Does pre-operative estimation of oesophageal tumour metabolic length using F-18-fluorodeoxyglucose PET/CT images compare with surgical pathology length?

PURPOSE:
The aim of the study was to compare the pre-operative metabolic tumour length on FDG PET/CT with the resected pathological specimen in patients with oesophageal cancer.

METHODS:
All patients diagnosed with oesophageal carcinoma who had undergone staging PET/CT imaging between the period of June 2002 and May 2008 who were then suitable for curative surgery, either with or without neo-adjuvant chemotherapy, were included in this study. Metabolic tumour length was assessed using both visual analysis and a maximum standardised uptake value (SUV(max)) cutoff of 2.5.

RESULTS:
Thirty-nine patients proceeded directly to curative surgical resection, whereas 48 patients received neo-adjuvant chemotherapy, followed by curative surgery. The 95% limits of agreement in the surgical arm were more accurate when the metabolic tumour length was visually assessed with a mean difference of -0.05 cm (SD 2.16 cm) compared to a mean difference of +2.42 cm (SD 3.46 cm) when assessed with an SUV(max) cutoff of 2.5. In the neo-adjuvant group, the 95% limits of agreement were once again more accurate when assessed visually with a mean difference of -0.6 cm (SD 1.84 cm) compared to a mean difference of +1.58 cm (SD 3.1 cm) when assessed with an SUV(max) cutoff of 2.5.

CONCLUSION:
This study confirms the high accuracy of PET/CT in measuring gross target volume (GTV) length. A visual method for GTV length measurement was demonstrated to be superior and more accurate than when using an SUV(max) cutoff of 2.5. This has the potential of reducing the planning target volume with dose escalation to the tumour with a corresponding reduction in normal tissue complication probability.

Runx2, p53, and pRB Status as Diagnostic Parameters for Deregulation of Osteoblast Growth and Differentiation in a New Pre-Chemotherapeutic Osteosarcoma Cell Line (OS1)

Osteosarcomas are the most prevalent primary bone tumors found in pediatric patients. To understand their molecular etiology, cell culture models are used to define disease mechanisms under controlled conditions. Many osteosarcoma cell lines (e.g., SAOS-2, U2OS, MG63) are derived from Caucasian patients. However, patients exhibit individual and ethnic differences in their responsiveness to irradiation and chemotherapy. This motivated the establishment of osteosarcoma cell lines (OS1, OS2, OS3) from three ethnically Chinese patients. OS1 cells, derived from a pre-chemotherapeutic tumor in the femur of a 6-year-old female, were examined for molecular markers characteristic for osteoblasts, stem cells, and cell cycle control by immunohistochemistry, reverse transcriptase-PCR, Western blotting and flow cytometry. OS I have aberrant G-banded karyotypes, possibly reflecting chromosomal abnormalities related to p53 deficiency. OS I had ossification profiles similar to human fetal osteoblasts rather than SAOS-2 which ossifies ab initio, (P

Optimal glycaemic control, pre-eclampsia and gestational hypertension in women with type 1 diabetes in the Diabetes and Pre-Eclampsia Intervention Trial

OBJECTIVE
To assess the relationship between glycemic control, pre-eclampsia, and gestational hypertension in women with type 1 diabetes.

RESEARCH DESIGN AND METHODS
Pregnancy outcome (pre-eclampsia or gestational hypertension) was assessed prospectively in 749 women from the randomized controlled Diabetes and Pre-eclampsia Intervention Trial (DAPIT). HbA1c (A1C) values were available up to 6 months before pregnancy (n = 542), at the first antenatal visit (median 9 weeks) (n = 721), at 26 weeks’ gestation (n = 592), and at 34 weeks’ gestation (n = 519) and were categorized as optimal (<6.1%: referent), good (6.1–6.9%), moderate (7.0–7.9%), and poor (=8.0%) glycemic control, respectively.

