The automatic recognition and counting of cough

BACKGROUND: Cough recordings have been undertaken for many years but the analysis of cough frequency and the temporal relation to trigger factors have proven problematic. Because cough is episodic, data collection over many hours is required, along with real-time aural analysis which is equally time-consuming. A method has been developed for the automatic recognition and counting of coughs in sound recordings. METHODS: The Hull Automatic Cough Counter (HACC) is a program developed for the analysis of digital audio recordings. HACC uses digital signal processing (DSP) to calculate characteristic spectral coefficients of sound events, which are then classified into cough and non-cough events by the use of a probabilistic neural network (PNN). Parameters such as the total number of coughs and cough frequency as a function of time can be calculated from the results of the audio processing. Thirty three smoking subjects, 20 male and 13 female aged between 20 and 54 with a chronic troublesome cough were studied in the hour after rising using audio recordings. RESULTS: Using the graphical user interface (GUI), counting the number of coughs identified by HACC in an hour long recording, took an average of 1 minute 35 seconds, a 97.5% reduction in counting time. HACC achieved a sensitivity of 80% and a specificity of 96%. Reproducibility of repeated HACC analysis is 100%. CONCLUSION: An automated system for the analysis of sound files containing coughs and other non-cough events has been developed, with a high robustness and good degree of accuracy towards the number of actual coughs in the audio recording

Global profiling of the muscle metabolome: method optimization, validation and application to determine exercise-induced metabolic effects

Skeletal muscle represents a crucial metabolic organ in the body characterized by a tremendous metabolic plasticity and the ability to influence important metabolic events elsewhere in the body. In order to understand the metabolic implications of skeletal muscle, it is imperative to characterize the metabolites within the tissue itself. In this work we aimed at developing a suitable analytical pipeline to analyze the metabolome of muscle tissue. Methanol/chloroform/water at neutral pH was selected as the method of choice for metabolite extraction prior to analysis by chromatographic-mass spectrometry systems in five different platforms covering a relevant part of the muscle metabolome: organic acids, amines, nucleotides, coenzymes, acylcarnitines and oxylipins. This analytical pipeline was extensively validated and proved to be robust, precise, accurate and biologically sound. The capability of our analytical method to capture metabolic alterations upon challenges was finally tested using a small proof-of-concept study involving an exercise intervention. Mild but consistent metabolic patterns were observed, allowing the discrimination between non-exercised and exercised muscles. Despite the low numbers of subjects enrolled in this study (5), these results are indicative that our method is suitable to determine intervention effects in skeletal muscle tissue whenever applied to adequately powered and well characterized studies.</p

First-Trimester Serum Acylcarnitine Levels to Predict Preeclampsia: A Metabolomics Approach

Objective. To expand the search for preeclampsia (PE) metabolomics biomarkers through the analysis of acylcarnitines in first-trimester maternal serum. Methods. This was a nested case-control study using serum from pregnant women, drawn between 8 and 14 weeks of gestational age. Metabolites were measured using an UPLC-MS/MS based method. Concentrations were compared between controls (n=500) and early-onset- (EO-) PE (n=68) or late-onset- (LO-) PE (n=99) women. Metabolites with a false discovery rate <10% for both EO-PE and LO-PE were selected and added to prediction models based on maternal characteristics (MC), mean arterial pressure (MAP), and previously established biomarkers (PAPPA, PLGF, and taurine). Results. Twelve metabolites were significantly different between EO-PE women and controls, with effect levels between −18% and 29%. For LO-PE, 11 metabolites were significantly different with effect sizes between −8% and 24%. Nine metabolites were significantly different for both comparisons. The best prediction model for EO-PE consisted of MC, MAP, PAPPA, PLGF, taurine, and stearoylcarnitine (AUC = 0.784). The best prediction model for LO-PE consisted of MC, MAP, PAPPA, PLGF, and stearoylcarnitine (AUC = 0.700). Conclusion. This study identified stearoylcarnitine as a novel metabolomics biomarker for EO-PE and LO-PE. Nevertheless, metabolomics-based assays for predicting PE are not yet suitable for clinical implementation

Metabolomics profiling for identification of novel potential markers in early prediction of preeclampsia.

