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"en": "# General\nGeoprivacy Open Data is an anonymised tracking dataset in gpx format based on voluntary donations of personal human mobility data. Anonymisation means that all direct and indirect references to a person have been removed, while at the same time, the original data is preserved as much as possible. The raw data donations have been done through the Geoprivacy platform operating at [https://geoprivacy.fi/](https://geoprivacy.fi/).\n\nThe Geoprivacy platform is a service where cyclists, joggers, and pedestrians can donate GPS (Global Positioning System) and other GNSS (Global Navigation Satellite System) tracking data for science. Additionally, the portal offers an open data repository to which users can choose to provide a privacy-protected version of their data. The open data is freely available to urban planners, scientists, and industry, i.e., everyone interested in the innovation potential of detailed personal-level mobility data. The hourly updates of the data are available from the platform, and this data is one of the monthly/yearly frozen versions of live data having exhaustive metadata descriptions, a persistent identifier, and access through the [https://etsin.fairdata.fi](https://etsin.fairdata.fi) service.\n\nThe platform was born as part of our research on privacy issues related to using precise individual-level location data. A prime example of this is activity tracking data, which citizens are recording using various mobile sports tracking applications. **Our vision is that this tracking data could be used to improve the infrastructure for non-motorized means of travel.** Making cycling and walking more safe and convenient could greatly help reduce the number of cars in cities.\n\nActivity tracking data is not easily accessible. Even when tracks are publicly visible on the web, the terms of use usually limit the ways in which the data can be used. Some companies grant specific types of users, e.g., urban planners, access to the data, but only in a heavily processed form. There are good reasons to limit the distribution of the data: Activity tracking data is sensitive data and can reveal surprising personal details. These include, for example, home and workplace, and repeating patterns such as commuting behavior.\n\nWe started the Geoprivacy platform to request voluntary participants to donate their tracking data for science. The original tracks are used only for research related to location data privacy within our research group. However, the participants have the additional option to donate a processed version of their tracks to an open data repository.\n\nWith the open, privacy-preserving data repository, we hope to provide the scientific community with a benchmark dataset for non-motorized mobility data, making research in this area more comparable and reproducible. Furthermore, the open repository can serve as a proof-of-concept for a service that allows citizens to share their data directly with urban planning authorities.\n\n# Privacy protection\nThe details of the privacy protection method are described at [https://geoprivacy.fi/#/privacy-mechanisms](https://geoprivacy.fi/#/privacy-mechanisms). In addition to the listed mechanisms, the population density of the data area needs to be more than 6 inhabitants per square kilometer, and the data has to be inside Finland.\n\n# Versions and release notes\nThis dataset is one of the frozen datasets in the series of Geoprivacy Open Data. The other frozen datasets can be found from the Fairdata.fi service [Etsin with the search term \"geoprivacy\"](https://etsin.fairdata.fi/datasets/geoprivacy?keys=&terms=&p=1&sort=best) .\n\n**Geoprivacy Open Data 2023/11 release** Added string \"cycling_\", \"running_\", or \"walking_\" to the filenames. Mode of movement is assessed based on the average speed computed for each trajectory. Note: The classification is prone to errors and should be refined for mode of travel -critical applications.\n\n# Format and coordinate system\nThe data is provided in the standard GPX format. The general GPX XML schema is available at [https://www.topografix.com/GPX/1/1/](https://www.topografix.com/GPX/1/1/).\n\nEach GPX file starts with definitions of the XML version and character encoding, the GPX version, and the library used for creating the file. The actual location data is given in <trk> and <trkseq> elements, where the latitude and longitude values of each track point are given after <lat> and <lon> attributes using decimal degrees in the WGS84 coordinate system (EPSG: 4326). The time stamp for each point is given in the <time> element in UTC. For day-time critical applications, conversion to EET/EEST taking into account the day-light saving is important (EET = UTC + 2h, EEST = UTC + 3h).\n\n# Terms of use\nThe dataset is provided as open data, and its use is controlled by [the Terms of Use for the GeoPrivacy Open Data](https://geoprivacy.fi/#/open-data-terms-of-use).\n\nIf you use the data in your work, please use the citation\n\n>Mäkinen, V., Brauer, A. and Oksanen, J. 2023. GeoPrivacy platform, available at: https://geoprivacy.fi\n\nand acknowledge\n\n>\"We made use of geospatial data provided by the Open Geospatial Information Infrastructure for Research (Geoportti, urn:nbn:fi:research-infras-2016072513) funded by the Academy of Finland, CSC – IT Center for Science, and other Geoportti consortium members.\"\n\n# Acknowledgements\nThe Geoprivacy project and platform have been funded by the Finnish Cultural Foundation and the Academy of Finland. The platform is the service pilot of the Geoportti RI (Open Geospatial Information Infrastructure for Research, urn:nbn:fi:research-infras-2016072513)."
