118 research outputs found
Correlated response to selection for litter size environmental variability in rabbits' resilience
[EN] Resilience is the ability of an animal to return soon to its initial productivity after facing diverse environmental challenges. This trait is directly related to animal welfare and it plays a key role in fluctuations of livestock productivity. A divergent selection experiment for environmental variance of litter size has been performed successfully in rabbits over ten generations. The objective of this study was to analyse resilience indicators of stress and disease in the divergent lines of this experiment. The high line showed a lower survival rate at birth than the low line (-4.1%). After correcting by litter size, the difference was -3.2%. Involuntary culling rate was higher in the high than in the low line (+12.4%). Before vaccination against viral haemorrhagic disease or myxomatosis, concentration of lymphocytes, C-reactive protein (CRP), complement C3, serum bilirubin, triglycerides and cholesterol were higher in the high line than in the low line (difference between lines +4.5%, +5.6 mu g/ml, +4.6 mg/ml, +7.9 mmol/l, +0.3 mmol/l and +0.4 mmol/l). Immunological and biochemical responses to the two vaccines were similar. After vaccination, the percentage of lymphocytes and CRP concentration were higher in the low line than in the high one (difference between lines +4.0% and +13.1 mu g/ml). The low line also showed a higher increment in bilirubin and triglycerides than the high line (+14.2 v. +8.7 mmol/l for bilirubin and +0.11 v. +0.01 mmol/l for triglycerides); these results would agree with the protective role of bilirubin and triglycerides against the larger inflammatory response found in this line. In relation to stress, the high line had higher basal concentration of cortisol than the low line (+0.2ng/ml); the difference between lines increased more than threefold after the injection of ACTH 1 to 24, the increase being greater in the high line (+0.9 ng/ml) than in the low line (+0.4 ng/ml). Selection for divergent environmental variability of litter size leads to dams with different culling rate for reproductive causes and different kits' neonatal survival. These associations suggest that the observed fitness differences are related to differences in the inflammatory response and the corticotrope response to stress, which are two important components of physiological adaptation to environmental aggressions.This study is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) with the Projects AGL2014-55921, C2-1-P and C2-2-P, and AGL2017-86083, C2-1-P and C2-2-P.Argente, M.; Garcia, M.; Zbynovska, K.; Petruska, P.; Capcarova, M.; Blasco Mateu, A. (2019). Correlated response to selection for litter size environmental variability in rabbits' resilience. Animal. 