| 1. |
- Donis, Daphne, et al.
(författare)
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Stratification strength and light climate explain variation in chlorophyll a at the continental scale in a European multilake survey in a heatwave summer
- 2021
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Ingår i: Limnology and Oceanography. - : John Wiley & Sons. - 0024-3590 .- 1939-5590. ; 66:12, s. 4314-4333
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Tidskriftsartikel (refereegranskat)abstract
- To determine the drivers of phytoplankton biomass, we collected standardized morphometric, physical, and biological data in 230 lakes across the Mediterranean, Continental, and Boreal climatic zones of the European continent. Multilinear regression models tested on this snapshot of mostly eutrophic lakes (median total phosphorus [TP] = 0.06 and total nitrogen [TN] = 0.7 mg L-1), and its subsets (2 depth types and 3 climatic zones), show that light climate and stratification strength were the most significant explanatory variables for chlorophyll a (Chl a) variance. TN was a significant predictor for phytoplankton biomass for shallow and continental lakes, while TP never appeared as an explanatory variable, suggesting that under high TP, light, which partially controls stratification strength, becomes limiting for phytoplankton development. Mediterranean lakes were the warmest yet most weakly stratified and had significantly less Chl a than Boreal lakes, where the temperature anomaly from the long-term average, during a summer heatwave was the highest (+4 degrees C) and showed a significant, exponential relationship with stratification strength. This European survey represents a summer snapshot of phytoplankton biomass and its drivers, and lends support that light and stratification metrics, which are both affected by climate change, are better predictors for phytoplankton biomass in nutrient-rich lakes than nutrient concentrations and surface temperature.
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| 2. |
- Mantzouki, Evanthia, et al.
(författare)
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Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins
- 2018
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Ingår i: Toxins. - : MDPI. - 2072-6651. ; 10:4
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Tidskriftsartikel (refereegranskat)abstract
- Insight into how environmental change determines the production and distribution of cyanobacterial toxins is necessary for risk assessment. Management guidelines currently focus on hepatotoxins (microcystins). Increasing attention is given to other classes, such as neurotoxins (e.g., anatoxin-a) and cytotoxins (e.g., cylindrospermopsin) due to their potency. Most studies examine the relationship between individual toxin variants and environmental factors, such as nutrients, temperature and light. In summer 2015, we collected samples across Europe to investigate the effect of nutrient and temperature gradients on the variability of toxin production at a continental scale. Direct and indirect effects of temperature were the main drivers of the spatial distribution in the toxins produced by the cyanobacterial community, the toxin concentrations and toxin quota. Generalized linear models showed that a Toxin Diversity Index (TDI) increased with latitude, while it decreased with water stability. Increases in TDI were explained through a significant increase in toxin variants such as MC-YR, anatoxin and cylindrospermopsin, accompanied by a decreasing presence of MC-LR. While global warming continues, the direct and indirect effects of increased lake temperatures will drive changes in the distribution of cyanobacterial toxins in Europe, potentially promoting selection of a few highly toxic species or strains.
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| 3. |
- Vialas, Vital, et al.
(författare)
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A multicentric study to evaluate the use of relative retention times in targeted proteomics
- 2017
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Ingår i: Journal of Proteomics. - : Elsevier BV. - 1874-3919. ; 152, s. 138-149
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Tidskriftsartikel (refereegranskat)abstract
- Despite the maturity reached by targeted proteomic strategies, reliable and standardized protocols are urgently needed to enhance reproducibility among different laboratories and analytical platforms, facilitating a more widespread use in biomedical research. To achieve this goal, the use of dimensionless relative retention times (iRT), defined on the basis of peptide standard retention times (RT), has lately emerged as a powerful tool. The robustness, reproducibility and utility of this strategy were examined for the first time in a multicentric setting, involving 28 laboratories that included 24 of the Spanish network of proteomics laboratories (ProteoRed-ISCIII). According to the results obtained in this study, dimensionless retention time values (iRTs) demonstrated to be a useful tool for transferring and sharing peptide retention times across different chromatographic set-ups both intra- and inter-laboratories. iRT values also showed very low variability over long time periods. Furthermore, parallel quantitative analyses showed a high reproducibility despite the variety of experimental strategies used, either MRM (multiple reaction monitoring) or pseudoMRM, and the diversity of analytical platforms employed. Biological significance From the very beginning of proteomics as an analytical science there has been a growing interest in developing standardized methods and experimental procedures in order to ensure the highest quality and reproducibility of the results. In this regard, the recent (2012) introduction of the dimensionless retention time concept has been a significant advance. In our multicentric (28 laboratories) study we explore the usefulness of this concept in the context of a targeted proteomics experiment, demonstrating that dimensionless retention time values is a useful tool for transferring and sharing peptide retention times across different chromatographic set-ups.
