Echinococcus metacestodes as laboratory models for the screening of drugs against cestodes and trematodes

Among the cestodes, Echinococcus granulosus, Echinococcus multilocularis and Taenia solium represent the most dangerous parasites. Their larval stages cause the diseases cystic echinococcosis (CE), alveolar echinococcosis (AE) and cysticercosis, respectively, which exhibit considerable medical and veterinary health concerns with a profound economic impact. Others caused by other cestodes, such as species of the genera Mesocestoides and Hymenolepis, are relatively rare in humans. In this review, we will focus on E. granulosus and E. multilocularis metacestode laboratory models and will review the use of these models in the search for novel drugs that could be employed for chemotherapeutic treatment of echinococcosis. Clearly, improved therapeutic drugs are needed for the treatment of AE and CE, and this can only be achieved through the development of medium-to-high throughput screening approaches. The most recent achievements in the in vitro culture and genetic manipulation of E. multilocularis cells and metacestodes, and the accessability of the E. multilocularis genome and EST sequence information, have rendered the E. multilocularis model uniquely suited for studies on drug-efficacy and drug target identification. This could lead to the development of novel compounds for the use in chemotherapy against echinococcosis, and possibly against diseases caused by other cestodes, and potentially also trematodes.

Three-dimensional printing of models for preoperative planning and simulation of transcatheter valve replacement

In this study, we show the use of three-dimensional printing models for preoperative planning of transcatheter valve replacement in a patient with an extreme porcelain aorta. A 70-year-old man with severe aortic stenosis and a porcelain aorta was referred to our center for transcatheter aortic valve replacement. Unfortunately, the patient died after the procedure because of a potential ischemic event. Therefore, we decided to fabricate three-dimensional models to evaluate the potential effects of these constructs for previous surgical planning and simulation of the transcatheter valve replacement.

Zinc Compounds as Models for Zinc Metalloenzymes

In this study zinc compounds containing coordination spheres similar to those in zinc metalloenzymes were identified as targets for potential investigation by ssNMR. The title compounds, zinc catena-(tetrakis(µ2-Benzoato-O,O’))-di-zinc(II), diaquabis(salicylato) zinc, Zn2PSAP(OAc)2, cis-diaqua-diglycolatozinc (II), hexaaquazinc(II)p-hydroxybenzoate dihydrate, and catena-((µ-3-nitrobenzoato) (µ-3-nitrobenzoic acid)sodium) were synthesized, crystallized and their structures solved using single crystal Xray diffraction

Simulator training on pulsatile vascular models significantly improves surgical skills and the quality of carotid patch plasty

Vascular surgeons perform numerous highly sophisticated and delicate procedures. Due to restrictions in training time and the advent of endovascular techniques, new concepts including alternative environments for training and assessment of surgical skills are required. Over the past decade, training on simulators and synthetic models has become more sophisticated and lifelike. This study was designed to evaluate the impact of a 3-day intense training course in open vascular surgery on both specific and global vascular surgical skills.

Pancreatic carcinoma, pancreatitis, and healthy controls: metabolite models in a three-class diagnostic dilemma

Metabolomics as one of the most rapidly growing technologies in the "-omics" field denotes the comprehensive analysis of low molecular-weight compounds and their pathways. Cancer-specific alterations of the metabolome can be detected by high-throughput mass-spectrometric metabolite profiling and serve as a considerable source of new markers for the early differentiation of malignant diseases as well as their distinction from benign states. However, a comprehensive framework for the statistical evaluation of marker panels in a multi-class setting has not yet been established. We collected serum samples of 40 pancreatic carcinoma patients, 40 controls, and 23 pancreatitis patients according to standard protocols and generated amino acid profiles by routine mass-spectrometry. In an intrinsic three-class bioinformatic approach we compared these profiles, evaluated their selectivity and computed multi-marker panels combined with the conventional tumor marker CA 19-9. Additionally, we tested for non-inferiority and superiority to determine the diagnostic surplus value of our multi-metabolite marker panels. Compared to CA 19-9 alone, the combined amino acid-based metabolite panel had a superior selectivity for the discrimination of healthy controls, pancreatitis, and pancreatic carcinoma patients [Formula: see text] We combined highly standardized samples, a three-class study design, a high-throughput mass-spectrometric technique, and a comprehensive bioinformatic framework to identify metabolite panels selective for all three groups in a single approach. Our results suggest that metabolomic profiling necessitates appropriate evaluation strategies and-despite all its current limitations-can deliver marker panels with high selectivity even in multi-class settings.

An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models

Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy.

