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Fitting the data of an electrochemical impedance spectroscopy (EIS) typically requires manual estimation of the initial values before regression algorithms such as complex nonlinear least squares (CNLS) can be applied. This makes the success rate of the fitting dependent on the user input. Furthermore, the Randles circuit consists of parameters with substantially differing magnitudes (e.g. capacitors and resistors), which can also strongly affect the success rate of the fitting due to numerical effects. The aim of this work is to investigate methods addressing the described limitations of fitting. Therefore, a Python implementation performing a fit for EIS is benchmarked with an equivalent open source library. The examined implementation optionally includes the normalization of the parameter values, the standardization of the impedances and a pre-fit. Applying the same equivalent circuit without additional signal processing steps and with fixed initial values defined by midpoints of the value ranges, both implementations were able to fit 46.50% of the simulated database with different spectra. Applying the normalization of the parameter values (76.25%) or the same method with additional pre-fit (97.75%) lead to a significant improvement of the success rate. The standardization of impedances did not affect the success rate.

Summary. We compared 123I-iodobenzamide single photon emission computed tomography (IBZM-SPECT) for imaging of striatal dopamine D2 receptors in vivo, and MRI in 32 patients with the clinical diagnosis of progressive supranuclear palsy (PSP). We found a significant inter-dependence of reduction of specific striatal IBZM binding indicative of striatal degeneration and of the absence of multiple signal hyperintensities in MRI; age had no influence neither on IBZM binding nor on signal hyperintensities. We conclude that the presence of multiple signal hyperintensities should raise doubt on the correct clinical diagnosis. [ABSTRACT FROM AUTHOR]

Summary. Twenty non-demented patients with idiopathic Parkinson's disease (PD) underwent single photon emission computed tomography (SPECT) with [123I]β-CIT to further investigate the contribution of nigrostriatal dysfunction to cognitive and motor deficits. Compared to matched controls PD patients showed normal verbal intelligence, short-term memory and phasic alertness. There were significant (p < 0.05) deficits in tests of verbal working memory (digit ordering, reading span), strategic memory (story recall) and executive functions (card sorting), indicating a "prefrontal" cognitive deficit. Significant (p < 0.05) correlations were observed between dopamine transporter (DAT) density in the putamen and motor deficits as well as between DAT density in both striatal compartments (head of the caudate nucleus and putamen) and prefrontal functioning. Age was a major contributing factor to both cognitive status and nigrostriatal integrity as measured by [123I]β-CIT SPECT. These results support the view that the striatum is part of a neuronal network that is mediating prefrontal cognitive functions. [ABSTRACT FROM AUTHOR]

Abstract Background Severe gait disturbances in idiopathic Parkinson's disease (PD) are observed in up to 80% of all patients in advanced disease stages with important impact on quality of life. There is an unmet need for further symptomatic therapeutic strategies, particularly as gait disturbances generally respond unfavourably to dopaminergic medication and conventional deep brain stimulation of the subthalamic nucleus in advanced disease stages. Recent pathophysiological research pointed to nigro-pontine networks entrained to locomotor integration. Stimulation of the pedunculopontine nucleus is currently under investigation, however, hitherto remains controversial. The substantia nigra pars reticulata (SNr) - entrained into integrative locomotor networks - is pathologically overactive in PD. High-frequent stimulation of the substantia nigra pars reticulata preferentially modulated axial symptoms and therefore is suggested as a novel therapeutic candidate target for neuromodulation of refractory gait disturbances in PD. Methods 12 patients with idiopathic Parkinson's disease and refractory gait disturbances under best individual subthalamic nucleus stimulation and dopaminergic medication will be enroled into this double-blind 2 × 2 cross-over clinical trial. The treatment consists of two different stimulation settings using (i) conventional stimulation of the subthalamic nucleus [STNmono] and (ii) combined stimulation of distant electrode contacts located in the subthalamic nucleus and caudal border zone of STN and substantia nigra pars reticulata [STN+SNr]. The primary outcome measure is the change of the cumulative 'axial score' (UPDRS II items '13-15' and UPRDS III items '27-31') at three weeks of constant stimulation in either condition. Secondary outcome measures include specific scores on freezing of gait, balance function, quality of life, non-motor symptoms, and neuropsychiatric symptoms. The aim of the present trial is to investigate the efficacy and safety of a three week constant combined stimulation on [STN+SNr] compared to [STNmono]. The results will clarify, whether stimulation on nigral contacts additional to subthalamic stimulation will improve therapeutic response of otherwise refractory gait disturbances in PD. Trial registration The trial was registered with the clinical trials register of http://www.clinicaltrials.gov (NCT01355835)

Abstract Background Ontology term labels can be ambiguous and have multiple senses. While this is no problem for human annotators, it is a challenge to automated methods, which identify ontology terms in text. Classical approaches to word sense disambiguation use co-occurring words or terms. However, most treat ontologies as simple terminologies, without making use of the ontology structure or the semantic similarity between terms. Another useful source of information for disambiguation are metadata. Here, we systematically compare three approaches to word sense disambiguation, which use ontologies and metadata, respectively. Results The 'Closest Sense' method assumes that the ontology defines multiple senses of the term. It computes the shortest path of co-occurring terms in the document to one of these senses. The 'Term Cooc' method defines a log-odds ratio for co-occurring terms including co-occurrences inferred from the ontology structure. The 'MetaData' approach trains a classifier on metadata. It does not require any ontology, but requires training data, which the other methods do not. To evaluate these approaches we defined a manually curated training corpus of 2600 documents for seven ambiguous terms from the Gene Ontology and MeSH. All approaches over all conditions achieve 80% success rate on average. The 'MetaData' approach performed best with 96%, when trained on high-quality data. Its performance deteriorates as quality of the training data decreases. The 'Term Cooc' approach performs better on Gene Ontology (92% success) than on MeSH (73% success) as MeSH is not a strict is-a/part-of, but rather a loose is-related-to hierarchy. The 'Closest Sense' approach achieves on average 80% success rate. Conclusion Metadata is valuable for disambiguation, but requires high quality training data. Closest Sense requires no training, but a large, consistently modelled ontology, which are two opposing conditions. Term Cooc achieves greater 90% success given a consistently modelled ontology. Overall, the results show that well structured ontologies can play a very important role to improve disambiguation. Availability The three benchmark datasets created for the purpose of disambiguation are available in Additional file 1. Additional file 1 Benchmark datasets used in the experiments. The three corpora (High quality/Low quantity corpus; Medium quality/Medium quantity corpus; Low quality/High quantity corpus) are given in the form of PubMed identifiers (PMID) for True/False cases for the 7 ambiguous terms examined (GO/MeSH/UMLS identifiers are also given). Click here for file