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| 4. |
- Bousquet, Jean, et al.
(författare)
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Allergic Rhinitis and its Impact on Asthma (ARIA) Phase 4 (2018) : Change management in allergic rhinitis and asthma multimorbidity using mobile technology
- 2019
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Ingår i: Journal of Allergy and Clinical Immunology. - : Elsevier. - 0091-6749 .- 1097-6825. ; 143:3, s. 864-879
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Tidskriftsartikel (refereegranskat)abstract
- Allergic Rhinitis and its Impact on Asthma (ARIA) has evolved from a guideline by using the best approach to integrated care pathways using mobile technology in patients with allergic rhinitis (AR) and asthma multimorbidity. The proposed next phase of ARIA is change management, with the aim of providing an active and healthy life to patients with rhinitis and to those with asthma multimorbidity across the lifecycle irrespective of their sex or socioeconomic status to reduce health and social inequities incurred by the disease. ARIA has followed the 8-step model of Kotter to assess and implement the effect of rhinitis on asthma multimorbidity and to propose multimorbid guidelines. A second change management strategy is proposed by ARIA Phase 4 to increase self-medication and shared decision making in rhinitis and asthma multimorbidity. An innovation of ARIA has been the development and validation of information technology evidence-based tools (Mobile Airways Sentinel Network [MASK]) that can inform patient decisions on the basis of a self-care plan proposed by the health care professional.
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| 5. |
- Colomé, Núria, et al.
(författare)
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Multi-laboratory experiment PME11 for the standardization of phosphoproteome analysis
- 2022
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Ingår i: Journal of Proteomics. - : Elsevier. - 1874-3919 .- 1876-7737. ; 251
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Tidskriftsartikel (refereegranskat)abstract
- Global analysis of protein phosphorylation by mass spectrometry proteomic techniques has emerged in the last decades as a powerful tool in biological and biomedical research. However, there are several factors that make the global study of the phosphoproteome more challenging than measuring non-modified proteins. The low stoichiometry of the phosphorylated species and the need to retrieve residue specific information require particular attention on sample preparation, data acquisition and processing to ensure reproducibility, qualitative and quantitative robustness and ample phosphoproteome coverage in phosphoproteomic workflows. Aiming to investigate the effect of different variables in the performance of proteome wide phosphoprotein analysis protocols, ProteoRed-ISCIII and EuPA launched the Proteomics Multicentric Experiment 11 (PME11). A reference sample consisting of a yeast protein extract spiked in with different amounts of a phosphomix standard (Sigma/Merck) was distributed to 31 laboratories around the globe. Thirty-six datasets from 23 laboratories were analyzed. Our results indicate the suitability of the PME11 reference sample to benchmark and optimize phosphoproteomics strategies, weighing the influence of different factors, as well as to rank intra and inter laboratory performance.
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| 6. |
- Galvez, Antonio, et al.
(författare)
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A Hybrid Model-Based Approach on Prognostics for Railway HVAC
- 2022
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 10, s. 108117-108127
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Tidskriftsartikel (refereegranskat)abstract
- Prognostics and health management (PHM) of systems usually depends on appropriate prior knowledge and sufficient condition monitoring (CM) data on critical components’ degradation process to appropriately estimate the remaining useful life (RUL). A failure of complex or critical systems such as heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage may adversely affect people or the environment. Critical systems must meet restrictive regulations and standards, and this usually results in an early replacement of components. Therefore, the CM datasets lack data on advanced stages of degradation, and this has a significant impact on developing robust diagnostics and prognostics processes; therefore, it is difficult to find PHM implemented in HVAC systems. This paper proposes a methodology for implementing a hybrid model-based approach (HyMA) to overcome the limited representativeness of the training dataset for developing a prognostic model. The proposed methodology is evaluated building an HyMA which fuses information from a physics-based model with a deep learning algorithm to implement a prognostics process for a complex and critical system. The physics-based model of the HVAC system is used to generate run-to-failure data. This model is built and validated using information and data on the real asset; the failures are modelled according to expert knowledge and an experimental test to evaluate the behaviour of the HVAC system while working, with the air filter at different levels of degradation. In addition to using the sensors located in the real system, we model virtual sensors to observe parameters related to system components’ health. The run-to-failure datasets generated are normalized and directly used as inputs to a deep convolutional neural network (CNN) for RUL estimation. The effectiveness of the proposed methodology and approach is evaluated on datasets containing the air filter’s run-to-failure data. The experimental results show remarkable accuracy in the RUL estimation, thereby suggesting the proposed HyMA and methodology offer a promising approach for PHM.
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| 7. |
- Galvez, Antonio, et al.
(författare)
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Development and synchronisation of a physics-based model for heating, ventilation and air conditioning system integrated into a hybrid model
- 2021
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Ingår i: International Journal of Hydromechatronics. - : InderScience Publishers. - 2515-0464 .- 2515-0472. ; 4:3
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Tidskriftsartikel (refereegranskat)abstract
- This paper proposes a physics-based model which is part of a hybrid model (HyM). The physics-based model is developed for a heating, ventilation, and air conditioning (HVAC) system installed in a passenger train carriage. This model will be used to generate data for building a data-driven mode. Thus, the combination of these two models provides the hybrid model-based approach (HyMAs). The physics-based model of the HVAC system is divided into four principal parts: cooling subsystems, heating subsystems, ventilation subsystems, and vehicle thermal networking. First, the subsystems are modelled, considering the sensors embedded in the real system. Next, the model is synchronised with the real system to give better simulation results and validate the model. The cooling subsystem, heating subsystem and ventilation subsystem are validated with the acceptable sum square error (SSE) results. Second, the new virtual sensors are defined in the model, and their value to future research is suggested
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| 8. |
- Galvez, Antonio, et al.
(författare)
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Fault Detection and RUL Estimation for Railway HVAC Systems Using a Hybrid Model-Based Approach
- 2021
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 13:12
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Tidskriftsartikel (refereegranskat)abstract
- Heating, ventilation, and air conditioning (HVAC) systems installed in a passenger train carriage are critical systems, whose failures can affect people or the environment. This, together with restrictive regulations, results in the replacement of critical components in initial stages of degradation, as well as a lack of data on advanced stages of degradation. This paper proposes a hybrid model-based approach (HyMA) to overcome the lack of failure data on a HVAC system installed in a passenger train carriage. The proposed HyMA combines physics-based models with data-driven models to deploy diagnostic and prognostic processes for a complex and critical system. The physics-based model generates data on healthy and faulty working conditions; the faults are generated in different levels of degradation and can appear individually or together. A fusion of synthetic data and measured data is used to train, validate, and test the proposed hybrid model (HyM) for fault detection and diagnostics (FDD) of the HVAC system. The model obtains an accuracy of 92.60%. In addition, the physics-based model generates run-to-failure data for the HVAC air filter to develop a remaining useful life (RUL) prediction model, the RUL estimations performed obtained an accuracy in the range of 95.21–97.80% Both models obtain a remarkable accuracy. The development presented will result in a tool which provides relevant information on the health state of the HVAC system, extends its useful life, reduces its life cycle cost, and improves its reliability and availability; thus enhancing the sustainability of the system.
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| 9. |
- Gálvez, Antonio, et al.
(författare)
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Feature Assessment for a Hybrid Model
- 2023
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Ingår i: Proceedings of the 5th International Conference on Maintenance, Condition Monitoring and Diagnostics 2021. - : Springer Nature. ; , s. 43-58
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Konferensbidrag (refereegranskat)abstract
- This paper proposes an assessment of features orientated to improve the accuracy of a hybrid model (HyM) used for detecting faults in a heating, ventilation, and air conditioning (HVAC) system. The HyM combines data collected by sensors embedded in the system with data generated by a physics-based model of the HVAC. The physics-based model includes sensors embedded in the real system and virtual sensors to represent the behaviour of the system when a failure mode (FM) is simulated. This fusion leads to improved maintenance actions to reduce the number of failures and predict the behaviour of the system. HyM can lead to improved fault detection and diagnostics (FDD) processes of critical systems, but multiple fault detection models are sometimes inaccurate. The paper assesses features extracted from synthetic signals. The results of the assessment are used to improve the accuracy of a multiple fault detection model developed in previous research. The assessment of features comprises the following: (1) generation of run-to-failure data using the physics-based model of the HVAC system; the FMs simulated in this paper are dust in the air filter, degradation of the CO2 sensor, degradation of the evaporator fan, and variations in the compression rate of the cooling system; (2) identification of the individual features that strongly distinguish the FM; (3) analysis of how the features selected vary when components degrade.
