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Glycation, oxidation, nitration, and crosslinking of proteins are implicated in the pathogenic mechanisms of type 2 diabetes, cardiovascular disease, and chronic kidney disease. Related modified amino acids formed by proteolysis are excreted in urine. We quantified urinary levels of these metabolites and branched-chain amino acids (BCAAs) in healthy subjects and assessed changes in early-stage decline in metabolic, vascular, and renal health and explored their diagnostic utility for a noninvasive health screen. We recruited 200 human subjects with early-stage health decline and healthy controls. Urinary amino acid metabolites were determined by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry. Machine learning was applied to optimise and validate algorithms to discriminate between study groups for potential diagnostic utility. Urinary analyte changes were as follows: impaired metabolic health—increased Nε-carboxymethyl-lysine, glucosepane, glutamic semialdehyde, and pyrraline; impaired vascular health—increased glucosepane; and impaired renal health—increased BCAAs and decreased Nε-(γ-glutamyl)lysine. Algorithms combining subject age, BMI, and BCAAs discriminated between healthy controls and impaired metabolic, vascular, and renal health study groups with accuracy of 84%, 72%, and 90%, respectively. In 2-step analysis, algorithms combining subject age, BMI, and urinary Nε-fructosyl-lysine and valine discriminated between healthy controls and impaired health (any type), accuracy of 78%, and then between types of health impairment with accuracy of 69%-78% (cf. random selection 33%). From likelihood ratios, this provided small, moderate, and conclusive evidence of early-stage cardiovascular, metabolic, and renal disease with diagnostic odds ratios of 6 – 7, 26 – 28, and 34 – 79, respectively. We conclude that measurement of urinary glycated, oxidized, crosslinked, and branched-chain amino acids provides the basis for a noninvasive health screen for early-stage health decline in metabolic, vascular, and renal health. Other information Published in: Oxidative Medicine and Cellular Longevity License: http://creativecommons.org/licenses/by/4.0 See article on publisher's website: http://dx.doi.org/10.1155/2019/4851323

Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we present, in a step-by-step guide, the use of ElectroMap for analysis, quantification, and mapping of high-resolution voltage and calcium datasets acquired by optical mapping. ElectroMap analysis options cover a wide variety of key electrophysiological parameters, and the graphical user interface allows straightforward modification of pre-processing and parameter definitions, making ElectroMap applicable to a wide range of experimental models. We show how built-in pacing frequency detection and signal segmentation allows high-throughput analysis of entire experimental recordings, acute responses, and single beat-to-beat variability. Additionally, ElectroMap incorporates automated multi-beat averaging to improve signal quality of noisy datasets, and here we demonstrate how this feature can help elucidate electrophysiological changes that might otherwise go undetected when using single beat analysis. Custom modules are included within the software for detailed investigation of conduction, single file analysis, and alternans, as demonstrated here. This software platform can be used to enable and accelerate the processing, analysis, and mapping of complex cardiac electrophysiology.

Background Clinical chemistry tests for autism spectrum disorder (ASD) are currently unavailable. The aim of this study was to explore the diagnostic utility of proteotoxic biomarkers in plasma and urine, plasma protein glycation, oxidation, and nitration adducts, and related glycated, oxidized, and nitrated amino acids (free adducts), for the clinical diagnosis of ASD. Methods Thirty-eight children with ASD (29 male, 9 female; age 7.6 ± 2.0 years) and 31 age-matched healthy controls (23 males, 8 females; 8.6 ± 2.0 years) were recruited for this study. Plasma protein glycation, oxidation, and nitration adducts and amino acid metabolome in plasma and urine were determined by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry. Machine learning methods were then employed to explore and optimize combinations of analyte data for ASD diagnosis. Results We found that children with ASD had increased advanced glycation endproducts (AGEs), Nε-carboxymethyl-lysine (CML) and Nω-carboxymethylarginine (CMA), and increased oxidation damage marker, dityrosine (DT), in plasma protein, with respect to healthy controls. We also found that children with ASD had increased CMA free adduct in plasma ultrafiltrate and increased urinary excretion of oxidation free adducts, alpha-aminoadipic semialdehyde and glutamic semialdehyde. From study of renal handling of amino acids, we found that children with ASD had decreased renal clearance of arginine and CMA with respect to healthy controls. Algorithms to discriminate between ASD and healthy controls gave strong diagnostic performance with features: plasma protein AGEs—CML, CMA—and 3-deoxyglucosone-derived hydroimidazolone, and oxidative damage marker, DT. The sensitivity, specificity, and receiver operating characteristic area-under-the-curve were 92%, 84%, and 0.94, respectively. Conclusions Changes in plasma AGEs were likely indicative of dysfunctional metabolism of dicarbonyl metabolite precursors of AGEs, glyoxal and 3-deoxyglucosone. DT is formed enzymatically by dual oxidase (DUOX); selective increase of DT as an oxidative damage marker implicates increased DUOX activity in ASD possibly linked to impaired gut mucosal immunity. Decreased renal clearance of arginine and CMA in ASD is indicative of increased arginine transporter activity which may be a surrogate marker of disturbance of neuronal availability of amino acids. Data driven combination of these biomarkers perturbed by proteotoxic stress, plasma protein AGEs and DT, gave diagnostic algorithms of high sensitivity and specificity for ASD. Electronic supplementary material The online version of this article (10.1186/s13229-017-0183-3) contains supplementary material, which is available to authorized users.