Quantification of atelectatic lung volumes in two different porcine models of ARDS

BACKGROUND: Cyclic recruitment during mechanical ventilation contributes to ventilator associated lung injury. Two different pathomechanisms in acute respiratory distress syndrome (ARDS) are currently discussed: alveolar collapse vs persistent flooding of small airways and alveoli. We compare two different ARDS animal models by computed tomography (CT) to describe different recruitment and derecruitment mechanisms at different airway pressures: (i) lavage-ARDS, favouring alveolar collapse by surfactant depletion; and (ii) oleic acid ARDS, favouring alveolar flooding by capillary leakage. METHODS: In 12 pigs [25 (1) kg], ARDS was randomly induced, either by saline lung lavage or oleic acid (OA) injection, and 3 animals served as controls. A respiratory breathhold manoeuvre without spontaneous breathing at different continuous positive airway pressure (CPAP) was applied in random order (CPAP levels of 5, 10, 15, 30, 35 and 50 cm H(2)O) and spiral-CT scans of the total lung were acquired at each CPAP level (slice thickness=1 mm). In each spiral-CT the volume of total lung parenchyma, tissue, gas, non-aerated, well-aerated, poorly aerated, and over-aerated lung was calculated. RESULTS: In both ARDS models non-aerated lung volume decreased significantly from CPAP 5 to CPAP 50 [oleic acid lung injury (OAI): 346.9 (80.1) to 96.4 (48.8) ml, P<0.001; lavage-ARDS: 245 17.6) to 42.7 (4.8) ml, P<0.001]. In lavage-ARDS poorly aerated lung volume decreased at higher CPAP levels [232 (45.2) at CPAP 10 to 84 (19.4) ml at CPAP 50, P<0.001] whereas in OAI poorly aerated lung volume did not vary at different airway pressures. CONCLUSIONS: In both ARDS models well-aerated and non-aerated lung volume respond to different CPAP levels in a comparable fashion: Thus, a cyclical alveolar collapse seems to be part of the derecruitment process also in the OA-ARDS. In OA-ARDS, the increase in poorly aerated lung volume reflects the specific initial lesion, that is capillary leakage with interstitial and alveolar oedema.

Bayesian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortality

In this paper, we develop Bayesian hierarchical distributed lag models for estimating associations between daily variations in summer ozone levels and daily variations in cardiovascular and respiratory (CVDRESP) mortality counts for 19 U.S. large cities included in the National Morbidity Mortality Air Pollution Study (NMMAPS) for the period 1987 - 1994. At the first stage, we define a semi-parametric distributed lag Poisson regression model to estimate city-specific relative rates of CVDRESP associated with short-term exposure to summer ozone. At the second stage, we specify a class of distributions for the true city-specific relative rates to estimate an overall effect by taking into account the variability within and across cities. We perform the calculations with respect to several random effects distributions (normal, t-student, and mixture of normal), thus relaxing the common assumption of a two-stage normal-normal hierarchical model. We assess the sensitivity of the results to: 1) lag structure for ozone exposure; 2) degree of adjustment for long-term trends; 3) inclusion of other pollutants in the model;4) heat waves; 5) random effects distributions; and 6) prior hyperparameters. On average across cities, we found that a 10ppb increase in summer ozone level for every day in the previous week is associated with 1.25 percent increase in CVDRESP mortality (95% posterior regions: 0.47, 2.03). The relative rate estimates are also positive and statistically significant at lags 0, 1, and 2. We found that associations between summer ozone and CVDRESP mortality are sensitive to the confounding adjustment for PM_10, but are robust to: 1) the adjustment for long-term trends, other gaseous pollutants (NO_2, SO_2, and CO); 2) the distributional assumptions at the second stage of the hierarchical model; and 3) the prior distributions on all unknown parameters. Bayesian hierarchical distributed lag models and their application to the NMMAPS data allow us estimation of an acute health effect associated with exposure to ambient air pollution in the last few days on average across several locations. The application of these methods and the systematic assessment of the sensitivity of findings to model assumptions provide important epidemiological evidence for future air quality regulations.

Substitutions due to antiretroviral toxicity or contraindication in the first 3 years of antiretroviral therapy in a large South African cohort

