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Introduction: Gingival thickness plays a very important role in framing the protocol in various dental treatments such as implantology, prosthodontics, and more importantly in periodontics. During periodontal management, it is important to consider the gingival thickness of the patient, which can result in more satisfactory treatment outcomes. Smoking has its effect on periodontium, affecting the physical and functional properties. Assessing the relation between these two entities is becoming important. This clinical study is sought to compare the thickness of gingiva in systemically healthy smokers and nonsmokers. Materials and Methods: A total of 180 periodontally healthy patients were divided into smokers and nonsmokers, and subdivided into Group 1 (18-25 years), Group 2 (26-39 years), and Group 3 (>40 years). Gingival thickness was assessed 6 mm from the gingival margin in the midbuccal area between maxillary central and lateral incisor. Statistical analysis was performed to assess the difference in gingival thickness among smokers and nonsmokers and correlated with age. Results: The results showed the presence of changes in gingival thickness for all the age groups. But, a significant P value was not obtained for the age groups 18-25 and 26-39 years. In >40 years of age group, there was a statistically significant change in the P value (0.008) of the mean and standard deviation in smokers and nonsmokers ( significance: P < 0.001). Conclusion: On the basis of the results of this study, gingival thickness was decreased with age among smokers and nonsmokers. This study also proved that smoking has a negative influence on the gingival thickness.

Iatrogenic trauma though not serious is very common in dental practice. Orthodontic treatment can inflict such injuries as they are prolonged over a long period of time. Ill-fabricated orthodontic appliances, such as wires and brackets, or the patients' habits such as application of constant pressure over the appliance can traumatize the adjacent oral soft tissues. In rare cases, these appliances can get embedded into the mucosa and gingival tissues. This case report describes one such case of iatrogenic trauma to the palatal mucosa due to entrapment of a tongue spike appliance and its surgical management.

Although microarray analysis has provided information regarding the dynamics of gene expression during development of the mouse lung, no extensive correlations have been made to the levels of corresponding protein products. Here, we present a global survey of protein expression during mouse lung organogenesis from embryonic day E13.5 until adulthood using gel‐free two‐dimensional liquid chromatography coupled to shotgun tandem mass spectrometry (MudPIT). Mathematical modeling of the proteomic profiles with parallel DNA microarray data identified large groups of gene products with statistically significant correlation or divergence in coregulation of protein and transcript levels during lung development. We also present an integrative analysis of mRNA and protein expression in Nmyc loss‐ and gain‐of‐function mutants. This revealed a set of 90 positively and negatively regulated putative target genes. These targets are evidence that Nmyc is a regulator of genes involved in mRNA processing and a repressor of the imprinted gene Igf2r in the developing lung. Synopsis The lung is a complex and highly organized tissue consisting of an epithelium in contact with the air, a mesenchyme layer allowing for the expansion and contraction of the lung during breathing and a complex vasculature to bring blood close to the site of gas exchange. The development of the lung is well defined morphologically and many genes have been shown to the critical for correct development using mouse genetic models such as gene knockout and misexpression (Kimura et al , 1996 ; Sekine et al , 1999 ). Several global microarray expression studies have investigated the profile of gene expression during normal lung development (Mariani et al , 2002 ; Bonner et al , 2003 ; Lu et al , 2004a ). However, the expression levels and subcellular localization of the cognate proteins are largely unknown. Here, we report the profiling of proteins in the lung by gel‐free two‐dimensional liquid chromatography coupled to shotgun tandem mass spectrometry (MudPIT) over six developmental time points covering most of the significant stages of lung development. A prefractionation into organellar compartments (cytosol, nucleus and mitochondria) was performed to assay both tissue and subcellular specificity from the same sample preparation (Kislinger et al , 2003 , 2006 ). Comparison of the proteomic data with mRNA expression profiles revealed a large number of gene products (protein and mRNA) that are coordinately regulated during development (Figure 5A ). We were also able to identify a smaller group of ∼30 genes whose levels of mRNA and protein expression are uncorrelated (Figure 5D ), suggesting regulation via post‐transcriptional or post‐translational control mechanisms. Having established a baseline of normal lung development, we next characterized the molecular changes that take place in mutant genetic backgrounds. One of the most powerful tools in understanding gene function is the combination of loss‐of‐function (nulls or hypomorophs) and gain‐of‐function (misexpression or overexpression) mutants. We used loss‐ and gain‐of‐function mutants of the gene Nmyc , a transcription factor that has previously been shown to be critical for lung development (Moens et al , 1992 , 1993 ; Okubo et al , 2005 ), to molecularly characterize its function in lung development by proteomics and microarray profiling. By combining these data sets, we identified several potential direct targets of Nmyc regulation (Figure 6 ). Furthermore, as Nmyc can function as both a transcriptional activator and repressor, we were able to classify these target genes as being activated or repressed by Nmyc. Along with several known targets of Nmyc, we also identified many genes involved in mRNA processing, splicing and export that appeared positively regulated by Nmyc. We identified four genes that appeared to be repressed by Nmyc including Igf2r an imprinted gene. In summary, we have shown that the technique of gel‐free two‐dimensional liquid chromatography coupled to shotgun tandem mass spectrometry (MudPIT) can be used to profile embryonic tissues during development. Mining of protein profiles and protein–protein interaction networks was used to identify proteins with potential developmental importance. Finally, integrative analysis of protein and mRNA levels in Nmyc hypomorph and overexpressing mutant mice identified a list of possible direct Nmyc target genes. Survey of expression of over 3300 proteins during lung development over six time points, E13.5 to adult Prediction of subcellular localization of 1000 proteins Correlation of microarray expression data with protein data to identify a set of over 600 gene products with correlated expression profiles Identification of 90 putative direct targets of the transcription factor Nmyc in the lung using loss and gain of function mutants