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"en": "<jats:p>Abstract. Devices with integrated global navigation satellite system (GNSS) receivers have enabled citizens to accurately record activities such as bicycle trips, runs, and walks. Due to its spatiotemporal extent and high level of detail, GNSS-based activity tracking data is a valuable source of information on active modes of transportation. At the same time, movement recordings of individuals are sensitive data and are associated with privacy concerns. In this work, we present a privacy-aware platform where citizens can contribute GNSS tracks to an open repository. The repository is published according to the FAIR data principles: findable, accessible, interoperable, and reusable. This provides the opportunity to use the data as a benchmark for the development of GNSS trajectory processing methods. The platform’s privacy module processes each track before publication, concealing stay points, generalizing the tracks in the temporal dimension, and suppressing tracks in sparsely populated areas. This approach mitigates the most likely re-identification attacks and limits the amount of information that could leak if an attacker succeeds with re-identification. As a residual risk remains, the platform sensitizes users to privacy risks and enables them to make informed decisions about publishing their data.\n </jats:p>",
"fi": "<jats:p>Abstract. Devices with integrated global navigation satellite system (GNSS) receivers have enabled citizens to accurately record activities such as bicycle trips, runs, and walks. Due to its spatiotemporal extent and high level of detail, GNSS-based activity tracking data is a valuable source of information on active modes of transportation. At the same time, movement recordings of individuals are sensitive data and are associated with privacy concerns. In this work, we present a privacy-aware platform where citizens can contribute GNSS tracks to an open repository. The repository is published according to the FAIR data principles: findable, accessible, interoperable, and reusable. This provides the opportunity to use the data as a benchmark for the development of GNSS trajectory processing methods. The platform’s privacy module processes each track before publication, concealing stay points, generalizing the tracks in the temporal dimension, and suppressing tracks in sparsely populated areas. This approach mitigates the most likely re-identification attacks and limits the amount of information that could leak if an attacker succeeds with re-identification. As a residual risk remains, the platform sensitizes users to privacy risks and enables them to make informed decisions about publishing their data.\n </jats:p>",
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"en": "<jats:title>Abstract</jats:title><jats:p>Cancer involves histological changes in tissue, which is of primary importance in pathological diagnosis and research. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue with all its variables. On the other hand, understanding connections between genetic alterations and histological attributes requires development of enhanced analysis methods suitable also for small sample sizes. Here, we set out to develop computational methods for early detection and distinction of prostate cancer-related pathological alterations. We use analysis of features from HE stained histological images of normal mouse prostate epithelium, distinguishing the descriptors for variability between ventral, lateral, and dorsal lobes. In addition, we use two common prostate cancer models, Hi-Myc and <jats:italic>Pten</jats:italic>+/− mice, to build a feature-based machine learning model separating the early pathological lesions provoked by these genetic alterations. This work offers a set of computational methods for separation of early neoplastic lesions in the prostates of model mice, and provides proof-of-principle for linking specific tumor genotypes to quantitative histological characteristics. The results obtained show that separation between different spatial locations within the organ, as well as classification between histologies linked to different genetic backgrounds, can be performed with very high specificity and sensitivity.</jats:p>",
"fi": "<jats:title>Abstract</jats:title><jats:p>Cancer involves histological changes in tissue, which is of primary importance in pathological diagnosis and research. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue with all its variables. On the other hand, understanding connections between genetic alterations and histological attributes requires development of enhanced analysis methods suitable also for small sample sizes. Here, we set out to develop computational methods for early detection and distinction of prostate cancer-related pathological alterations. We use analysis of features from HE stained histological images of normal mouse prostate epithelium, distinguishing the descriptors for variability between ventral, lateral, and dorsal lobes. In addition, we use two common prostate cancer models, Hi-Myc and <jats:italic>Pten</jats:italic>+/− mice, to build a feature-based machine learning model separating the early pathological lesions provoked by these genetic alterations. This work offers a set of computational methods for separation of early neoplastic lesions in the prostates of model mice, and provides proof-of-principle for linking specific tumor genotypes to quantitative histological characteristics. The results obtained show that separation between different spatial locations within the organ, as well as classification between histologies linked to different genetic backgrounds, can be performed with very high specificity and sensitivity.</jats:p>",
"und": "<jats:title>Abstract</jats:title><jats:p>Cancer involves histological changes in tissue, which is of primary importance in pathological diagnosis and research. Automated histological analysis requires ability to computationally separate pathological alterations from normal tissue with all its variables. On the other hand, understanding connections between genetic alterations and histological attributes requires development of enhanced analysis methods suitable also for small sample sizes. Here, we set out to develop computational methods for early detection and distinction of prostate cancer-related pathological alterations. We use analysis of features from HE stained histological images of normal mouse prostate epithelium, distinguishing the descriptors for variability between ventral, lateral, and dorsal lobes. In addition, we use two common prostate cancer models, Hi-Myc and <jats:italic>Pten</jats:italic>+/− mice, to build a feature-based machine learning model separating the early pathological lesions provoked by these genetic alterations. This work offers a set of computational methods for separation of early neoplastic lesions in the prostates of model mice, and provides proof-of-principle for linking specific tumor genotypes to quantitative histological characteristics. The results obtained show that separation between different spatial locations within the organ, as well as classification between histologies linked to different genetic backgrounds, can be performed with very high specificity and sensitivity.</jats:p>"
