La torta de ajonjolí en alimentación de cerdos. 2: efecto de la suplementación de las tortas de soya y ajonjolí con metionina, en raciones para cerdos.

Se estudian los efectos de la suplementación con metionina en dietas a base de soya y ajonjolí. El experimento se realiza en Tibaitatá, Colombia, y dura 77 días. Se toman 98 cerdos y se distribuyen con base en sexo y peso en 14 lotes de 7 animales cada uno. Se hacen 2 repeticiones. Los cerdos tuvieron un peso promedio inicial de 26.9 kg. El experimento termina cuando el primer lote de cada repetición alcanza los 100 kg de peso promedio. Se manifestó ligeramente el efecto complementario de las tortas de soya y ajonjolí a nivel de 5 por ciento. La suplementación con metionina mostró una tendencia no significativa a causar depresión de la rata de crecimiento, siendo menos marcado el efecto en la dieta a base de torta de soya. La mejor rata de crecimiento se obtuvo consistentemente con la dieta a base de soya y ajonjolí al 5 por ciento, lo que sugiere que los efectos complementarios de la torta de ajonjolí se deben al mejoramiento de otros aminoácidos y no solo a la metionina. No se obtiene una marcada respuesta a la inclusión de harina de pescadoSoya-Soja- Glycine maxAjonjolí-Sésamo - Sesamum indicumPorcicultur

La torta de ajonjolí en la alimentación de cerdos. 1: Valor nutritivo de la torta de ajonjolí para cerdos en crecimiento y acabado.

Se estudia el efecto de la inclusión de torta de ajonjolí a niveles de 5 a 10 por ciento en dietas a base de maíz y soya para cerdos en crecimiento y acabado. 2 repeticiones se hacen en la Estación Experimental de Palmira y 2 en la de Tibaitatá. En Tibaitatá 64 cerdos se distribuyeron con base en sexo y raza en 8 lotes de 8 animales cada uno, la duración del experimento es de 84 días para ambas repeticiones. En Palmira 48 animales se distribuyeron en 8 lotes de 6 animales cada uno, la duración del experimento es de 98 días para la primera repetición y de 112 días para la segunda. El experimento se termina cuando el primer lote en cada repetición alcanza 100 kg de peso promedio. La dieta basal estaba compuesta de maíz, soya y pescado, la segunda dieta llevaba solo soya como fuente proteíca, las raciones 3 y 4 incluían además de soya y pescado, ajonjolí a niveles de 5 y 10 por ciento, respectivamente. El ajonjolí puede agregarse a las dietas a base de maíz y soya a niveles hasta del 10 por ciento sin afectar significativamente el rendimiento de los cerdos. Los mejores aumentos se obtuvieron en Tibaitatá con la dieta a base de soya y 5 por ciento de ajonjolí. En Palmira los mejores aumentos y la mejor conversión se obtuvieron con la dieta a base de soya. La inclusión de harina de pescado al nivel del 4 por ciento no tuvo efecto sobre el rendimiento de los cerdosAjonjolí-Sésamo - Sesamum indicu

El azúcar en la alimentación de cerdos. 1: Rendimiento de las hembras lactantes y lechones alimentados con altos niveles de azúcar refinada.

Se estudia el efecto de la inclusión de altos niveles de azúcar en dietas de hembras en lactancia y de lechones. En la Sabana de Bogotá, Colombia. Se emplean 48 cerdas de diferentes edades y pesos. Las hembras se asignan completamente al azar a 2 dietas de lactancia (0 y 60 por ciento de azúcar). En cada uno de los tratamientos de lactancia se asignan 6 camadas, a las siguientes dietas de preiniciación: 10 por ciento de azúcar, 10 por ciento de azúcar más 0.10 por ciento de metionina, 25 por ciento de azúcar más 0.10 por ciento de metionina. Se obtienen los siguientes resultados: La inclusión de diferentes niveles de azúcar, con o sin metionina en las dietas de los lechones no produjo diferencias significativas de peso. La dieta de lactancia con alto nivel de azúcar produjo aumentos de peso en los lechones (16.3 kg) superiores a los producidos por la dieta sin azúcar (14.3 kg). El consumo de la dieta de preiniciación fue menor en los lechones cuyas madres consumieron dieta con 60 por ciento de azúcar. La dieta de lactancia con 60 por ciento de azúcar en cerdas produjo un aumento de peso de 6.8 kg, la dieta a base de maíz y soya dió pérdidas de peso de 9.1 kg por hembra. El consumo de alimento de la cerda fue mayor en 18.8 por ciento con la dieta que contenía 60 por ciento de azúcar. En épocas de alto costo del máiz, la utilización de azúcar parece factible económicamente. La inclusión de azúcar en las dietas de preiniciación o lactancia no causa trastornos digestivosPorcicultur

