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"en": "The study aimed at finding gut microbiota signatures and metabolite markers in plasma and feces related to high liver fat content in humans. Based on imaging, we divided study participants into low (<5%, LF, n=25) and high (>5%, HF, n=39) liver fat groups. Fecal (LF n=14, HF n=25) and plasma (LF n=11, HF n=7) metabolomes of subsets of participants were studied using liquid chromatography/high resolution mass spectrometry. The gut microbiota were analyzed using 16S rRNA gene sequencing. Additionally, blood clinical variables, body composition and diet were studied.\r\n\r\nThe list of metadata variables can be found below. They are categorized by the type of sample and/or analysis.\r\n\r\nBLOOD CLINICAL VARIABLES & UNITS\r\n\r\nalanine aminotransferase, ALT (U/L)\r\naspartate aminotransferase, AST (U/L)\r\ngamma-glutamyltransferase, GGT(U/L)\r\ntotal cholesterol (mmol/L)\r\nhigh-density lipoprotein, HDL (mmol/L)\r\nlow-density lipoprotein, LDL (mmol/L)\r\ntriglycrides (mmol/L)\r\nglucose (mmol/L) \r\ninsulin (iU/L) \r\nHOMA-IR\r\n \r\n\r\nBODY COMPOSITION VARIABLES & UNITS\r\n\r\nHeight (cm)\r\nWeight (kg)\r\nbody mass index, BMI (kg/m2)\r\nWaist circumference (cm)\r\nblood pressure (BP) systolic (mm Hg)\r\nblood pressure (BP) diastolic (mm Hg)\r\nLiver fat %\r\n\r\n\r\nGUT MICROBIOTA COMPOSITION\r\n\r\nfastq files of the sequences\r\nOUT counts per each sample\r\nrelative abundance per each sample\r\ndiversity measures (Shannon, Chao1, phylogenetic diversity, Bray Curtis distance)\r\n\r\n\r\nMETABOLITES\r\n\r\nplasma metabolites & identification information\r\nfecal metabolites & identification information\r\n\r\n\r\nDIETARY INTAKES & UNITS (from 3 different days & average from 3 days)\r\n\r\nEnergy (kJ)\r\nEnergy (kcal)\r\nProtein (g)\r\nCarbohydrates (g)\r\nFat (g)\r\nAlcohol\r\nCoffee (dl/3 days) *\r\nFatty acids (g)\r\nMonosaturated Fatty acids (g)\r\nPolyunsaturated Fatty acids (g)\r\nSucrose (g)\r\nFiber, total (g)\r\nVitamin C (mg)\r\nThiamine (B1) (mg)\r\nVitamin E (mg)\r\nVitamin A (µg)\r\nVitamin D (µg)\r\nRiboflavin (B2) (mg)\r\nVitamin K (mg)\r\nVitamin B6 (mg)\r\nFolate (B9) (µg)\r\nSodium (mg)\r\nMagnesium (mg)\r\nCalcium (mg)\r\nPotassium (mg)\r\nIron (mg)\r\nIodine (µg)\r\nPhosphorus (mg)\r\nSelenium (µg)\r\nZinc (mg)\r\nFatty acids, total (g)\r\nCholesterol (mg)\r\nFatty acids, trans (g)\r\nLactose (g)\r\nFiber, total amount (g)\r\nVitamin B12 (cobalamine) (µg)\r\nSalt (mg)\r\nOmega-6 Fatty acids (g)\r\nOmega-3 Fatty acids (g)."
