Is a change in patient-reported dysphagia after induction chemotherapy in locally advanced esophageal cancer a predictive factor for pathological response to neoadjuvant chemoradiation?

GOALS OF WORK: In patients with locally advanced esophageal cancer, only those responding to the treatment ultimately benefit from preoperative chemoradiation. We investigated whether changes in subjective dysphagia or eating restrictions after two cycles of induction chemotherapy can predict histopathological tumor response observed after chemoradiation. In addition, we examined general long-term quality of life (QoL) and, in particular, eating restrictions after esophagectomy. MATERIALS AND METHODS: Patients with resectable, locally advanced squamous cell- or adenocarcinoma of the esophagus were treated with two cycles of chemotherapy followed by chemoradiation and surgery. They were asked to complete the EORTC oesophageal-specific QoL module (EORTC QLQ-OES24), and linear analogue self-assessment QoL indicators, before and during neoadjuvant therapy and quarterly until 1 year postoperatively. A median change of at least eight points was considered as clinically meaningful. MAIN RESULTS: Clinically meaningful improvements in the median scores for dysphagia and eating restrictions were found during induction chemotherapy. These improvements were not associated with a histopathological response observed after chemoradiation, but enhanced treatment compliance. Postoperatively, dysphagia scores remained low at 1 year, while eating restrictions persisted more frequently in patients with extended transthoracic resection compared to those with limited transhiatal resection. CONCLUSIONS: The improvement of dysphagia and eating restrictions after induction chemotherapy did not predict tumor response observed after chemoradiation. One year after esophagectomy, dysphagia was a minor problem, and global QoL was rather good. Eating restrictions persisted depending on the surgical technique used.

Admission blood glucose is an independent predictive factor for hospital mortality in polytraumatised patients

PURPOSE: The goal of this study was to analyse a possible association of admission blood glucose with hospital mortality of polytraumatised patients and to develop an outcome prediction model for this patient group. METHODS: The outcome of adult polytraumatised patients admitted to the University Hospital of Berne, Switzerland, between 2002 and 2004 with an ISS > or = 17, and more than one severely injured organ system was retrospectively analysed. RESULTS: The inclusion criteria were met by 555 patients, of which 108 (19.5%) died. Hyperglycaemia proved to be an independent predictor for hospital mortality (P < 0.0001), following multiple regression analysis. After inclusion of admission blood glucose, the calculated mortality prediction model performed better than currently described models (P < 0.0001, AUC 0.924). CONCLUSION: In this retrospective, single-centre study in polytraumatised patients, admission blood glucose proved to be an independent predictor of hospital mortality following regression analysis controlling for age, gender, injury severity and other laboratory parameters. A reliable admission blood glucose-based mortality prediction model for polytraumatised patients could be established. This observation may be helpful in improving the precision of future outcome prediction models for polytraumatised patients. These observations warrant further prospective evaluation.

Limited predictive value of FDG-PET for response assessment in the preoperative treatment of esophageal cancer: results of a prospective multi-center trial (SAKK 75/02)

BACKGROUND: Only responding patients benefit from preoperative therapy for locally advanced esophageal carcinoma. Early detection of non-responders may avoid futile treatment and delayed surgery. PATIENTS AND METHODS: In a multi-center phase ll trial, patients with resectable, locally advanced esophageal carcinoma were treated with 2 cycles of induction chemotherapy followed by chemoradiotherapy (CRT) and surgery. Positron emission tomography with 2[fluorine-18]fluoro-2-deoxy-d-glucose (FDG-PET) was performed at baseline and after induction chemotherapy. The metabolic response was correlated with tumor regression grade (TRG). A decrease in FDG tumor uptake of less than 40% was prospectively hypothesized as a predictor for histopathological non-response (TRG > 2) after CRT. RESULTS: 45 patients were included. The median decrease in FDG tumor uptake after chemotherapy correlated well with TRG after completion of CRT (p = 0.021). For an individual patient, less than 40% decrease in FDG tumor uptake after induction chemotherapy predicted histopathological non-response after completion of CRT, with a sensitivity of 68% and a specificity of 52% (positive predictive value 58%, negative predictive value 63%). CONCLUSIONS: Metabolic response correlated with histopathology after preoperative therapy. However, FDG-PET did not predict non-response after induction chemotherapy with sufficient clinical accuracy to justify withdrawal of subsequent CRT and selection of patients to proceed directly to surgery.

