Metagenomic sequencing of expressed prostate secretions.

To investigate which microorganisms may be present in expressed prostate secretions (EPS) metagenomic sequencing (MGS) was applied to prostate secretion samples from five men with prostatitis and five matched control men as well as to combined expressed prostate secretion and urine from six patients with prostate cancer and six matched control men. The prostate secretion samples contained a variety of bacterial sequences, mostly belonging to the Proteobacteria phylum. The combined prostate secretion and urine samples were dominated by abundant presence of the JC polyomavirus, representing >20% of all detected metagenomic sequence reads. There were also other viruses detected, for example, human papillomavirus type 81. All combined prostate secretion and urine samples were also positive for Proteobacteria. In summary, MGS of expressed prostate secretion is informative for detecting a variety of bacteria and viruses, suggesting that a more large-scale use of MGS of prostate secretions may be useful in medical and epidemiological studies of prostate infections.

Relationship between size and latency of action potentials in human muscle sympathetic nerve activity.

We employed a novel action potential detection and classification technique to study the relationship between the recruitment of sympathetic action potentials (i.e., neurons) and the size of integrated sympathetic bursts in human muscle sympathetic nerve activity (MSNA). Multifiber postganglionic sympathetic nerve activity from the common fibular nerve was collected using microneurography in 10 healthy subjects at rest and during activation of sympathetic outflow using lower body negative pressure (LBNP). Burst occurrence increased with LBNP. Integrated burst strength (size) varied from 0.22 ± 0.07 V at rest to 0.28 ± 0.09 V during LBNP. Sympathetic burst size (i.e., peak height) was directly related to the number of action potentials within a sympathetic burst both at baseline (r = 0.75 ± 0.13; P < 0.001) and LBNP (r = 0.75 ± 0.12; P < 0.001). Also, the amplitude of detected action potentials within sympathetic bursts was directly related to the increased burst size at both baseline (r = 0.59 ± 0.16; P < 0.001) and LBNP (r = 0.61 ± 0.12; P < 0.001). In addition, the number of detected action potentials and the number of distinct action potential clusters within a given sympathetic burst were correlated at baseline (r = 0.7 ± 0.1; P < 0.001) and during LBNP (r = 0.74 ± 0.03; P < 0.001). Furthermore, action potential latency (i.e., an inverse index of neural conduction velocity) was decreased as a function of action potential size at baseline and LBNP. LBNP did not change the number of action potentials and unique clusters per sympathetic burst. It was concluded that there exists a hierarchical pattern of recruitment of additional faster conducting neurons of larger amplitude as the sympathetic bursts become stronger (i.e., larger amplitude bursts). This fundamental pattern was evident at rest and was not altered by the level of baroreceptor unloading applied in this study.

Spike detection in human muscle sympathetic nerve activity using a matched wavelet approach.

Sympathetic nerve recordings associated with blood pressure regulation can be recorded directly using microneurography. A general characteristic of this signal is spontaneous burst activity of spikes (action potentials) separated by silent periods against a background of considerable Gaussian noise. During measurement with electrodes, the raw muscle sympathetic nerve activity (MSNA) signal is amplified, band-pass filtered, rectified and integrated. This integration process removes information regarding action potential content and their discharge properties. This paper proposes a new method for detecting action potentials from the raw MSNA signal to enable investigation of post-ganglionic neural discharge properties. The new method is based on the design of a mother wavelet that is matched to an actual mean action potential template extracted from a real raw MSNA signal. To detect action potentials, the new matched wavelet is applied to the MSNA signal using a continuous wavelet transform following a thresholding procedure and finding of a local maxima that indicates the location of action potentials. The performance of the proposed method versus two previous wavelet-based approaches was evaluated using (1) real MSNA recorded from seven healthy participants and, (2) simulated MSNA. The results show that the new matched wavelet performs better than the previous wavelet-based methods that use a non-matched wavelet in detecting action potentials in the MSNA signal.

Detection and classification of raw action potential patterns in human Muscle Sympathetic Nerve Activity.

The Muscle Sympathetic Nerve Activity (MSNA) consists of synchronous neural discharges separated by periods of neural silence dominated by heavy background noise. During measurement with electrodes, the raw MSNA signal is amplified, band-pass filtered, rectified and integrated. This integration process removes much neurophysiological information. In this paper a method for detecting a raw action potential (before the pre-amplifier) and filtered action potential (after the band-pass filter) is presented. This method is based on stationary wavelet transform (SWT) and a peak detection algorithm. Also, the detected action potentials were clustered using the k-means method and the cluster averages were calculated. The action potential detector and classification algorithm are evaluated using real MSNA recorded from three healthy subjects.

Performance analysis of stationary and discrete wavelet transform for action potential detection from sympathetic nerve recordings in humans.

Accurate investigation of the sympathetic nervous system is important in the diagnosis and study of various autonomic and cardiovascular control and disorders. Sympathetic function associated with blood pressure regulation in humans can be evaluated by recording muscle sympathetic nerve activity (MSNA), which is characterised by synchronous neuronal discharges separated by periods of neural silence dominated by colored gaussian noise. In this paper two common methods for detecting filtered action potential in MSNA recordings is compared. These methods are based on stationary wavelet transform (SWT) and discrete wavelet transform (DWT). The performance analysis are evaluated using simulated MSNA using templates extracted from real MSNA recorded from three healthy subjects.