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"en": "<jats:title>Abstract</jats:title><jats:sec>\n <jats:title>Background</jats:title>\n <jats:p>Virtual reality (VR) enables data visualization in an immersive and engaging manner, and it can be used for creating ways to explore scientific data. Here, we use VR for visualization of 3D histology data, creating a novel interface for digital pathology to aid cancer research.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Methods</jats:title>\n <jats:p>Our contribution includes 3D modeling of a whole organ and embedded objects of interest, fusing the models with associated quantitative features and full resolution serial section patches, and implementing the virtual reality application. Our VR application is multi-scale in nature, covering two object levels representing different ranges of detail, namely organ level and sub-organ level. In addition, the application includes several data layers, including the measured histology image layer and multiple representations of quantitative features computed from the histology.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Results</jats:title>\n <jats:p>In our interactive VR application, the user can set visualization properties, select different samples and features, and interact with various objects, which is not possible in the traditional 2D-image view used in digital pathology. In this work, we used whole mouse prostates (organ level) with prostate cancer tumors (sub-organ objects of interest) as example cases, and included quantitative histological features relevant for tumor biology in the VR model.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Conclusions</jats:title>\n <jats:p>Our application enables a novel way for exploration of high-resolution, multidimensional data for biomedical research purposes, and can also be used in teaching and researcher training. Due to automated processing of the histology data, our application can be easily adopted to visualize other organs and pathologies from various origins.</jats:p>\n </jats:sec>",
"fi": "<jats:title>Abstract</jats:title><jats:sec>\n <jats:title>Background</jats:title>\n <jats:p>Virtual reality (VR) enables data visualization in an immersive and engaging manner, and it can be used for creating ways to explore scientific data. Here, we use VR for visualization of 3D histology data, creating a novel interface for digital pathology to aid cancer research.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Methods</jats:title>\n <jats:p>Our contribution includes 3D modeling of a whole organ and embedded objects of interest, fusing the models with associated quantitative features and full resolution serial section patches, and implementing the virtual reality application. Our VR application is multi-scale in nature, covering two object levels representing different ranges of detail, namely organ level and sub-organ level. In addition, the application includes several data layers, including the measured histology image layer and multiple representations of quantitative features computed from the histology.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Results</jats:title>\n <jats:p>In our interactive VR application, the user can set visualization properties, select different samples and features, and interact with various objects, which is not possible in the traditional 2D-image view used in digital pathology. In this work, we used whole mouse prostates (organ level) with prostate cancer tumors (sub-organ objects of interest) as example cases, and included quantitative histological features relevant for tumor biology in the VR model.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Conclusions</jats:title>\n <jats:p>Our application enables a novel way for exploration of high-resolution, multidimensional data for biomedical research purposes, and can also be used in teaching and researcher training. Due to automated processing of the histology data, our application can be easily adopted to visualize other organs and pathologies from various origins.</jats:p>\n </jats:sec>",
"und": "<jats:title>Abstract</jats:title><jats:sec>\n <jats:title>Background</jats:title>\n <jats:p>Virtual reality (VR) enables data visualization in an immersive and engaging manner, and it can be used for creating ways to explore scientific data. Here, we use VR for visualization of 3D histology data, creating a novel interface for digital pathology to aid cancer research.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Methods</jats:title>\n <jats:p>Our contribution includes 3D modeling of a whole organ and embedded objects of interest, fusing the models with associated quantitative features and full resolution serial section patches, and implementing the virtual reality application. Our VR application is multi-scale in nature, covering two object levels representing different ranges of detail, namely organ level and sub-organ level. In addition, the application includes several data layers, including the measured histology image layer and multiple representations of quantitative features computed from the histology.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Results</jats:title>\n <jats:p>In our interactive VR application, the user can set visualization properties, select different samples and features, and interact with various objects, which is not possible in the traditional 2D-image view used in digital pathology. In this work, we used whole mouse prostates (organ level) with prostate cancer tumors (sub-organ objects of interest) as example cases, and included quantitative histological features relevant for tumor biology in the VR model.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Conclusions</jats:title>\n <jats:p>Our application enables a novel way for exploration of high-resolution, multidimensional data for biomedical research purposes, and can also be used in teaching and researcher training. Due to automated processing of the histology data, our application can be easily adopted to visualize other organs and pathologies from various origins.</jats:p>\n </jats:sec>"
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"en": "<jats:sec><jats:title>Background</jats:title><jats:p>Detailed characterization of early pathophysiological changes in preclinical Alzheimer's disease (AD) is necessary to enable development of correctly targeted and timed disease-modifying treatments. ASIC-E4 study (“Beta-Amyloid, Synaptic loss, Inflammation and Cognition in healthy <jats:italic>APOE</jats:italic> ε4 carriers”) combines state-of-the-art neuroimaging and fluid-based biomarker measurements to study the early interplay of three key pathological features of AD, i.e., beta-amyloid (Aβ) deposition, neuroinflammation and synaptic dysfunction and loss in cognitively normal volunteers with three different levels of genetic (<jats:italic>APOE-</jats:italic>related) risk for late-onset AD.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>Here, our objective is to describe the study design, used protocols and baseline demographics of the ASIC-E4 study.