The purpose of this study was to determine whether (a) an uptake system for gamma-aminobutyric acid (GABA) exists in human dental pulp, (b) GABA can be released from nerves in this tissue, and (c) GABA(B) autoreceptors modulate release of this transmitter. Segments of vital pulp were incubated in [(3)H]GABA (0.1-10 microM) for up to 120 min, washed, and the retained [(3)H] extracted and assayed. Some tissues were treated with GABA uptake inhibitors (nipecotic acid or NO-711) prior to incubation. At concentrations of 0.1 and 1.0 microM the uptake of [(3)H]GABA was saturated after 90 min of incubation. At 10 microM, at least two uptake compartments were apparent, and the amount of [(3)H]GABA retained was five-fold greater than 0.1 microM. The uptake inhibitors reduced [(3)H]GABA accumulation by more than 80%. In the release study, pulp was incubated in [(3)H]GABA (0.5 microM) for 90 min, and superfused with Krebs solution containing NO-711 (5 microM). Electrical stimulation increased the overflow of [(3)H]; a GABA(B) autoreceptor agonist (baclofen) inhibited, whilst an antagonist, Sch 50911, enhanced this release. The effects of baclofen were reversed by Sch 50911. These results imply that GABA can be taken up and bound firmly in compartments within human dental pulp, GABA can be released from isolated pulp segments by electrical stimulation, and this release is modulated by GABA(B) autoreceptors.
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http://dx.doi.org/10.1016/j.archoralbio.2006.12.005 | DOI Listing |
Clin Oral Investig
January 2025
Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
Objectives: To evaluate recent advances in the automatic multimodal registration of cone-beam computed tomography (CBCT) and intraoral scans (IOS) and their clinical significance in dentistry.
Methods: A comprehensive literature search was conducted in October 2024 across the PubMed, Web of Science, and IEEE Xplore databases, including studies that were published in the past decade. The inclusion criteria were as follows: English-language studies, randomized and nonrandomized controlled trials, cohort studies, case-control studies, cross-sectional studies, and retrospective studies.
Curr Med Chem
January 2025
Council for Nutritional and Environmental Medicine (CONEM), Mo i Rana, Norway.
Mercury is a pervasive global pollutant, with primary anthropogenic sources including mining, industrial processes, and mercury-containing products such as dental amalgams. These sources release mercury into the environment, where it accumulates in ecosystems and enters the food chain, notably through bioamplification in marine life, posing a risk to human health. Dental amalgams, widely used for over a century, serve as a significant endogenous source of inorganic mercury.
View Article and Find Full Text PDFFront Public Health
January 2025
Party Committee Office, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China.
Background: This study aimed to investigate and analyze the current status of oral disease treatment among the older adult in Guangxi Zhuang Autonomous Region, while also assessing the continuing medical education (CME) needs of dental institution personnel regarding oral diseases in this population.
Methods: Convenience sampling was used to investigate the oral disease treatment among older adults and to assess CME needs of dental institution personnel regarding oral diseases in this population across various oral medical and health institutions in Guangxi.
Results: A total of 754 valid questionnaires were collected, of which 70.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFCureus
December 2024
Faculty of Dentistry, Pharos University, Alexandria, EGY.
Background Odontogenic maxillary sinusitis arises mainly from dental origins, emphasizing the connection between dental health and sinus issues. Understanding these relationships is crucial for implant planning, sinus augmentation procedures, and managing post-extraction complications. This knowledge can help clinicians make informed decisions about treatment timing and approach.
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