13(10):2348-2355. https://doi.org/10.1017/S1751731119000302S234823551310Glaser, R., & Kiecolt-Glaser, J. K. (2005). Stress-induced immune dysfunction: implications for health. Nature Reviews Immunology, 5(3), 243-251. doi:10.1038/nri1571Markanday, A. (2015). Acute Phase Reactants in Infections: Evidence-Based Review and a Guide for Clinicians. Open Forum Infectious Diseases, 2(3). doi:10.1093/ofid/ofv098Rauw, W. ., Kanis, E., Noordhuizen-Stassen, E. ., & Grommers, F. . (1998). Undesirable side effects of selection for high production efficiency in farm animals: a review. Livestock Production Science, 56(1), 15-33. doi:10.1016/s0301-6226(98)00147-xPiles, M., GarciÌa, M. L., Rafel, O., Ramon, J., & Baselga, M. (2006). Genetics of litter size in three maternal lines of rabbits: Repeatability versus multiple-trait models. Journal of Animal Science, 84(9), 2309-2315. doi:10.2527/jas.2005-622Guelfi, G., Zerani, M., Brecchia, G., Parillo, F., DallâAglio, C., Maranesi, M., & Boiti, C. (2011). Direct actions of ACTH on ovarian function of pseudopregnant rabbits. Molecular and Cellular Endocrinology, 339(1-2), 63-71. doi:10.1016/j.mce.2011.03.017GarcĂa ML , Blasco A , GarcĂa ME and Argente MJ 2018. Body condition and energy mobilisation in rabbits selected for litter size variability. Animal, 1â6, https://doi.org/10.1017/S1751731118002203, Published online by Cambridge University Press 28 August 2018.Furze, R. C., & Rankin, S. M. (2008). Neutrophil mobilization and clearance in the bone marrow. Immunology, 125(3), 281-288. doi:10.1111/j.1365-2567.2008.02950.xMcDade, T. W., Borja, J. B., Kuzawa, C. W., Perez, T. L. L., & Adair, L. S. (2015). C-reactive protein response to influenza vaccination as a model of mild inflammatory stimulation in the Philippines. Vaccine, 33(17), 2004-2008. doi:10.1016/j.vaccine.2015.03.019Blasco, A. (2017). Bayesian Data Analysis for Animal Scientists. doi:10.1007/978-3-319-54274-4Castellini, C., Dal Bosco, A., Arias-Ălvarez, M., Lorenzo, P. L., Cardinali, R., & Rebollar, P. G. (2010). The main factors affecting the reproductive performance of rabbit does: A review. Animal Reproduction Science, 122(3-4), 174-182. doi:10.1016/j.anireprosci.2010.10.003Rosa Neto, N. S., & Carvalho, J. F. de. (2009). O uso de provas de atividade inflamatĂłria em reumatologia. Revista Brasileira de Reumatologia, 49(4), 413-430. doi:10.1590/s0482-50042009000400008Argente, M. J., Calle, E. W., GarcĂa, M. L., & Blasco, A. (2017). Correlated response in litter size components in rabbits selected for litter size variability. Journal of Animal Breeding and Genetics, 134(6), 505-511. doi:10.1111/jbg.12283Mirkena, T., Duguma, G., Haile, A., Tibbo, M., Okeyo, A. M., Wurzinger, M., & Sölkner, J. (2010). Genetics of adaptation in domestic farm animals: A review. Livestock Science, 132(1-3), 1-12. doi:10.1016/j.livsci.2010.05.003GarcĂa, M. L., Blasco, A., & Argente, M. J. (2016). Embryologic changes in rabbit lines selected for litter size variability. Theriogenology, 86(5), 1247-1250. doi:10.1016/j.theriogenology.2016.04.065Feingold KR and Grunfeld C 2015. The effect of inflammation and infection on lipids and lipoproteins. In: De Groot LJ, Chrousos G, Dungan K, Feingold KR, Grossman A, Hershman JM, Koch C, Korbonits M, McLachlan R, New M, Purnell J, Rebar R, Singer F and Vinik A. Endotext, South Dartmouth, MA, USA. Retrieved on 7 June 2018 from https://www.ncbi.nlm.nih.gov/books/NBK326741/.Minemura, M. (2014). Liver involvement in systemic infection. World Journal of Hepatology, 6(9), 632. doi:10.4254/wjh.v6.i9.632Knap, P. W. (2005). Breeding robust pigs. Australian Journal of Experimental Agriculture, 45(8), 763. doi:10.1071/ea05041Barcia, A. M., & Harris, H. W. (2005). Triglyceride-Rich Lipoproteins as Agents of Innate Immunity. Clinical Infectious Diseases, 41(Supplement_7), S498-S503. doi:10.1086/432005Webster, J. I., Tonelli, L., & Sternberg, E. M. (2002). NEUROENDOCRINEREGULATION OFIMMUNITY. Annual Review of Immunology, 20(1), 125-163. doi:10.1146/annurev.immunol.20.082401.104914Fortun-Lamothe, L. (2006). Energy balance and reproductive performance in rabbit does. Animal Reproduction Science, 93(1-2), 1-15. doi:10.1016/j.anireprosci.2005.06.009Cabezas, S., Blas, J., Marchant, T. A., & Moreno, S. (2007). Physiological stress levels predict survival probabilities in wild rabbits. Hormones and Behavior, 51(3), 313-320. doi:10.1016/j.yhbeh.2006.11.004De Nardo, D., Labzin, L. I., Kono, H., Seki, R., Schmidt, S. V., Beyer, M., ⊠Latz, E. (2013). High-density lipoprotein mediates anti-inflammatory reprogramming of macrophages via the transcriptional regulator ATF3. Nature Immunology, 15(2), 152-160. doi:10.1038/ni.2784BURKUĆ , J., KAÄMAROVĂ, M., KUBANDOVĂ, J., KOKOĆ OVĂ, N., FABIANOVĂ, K., FABIAN, D., ⊠ÄIKOĆ , Ć . (2015). Stress exposure during the preimplantation period affects blastocyst lineages and offspring development. Journal of Reproduction and Development, 61(4), 325-331. doi:10.1262/jrd.2015-012Posthouwer, D., Voorbij, H. A. M., Grobbee, D. E., Numans, M. E., & van der Bom, J. G. (2004). Influenza and pneumococcal vaccination as a model to assess C-reactive protein response to mild inflammation. Vaccine, 23(3), 362-365. doi:10.1016/j.vaccine.2004.05.035Ibåñez-Escriche, N., Sorensen, D., Waagepetersen, R., & Blasco, A. (2008). Selection for Environmental Variation: A Statistical Analysis and Power Calculations to Detect Response. Genetics, 180(4), 2209-2226. doi:10.1534/genetics.108.091678Colditz, I. G., & Hine, B. C. (2016). Resilience in farm animals: biology, management, breeding and implications for animal welfare. Animal Production Science, 56(12), 1961. doi:10.1071/an15297Blasco, A., MartĂnez-Ălvaro, M., GarcĂa, M.-L., Ibåñez-Escriche, N., & Argente, M.