Context: Invasion of the bifurcation and trifurcation of the multi-rooted teeth resulting in furcation involvement is one of the serious complications of periodontitis. Aim: The purpose of the study was to evaluate the efficacy of combination therapy using anorganic bovine bone graft and resorbable guided tissue regeneration (GTR) membrane versus open flap debridement alone in the management of Grade II furcation defects in mandibular molars. Materials and Methods: The study included a total number of 20 sites in 10 patients with bilateral mandibular furcation defects, out of which 10 sites were treated as test group and 10 as control group. The test group was treated with combination therapy and the control group with open flap debridement alone. The parameters were recorded on 0 day (baseline), 90th day, and 180th day, which included vertical probing depth and horizontal probing depth of the furcation defect, clinical attachment level, and defect fill. Statistical Analysis Used: Mean and standard deviation were calculated for different variables in each study group at different time points. Mean values were compared by using Wilcoxon signed ranks test, after adjusting the P values for multiple comparison by using Bonferroni correction method. Results: Both the test and control groups showed a definitive improvement in clinical parameters, which was statistically significant. On comparison, the vertical probing depth showed significant reduction in the test group with a mean reduction of 3.1 +- 0.7 mm, when compared to the control group which showed a mean reduction of 1.5 +- 0.5 mm. The horizontal probing depth of furcation defects was also significantly reduced in the test group with a mean reduction of 2.2 +- 0.6 mm, when compared to the control group in which the mean reduction was 0.9 +- 0.3 mm. There was also significant gain in attachment level in the test group which showed a mean gain of 3.2 +- 0.6 mm, when compared to the control group which showed a gain of 1.2 +- 0.6 mm. Radiographic defect fill was found to be more in the test group with a mean gain of 2.0 +- 0.1 mm, when compared to the control group which showed a defect fill of 0.2 +- 0.1 mm. Conclusions: The results of this study demonstrated that the combined use of anorganic bovine bone graft and resorbable GTR membrane is effective than open flap debridement alone in the treatment of mandibular grade II furcation defects.

Dimensionality reduction techniques such as principal component analysis and factor analysis are used to discover a linear mapping between high-dimensional data samples and points in a lower-dimensional subspace. Previously, Frey and Jojic introduced transformation-invariant component analysis (TCA) to learn a linear mapping, invariant to a set of known form of global transformations. However, parameter estimation in that model using the previously-proposed expectation maximization (EM) algorithm required scalar operations in the order of N 2 where N is the dimensionality of each training example. This is prohibitive for many applications of interest such as modeling mid-to large-size images, where, for instance, N may be as high as 786432 (512×512 RGB image). In this paper, we present an efficient algorithm that reduces the computational requirements to order of N log  N . With this speedup, we show the effectiveness of transformation-invariant component analysis in various applications including tracking, learning video textures, clustering, object recognition and object detection in images. Software for TCA can be downloaded from http://www.psi.toronto.edu/fastTCA.htm .

