| 41. |
- Cann, Sophie Le, et al.
(författare)
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Spatio-temporal evolution of hydroxyapatite crystal thickness at the bone-implant interface
- 2020
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Ingår i: Acta Biomaterialia. - : Elsevier BV. - 1878-7568 .- 1742-7061. ; 116, s. 391-399
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Tidskriftsartikel (refereegranskat)abstract
- A better understanding of bone nanostructure around the bone-implant interface is essential to improve longevity of clinical implants and decrease failure risks. This study investigates the spatio-temporal evolution of mineral crystal thickness and plate orientation in newly formed bone around the surface of a metallic implant. Standardized coin-shaped titanium implants designed with a bone chamber were inserted into rabbit tibiae for 7 and 13 weeks. Scanning measurements with micro-focused small-angle X-ray scattering (SAXS) were carried out on newly formed bone close to the implant and in control mature cortical bone. Mineral crystals were thinner close to the implant (1.8 ± 0.45 nm at 7 weeks and 2.4 ± 0.57 nm at 13 weeks) than in the control mature bone tissue (2.5 ± 0.21 nm at 7 weeks and 2.8 ± 0.35 nm at 13 weeks), with increasing thickness over healing time (+30 % in 6 weeks). These results are explained by younger bone close to the implant, which matures during osseointegration. Thinner mineral crystals parallel to the implant surface within the first 100 µm close to the implant indicate that the implant affects bone ultrastructure close to the implant, potentially due to heterogeneous interfacial stresses, and suggest a longer maturation process of bone tissue and difficulty in binding to the metal. The bone growth kinetics within the bone chamber was derived from the spatio-temporal evolution of bone tissue's nanostructure, coupled with microtomographic imaging. The findings indicate that understanding mineral crystal thickness or plate orientation can improve our knowledge of osseointegration.
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| 42. |
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| 43. |
- Dodig-Crnkovic, Tea, et al.
(författare)
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Facets of individual-specific health signatures determined from longitudinal plasma proteome profiling
- 2020
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Ingår i: Ebiomedicine. - : Elsevier BV. - 2352-3964. ; 57
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Tidskriftsartikel (refereegranskat)abstract
- Background: Precision medicine approaches aim to tackle diseases on an individual level through molecular profiling. Despite the growing knowledge about diseases and the reported diversity of molecular phenotypes, the descriptions of human health on an individual level have been far less elaborate. Methods: To provide insights into the longitudinal protein signatures of well-being, we profiled blood plasma collected over one year from 101 clinically healthy individuals using multiplexed antibody assays. After applying an antibody validation scheme, we utilized > 700 protein profiles for in-depth analyses of the individuals' short-term health trajectories. Findings: We found signatures of circulating proteomes to be highly individual-specific. Considering technical and longitudinal variability, we observed that 49% of the protein profiles were stable over one year. We also identified eight networks of proteins in which 11-242 proteins covaried over time. For each participant, there were unique protein profiles of which some could be explained by associations to genetic variants. Interpretation: This observational and non-interventional study identifyed noticeable diversity among clinically healthy subjects, and facets of individual-specific signatures emerged by monitoring the variability of the circulating proteomes over time. To enable more personal hence precise assessments of health states, longitudinal profiling of circulating proteomes can provide a valuable component for precision medicine approaches.
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| 44. |
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| 45. |
- Gharehbaghi, Arash, et al.
(författare)
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A pattern recognition framework for detecting dynamic changes on cyclic time series
- 2015
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Ingår i: Pattern Recognition. - : Elsevier. - 0031-3203 .- 1873-5142. ; 48:3, s. 696-708
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a framework for binary classification of the time series with cyclic characteristics. The framework presents an iterative algorithm for learning the cyclic characteristics by introducing the discriminative frequency bands (DFBs) using the discriminant analysis along with k-means clustering method. The DFBs are employed by a hybrid model for learning dynamic characteristics of the time series within the cycles, using statistical and structural machine learning techniques. The framework offers a systematic procedure for finding the optimal design parameters associated with the hybrid model. The proposed model is optimized to detect the changes of the heart sound recordings (HSRs) related to aortic stenosis. Experimental results show that the proposed framework provides efficient tools for classification of the HSRs based on the heart murmurs. It is also evidenced that the hybrid model, proposed by the framework, substantially improves the classification performance when it comes to detection of the heart disease.
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| 46. |
- Gharehbaghi, Arash, et al.
(författare)
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An Automatic Tool for Pediatric Heart Sounds Segmentation
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we present a novel algorithm for pediatric heart sound segmentation, incorporated into a graphical user interface. The algorithm employs both the Electrocardiogram (ECG) and Phonocardiogram (PCG) signals for an efficient segmentation under pathological circumstances.First, the ECG signal is invoked in order to determine the beginning and end points of each cardiac cycle by using wavelet transform technique. Then, first and second heart sounds within the cycles are identified over the PCG signal by paying attention to the spectral properties of the sounds. The algorithm is applied on 120 recordings of normal and pathological children, totally containing 1976 cardiac cycles. The accuracy of the segmentation algorithm is 97% for S1 and 94% for S2 identification while all the cardiac cycles are correctly determined.
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| 47. |
- Gharehbaghi, Arash, et al.