-J. (2017). Selection for environmental variance of litter size in rabbits. Genetics Selection Evolution, 49(1). doi:10.1186/s12711-017-0323-4Argente MJ , Santacreu MA , Climen A and Blasco A 2000. Genetic correlations between litter size and uterine capacity. In Proceeding of the 8th World Rabbit Congress, 4â7 July 2000, Valencia, Spain, pp. 333â338.Janssens, C. J., Helmond, F. A., & Wiegant, V. M. (1995). Chronic stress and pituitaryâadrenocortical responses to corticotropin-releasing hormone and vasopressin in female pigs. European Journal of Endocrinology, 132(4), 479-486. doi:10.1530/eje.0.132047
Erratum to: Study protocol: differential effects of diet and physical activity based interventions in pregnancy on maternal and fetal outcomes: individual patient data (IPD) meta-analysis and health economic evaluation.
Erratum to: Study protocol: differential
effects of diet and physical activity based
interventions in pregnancy on maternal
and fetal outcomes: individual patient data
(IPD) meta-analysis and health economic
evaluatio
ĐĐŽĐœĐŸĐșĐŸĐ»Đ”ĐčĐœŃĐ” ŃŃĐ°ĐșŃĐŸŃĐœĐŸ-лДЎŃĐœŃĐ” ĐŽĐŸŃĐŸĐłĐž: ŃŃĐ”Đ±ĐœĐŸĐ” ĐżĐŸŃĐŸĐ±ĐžĐ” ĐŽĐ»Ń Đ»Đ”ŃĐŸŃĐ”Ń ĐœĐžŃĐ”ŃĐșĐžŃ ĐČŃĐ·ĐŸĐČ
ĐĐœĐžĐłĐ° ŃĐŸĐŽĐ”ŃĐ¶ĐžŃ ĐŸĐżĐžŃĐ°ĐœĐžĐ” ĐșĐŸĐœŃŃŃŃĐșŃĐžĐč ĐŸĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃŃ
ŃŃĐ°ĐșŃĐŸŃĐœŃŃ
ŃĐ°ĐœĐ”Đč, ŃĐ°ŃŃĐ”Ń ĐŸŃĐœĐŸĐČĐœŃŃ
ĐŽĐ”ŃалДĐč ŃĐ°ĐœĐ”Đč, ĐșŃĐ°ŃĐșОД ŃĐ”Ń
ĐœĐžŃĐ”ŃĐșОД ŃŃĐ»ĐŸĐČĐžŃ ĐżŃĐŸĐ”ĐșŃĐžŃĐŸĐČĐ°ĐœĐžŃ ĐŸĐŽĐœĐŸĐșĐŸĐ»Đ”ĐčĐœŃŃ
ŃŃĐ°ĐșŃĐŸŃĐœĐŸ-лДЎŃĐœŃŃ
ĐŽĐŸŃĐŸĐł, ĐżŃĐ°ĐČОла ĐżĐŸŃŃŃĐŸĐčĐșĐž Đž ŃĐșŃплŃĐ°ŃĐ°ŃОО лДЎŃĐœŃŃ
ĐŽĐŸŃĐŸĐł Đž ĐŸŃĐœĐŸĐČŃ ĐŸŃĐłĐ°ĐœĐžĐ·Đ°ŃОО ŃŃĐ°ĐșŃĐŸŃĐœĐŸĐłĐŸ Ń
ĐŸĐ·ŃĐčŃŃĐČĐ° ĐœĐ° базД ĐŸĐŽĐœĐŸĐșĐŸĐ»Đ”ĐčĐœŃŃ
лДЎŃĐœŃŃ
ĐŽĐŸŃĐŸĐł. ĐĐœĐžĐłĐ° ĐżŃĐ”ĐŽĐœĐ°Đ·ĐœĐ°ŃĐ”ĐœĐ° ĐČ ĐșĐ°ŃĐ”ŃŃĐČĐ” ŃŃĐ”Đ±ĐœĐŸĐłĐŸ ĐżĐŸŃĐŸĐ±ĐžŃ ĐŽĐ»Ń Đ»Đ”ŃĐŸŃĐ”Ń
ĐœĐžŃĐ”ŃĐșĐžŃ
ĐČŃĐ·ĐŸĐČ, ĐœĐŸ ĐŒĐŸĐ¶Đ”Ń ŃĐ°ĐșжД ŃĐ»ŃжОŃŃ ĐżŃĐ°ĐșŃĐžŃĐ”ŃĐșĐžĐŒ ĐżĐŸŃĐŸĐ±ĐžĐ”ĐŒ Đž ĐŽĐ»Ń ĐČŃŃŃĐ”ĐłĐŸ ŃĐ”Ń
ĐœĐžŃĐ”ŃĐșĐŸĐłĐŸ пДŃŃĐŸĐœĐ°Đ»Đ° лДŃĐŸĐ·Đ°ĐłĐŸŃĐŸĐČĐžŃДлŃĐœŃŃ
ĐżŃДЎпŃĐžŃŃĐžĐč ĐĐ°ŃĐșĐŸĐŒĐ»Đ”ŃĐ° ĐĄĐĄĐĄĐ .0|7|ĐŃДЎОŃĐ»ĐŸĐČОД [c. 7]0|8|ĐĐČĐ”ĐŽĐ”ĐœĐžĐ” [c. 8]0|11|ĐĐŸĐ·ĐœĐžĐșĐœĐŸĐČĐ”ĐœĐžĐ” Đž ŃĐ°Đ·ĐČĐžŃОД ĐșĐŸĐœŃŃŃŃĐșŃОО ĐŸĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃŃ
ŃĐ°ĐœĐ”Đč [c. 11]1|11|ĐĐ”ŃĐČŃĐ” ĐŸĐżŃŃŃ [c. 11]1|12|ĐŃĐžĐœŃОп ŃĐ°Đ±ĐŸŃŃ ĐŸĐŽĐœĐŸĐșĐŸĐ»Đ”ĐčĐœĐŸĐč лДЎŃĐœĐŸĐč ĐŽĐŸŃĐŸĐłĐž Đž ŃĐ”ĐŸŃĐ”ŃĐžŃĐ”ŃĐșОД ĐŸŃĐœĐŸĐČĐ°ĐœĐžŃ ĐżŃĐŸĐ”ĐșŃĐžŃĐŸĐČĐ°ĐœĐžŃ ĐŸĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃŃ
ŃĐ°ĐœĐ”Đč [c. 12]1|17|ĐĐŸĐœŃŃŃŃĐșŃĐžŃ ĐżĐ”ŃĐČŃŃ
ĐŸĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃŃ
ŃĐ°ĐœĐ”Đč [c. 17]1|17|ĐĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃĐ” ŃĐ°ĐœĐž ĐĐŸŃŃĐŸĐșĐŸŃŃĐ°Đ»ŃлДŃĐ° [c. 17]1|19|ĐĐŽĐșĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃĐ” ŃĐ°ĐœĐž ĐŠĐĐĐĐĐ, ĐŒĐŸĐŽĐ”Đ»Ń Đ [c. 19]1|21|ĐĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃĐ” ŃĐ°ĐœĐž ĐœĐ° базД ĐżĐŸĐșĐŸĐČĐŸĐș ŃŃĐ°ĐșŃĐŸŃĐœŃŃ
ĐŽĐČŃŃ
ĐżĐŸĐ»ĐŸĐ·ĐœŃŃ