Optogenetic control of the heart is an emergent technology that offers unparalleled spatio-temporal control of cardiac dynamics via light-sensitive ion pumps and channels (opsins). This fast-evolving technique holds broad scope in both clinical and basic research setting. Combination of optogenetics with optical mapping of voltage or calcium fluorescent probes facilitates ‘all-optical’ electrophysiology, allowing precise optogenetic actuation of cardiac tissue with high spatio-temporal resolution imaging of action potential and calcium transient morphology and conduction patterns. In this review, we provide a synopsis of optogenetics and discuss in detail its use and compatibility with optical interrogation of cardiac electrophysiology. We briefly discuss the benefits of all-optical cardiac control and electrophysiological interrogation compared to traditional techniques, and describe mechanisms, unique features and limitations of optically induced cardiac control. In particular, we focus on state-of-the-art setup design, challenges in light delivery and filtering, and compatibility of opsins with fluorescent reporters used in optical mapping. The interaction of cardiac tissue with light, and physical and computational approaches to overcome the ‘spectral congestion’ that arises from the combination of optogenetics and optical mapping are discussed. Finally, we summarize recent preclinical work applications of combined cardiac optogenetics and optical mapping approach.

The ability to record and analyse electrical behaviour across the heart using optical and electrode mapping has revolutionised cardiac research. However, wider uptake of these technologies is constrained by the lack of multi-functional and robustly characterised analysis and mapping software. We present ElectroMap, an adaptable, high-throughput, open-source software for processing, analysis and mapping of complex electrophysiology datasets from diverse experimental models and acquisition modalities. Key innovation is development of standalone module for quantification of conduction velocity, employing multiple methodologies, currently not widely available to researchers. ElectroMap has also been designed to support multiple methodologies for accurate calculation of activation, repolarisation, arrhythmia detection, calcium handling and beat-to-beat heterogeneity. ElectroMap implements automated signal segmentation, ensemble averaging and integrates optogenetic approaches. Here we employ ElectroMap for analysis, mapping and detection of pro-arrhythmic phenomena in silico, in cellulo, animal model and in vivo patient datasets. We anticipate that ElectroMap will accelerate innovative cardiac research and enhance the uptake, application and interpretation of mapping technologies leading to novel approaches for arrhythmia prevention.

Background Glycation, oxidation and nitration of proteins are reactions involved in accelerated ageing of tissues. The products of these reactions are used as biomarkers of chronic pathologies such as diabetes or chronic inflammatory states. Objectives In this work, we studied by mass spectrometry the levels of amino acids and glycated, oxidised or nitrated proteins in culture media of chondrocytes cultivated in multi-layers and in the blood of guinea pigs or osteoarthritis patients. Methods Sixty male, 3-week-old Dunkin-Hartley guinea pigs were used in this work. At 4-weeks-old and 8 week intervals until week 36, twelve animals were sacrificed and histological severity of knee osteoarthritis evaluated and cartilage rheological properties. Human patients with early and advanced osteoarthritis and healthy subjects were recruited. Human chondrocytes cultured in multilayers were treated for 10 days with interleukin (IL) −1β. Amino acids and glycated, oxidised and nitrated proteins were analysed in the serum of guinea pigs, osteoarthritis patients and in the culture medium conditioned by chondrocytes by stable isotopic dilution analysis liquid chromatography-tandem mass spectrometry using the Acquity UPLC system. Results Severity of osteoarthritis increased progressively in guinea pigs with age. Glycated, oxidised and nitrated amino acids were increased markedly at week 36. Glucosepane and dityrosine increased progressively from weeks 20 and 28, respectively. Glucosepane was positively correlated with the OA histological severity (r=0.58, p Conclusions The glycation, oxidation and nitration of proteins are reactions related to the severity of osteoarthritis. The products of these reactions are measurable in blood by mass spectrometry and could be biomarkers of osteoarthritis. More specifically, glucosepane is an advanced glycation product very strongly increased in the severe form of the disease. In conclusion, serum glucosepane is a potential biomarker for diagnosis and progression of osteoarthritis. Disclosure of Interest None declared

