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Background The aim of this Italian multicenter study was to evaluate the diagnostic performance of a minimally invasive method for the detection of oral squamous cell carcinoma (OSCC) based on 13‐gene DNA methylation analysis in oral brushing samples. Methods Oral brushing specimens were collected in 11 oral medicine centers across Italy. Twenty brushing specimens were collected by each center, 10 from patients with OSCC, and 10 from healthy volunteers. DNA methylation analysis was performed in blindness, and each sample was determined as positive or negative based on a predefined cutoff value. Results DNA amplification failed in 4 of 220 (1.8%) samples. Of the specimens derived from patients with OSCC, 93.6% (103/110) were detected as positive, and 84.9% (90/106) of the samples from healthy volunteers were negative. Conclusion These data confirmed the diagnostic performance of our novel procedure in a large cohort of brushing specimens collected from 11 different centers and analyzed in blindness.

The aim of this case report was to evaluate the use of Partsch I cystotomy in order to preserve a dental implant located in an odontogenic cyst extended from 3.2 to 4.4. A 50 year-old woman showed a circular, well-defined unilocular radiolucent area, Ø2.5 cm, in the right mandibular region with an oral implant intruding inside it. The overdenture in the mandibular right site showed no clinical mobility. The authors decided to perform a surgical treatment aimed to preserve the implant. The patient underwent Partsch I surgery followed by iodoform gauze insertion replaced weekly for one month, revision of the previous orthograde endodontic treatments, and an acrylic resin obturator prosthesis application for the following two months. The twelve month follow-up showed no clinical mobility of the right lateral mandibular implant prostheses. Radiographical analysis revealed cystic lesion healing and perimplant bone regeneration. This report highlights the opportunity to apply cystotomy when the cyst involves a dental implant and undermines its stability. This possibility is offered by the peculiar clinical scenario where the implant was stabilized by the presence of a previous prosthetic fixation. Our study led to the application of an operative protocol that allowed for the preservation of the implant.

PurposeThe recent innovations of Industry 4.0 have made it possible to easily collect data related to a production environment. In this context, information about industrial equipment – gathered by proper sensors – can be profitably used for supporting predictive maintenance (PdM) through the application of data-driven analytics based on artificial intelligence (AI) techniques. Although deep learning (DL) approaches have proven to be a quite effective solutions to the problem, one of the open research challenges remains – the design of PdM methods that are computationally efficient, and most importantly, applicable in real-world internet of things (IoT) scenarios, where they are required to be executable directly on the limited devices’ hardware.Design/methodology/approachIn this paper, the authors propose a DL approach for PdM task, which is based on a particular and very efficient architecture. The major novelty behind the proposed framework is to leverage a multi-head attention (MHA) mechanism to obtain both high results in terms of remaining useful life (RUL) estimation and low memory model storage requirements, providing the basis for a possible implementation directly on the equipment hardware.FindingsThe achieved experimental results on the NASA dataset show how the authors’ approach outperforms in terms of effectiveness and efficiency the majority of the most diffused state-of-the-art techniques.Research limitations/implicationsA comparison of the spatial and temporal complexity with a typical long-short term memory (LSTM) model and the state-of-the-art approaches was also done on the NASA dataset. Despite the authors’ approach achieving similar effectiveness results with respect to other approaches, it has a significantly smaller number of parameters, a smaller storage volume and lower training time.Practical implicationsThe proposed approach aims to find a compromise between effectiveness and efficiency, which is crucial in the industrial domain in which it is important to maximize the link between performance attained and resources allocated. The overall accuracy performances are also on par with the finest methods described in the literature.Originality/valueThe proposed approach allows satisfying the requirements of modern embedded AI applications (reliability, low power consumption, etc.), finding a compromise between efficiency and effectiveness.