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| 10. |
- Galvez, Antonio
(författare)
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Hybrid digital twins: A co-creation of data science and physics
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Safety is more important than reliability or efficiency in railway, aerospace, oil & gas, and chemical industries. Regulations are very restrictive in sectors where safety is paramount. This makes maintainers replace critical components in initial stages of degradation, which implies a loss of useful life and a lack of information about advanced stages of degradation for those components. Nevertheless, this lack of data can be overcome using hybrid digital twins, also known as hybrid-model based approaches (HyMAs), which combine data-driven models with physics-based models. This fusion minimizes the occurrence of undesirable failures that may interrupt the functionality of critical systems in a safe or cost-efficient manner.HyMAs have been studied at Luleå University of Technology by other Ph.D. students who found promising direction for future research in prognostics and health management (PHM) applications. Thus, this research work continues the direction defined in previous research with the proposal of HyMAs for a heating, ventilation, and air conditioning (HVAC) system installed in a passenger train carriage orientated to diagnostics and prognostics processes. The proposed hybrid modelling consists of the fusion of data obtained from two sources: data obtained from the real system and synthetic data generated by a developed physics-based model of the HVAC.The HVAC system is considered a system of systems (SoS). Therefore, the physics-based model of the HVAC system is divided into four main systems: heating subsystem, cooling subsystem, ventilation subsystem, and cabin thermal networking subsystem. These subsystems are modelled considering the sensors installed in the real system and soft sensors, also known as virtual sensors, which provide crucial information for fault detection, diagnostics, and prognostics. These sensors defined in the physics-based model generate synthetic data which reproduce the behaviour of the system while a failure mode (FM) is simulated. Verification and validation are key processes to synchronise the response of the physics-based model with the signals obtained from the real system. Hence, the physics-based model is synchronised, verified, and validated using data collected by sensors located in the real system. These steps are conducted following guidelines suggested in the literature.Different datasets containing real data and synthetic data while the HVAC system works in faulty and healthy states are used to train data-driven models for fault detection and diagnostics and to train data-driven models for prognostics.Statistical features, such as shape factor, kurtosis, skewness, and sum square error, among others, are calculated from the selected signals. These features are labelled according to the related FMs and are merged with the features calculated from the data obtained from the real system. The data fusion is classified according to the condition indicators of the system in terms of FMs and level of degradation. The merged features are used to train data-driven models for fault detection and diagnostics. In addition, the real data can be loaded to the physics-based model to predict the degradation of the air filter.Then, the prediction data are loaded to an exponential model that provides an estimation of the remaining useful life (RUL) of the air filter. To improve the prognostics model, the physics-based model is used to generate run-to-failure data which are used to train and test a deep convolutional neural network (CNN) which accurately estimates the RUL of the air filter.
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| 11. |
- Gálvez, Antonio, et al.
(författare)
-
Hybrid Model Development for HVAC System in Transportation
- 2021
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Ingår i: Technologies. - : MDPI. - 2227-7080. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- Hybrid models combine physics-based models and data-driven models. This combination is a useful technique to detect fault and predict the current degradation of equipment. This paper proposes a physics-based model, which will be part of a hybrid model, for a heating, ventilation, and air conditioning system installed in the passenger vehicle of a train. The physics-based model is divided into four main parts: heating subsystems, cooling subsystems, ventilation subsystems, and cabin thermal networking subsystems. These subsystems are developed when considering the sensors that are located in the real system, so the model can be linked via the acquired sensor data and virtual sensor data to improve the detectability of failure modes. Thus, the physics-based model can be synchronized with the real system to provide better simulation results. The paper also considers diagnostics and prognostics performance. First, it looks at the current situation of the maintenance strategy for the heating, ventilation, air conditioning system, and the number of failure modes that the maintenance team can detect. Second, it determines the expected improvement using hybrid modelling to maintain the system. This improvement is based on the capabilities of detecting new failure modes. The paper concludes by suggesting the future capabilities of hybrid models.
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| 12. |
- Gálvez, Antonio, et al.
(författare)
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Hybrid Models and Digital Twins for Condition Monitoring : HVAC System for Railway
- 2019
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Ingår i: EUROSIM 2019. - : ARGESIM Publisher. ; , s. 52-52
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- The passenger transportation,safety is more important than efficiency or reliability. Therefore, it is vital to maintain the proper condition of the equipment related to the passengers’ comfort and safety.This manuscriptexplainsthe methodology ofcomplete development and implementation of both hybrid model and digital twin3.0for a HVAC inrailways. The objective of this is to monitor the condition of the HVAC where it matters to the comfort and safety of the passengers in the trains. The level3.0of digital twinwill be develop for the diagnosis and prognosis of HVAC by using the hybrid modeling.The descriptionillustratedin this paper is focused on the methodology used to implement the hybrid model approach and both the need and advantages to use a hybrid model approach instead of thedata-based approach.One of the particularitiesconsidered doing the developmentwas the importanceof the safety and environmental risk whichwere included in the risk quantification of the failure modes. In train companies the maintainers replace critical components in early stages of degradation, thus, using a data-based model might loses important information and does not givegood support to manage the maintenance instructions.Developing a physicsbased-model will be able to generate synthetic data for the behavior of the components in advanced stages of degradation and combiningitwith the data-based modellead to formhybrid model with a large number of failure modes that were unpredictable. Finally,the outcome is beneficialfor the proper functioning of the systems, hence, the safety of the passengers.
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| 13. |
- Gálvez, Antonio, et al.
(författare)
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Hybrid Models and Digital Twins for Condition Monitoring: HVAC System for Railway
- 2021
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Ingår i: Simulation Notes Europe. - : ARGESIM Publisher. - 2306-0271 .- 2305-9974. ; 31:3, s. 121-126
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Tidskriftsartikel (refereegranskat)abstract
- Safety passenger transportation is more important than efficiency or reliability. Therefore, it is vital to maintain the proper condition of the equipment related to the passengers’ comfort and safety. This manuscript presents the methodology of complete development and implementation of both hybrid model and digital twin 3.0 for an HVAC in railways. The objective of this is to monitor the condition of the HVAC where it matters to the comfort and safety of the passengers in the trains. The level 3.0 of digital twin will be developed for the diagnosis and prognosis of HVAC by using hybrid modeling. The description illustrated in this paper is focused on the methodology used to implement a hybrid model-based approach, and both the need and advantages of using hybrid model approaches instead of data-based approaches. The development considers the importance of safety and environmental risks, which are included in the risk quantification of failure modes. Railway’s maintainers replace critical components in early stages of degradation; thus, the use of a data-driven model loses essential information related to advanced stages of degradation which might decrease the accuracy of the maintenance instructions provided. Physics-based model can be used to generate synthetic data to overcome the lack of data in advanced stages of degradation, and then, the synthetic data can be combined with the real data, which is collected by sensor located in the real system, to build the data-driven model. The combination leads to form hybrid-model based approach with a large number of failure modes that were unpredictable. Finally, the outcome is beneficial for the proper functioning of systems; hence, safety of the passengers.
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| 14. |
- Gálvez, Antonio, et al.
(författare)
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Synthetic Data Generation in Hybrid Modelling of Railway HVAC System
- 2020
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Ingår i: 17th IMEKO TC 10 and EUROLAB Virtual Conference. - : International Measurement Confederation (IMEKO). ; , s. 79-84
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a hybrid model (HyM)for a heating, ventilation and air conditioning (HVAC) system installed in a passenger train. This HyM fuses data from two sources: data taken from the real system and synthetic data generated using a physics-based model of the HVAC.The physical model of the HVAC was developed to include the sensors located in the real system and new virtual sensors reproducing the behaviour of the system while a failure mode (FM) is simulated.Statistical features are calculated from the selected signals. These features are labelled according to the related FMs and are merged with the features calculated from the data from the real system. This data fusion allows us to classify the condition indicators of the system according to the FMs. The merged features are used to train a neural network (NN), which achieves a remarkable accuracy.Accuracy is a key concern of future research on the detection and diagnosis of a multiple faults and the estimation of the remaining useful life (RUL) through prognosis. The outcome is beneficial for the proper functioning of the system and the safety of the passengers.
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| 15. |
- Gonzalez-Gaya, Belen, et al.