</jats:p></jats:sec><jats:sec><jats:title>Methods/Design</jats:title><jats:p>ASIC-E4 is a prospective observational multimodal imaging study performed in Turku PET Centre in collaboration with University of Gothenburg. Cognitively normal 60–75-year-old-individuals with known <jats:italic>APOE</jats:italic> ε4/ε4 genotype were recruited via local Auria Biobank (Turku, Finland). Recruitment of the project has been completed in July 2020 and 63 individuals were enrolled to three study groups (Group 1: <jats:italic>APOE</jats:italic> ε4/ε4, <jats:italic>N</jats:italic> = 19; Group 2: <jats:italic>APOE</jats:italic> ε4/ε3, <jats:italic>N</jats:italic> = 22; Group 3: <jats:italic>APOE</jats:italic> ε3/ε3, <jats:italic>N</jats:italic> = 22). At baseline, all participants will undergo positron emission tomography imaging with tracers targeted against Aβ deposition (<jats:sup>11</jats:sup>C-PIB), activated glia (<jats:sup>11</jats:sup>C-PK11195) and synaptic vesicle glycoprotein 2A (<jats:sup>11</jats:sup>C-UCB-J), two brain magnetic resonance imaging scans, and extensive cognitive testing. In addition, blood samples are collected for various laboratory measurements and blood biomarker analysis and cerebrospinal fluid samples are collected from a subset of participants based on additional voluntary informed consent. To evaluate the predictive value of the early neuroimaging findings, neuropsychological evaluation and blood biomarker measurements will be repeated after a 4-year follow-up period.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>Results of the ASIC-E4 project will bridge the gap related to limited knowledge of the synaptic and inflammatory changes and their association with each other and Aβ in “at-risk” individuals. Thorough <jats:italic>in vivo</jats:italic> characterization of the biomarker profiles in this population will produce valuable information for diagnostic purposes and future drug development, where the field has already started to look beyond Aβ.</jats:p></jats:sec>",
"fi": "<jats:sec><jats:title>Background</jats:title><jats:p>Detailed characterization of early pathophysiological changes in preclinical Alzheimer's disease (AD) is necessary to enable development of correctly targeted and timed disease-modifying treatments. ASIC-E4 study (“Beta-Amyloid, Synaptic loss, Inflammation and Cognition in healthy <jats:italic>APOE</jats:italic> ε4 carriers”) combines state-of-the-art neuroimaging and fluid-based biomarker measurements to study the early interplay of three key pathological features of AD, i.e., beta-amyloid (Aβ) deposition, neuroinflammation and synaptic dysfunction and loss in cognitively normal volunteers with three different levels of genetic (<jats:italic>APOE-</jats:italic>related) risk for late-onset AD.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>Here, our objective is to describe the study design, used protocols and baseline demographics of the ASIC-E4 study.</jats:p></jats:sec><jats:sec><jats:title>Methods/Design</jats:title><jats:p>ASIC-E4 is a prospective observational multimodal imaging study performed in Turku PET Centre in collaboration with University of Gothenburg. Cognitively normal 60–75-year-old-individuals with known <jats:italic>APOE</jats:italic> ε4/ε4 genotype were recruited via local Auria Biobank (Turku, Finland). Recruitment of the project has been completed in July 2020 and 63 individuals were enrolled to three study groups (Group 1: <jats:italic>APOE</jats:italic> ε4/ε4, <jats:italic>N</jats:italic> = 19; Group 2: <jats:italic>APOE</jats:italic> ε4/ε3, <jats:italic>N</jats:italic> = 22; Group 3: <jats:italic>APOE</jats:italic> ε3/ε3, <jats:italic>N</jats:italic> = 22). At baseline, all participants will undergo positron emission tomography imaging with tracers targeted against Aβ deposition (<jats:sup>11</jats:sup>C-PIB), activated glia (<jats:sup>11</jats:sup>C-PK11195) and synaptic vesicle glycoprotein 2A (<jats:sup>11</jats:sup>C-UCB-J), two brain magnetic resonance imaging scans, and extensive cognitive testing. In addition, blood samples are collected for various laboratory measurements and blood biomarker analysis and cerebrospinal fluid samples are collected from a subset of participants based on additional voluntary informed consent. To evaluate the predictive value of the early neuroimaging findings, neuropsychological evaluation and blood biomarker measurements will be repeated after a 4-year follow-up period.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>Results of the ASIC-E4 project will bridge the gap related to limited knowledge of the synaptic and inflammatory changes and their association with each other and Aβ in “at-risk” individuals. Thorough <jats:italic>in vivo</jats:italic> characterization of the biomarker profiles in this population will produce valuable information for diagnostic purposes and future drug development, where the field has already started to look beyond Aβ.</jats:p></jats:sec>",
"und": "<jats:sec><jats:title>Background</jats:title><jats:p>Detailed characterization of early pathophysiological changes in preclinical Alzheimer's disease (AD) is necessary to enable development of correctly targeted and timed disease-modifying treatments. ASIC-E4 study (“Beta-Amyloid, Synaptic loss, Inflammation and Cognition in healthy <jats:italic>APOE</jats:italic> ε4 carriers”) combines state-of-the-art neuroimaging and fluid-based biomarker measurements to study the early interplay of three key pathological features of AD, i.e., beta-amyloid (Aβ) deposition, neuroinflammation and synaptic dysfunction and loss in cognitively normal volunteers with three different levels of genetic (<jats:italic>APOE-</jats:italic>related) risk for late-onset AD.</jats:p></jats:sec><jats:sec><jats:title>Objective</jats:title><jats:p>Here, our objective is to describe the study design, used protocols and baseline demographics of the ASIC-E4 study.</jats:p></jats:sec><jats:sec><jats:title>Methods/Design</jats:title><jats:p>ASIC-E4 is a prospective observational multimodal imaging study performed in Turku PET Centre in collaboration with University of Gothenburg. Cognitively normal 60–75-year-old-individuals with known <jats:italic>APOE</jats:italic> ε4/ε4 genotype were recruited via local Auria Biobank (Turku, Finland). Recruitment of the project has been completed in July 2020 and 63 individuals were enrolled to three study groups (Group 1: <jats:italic>APOE</jats:italic> ε4/ε4, <jats:italic>N</jats:italic> = 19; Group 2: <jats:italic>APOE</jats:italic> ε4/ε3, <jats:italic>N</jats:italic> = 22; Group 3: <jats:italic>APOE</jats:italic> ε3/ε3, <jats:italic>N</jats:italic> = 22). At baseline, all participants will undergo positron emission tomography imaging with tracers targeted against Aβ deposition (<jats:sup>11</jats:sup>C-PIB), activated glia (<jats:sup>11</jats:sup>C-PK11195) and synaptic vesicle glycoprotein 2A (<jats:sup>11</jats:sup>C-UCB-J), two brain magnetic resonance imaging scans, and extensive cognitive testing. In addition, blood samples are collected for various laboratory measurements and blood biomarker analysis and cerebrospinal fluid samples are collected from a subset of participants based on additional voluntary informed consent. To evaluate the predictive value of the early neuroimaging findings, neuropsychological evaluation and blood biomarker measurements will be repeated after a 4-year follow-up period.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>Results of the ASIC-E4 project will bridge the gap related to limited knowledge of the synaptic and inflammatory changes and their association with each other and Aβ in “at-risk” individuals. Thorough <jats:italic>in vivo</jats:italic> characterization of the biomarker profiles in this population will produce valuable information for diagnostic purposes and future drug development, where the field has already started to look beyond Aβ.</jats:p></jats:sec>"
