This investigation provides molecular analyses of the periodontal microbiota in health and disease. Subgingival samples from 47 volunteers with healthy gingivae or clinically diagnosed chronic periodontitis were characterized by PCR-denaturing gradient gel electrophoresis (DGGE) with primers specific for the V2-V3 region of the eubacterial 16S rRNA gene. A hierarchical dendrogram was constructed from band patterns. All unique PCR amplicons (DGGE bands) were sequenced for identity. Samples were also analyzed for the presence of Actinobacillus actinomycetemcomitans, Porphyromonas gingivalis, and Tannerella forsythensis by multiplex PCR. Associations of patient age, gender, and smoking status together with the presence of each unique band and putative periodontal pathogens with disease were assessed by logistic regression. Periodontal pockets were colonized by complex eubacterial communities (10 to 40 distinct DGGE bands) with substantial individual variation in the community profile. Species diversity in health and disease was determined by the ShannonWeaver index of diversity and compared by the Mann-Whitney U test. Sequence analyses of DGGE amplicons indicated the occurrence of many nontypical oral species and eubacteria previously associated with this environment. With the exception of T. forsythensis, the putative pathogens were not detected by DGGE. Multiplex PCR, however, detected T. forsythensis, A. actinomycetemcomitans, and P. gingivalis in 9% 16%, and 29% of the patients with disease, respectively. The presence of A. actinomycetemcomitans was significantly associated with disease (P < 0.01). Statistical analyses indicated that the presence of Treponema socranskii and Pseudomonas sp. was a significant predictor of disease (P < 0.05) and that there was no significant difference (P > 0.05) in terms of eubacterial species diversity between health and disease.Periodontitis is a generic term relating to inflammation of the tissues supporting the teeth but is widely attributed to succession by polymicrobial communities (36,58,74). The etiology of the condition is further complicated by the presence of a complex resident subgingival microbiota that underlies both periodontal health and disease (22,45). Periodontitis is often self-limiting; invasion of bacteria beyond the gingival tissue is rare (32). No single etiologic agent has been identified; rather, specific groups and combinations of bacteria including Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythensis have been strongly associated with pathology (11, 32, 58). Emerging research now implicates both host genetic and immunological factors as being important in disease susceptibility (9,11,23,24), further demonstrating the complex nature of this condition.Plaque accumulates in the mouth at sites such as the gingival margin, where shear forces are low (36). Chronic bacterial colonization of this site, often in the absence of effective oral hygiene, leads to inflammation of the adjacent gingival tissue, termed gingivitis. Chronic gingivit...

There are several methods available for the replacement of missing anterior teeth. These include a removable prosthesis, a conventional fixed bridge, an adhesive bridge or a dental implant. The choice of the most suitable technique depends on several factors, such as the condition of the adjacent teeth, the occlusion, the patient's wishes and the financial implications of the proposed treatment.

Extraction of the wrong tooth or teeth is a serious and avoidable clinical error causing harm to the patient. All NHS Trusts in England are required to use a surgical safety checklist in operating theatres to prevent incorrect site surgery and ensure safe management of patients. However, the majority of patients have dental extractions and other oral surgical procedures undertaken on an outpatient basis and these patients are also at risk of having an incorrect site surgical procedure such as a wrong tooth extraction. We describe our experience in developing, introducing and refining a surgical safety checklist for outpatient oral surgery along with the key strategic actions needed to ensure effective cultural change and optimum patient safety in the outpatient setting.

The purpose of this clinical article is to emphasise that root perforations can occur both during and after endodontic treatment. These reduce the chance of a successful treatment outcome and can jeopardise the survival of the tooth. The aetiology and diagnosis of root perforations are described. The article also focusses on the non-surgical and surgical management of root perforations and describes how selection of the appropriate treatment depends on an accurate diagnosis.

Paediatric dentistry is not my usual field of work. I am now based almost entirely in restorative dentistry and it is five years since I worked in the dental department of a children's hospital. An essay on teething would appear to be an unusual choice of topic. With the current professional climate of 'general professional education' and 'lifelong learning' I can easily justify my time and effort studying a subject somewhat removed from my regular work. However, to be completely honest, I have reached that age when many of my friends, relatives and colleagues are enjoying the sleepless nights that accompany expanding families. Add to this the fact that I have recently married into a family of midwives, health visitors, nurses and new mothers. I was not sure that I was giving the best, most up to date advice when asked about teething. So some reading around was required. If only it were that simple. I now feel equipped to give a little more help than simply saying, "It's only teething..."

Introduction Reversal of enamel-only proximal caries by non-invasive treatments is important in preventive dentistry. However, detecting such caries using bitewing radiography is difficult and the subtle patterns are often missed by dental practitioners.Aims To investigate whether the ability of dentists to detect enamel-only proximal caries is enhanced by the use of AssistDent artificial intelligence (AI) software.Materials and methods In the ADEPT (AssistDent Enamel-only Proximal caries assessmenT) study, 23 dentists were randomly divided into a control arm, without AI assistance, and an experimental arm, in which AI assistance provided on-screen prompts indicating potential enamel-only proximal caries. All participants analysed a set of 24 bitewings in which an expert panel had previously identified 65 enamel-only carious lesions and 241 healthy proximal surfaces.Results The control group found 44.3% of the caries, whereas the experimental group found 75.8%. The experimental group incorrectly identified caries in 14.6% of the healthy surfaces compared to 3.7% in the control group. The increase in sensitivity of 71% and decrease in specificity of 11% are statistically significant (p <0.01).Conclusions AssistDent AI software significantly improves dentists' ability to detect enamel-only proximal caries and could be considered as a tool to support preventive dentistry in general practice.

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Enamel-only proximal caries, if detected, can be reversed by non-invasive treatments. Dental bitewing radiograph analysis is central to diagnosis and treatment planning and when used to detect enamel-only proximal caries it is an important tool in minimum intervention and preventive dentistry. However, the subtle patterns of enamel-only proximal caries visible in a bitewing radiographs are difficult to detect and often missed by dental practitioners. This pilot study measures the ability of a cohort of third-year dental students to detect enamel-only proximal caries in bitewing radiographs with and without the use of a deep learning assistive software AssistDent®. We demonstrate an increased ability in the detection of enamel-only proximal caries by the students using AssistDent, showing a mean sensitivity level of 0.80 (95%CI ± 0.04), increased from 0.50 (95%CI ± 0.13) p<0.01 shown by students not using AssistDent. This improvement in ability was achieved without an increase in false positives. Mean false positives per bitewing radiograph recorded by students when using AssistDent was 2.64 (95%CI ± 0.57), and by students without using AssistDent was 2.46 (95%CI ± 1.51). Based on these results we conclude that the AI-based software AssistDent significantly improves third-year dental students' ability to detect enamel-only proximal caries and could be considered as a tool to support minimum intervention and preventive dentistry in teaching hospitals and general practice. We also discuss how the experience of conducting this pilot study can be used to inform the design and methodology of a follow-on study of AssistDent in dental practice use.