Cholangiocarcinoma presenting as hemobilia and recurrent iron-deficiency anemia: a case report.

INTRODUCTION: Iron-deficiency anemia is a relatively common presenting feature of several gastrointestinal malignancies. However, cholangiocarcinoma has rarely been reported as an underlying cause. The association of cholangiocarcinoma with the rare clinical finding of hemobilia is also highly unusual. To our knowledge, this is the first case report of cholangiocarcinoma presenting with acute hemobilia and chronic iron-deficiency anemia. CASE PRESENTATION: We report the case of a Caucasian, 84-year-old woman presenting with recurrent, severe iron-deficiency anemia who was eventually diagnosed with intra-hepatic cholangiocarcinoma, following an acute episode of hemobilia. A right hepatectomy was subsequently performed with curative intent, and our patient has now fully recovered. CONCLUSION: This is a rare example of hemobilia and chronic iron-deficiency anemia in association with cholangiocarcinoma. We suggest that a diagnosis of cholangiocarcinoma should be considered in patients who present with iron-deficiency anemia of unknown cause, particularly in the presence of abnormal liver function.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Repeatability and Reliability of New Air and Water Permeability Tests for Assessing the Durability of High Performance Concretes

This paper reports on the accuracy of new test methods developed to measure the air and water permeability of high-performance concretes (HPCs). Five representative HPC and one normal concrete (NC) mixtures were tested to estimate both repeatability and reliability of the proposed methods. Repeatability acceptance was adjudged using values of signal-noise ratio (SNR) and discrimination ratio (DR), and reliability was investigated by comparing against standard laboratory-based test methods (i.e., the RILEM gas permeability test and BS EN water penetration test). With SNR and DR values satisfying recommended criteria, it was concluded that test repeatability error has no significant influence on results. In addition, the research confirmed strong positive relationships between the proposed test methods and existing standard permeability assessment techniques. Based on these findings, the proposed test methods show strong potential to become recognized as international methods for determining the permeability of HPCs

Expert system for early diagnosis of eye diseases infecting the Malaysian population

This paper describes a knowledge based system employing certain expert system rules to detect different kind of eye diseases found in Malaysia. The types of eye diseases that can be detected with this system are allergic or infectious conjunctivitis, secondary and senile cataract, open angle glaucoma and acute glaucoma, keratitis and dry eyes syndrome. These are the most frequent eye diseases infecting the Malaysian population. The project was designed and programmed via the object-oriented expert system shell software, EXSYS. Expert rules were developed based on the symptoms of each type of the eye diseases, and they were presented using a tree graph forward chaining with depth search first method. In order to enhance user interaction with the system, graphical user interfaces were employed. Previously, several similar works have been published, but they are limited to detecting a single disease and also required expert medical officer to operate. The expert system described in this paper is able to detect and gives early diagnosis of five types of eye diseases; inclusive of senile, secondary, open angle, acute, allergic and infections

Expert system for early diagnosis of eye diseases infecting the Malaysian population

This paper describes a knowledge based system employing certain expert system rules to detect different kind of eye diseases found in Malaysia. The types of eye diseases that can be detected with this system are allergic or infectious conjunctivitis, secondary and senile cataract, open angle glaucoma and acute glaucoma, keratitis and dry eyes syndrome. These are the most frequent eye diseases infecting the Malaysian population. The project was designed and programmed via the object-oriented expert system shell software, EXSYS. Expert rules were developed based on the symptoms of each type of the eye diseases, and they were presented using a tree graph forward chaining with depth search first method. In order to enhance user interaction with the system, graphical user interfaces were employed. Previously, several similar works have been published, but they are limited to detecting a single disease and also required expert medical officer to operate. The expert system described in this paper is able to detect and gives early diagnosis of five types of eye diseases; inclusive of senile, secondary, open angle, acute, allergic and infections

The role of calcium stearate on regulating activation to form stable, uniform and flawless reaction products in alkali-activated slag cement

In the course of an investigation on using calcium stearate (CaSt) to improve performance of the alkali-activated slag (AAS) cement, the objective of the present work is to discovery its role in the AAS system. Special interest is devoted to understand the influence of CaSt on the reaction process, reaction products and microstructural features of the AAS cement. To achieve this, isothermal calorimetry, impedance characteristics, infrared spectroscopy, X-ray diffraction, thermogravimetry, nitrogen sorption, mercury intrusion porosimetry and scanning electron microscopy were carried out. According to results obtained, the CaSt has three important effects on the AAS cement. Firstly, it inhibited slag reaction with the activator through decreasing activity of alkalis, whereas the amount of C-(A)-S-H gels in the system depended on the usage of CaSt, because the CaSt could have chemical reactions with the alkali-solution and form similar reaction products. Secondly, there is less sodium and more calcium in reaction products of the CaSt added mix, which improve their stability and uniformity. Finally, microstructure characteristics (e.g. pore size distribution, pore connectivity) are optimised and defects are reduced significantly, when CaSt is added in the AAS mix

Design of a smart lime mortar with conductive micro and nano fillers for structural health monitoring

Structural health monitoring is an essential tool for assessing the performance of buildings and infrastructure, especially after critical events or the application of structural interventions. When dealing with architectural heritage structures, both structural health monitoring instrumentation and intervention materials need to be as inconspicuous and unintrusive as possible, both in terms of mechanical compatibility and aesthetics. Therefore, the design of smart sensors based on construction materials used in conservation engineering promises to provide an acceptable integrated structural health monitoring and upgrading solution for historic structures. In this paper an experimental investigation of smart intervention materials for historic masonry structures is presented. The materials consisted of natural hydraulic lime mortars modified through the inclusion of electrically conductive micro- and nanofillers: graphite, carbon nanotubes and carbon microfibres. The fillers provide multifunctionality to the matrix material based on an enhancement of its piezoresistive characteristics. Further, they result in an improvement of the mechanical properties of the intervention material without compromising its mechanical and chemical compatibility with the original structure. The resulting materials were evaluated based on mechanical property improvement, piezoresistivity enhancement and ease of production

Sleep-wake sensitive mechanisms of adenosine release in the basal forebrain of rodents : an in vitro study

Adenosine acting in the basal forebrain is a key mediator of sleep homeostasis. Extracellular adenosine concentrations increase during wakefulness, especially during prolonged wakefulness and lead to increased sleep pressure and subsequent rebound sleep. The release of endogenous adenosine during the sleep-wake cycle has mainly been studied in vivo with microdialysis techniques. The biochemical changes that accompany sleep-wake status may be preserved in vitro. We have therefore used adenosine-sensitive biosensors in slices of the basal forebrain (BFB) to study both depolarization-evoked adenosine release and the steady state adenosine tone in rats, mice and hamsters. Adenosine release was evoked by high K+, AMPA, NMDA and mGlu receptor agonists, but not by other transmitters associated with wakefulness such as orexin, histamine or neurotensin. Evoked and basal adenosine release in the BFB in vitro exhibited three key features: the magnitude of each varied systematically with the diurnal time at which the animal was sacrificed; sleep deprivation prior to sacrifice greatly increased both evoked adenosine release and the basal tone; and the enhancement of evoked adenosine release and basal tone resulting from sleep deprivation was reversed by the inducible nitric oxide synthase (iNOS) inhibitor, 1400 W. These data indicate that characteristics of adenosine release recorded in the BFB in vitro reflect those that have been linked in vivo to the homeostatic control of sleep. Our results provide methodologically independent support for a key role for induction of iNOS as a trigger for enhanced adenosine release following sleep deprivation and suggest that this induction may constitute a biochemical memory of this state

Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa