Anti-heat shock protein 70 levels in gingival crevicular fluid of Japanese patients with chronic periodontitis.
Anti-heat shock protein 70 levels in gingival crevicular fluid of Japanese patients with chronic periodontitis. J Oral Sci. 2020 Jun 04;: Authors: Takai H, Furuse N, Ogata Y Abstract Periodontitis is an inflammatory disease involving complex tripartite cross-interactions among bacterial, host and environment factors. Heat shock proteins (Hsps) are a protein family produced in response to stress conditions. Hsps protect cells under adverse circumstances such as infection, inflammation and disease. One of the causes of periodontal disease is thought to be an imbalance in the expression of Hsps and anti-Hsp antibodies. Hsps are classified according to their molecular weight, and one of the major ones is Hsp70. In the present study, enzyme-linked immunosorbent assay was used to measure the levels of anti-Hsp70 antibody in gingival crevicular fluid (GCF) from two gingival sulci in each of nine patients with chronic periodontitis (CP): one healthy control (HC) site with a probing pocket depth (PPD) of ≤3 mm and one CP site with a PPD of>5 mm. Anti-Hsp70 antibody levels in GCF were higher at HC sites than at CP sites. Moreover, the anti-Hsp70 antibody levels were found to increase after initial periodontal therapy at both HC and CP sites. These results suggest an association of anti-Hsp70 antibody with periodontitis. PMID: 32493865 [PubMed - as supplied by publisher]
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ConclusionsThe study suggests that EA-PM TCC cannot be diagnosed based on the classical indirect radiological signs of TCC, but can be identified by prominence of the posterior subtalar joint.
ConclusionFDG-PET/CT may steer the diagnosis (particularly thanks to a relatively high PPV and value of semiquantitative measurements), but cannot always classify vertebral bone lesions as malignant or benign with sufficient certainty. In these cases, biopsy and/or follow-up remain necessary to establish a final diagnosis.
ConclusionWe achieved promising results with this computer-aided diagnosis method that we tried to develop using convolutional neural networks based on transfer learning. This method can help clinicians for the diagnosis of hip osteoarthritis while interpreting plain pelvic radiographs, also provides assistance for a second objective interpretation. It may also reduce the need for advanced imaging methods in the diagnosis of hip osteoarthritis.
ConclusionThe shorthand bone age method and the automated algorithm produced values that are in agreement with the gold standard while reducing analysis time.