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  1. 1681
    “…Methods: We used summary statistics data for single-nucleotide polymorphisms associated with plasma levels of saturated fatty acids (palmitic acid, stearic acid), mono-unsaturated fatty acids (MUFAs) (palmitoleic acid, oleic acid), n-6 PUFAs (linoleic acid, arachidonic acid), and n-3 PUFAs (alpha-linolenic acid, eicosapentaenoic acid, docosapentaenoic acid, docosahexaenoic acid), and the corresponding data for frailty index (FI) in 356,432 individuals in the UK Biobank. …”
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    Fatty Acids and Frailty: A Mendelian Randomization Study
  2. 1682
    por Abreu, P., Adam, W., Adye, T., Adzic, P., Albrecht, Z., Alderweireld, T., Alekseev, G.D., Alemany, R., Allmendinger, T., Allport, P.P., Almehed, S., Amaldi, U., Amapane, N., Amato, S., Anassontzis, E.G., Andersson, P., Andreazza, A., Andringa, S., Antilogus, P., Apel, W.D., Arnoud, Y., Asman, B., Augustin, J.E., Augustinus, A., Baillon, P., Bambade, P., Barao, F., Barbiellini, G., Barbier, R., Bardin, D.Yu., Barker, G.J., Baroncelli, A., Battaglia, M., Baubillier, M., Becks, K.H., Begalli, M., Behrmann, A., Beilliere, P., Belokopytov, Yu., Benekos, N.C., Benvenuti, A.C., Berat, C., Berggren, M., Bertini, D., Bertrand, D., Besancon, M., Bigi, M., Bilenky, Mikhail S., Bizouard, M.A., Bloch, D., Blom, H.M., Bonesini, M., Bonivento, W., Boonekamp, M., Booth, P.S.L., Borgland, A.W., Borisov, G., Bosio, C., Botner, O., Boudinov, E., Bouquet, B., Bourdarios, C., Bowcock, T.J.V., Boyko, I., Bozovic, I., Bozzo, M., Branchini, P., Brenke, T., Brenner, R.A., Bruckman, P., Brunet, J.M., Bugge, L., Buran, T., Burgsmuller, T., Buschbeck, B., Buschmann, P., Cabrera, S., Caccia, M., Calvi, M., Camporesi, T., Canale, V., Carena, F., Carroll, L., Caso, C., Castillo Gimenez, M.V., Cattai, A., Cavallo, F.R., Chabaud, V., Charpentier, P., Chaussard, L., Checchia, P., Chelkov, G.A., Chierici, R., Chochula, P., Chorowicz, V., Chudoba, J., Cieslik, K., Collins, P., Contri, R., Cortina, E., Cosme, G., Cossutti, F., Cowell, J.H., Crawley, H.B., Crennell, D., Crepe-Renaudin, Sabine, Crosetti, G., Cuevas Maestro, J., Czellar, S., Davenport, M., Da Silva, W., Deghorain, A., Della Ricca, G., Delpierre, P., Demaria, N., De Angelis, A., De Boer, W., De Clercq, C., De Lotto, B., De Min, A., De Paula, L., Dijkstra, H., Di Ciaccio, L., Dolbeau, J., Doroba, K., Dracos, M., Drees, J., Dris, M., Duperrin, A., Durand, J.D., Eigen, G., Ekelof, T., Ekspong, G., Ellert, M., Elsing, M., Engel, J.P., Erzen, B., Espirito Santo, M.C., Fanourakis, G., Fassouliotis, D., Fayot, J., Feindt, M., Ferrari, P., Ferrer, A., Ferrer-Ribas, E., Ferro, F., Fichet, S., Firestone, A., Flagmeyer, U., Foeth, H., Fokitis, E., Fontanelli, F., Franek, B., Frodesen, A.G., Fruhwirth, R., Fulda-Quenzer, F., Fuster, J., Galloni, A., Gamba, D., Gamblin, S., Gandelman, M., Garcia, C., Gaspar, C., Gaspar, M., Gasparini, U., Gavillet, P., Gazis, Evangelos, Gele, D., Ghodbane, N., Gil Botella, Ines, Glege, F., Gokieli, R., Golob, B., Gomez-Ceballos, G., Goncalves, P., Gonzalez Caballero, I., Gopal, G., Gorn, L., Gracco, V., Grahl, J., Graziani, E., Green, C., Grimm, H.J., Gris, P., Grosdidier, G., Grzelak, K., Gunther, M., Guy, J., Hahn, F., Hahn, S., Haider, S., Hallgren, A., Hamacher, K., Hansen, J., Harris, F.J., Hedberg, V., Heising, S., Hernandez, J.J., Herquet, P., Herr, H., Hessing, T.L., Heuser, J.M., Higon, E., Holmgren, S.O., Holt, P.J., Hoorelbeke, S., Houlden, M., Hrubec, J., Huet, K., Hughes, G.J., Hultqvist, K., Jackson, John