(författare)
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Biodegradation as an important sink of aromatic hydrocarbons in the oceans
- 2019
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Ingår i: Nature Geoscience. - : Nature Publishing Group. - 1752-0894 .- 1752-0908. ; 12:2, s. 119-125+2
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Tidskriftsartikel (refereegranskat)abstract
- Atmospheric deposition of semivolatile aromatic hydrocarbons accounts for an important input of organic matter to the surface ocean. Nevertheless, the biogeochemical cycling and sinks of semivolatile aromatic hydrocarbons in the ocean remain largely uncharacterized. Here we present measurements of 64 polycyclic aromatic hydrocarbons in plankton and seawater from the Atlantic, Pacific, Indian and Southern Oceans, as well an assessment of their microbial degradation genes. Concentrations of the more hydrophobic compounds decreased when the plankton biomass was higher, consistent with the relevance of the biological pump. The mass balance for the global oceans showed that the settling fluxes of aromatic hydrocarbons in the water column were two orders of magnitude lower than the atmospheric deposition fluxes. This imbalance was high for low molecular weight hydrocarbons, such as phenanthrene and methylphenanthrenes, highly abundant in the dissolved phase. Parent polycyclic aromatic hydrocarbons were depleted to a higher degree than alkylated polycyclic aromatic hydrocarbons, and the degradation genes for polycyclic aromatic hydrocarbons were found to be ubiquitous in oceanic metagenomes. These observations point to a key role of biodegradation in depleting the bioavailable dissolved hydrocarbons and to the microbial degradation of atmospheric inputs of organic matter as a relevant process for the marine carbon cycle.
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| 16. |
- Munoz-Gama, Jorge, et al.
(författare)
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Process mining for healthcare : Characteristics and challenges
- 2022
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Ingår i: Journal of Biomedical Informatics. - : Elsevier BV. - 1532-0464 .- 1532-0480. ; 127
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Tidskriftsartikel (refereegranskat)abstract
- Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.
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| 17. |
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| 18. |
- Santos-Lozano, Alejandro, et al.
(författare)
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Successful aging : Insights from proteome analyses of healthy centenarians
- 2020
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Ingår i: Aging. - : Impact Journals, LLC. - 1945-4589. ; 12:4, s. 3502-3515
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Tidskriftsartikel (refereegranskat)abstract
- Healthy aging depends on a complex gene-environment network that is ultimately reflected in the expression of different proteins. We aimed to perform a comparative analysis of the plasma proteome of healthy centenarians (n=9, 5 women, age range 100-103 years) with a notably preserved ambulatory capacity (as a paradigm of 'successful' aging), and control individuals who died from a major age-related disease before the expected life expectancy (n=9, 5 women, age range: 67-81 years), and while having impaired ambulatory capacity (as a paradigm of 'unsuccessful' aging). We found that the expression of 49 proteins and 86 pathways differed between the two groups. Overall, healthy centenarians presented with distinct expression of proteins/pathways that reflect a healthy immune function, including a lower pro-inflammatory status (less 'inflammaging' and autoimmunity) and a preserved humoral immune response (increased B cell-mediated immune response). Compared with controls, healthy centenarians also presented with a higher expression of proteins involved in angiogenesis and related to enhanced intercellular junctions, as well as a lower expression of proteins involved in cardiovascular abnormalities. The identification of these proteins/pathways might provide new insights into the biological mechanisms underlying the paradigm of healthy aging.
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| 19. |
- Schneider, K. M., et al.
(författare)
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Gut microbiota depletion exacerbates cholestatic liver injury via loss of FXR signalling
- 2021
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Ingår i: Nature Metabolism. - : Springer Science and Business Media LLC. - 2522-5812. ; 3:9, s. 1228-1241
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Tidskriftsartikel (refereegranskat)abstract
- Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease of unknown aetiology for which there are no approved therapeutic options. Patients with PSC display changes in gut microbiota and in bile acid (BA) composition; however, the contribution of these alterations to disease pathogenesis remains controversial. Here we identify a role for microbiota-dependent changes in BA synthesis that modulates PSC pathophysiology. In a genetic mouse model of PSC, we show that loss of microbiota-mediated negative feedback control of BA synthesis results in increased hepatic BA concentrations, disruption of bile duct barrier function and, consequently, fatal liver injury. We further show that these changes are dependent on decreased BA signalling to the farnesoid X receptor, which modulates the activity of the rate-limiting enzyme in BA synthesis, CYP7A1. Moreover, patients with advanced stages of PSC show suppressed BA synthesis as measured by serum C4 levels, which is associated with poor disease prognosis. Our preclinical data highlight the microbiota-dependent dynamics of BA metabolism in cholestatic liver disease, which could be important for future therapies targeting BA and gut microbiome interactions, and identify C4 as a potential biomarker to functionally stratify patients with PSC and predict disease outcomes. Patients with primary sclerosing cholangitis (PSC), a chronic cholestatic liver disease, display changes in the gut microbiota and in bile acid composition. Schneider, Candels and colleagues identify a role for microbiota-dependent regulation of bile acid synthesis through farnesoid X receptor signalling, which is relevant for PSC disease progression.
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