◽  
M R Shalaby ◽  
T Espevik
Keyword(s):  
B Cells ◽  
B Lymphocytes ◽  
Interleukin 6 ◽  
Specific Effect ◽  
Interleukin 4 ◽  
Tnf Alpha ◽  
Ifn Gamma ◽  
Normal Human ◽  
Tnf Secretion

In this paper we have shown that extensively purified human B lymphocytes respond to IL-4 treatment with a marked production of IL-6. Addition of anti-mu potentiated the effect of IL-4 on IL-6 production. Other cytokines tested like TNF-alpha and-beta, IFN-gamma, IL-1, IL-2, and IL-5 did not induce IL-6 secretion when given to resting B cells. Although B cells generally also produced TNF-alpha and TNF-beta upon stimulation, IL-4 did not induce TNF secretion and seemingly had a specific effect on IL-6 production.

scholarly journals Regulation of the Anti-Inflammatory Cytokines Interleukin-4 and Interleukin-10 during Pregnancy

2014 ◽  
Vol 5 ◽  
Author(s):  
Piyali Chatterjee ◽  
Valorie L. Chiasson ◽  
Kelsey R. Bounds ◽  
Brett M. Mitchell
Keyword(s):  
Inflammatory Cytokines ◽  
Interleukin 10 ◽  
Interleukin 4 ◽  
Anti Inflammatory

Personalized mathematical model of endotoxin-induced inflammatory responses in young men and associated changes in heart rate variability

2018 ◽  
Vol 13 (5) ◽  
pp. 42 ◽  
Author(s):  
R. Brady ◽  
D.O. Frank-Ito ◽  
H.T. Tran ◽  
S. Janum ◽  
K. Møller ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Inflammatory Responses ◽  
Interleukin 10 ◽  
Tnf Alpha ◽  
Non Invasive ◽  
Estimated Parameters ◽  
Subject Specific ◽  
The Subject ◽  
Necrosis Factor

The objective of this study was to develop a personalized inflammatory model and estimate subject-specific parameters that could be related to changes in heart rate variability (HRV), a measure that can be obtained non-invasively in real time. An inflammatory model was developed and calibrated to measurements of interleukin-6 (IL-6), tumor necrosis factor (TNF-alpha), interleukin-8 (IL-8) and interleukin-10 (IL-10) over 8 hours in 20 subjects administered a low dose of lipopolysaccharide. For this model, we estimated 11 subject-specific parameters for all 20 subjects. Estimated parameters were correlated with changes in HRV, computed from ECG measurements using a built-in HRV module available in Labchart. Results revealed that patients could be separated into two groups expressing normal and abnormal responses to endotoxin. Abnormal responders exhibited increased HRV, most likely as a result of increased vagal firing. The observed correlation between the inflammatory response and HRV brings us a step further towards understanding if HRV predictions can be used as a marker for inflammation. Analyzing HRV parameters provides an easy, non-invasively obtained measure that can be used to assess the state of the subject, potentially translating to identifying a non-invasive marker that can be used to detect the onset of sepsis.

scholarly journals The Impact of Oxidative Stress on Dental Implants

2021 ◽  
Vol 2 (1) ◽  
pp. 1-8
Author(s):  
César Esquivel-Chirino ◽  
Juan Carlos Gómez-Landeros ◽  
Erika Patricia Carabantes-Campos ◽  
Daniela Carmona-Ruiz ◽  
Yolanda Valero-Princet ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Dental Implants ◽  
Alveolar Bone ◽  
Inflammatory Responses ◽  
Cell Damage ◽  
Pathological Condition ◽  
Periodontal Diseases ◽  
Tissue Destruction ◽  
Waste Products ◽  
The Impact

Periodontal disease is an inflammatory condition that alters the periodontium, resulting in destruction of the alveolar bone; without treatment the condition may lead to tooth loss. Dental implants are an alternative for substitution of naturally lost teeth as they have high success rates; however, some factors are related to its failure. Peri-implantitis (PI) is a pathological condition that affects the tissues surrounding dental implants and has been reported as the major cause of implant failure; PI and periodontal diseases are characterized by tissue inflammation and bone damage. In homeostasis conditions, reactive oxygen species (ROS) have been shown to be involved in cell maintenance, signal transduction, and repair of all tissues, but ROS overaccumulation leads to oxidative stress, which generates cell damage and tissue destruction; likewise, antioxidants protect against the destructive effects of ROS by turning free radicals into waste products. The main purpose of this review was to determine some aspects of inflammatory responses and oxidative stress and analyze their relationship with the lack of osseointegration and PI.

Sign in / Sign up

Export Citation Format

Share Document