Neil, Jacobsson, R., Jalocha, P., Janik, R., Jarlskog, C., Jarlskog, G., Jarry, P., Jean-Marie, B., Johansson, Erik Karl, Jonsson, P., Joram, C., Juillot, P., Kapusta, Frederic, Karafasoulis, K., Katsanevas, S., Katsoufis, E.C., Keranen, R., Kersevan, B.P., Khomenko, B.A., Khovansky, N.N., Kiiskinen, A., King, B., Kinvig, A., Kjaer, N.J., Klapp, O., Klein, Hansjorg, Kluit, P., Kokkinias, P., Koratzinos, M., Kourkoumelis, C., Kuznetsov, O., Krammer, M., Kriznic, E., Krumshtein, Z., Kubinec, P., Kurowska, J., Kurvinen, K., Lamsa, J.W., Lane, D.W., Langefeld, P., Laugier, J.P., Lauhakangas, R., Leder, G., Ledroit, Fabienne, Lefebure, V., Leinonen, L., Leisos, A., Leitner, R., Lemonne, J., Lenzen, G., Lepeltier, V., Lesiak, T., Lethuillier, M., Libby, J., Liko, D., Lipniacka, A., Lippi, I., Lorstad, B., Loken, J.G., Lopes, J.H., Lopez, J.M., Lopez-Fernandez, R., Loukas, D., Lutz, P., Lyons, L., MacNaughton, J., Mahon, J.R., Maio, A., Malek, A., Malmgren, T.G.M., Maltezos, S., Malychev, V., Mandl, F., Marco, J., Marco, R., Marechal, B., Margoni, M., Marin, J.C., Mariotti, C., Markou, A., Martinez-Rivero, C., Martinez-Vidal, F., Marti i Garcia, S., Masik, J., Mastroyiannopoulos, N., Matorras, F., Matteuzzi, C., Matthiae, G., Mazzucato, F., Mazzucato, M., McCubbin, M., McKay, R., McNulty, R., McPherson, G., Meroni, C., Meyer, W.T., Migliore, E., Mirabito, L., Mitaroff, W.A., Mjornmark, U., Moa, T., Moch, M., Moller, Rasmus, Monig, Klaus, Monge, M.R., Moreau, X., Morettini, P., Morton, G., Muller, U., Munich, K., Mulders, M., Mulet-Marquis, C., Muresan, R., Murray, W.J., Muryn, B., Myatt, G., Myklebust, T., Naraghi, F., Nassiakou, M., Navarria, F.L., Navas, Sergio, Nawrocki, K., Negri, P., Nemecek, S., Neufeld, N., Nicolaidou, R., Nielsen, B.S., Niezurawski, P., Nikolenko, M., Nomokonov, V., Normand, A., Nygren, A., Olshevsky, A.G., Onofre, A., Orava, R., Orazi, G., Osterberg, K., Ouraou, A., Paganoni, M., Paiano, S., Pain, R., Paiva, R., Palacios, J., Palka, H., Papadopoulou, T.D., Papageorgiou, K., Pape, L., Parkes, C., Parodi, F., Parzefall, U., Passeri, A., Passon, O., Pegoraro, M., Peralta, L., Pernicka, M., Perrotta, A., Petridou, C., Petrolini, A., Phillips, H.T., Pierre, F., Pimenta, M., Piotto, E., Podobnik, T., Pol, M.E., Polok, G., Poropat, P., Pozdnyakov, V., Privitera, P., Pukhaeva, N., Pullia, A., Radojicic, D., Ragazzi, S., Rahmani, H., Ratoff, P.N., Read, Alexander L., Rebecchi, P., Redaelli, Nicola Giuseppe, Regler, M., Reid, D., Reinhardt, R., Renton, P.B., Resvanis, L.K., Richard, F., Ridky, J., Rinaudo, G., Rodrigo, German, Rohne, O., Romero, A., Ronchese, P., Rosenberg, E.I., Rosinsky, P., Roudeau, P., Rovelli, T., Royon, C., Ruhlmann-Kleider, V., Ruiz, A., Saarikko, H., Sacquin, Y., Sadovsky, A., Sajot, G., Salt, J., Sampsonidis, D., Sannino, M., Schneider, H., Schwemling, P., Schwering, B., Schwickerath, U., Schyns, M.A.E., Scuri, Fabrizio, Seager, P., Sedykh, Yu., Segar, A.M., Sekulin, R., Shellard, R.C., Sheridan, A., Siebel, M., Simard, L., Simonetto, F., Sisakian, A.N., Smadja, G., Smirnova, O., Smith, G.R., Sopczak, A., Sosnowski, R., Spassoff, T., Spiriti, E., Sponholz, P., Squarcia, S., Stanescu, C., Stanic, S., Stevenson, K., Stocchi, A., Strauss, J., Strub, R., Stugu, B., Szczekowski, M., Szeptycka, M., Tabarelli, T., Tegenfeldt, F., Terranova, F., Thomas, J., Timmermans, Jan, Tinti, N., Tkachev, L.G., Todorova, S., Tomaradze, A., Tome, B., Tonazzo, A., Tortora, L., Transtromer, G., Treille, D., Tristram, G., Trochimczuk, M., Troncon, C., Tsirou, A., Turluer, M.L., Tyapkin, I.A., Tzamarias, S., Ullaland, O., Valenti, G., Vallazza, E., Vander