ŃĐ°ĐœĐ”Đč ĐŒĐŸĐŽĐ”Đ»Đž Đ [c. 21]1|22|ĐĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃĐ” ŃĐ°ĐœĐž ĐŻ. Đ. ĐĐžĐœĐ·Đ±ŃŃга ĐŒĐŸĐŽĐ”Đ»Đž 1939 Đł. [c. 22]1|33|ĐĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃĐ” ŃĐ°ĐœĐž ĐĐĐŻ-2 [c. 33]1|39|ĐĐ°ŃĐžĐ°ĐœŃŃ ŃĐŸĐ”ĐŽĐžĐœĐ”ĐœĐžŃ ĐșĐŸĐœĐžĐșĐ° Ń ĐżĐŸĐ»ĐŸĐ·ĐŸĐŒ [c. 39]1|39|ĐĐŸĐŽĐ”ŃĐœĐžĐ·ĐžŃĐŸĐČĐ°ĐœĐœŃĐ” ĐŸĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃĐ” ŃĐ°ĐœĐž ĐœĐ° базД ĐżĐŸĐșĐŸĐČĐŸĐș ŃĐ°ĐœĐ”Đč ĐŒĐŸĐŽĐ”Đ»Đž ĐĄĐČĐ”ŃЎлДŃĐ° Đž ĐĐŸŃŃĐŸĐșĐŸŃŃĐ°Đ»ŃлДŃĐ° [c. 39]1|44|ĐĐ”ŃĐșĐŸĐœĐžĐșĐŸĐČŃĐ” ĐŸĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃĐ” ŃĐ°ĐœĐž ĐșĐŸĐœŃŃŃŃĐșŃОО ХОбĐĐĐĐĐ„Đ [c. 44]1|46|ĐŃŃĐ”ŃĐœĐŸ-ĐżŃĐžŃĐ”ĐżĐœŃĐ” ŃŃŃŃĐŸĐčŃŃĐČĐ° ŃŃĐ°ĐșŃĐŸŃĐ° ĐșĐŸĐœŃŃŃŃĐșŃОО ĐŁĐĐąĐ, ĐĄĐŸŃŃĐžĐœŃĐșĐŸĐłĐŸ ĐŒĐ”Ń
лДŃĐŸĐżŃĐœĐșŃĐ° Đž ĐĄŃŃĐŸĐčлДŃĐżŃĐŸĐ”ĐșŃĐ° [c. 46]1|48|ĐĐČŃĐŸĐŒĐ°ŃĐžŃĐ”ŃĐșĐ°Ń ŃŃДпĐșĐ° ŃŃĐ°ĐșŃĐŸŃĐœŃŃ
ŃĐ°ĐœĐ”Đč [c. 48]1|49|Đ Đ°ĐŒĐ° ĐŽĐ»Ń ĐżĐ”ŃĐ”ĐČĐŸĐ·ĐșĐž ĐșĐŸŃĐŸŃŃŃ ĐœĐ° ĐŸĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃŃ
ŃĐ°ĐœŃŃ
[c. 49]1|51|Đ Đ°ŃŃĐ”Ń ŃĐ°ĐœĐ”Đč [c. 51]1|51|Đ Đ°ŃŃĐ”Ń ĐżĐŸĐ»ĐŸĐ·Đ° [c. 51]1|58|Đ ŃĐŸŃĐŒĐ” ĐżĐŸĐŽŃĐ”Đ·ĐŸĐČ [c. 58]0|61|ĐĐŸŃŃŃĐŸĐčĐșĐ° ĐŸĐŽĐœĐŸĐșĐŸĐ»Đ”ĐčĐœŃŃ
лДЎŃĐœŃŃ
ĐŽĐŸŃĐŸĐł [c. 61]1|61|ĐŁŃĐ»ĐŸĐČĐžŃ ĐżŃĐžĐŒĐ”ĐœĐ”ĐœĐžŃ, ŃŃŃŃĐ”ĐČĐ°Ń Đ±Đ°Đ·Đ° Đž ĐżĐŸŃŃĐŽĐŸĐș ĐŸŃĐŸŃĐŒĐ»Đ”ĐœĐžŃ ŃŃŃĐŸĐžŃДлŃŃŃĐČĐ° [c. 61]1|62|йДŃ
ĐœĐžŃĐ”ŃĐșОД ŃŃĐ»ĐŸĐČĐžŃ ĐżŃĐŸĐ”ĐșŃĐžŃĐŸĐČĐ°ĐœĐžŃ ĐŸĐŽĐœĐŸĐșĐŸĐ»Đ”ĐčĐœŃŃ
лДЎŃĐœŃŃ
ĐŽĐŸŃĐŸĐł [c. 62]1|72|ĐĐ·ŃŃĐșĐ°ĐœĐžŃ ŃŃĐ°ŃŃ ĐŸĐŽĐœĐŸĐșĐŸĐ»Đ”ĐčĐœŃŃ
лДЎŃĐœŃŃ
ĐŽĐŸŃĐŸĐł [c. 72]1|73|ĐĄŃŃĐŸĐžŃДлŃĐœŃĐ” ŃĐ°Đ±ĐŸŃŃ ĐœĐ° ĐŸĐŽĐœĐŸĐșĐŸĐ»Đ”ĐčĐœŃŃ
лДЎŃĐœŃŃ
ĐŽĐŸŃĐŸĐłĐ°Ń
[c. 73]1|85|ĐĐŸŃĐŸĐ¶ĐœŃĐ” ĐŸŃŃĐŽĐžŃ ĐŽĐ»Ń ŃŃŃĐŸĐžŃДлŃŃŃĐČĐ° ĐŸĐŽĐœĐŸĐșĐŸĐ»Đ”ĐčĐœŃŃ
лДЎŃĐœŃŃ
ĐŽĐŸŃĐŸĐł [c. 85]1|91|ĐŠĐžŃŃĐ”ŃĐœŃ ĐŽĐ»Ń ĐżĐŸĐ»ĐžĐČĐșĐž лДЎŃĐœĐŸĐč ĐŽĐŸŃĐŸĐłĐž [c. 91]1|91|ĐĐ°ŃĐŸŃĐœŃĐ” ŃŃĐ°ĐœŃОО [c. 91]0|95|ĐĐșŃплŃĐ°ŃĐ°ŃĐžŃ Đ»Đ”ĐŽŃĐœŃŃ
ĐŽĐŸŃĐŸĐł [c. 95]1|95|йДŃ
ĐœĐžŃĐ”ŃĐșĐ°Ń Ń
Đ°ŃĐ°ĐșŃĐ”ŃĐžŃŃĐžĐșĐ° ŃŃĐłĐŸĐČŃŃ
ĐŒĐ°ŃĐžĐœ [c. 95]1|107|ĐĐșŃплŃĐ°ŃĐ°ŃĐžŃ ĐłĐ°Đ·ĐŸĐłĐ”ĐœĐ”ŃĐ°ŃĐŸŃĐœŃŃ
ŃŃĐ°ĐșŃĐŸŃĐŸĐČ ĐœĐ° лДŃĐŸĐČŃĐČĐŸĐ·ĐșĐ” ĐżĐŸ лДЎŃĐœŃĐŒ ĐŽĐŸŃĐŸĐłĐ°ĐŒ [c. 107]1|115|ĐŃĐ°ĐČОла ĐČĐŸĐ¶ĐŽĐ”ĐœĐžŃ ĐżĐŸĐ”Đ·ĐŽĐŸĐČ [c. 115]1|117|Đ€ĐŸŃĐŒĐžŃĐŸĐČĐ°ĐœĐžĐ” ŃĐŸŃŃĐ°ĐČĐ° Đž ĐŒĐ°ĐœĐ”ĐČŃŃ [c. 117]1|117|ĐĄĐŸĐŽĐ”ŃĐ¶Đ°ĐœĐžĐ” Đž ŃĐ”ĐŒĐŸĐœŃ ĐżŃŃĐž лДЎŃĐœĐŸĐč ĐŽĐŸŃĐŸĐłĐž [c. 117]1|119|йДŃ
ĐœĐžĐșĐ° Đ±Đ”Đ·ĐŸĐżĐ°ŃĐœĐŸŃŃĐž ĐżŃĐž ĐČŃĐČĐŸĐ·ĐșĐ” лДŃĐ° ĐżĐŸ ŃŃĐ°ĐșŃĐŸŃĐœŃĐŒ лДЎŃĐœŃĐŒ ĐŽĐŸŃĐŸĐłĐ°ĐŒ [c. 119]1|121|ĐŃĐœĐŸĐČĐœŃĐ” ĐżŃĐ°ĐČОла ĐżĐŸ ŃĐ”Ń
ĐœĐžĐșĐ” Đ±Đ”Đ·ĐŸĐżĐ°ŃĐœĐŸŃŃĐž ĐŽĐ»Ń ŃŃĐ°ĐșŃĐŸŃĐœĐŸĐłĐŸ лДŃĐŸŃŃĐ°ĐœŃĐżĐŸŃŃĐ° [c. 121]0|123|ĐŃĐžĐ»ĐŸĐ¶Đ”ĐœĐžŃ [c. 123]1|123|ĐĐ”ŃалО ĐŸĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃŃ
ŃĐ°ĐœĐ”Đč ĐĐĐŻ-1 [c. 123]1|136|ĐĐ”ŃалО ĐŒĐŸĐŽĐ”ŃĐœĐžĐ·ĐžŃĐŸĐČĐ°ĐœĐœŃŃ
ĐŸĐŽĐœĐŸĐżĐŸĐ»ĐŸĐ·ĐœŃŃ
ŃĐ°ĐœĐ”Đč ĐœĐ° базД ĐżĐŸĐșĐŸĐČĐŸĐș ŃĐ°ĐœĐ”Đč ĐĄĐČĐ”ŃЎллДŃĐ° [c. 136]1|141|ĐŃĐ°ŃĐșĐ°Ń ŃĐ”Ń
ĐœĐžŃĐ”ŃĐșĐ°Ń Ń
Đ°ŃĐ°ĐșŃĐ”ŃĐžŃŃĐžĐșĐ° ĐłŃŃĐ”ĐœĐžŃĐœŃŃ
ŃŃĐ°ĐșŃĐŸŃĐŸĐČ Đ§Đ”Đ»ŃĐ±ĐžĐœŃĐșĐŸĐłĐŸ ŃŃĐ°ĐșŃĐŸŃĐœĐŸĐłĐŸ Đ·Đ°ĐČĐŸĐŽĐ° [c. 141]0|143|ĐглаĐČĐ»Đ”ĐœĐžĐ” [c. 143
Impact of maternal education on response to lifestyle interventions to reduce gestational weight gain: Individual participant data meta-Analysis
