Minimally invasive thoracoscopic approach for anterior decompression and stabilization of metastatic spine disease

Journal ArticleObject. The choices available in the management of metastatic spine disease are complex, and the role of surgical therapy is increasing. Recent studies have indicated that patients treated with direct surgical decompression and stabilization before radiation have better functional outcomes than those treated with radiation alone. The most common anterior surgical approach for direct spinal cord decompression and stabilization in the thoracic spine is open thoracotomy; however, thoracotomy for spinal access is associated with morbidity that can be avoided with minimally invasive techniques like thoracoscopy. Methods. A minimally invasive thoracoscopic approach was used for the surgical treatment of thoracic and thoracolumbar metastatic spinal cord compression. This technique allows ventral decompression via corpectomy, interbody reconstruction with expandable cages, and stabilization with an anterolateral plating system designed specifically for minimally invasive implantation. This technique was performed in 5 patients with metastatic disease of the thoracic spine, including the thoracolumbar junction. Results. All patients had improvement in preoperative symptoms and neurological deficits. No complications occurred in this small series. Conclusions. The minimally invasive thoracoscopic approach can be applied to the treatment of thoracic and thoracolumbar metastatic spine disease in an effort to reduce access morbidity. Preliminary results have indicated that adequate decompression, reconstruction, and stabilization can be achieved with this technique

An Investigation on Integrating Eastern and Western Medicine with Informatics

Today, in many western countries, acceptance of alternate forms of healthcare such as Chinese medicine (CM) is increasing. In fact, countries such as Australia, Canada, and England are going so far as to set regulations, education, and standards regarding the practice of CM in these respective countries. Further, we can see the integration between western and Chinese medicine delivery of care and treatments in many instances. Information Systems and Information Technology (IS/IT) can be a key enabler in assisting this integration. The following study examines aspects of such integrations using IS/IT and identifies that CM IS/IT is more likely to succeed when there is synthesis between key aspects of the unique environment and user requirements. This perspective is supported theoretically by adapting Churchman’s Inquiring Systems to frame CM as a combination of Hegelian and Kantian inquiring systems with the support of Singerian, Lockean, and Leibnizian inquiring systems and Knowledge Management (KM) features. Based on this, the study then proposes a new design for a patient management system in clinics and hospitals

Chronic toxicity of double-walled carbon nanotubes to three marine organisms: influence of different dispersion methods

Double-walled carbon nanotubes (DWNTs) are found in a variety of consumer products, but there are no ecotoxicity data of DWNTs into marine organisms. Materials & methods: Chronic toxicity of DWNTs was investigated with the diatom Thalassiosira pseudonana, copepod Tigriopus japonicus and medaka Oryzias melastigma. DWNTs were dispersed using sonication (so-DWNTs) and stirring (st-DWNTs) for comparison. Results: The median aggregation size (0.89 μm2) of so-DWNTs was smaller than that of st-DWNTs (21.8 μm2). Exposure to DWNTs led to growth inhibition of T. pseudonana with EC50s of 1.86 and 22.7 mg/l for so- and st-DWNTs, respectively. Population growth of T. japonicus was reduced to 0.1 mg/l for so-DWNTs and 10 mg/l for st-DWNTs. Growth inhibition in O. melastigma was observed at 10 mg/l for so-DWNTs but not for st-DWNTs. Conclusion:Given that so-DWNTs are consistently significantly more toxic than st-DWNTs, dispersion method and size of aggregations should be considered in DWNT toxicity testing

Endobronchial Tuberculosis Simulating Lung Cancer and Healing without Bronchial Stenosis

Endobroncheal tuberculosis is defined as tuberculous infection of the tracheobronchial tree with microbial and histopathological evidence. The disease is usually mistaken for other lung diseases including lung cancer. Bronchial stenosis is a common complication of this type of tuberculosis despite the use of effective anti-tuberculous chemotherapy. We are presenting a case of endobronchial tuberculosis that simulated lung cancer and healed without residual bronchial stenosis

Security challenges of small cell as a service in virtualized mobile edge computing environments

Research on next-generation 5G wireless networks is currently attracting a lot of attention in both academia and industry. While 5G development and standardization activities are still at their early stage, it is widely acknowledged that 5G systems are going to extensively rely on dense small cell deployments, which would exploit infrastructure and network functions virtualization (NFV), and push the network intelligence towards network edges by embracing the concept of mobile edge computing (MEC). As security will be a fundamental enabling factor of small cell as a service (SCaaS) in 5G networks, we present the most prominent threats and vulnerabilities against a broad range of targets. As far as the related work is concerned, to the best of our knowledge, this paper is the first to investigate security challenges at the intersection of SCaaS, NFV, and MEC. It is also the first paper that proposes a set of criteria to facilitate a clear and effective taxonomy of security challenges of main elements of 5G networks. Our analysis can serve as a staring point towards the development of appropriate 5G security solutions. These will have crucial effect on legal and regulatory frameworks as well as on decisions of businesses, governments, and end-users