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"bibliographic_citation": "Kokko, Katja; Saajanaho, Milla; Kekäläinen, Tiia; Reinilä, Emmi; Ahola, Johanna. Developmental Psychological Perspectives on Transitions at Age 60: Individuals Navigating Across the Lifespan (TRAILS). V. 2.7.2022.",
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"description": {
"en": "The TRAILS (Developmental Psychological Perspectives on Transitions at Age 60: Individuals Navigating Across the Lifespan) dataset is the latest follow-up of the Jyväskylä Longitudinal Study of Personality and Social Development (JYLS). The data was gathered in 2020-21, when the participants were about 61 years old. Previously, they have been studied at ages 8, 14, 20, 27, 36, 42 and 50. A total of 206 Finnish men (n=99) and women (n=107) participated in TRAILS. The data was gathered via life situation questionnaires, psychological interviews, self-report questionnaires, health examinations (inc. blood samples) and accelerometers. The topics covered in the dataset include e.g., personality, identity, mental well-being, life situation, work and retirement, future orientation, social relationships, leisure-time activities, use of modern technology, physical activity, health and health behavior, and physical and cognitive functioning. \r\n\r\nThe former JYLS datasets (8- to 50-year-olds) are archived at the Finnish Social Science Data Archive (FSD): https://www.fsd.tuni.fi/en/data/misc/laku/. Detailed methodology descriptions can be accessed at the FSD data portal.",
"fi": "TRAILS-tutkimuksen (Developmental Psychological Perspectives on Transitions at Age 60: Individuals Navigating Across the Lifespan) aineisto on vuonna 1968 alkaneen JYLS-tutkimuksen (the Jyväskylä Longitudinal Study of Personality and Social Development, JYLS; suom. Lapsesta aikuiseksi) uusin seuranta-aineisto. TRAILS-aineisto kerättiin vuosina 2020-21, jolloin tutkittavat olivat noin 61-vuotiaita. JYLS-tutkimuksen aloitti vuonna 1968 professori Lea Pulkkinen, joka valitsi satunnaisesti mukaan 12 kansakoulun toista luokkaa. Tutkittavat (syntyneet 1959) olivat tuolloin n. 8-vuotiaita. Sittemmin JYLS:in pääaineistonkeruut on toteutettu tutkittavien ollessa 14-, 20-, 27-, 36-, 42- ja 50-vuotiaita. Tähän uusimpaan TRAILS-aineistonkeruuvaiheeseen osallistui 206 tutkittavaa (99 miestä, 107 naista). TRAILS-aineiston avulla tutkitaan noin 60 vuoden iässä alkavan myöhäisen aikuisiän vaiheen mahdollisia elämänsiirtymiä ja niiden yhteyksiä erityisesti mielen hyvinvointiin ja toimintakykyyn. Erityisenä tarkastelun kohteena ovat eläköitymisen tai sen ennakoinnin, työnteon jatkamisen, mahdollisen isovanhemmuuden, terveyden sekä vapaa-ajan aktiviteettien yhteydet hyvinvointiin ja toimintakykyyn. Lisäksi tarkastellaan persoonallisuuden, varhaisemman kehityksen ja yhteiskunnallisten vaatimusten roolia näissä yhteyksissä sekä pitkän aikavälin kehityspolkuja yleensäkin. TRAILS-aineisto koostuu elämäntilannekyselyn, psykologisen haastattelun, kyselylomakkeiden, terveystarkastuksen (sis. verinäytteet) ja liikemittareiden avulla kerätyistä tiedoista. Tutkimustietoja on kerätty mm. seuraavista aihepiireistä: persoonallisuus, identiteetti, mielen hyvinvointi, elämäntilanne ja elämäntapahtumat, työ ja eläköityminen, tulevaisuuteen suuntautuminen, ihmissuhteet, vapaa-ajan aktiviteetit ja tietotekniikan käyttö, fyysinen aktiivisuus, terveys ja terveyskäyttäytyminen sekä fyysinen ja kognitiivinen toimintakyky. Tarkempi menetelmäkuvaus tulee saataville erillisenä dokumenttina.\r\n\r\nAiemmat JYLS-tutkimuksen aineistot (8–50-vuotiaat) on tallennettu Yhteiskuntatieteelliseen Tietoarkistoon (https://www.fsd.tuni.fi/fi/aineistot/taustatietoa/laku/) Tietoarkiston sivujen kautta pääsee tutustumaan niissä käytettyihin menetelmiin."
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"fi": "Osteoarthritis is one of the most common musculoskeletal diseases. When osteoarthritis develops after a joint injury, it is designated as post-traumatic osteoarthritis. A defining feature of osteoarthritis is degeneration of articular cartilage, which is partly driven by cartilage cells after joint injury, and further accelerated by inflammation. The degeneration triggered by these biomechanical and biochemical mechanisms is currently irreversible. Thus, early prevention/mitigation of disease progression is a key to avoiding PTOA. Prior computational models have been developed to provide insights into the complex mechanisms of cartilage degradation, but they rarely include cell-level cartilage degeneration mechanisms. Here, we present a novel approach to simulate how the early post-traumatic biomechanical and inflammatory effects on cartilage cells eventually influence tissue composition. Our model includes the key regulators of early post-traumatic osteoarthritis: chondral lesions, cell death, reactive oxygen species, and inflammatory cytokines. The model is supported by several experimental explant culture findings. Interestingly, we found that when post-injury inflammation is mitigated, cartilage composition can partially recover. We suggest that mechanobiological models including cell–tissue-level mechanisms can serve as future tools for evaluating high-risk lesions and developing new intervention strategies.\n\nThis dataset includes the files needed to run the computational models."
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"en": "Injury-related cell death and proteoglycan loss in articular cartilage: Numerical model combining necrosis, reactive oxygen species, and inflammatory cytokines",
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