Single‐rooted extraction socket classification: A systematic review and proposal of a new classification system based on morphologic and patient‐related factors

alexandrossfakianakis shared this article with you from Inoreader Single‐rooted extraction socket classification: A systematic review and proposal of a new classification system based on morphologic and patient‐related factors via Journal of Esthetic and Restorative Dentistry Abstract Taxonomy and classification of a disease contributes to facilitating the diagnosis and treatment planning process and simplifies communication between clinicians. The aim of this study was to provide a critical appraisal based on a systematic review of the single-rooted extraction socket (ES) classifications and subsequently, introduce a new classification system combining the cornerstones of the previously proposed systems and based on the latest consensus in implant dentistry. Following the systematic search process in PubMed, EMBASE, and SCOPUS databases 13 ES classifications were detected. The most repeated hard and soft tissue factors in the previous classifications were buccal bone dehiscence, interproximal bone, gingival recession, and soft tissue phenotype. However, there was minimal attention to patient-related factors such as systemic conditions and smoking. Therefore, a new classification system based on the combination of patient-related factors, clinical and radiograp hical parameters was proposed. This divides an ES into three types. Class I and II sockets are candidates for receiving immediate implant placement and conversely, a class III socket includes a compromised condition that requires multiple-stage reconstruction mostly suitable for standard delayed implant placement with alveolar ridge preservation. Within the limitations of this study, the new classification system not only provides comprehensive inclusion of various crucial parameters in implant placement (such as prediction of future implant position and osteotomy difficulty, etc.) but also, in contrast to the previously introduced systems, is able to classify the ES prior to extraction and also, takes into the account the patient-related factors as the class modifiers following the extraction. View on Web

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Acinetobacter baumannii: Pathogenesis, virulence factors, novel therapeutic options and mechanisms of resistance to antimicrobial agents with emphasis on tigecycline

alexandrossfakianakis shared this article with you from Inoreader Acinetobacter baumannii: Pathogenesis, virulence factors, novel therapeutic options and mechanisms of resistance to antimicrobial agents with emphasis on tigecycline via Journal of Clinical Pharmacy and Therapeutics This article summarizes the microbiological and virulence traits in A.baumannii. In addition, in this study, the mechanisms of resistance to tigecycline have been comprehensively investigated and novel therapeutic strategies have been expressed. Abstract What is known and objective Acinetobacter baumannii is one of the most important nosocomial pathogens with the ability to cause infections such as meningitis, pneumonia, urinary tract, septicaemia and wound infections. A wide range of virulence factors are responsible for pathogenesis and high mortality of A. baumannii including outer membrane proteins, lipopolysaccharide, capsule, phospholipase, nutrient- acquisition systems, efflux pumps, protein secretion systems, quarom sensing and biofilm production. These virulence factors contribute in pathogen survival in stressful conditions and antimicrobial resistance. Comment According to the World Health Organization (WHO), A. baumannii is one of the most resistant pathogens of ESKAPE group (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, A. baumannii, Pseudomonas aeruginosa and Enterobacter spp.). In recent years, resistance to a wide range of antibiotics in A. baumannii has significantly increased and the high emergence of extensively drug resistant (XDR) isolates is challenging. Among therapeutic antibiotics, resistance to tigecycline as a last resort antibiotic has become a global concern. Several mechanisms are involved in tigecycline resistance, the most important of which is RND (Resistance-Nodulation-Division) family efflux pumps overexpression. The development of new therapeutic strategies to confront A. baumannii infections has been very promising in recent years. What is new and conclusion In the present review we highlight microbiological and virulence traits in A. baumannii and peruse the tigecycline resistance mechanisms and novel therapeutic options. Among the novel therapeutic strategies we focus on combination therapy, drug repurposing, novel antibiotics, bacteriophage therapy, antimicrobial peptides (AMPs), human monoclonal antibodies (Hu-mAbs), nanoparticles and gene editing. View on Web

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Differentiation of eosinophilic and non‐eosinophilic chronic rhinosinusitis on preoperative computed tomography using deep learning

alexandrossfakianakis shared this article with you from Inoreader Differentiation of eosinophilic and non‐eosinophilic chronic rhinosinusitis on preoperative computed tomography using deep learning via Clinical Otolaryngology Abstract Objective This study aimed to develop deep learning (DL) models for differentiating between eosinophilic chronic rhinosinusitis (ECRS) and non-ECRS (NECRS) on preoperative CT. Methods A total of 878 chronic rhinosinusitis (CRS) patients undergoing nasal endoscopic surgery at Renmin Hospital of Wuhan University (Hubei, China) between October 2016 to June 2021 were included. Axial spiral CT images were pre-processed and used to build the dataset. Two semantic segmentation models based on U-net and Deeplabv3 were trained to segment the sinus area on CT images. All patient images were segmented using the better-performing segmentation model and used for training and testing of the transferred efficientnet_b0, resnet50, inception_resnet_v2, and Xception neural networks. Additionally, we evaluated the performances of the models trained using each image and each patient as a unit. The precision of each model was assessed based on the receiver operating characteristic curve. Further, we analyzed the confusion matrix and accuracy of each model. Results The Dice coefficients of U-net and Deeplabv3 were 0.953 and 0.961, respectively. The average area under the curve and mean accuracy values of the four networks were 0.848 and 0.762 for models trained using a single image as a unit, while the corresponding values for models trained using each patient as a unit were 0.893 and 0.853, respectively. Conclusion Combining semantic segmentation with classification networks could effectively distinguish between patients with ECRS and those with NECRS based on preoperative sinus CT images. Furthermore, labeling each patient to build a dataset for classification may be more reliable than labeling each medical image. This article is protected by copyright. All rights reserved. View on Web

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