Evaluation of appendicitis risk prediction models in adults with suspected appendicitis

Background Appendicitis is the most common general surgical emergency worldwide, but its diagnosis remains challenging. The aim of this study was to determine whether existing risk prediction models can reliably identify patients presenting to hospital in the UK with acute right iliac fossa (RIF) pain who are at low risk of appendicitis. Methods A systematic search was completed to identify all existing appendicitis risk prediction models. Models were validated using UK data from an international prospective cohort study that captured consecutive patients aged 16–45 years presenting to hospital with acute RIF in March to June 2017. The main outcome was best achievable model specificity (proportion of patients who did not have appendicitis correctly classified as low risk) whilst maintaining a failure rate below 5 per cent (proportion of patients identified as low risk who actually had appendicitis). Results Some 5345 patients across 154 UK hospitals were identified, of which two‐thirds (3613 of 5345, 67·6 per cent) were women. Women were more than twice as likely to undergo surgery with removal of a histologically normal appendix (272 of 964, 28·2 per cent) than men (120 of 993, 12·1 per cent) (relative risk 2·33, 95 per cent c.i. 1·92 to 2·84; P < 0·001). Of 15 validated risk prediction models, the Adult Appendicitis Score performed best (cut‐off score 8 or less, specificity 63·1 per cent, failure rate 3·7 per cent). The Appendicitis Inflammatory Response Score performed best for men (cut‐off score 2 or less, specificity 24·7 per cent, failure rate 2·4 per cent). Conclusion Women in the UK had a disproportionate risk of admission without surgical intervention and had high rates of normal appendicectomy. Risk prediction models to support shared decision‐making by identifying adults in the UK at low risk of appendicitis were identified

GWAS and meta-analysis identifies 49 genetic variants underlying critical COVID-19

