Denture induced stomatitis, patient and denture related factors

Purpose: Denture hygiene habits are highly variable amongst denture wearers, and can be frequently non-existent. Approximately 20% of the UK population wear some form of denture prosthesis. Almost half of these individuals show signs of denture induced stomatitis. There is currently a lack of evidence of how certain denture hygiene habits influence denture plaque composition, and whether this directly contributes towards oral inflammation and associated morbidity. Moreover, Candida albicans is primarily attributed as the causative agent due to its biofilm forming ability, thought to be influenced by the denture material, and in turn their effectiveness of decontamination. Aims: To assess denture hygiene habits and risk factors with respect to host and microbial factors on the clinical presence of denture induced stomatitis. Also, to conduct in vitro analyses of biofilm formation of clinical denture isolates on various denture substrates and the antimicrobial activity of common denture cleansers. Materials and methods: Data regarding participant demographics and denture hygiene habits were collected using a standardised questionnaire. Denture plaque samples were analysed from participants wearing either a complete or partial denture, and Candida spp. enumerated. Data on the bacterial microbiome composition of each participant was analysed in respect to denture hygiene habits. C. albicans isolated from dentures of healthy and diseased individuals was quantified using real-time polymerase chain reaction and biofilm biomass assessed using crystal violet. Biofilm development on the denture substratum polymethylmethacrylate, Molloplast B and Ufi-gel was determined. Early and mature biofilms were treated with popular over the counter denture hygiene products and assessed using metabolic and biomass stains. Results: Clinical data suggests the presence of denture induced stomatitis was positively associated with a history of smoking, denture design, poor denture hygiene, and retention of dentures whilst sleeping. Although C. albicans was detected in greater quantities in diseased individuals, it was not significantly associated with denture induced stomatitis. Microbiome analysis indicated that poor denture hygiene did not reveal any significant changes in microbiome composition in comparison to satisfactory oral hygiene. Neither did frequency of denture cleaning or sleeping whilst wearing a denture in situ reveal significant changes in the denture plaque composition. Denture substrata were shown to influence biofilm biomass, with polymethylmethacrylate providing the most suitable environment for C. albicans to reside. Of all the denture hygiene products tested, Milton had the most effective antimicrobial activity on early biofilms, reducing biofilm biomass and viability the greatest. Conclusions: This study has shown that denture hygiene practices appear to have minimal direct influence on the composition of the denture microbiome and clinical presence of denture induced stomatitis, reinforcing the idea that denture induced stomatitis is a multifactorial disease, influenced by host, microbial and environmental factors

Multi-defect modelling of bridge deterioration using truncated inspection records

Bridge Management Systems (BMS) are decision support tools that have gained widespread use across the transportation infrastructure management industry. The Whole Life Cycle Cost (WLCC) modelling in a BMS is typically composed of two main components: a deterioration model and a decision model. An accurate deterioration model is fundamental to any quality decision output.There are examples of deterministic and stochastic models for predictive deterioration modelling in the literature, however the condition of a bridge in these models is considered as an ‘overall’ condition which is either the worst condition or some aggregation of all the defects present. This research proposes a predictive bridge deterioration model which computes deterioration profiles for several distinct deterioration mechanisms on a bridge.The predictive deterioration model is composed of multiple Markov Chains, estimated using a method of maximum likelihood applied to panel data. The data available for all the defects types at each inspection is incomplete. As such, the proposed method considers that only the most significant defects are recorded, and inference is required regarding the less severe defects. A portfolio of 9,726 masonry railway bridges, with an average of 2.47 inspections per bridge, in the United Kingdom is the case study considered

