Modellierung von Sensornetz-basierten Logistikdienstleistungen - Evaluierung von drei Modellierungssprachen anhand des Projekts ALETHEIA

Für die Logistikbranche ist das Krisenjahr 2009 bereits Ende 2011 nur noch ein Schatten der Vergangenheit. Bis Ende 2011 prognostiziert die Bundesvereinigung Logistik e.V. einen Umsatz von 222 Milliarden Euro und die Branche erwartet eine Beschäftigtenzahl von bis zu 2,9 Millionen Arbeitnehmern (Bundesvereinigung Logistik e.V. 2011)

Wireless Sensor Network-Based Asset Management for Mobile Measurement Devices

The maturity level of Wireless Sensor Networks (WSN) has grown constantly and the market offers first components and products. Nevertheless the commercial breakthrough in industries has not yet happened due to the lack of economically justifiable applications. This paper presents the management of assets as one promising field of application for WSN. In particular we show how the existing asset management process for mobile measurement devices at Fraunhofer IIS is optimized with a WSN-based asset management system

Cerebral hemodynamic response to upright position in acute ischemic stroke

IntroductionConcerns exist that a potential mechanism for harm from upright activity (sitting, standing, and walking) early after an acute ischaemic stroke could be the reduction of cerebral perfusion during this critical phase. We aimed to estimate the effects of upright positions (sitting and standing) on cerebral hemodynamics within 48 h and later, 3–7 days post-stroke, in patients with strokes with and without occlusive disease and in controls.MethodsWe investigated MCAv using transcranial Doppler in 0° head position, then at 30°, 70°, 90° sitting, and 90° standing, at <48 h post-stroke, and later at 3–7 days post-stroke. Mixed-effect linear regression modeling was used to estimate differences in MCAv between the 0° and other positions and to compare MCAv changes across groups.ResultsA total of 42 stroke participants (anterior and posterior circulation) (13 with occlusive disease, 29 without) and 22 controls were recruited. Affected hemisphere MCAv decreased in strokes with occlusive disease (<48 h post-stroke): from 0° to 90° sitting (−9.9 cm/s, 95% CI[−16.4, −3.4]) and from 0° to 90° standing (−7.1 cm/s, 95%CI[−14.3, −0.01]). Affected hemisphere MCAv also decreased in strokes without occlusive disease: from 0° to 90° sitting (−3.3 cm/s, 95%CI[−5.6, −1.1]) and from 0° to 90° standing (−3.6 cm/s, 95%CI [−5.9, −1.3]) (p-value interaction stroke with vs. without occlusive disease = 0.07). A decrease in MCAv when upright was also observed in controls: from 0° to 90° sitting (−3.8 cm/s, 95%CI[−6.0, −1.63]) and from 0° to 90° standing (−3 cm/s, 95%CI[−5.2, −0.81]) (p-value interaction stroke vs. controls = 0.85). Subgroup analysis of anterior circulation stroke showed similar patterns of change in MCAv in the affected hemisphere, with a significant interaction between those with occlusive disease (n = 11) and those without (n = 26) (p = 0.02). Changes in MCAv from 0° to upright at <48 h post-stroke were similar to 3–7 days. No association between changes in MCAv at <48 h and the 30-day modified Rankin Scale was found.DiscussionMoving to more upright positions <2 days post-stroke does reduce MCAv in the affected hemisphere; however, these changes were not significantly different for stroke participants (anterior and posterior circulation) with and without occlusive disease, nor for controls. The decrease in MCAv in anterior circulation stroke with occlusive disease significantly differed from without occlusive disease. However, the sample size was small, and more research is warranted to confirm these findings

Performance of clinicopathologic models in men with high risk localized prostate cancer: impact of a 22-gene genomic classifier

BackgroundProstate cancer exhibits biological and clinical heterogeneity even within established clinico-pathologic risk groups. The Decipher genomic classifier (GC) is a validated method to further risk-stratify disease in patients with prostate cancer, but its performance solely within National Comprehensive Cancer Network (NCCN) high-risk disease has not been undertaken to date.MethodsA multi-institutional retrospective study of 405 men with high-risk prostate cancer who underwent primary treatment with radical prostatectomy (RP) or radiation therapy (RT) with androgen-deprivation therapy (ADT) at 11 centers from 1995 to 2005 was performed. Cox proportional hazards models were used to determine the hazard ratios (HR) for the development of metastatic disease based on clinico-pathologic variables, risk groups, and GC score. The area under the receiver operating characteristic curve (AUC) was determined for regression models without and with the GC score.ResultsOver a median follow-up of 82 months, 104 patients (26%) developed metastatic disease. On univariable analysis, increasing GC score was significantly associated with metastatic disease ([HR]: 1.34 per 0.1 unit increase, 95% confidence interval [CI]: 1.19-1.50, p < 0.001), while age, serum PSA, biopsy GG, and clinical T-stage were not (all p > 0.05). On multivariable analysis, GC score (HR: 1.33 per 0.1 unit increase, 95% CI: 1.19-1.48, p < 0.001) and GC high-risk (vs low-risk, HR: 2.95, 95% CI: 1.79-4.87, p < 0.001) were significantly associated with metastasis. The addition of GC score to regression models based on NCCN risk group improved model AUC from 0.46 to 0.67, and CAPRA from 0.59 to 0.71.ConclusionsAmong men with high-risk prostate cancer, conventional clinico-pathologic data had poor discrimination to risk stratify development of metastatic disease. GC score was a significant and independent predictor of metastasis and may help identify men best suited for treatment intensification/de-escalation

