Stamp Technique: An Explorative SEM Analysis.

BACKGROUND: Achieving correct tooth anatomy and saving time at the dental chair are some of the goals of modern restorative dentistry. Stamp technique has gained acceptance in clinical practice. The aim of this study was to evaluate the effectiveness of this technique in terms of microleakage, voids, overhangs and marginal adaptation of Class I restorations, and to analyse the operative times in comparison with traditional restorative procedures. METHODS: Twenty extracted teeth were divided into 2 groups. Ten teeth in the study group (SG) were Class I prepared and restored using stamp technique, and ten teeth in the control group (CG) were Class I restored traditionally. SEM analysis was performed to evaluate voids, microleakage, overhangs, and marginal adaptation, and operative times were recorded. A statistical analysis was performed. RESULTS: There were no significant differences in microleakage, marginal adaptation and filling defects between the two groups, however, the stamp technique seems to facilitate the formation of large overflowing margins that require a careful finishing phase. CONCLUSIONS: Stamp technique does not seem to have any critical aspects in terms of restoration durability and it can be performed in a short time.

Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs.

The slaughterhouse can act as a valid checkpoint to estimate the prevalence and the economic impact of diseases in farm animals. At present, scoring lesions is a challenging and time-consuming activity, which is carried out by veterinarians serving the slaughter chain. Over recent years, artificial intelligence(AI) has gained traction in many fields of research, including livestock production. In particular, AI-based methods appear able to solve highly repetitive tasks and to consistently analyze large amounts of data, such as those collected by veterinarians during postmortem inspection in high-throughput slaughterhouses. The present study aims to develop an AI-based method capable of recognizing and quantifying enzootic pneumonia-like lesions on digital images captured from slaughtered pigs under routine abattoir conditions. Overall, the data indicate that the AI-based method proposed herein could properly identify and score enzootic pneumonia-like lesions without interfering with the slaughter chain routine. According to European legislation, the application of such a method avoids the handling of carcasses and organs, decreasing the risk of microbial contamination, and could provide further alternatives in the field of food hygiene.