INTRODUCTION: The patterns and reasons for antiretroviral therapy (ART) drug substitutions are poorly described in resource-limited settings. METHODS: Time to and reason for drug substitution were recorded in treatment-naive adults receiving ART in two primary care treatment programmes in Cape Town. The cumulative proportion of patients having therapy changed because of toxicity was described for each drug, and associations with these changes were explored in multivariate models. RESULTS: Analysis included 2,679 individuals followed for a median of 11 months. Median CD4+ T-cell count at baseline was 85 cells/microl. Mean weight was 59 kg, mean age was 32 years and 71% were women. All started non-nucleoside reverse transcriptase inhibitor-based ART (60% on efavrienz) and 75% started on stavudine (d4T). After 3 years, 75% remained in care on-site, of whom 72% remained on their initial regimen. Substitutions due to toxicity of nevirapine (8% by 3 years), efavirenz (2%) and zidovudine (8%) occurred early. Substitutions on d4T occurred in 21% of patients by 3 years, due to symptomatic hyperlactataemia (5%), lipodystrophy (9%) or peripheral neuropathy (6%), and continued to accumulate over time. Those at greatest risk of hyperlactataemia or lipodystrophy were women on ART > or =6 months, weighing > or =75 kg at baseline. DISCUSSION: A high proportion of adult patients are able to tolerate their initial ART regimen for up to 3 years. In most instances treatment-limiting toxicities occur early, but continue to accumulate over time in patients on d4T. Whilst awaiting other treatment options, the risks of known toxicities could be minimized through early identification of patients at the highest risk.

CA15-3 and alkaline phosphatase as predictors for breast cancer recurrence: a combined analysis of seven International Breast Cancer Study Group trials

BACKGROUND: We evaluated the ability of CA15-3 and alkaline phosphatase (ALP) to predict breast cancer recurrence. PATIENTS AND METHODS: Data from seven International Breast Cancer Study Group trials were combined. The primary end point was relapse-free survival (RFS) (time from randomization to first breast cancer recurrence), and analyses included 3953 patients with one or more CA15-3 and ALP measurement during their RFS period. CA15-3 was considered abnormal if >30 U/ml or >50% higher than the first value recorded; ALP was recorded as normal, abnormal, or equivocal. Cox proportional hazards models with a time-varying indicator for abnormal CA15-3 and/or ALP were utilized. RESULTS: Overall, 784 patients (20%) had a recurrence, before which 274 (35%) had one or more abnormal CA15-3 and 35 (4%) had one or more abnormal ALP. Risk of recurrence increased by 30% for patients with abnormal CA15-3 [hazard ratio (HR) = 1.30; P = 0.0005], and by 4% for those with abnormal ALP (HR = 1.04; P = 0.82). Recurrence risk was greatest for patients with either (HR = 2.40; P < 0.0001) and with both (HR = 4.69; P < 0.0001) biomarkers abnormal. ALP better predicted liver recurrence. CONCLUSIONS: CA15-3 was better able to predict breast cancer recurrence than ALP, but use of both biomarkers together provided a better early indicator of recurrence. Whether routine use of these biomarkers improves overall survival remains an open question.

Simple beam models for Monte Carlo photon beam dose calculations in radiotherapy

Monte Carlo (code GEANT) produced 6 and 15 MV phase space (PS) data were used to define several simple photon beam models. For creating the PS data the energy of starting electrons hitting the target was tuned to get correct depth dose data compared to measurements. The modeling process used the full PS information within the geometrical boundaries of the beam including all scattered radiation of the accelerator head. Scattered radiation outside the boundaries was neglected. Photons and electrons were assumed to be radiated from point sources. Four different models were investigated which involved different ways to determine the energies and locations of beam particles in the output plane. Depth dose curves, profiles, and relative output factors were calculated with these models for six field sizes from 5x5 to 40x40cm2 and compared to measurements. Model 1 uses a photon energy spectrum independent of location in the PS plane and a constant photon fluence in this plane. Model 2 takes into account the spatial particle fluence distribution in the PS plane. A constant fluence is used again in model 3, but the photon energy spectrum depends upon the off axis position. Model 4, finally uses the spatial particle fluence distribution and off axis dependent photon energy spectra in the PS plane. Depth dose curves and profiles for field sizes up to 10x10cm2 were not model sensitive. Good agreement between measured and calculated depth dose curves and profiles for all field sizes was reached for model 4. However, increasing deviations were found for increasing field sizes for models 1-3. Large deviations resulted for the profiles of models 2 and 3. This is due to the fact that these models overestimate and underestimate the energy fluence at large off axis distances. Relative output factors consistent with measurements resulted only for model 4.

An integrated approach for reconstructing surface models of the proximal femur from sparse input data for surgical navigation

A patient-specific surface model of the proximal femur plays an important role in planning and supporting various computer-assisted surgical procedures including total hip replacement, hip resurfacing, and osteotomy of the proximal femur. The common approach to derive 3D models of the proximal femur is to use imaging techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). However, the high logistic effort, the extra radiation (CT-imaging), and the large quantity of data to be acquired and processed make them less functional. In this paper, we present an integrated approach using a multi-level point distribution model (ML-PDM) to reconstruct a patient-specific model of the proximal femur from intra-operatively available sparse data. Results of experiments performed on dry cadaveric bones using dozens of 3D points are presented, as well as experiments using a limited number of 2D X-ray images, which demonstrate promising accuracy of the present approach.