Disturbance Observer

Disturbance observer is an inner-loop output-feedback controller whose role is to reject external disturbances and to make the outer-loop baseline controller robust against plant's uncertainties. Therefore, the closed-loop system with the DOB approximates the nominal closed-loop by the baseline controller and the nominal plant model with no disturbances. This article presents how the disturbance observer works under what conditions, and how one can design a disturbance observer to guarantee robust stability and to recover the nominal performance not only in the steady-state but also for the transient response under large uncertainty and disturbance

Muon to electron conversion in the Littlest Higgs model with T-parity

Little Higgs models provide a natural explanation of the little hierarchy between the electroweak scale and a few TeV scale, where new physics is expected. Under the same inspiring naturalness arguments, this work completes a previous study on lepton flavor-changing processes in the Littlest Higgs model with T-parity exploring the channel that will eventually turn out to be the most sensitive, \mu-e conversion in nuclei. All one-loop contributions are carefully taken into account, results for the most relevant nuclei are provided and a discussion of the influence of the quark mixing is included. The results for the Ti nucleus are in good agreement with earlier work by Blanke et al., where a degenerate mirror quark sector was assumed. The conclusion is that, although this particular model reduces the tension with electroweak precision tests, if the restrictions on the parameter space derived from lepton flavor violation are taken seriously, the degree of fine tuning necessary to meet these constraints also disfavors this model.Comment: 26 pages, 7 figures, 4 tables; discussion improved, results unchanged, one reference added, version to appear in JHE

Recurrent lower gastrointestinal bleeding from idiopathic ileocolonic varices: a case report

<p>Abstract</p> <p>Introduction</p> <p>Varices of the colon are a rare cause of lower gastrointestinal bleeding, usually associated with portal hypertension due to liver cirrhosis or other causes of portal venous obstruction. Idiopathic colonic varices are extremely rare. Recognition of this condition is important as idiopathic colonic varices may be a cause of recurrent lower gastrointestinal bleeding.</p> <p>Case presentation</p> <p>We report the case of a 21-year-old Asian man from north India who presented with recurrent episodes of lower gastrointestinal bleeding. Colonoscopy revealed varices involving the terminal ileum and colon to the sigmoid. Thorough evaluation was undertaken to rule out any underlying portal hypertension. Our patient underwent subtotal colectomy including resection of involved terminal ileum and an ileorectal anastomosis.</p> <p>Conclusion</p> <p>Colonic varices are an uncommon cause of lower gastrointestinal bleeding. Idiopathic colonic varices are diagnosed after excluding underlying liver disease and portal hypertension. Recognition of this condition is important as prognosis is good in the absence of liver disease and is curable by resection of the involved bowel.</p

Comparison of Artificial Neural Network and Logistic Regression Models for Predicting In-Hospital Mortality after Primary Liver Cancer Surgery

BACKGROUND: Since most published articles comparing the performance of artificial neural network (ANN) models and logistic regression (LR) models for predicting hepatocellular carcinoma (HCC) outcomes used only a single dataset, the essential issue of internal validity (reproducibility) of the models has not been addressed. The study purposes to validate the use of ANN model for predicting in-hospital mortality in HCC surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model. METHODOLOGY/PRINCIPAL FINDINGS: Patients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC) curves, Hosmer-Lemeshow (H-L) statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive) parameter affecting in-hospital mortality followed by age and lengths of stay. CONCLUSIONS/SIGNIFICANCE: In comparison with the conventional LR model, the ANN model in the study was more accurate in predicting in-hospital mortality and had higher overall performance indices. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data

Entity linking of tweets based on dominant entity candidates

© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Entity linking, also known as semantic annotation, of textual content has received increasing attention. Recent works in this area have focused on entity linking on text with special characteristics such as search queries and tweets. The semantic annotation of tweets is specially proven to be challenging given the informal nature of the writing and the short length of the text. In this paper, we propose a method to perform entity linking on tweets built based on one primary hypothesis. We hypothesize that while there are formally many possible entity candidates for an ambiguous mention in a tweet, as listed on the disambiguation page of the corresponding entity on Wikipedia, there are only few entity candidates that are likely to be employed in the context of Twitter. Based on this hypothesis, we propose a method to identify such dominant entity candidates for each ambiguous mention and use them in the annotation process. Particularly, our proposed work integrates two phases (i) dominant entity candidate detection, which applies community detection methods for finding the dominant candidates of ambiguous mentions; and (ii) named entity disambiguation that links a tweet to entities in Wikipedia by only considering the identified dominant entity candidates. Our investigations show that: (1) there are only very few entity candidates for each ambiguous mention in a tweet that need to be considered when performing disambiguation. This helps us limit the candidate search space and hence noticeably reduce the entity linking time; (2) limiting the search space to only a subset of disambiguation options will not only improve entity linking execution time but will also lead to improved accuracy of the entity linking process when the main entity candidates of each mention are mined from a temporally aligned corpus. We show that our proposed method offers competitive results with the state-of-the-art methods in terms of precision and recall on widely used gold standard datasets while significantly reducing the time for processing each tweet