Modelling interactions between multiple bridge deterioration mechanisms

Bridge asset managers are tasked with developing effective maintenance strategies by the stakeholders of transportation networks. Any presentation of maintenance strategies requires an estimate of the consequence on the Whole Life Cycle Cost (WLCC), which is contingent on an accurate deterioration model. Bridge deterioration has previously been demonstrated to exhibit non-constant behaviour in literature. However, commonly industrial data constrains deterioration models to use exponential distributions. In this study, a Dynamic Bayesian Network (DBN) is proposed to model bridge deterioration, which considers the initiation of different defect mechanisms and the interactions between the mechanisms. The model is parameterised using an exponential distribution, however through the consideration of defect interactions, non-constant deterioration behaviour can still be incorporated in the model. The deterioration of pointing, displacement of block work alongside the presence of spalling, hollowness and masonry cracking are the defect mechanisms considered, with masonry railway bridges in the United Kingdom serving as a case study

Incorporating defect specific condition indicators in a bridge life cycle analysis

Bridges are critical assets for the safe, reliable and functional operation of transportation networks. Infrastructure asset managers are responsible for ensuring that these bridges adhere to rigorous safety standards using the finite resources available to transportation agencies. To facilitate strategy development and to present decisions to stakeholders, a life cycle analysis is commonly performed. Many bridge owners use stochastic models that are calibrated using condition records from visual examinations, however condition records typically report bridge condition on a single condition scale. In this study, defect specific condition scales are utilised to implement multiple defect specific condition indicators in the modelling of deterioration. These additional indicators enable the modelling of the interactions between defects during deterioration. Moreover, the indicators are used in the modelling of different defect specific maintenance interventions providing the scope to quantitatively assess the effects of strategies that favour early intervention. A multiple defect deterioration model is presented as a dynamic Bayesian network, which is calibrated using records for metallic girders from railway bridges in the United Kingdom. A Petri net model is then used to perform a life cycle analysis, which incorporates a novel dynamic conditional approach for Petri net modelling to utilise the multiple condition indicators

Masthead

PubMed: 31444906Aims: Periodontal diseases negatively affect implant osseointegration. Perturbations in non-neuronal cholinergic signalling mechanisms are associated with periodontitis; however, their role in generalized aggressive periodontitis (GAgP) is unknown. The aim of this prospective case–control study was to determine the relationship between non-neuronal cholinergic signalling mechanisms, secreted Ly-6/uPAR-related protein-1 (SLURP-1), interleukin-17 (IL-17) family cytokines and healing of dental implants in health and GAgP. Material and Methods: Thirteen GAgP patients and seven periodontally healthy individuals (PH) were recruited. Peri-implant crevicular fluid (PICF) was obtained at baseline and 1 month post-placement. Acetylcholine (ACh) levels and cholinesterase activity were determined biochemically. SLURP-1, IL-17A and IL-17E levels were determined by ELISA. Marginal bone loss (MBL) at 1 and 6 months post-placement was determined radiographically. Results: The concentration of ACh, cholinesterase activity and IL-17A levels was elevated in PICF of patients with GAgP compared to PH individuals at baseline and 1 month post-placement. The concentration of ACh and cholinesterase activity levels in PICF correlated with levels of IL-17A and MBL around implants 1 month post-placement in patients with GAgP. Conclusions: Non-neuronal cholinergic mechanisms may play a role in the aetiopathogenesis of GAgP and may directly or indirectly, through modulation of IL-17A, influence early implant osseointegration and potential long-term implant survival. © 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Lt

Candida albicans biofilm heterogeneity does not influence denture stomatitis but strongly influences denture cleansing capacity

Approximately 20  % of the UK population wear some form of denture prosthesis, resulting in denture stomatitis in half of these individuals. Candida albicans is primarily attributed as the causative agent, due to its biofilm -forming ability. Recently, there has been increasing evidence of C. albicans biofilm heterogeneity and the negative impact it can have clinically; however, this phenomenon has yet to be studied in relation to denture isolates. The aims of this study were to evaluate C. albicans biofilm formation of clinical denture isolates in a denture environment and to assess antimicrobial activity of common denture cleansers against these tenacious communities. C. albicans isolated from dentures of healthy and diseased individuals was quantified using real-time PCR and biofilm biomass assessed using crystal violet. Biofilm development on the denture substratum poly(methyl methacrylate), Molloplast B and Ufi-gel was determined. Biofilm formation was assessed using metabolic and biomass stains, following treatment with denture hygiene products. Although C. albicans was detected in greater quantities in diseased individuals, it was not associated with increased biofilm biomass. Denture substrata were shown to influence biofilm biomass, with poly(methyl methacrylate) providing the most suitable environment for C. albicans to reside. Of all denture hygiene products tested, Milton had the most effective antimicrobial activity, reducing biofilm biomass and viability the greatest. Overall, our results highlight the complex nature of denture- related disease, and disease development cannot always be attributed to a sole cause. It is the distinct combination of various factors that ultimately determines the pathogenic outcome