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"en": "Ekblad, Tuisku, Koivumäki, Helin, Rinne, Snellman: Insulin resistance and body mass index are associated with TSPO PET in cognitively unimpaired elderly",
"fi": "Ekblad, Tuisku, Koivumäki, Helin, Rinne, Snellman: Insulin resistance and body mass index are associated with TSPO PET in cognitively unimpaired elderly"
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"en": "<jats:p> Metabolic risk factors are associated with peripheral low-grade inflammation and an increased risk for dementia. We evaluated if metabolic risk factors i.e. insulin resistance, body mass index (BMI), serum cholesterol values, or high sensitivity C-reactive protein associate with central inflammation or beta-amyloid (Aβ) accumulation in the brain, and if these associations are modulated by APOE4 gene dose. Altogether 60 cognitively unimpaired individuals (mean age 67.7 years (SD 4.7); 63% women; 21 APOE3/3, 20 APOE3/4 and 19 APOE4/ 4) underwent positron emission tomography with [<jats:sup>11</jats:sup>C]PK11195 targeting TSPO (18 kDa translocator protein) and [<jats:sup>11</jats:sup>C]PIB targeting fibrillar Aβ. [<jats:sup>11</jats:sup>C]PK11195 distribution value ratios and [<jats:sup>11</jats:sup>C]PIB standardized uptake values were calculated in a cortical composite region of interest typical for Aβ accumulation in Alzheimer’s disease. Associations between metabolic risk factors, [<jats:sup>11</jats:sup>C]PK11195, and [<jats:sup>11</jats:sup>C]PIB uptake were evaluated with linear models adjusted for age and sex. Higher logarithmic HOMA-IR (standardized beta 0.40, p = 0.002) and BMI (standardized beta 0.27, p = 0.048) were associated with higher TSPO availability. Voxel-wise analyses indicated that this association was mainly seen in the parietal cortex. Higher logarithmic HOMA-IR was associated with higher [<jats:sup>11</jats:sup>C]PIB (standardized beta 0.44, p = 0.02), but only in APOE4/4 homozygotes. BMI and HOMA-IR seem to influence TSPO availability in the brain. </jats:p>",
"fi": "<jats:p> Metabolic risk factors are associated with peripheral low-grade inflammation and an increased risk for dementia. We evaluated if metabolic risk factors i.e. insulin resistance, body mass index (BMI), serum cholesterol values, or high sensitivity C-reactive protein associate with central inflammation or beta-amyloid (Aβ) accumulation in the brain, and if these associations are modulated by APOE4 gene dose. Altogether 60 cognitively unimpaired individuals (mean age 67.7 years (SD 4.7); 63% women; 21 APOE3/3, 20 APOE3/4 and 19 APOE4/ 4) underwent positron emission tomography with [<jats:sup>11</jats:sup>C]PK11195 targeting TSPO (18 kDa translocator protein) and [<jats:sup>11</jats:sup>C]PIB targeting fibrillar Aβ. [<jats:sup>11</jats:sup>C]PK11195 distribution value ratios and [<jats:sup>11</jats:sup>C]PIB standardized uptake values were calculated in a cortical composite region of interest typical for Aβ accumulation in Alzheimer’s disease. Associations between metabolic risk factors, [<jats:sup>11</jats:sup>C]PK11195, and [<jats:sup>11</jats:sup>C]PIB uptake were evaluated with linear models adjusted for age and sex. Higher logarithmic HOMA-IR (standardized beta 0.40, p = 0.002) and BMI (standardized beta 0.27, p = 0.048) were associated with higher TSPO availability. Voxel-wise analyses indicated that this association was mainly seen in the parietal cortex. Higher logarithmic HOMA-IR was associated with higher [<jats:sup>11</jats:sup>C]PIB (standardized beta 0.44, p = 0.02), but only in APOE4/4 homozygotes. BMI and HOMA-IR seem to influence TSPO availability in the brain. </jats:p>",
"und": "<jats:p> Metabolic risk factors are associated with peripheral low-grade inflammation and an increased risk for dementia. We evaluated if metabolic risk factors i.e. insulin resistance, body mass index (BMI), serum cholesterol values, or high sensitivity C-reactive protein associate with central inflammation or beta-amyloid (Aβ) accumulation in the brain, and if these associations are modulated by APOE4 gene dose. Altogether 60 cognitively unimpaired individuals (mean age 67.7 years (SD 4.7); 63% women; 21 APOE3/3, 20 APOE3/4 and 19 APOE4/ 4) underwent positron emission tomography with [<jats:sup>11</jats:sup>C]PK11195 targeting TSPO (18 kDa translocator protein) and [<jats:sup>11</jats:sup>C]PIB targeting fibrillar Aβ. [<jats:sup>11</jats:sup>C]PK11195 distribution value ratios and [<jats:sup>11</jats:sup>C]PIB standardized uptake values were calculated in a cortical composite region of interest typical for Aβ accumulation in Alzheimer’s disease. Associations between metabolic risk factors, [<jats:sup>11</jats:sup>C]PK11195, and [<jats:sup>11</jats:sup>C]PIB uptake were evaluated with linear models adjusted for age and sex. Higher logarithmic HOMA-IR (standardized beta 0.40, p = 0.002) and BMI (standardized beta 0.27, p = 0.048) were associated with higher TSPO availability. Voxel-wise analyses indicated that this association was mainly seen in the parietal cortex. Higher logarithmic HOMA-IR was associated with higher [<jats:sup>11</jats:sup>C]PIB (standardized beta 0.44, p = 0.02), but only in APOE4/4 homozygotes. BMI and HOMA-IR seem to influence TSPO availability in the brain. </jats:p>"
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"en": "Snellman, Ekblad, Ashton, Karikari, Lantero-Rodriguez, Pietilä, Koivumäki, Helin, Karrasch, Zetterberg, Blennow, Rinne: Head-to-head comparison of plasma p-tau181, p-tau231 and glial fibrillary acidic protein in clinically unimpaired elderly with three levels of APOE4-related risk for Alzheimer's disease",
"fi": "Snellman, Ekblad, Ashton, Karikari, Lantero-Rodriguez, Pietilä, Koivumäki, Helin, Karrasch, Zetterberg, Blennow, Rinne: Head-to-head comparison of plasma p-tau181, p-tau231 and glial fibrillary acidic protein in clinically unimpaired elderly with three levels of APOE4-related risk for Alzheimer's disease"
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"en": "Snellman, Ekblad, Tuisku, Koivumäki, Ashton, Lantero-Rodriguez, Karikari, Helin, Bucci, Löyttyniemi, Parkkola, Karrasch, Schöll, Zetterberg, Blennow, Rinne: APOE ε4 gene dose effect on imaging and blood biomarkers of neuroinflammation and beta-amyloid in cognitively unimpaired elderly",
"fi": "Snellman, Ekblad, Tuisku, Koivumäki, Ashton, Lantero-Rodriguez, Karikari, Helin, Bucci, Löyttyniemi, Parkkola, Karrasch, Schöll, Zetterberg, Blennow, Rinne: APOE ε4 gene dose effect on imaging and blood biomarkers of neuroinflammation and beta-amyloid in cognitively unimpaired elderly"
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"description": {
