Background:Cold-atmospheric plasma (CAP) is an ionized gas produced at an atmospheric pressure. The aim of this systematic review is to map the use of CAP in oncology and the implemented methodologies (cell targets, physical parameters, direct or indirect therapies).Methods:PubMed, the International Clinical Trials Registry Platform and Google Scholar were explored until 31 December 2017 for studies regarding the use of plasma treatment in oncology (in vitro, in vivo, clinical trials).Results:190 original articles were included. Plasma jets are the most-used production systems (72.1%). Helium alone was the most-used gas (35.8%), followed by air (26.3%) and argon (22.1%). Studies were mostly in vitro (94.7%) and concerned direct plasma treatments (84.2%). The most targeted cancer cell lines are human cell lines (87.4%), in particular, in brain cancer (16.3%).Conclusions:This study highlights the multiplicity of means of production and clinical applications of the CAP in oncology. While some devices may be used directly at the bedside, others open the way for the development of new pharmaceutical products that could be generated at an industrial scale. However, its clinical use strongly needs the development of standardized reliable protocols, to determine the more efficient type of plasma for each type of cancer, and its combination with conventional treatments.

The reconstruction of cephalic defect and more precisely from the face is not a recent issue. Indeed, the use of facial masks in a symbolic perspective was reported in ancient Egypt. Few references to facial prostheses are then found. It is really only with the work of the French surgeon Ambroise Paré that the first surgical techniques concerning facial epithetics are described. Techniques and materials tend to evolve over the centuries. But then came WWI, which marked a major turning point and brought to light the broken faces and the impact of maxillofacial trauma. Rehabilitation became a major issue in society. The war was a driving force for change from both a surgical and prosthetic point of view, revealing in particular such brilliant designers as the American sculptor Anna Coleman Ladd. Today, the profession is undergoing a major upheaval, linked to the growing development of biotechnological constructions. This historical review aims to retrace the evolution of the rehabilitation of facial substance loss over the ages and to outline the prospects for the foreseeable future. (Int J Maxillofac Prosthetics 2021;4:2-8)

Lateral segmental mandibulectomy can be associated with sequelae, such as mouth opening limitation and mandibular deviation, that lead to altered oral functions (chewing, swallowing, speech) and complex prosthetic management. The authors present a new protocol for surface electromyography (sEMG) and mandibular motion recording to help clinicians with decision-making and dental prosthetic treatment planning for segmental mandibulectomy. The clinical case of a patient with a reconstructed titanium endoprosthesis is presented. The MAC2 protocol is used and consists of chronologically performing various recordings by using a device for sEMG and mandibular tracking. During the orofacial reeducation and dental prosthetic reconstruction, three therapeutic steps can benefit from the MAC2 protocol: to analyze the initial muscular imbalance, to provide guidance in the choice of maxillomandibular relationship and to quantify the functional improvement. sEMG of masticatory muscles is a useful diagnostic tool in a context of mandibular kinematic disorders and the MAC2 protocol adds some guidance for dental prosthetic rehabilitation in the context of segmental mandibulectomy. (Int J Maxillofac Prosthetics 2021;4:47-54)

Synovial chondromatosis is a non-cancerous tumor characterized by the formation of multiple nodules of cartilage due to metaplastic development of the synovial membrane. Etiology can be a primary lesion, of which pathogenesis remains unknown, or low-grade trauma or internal disorders. This pathology can long remain undiagnosed and leads to therapeutic wandering, especially since clinical manifestations are non-specific. Symptoms may mimic temporomandibular disorders and dental orthopantomogram does not always show intra-articular calcified bodies. Cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI) are tests of choice for the diagnosis of this pathology. This case report describes the clinical manifestations, diagnosis and management of a case of synovial chondromatosis involving the temporomandibular joint, in a 21-year-old woman who was initially treated for two years for a common temporo-mandibular disorder. The evidence gathered during the medical interview and clinical examination led us to suspect synovial chondromatosis of the temporomandibular joint. Prescription of a CBCT and MRI confirmed the diagnosis of her temporomandibular joint disorder and led to a successful arthroplasty.

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Despite artificial intelligence used in skin dermatology diagnosis is booming, application in oral pathology remains to be developed. Early diagnosis and therefore early management, remain key points in the successful management of oral mucosa cancers. The objective was to develop and evaluate a machine learning algorithm that allows the prediction of oral mucosa lesions diagnosis. This cohort study included patients followed between January 2015 and December 2020 in the oral mucosal pathology consultation of the Toulouse University Hospital. Photographs and demographic and medical data were collected from each patient to constitute clinical cases. A machine learning model was then developed and optimized and compared to 5 models classically used in the field. A total of 299 patients representing 1242 records of oral mucosa lesions were used to train and evaluate machine learning models. Our model reached a mean accuracy of 0.84 for diagnostic prediction. The specificity and sensitivity range from 0.89 to 1.00 and 0.72 to 0.92, respectively. The other models were proven to be less efficient in performing this task. These results suggest the utility of machine learning-based tools in diagnosing oral mucosal lesions with high accuracy. Moreover, the results of this study confirm that the consideration of clinical data and medical history, in addition to the lesion itself, appears to play an important role.

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