Is increased joint loading detrimental to obese patients with knee osteoarthritis? A secondary data analysis from a randomized trial

SummaryObjectiveTo investigate whether increased knee joint loading due to improved ambulatory function and walking speed following weight loss achieved over 16 weeks accelerates symptomatic and structural disease progression over a subsequent 1 year weight maintenance period in an obese population with knee osteoarthritis (OA).MethodsData from a prospective study of weight loss in obese patients with knee OA (the CARtilage in obese knee OsteoarThritis (CAROT) study) were used to determine changes in knee joint compressive loadings (model estimated) during walking after a successful 16 week weight loss intervention. The participants were divided into ‘Unloaders’ (participants that reduced joint loads) and ‘Loaders’ (participants that increased joint loads). The primary symptomatic outcome was changes in knee symptoms, measured with the Knee injury and Osteoarthritis Outcome Score (KOOS) questionnaire, during a subsequent 52 weeks weight maintenance period. The primary structural outcome was changes in tibiofemoral cartilage loss assessed semi-quantitatively (Boston Leeds Knee Osteoarthritis Score (BLOKS) from MRI after the 52 weight maintenance period.Results157 participants (82% of the CAROT cohort) with medial and/or lateral knee OA were classified as Unloaders (n = 100) or Loaders (n = 57). The groups showed similar significant changes in symptoms (group difference: −2.4 KOOS points [95% CI −6.8:1.9]) and cartilage loss (group difference: −0.06 BLOKS points [95% CI −0.22:0.11) after 1 year, with no statistically significant differences between Loaders and Unloaders.ConclusionFor obese patients undergoing a significant weight loss, increased knee joint loading for 1 year was not associated with accelerated symptomatic and structural disease progression compared to a similar weight loss group that had reduced ambulatory compressive knee joint loads.Clinicaltrials.govNCT00655941

P24-9 Extended seizure detection algorithm for intracranial EEG recordings

Objective: We implemented and tested an existing seizure detection algorithm for scalp EEG (sEEG) with the purpose of improving it to intracranial EEG (iEEG) recordings. Method: iEEG was obtained from 16 patients with focal epilepsy undergoing work up for resective epilepsy surgery. Each patient had 4 or 5 recorded seizures and 24 hours of non-ictal data were used for evaluation. Data from three electrodes placed at the ictal focus were used for the analysis. A wavelet based feature extraction algorithm delivered input to a support vector machine (SVM) classifier for distinction between ictal and non-ictal iEEG. We compare our results to a method published by Shoeb in 2004. While the original method on sEEG was optimal with the use of only four subbands in the wavelet analysis, we found that better seizure detection could be made if all subbands were used for iEEG. Results: When using the original implementation a sensitivity of 92.8% and a false positive ratio (FPR) of 0.93/h were obtained. Our extension of the algorithm rendered a 95.9% sensitivity and only 0.65 false detections per hour. Conclusion: Better seizure detection can be performed when the higher frequencies in the iEEG were included in the feature extraction. Our future work will concentrate on development of a method for identification of the most prominent nodes in the wavelet packets analysis for optimization of an automatic seizure detection algorithm

CN-SIM - a model for the turnover of soil organic mattter. II: Short term carbon and nitrogen development

A computer model is presented, which describes the transformations of C and N in the soil. The development has been divided into two interdependent tasks, the first being development of long-term SOC simulation capabilities, and the second being short-term simulations of C and N, as described in this paper. A number of existing, independent laboratory experiments, covering a range of amendments, have been used for this task. The amendments includes a variety of different crop residues and animal manure. These experiments have included measurements of 13C, 14C and 15N in various pools, and the model facilitates the simulation of these isotopes. Non-linear, automated optimisation procedures were utilised wherever feasible. The model generally yielded good descriptions of the measured data. A new method for subdividing the added matter was developed, which gave results generally superior to subdivision according to van Soest pools

Near-infra-red spectroscopy (NIR) for characterisation of plant residue quality - A new approach for predicting decomposition and nitrogen release in agricultural soils

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Criteria for describing decomposition of plant materials in a dynamic simulation model

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Characterisation of plant residue quality for prediction of decomposition and nitrogen release in agricultural soils

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Prediction of plant residue C and N mineralization dynamics by near infrared (NIR) spectroscopy and residue quality fractions

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