Monitoring food costs informs governments of the affordability of healthy diets. Many countries have adopted a standardized healthy food basket. The Victorian Healthy Food Basket contains 44 food items necessary to meet the nutritional requirements of four different Australian family types for a fortnight. The aim of this study was to describe the development of a new iPad app as core to the implementation of the Victorian Healthy Food Basket. The app significantly automates the data collection. We evaluate if the new technology enhanced the quality and efficacy of the research. Time taken for data collection and entry was recorded. Semi-structured evaluative interviews were conducted with five field workers during the pilot of the iPad app. Field workers were familiar with previous manual data collection methods. Qualitative process evaluation data was summarized against key evaluation questions. Field workers reported that using the iPad for data collection resulted in increased data accuracy, time savings, and efficient data management, and was preferred over manual collection. Portable digital devices may be considered to improve and extend data collection in the field of food cost monitoring.

A medical provider's summary of a patient visit serves several critical purposes, including clinical decision-making, facilitating hand-offs between providers, and as a reference for the patient. An effective summary is required to be coherent and accurately capture all the medically relevant information in the dialogue, despite the complexity of patient-generated language. Even minor inaccuracies in visit summaries (for example, summarizing \"patient does not have a fever\" when a fever is present) can be detrimental to the outcome of care for the patient. This paper tackles the problem of medical conversation summarization by discretizing the task into several smaller dialogue-understanding tasks that are sequentially built upon. First, we identify medical entities and their affirmations within the conversation to serve as building blocks. We study dynamically constructing few-shot prompts for tasks by conditioning on relevant patient information and use GPT-3 as the backbone for our experiments. We also develop GPT-derived summarization metrics to measure performance against reference summaries quantitatively. Both our human evaluation study and metrics for medical correctness show that summaries generated using this approach are clinically accurate and outperform the baseline approach of summarizing the dialog in a zero-shot, single-prompt setting.

AI-driven medical history-taking is an important component in symptom checking, automated patient intake, triage, and other AI virtual care applications. As history-taking is extremely varied, machine learning models require a significant amount of data to train. To overcome this challenge, existing systems are developed using indirect data or expert knowledge. This leads to a training-inference gap as models are trained on different kinds of data than what they observe at inference time. In this work, we present a two-stage re-ranking approach that helps close the training-inference gap by re-ranking the first-stage question candidates using a dialogue-contextualized model. For this, we propose a new model, global re-ranker, which cross-encodes the dialogue with all questions simultaneously, and compare it with several existing neural baselines. We test both transformer and S4-based language model backbones. We find that relative to the expert system, the best performance is achieved by our proposed global re-ranker with a transformer backbone, resulting in a 30% higher normalized discount cumulative gain (nDCG) and a 77% higher mean average precision (mAP).

A wave of new task-based virtual assistants has been fueled by increasingly powerful large language models (LLMs), such as GPT-4 (OpenAI, 2023). A major challenge in deploying LLM-based virtual conversational assistants in real world settings is ensuring they operate within what is admissible for the task. To overcome this challenge, the designers of these virtual assistants rely on an independent guardrail system that verifies the virtual assistant's output aligns with the constraints required for the task. However, relying on commonly used, prompt-based guardrails can be difficult to engineer correctly and comprehensively. To address these challenges, we propose CONSCENDI. We use CONSCENDI to exhaustively generate training data with two key LLM-powered components: scenario-augmented generation and contrastive training examples. When generating conversational data, we generate a set of rule-breaking scenarios, which enumerate a diverse set of high-level ways a rule can be violated. This scenario-guided approach produces a diverse training set and provides chatbot designers greater control. To generate contrastive examples, we prompt the LLM to alter conversations with violations into acceptable conversations to enable fine-grained distinctions. We then use this data, generated by CONSCENDI, to train a smaller model. We find that CONSCENDI results in guardrail models that improve over baselines in multiple dialogue domains.