An insulin infusion advisory system for type 1 diabetes patients based on non-linear model predictive control methods

In this paper, an Insulin Infusion Advisory System (IIAS) for Type 1 diabetes patients, which use insulin pumps for the Continuous Subcutaneous Insulin Infusion (CSII) is presented. The purpose of the system is to estimate the appropriate insulin infusion rates. The system is based on a Non-Linear Model Predictive Controller (NMPC) which uses a hybrid model. The model comprises a Compartmental Model (CM), which simulates the absorption of the glucose to the blood due to meal intakes, and a Neural Network (NN), which simulates the glucose-insulin kinetics. The NN is a Recurrent NN (RNN) trained with the Real Time Recurrent Learning (RTRL) algorithm. The output of the model consists of short term glucose predictions and provides input to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. For the development and the evaluation of the IIAS, data generated from a Mathematical Model (MM) of a Type 1 diabetes patient have been used. The proposed control strategy is evaluated at multiple meal disturbances, various noise levels and additional time delays. The results indicate that the implemented IIAS is capable of handling multiple meals, which correspond to realistic meal profiles, large noise levels and time delays.

Predictive-DCT Coding for 3D Mesh Sequences Compression

This paper proposes a new compression algorithm for dynamic 3d meshes. In such a sequence of meshes, neighboring vertices have a strong tendency to behave similarly and the degree of dependencies between their locations in two successive frames is very large which can be efficiently exploited using a combination of Predictive and DCT coders (PDCT). Our strategy gathers mesh vertices of similar motions into clusters, establish a local coordinate frame (LCF) for each cluster and encodes frame by frame and each cluster separately. The vertices of each cluster have small variation over a time relative to the LCF. Therefore, the location of each new vertex is well predicted from its location in the previous frame relative to the LCF of its cluster. The difference between the original and the predicted local coordinates are then transformed into frequency domain using DCT. The resulting DCT coefficients are quantized and compressed with entropy coding. The original sequence of meshes can be reconstructed from only a few non-zero DCT coefficients without significant loss in visual quality. Experimental results show that our strategy outperforms or comes close to other coders.

Learning Analytics

This paper provides a brief introduction to the domain of ‘learning analytics’. We first explain the background and idea behind the concept. Then we give a brief overview of current research issues. We briefly list some more controversial issues before concluding.

Action Research and Learning Analytics in Higher Education

Teaching is a dynamic activity. It can be very effective, if its impact is constantly monitored and adjusted to the demands of changing social contexts and needs of learners. This implies that teachers need to be aware about teaching and learning processes. Moreover, they should constantly question their didactical methods and the learning resources, which they provide to their students. They should reflect if their actions are suitable, and they should regulate their teaching, e.g., by updating learning materials based on new knowledge about learners, or by motivating learners to engage in further learning activities. In the last years, a rising interest in ‘learning analytics’ is observable. This interest is motivated by the availability of massive amounts of educational data. Also, the continuously increasing processing power, and a strong motivation for discovering new information from these pools of educational data, is pushing further developments within the learning analytics research field. Learning analytics could be a method for reflective teaching practice that enables and guides teachers to investigate and evaluate their work in future learning scenarios. However, this potentially positive impact has not yet been sufficiently verified by learning analytics research. Another method that pursues these goals is ‘action research’. Learning analytics promises to initiate action research processes because it facilitates awareness, reflection and regulation of teaching activities analogous to action research. Therefore, this thesis joins both concepts, in order to improve the design of learning analytics tools. Central research question of this thesis are: What are the dimensions of learning analytics in relation to action research, which need to be considered when designing a learning analytics tool? How does a learning analytics dashboard impact the teachers of technology-enhanced university lectures regarding ‘awareness’, ‘reflection’ and ‘action’? Does it initiate action research? Which are central requirements for a learning analytics tool, which pursues such effects? This project followed design-based research principles, in order to answer these research questions. The main contributions are: a theoretical reference model that connects action research and learning analytics, the conceptualization and implementation of a learning analytics tool, a requirements catalogue for useful and usable learning analytics design based on evaluations, a tested procedure for impact analysis, and guidelines for the introduction of learning analytics into higher education.