OBJECTIVE: The first aim was to investigate specific signature patterns of metabolites that are significantly altered in first-trimester serum of women who subsequently developed preeclampsia (PE) compared to healthy pregnancies. The second aim of this study was to examine the predictive performance of the selected metabolites for both early onset [EO-PE] and late onset PE [LO-PE]. METHODS: This was a case-control study of maternal serum samples collected between 8+0 and 13+6 weeks of gestation from 167 women who subsequently developed EO-PE n = 68; LO-PE n = 99 and 500 controls with uncomplicated pregnancies. Metabolomics profiling analysis was performed using two methods. One has been optimized to target eicosanoids/oxylipins, which are known inflammation markers and the other targets compounds containing a primary or secondary biogenic amine group. Logistic regression analyses were performed to predict the development of PE using metabolites alone and in combination with first trimester mean arterial pressure (MAP) measurements. RESULTS: Two metabolites were significantly different between EO-PE and controls (taurine and asparagine) and one in case of LO-PE (glycylglycine). Taurine appeared the most discriminative biomarker and in combination with MAP predicted EO-PE with a detection rate (DR) of 55%, at a false-positive rate (FPR) of 10%. CONCLUSION: Our findings suggest a potential role of taurine in both PE pathophysiology and first trimester screening for EO-PE

Alterations in ether lipid metabolism and the consequences for the mouse lipidome

Alkylglycerol monooxygenase (AGMO) and plasmanylethanolamine desaturase (PEDS1) are enzymes involved in ether lipid metabolism. While AGMO degrades plasmanyl lipids by oxidative cleavage of the ether bond, PEDS1 exclusively synthesizes a specific subclass of ether lipids, the plasmalogens, by introducing a vinyl ether double bond into plasmanylethanolamine phospholipids. Ether lipids are characterized by an ether linkage at the sn-1 position of the glycerol backbone and they are found in membranes of different cell types. Decreased plasmalogen levels have been associated with neurological diseases like Alzheimer's disease. Agmo-deficient mice do not present an obvious phenotype under unchallenged conditions. In contrast, Peds1 knockout mice display a growth phenotype. To investigate the molecular consequences of Agmo and Peds1 deficiency on the mouse lipidome, five tissues from each mouse model were isolated and subjected to high resolution mass spectrometry allowing the characterization of up to 2013 lipid species from 42 lipid subclasses. Agmo knockout mice moderately accumulated plasmanyl and plasmenyl lipid species. Peds1-deficient mice manifested striking changes characterized by a strong reduction of plasmenyl lipids and a concomitant massive accumulation of plasmanyl lipids resulting in increased total ether lipid levels in the analyzed tissues except for the class of phosphatidylethanolamines where total levels remained remarkably constant also in Peds1 knockout mice. The rate-limiting enzyme in ether lipid metabolism, FAR1, was not upregulated in Peds1-deficient mice, indicating that the selective loss of plasmalogens is not sufficient to activate the feedback mechanism observed in total ether lipid deficiency

Alterations in ether lipid metabolism and the consequences for the mouse lipidome

Alkylglycerol monooxygenase (AGMO) and plasmanylethanolamine desaturase (PEDS1) are enzymes involved in ether lipid metabolism. While AGMO degrades plasmanyl lipids by oxidative cleavage of the ether bond, PEDS1 exclusively synthesizes a specific subclass of ether lipids, the plasmalogens, by introducing a vinyl ether double bond into plasmanylethanolamine phospholipids. Ether lipids are characterized by an ether linkage at the sn-1 position of the glycerol backbone and they are found in membranes of different cell types. Decreased plasmalogen levels have been associated with neurological diseases like Alzheimer's disease. Agmo-deficient mice do not present an obvious phenotype under unchallenged conditions. In contrast, Peds1 knockout mice display a growth phenotype. To investigate the molecular consequences of Agmo and Peds1 deficiency on the mouse lipidome, five tissues from each mouse model were isolated and subjected to high resolution mass spectrometry allowing the characterization of up to 2013 lipid species from 42 lipid subclasses. Agmo knockout mice moderately accumulated plasmanyl and plasmenyl lipid species. Peds1-deficient mice manifested striking changes characterized by a strong reduction of plasmenyl lipids and a concomitant massive accumulation of plasmanyl lipids resulting in increased total ether lipid levels in the analyzed tissues except for the class of phosphatidylethanolamines where total levels remained remarkably constant also in Peds1 knockout mice. The rate-limiting enzyme in ether lipid metabolism, FAR1, was not upregulated in Peds1-deficient mice, indicating that the selective loss of plasmalogens is not sufficient to activate the feedback mechanism observed in total ether lipid deficiency

Studies that assessed preeclampsia screening using metabolomics techniques.

<p>PE: preeclampsia; DR: detection rate; FPR: false positive rate; MC: maternal characteristics; UtA: Uterine artery Doppler measurement; CRL: crown-rump-length.</p