An alternative approach to railway asset management value analysis: application to a UK railway corridor

Railway networks are complex systems and the management of such systems is a challenging task for railway asset managers. It is their responsibility to ensure that the network delivers the highest level of performance for all stakeholders, whilst adhering to strict safety regulations and financial constraints. Historically, Reliability, Availability, Maintainability and Safety (RAMS) analysis has been used to assess the performance and safety of railway networks, nonetheless there is a lack of consistency in approaches across the industry, with analysis often influenced by the key stakeholders at the time. This research demonstrates an application of an Extended RAMS framework on the UK Railway Network, the Extended RAMS frameworks aims to consolidate various extensions to the traditional RAMS approach in to a single universal approach, which is beneficial to all stakeholders. This paper explores the data currently available within the rail industry and how it can be used to assess the ten metrics within the framework. The final part of the paper explores how the parameters within the ExRAMS framework can be used as the bases of a value analysis, which can be used to assist with asset management decisions

An alternative approach to railway asset management value analysis: framework development

The management of a diverse asset portfolio is a demanding task for railway asset managers. They must ensure that the network delivers a high level of performance for customers and adheres to safety limits. Reliability, availability, maintainability and safety (RAMS) analysis is regularly used to assess the performance of systems, including railway networks. However, currently there are a wide range of different approaches to RAMS analysis in the railway industry. This research seeks to identify and consolidate any potential extensions to the traditional RAMS approach into a single framework: extended RAMS. The framework comprises ten parameters, RAMS and six additional parameters of particular interest to railway asset managers, including capacity and train performance. The framework is intended for use by asset managers to evaluate the attributes and current status of the railway infrastructure and enable comparison between different parts of the network and to evaluate different stakeholder needs

Classifying Bias in Large Multilingual Corpora via Crowdsourcing and Topic Modeling

Our project extends previous algorithmic approaches to finding bias in large text corpora. We used multilingual topic modeling to examine language-specific bias in the English, Spanish, and Russian versions of Wikipedia. In particular, we placed Spanish articles discussing the Cold War on a Russian-English viewpoint spectrum based on similarity in topic distribution. We then crowdsourced human annotations of Spanish Wikipedia articles for comparison to the topic model. Our hypothesis was that human annotators and topic modeling algorithms would provide correlated results for bias. However, that was not the case. Our annotators indicated that humans were more perceptive of sentiment in article text than topic distribution, which suggests that our classifier provides a different perspective on a text’s bias

Sensory sociological phenomenology, somatic learning and 'lived' temperature in competitive pool swimming

In this article, we address an existing lacuna in the sociology of the senses, by employing sociological phenomenology to illuminate the under-researched sense of temperature, as lived by a social group for whom water temperature is particularly salient: competitive pool swimmers. The research contributes to a developing ‘sensory sociology’ that highlights the importance of the socio-cultural framing of the senses and ‘sensory work’, but where there remains a dearth of sociological exploration into senses extending beyond the ‘classic five’ sensorium. Drawing on data from a three-year ethnographic study of competitive swimmers in the UK, our analysis explores the rich sensuousities of swimming, and highlights the role of temperature as fundamentally affecting the affordances offered by the aquatic environment. The article contributes original theoretical perspectives to the sociology of the senses and of sport in addressing the ways in which social actors in the aquatic environment interact, both intersubjectively and intercorporeally, as thermal beings