RESULTS
Pre-eclampsia and gestational hypertension developed in 17 and 11% of pregnancies, respectively. Women who developed pre-eclampsia had significantly higher A1C values before and during pregnancy compared with women who did not develop pre-eclampsia (P < 0.05, respectively). In early pregnancy, A1C =8.0% was associated with a significantly increased risk of pre-eclampsia (odds ratio 3.68 [95% CI 1.17–11.6]) compared with optimal control. At 26 weeks’ gestation, A1C values =6.1% (good: 2.09 [1.03–4.21]; moderate: 3.20 [1.47–7.00]; and poor: 3.81 [1.30–11.1]) and at 34 weeks’ gestation A1C values =7.0% (moderate: 3.27 [1.31–8.20] and poor: 8.01 [2.04–31.5]) significantly increased the risk of pre-eclampsia compared with optimal control. The adjusted odds ratios for pre-eclampsia for each 1% decrement in A1C before pregnancy, at the first antenatal visit, at 26 weeks’ gestation, and at 34 weeks’ gestation were 0.88 (0.75–1.03), 0.75 (0.64–0.88), 0.57 (0.42–0.78), and 0.47 (0.31–0.70), respectively. Glycemic control was not significantly associated with gestational hypertension.

CONCLUSIONS
Women who developed pre-eclampsia had significantly higher A1C values before and during pregnancy. These data suggest that optimal glycemic control both early and throughout pregnancy may reduce the risk of pre-eclampsia in women with type 1 diabetes.

Religious Geographies in Ireland: Pre-Famine, Post-Famine, Pre-Partition

This paper examines changes in religious geographies for Ireland from 1834 to 1911. It shows that in a period of dramatic social and economic change religious geographies remained remarkably stable. In this it challenges the accepted historiography. It makes use of new data in new ways with the full exploitation of the 1834 Enumeration of Religion and, in so doing, is able to examine the impact of the Great Irish Famine on geographies of religion. These data are visualised both using traditional choropleth maps and, more innovatively in this subject area, cartograms.

A method to reconstruct patient-specific proximal femur surface models from planar pre-operative radiographs

Three-dimensional reconstruction from volumetric medical images (e.g. CT, MRI) is a well-established technology used in patient-specific modelling. However, there are many cases where only 2D (planar) images may be available, e.g. if radiation dose must be limited or if retrospective data is being used from periods when 3D data was not available. This study aims to address such cases by proposing an automated method to create 3D surface models from planar radiographs. The method consists of (i) contour extraction from the radiograph using an Active Contour (Snake) algorithm, (ii) selection of a closest matching 3D model from a library of generic models, and (iii) warping the selected generic model to improve correlation with the extracted contour.

This method proved to be fully automated, rapid and robust on a given set of radiographs. Measured mean surface distance error values were low when comparing models reconstructed from matching pairs of CT scans and planar X-rays (2.57–3.74 mm) and within ranges of similar studies. Benefits of the method are that it requires a single radiographic image to perform the surface reconstruction task and it is fully automated. Mechanical simulations of loaded bone with different levels of reconstruction accuracy showed that an error in predicted strain fields grows proportionally to the error level in geometric precision. In conclusion, models generated by the proposed technique are deemed acceptable to perform realistic patient-specific simulations when 3D data sources are unavailable.

A heterosynaptic learning rule for neural networks

In this article we intoduce a novel stochastic Hebb-like learning rule for neural networks that is neurobiologically motivated. This learning rule combines features of unsupervised (Hebbian) and supervised (reinforcement) learning and is stochastic with respect to the selection of the time points when a synapse is modified. Moreover, the learning rule does not only affect the synapse between pre- and postsynaptic neuron, which is called homosynaptic plasticity, but effects also further remote synapses of the pre-and postsynaptic neuron. This more complex form of synaptic plasticity has recently come under investigations in neurobiology and is called heterosynaptic plasticity. We demonstrate that this learning rule is useful in training neural networks by learning parity functions including the exclusive-or (XOR) mapping in a multilayer feed-forward network. We find, that our stochastic learning rule works well, even in the presence of noise. Importantly, the mean leaxning time increases with the number of patterns to be learned polynomially, indicating efficient learning.