"en": "<jats:title>Abstract</jats:title><jats:sec>\n <jats:title>Background</jats:title>\n <jats:p>Neuroinflammation, characterized by increased reactivity of microglia and astrocytes in the brain, is known to be present at various stages of the Alzheimer’s disease (AD) <jats:italic>continuum</jats:italic>. However, its presence and relationship with amyloid pathology in cognitively normal at-risk individuals is less clear. Here, we used positron emission tomography (PET) and blood biomarker measurements to examine differences in neuroinflammation and beta-amyloid (Aβ) and their association in cognitively unimpaired homozygotes, heterozygotes, or non-carriers of the <jats:italic>APOE</jats:italic> ε4 allele, the strongest genetic risk for sporadic AD.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Methods</jats:title>\n <jats:p>Sixty 60–75-year-old <jats:italic>APOE</jats:italic> ε4 homozygotes (<jats:italic>n</jats:italic> = 19), heterozygotes (<jats:italic>n</jats:italic> = 21), and non-carriers (<jats:italic>n</jats:italic> = 20) were recruited in collaboration with the local Auria biobank. The participants underwent <jats:sup>11</jats:sup>C-PK11195 PET (targeting 18-kDa translocator protein, TSPO), <jats:sup>11</jats:sup>C-PiB PET (targeting Aβ), brain MRI, and neuropsychological testing including a preclinical cognitive composite (APCC). <jats:sup>11</jats:sup>C-PK11195 distribution volume ratios and <jats:sup>11</jats:sup>C-PiB standardized uptake value ratios (SUVRs) were calculated for regions typical for early Aβ accumulation in AD. Blood samples were drawn for measuring plasma glial fibrillary acidic protein (GFAP) and plasma Aβ<jats:sub>1-42/1.40</jats:sub>.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Results</jats:title>\n <jats:p>In our cognitively unimpaired sample, cortical <jats:sup>11</jats:sup>C-PiB-binding increased according to <jats:italic>APOE</jats:italic> ε4 gene dose (median composite SUVR 1.47 (range 1.38–1.66) in non-carriers, 1.55 (1.43–2.02) in heterozygotes, and 2.13 (1.61–2.83) in homozygotes, <jats:italic>P</jats:italic> = 0.002). In contrast, cortical composite <jats:sup>11</jats:sup>C-PK11195-binding did not differ between the <jats:italic>APOE</jats:italic> ε4 gene doses (<jats:italic>P</jats:italic> = 0.27) or between Aβ-positive and Aβ-negative individuals (<jats:italic>P</jats:italic> = 0.81) and associated with higher Aβ burden only in <jats:italic>APOE</jats:italic> ε4 homozygotes (Rho = 0.47, <jats:italic>P</jats:italic> = 0.043). Plasma GFAP concentration correlated with cortical <jats:sup>11</jats:sup>C-PiB (Rho = 0.35, <jats:italic>P</jats:italic> = 0.040), but not <jats:sup>11</jats:sup>C-PK11195-binding (Rho = 0.13, <jats:italic>P</jats:italic> = 0.47) in Aβ-positive individuals. In the total cognitively unimpaired population, both higher composite <jats:sup>11</jats:sup>C-PK11195-binding and plasma GFAP were associated with lower hippocampal volume, whereas elevated <jats:sup>11</jats:sup>C-PiB-binding was associated with lower APCC scores.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Conclusions</jats:title>\n <jats:p>Only Aβ burden measured by PET, but not markers of neuroinflammation, differed among cognitively unimpaired elderly with different <jats:italic>APOE</jats:italic> ε4 gene dose. However, <jats:italic>APOE</jats:italic> ε4 gene dose seemed to modulate the association between neuroinflammation and Aβ.</jats:p>\n </jats:sec>",
"fi": "<jats:title>Abstract</jats:title><jats:sec>\n <jats:title>Background</jats:title>\n <jats:p>Neuroinflammation, characterized by increased reactivity of microglia and astrocytes in the brain, is known to be present at various stages of the Alzheimer’s disease (AD) <jats:italic>continuum</jats:italic>. However, its presence and relationship with amyloid pathology in cognitively normal at-risk individuals is less clear. Here, we used positron emission tomography (PET) and blood biomarker measurements to examine differences in neuroinflammation and beta-amyloid (Aβ) and their association in cognitively unimpaired homozygotes, heterozygotes, or non-carriers of the <jats:italic>APOE</jats:italic> ε4 allele, the strongest genetic risk for sporadic AD.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Methods</jats:title>\n <jats:p>Sixty 60–75-year-old <jats:italic>APOE</jats:italic> ε4 homozygotes (<jats:italic>n</jats:italic> = 19), heterozygotes (<jats:italic>n</jats:italic> = 21), and non-carriers (<jats:italic>n</jats:italic> = 20) were recruited in collaboration with the local Auria biobank. The participants underwent <jats:sup>11</jats:sup>C-PK11195 PET (targeting 18-kDa translocator protein, TSPO), <jats:sup>11</jats:sup>C-PiB PET (targeting Aβ), brain MRI, and neuropsychological testing including a preclinical cognitive composite (APCC). <jats:sup>11</jats:sup>C-PK11195 distribution volume ratios and <jats:sup>11</jats:sup>C-PiB standardized uptake value ratios (SUVRs) were calculated for regions typical for early Aβ accumulation in AD. Blood samples were drawn for measuring plasma glial fibrillary acidic protein (GFAP) and plasma Aβ<jats:sub>1-42/1.40</jats:sub>.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Results</jats:title>\n <jats:p>In our cognitively unimpaired sample, cortical <jats:sup>11</jats:sup>C-PiB-binding increased according to <jats:italic>APOE</jats:italic> ε4 gene dose (median composite SUVR 1.47 (range 1.38–1.66) in non-carriers, 1.55 (1.43–2.02) in heterozygotes, and 2.13 (1.61–2.83) in homozygotes, <jats:italic>P</jats:italic> = 0.002). In contrast, cortical composite <jats:sup>11</jats:sup>C-PK11195-binding did not differ between the <jats:italic>APOE</jats:italic> ε4 gene doses (<jats:italic>P</jats:italic> = 0.27) or between Aβ-positive and Aβ-negative individuals (<jats:italic>P</jats:italic> = 0.81) and associated with higher Aβ burden only in <jats:italic>APOE</jats:italic> ε4 homozygotes (Rho = 0.47, <jats:italic>P</jats:italic> = 0.043). Plasma GFAP concentration correlated with cortical <jats:sup>11</jats:sup>C-PiB (Rho = 0.35, <jats:italic>P</jats:italic> = 0.040), but not <jats:sup>11</jats:sup>C-PK11195-binding (Rho = 0.13, <jats:italic>P</jats:italic> = 0.47) in Aβ-positive individuals. In the total cognitively unimpaired population, both higher composite <jats:sup>11</jats:sup>C-PK11195-binding and plasma GFAP were associated with lower hippocampal volume, whereas elevated <jats:sup>11</jats:sup>C-PiB-binding was associated with lower APCC scores.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Conclusions</jats:title>\n <jats:p>Only Aβ burden measured by PET, but not markers of neuroinflammation, differed among cognitively unimpaired elderly with different <jats:italic>APOE</jats:italic> ε4 gene dose. However, <jats:italic>APOE</jats:italic> ε4 gene dose seemed to modulate the association between neuroinflammation and Aβ.</jats:p>\n </jats:sec>",
"und": "<jats:title>Abstract</jats:title><jats:sec>\n <jats:title>Background</jats:title>\n <jats:p>Neuroinflammation, characterized by increased reactivity of microglia and astrocytes in the brain, is known to be present at various stages of the Alzheimer’s disease (AD) <jats:italic>continuum</jats:italic>. However, its presence and relationship with amyloid pathology in cognitively normal at-risk individuals is less clear. Here, we used positron emission tomography (PET) and blood biomarker measurements to examine differences in neuroinflammation and beta-amyloid (Aβ) and their association in cognitively unimpaired homozygotes, heterozygotes, or non-carriers of the <jats:italic>APOE</jats:italic> ε4 allele, the strongest genetic risk for sporadic AD.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Methods</jats:title>\n <jats:p>Sixty 60–75-year-old <jats:italic>APOE</jats:italic> ε4 homozygotes (<jats:italic>n</jats:italic> = 19), heterozygotes (<jats:italic>n</jats:italic> = 21), and non-carriers (<jats:italic>n</jats:italic> = 20) were recruited in collaboration with the local Auria biobank. The participants underwent <jats:sup>11</jats:sup>C-PK11195 PET (targeting 18-kDa translocator protein, TSPO), <jats:sup>11</jats:sup>C-PiB PET (targeting Aβ), brain MRI, and neuropsychological testing including a preclinical cognitive composite (APCC). <jats:sup>11</jats:sup>C-PK11195 distribution volume ratios and <jats:sup>11</jats:sup>C-PiB standardized uptake value ratios (SUVRs) were calculated for regions typical for early Aβ accumulation in AD. Blood samples were drawn for measuring plasma glial fibrillary acidic protein (GFAP) and plasma Aβ<jats:sub>1-42/1.40</jats:sub>.