Biomedical subjects

Oliver Lange

Publications and source records attributed to Oliver Lange.

8 recordsLinked to original sources

PubMed2006

Dynamic range of mass accuracy in LTQ Orbitrap hybrid mass spectrometer.

Using a novel orbitrap mass spectrometer, the authors investigate the dynamic range over which accurate masses can be determined (extent of mass accuracy) for short duration experiments typical for LC/MS. A linear ion trap is used to selectively fill an intermediate ion storage device (C-trap) with ions of interest, following which the ensemble of ions is injected into an orbitrap mass analyzer and analyzed using image current detection and fast Fourier transformation. Using this technique, it is possible to generate ion populations with intraspectrum intensity ranges up to 10(4). All measurements (including ion accumulation and image current detection) were performed in less than 1 s at a resolving power of 30,000. It was shown that 5-ppm mass accuracy of the orbitrap mass analyzer is reached with >95% probability at a dynamic range of more than 5000, which is at least an order of magnitude higher than typical values for time-of-flight instruments. Due to the high resolving power of the orbitrap, accurate mass of an ion could be determined when the signal was reliably distinguished from noise (S/Np-p)>2...3).

Journal Article↗

PubMed2006

Performance evaluation of a hybrid linear ion trap/orbitrap mass spectrometer.

Design and performance of a novel hybrid mass spectrometer is described. It couples a linear ion trap mass spectrometer to an orbitrap mass analyzer via an rf-only trapping quadrupole with a curved axis. The latter injects pulsed ion beams into a rapidly changing electric field in the orbitrap wherein they are trapped at high kinetic energies around an inner electrode. Image current detection is subsequently performed after a stable electrostatic field is achieved. Fourier transformation of the acquired transient allows wide mass range detection with high resolving power, mass accuracy, and dynamic range. The entire instrument operates in LC/MS mode (1 spectrum/s) with nominal mass resolving power of 60,000 and uses automatic gain control to provide high-accuracy mass measurements, within 2 ppm using internal standards and within 5 ppm with external calibration. The maximum resolving power exceeds 100,000 (fwhm). Rapid, automated data-dependent capabilities enable real-time acquisition of up to three high-mass accuracy MS/MS spectra per second.

Journal Article↗

PubMed2006

Cluster analysis of signal-intensity time course in dynamic breast MRI: does unsupervised vector quantization help to evaluate small mammographic lesions?

We examined whether neural network clustering could support the characterization of diagnostically challenging breast lesions in dynamic magnetic resonance imaging (MRI). We examined 88 patients with 92 breast lesions (51 malignant, 41 benign). Lesions were detected by mammography and classified Breast Imaging and Reporting Data System (BIRADS) III (median diameter 14 mm). MRI was performed with a dynamic T1-weighted gradient echo sequence (one precontrast and five postcontrast series). Lesions with an initial contrast enhancement >or=50% were selected with semiautomatic segmentation. For conventional analysis, we calculated the mean initial signal increase and postinitial course of all voxels included in a lesion. Secondly, all voxels within the lesions were divided into four clusters using minimal-free-energy vector quantization (VQ). With conventional analysis, maximum accuracy in detecting breast cancer was 71%. With VQ, a maximum accuracy of 75% was observed. The slight improvement using VQ was mainly achieved by an increase of sensitivity, especially in invasive lobular carcinoma and ductal carcinoma in situ (DCIS). For lesion size, a high correlation between different observers was found (R(2) = 0.98). VQ slightly improved the discrimination between malignant and benign indeterminate lesions (BIRADS III) in comparison with a standard evaluation method.

Algorithms↗

PubMed2005

Parts per million mass accuracy on an Orbitrap mass spectrometer via lock mass injection into a C-trap.