(författare)
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Severity assessments of aortic stenosis using intelligent phonocardiography
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Objectives: To study capabilities of the intelligent phonocardiography (IPCG) in automatic grading severity of the aortic stenosis (AS).Methods: Phonocardiogram signals were recorded from the patients with AS, as diagnosed by echocardiography. The patient group is comprised of signals, recorded from 5 patients (2 recordings from each), mostly elderly referrals (>60 years) with mild to severe AS. An advanced processing algorithm, consisted of the wavelet transform and the stepwise regression analysis, characterizes the systolic murmur caused by the AS in order to predict the 5 indicators; mean pressure gradient over the aortic valve (MPG), maximum jet velocity (MJV), aortic valve area (AVA), velocity time integral and the ejection period. The automatic assessment is performed by an artificial neural network using the predicted values of the indicators as the input data. Reliability of the IPCG is validated by applying repeated random sub-sampling (RRSS) with 70%/30% of the training/testing data, and calculating the accuracy. The RRSS is also employed to validate reproducibility of the IPCG by using 70% of the signals for training and the second recording of the same individuals for testing.Results: Accuracy of the IPCG is estimated to be and (95% confidence interval) for the reliability and the reproducibility, respectively. Linear correlation between the characterized systolic murmur and the MPG (r>0.81), the MJV (r>0.82) and the AVA (r>0.85) is observed.Conclusions: The IPCG has the potential to objectively serve as a clinical tool for grading severity of the aortic stenosis.
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| 48. |
- Hafid, Abdelakram, et al.
(författare)
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Full Impedance Cardiography Measurement Device Using Raspberry PI3 and System-on-Chip Biomedical Instrumentation Solutions
- 2018
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2168-2194 .- 2168-2208. ; 22:6, s. 1883-1894
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Tidskriftsartikel (refereegranskat)abstract
- Impedance cardiography (ICG) is a noninvasive method for monitoring cardiac dynamics using electrical bioimpedance (EBI) measurements. Since its appearance more than 40 years ago, ICG has been used for assessing hemodynamic parameters. This paper presents a measurement system based on two System on Chip (SoC) solutions and Raspberry PI, implementing both a full three-lead ECG recorder and an impedance cardiographer, for educational and research development purposes. Raspberry PI is a platform supporting Do-I t-Yourself project and education applications across the world. The development is part of Biosignal PI, an open hardware platform focusing in quick prototyping of physiological measurement instrumentation. The SoC used for sensing cardiac biopotential is the ADAS1000, and for the EBI measurement is the AD5933. The recordings were wirelessly transmitted through Bluetooth to a PC, where the waveforms were displayed, and hemodynamic parameters such as heart rate, stroke volume, ejection time and cardiac output were extracted from the ICG and ECG recordings. These results show how Raspberry PI can be used for quick prototyping using relatively widely available and affordable components, for supporting developers in research and engineering education. The design and development documents will be available on www.BiosignalPl.com, for open access under a Non Commercial-Share A like 4.0 International License.
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| 49. |
- Iredahl, Fredrik, 1988-
(författare)
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Assessment of microvascular and metabolic responses in the skin
- 2016
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The general aim of this project was to develop experimental in vivo models that allow for minimally invasive investigations of responses in the skin to microvascular and metabolic provocations. The cutaneous microvasculature has emerged as a valuable model and been proposed to mirror the microcirculation in other organs. Dysfunction in the cutaneous microcirculation has thus been linked to systemic diseases such as hypertension and diabetes mellitus. Models for investigating skin responses could facilitate the understanding of pathophysiological mechanisms as well as effects of drugs.In the first study, three optical measurement techniques (laser Doppler flowmetry (LDF), laser speckle contrast imaging (LSCI) and tissue viability imaging (TiVi)) were compared against each other and showed differences in their ability to detect microvascular responses to provocations in the skin. TiVi was found more sensitive for measurement of noradrenaline-induced vasoconstriction, while LSCI was more sensitive for measurement of vascular occlusion. In the second study, microvascular responses in the skin to iontophoresis of vasoactive drugs were found to depend on the drug delivery protocol. Perfusion half-life was defined and used to describe the decay in the microvascular response to a drug after iontophoresis. In the third study, the role of nitric oxide (NO) was assessed during iontophoresis of insulin. The results showed a NO-dependent vasodilation in the skin by insulin. In the fourth study the vasoactive and metabolic effects of insulin were studied after both local and endogenous administration. Local delivery of insulin increased skin blood flow, paralleled by increased skin concentrations of interstitial pyruvate and lactate, although no change in glucose concentration was observed. An oral glucose load resulted in an increased insulin concentration in the skin paralleled by an increase in blood flow, as measured using the microdialysis urea clearance technique, although no changes in perfusion was measured by LSCI.The thesis concludes that when studying skin microvascular responses, the choice of measurement technique and the drug delivery protocol has an impact on the measurement results, and should therefore be carefully considered. The thesis also concludes that insulin has metabolic and vasodilatory effects in the skin both when administered locally and as an endogenous response to an oral glucose load. The vasodilatory effect of insulin in the skin is mediated by nitric oxide.
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| 50. |
- Messner, Christoph B., et al.
(författare)
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Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection
- 2020
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Ingår i: Cell Systems. - : Elsevier BV. - 2405-4712 .- 2405-4720. ; 11:1, s. 11-24.E4
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Tidskriftsartikel (refereegranskat)abstract
- The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.
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