Evaluation of Glove Damage during Dental Procedures among Dental Specialists in Tabriz

<p><strong>Background and aims.</strong> Dental practitioners are prone to occupational risk of infection. This can be prevented in part by wearing gloves. However, for this to be effective, gloves should be intact during the entire course of dental procedure. Leaky surgical latex gloves have been seen in 0.9% of cases before use. As much as 1.9% of latex gloves have been reported to be damaged during dental procedures. In this study, we decided to assess glove damage during dental procedures among dental specialists in Tabriz. </p><p><strong>Materials and methods.</strong> Thirty-six dental specialists were selected for this study. Each practitioner received 40 pairs of intact powdered latex gloves. Upon the completion of dental procedures, the gloves were retrieved and any tears were evaluated separately for right and left hands. Data was analyzed using chi-square test. </p><p><strong>Results.</strong> 159 punctures were detected in 144 gloves (5%) out of 2880 unpaired gloves used by practitioners. They noticed the tear(s) in 60 cases (2%), however, 99 cases (3%) of tear(s) were not noted during the procedure. The highest rate of glove damage was observed in the prosthodontists’ group (12.3%), which was statistically significant comparing to other groups (p=0.048). The lowest rate of the damage was observed in the oral surgeons’ group (2%) which showed no significant difference (p=0.134). The highest rate of punctures in the gloves was observed in the first and second fingers of the non-dominant hand. </p><p><strong>Conclusion.</strong> The damage to 5% of the gloves is highly significant, with a potential role in occupational hazards. The higher rate of leaks in the prosthodontists’ group compared to other groups demands for greater prudence in this field. The high rate of leaks in the first and second fingers of the non-dominant hand requires more attention to this area during daily practice.</p&gt

Classification algorithms with multi-modal data fusion could accurately distinguish neuromyelitis optica from multiple sclerosis

International audienceNeuromyelitis optica (NMO) exhibits substantial similarities to multiple sclerosis (MS) in clinical manifestations and imaging results and has long been considered a variant of MS. With the advent of a specific biomarker in NMO, known as anti-aquaporin 4, this assumption has changed; however, the differential diagnosis remains challenging and it is still not clear whether a combination of neuroimaging and clinical data could be used to aid clinical decision-making. Computer-aided diagnosis is a rapidly evolving process that holds great promise to facilitate objective differential diagnoses of disorders that show similar presentations. In this study, we aimed to use a powerful method for multi-modal data fusion, known as a multi-kernel learning and performed automatic diagnosis of subjects. We included 30 patients with NMO, 25 patients with MS and 35 healthy volunteers and performed multi-modal imaging with T1-weighted high resolution scans, diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). In addition, subjects underwent clinical examinations and cogni-tive assessments. We included 18 a priori predictors from neuroimaging, clinical and cognitive measures in the initial model. We used 10-fold cross-validation to learn the importance of each modality, train and finally test the model performance. The mean accuracy in differentiating between MS and NMO was 88%, where visible white matter lesion load, normal appearing white matter (DTI) and functional connectivity had the most important contributions to the final classification. In a multi-class classification problem we distinguished between all of 3 groups (MS, NMO and healthy controls) with an average accuracy of 84%. In this classification, visible white matter lesion load, functional connectivity, and cognitive scores were the 3 most important modalities. Our work provides preliminary evidence that computational tools can be used to help make an objective differential diagnosis of NMO and MS