Data availability: Downloadable summary data are available through the GenOMICC data site (https://genomicc.org/data). Summary statistics are available, but without the 23andMe summary statistics, except for the 10,000 most significant hits, for which full summary statistics are available. The full GWAS summary statistics for the 23andMe discovery dataset will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. For further information and to apply for access to the data, see the 23andMe website (https://research.23andMe.com/dataset-access/). All individual-level genotype and whole-genome sequencing data (for both academic and commercial uses) can be accessed through the UKRI/HDR UK Outbreak Data Analysis Platform (https://odap.ac.uk). A restricted dataset for a subset of GenOMICC participants is also available through the Genomics England data service. Monocyte RNA-seq data are available under the title ‘Monocyte gene expression data’ within the Oxford University Research Archives (https://doi.org/10.5287/ora-ko7q2nq66). Sequencing data will be made freely available to organizations and researchers to conduct research in accordance with the UK Policy Framework for Health and Social Care Research through a data access agreement. Sequencing data have been deposited at the European Genome–Phenome Archive (EGA), which is hosted by the EBI and the CRG, under accession number EGAS00001007111.Extended data figures and tables are available online at https://www.nature.com/articles/s41586-023-06034-3#Sec21 .Supplementary information is available online at https://www.nature.com/articles/s41586-023-06034-3#Sec22 .Code availability: Code to calculate the imputation of P values on the basis of SNPs in linkage disequilibrium is available at GitHub (https://github.com/baillielab/GenOMICC_GWAS).Acknowledgements: We thank the members of the Banco Nacional de ADN and the GRA@CE cohort group; and the research participants and employees of 23andMe for making this work possible. A full list of contributors who have provided data that were collated in the HGI project, including previous iterations, is available online (https://www.covid19hg.org/acknowledgements).Change history: 11 July 2023: A Correction to this paper has been published at: https://doi.org/10.1038/s41586-023-06383-z. -- In the version of this article initially published, the name of Ana Margarita Baldión-Elorza, of the SCOURGE Consortium, appeared incorrectly (as Ana María Baldion) and has now been amended in the HTML and PDF versions of the article.Copyright © The Author(s) 2023, Critical illness in COVID-19 is an extreme and clinically homogeneous disease phenotype that we have previously shown1 to be highly efficient for discovery of genetic associations2. Despite the advanced stage of illness at presentation, we have shown that host genetics in patients who are critically ill with COVID-19 can identify immunomodulatory therapies with strong beneficial effects in this group3. Here we analyse 24,202 cases of COVID-19 with critical illness comprising a combination of microarray genotype and whole-genome sequencing data from cases of critical illness in the international GenOMICC (11,440 cases) study, combined with other studies recruiting hospitalized patients with a strong focus on severe and critical disease: ISARIC4C (676 cases) and the SCOURGE consortium (5,934 cases). To put these results in the context of existing work, we conduct a meta-analysis of the new GenOMICC genome-wide association study (GWAS) results with previously published data. We find 49 genome-wide significant associations, of which 16 have not been reported previously. To investigate the therapeutic implications of these findings, we infer the structural consequences of protein-coding variants, and combine our GWAS results with gene expression data using a monocyte transcriptome-wide association study (TWAS) model, as well as gene and protein expression using Mendelian randomization. We identify potentially druggable targets in multiple systems, including inflammatory signalling (JAK1), monocyte–macrophage activation and endothelial permeability (PDE4A), immunometabolism (SLC2A5 and AK5), and host factors required for viral entry and replication (TMPRSS2 and RAB2A).GenOMICC was funded by Sepsis Research (the Fiona Elizabeth Agnew Trust), the Intensive Care Society, a Wellcome Trust Senior Research Fellowship (to J.K.B., 223164/Z/21/Z), the Department of Health and Social Care (DHSC), Illumina, LifeArc, the Medical Research Council, UKRI, a BBSRC Institute Program Support Grant to the Roslin Institute (BBS/E/D/20002172, BBS/E/D/10002070 and BBS/E/D/30002275) and UKRI grants MC_PC_20004, MC_PC_19025, MC_PC_1905 and MRNO2995X/1. A.D.B. acknowledges funding from the Wellcome PhD training fellowship for clinicians (204979/Z/16/Z), the Edinburgh Clinical Academic Track (ECAT) programme. This research is supported in part by the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant MC_PC_20029). Laboratory work was funded by a Wellcome Intermediate Clinical Fellowship to B.F. (201488/Z/16/Z). We acknowledge the staff at NHS Digital, Public Health England and the Intensive Care National Audit and Research Centre who provided clinical data on the participants; and the National Institute for Healthcare Research Clinical Research Network (NIHR CRN) and the Chief Scientist’s Office (Scotland), who facilitate recruitment into research studies in NHS hospitals, and to the global ISARIC and InFACT consortia. GenOMICC genotype controls were obtained using UK Biobank Resource under project 788 funded by Roslin Institute Strategic Programme Grants from the BBSRC (BBS/E/D/10002070 and BBS/E/D/30002275) and Health Data Research UK (HDR-9004 and HDR-9003). UK Biobank data were used in the GSMR analyses presented here under project 66982. The UK Biobank was established by the Wellcome Trust medical charity, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government, British Heart Foundation and Diabetes UK. The work of L.K. was supported by an RCUK Innovation Fellowship from the National Productivity Investment Fund (MR/R026408/1). J.Y. is supported by the Westlake Education Foundation. SCOURGE is funded by the Instituto de Salud Carlos III (COV20_00622 to A.C., PI20/00876 to C.F.), European Union (ERDF) ‘A way of making Europe’, Fundación Amancio Ortega, Banco de Santander (to A.C.), Cabildo Insular de Tenerife (CGIEU0000219140 ‘Apuestas científicas del ITER para colaborar en la lucha contra la COVID-19’ to C.F.) and Fundación Canaria Instituto de Investigación Sanitaria de Canarias (PIFIISC20/57 to C.F.). We also acknowledge the contribution of the Centro National de Genotipado (CEGEN) and Centro de Supercomputación de Galicia (CESGA) for funding this project by providing supercomputing infrastructures. A.D.L. is a recipient of fellowships from the National Council for Scientific and Technological Development (CNPq)-Brazil (309173/2019-1 and 201527/2020-0)