Acute cholestatic hepatitis caused by amoxicillin/ clavulanate

Amoxicillin/clavulanate is a synthetic penicillin that is currently commonly used,especially for the treatment of respiratory and cutaneous infections.In general,it is a well-tolerated oral antibiotic.However,amoxicillin/clavulanate can cause adverse effects,mainly cutaneous,gastrointestinal,hepatic and hematologic,in some cases.Presented here is a case report of a 63-yearold male patient who developed cholestatic hepatitis after recent use of amoxicillin/clavulanate.After 6 wk of prolonged use of the drug,he began to show signs of cholestatic icterus and developed severe hyperbilirubinemia(total bilirubin>300 mg/L).Diagnostic investigation was conducted by ultrasonography of the upper abdomen,serum tests for infection history,laboratory screening of autoimmune diseases,nuclear magnetic resonance(NMR)of the abdomen with bile duct-NMR and transcutaneous liver biopsy guided by ultrasound.The duration of disease was approximately 4 mo,with complete resolution of symptoms and laboratory changes at the end of that time period.Specific treatment was not instituted,only a combination of anti-emetic(metoclopramide)and cholestyramine for pruritus.     

Calcimimetics maintain bone turnover in uremic rats despite the concomitant decrease in parathyroid hormone concentration

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Criteria for Emergency Brain MRI During Stroke-Alert

ObjectivesIntravenous (IV) tissue plasminogen activator (tPA) should be given to patients with acute ischemic stroke (AIS) and avoided in stroke mimics (SM). Select use of emergency brain magnetic resonance imaging (eMRI-brain) in stroke-alerts aids diagnosis, but accepted utilization criteria for eMRI-brain do not currently exist. We developed criteria for eMRI-brain and report the yield of eMRI-brain in stroke-alert patients.Materials and MethodsWe developed three history-based criteria for performing eMRI-brain during stroke-alerts: (1) history of previous similar deficits, (2) change in consciousness at onset of symptoms, (3) symptom presentation consistent with migraine aura. We then performed a retrospective chart review of patients who presented as a stroke-alert over a 5-year period and determined how these criteria affected administration of IV tPA to AIS and SM patients.ResultsAmong 3,512 stroke-alerts, 230 (8.1%) patients met our criteria for eMRI-brain exams: 217 (92.6%) had SM and 17 (7.4%) had AIS. Our IV tPA decision-making analysis showed that based on eMRI-brain IV tPA was less frequently administered to SM patients (PCC-0.841, p=0.036) with less failures to administer IV tPA to patients with AIS (PCC -0.907, p-value=0.013, Pearson correlation coefficient). No patients became ineligible for IV tPA due to MRI-related time delays.ConclusionsOur history based criteria for performing eMRI-brain during stroke-alerts show a high yield of stroke mimics. Selective eMRI-brain improves decision-making accuracy regarding IV tPA administration.     

Moderately Hypofractionated Helical IMRT,FDG–PET/CT-guided,for Progressive Malignant Pleural Mesothelioma in Patients With Intact Lungs

Introduction

The objective of this study was to present the outcomes of moderately hypofractionated helical intensity-modulated radiation therapy (HT) with/without simultaneous integrated boost (SIB) on fluorodeoxyglucose-positron emission tomography (FDG-PET) positive areas (gross tumor volume [GTV]-PET) for patients with progressive malignant pleural mesothelioma (MPM) after previous treatments.

Influence of surface properties of resin‐based composites on in vitro Streptococcus mutans biofilm development

The aim of this in vitro study was to evaluate the influence of physicochemical surface properties of resin‐based composites on Streptococcus mutans biofilm formation. Specimens were prepared from each of four resin‐based composites by polymerization against Mylar strips. Half of the number of specimens received no further surface treatment, whereas the other half were subjected to a polishing treatment. Surface roughness (SR) and topography were assessed using profilometry and atomic force microscopy. Surface free‐energy (SFE) was determined, and the chemical surface composition was analysed by X‐ray photoelectron spectroscopy (XPS). S. mutans biofilms were formed on the surface of the resin‐based composite specimens for either 48 or 96 h using an artificial mouth system (AMS). Polishing caused a significant decrease in SFE, and XPS analysis indicated an increase of surface silicon and a decrease of surface carbon. Only for Grandio was a significant increase in SR identified after polishing, which was probably related to the higher concentration of filler particles on its surface. Significantly less S. mutans biofilm formation was observed on polished resin‐based composites than on unpolished resin‐based composites. These results indicate that the proportions of resin matrix and filler particles on the surface of resin‐based composites strongly influence S. mutans biofilm formation in vitro, suggesting that minimization of resin matrix exposure might be useful to reduce biofilm formation on the surface of resin‐based composites.     

Current knowledge on tumour induction by computed tomography should be carefully used

Risks associated to ionising radiation from medical imaging techniques have focused the attention of the medical society and general population. This risk is aimed to determine the probability that a tumour is induced as a result of a computed tomography (CT) examination since it makes nowadays the biggest contribution to the collective dose. Several models of cancer induction have been reported in the literature, with diametrically different implications. This article reviews those models, focusing on the ones used by the scientific community to estimate CT detriments. Current estimates of the probability that a CT examination induces cancer are reported, highlighting its low magnitude (near the background level) and large sources of uncertainty. From this objective review, it is concluded that epidemiological data with more accurate dosimetric estimates are needed. Prediction of the number of tumours that will be induced in population exposed to ionising radiation should be avoided or, if given, it should be accompanied by a realistic evaluation of its uncertainty and of the advantages of CTs. Otherwise they may have a negative impact in both the medical community and the patients. Reducing doses even more is not justified if that compromises clinical image quality in a necessary investigation. Key Points ? Predictions of radiation-induced cancer should be discussed alongside benefits of imaging. ? Estimates of induced cancers have noticeable uncertainties that should always be highlighted. ? There is controversy about the acceptance of the linear no-threshold model. ? Estimated extra risks of cancer are close to the background level. ? Patients should not be alarmed by potential cancer induction by CT examinations.     

Predicting cardiac autonomic neuropathy category for diabetic data with missing values

Cardiovascular autonomic neuropathy (CAN) is a serious and well known complication of diabetes. Previous articles circumvented the problem of missing values in CAN data by deleting all records and fields with missing values and applying classifiers trained on different sets of features that were complete. Most of them also added alternative features to compensate for the deleted ones. Here we introduce and investigate a new method for classifying CAN data with missing values. In contrast to all previous papers, our new method does not delete attributes with missing values, does not use classifiers, and does not add features. Instead it is based on regression and meta-regression combined with the Ewing formula for identifying the classes of CAN. This is the first article using the Ewing formula and regression to classify CAN. We carried out extensive experiments to determine the best combination of regression and meta-regression techniques for classifying CAN data with missing values. The best outcomes have been obtained by the additive regression meta-learner based on M5Rules and combined with the Ewing formula. It has achieved the best accuracy of 99.78% for two classes of CAN, and 98.98% for three classes of CAN. These outcomes are substantially better than previous results obtained in the literature by deleting all missing attributes and applying traditional classifiers to different sets of features without regression. Another advantage of our method is that it does not require practitioners to perform more tests collecting additional alternative features.