Objectives To identify if maternal educational attainment is a prognostic factor for gestational weight gain (GWG), and to determine the differential effects of lifestyle interventions (diet based, physical activity based or mixed approach) on GWG, stratified by educational attainment. Design Individual participant data meta-Analysis using the previously established International Weight Management in Pregnancy (i-WIP) Collaborative Group database (https://iwipgroup.wixsite.com/collaboration). Preferred Reporting Items for Systematic reviews and Meta-Analysis of Individual Participant Data Statement guidelines were followed. Data sources Major electronic databases, from inception to February 2017. Eligibility criteria Randomised controlled trials on diet and physical activity-based interventions in pregnancy. Maternal educational attainment was required for inclusion and was categorised as higher education ( 65tertiary) or lower education ( 64secondary). Risk of bias Cochrane risk of bias tool was used. Data synthesis Principle measures of effect were OR and regression coefficient. Results Of the 36 randomised controlled trials in the i-WIP database, 21 trials and 5183 pregnant women were included. Women with lower educational attainment had an increased risk of excessive (OR 1.182; 95% CI 1.008 to 1.385, p =0.039) and inadequate weight gain (OR 1.284; 95% CI 1.045 to 1.577, p =0.017). Among women with lower education, diet basedinterventions reduced risk of excessive weight gain (OR 0.515; 95% CI 0.339 to 0.785, p = 0.002) and inadequate weight gain (OR 0.504; 95% CI 0.288 to 0.884, p=0.017), and reduced kg/week gain (B-0.055; 95% CI-0.098 to-0.012, p=0.012). Mixed interventions reduced risk of excessive weight gain for women with lower education (OR 0.735; 95% CI 0.561 to 0.963, p=0.026). Among women with high education, diet based interventions reduced risk of excessive weight gain (OR 0.609; 95% CI 0.437 to 0.849, p=0.003), and mixed interventions reduced kg/week gain (B-0.053; 95% CI-0.069 to-0.037,p<0.001). Physical activity based interventions did not impact GWG when stratified by education. Conclusions Pregnant women with lower education are at an increased risk of excessive and inadequate GWG. Diet based interventions seem the most appropriate choice for these women, and additional support through mixed interventions may also be beneficial
Ammonia and carbon dioxide emissions by stabilized conventional nitrogen fertilizers and controlled release in corn crop
An update of the Worldwide Integrated Assessment (WIA) on systemic insecticides. Part 2: impacts on organisms and ecosystems
