Recent Progress in Phage Therapy to Modulate Multidrug-Resistant Acinetobacter baumannii, Including in Human and Poultry

Acinetobacter baumannii is a multidrug-resistant and invasive pathogen associated with the etiopathology of both an increasing number of nosocomial infections and is of relevance to poultry production systems. Multidrug-resistant Acinetobacter baumannii has been reported in connection to severe challenges to clinical treatment, mostly due to an increased rate of resistance to carbapenems. Amid the possible strategies aiming to reduce the insurgence of antimicrobial resistance, phage therapy has gained particular importance for the treatment of bacterial infections. This review summarizes the different phage-therapy approaches currently in use for multiple-drug resistant Acinetobacter baumannii, including single phage therapy, phage cocktails, phage–antibiotic combination therapy, phage-derived enzymes active on Acinetobacter baumannii and some novel technologies based on phage interventions. Although phage therapy represents a potential treatment solution for multidrug-resistant Acinetobacter baumannii, further research is needed to unravel some unanswered questions, especially in regard to its in vivo applications, before possible routine clinical use

Integrative network analysis reveals subtype-specific long non-coding RNA regulatory mechanisms in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSC) is one of most common malignancies with high mortality worldwide. Importantly, the molecular heterogeneity of HNSC complicates the clinical diagnosis and treatment, leading to poor overall survival outcomes. To dissect the complex heterogeneity, recent studies have reported multiple molecular subtyping systems. For instance, HNSC can be subdivided to four distinct molecular subtypes: atypical, basal, classical, and mesenchymal, of which the mesenchymal subtype is characterized by upregulated epithelial-mesenchymal transition (EMT) and associated with poorer survival outcomes. Despite a wealth of studies into the complex molecular heterogeneity, the regulatory mechanism specific to this aggressive subtype remain largely unclear. Herein, we developed a network-based bioinformatics framework that integrates lncRNA and mRNA expression profiles to elucidate the subtype-specific regulatory mechanisms. Applying the framework to HNSC, we identified a clinically relevant lncRNA LNCOG as a key master regulator mediating EMT underlying the mesenchymal subtype. Five genes with strong prognostic values, namely ANXA5, ITGA5, CCBE1, P4HA2, and EPHX3, were predicted to be the putative targets of LNCOG and subsequently validated in other independent datasets. By integrative analysis of the miRNA expression profiles, we found that LNCOG may act as a ceRNA to sponge miR-148a-3p thereby upregulating ITGA5 to promote HNSC progression. Furthermore, our drug sensitivity analysis demonstrated that the five putative targets of LNCOG were also predictive of the sensitivities of multiple FDA-approved drugs. In summary, our bioinformatics framework facilitates the dissection of cancer subtype-specific lncRNA regulatory mechanisms, providing potential novel biomarkers for more optimized treatment of HNSC

Neuroprotective Effect of the Inhibitor Salubrinal after Cardiac Arrest in a Rodent Model

Cardiac arrest (CA) yields poor neurological outcomes. Salubrinal (Sal), an endoplasmic reticulum (ER) stress inhibitor, has been shown to have neuroprotective effects in both in vivo and in vitro brain injury models. This study investigated the neuroprotective mechanisms of Sal in postresuscitation brain damage in a rodent model of CA. In the present study, rats were subjected to 6 min of CA and then successfully resuscitated. Either Sal (1 mg/kg) or vehicle (DMSO) was injected blindly 30 min before the induction of CA. Neurological status was assessed 24 h after CA, and the cortex was collected for analysis. As a result, we observed that, compared with the vehicle-treated animals, the rats pretreated with Sal exhibited markedly improved neurological performance and cortical mitochondrial morphology 24 h after CA. Moreover, Sal pretreatment was associated with the following: (1) upregulation of superoxide dismutase activity and a reduction in maleic dialdehyde content; (2) preserved mitochondrial membrane potential; (3) amelioration of the abnormal distribution of cytochrome C; and (4) an increased Bcl-2/Bax ratio, decreased cleaved caspase 3 upregulation, and enhanced HIF-1α expression. Our findings suggested that Sal treatment improved neurological dysfunction 24 h after CPR (cardiopulmonary resuscitation), possibly through mitochondrial preservation and stabilizing the structure of HIF-1α