Velde, C., Van Apeldoorn, G.W., Van Dam, Piet, Van Doninck, Walter, Van Eldik, J., Van Lysebetten, A., Van Remortel, N., Van Vulpen, I., Vassilopoulos, N., Vegni, G., Ventura, L., Venus, W., Verbeure, F., Verlato, M., Vertogradov, L.S., Verzi, V., Vilanova, D., Vitale, L., Vodopianov, A.S., Vollmer, C., Voulgaris, G., Vrba, V., Wahlen, H., Walck, C., Weiser, C., Wicke, D., Wickens, J.H., Wilkinson, G.R., Winter, M., Witek, M., Wolf, G., Yi, J., Zalewska, A., Zalewski, P., Zavrtanik, D., Zevgolatakos, E., Zimine, N.I., Zucchelli, G.C., Zumerle, G.
    Publicado 2000
    “…A combined fit of $\alpha_s$ and of the renormalization scale $x_{\mu}$ in $\cal O(\alpha_s^2$)yields an excellent description of the high statistics data. The weighted average from 18 observables including quark mass effects and correlations is $\alpha_s(M_Z^2) = 0.1174 \pm 0.0026$. …”
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    Consistent Measurements of $\alpha_{s}$ from Precise Oriented Event Shape Distributions
  3. 1683
    “…Incidence rates for biopsy-proven GCA were calculated using Australian Bureau of Statistics data for South Australian population sizes by age, sex, and calendar year. …”
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    Incidence of biopsy-proven giant cell arteritis (GCA) in South Australia 2014–2020
  4. 1684
    “…To better clarify the causal effects between matrix metalloproteinases (MMPs) and estrogen-receptor (ER)-negative breast cancer (BC), we investigated the bidirectional causal relationship between MMPs and ER-negative BC by mendelian randomization (MR) analysis. Summary statistic data of five MMPs were extracted from European participants in 13 cohorts. …”
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    Causal relationships between serum matrix metalloproteinases and estrogen receptor-negative breast cancer: a bidirectional mendelian randomization study
  5. 1685
    “…We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. …”
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    GCPBayes pipeline: a tool for exploring pleiotropy at the gene level
  6. 1686
    “…To evaluate the effect of the progress in the treatment of ATLL in a whole patient population, we used vital statistics data and estimated age-adjusted mortality and trends in the mortality from 1995 to 2009. …”
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    Association between decreasing trend in the mortality of adult T-cell leukemia/lymphoma and allogeneic hematopoietic stem cell transplants in Japan: analysis of Japanese vital statistics and Japan Society for Hematopoietic Cell Transplantation (JSHCT)
  7. 1687
    “…Official medical statistics data and the data collected from the Global Alliance against Chronic Respiratory Diseases program 2011 among 15,000 inhabitants of the region aged 18 years and older were analyzed. …”
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    Epidemiology of chronic obstructive pulmonary disease: a population-based study in Krasnoyarsk region, Russia
  8. 1688
    “…This cost difference or benefit per case was then used to calculate the duration in years required to recover the cost of the ultrasound machine. STATISTICS: Data were analyzed using SPSS 15.0. Analysis of variance was applied to compare mean benefits as per surgery, block, and duration. …”
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    Is dedicating an ultrasound machine to regional anesthesia an economically viable option?