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Results</jats:title>\n <jats:p>In our cognitively unimpaired sample, cortical <jats:sup>11</jats:sup>C-PiB-binding increased according to <jats:italic>APOE</jats:italic> ε4 gene dose (median composite SUVR 1.47 (range 1.38–1.66) in non-carriers, 1.55 (1.43–2.02) in heterozygotes, and 2.13 (1.61–2.83) in homozygotes, <jats:italic>P</jats:italic> = 0.002). In contrast, cortical composite <jats:sup>11</jats:sup>C-PK11195-binding did not differ between the <jats:italic>APOE</jats:italic> ε4 gene doses (<jats:italic>P</jats:italic> = 0.27) or between Aβ-positive and Aβ-negative individuals (<jats:italic>P</jats:italic> = 0.81) and associated with higher Aβ burden only in <jats:italic>APOE</jats:italic> ε4 homozygotes (Rho = 0.47, <jats:italic>P</jats:italic> = 0.043). Plasma GFAP concentration correlated with cortical <jats:sup>11</jats:sup>C-PiB (Rho = 0.35, <jats:italic>P</jats:italic> = 0.040), but not <jats:sup>11</jats:sup>C-PK11195-binding (Rho = 0.13, <jats:italic>P</jats:italic> = 0.47) in Aβ-positive individuals. In the total cognitively unimpaired population, both higher composite <jats:sup>11</jats:sup>C-PK11195-binding and plasma GFAP were associated with lower hippocampal volume, whereas elevated <jats:sup>11</jats:sup>C-PiB-binding was associated with lower APCC scores.</jats:p>\n </jats:sec><jats:sec>\n <jats:title>Conclusions</jats:title>\n <jats:p>Only Aβ burden measured by PET, but not markers of neuroinflammation, differed among cognitively unimpaired elderly with different <jats:italic>APOE</jats:italic> ε4 gene dose. However, <jats:italic>APOE</jats:italic> ε4 gene dose seemed to modulate the association between neuroinflammation and Aβ.</jats:p>\n </jats:sec>"
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"en": "Koivumäki, Ekblad, Lantero-Rodriguez, Ashton, Karikari, Helin, Parkkola, Lötjönen, Zetterberg, Blennow, Rinne, Snellman: Blood biomarkers of neurodegeneration associate differently with amyloid deposition, medial temporal atrophy, and cerebrovascular changes in APOE ε4-enriched cognitively unimpaired elderly",
"fi": "Koivumäki, Ekblad, Lantero-Rodriguez, Ashton, Karikari, Helin, Parkkola, Lötjönen, Zetterberg, Blennow, Rinne, Snellman: Blood biomarkers of neurodegeneration associate differently with amyloid deposition, medial temporal atrophy, and cerebrovascular changes in APOE ε4-enriched cognitively unimpaired elderly"
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"en": "<jats:title>Abstract</jats:title>\n <jats:p><jats:bold>Background: </jats:bold>Alzheimer’s disease (AD) is characterized by the accumulation of amyloid-β (Aβ) plaques, neurofibrillary tau tangles, and neurodegeneration in the brain parenchyma. Here, we aimed to i) assess differences in blood and imaging biomarkers used to evaluate neurodegeneration among cognitively unimpaired <jats:italic>APOE</jats:italic> ε4 homozygotes, heterozygotes, and non-carriers with varying risk for sporadic AD, and ii) to determine how different cerebral pathologies (<jats:italic>i.e.</jats:italic>, Aβ deposition, medial temporal atrophy, and cerebrovascular pathology) contribute to blood biomarker concentrations in this sample.\n<jats:bold>Methods</jats:bold>: Sixty <jats:italic>APOE </jats:italic>ε4 homozygotes (n = 19), heterozygotes (n = 21), and non-carriers (n = 20) ranging from 60–75 years, were recruited in collaboration with Auria biobank (Turku, Finland). Participants underwent Aβ-PET ([<jats:sup>11</jats:sup>C]PiB), structural brain MRI including T1-weighted and T2-FLAIR sequences, and blood sampling for measuring serum neurofilament light chain (NfL), plasma total tau (t-tau), plasma N-terminal tau fragments (NTA-tau) and plasma glial fibrillary acidic protein (GFAP). [<jats:sup>11</jats:sup>C]PiB standardized uptake value ratio was calculated for regions typical for Aβ accumulation in AD. MRI images were analysed for regional volumes, atrophy scores, and volumes of white matter hyperintensities. Differences in biomarker levels and associations between blood and imaging biomarkers were tested using uni- and multivariable linear models (unadjusted and adjusted for age and sex).\n<jats:bold>Results: </jats:bold>Serum NfL concentration was increased in <jats:italic>APOE</jats:italic> ε4 homozygotes compared with non-carriers (mean 21.4 pg/ml (SD 9.5) vs. 15.5 pg/ml (3.8), p = 0.013), whereas other blood biomarkers did not differ between the groups (p > 0.077 for all). From imaging biomarkers, hippocampal volume was significantly decreased in <jats:italic>APOE</jats:italic> ε4 homozygotes compared with non-carriers (6.71 ml (0.86) vs. 7.2 ml (0.7), p = 0.029). In the whole sample, blood biomarker levels were differently predicted by the three measured cerebral pathologies; serum NfL concentration was associated with cerebrovascular pathology and medial temporal atrophy, while plasma NTA-tau associated with medial temporal atrophy. Plasma GFAP showed significant association with both medial temporal atrophy and Aβ pathology. Plasma t-tau concentration did not associate with any of the measured pathologies.\n<jats:bold>Conclusions: </jats:bold>Only increased serum NfL concentrations and decreased hippocampal volume was observed in cognitively unimpaired APOEε4 homozygotes compared to non-carriers. In the whole population the concentrations of blood biomarkers were affected in distinct ways by different pathologies.</jats:p>",
"fi": "<jats:title>Abstract</jats:title>\n <jats:p><jats:bold>Background: </jats:bold>Alzheimer’s disease (AD) is characterized by the accumulation of amyloid-β (Aβ) plaques, neurofibrillary tau tangles, and neurodegeneration in the brain parenchyma. Here, we aimed to i) assess differences in blood and imaging biomarkers used to evaluate neurodegeneration among cognitively unimpaired <jats:italic>APOE</jats:italic> ε4 homozygotes, heterozygotes, and non-carriers with varying risk for sporadic AD, and ii) to determine how different cerebral pathologies (<jats:italic>i.e.</jats:italic>, Aβ deposition, medial temporal atrophy, and cerebrovascular pathology) contribute to blood biomarker concentrations in this sample.\n<jats:bold>Methods</jats:bold>: Sixty <jats:italic>APOE </jats:italic>ε4 homozygotes (n = 19), heterozygotes (n = 21), and non-carriers (n = 20) ranging from 60–75 years, were recruited in collaboration with Auria biobank (Turku, Finland). Participants underwent Aβ-PET ([<jats:sup>11</jats:sup>C]PiB), structural brain MRI including T1-weighted and T2-FLAIR sequences, and blood sampling for measuring serum neurofilament light chain (NfL), plasma total tau (t-tau), plasma N-terminal tau fragments (NTA-tau) and plasma glial fibrillary acidic protein (GFAP). [<jats:sup>11</jats:sup>C]PiB standardized uptake value ratio was calculated for regions typical for Aβ accumulation in AD. MRI images were analysed for regional volumes, atrophy scores, and volumes of white matter hyperintensities. Differences in biomarker levels and associations between blood and imaging biomarkers were tested using uni- and multivariable linear models (unadjusted and adjusted for age and sex).