Learning Analytics: Challenges and Future Research Directions

In recent years, learning analytics (LA) has attracted a great deal of attention in technology-enhanced learning (TEL) research as practitioners, institutions, and researchers are increasingly seeing the potential that LA has to shape the future TEL landscape. Generally, LA deals with the development of methods that harness educational data sets to support the learning process. This paper provides a foundation for future research in LA. It provides a systematic overview on this emerging field and its key concepts through a reference model for LA based on four dimensions, namely data, environments, context (what?), stakeholders (who?), objectives (why?), and methods (how?). It further identifies various challenges and research opportunities in the area of LA in relation to each dimension.

The Costs and Predictive Factors of Bovine Respiratory Disease in Standardized Steer Tests

A retrospective study of 2,146 feedlot cattle in 17 feedlot tests from 1988 to 1997 was conducted to determine the impact of bovine respiratory disease (BRD) on veterinary treatment costs, average daily gain, carcass traits, mortality, and net profit. Morbidity caused by BRD was 20.6%. The average cost to treat each case of BRD was $12.39. Mortality rate of calves diagnosed and treated for BRD was 5.9% vs. .35% for those not diagnosed with BRD. Average daily gain differed between treated and non-treated steers during the first 28 days on feed but did not differ from 28 days to harvest. Net profit was $57.48 lower for treated steers. Eighty-two percent of this difference was due to a combination of mortality and treatment costs. Eighteen percent of the net profit difference was due to improved performance and carcass value of the non-treated steers. Data from 496 steers and heifers in nine feedlot tests were used to determine the effects of age, weaning, and use of modified live virus or killed vaccines prior to the test to predict BRD. Younger calves, non-weaned calves, and calves vaccinated with killed vaccines prior to the test had higher BRD morbidity than those that were older, weaned, or vaccinated with modified live virus vaccines, respectively. Treatment regimes that precluded relapse resulting in re-treatment prevented reduced performance and loss of carcass value. Using modified live virus vaccines and weaning calves 30 days prior to shipment reduced the incidence of BRD.

Mid-term results in relation to age and analysis of predictive factors after fixation of acetabular fractures using the modified Stoppa approach.

INTRODUCTION Data concerning outcome after management of acetabular fractures by anterior approaches with focus on age and fractures associated with roof impaction, central dislocation and/or quadrilateral plate displacement are rare. METHODS Between October 2005 and April 2009 a series of 59 patients (mean age 57 years, range 13-91) with fractures involving the anterior column was treated using the modified Stoppa approach alone or for reduction of displaced iliac wing or low anterior column fractures in combination with the 1st window of the ilioinguinal approach or the modified Smith-Petersen approach, respectively. Surgical data, accuracy of reduction, clinical and radiographic outcome at mid-term and the need for endoprosthetic replacement in the postoperative course (defined as failure) were assessed; uni- and multivariate regression analysis were performed to identify independent predictive factors (e.g. age, nonanatomical reduction, acetabular roof impaction, central dislocation, quadrilateral plate displacement) for a failure. Outcome was assessed for all patients in general and in accordance to age in particular; patients were subdivided into two groups according to their age (group "<60yrs", group "≥60yrs"). RESULTS Forty-three of 59 patients (mean age 54yrs, 13-89) were available for evaluation. Of these, anatomic reduction was achieved in 72% of cases. Nonanatomical reduction was identified as being the only multivariate predictor for subsequent total hip replacement (Adjusted Hazard Ratio 23.5; p<0.01). A statistically significant higher rate of nonanatomical reduction was observed in the presence of acetabular roof impaction (p=0.01). In 16% of all patients, total hip replacement was performed and in 69% of patients with preserved hips the clinical results were excellent or good at a mean follow up of 35±10 months (range: 24-55). No statistical significant differences were observed between both groups. CONCLUSION Nonanatomical reconstruction of the articular surfaces is at risk for failure of joint-preserving management of acetabular fractures through an isolated or combined modified Stoppa approach resulting in total joint replacement at mid-term. In the elderly, joint-preserving surgery is worth considering as promising clinical and radiographic results might be obtained at mid-term.