Data_Sheet_1_A simple-to-use score system for predicting HBsAg clearance to peginterferon alfa-2b in nucleoside analogs-experienced chronic hepatitis B patients.docx

ObjectivePatients with chronic hepatitis B (CHB) often fail to achieve clearance of the hepatitis B surface antigen (HBsAg) with peginterferon treatment. Our study aimed to develop a simple-to-use scoring system to predict the likelihood of HBsAg clearance following treatment with peginterferon alfa-2b(PEG-IFN-α2b) in patients with CHB.MethodsA total of 231 patients were enrolled and divided into HBsAg clearance (n = 37) and non-HBsAg clearance (n = 194) groups. Multifactor logistic models were constructed using univariate and multiple logistic regression analyses. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis were used to evaluate the discrimination, calibration, and clinical applicability of the predictive scoring system.ResultsFour clinical variables (age, baseline HBsAg level, HBsAg level decline at week 12, and alanine aminotransferase ratio at week 12) were independently associated with HBsAg clearance after PEG-IFN-α2b treatment and, therefore, were used to develop a predictive scoring system ranging from 0 to 13. The optimal cut-off value was >4, with a sensitivity of 86.49%, specificity of 72.16%, positive predictive value of 37.2%, negative predictive value of 96.6%, and an AUC of 0.872. This model exhibited good discrimination, calibration, and clinical applicability. Among patients with scores  4 HBsAg clearance was achieved in 0.85, 14.29, and 37.21% of the patients, respectively.ConclusionThe scoring system could effectively predict the predominance of HBsAg clearance after PEG-IFN-α2b treatment in the early stage. This may be helpful when making clinical decisions for the treatment of patients with CHB.</p

SHREC\u2717 Track Large-Scale 3D Shape Retrieval From Shapenet Core55

© 2017 The Eurographics Association. With the advent of commodity 3D capturing devices and better 3D modeling tools, 3D shape content is becoming increasingly prevalent. Therefore, the need for shape retrieval algorithms to handle large-scale shape repositories is more and more important. This track provides a benchmark to evaluate large-scale 3D shape retrieval based on the ShapeNet dataset. It is a continuation of the SHREC 2016 large-scale shape retrieval challenge with a goal of measuring progress with recent developments in deep learning methods for shape retrieval. We use ShapeNet Core55, which provides more than 50 thousands models over 55 common categories in total for training and evaluating several algorithms. Eight participating teams have submitted a variety of retrieval methods which were evaluated on several standard information retrieval performance metrics. The approaches vary in terms of the 3D representation, using multi-view projections, point sets, volumetric grids, or traditional 3D shape descriptors. Overall performance on the shape retrieval task has improved significantly compared to the iteration of this competition in SHREC 2016. We release all data, results, and evaluation code for the benefit of the community and to catalyze future research into large-scale 3D shape retrieval (website: https://www.shapenet.org/shrec17)

Large-Scale 3D Shape Retrieval from ShapeNet Core55

With the advent of commodity 3D capturing devices and better 3D modeling tools, 3D shape content is becoming increasingly prevalent. Therefore, the need for shape retrieval algorithms to handle large-scale shape repositories is more and more important. This track provides a benchmark to evaluate large-scale 3D shape retrieval based on the ShapeNet dataset. It is a continuation of the SHREC 2016 large-scale shape retrieval challenge with a goal of measuring progress with recent developments in deep learning methods for shape retrieval. We use ShapeNet Core55, which provides more than 50 thousands models over 55 common categories in total for training and evaluating several algorithms. Eight participating teams have submitted a variety of retrieval methods which were evaluated on several standard information retrieval performance metrics. The approaches vary in terms of the 3D representation, using multi-view projections, point sets, volumetric grids, or traditional 3D shape descriptors. Overall performance on the shape retrieval task has improved significantly compared to the iteration of this competition in SHREC 2016. We release all data, results, and evaluation code for the benefit of the community and to catalyze future research into large-scale 3D shape retrieval