  9. 1689
    “…Due to a large number of missing values, both spatially and temporally, China has not published a complete official socioeconomic statistics dataset at the county level, which is the country’s basic scale of official statistics data collection. We developed a procedure to impute the missing values under the Bayesian hierarchical modeling framework. …”
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    Estimating missing values in China’s official socioeconomic statistics using progressive spatiotemporal Bayesian hierarchical modeling
  10. 1690
    “…METHODS: Linkage of vital statistics data of births to female Bogalusa Heart Study participants was compared to interviewing of female participants. …”
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    Comparison of reproductive history gathered by interview and by vital records linkage after 40 years of follow-up: Bogalusa Babies
  11. 1691
    “…Using English National Health Service (NHS) Hospital Episode Statistics data, we analysed patient movements across England and assessed the number of hospitals required to participate in such a reporting scheme to deliver robust estimates of incidence. …”
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    Using hospital network-based surveillance for antimicrobial resistance as a more robust alternative to self-reporting
  12. 1692
    “…Heterogeneity of studies was assessed using I2 statistic . Data were analyzed using STATA 11.1. RESULTS: In 66 reviewed studies with a sample of 111,406 participants, the prevalence of hypertension was 44% in Iranian patients with cardiovascular disease 67%(95%CI: 38%–49%) in women and 42% in men. …”
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    Prevalence of Hypertension in Cardiovascular Disease in Iran: Systematic Review and Meta-Analysis
  13. 1693
    “…METHODS: We use provisional vital statistics data for births occurring in the central region of New Jersey. …”
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    Postpartum Depressive Symptoms during the Beginning of the COVID-19 Pandemic: An Examination of Population Birth Data from Central New Jersey
  14. 1694
    “…Using a cohort of centenarians with maintained cognitive health (N = 343), a population-matched cohort of older adults from 5 cohorts (N = 2905), and summary statistics data from genome-wide association studies on parental longevity, we constructed a PRS including 330 variants that significantly discriminated between centenarians and older adults. …”
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    Polygenic Risk Score of Longevity Predicts Longer Survival Across an Age Continuum
  15. 1695
    “…Using documentary analysis and secondary data, this article examines the diversity of NEET situations for the youth in Spain, which is generally not captured in large national statistics data-sets and policies. Furthermore, it analyses the EU Youth Guarantee and its application in Spain, highlighting where official objectives have not been met, and includes an overview of the current effects of the coronavirus crisis. …”
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    Examining NEET situations in Spain: Labour Market, Discourses and Policies
  16. 1696
    “…Based on the current global pandemic statistic data, here we developed a logistic probability function configured SEIR model to analyse the COVID-19 outbreak and estimate its transmission pattern under different “anticipate- or delay-to-activate” policy response scenarios in containing the pandemic. …”
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    To Procrastinate or Not to Procrastinate: A Retrospective Study of the Optimal Timing of Containing the Global Spread of the COVID-19 Pandemic
  17. 1697
    “…To analyse large scale COVID-19 statistics data for extracting aggregate information of the disease spread, the cloud servers are leveraged due to its scalable computational and storage capabilities. …”
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    CONFRONT: Cloud-fog-dew based monitoring framework for COVID-19 management
  18. 1698
    “…National Population Statistics data and AFP demographic data during 2008-2013 intervals were obtained from the relevant authorities in the Ministry of Health in Iran. …”
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    Epidemiology of Guillain-Barré Syndrome in Iranian Children Aged 0-15 Years (2008-2013)
  19. 1699
    “…The feasibility and plausibility of a common OECD Eurostat list are examined using official statistics data from Bavaria from 2016 to 2018. The analysis includes an examination of the variability over time and within the Bavarian administrative districts, as well as possible systematic errors through regional differences in coding behaviour or changes over time. …”
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    Vermeidbare Sterblichkeit – Neufassung eines Indikators für die Präventionsberichterstattung
  20. 1700
    por Chen, Bingxia, Han, Zemin, Geng, Lanlan
    Publicado 2022
    “…This study used summary statistics data from large-scale genome-wide association studies (GWAS) on food intakes, Crohn’s disease (CD), and ulcerative colitis (UC). …”
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    Mendelian randomization analysis reveals causal effects of food intakes on inflammatory bowel disease risk
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