Serial lactate and admission SOFA scores in trauma: an analysis of predictive value in 724 patients with and without traumatic brain injury

Objective Arterial lactate, base excess (BE), lactate clearance, and Sequential Organ Failure Assessment (SOFA) score have been shown to correlate with outcome in severely injured patients. The goal of the present study was to separately assess their predictive value in patients suffering from traumatic brain injury (TBI) as opposed to patients suffering from injuries not related to the brain. Materials and methods A total of 724 adult trauma patients with an Injury Severity Score (ISS) ≥ 16 were grouped into patients without TBI (non-TBI), patients with isolated TBI (isolated TBI), and patients with a combination of TBI and non-TBI injuries (combined injuries). The predictive value of the above parameters was then analyzed using both uni- and multivariate analyses. Results The mean age of the patients was 39 years (77 % males), with a mean ISS of 32 (range 16–75). Mortality ranged from 14 % (non-TBI) to 24 % (combined injuries). Admission and serial lactate/BE values were higher in non-survivors of all groups (all p < 0.01), but not in patients with isolated TBI. Admission SOFA scores were highest in non-survivors of all groups (p = 0.023); subsequently septic patients also showed elevated SOFA scores (p < 0.01), except those with isolated TBI. In this group, SOFA score was the only parameter which showed significant differences between survivors and non-survivors. Receiver operating characteristic (ROC) analysis revealed lactate to be the best overall predictor for increased mortality and further septic complications, irrespective of the leading injury. Conclusion Lactate showed the best performance in predicting sepsis or death in all trauma patients except those with isolated TBI, and the differences were greatest in patients with substantial bleeding. Following isolated TBI, SOFA score was the only parameter which could differentiate survivors from non-survivors on admission, although the SOFA score, too, was not an independent predictor of death following multivariate analysis.

SLA-driven predictive orchestration for distributed cloud-based mobile services

We describe a system for performing SLA-driven management and orchestration of distributed infrastructures composed of services supporting mobile computing use cases. In particular, we focus on a Follow-Me Cloud scenario in which we consider mobile users accessing cloud-enable services. We combine a SLA-driven approach to infrastructure optimization, with forecast-based performance degradation preventive actions and pattern detection for supporting mobile cloud infrastructure management. We present our system's information model and architecture including the algorithmic support and the proposed scenarios for system evaluation.

Clinical signs of deformed wing virus infection are predictive markers for honey bee colony losses.

The ectoparasitic mite Varroa destructor acting as a virus vector constitutes a central mechanism for losses of managed honey bee, Apis mellifera, colonies. This creates demand for an easy, accurate and cheap diagnostic tool to estimate the impact of viruliferous mites in the field. Here we evaluated whether the clinical signs of the ubiquitous and mite-transmitted deformed wing virus (DWV) can be predictive markers of winter losses. In fall and winter 2007/2008, A.m. carnica workers with apparent wing deformities were counted daily in traps installed on 29 queenright colonies. The data show that colonies which later died had a significantly higher proportion of workers with wing deformities than did those which survived. There was a significant positive correlation between V. destructor infestation levels and the number of workers displaying DWV clinical signs, further supporting the mite's impact on virus infections at the colony level. A logistic regression model suggests that colony size, the number of workers with wing deformities and V. destructor infestation levels constitute predictive markers for winter colony losses in this order of importance and ease of evaluation.