The advent of real-time 3-D echocardiography (RT3DE) promised dynamic 3-D image acquisition with the potential of a more objective and complete functional analysis. In spite of that, 2-D echocardiography remains the backbone of echocardiography imaging in current clinical practice, with RT3DE mainly used for clinical research. The uptake of RT3DE has been slow because of missing anatomic information, limited field-of-view (FOV) and tedious analysis procedures. This paper presents multiview fusion 3D echocardiography, where multiple images with complementary information are acquired from different probe positions. These multiple images are subsequently aligned and fused together for preserving salient structures in a single, multiview fused image. A novel and simple wavelet-based fusion algorithm is proposed that exploits the low- and high-frequency separation capability of the wavelet analysis. The results obtained show that the proposed multiview fusion considerably improves the contrast (31.1%), contrast-to-noise ratio (46.7%), signal-to-noise ratio (44.7%) and anatomic features (12%) in 3-D echocardiography, and enlarges the FOV (28.2%). This indicates that multiview fusion substantially enhances the image quality and information.

Background Optical mapping is a powerful research tool that is revolutionising study of cardiac electrophysiology. However, processing and analysis of optical mapping data is computationally challenging to design and implement, a difficulty further enhanced by novel camera technology providing higher temporal and spatial resolution. We have previously developed algorithms capable of objective processing and analysis of electrophysiological parameters acquired using a second-generation complementary metal-oxide semiconductor camera.1 Here we report development of improved algorithms packaged in a user-friendly graphical interface, capable of high-throughput processing and analysis of voltage and calcium optical mapping data from a wide spectrum of cameras. Functionality and processing speed is further improved through automated recognition of pacing frequency and analysis of activation and repolarisation of electrophysiological parameters at high spatiotemporal resolution (200 × 2400 pixels; sampling rate 1 kHz). Processing options allow averaging of multiple beats as well as individual beat segmentation within the whole experimental trace, thus allowing for study of dynamic changes in key parameters such as action potential duration (APD), activation times, conduction velocity (CV) and phase mapping. Methods and results We have compared how our software performs in terms of analysis and processing time with our previously published methods.1Results remain consistent between both methods (APD50=10.41±0.62 vs 10.36±0.63 ms; CV=24.3±1.68 vs 23.3±1.69 cm/s, new software vs published methods; n=5, murine atria). Substantial improvements in processing speed (up to 4 times) are achieved, compared to our previously published methods. This improvement, coupled with automatic recognition and segmentation of whole experiments by cycle length, enables analysis of frequency dependent beat-to-beat changes in APD, CV and activation times. We demonstrate dynamic beat-to-beat frequency-dependent changes in APD50 in isolated superfused murine atria over a long experimental timeframe. As expected, increased frequency reduces APD50 (3 Hz=12.56±0.08; 8.33Hz=11.78±0.29; 10 Hz=11.47±0.3; 12.5Hz=10.88±0.29 ms; n=3), however, extensive beat-to-beat analysis allows investigation of electophysiological changes over whole experimental protocols, potentially providing crucial insight into novel arrhythmic mechanisms. Conclusions We have developed high-throughput software for analysis and processing of optical mapping imaging data compatible with a wide range of optical mapping cameras/systems. Our software offers enhanced processing speeds of key electrophysiological parameters across the heart and allows beat-to-beat analysis of large computationally challenging datasets. Reference 1. Yue et al. Prog Biophys Mol Biol. 2014 Aug;115(2-3):340–8.

This paper presents left ventricle (LV) endocardial segmentation from contrast 3D echocardiography (C3DE) images. The C3DE image segmentation is a very challenging problem. Though the image quality is perceived to be improved for visual analysis, the image quality actually deteriorates for the purpose of automatic/semi-automatic analysis due to high speckle noise. To overcome the speckle noise and low contrast between myocardium and LV cavity, we employ a simple inverse image construction method to ease the segmentation challenge. This reduces the failure rate of segmentation which was otherwise very high on C3DE. The image inversion method inverts the C3DE image appearance, thus having high contrast and low noise appearance while the endocardial delineation becomes clearer. This inverted image information is utilized in a semi-automatic image-driven LV segmentation method. The segmentation results of LV boundary delineation are compared to a manual LV boundary delineation and a good match is observed.