\n<jats:bold>Results: </jats:bold>Serum NfL concentration was increased in <jats:italic>APOE</jats:italic> ε4 homozygotes compared with non-carriers (mean 21.4 pg/ml (SD 9.5) vs. 15.5 pg/ml (3.8), p = 0.013), whereas other blood biomarkers did not differ between the groups (p > 0.077 for all). From imaging biomarkers, hippocampal volume was significantly decreased in <jats:italic>APOE</jats:italic> ε4 homozygotes compared with non-carriers (6.71 ml (0.86) vs. 7.2 ml (0.7), p = 0.029). In the whole sample, blood biomarker levels were differently predicted by the three measured cerebral pathologies; serum NfL concentration was associated with cerebrovascular pathology and medial temporal atrophy, while plasma NTA-tau associated with medial temporal atrophy. Plasma GFAP showed significant association with both medial temporal atrophy and Aβ pathology. Plasma t-tau concentration did not associate with any of the measured pathologies.\n<jats:bold>Conclusions: </jats:bold>Only increased serum NfL concentrations and decreased hippocampal volume was observed in cognitively unimpaired APOEε4 homozygotes compared to non-carriers. In the whole population the concentrations of blood biomarkers were affected in distinct ways by different pathologies.</jats:p>",
"und": "<jats:title>Abstract</jats:title>\n <jats:p><jats:bold>Background: </jats:bold>Alzheimer’s disease (AD) is characterized by the accumulation of amyloid-β (Aβ) plaques, neurofibrillary tau tangles, and neurodegeneration in the brain parenchyma. Here, we aimed to i) assess differences in blood and imaging biomarkers used to evaluate neurodegeneration among cognitively unimpaired <jats:italic>APOE</jats:italic> ε4 homozygotes, heterozygotes, and non-carriers with varying risk for sporadic AD, and ii) to determine how different cerebral pathologies (<jats:italic>i.e.</jats:italic>, Aβ deposition, medial temporal atrophy, and cerebrovascular pathology) contribute to blood biomarker concentrations in this sample.\n<jats:bold>Methods</jats:bold>: Sixty <jats:italic>APOE </jats:italic>ε4 homozygotes (n = 19), heterozygotes (n = 21), and non-carriers (n = 20) ranging from 60–75 years, were recruited in collaboration with Auria biobank (Turku, Finland). Participants underwent Aβ-PET ([<jats:sup>11</jats:sup>C]PiB), structural brain MRI including T1-weighted and T2-FLAIR sequences, and blood sampling for measuring serum neurofilament light chain (NfL), plasma total tau (t-tau), plasma N-terminal tau fragments (NTA-tau) and plasma glial fibrillary acidic protein (GFAP). [<jats:sup>11</jats:sup>C]PiB standardized uptake value ratio was calculated for regions typical for Aβ accumulation in AD. MRI images were analysed for regional volumes, atrophy scores, and volumes of white matter hyperintensities. Differences in biomarker levels and associations between blood and imaging biomarkers were tested using uni- and multivariable linear models (unadjusted and adjusted for age and sex).\n<jats:bold>Results: </jats:bold>Serum NfL concentration was increased in <jats:italic>APOE</jats:italic> ε4 homozygotes compared with non-carriers (mean 21.4 pg/ml (SD 9.5) vs. 15.5 pg/ml (3.8), p = 0.013), whereas other blood biomarkers did not differ between the groups (p > 0.077 for all). From imaging biomarkers, hippocampal volume was significantly decreased in <jats:italic>APOE</jats:italic> ε4 homozygotes compared with non-carriers (6.71 ml (0.86) vs. 7.2 ml (0.7), p = 0.029). In the whole sample, blood biomarker levels were differently predicted by the three measured cerebral pathologies; serum NfL concentration was associated with cerebrovascular pathology and medial temporal atrophy, while plasma NTA-tau associated with medial temporal atrophy. Plasma GFAP showed significant association with both medial temporal atrophy and Aβ pathology. Plasma t-tau concentration did not associate with any of the measured pathologies.\n<jats:bold>Conclusions: </jats:bold>Only increased serum NfL concentrations and decreased hippocampal volume was observed in cognitively unimpaired APOEε4 homozygotes compared to non-carriers. In the whole population the concentrations of blood biomarkers were affected in distinct ways by different pathologies.</jats:p>"
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"en": "<jats:p> Abstract. Emotions are an important element of human interactions, including those in social media. Despite the prominence of text-based messages in online communication, little is known about how the emotional tone of the messages affects the emotions of the recipients. With three experiments, the current study investigated these effects in the context of online news discussions. Participants first read news discussion threads with a negative, neutral, and positive tone, and then rated their subjective emotional state in terms of valence and arousal. Results showed that negatively toned discussions induced more negative valence and higher arousal ratings than the other conditions. Positive discussions had an opposite effect. Emotionally toned online comments evidently affect the quality and the intensity of the readers’ subjective emotions. We discuss implications for future research and emotion mitigation strategies to improve online discussion quality. </jats:p>",
"fi": "<jats:p> Abstract. Emotions are an important element of human interactions, including those in social media. Despite the prominence of text-based messages in online communication, little is known about how the emotional tone of the messages affects the emotions of the recipients. With three experiments, the current study investigated these effects in the context of online news discussions. Participants first read news discussion threads with a negative, neutral, and positive tone, and then rated their subjective emotional state in terms of valence and arousal. Results showed that negatively toned discussions induced more negative valence and higher arousal ratings than the other conditions. Positive discussions had an opposite effect. Emotionally toned online comments evidently affect the quality and the intensity of the readers’ subjective emotions. We discuss implications for future research and emotion mitigation strategies to improve online discussion quality. </jats:p>",
"und": "<jats:p> Abstract. Emotions are an important element of human interactions, including those in social media. Despite the prominence of text-based messages in online communication, little is known about how the emotional tone of the messages affects the emotions of the recipients. With three experiments, the current study investigated these effects in the context of online news discussions. Participants first read news discussion threads with a negative, neutral, and positive tone, and then rated their subjective emotional state in terms of valence and arousal. Results showed that negatively toned discussions induced more negative valence and higher arousal ratings than the other conditions. Positive discussions had an opposite effect. Emotionally toned online comments evidently affect the quality and the intensity of the readers’ subjective emotions. We discuss implications for future research and emotion mitigation strategies to improve online discussion quality. </jats:p>"
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