New information on the lethal and sublethal effects of neonicotinoids and fipronil on organisms is presented in this review, complementing the previous WIA in 2015. The high toxicity of these systemic insecticides to invertebrates has been confirmed and expanded to include more species and compounds. Most of the recent research has focused on bees and the sublethal and ecological impacts these insecticides have on pollinators. Toxic effects on other invertebrate taxa also covered predatory and parasitoid natural enemies and aquatic arthropods. Little, while not much new information has been gathered on soil organisms. The impact on marine coastal ecosystems is still largely uncharted. The chronic lethality of neonicotinoids to insects and crustaceans, and the strengthened evidence that these chemicals also impair the immune system and reproduction, highlights the dangers of this particular insecticidal classneonicotinoids and fipronil. , withContinued large scale â mostly prophylactic â use of these persistent organochlorine pesticides has the potential to greatly decreasecompletely eliminate populations of arthropods in both terrestrial and aquatic environments. Sublethal effects on fish, reptiles, frogs, birds and mammals are also reported, showing a better understanding of the mechanisms of toxicity of these insecticides in vertebrates, and their deleterious impacts on growth, reproduction and neurobehaviour of most of the species tested. This review concludes with a summary of impacts on the ecosystem services and functioning, particularly on pollination, soil biota and aquatic invertebrate communities, thus reinforcing the previous WIA conclusions (van der Sluijs et al. 2015)
Gender differences in the use of cardiovascular interventions in HIV-positive persons; the D:A:D Study
Peer reviewe
Global proteome changes in the rat diaphragm induced by endurance exercise training
Mechanical ventilation (MV) is a life-saving intervention for many critically ill patients. Unfor- tunately, prolonged MV results in the rapid development of diaphragmatic atrophy and weakness. Importantly, endurance exercise training results in a diaphragmatic phenotype that is protected against ventilator-induced diaphragmatic atrophy and weakness. The mechanisms responsible for this exercise-induced protection against ventilator-induced dia- phragmatic atrophy remain unknown. Therefore, to investigate exercise-induced changes in diaphragm muscle proteins, we compared the diaphragmatic proteome from sedentary and exercise-trained rats. Specifically, using label-free liquid chromatography-mass spectrome- try, we performed a proteomics analysis of both soluble proteins and mitochondrial proteins isolated from diaphragm muscle. The total number of diaphragm proteins profiled in the sol- uble protein fraction and mitochondrial protein fraction were 813 and 732, respectively. Endurance exercise training significantly (P<0.05, FDR <10%) altered the abundance of 70 proteins in the soluble diaphragm proteome and 25 proteins of the mitochondrial proteome. In particular, key cytoprotective proteins that increased in