Mass accuracy is a key parameter of mass spectrometric performance. TOF instruments can reach low parts per million, and FT-ICR instruments are capable of even greater accuracy provided ion numbers are well controlled. Here we demonstrate sub-ppm mass accuracy on a linear ion trap coupled via a radio frequency-only storage trap (C-trap) to the orbitrap mass spectrometer (LTQ Orbitrap). Prior to acquisition of a spectrum, a background ion originating from ambient air is first transferred to the C-trap. Ions forming the MS or MS(n) spectrum are then added to this species, and all ions are injected into the orbitrap for analysis. Real time recalibration on the "lock mass" by corrections of mass shift removes mass error associated with calibration of the mass scale. The remaining mass error is mainly due to imperfect peaks caused by weak signals and is addressed by averaging the mass measurement over the LC peak, weighted by signal intensity. For peptide database searches in proteomics, we introduce a variable mass tolerance and achieve average absolute mass deviations of 0.48 ppm (standard deviation 0.38 ppm) and maximal deviations of less than 2 ppm. For tandem mass spectra we demonstrate similarly high mass accuracy and discuss its impact on database searching. High and routine mass accuracy in a compact instrument will dramatically improve certainty of peptide and small molecule identification.

Freeze Drying↗

PubMed2004

Measuring the effects of acupuncture and homoeopathy in general practice: an uncontrolled prospective documentation approach.

BACKGROUND: Despite the increasing demand for acupuncture and homoeopathy in Germany, little is known about the effects of these treatments in routine care. We set up a pragmatic documentation study in general practice funded within the scope of project launched by a German health insurer. Patients were followed-up for up to four years. METHODS: The aim of the project was to study the effects and benefits of acupuncture and/or homoeopathy, and to assess patient satisfaction within a prospective documentation of over 5000 acupuncture and over 900 homoeopathy patients. As data sources, we used the documentation made available by therapists on every individual visit and a standardised quality-of-life questionnaire (MOS SF-36); these were complemented by questions concerning the patient's medical history and by questions on patient satisfaction. The health insurer provided us with data on work absenteeism. RESULTS: Descriptive analyses of the main outcomes showed benefit of treatment with middle to large-sized effects for the quality of life questionnaire SF-36 and about 1 point improvement on a rating scale of effects, given by doctors. Data on the treatment and the patients' and physicians' background suggests chronically ill patients treated by fairly regular schemes. CONCLUSION: Since the results showed evidence of a subjective benefit for patients from acupuncture and homoeopathy, this may account for the increase in demand for these treatments especially when patients are chronically ill and unsatisfied with the conventional treatment given previously.

Absenteeism↗

PubMed2004

Model-free functional MRI analysis based on unsupervised clustering.

Conventional model-based or statistical analysis methods for functional MRI (fMRI) are easy to implement, and are effective in analyzing data with simple paradigms. However, they are not applicable in situations in which patterns of neural response are complicated and when fMRI response is unknown. In this paper the "neural gas" network is adapted and rigourosly studied for analyzing fMRI data. The algorithm supports spatial connectivity aiding in the identification of activation sites in functional brain imaging. A comparison of this new method with Kohonen's self-organizing map and with a fuzzy clustering scheme based on deterministic annealing is done in a systematic fMRI study showing comparative quantitative evaluations. The most important findings in this paper are: (1) both "neural gas" and the fuzzy clustering technique outperform Kohonen's map in terms of identifying signal components with high correlation to the fMRI stimulus, (2) the "neural gas" outperforms the two other methods with respect to the quantization error, and (3) Kohonen's map outperforms the two other methods in terms of computational expense. The applicability of the new algorithm is demonstrated on experimental data.

Adult↗

PubMed2004

Comparison of two exploratory data analysis methods for fMRI: unsupervised clustering versus independent component analysis.

Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between unsupervised clustering and ICA in a systematic fMRI study. The comparative results were evaluated by 1) task-related activation maps, 2) associated time-courses, and 3) receiver operating characteristic analysis. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, self-organizing map, "neural gas" network, and fuzzy clustering based on deterministic annealing, and the three ICA methods, FastICA, Infomax and topographic ICA was performed. The ICA methods proved to extract features relatively well for a small number of independent components but are limited to the linear mixture assumption. The unsupervised Clustering outperforms ICA in terms of classification results but requires a longer processing time than the ICA methods.

Adult↗

PubMed2004

Model-free functional MRI analysis using topographic independent component analysis.

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.

Algorithms↗