Performance of clinicopathologic models in men with high risk localized prostate cancer: impact of a 22-gene genomic classifier

Prostate cancer exhibits biological and clinical heterogeneity even within established clinico-pathologic risk groups. The Decipher genomic classifier (GC) is a validated method to further risk-stratify disease in patients with prostate cancer, but its performance solely within National Comprehensive Cancer Network (NCCN) high-risk disease has not been undertaken to date. A multi-institutional retrospective study of 405 men with high-risk prostate cancer who underwent primary treatment with radical prostatectomy (RP) or radiation therapy (RT) with androgen-deprivation therapy (ADT) at 11 centers from 1995 to 2005 was performed. Cox proportional hazards models were used to determine the hazard ratios (HR) for the development of metastatic disease based on clinico-pathologic variables, risk groups, and GC score. The area under the receiver operating characteristic curve (AUC) was determined for regression models without and with the GC score. Over a median follow-up of 82 months, 104 patients (26%) developed metastatic disease. On univariable analysis, increasing GC score was significantly associated with metastatic disease ([HR]: 1.34 per 0.1 unit increase, 95% confidence interval [CI]: 1.19-1.50, p  0.05). On multivariable analysis, GC score (HR: 1.33 per 0.1 unit increase, 95% CI: 1.19-1.48, p < 0.001) and GC high-risk (vs low-risk, HR: 2.95, 95% CI: 1.79-4.87, p < 0.001) were significantly associated with metastasis. The addition of GC score to regression models based on NCCN risk group improved model AUC from 0.46 to 0.67, and CAPRA from 0.59 to 0.71. Among men with high-risk prostate cancer, conventional clinico-pathologic data had poor discrimination to risk stratify development of metastatic disease. GC score was a significant and independent predictor of metastasis and may help identify men best suited for treatment intensification/de-escalation

Development and Validation of a Clinical Prognostic Stage Group System for Nonmetastatic Prostate Cancer Using Disease-Specific Mortality Results From the International Staging Collaboration for Cancer of the Prostate

IMPORTANCE: In 2016, the American Joint Committee on Cancer (AJCC) established criteria to evaluate prediction models for staging. No localized prostate cancer models were endorsed by the Precision Medicine Core committee, and 8th edition staging was based on expert consensus. OBJECTIVE: To develop and validate a pretreatment clinical prognostic stage group system for nonmetastatic prostate cancer. DESIGN, SETTING, AND PARTICIPANTS: This multinational cohort study included 7 centers from the United States, Canada, and Europe, the Shared Equal Access Regional Cancer Hospital (SEARCH) Veterans Affairs Medical Centers collaborative (5 centers), and the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE) registry (43 centers) (the STAR-CAP cohort). Patients with cT1-4N0-1M0 prostate adenocarcinoma treated from January 1, 1992, to December 31, 2013 (follow-up completed December 31, 2017). The STAR-CAP cohort was randomly divided into training and validation data sets; statisticians were blinded to the validation data until the model was locked. A Surveillance, Epidemiology, and End Results (SEER) cohort was used as a second validation set. Analysis was performed from January 1, 2018, to November 30, 2019. EXPOSURES: Curative intent radical prostatectomy (RP) or radiotherapy with or without androgen deprivation therapy. MAIN OUTCOMES AND MEASURES: Prostate cancer–specific mortality (PCSM). Based on a competing-risk regression model, a points-based Score staging system was developed. Model discrimination (C index), calibration, and overall performance were assessed in the validation cohorts. RESULTS: Of 19 684 patients included in the analysis (median age, 64.0 [interquartile range (IQR), 59.0-70.0] years), 12 421 were treated with RP and 7263 with radiotherapy. Median follow-up was 71.8 (IQR, 34.3-124.3) months; 4078 (20.7%) were followed up for at least 10 years. Age, T category, N category, Gleason grade, pretreatment serum prostate-specific antigen level, and the percentage of positive core biopsy results among biopsies performed were included as variables. In the validation set, predicted 10-year PCSM for the 9 Score groups ranged from 0.3% to 40.0%. The 10-year C index (0.796; 95% CI, 0.760-0.828) exceeded that of the AJCC 8th edition (0.757; 95% CI, 0.719-0.792), which was improved across age, race, and treatment modality and within the SEER validation cohort. The Score system performed similarly to individualized random survival forest and interaction models and outperformed National Comprehensive Cancer Network (NCCN) and Cancer of the Prostate Risk Assessment (CAPRA) risk grouping 3- and 4-tier classification systems (10-year C index for NCCN 3-tier, 0.729; for NCCN 4-tier, 0.746; for Score, 0.794) as well as CAPRA (10-year C index for CAPRA, 0.760; for Score, 0.782). CONCLUSIONS AND RELEVANCE: Using a large, diverse international cohort treated with standard curative treatment options, a proposed AJCC-compliant clinical prognostic stage group system for prostate cancer has been developed. This system may allow consistency of reporting and interpretation of results and clinical trial design