Identification of functional brain connectivity differences induced by certain neurological disorders from resting state functional MRI (rfMRI) is generally considered a difficult task. This challenging task requires the identification of discriminative neuroimaging markers. In this paper, we propose a two-stage algorithm to identify functional connectivity differences that can discriminate epileptic patients and healthy subjects. In the first stage, we determine the functional connectivity matrix between brain cortical regions for identification of potentially discriminative neuroimaging markers using a novel affinity propagation clustering method. Next, we propose a difference statistic to select the most discriminative connections between the cortical regions. Using selected connections and a support vector machine classifier, we achieve classification accuracy of 81.33% on unseen dataset. The results demonstrate that the proposed algorithm is capable of determining functional connections between brain regions which aid in discrimination of epileptic patients versus healthy subjects.

3D echocardiography offers the ability to perform cardiac functional analysis by visualizing the full 3D geometry of the heart. The full potential of 3D echocardiography has still not been achieved due to problems with image quality and automated quantitative analysis. Native single-view images often lack sufficient anatomical information and are low in contrast and noisy in nature due to poor acoustic window and ultrasound physics limitations. In this work, we explore various ways of fusing the multiple single-view 3D echocardiography images in order to obtain a complete 3D view of the heart by preserving maximum salient information from individual images. Three fusion techniques have been explored for image fusion that include: maximum, averaging, and wavelet image fusion. A novel method of 3D echocardiography fusion utilizing principal component analysis is proposed and a comparative analysis of all discussed techniques is conducted Results obtained from 10 subjects demonstrate that 3D echocardiography image fusion helps in improving quantitative evaluation measures SNR, CNR and contrast while extending FOV and thus filling the missing information in the individual source images. It is hoped that this improved image quality leads to an improved cardiac functional analysis as the multi-view fused image shows the whole picture of the heart.

In an ultrasound system, a digital beamformer is a critical component, which beamforms the data in a desired direction. A 96 channel parameterized beamforming system is designed using VHDL tools. Incoming sampled signals are delayed using FIFO chains and receive apodization is applied using a single multiplier on a sample by sample basis. Each delayed channel data is then summed using a pipelined adder tree to give a beamformed line. The design is evaluated using a 96 channel hardware research platform (UARP), but can easily be extended to more than 96 channels for different imaging techniques. It supports variable channel count, aperture sizes and delays. It increases system frame rate as compared to PC based beamforming with just another 15% logic utilization and 3% memory resources using custom FPGA board, making it viable for real-time processing and measurements.

Real-time 3D echocardiography (RT3DE) promises a more objective and complete cardiac functional analysis by dynamic 3D image acquisition. Despite several efforts towards automation of left ventricle (LV) segmentation and tracking, these remain challenging research problems due to the poor-quality nature of acquired images usually containing missing anatomical information, speckle noise, and limited field-of-view (FOV). Recently, multi-view fusion 3D echocardiography has been introduced as acquiring multiple conventional single-view RT3DE images with small probe movements and fusing them together after alignment. This concept of multi-view fusion helps to improve image quality and anatomical information and extends the FOV. We now take this work further by comparing single-view and multi-view fused images in a systematic study. In order to better illustrate the differences, this work evaluates image quality and information content of single-view and multi-view fused images using image-driven LV endocardial segmentation and tracking. The image-driven methods were utilized to fully exploit image quality and anatomical information present in the image, thus purposely not including any high-level constraints like prior shape or motion knowledge in the analysis approaches. Experiments show that multi-view fused images are better suited for LV segmentation and tracking, while relatively more failures and errors were observed on single-view images.

Recent advances in four-dimensional (4-D) ultrasound enable the acquisition and visualisation of the entire fetal heart. However, getting consistent, shadow free, data remains problematic due to the uncontrollable nature of fetal orientation. This article presents the first investigation into the utility of image fusion to improve the quality of volumetric fetal cardiac imaging. Multiple volume scans are registered using a semiautomatic approach and five fusion methods are assessed for their ability to remove artefacts and improve image quality. Image quality is assessed in terms of signal-to-noise ratio, contrast and contrast-to-noise ratio. Qualitative results are presented for the ability to remove artefacts. The fusion methods assessed were found to be divided into those that reduce noise and those that increase contrast. The effect of fusion on left ventricle segmentation using commercial state-of-the-art software is also considered. The use of image fusion is shown to reduce the variability of volume estimates by about 50% relative to measurement on a single scan.

