Bone regeneration using titanium plate stabilization for the treatment of peri‐implant bone defects: A retrospective radiologic pilot study

alexandrossfakianakis shared this article with you from Inoreader Bone regeneration using titanium plate stabilization for the treatment of peri‐implant bone defects: A retrospective radiologic pilot study via Clinical Implant Dentistry and Related Research Abstract Aim To 3-dimensional radiographically assess the effect of titanium plate in guided bone regeneration (GBR) for the treatment of peri-implant ridge defects in esthetic zone. Material and Methods Nineteen patients with buccal peri-implant defects in the maxillary esthetic zone were treated with GBR using xenograft, autogenous bone, and collagen membrane. Subjects were divided into two groups: control (conventional GBR, 10 patients with 16 implants) and test (GBR with an adjunctive titanium plate; nine patients with 15 implants). Cone-beam computed tomography (CBCT) images obtained immediately after and 5–7 months following GBR were used to assess buccal crestal bone level (BBL) and buccal bone thickness (BBT) at different implant levels. Results Thirty-one implants in 19 patients were evaluated. Titanium plate exposure occurred in three cases (33.33%) of the test group. After 5–7 months, the mean BBL was located 1.48 ± 0.71 mm coronal to the platform in the test group and 0.90 ± 3.03 mm coronal to the platform in the control group (p = 0.03). The mean over all BBT (BBT-M) was 4.16 ± 0.48 mm in the test group and 2.38 ± 0.97 mm in the control group (p < 0.01). More resorption occurred in the control group than in the test group regarding mean BBL (3.00 ± 3.11 mm vs. 0.78 ± 0.79 mm, respectively; p = 0.04), BBT-M change (1.87 ± 1.59 mm vs. 0.56 ± 0.33 mm, respectively; p = 0.02), and percentage change in BBT-M (40.69 ± 24.01% vs. 11.53 ± 5.86%, respectively; p < 0.01). Conclusion In the short-term, titanium plate-enhanced GBR maintained ridge dimensions better than conventional GBR did. 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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Clinical, Virologic, and Immunologic Evaluation of Symptomatic Coronavirus Disease 2019 Rebound Following Nirmatrelvir/Ritonavir Treatment

alexandrossfakianakis shared this article with you from Inoreader Clinical, Virologic, and Immunologic Evaluation of Symptomatic Coronavirus Disease 2019 Rebound Following Nirmatrelvir/Ritonavir Treatment via Clinical Infectious Diseases Advance Access Abstract BackgroundNirmatrelvir/ritonavir, the first severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) protease inhibitor, reduces the risk of hospitalization and death by coronavirus disease 2019 (COVID-19) but has been associated with symptomatic rebound after therapy completion. Methods Six individuals with relapse of COVID-19 symptoms after treatment with nirmatrelvir/ritonavir, 2 individuals with rebound symptoms without prior antiviral therapy and 7 patients with acute Omicron infection (controls) were studied. Soluble biomarkers and serum SARS-CoV-2 nucleocapsid protein were measured. Nasal swabs positive for SARS-CoV-2 underwent viral isolation and targeted viral sequencing. SARS-CoV-2 anti-spike, anti–receptor-binding domain, and anti-nucleocapsid antibodies were measured. Surrogate viral neutralization tests against wild-type and Omicron spike protein, as well as T-cell stimulation assays, were performed. Results High levels of SARS-CoV-2 anti-spike immunoglo bulin G (IgG) antibodies were found in all participants. Anti-nucleocapsid IgG and Omicron-specific neutralizing antibodies increased in patients with rebound. Robust SARS-CoV-2–specific T-cell responses were observed, higher in rebound compared with early acute COVID-19 patients. Inflammatory markers mostly decreased during rebound. Two patients sampled longitudinally demonstrated an increase in activated cytokine-producing CD4+ T cells against viral proteins. No characteristic resistance mutations were identified. SARS-CoV-2 was isolated by culture from 1 of 8 rebound patients; Polybrene addition increased this to 5 of 8. Conclusions Nirmatrelvir/ritonavir treatment does not impede adaptive immune responses to SARS-CoV-2. Clinical rebound corresponds to development of a robust antibody and T-cell immune response, arguing against a high risk of disease progression. The presence of infectious virus supports the need for isolation and assessment of longer treatm ent courses.Clinical trials registration. NCT04401436. 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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