relative abundance following exer- cise training included mitochondrial fission process 1 (Mtfp1; MTP18), 3-mercaptopyruvate sulfurtransferase (3MPST), microsomal glutathione S-transferase 3 (Mgst3; GST-III), and heat shock protein 70 kDa protein 1A/1B (HSP70). While these proteins are known to be cytoprotective in several cell types, the cyto-protective roles of these proteins have yet to be fully elucidated in diaphragm muscle fibers. Based upon these important findings, future experiments can now determine which of these diaphragmatic proteins are sufficient and/or required to promote exercise-induced protection against inactivity-induced muscle atrophy
Identification of sixteen novel candidate genes for late onset Parkinsonâs disease
Background
Parkinsonâs disease (PD) is a neurodegenerative movement disorder affecting 1â5% of the general population for which neither effective cure nor early diagnostic tools are available that could tackle the pathology in the early phase. Here we report a multi-stage procedure to identify candidate genes likely involved in the etiopathogenesis of PD.
Methods
The study includes a discovery stage based on the analysis of whole exome data from 26 dominant late onset PD families, a validation analysis performed on 1542 independent PD patients and 706 controls from different cohorts and the assessment of polygenic variants load in the Italian cohort (394 unrelated patients and 203 controls).
Results
Family-based approach identified 28 disrupting variants in 26 candidate genes for PD including PARK2, PINK1, DJ-1(PARK7), LRRK2, HTRA2, FBXO7, EIF4G1, DNAJC6, DNAJC13, SNCAIP, AIMP2, CHMP1A, GIPC1, HMOX2, HSPA8, IMMT, KIF21B, KIF24, MAN2C1, RHOT2, SLC25A39, SPTBN1, TMEM175, TOMM22, TVP23A and ZSCAN21. Sixteen of them have not been associated to PD before, were expressed in mesencephalon and were involved in pathways potentially deregulated in PD. Mutation analysis in independent cohorts disclosed a significant excess of highly deleterious variants in cases (pâ=â0.0001), supporting their role in PD.
Moreover, we demonstrated that the co-inheritance of multiple rare variants (â„ 2) in the 26 genes may predict PD occurrence in about 20% of patients, both familial and sporadic cases, with high specificity (>â93%; pâ=â4.4âĂâ10ââ5). Moreover, our data highlight the fact that the genetic landmarks of late onset PD does not systematically differ between sporadic and familial forms, especially in the case of small nuclear families and underline the importance of rare variants in the genetics of sporadic PD.
Furthermore, patients carrying multiple rare variants showed higher risk of manifesting dyskinesia induced by levodopa treatment.
Conclusions
Besides confirming the extreme genetic heterogeneity of PD, these data provide novel insights into the genetic of the disease and may be relevant for its prediction, diagnosis and treatment
- âŠ