Real-time 3-dimensional echocardiography (RT3DE) permits the acquisition and visualization of the beating heart in 3D. Despite a number of efforts to automate the left ventricle (LV) delineation from RT3DE images, this remains a challenging problem due to the poor nature of the acquired images usually containing missing anatomical information and high speckle noise. Recently, there have been efforts to improve image quality and anatomical definition by acquiring multiple single-view RT3DE images with small probe movements and fusing them together after alignment. In this work, we evaluate the quality of the multiview fused images using an image-driven semiautomatic LV segmentation method. The segmentation method is based on an edge-driven level set framework, where the edges are extracted using a local-phase inspired feature detector for low-contrast echocardiography boundaries. This totally image-driven segmentation method is applied for the evaluation of end-diastolic (ED) and end-systolic (ES) single-view and multiview fused images. Experiments were conducted on 17 cases and the results show that multiview fused images have better image segmentation quality, but large failures were observed on ED (88.2%) and ES (58.8%) single-view images.

Real-time 3-dimensional echocardiography (RT3DE) permits the acquisition and visualization of the beating heart in 3D. Despite a number of efforts to automate the left ventricle (LV) delineation from RT3DE images, this remains a challenging problem due to the poor nature of the acquired images usually containing missing anatomical information and high speckle noise. Recently, there have been efforts to improve image quality and anatomical definition by acquiring multiple single-view RT3DE images with small probe movements and fusing them together after alignment. In this work, we evaluate the quality of the multiview fused images using an image-driven semi-automatic LV segmentation method. The segmentation method is based on an edge-driven level set framework, where the edges are extracted using a local-phase inspired feature detector for low-contrast echocardiography boundaries. This totally image-driven segmentation method is applied for the evaluation of end-diastolic (ED) and end-systolic (ES) single-view and multiview fused images. Experiments were conducted on 17 cases and the results show that multiview fused images have better image segmentation quality, but large failures were observed on ED (88.2%) and ES (58.8%) single-view images.

Real-time three-dimensional echocardiography (RT3DE) permits the acquisition and visualization of the beating heart in 3D. However, its actual utility is limited due to missing anatomical structures and limited field-of-view (FOV). We present an automatic two-stage registration and fusion method to integrate multiple single-view RT3DE images. The registration scheme finds a rigid transformation by using a multiresolution algorithm. The fusion is based on the 3D wavelet transform, utilizing the separation of the image into low- and high-frequency wavelet subbands. The qualitative and quantitative results, from 12 subjects, demonstrate that the proposed fusion framework helps in: (i) filling-in missing anatomical information, (ii) extending the FOV, and (iii) increasing the structural information and image contrast. © 2009 Springer Berlin Heidelberg.

OBJECTIVES: This study assessed 3-dimensional fusion echocardiography (3DFE), combining several real-time 3-dimensional echocardiography (RT3DE) full volumes from different transducer positions, for improvement in quality and completeness of the reconstructed image. BACKGROUND: The RT3DE technique has limited image quality and completeness of datasets even with current matrix transducers. METHODS: RT3DE datasets were acquired in 32 participants (mean age 33.7 +/- 18.8 years; 27 men, 5 women). The 3DFE technique was also performed on a cardiac phantom. The endocardial border definition of RT3DE and 3DFE segments was graded for quality: good (2), intermediate (1), poor (0), or out of sector. Short-axis and apical images were compared in RT3DE and 3DFE, yielding 2,048 segments. The images were processed to generate 3DFE and then compared with cardiac magnetic resonance. RESULTS: In the heart phantom, fused datasets showed improved contrast to noise ratio from 49.4 +/- 25.1 (single dataset) to 125.4 +/- 25.1 (6 datasets fused together). In subjects, more segments were graded as good quality with 3DFE (805 vs. 435; p < 0.0001) and fewer as intermediate (184 vs. 283; p = 0.017), poor (31 vs. 265; p < 0.0001), or out of sector (4 vs. 41; p < 0.001) compared with the single 3-dimensional dataset. End-diastolic volume (EDV) and end-systolic volume (ESV) obtained from 3-dimensional fused datasets were equivalent to those from single datasets (EDV 118.2 +/- 39 ml vs. 119.7 +/- 43 ml; p = 0.41; ESV 48.1 +/- 30 ml vs. 48.4 +/- 35 ml; p = 0.87; ejection fraction [EF] 61.0 +/- 10% vs. 61.8 +/- 10%; p = 0.44). Bland-Altman analysis showed good 95% limits of agreement for the nonfused datasets (EDV +/-46 ml; ESV +/-36 ml; EF +/-14%) and the fused datasets (EDV +/-45 ml; ESV +/-35 ml; EF +/-16%), when compared with cardiac magnetic resonance. CONCLUSIONS: Fusion of full-volume datasets resulted in an improvement in endocardial borders, image quality, and completeness of the datasets.