Prevalencia de infección de herida quirúrgica en pacientes con profilaxis antimicrobiana en cesárea programada / Prevalence of surgical wound infection in patients with antimicrobial prophylaxis in planned cesarean section

Resumen: Objetivo: Determinar la prevalencia de infecciones en la herida quirúrgica en cesáreas programadas del HRAEV. Materiales y método: Estudio retrospectivo, descriptivo, observacional tipo corte transversal para determinar la prevalencia de IHQ en pacientes llevadas a cesárea programada con profilaxis antibiótica en HRAEV. Resultados: Se evaluaron 185 expedientes de pacientes sometidas a cesárea programada, con edad entre 28 a 37 años (48.1%) con un peso promedio 81 kg (DE=10.1) con un índice de masa corporal (IMC) promedio de 30 (DE=4.24) es decir un IMC entre 25.76 a 34.24. De ellas, 4 pacientes (2.16%) presentaron infección de herida quirúrgica durante cesárea programada, las cuales recibieron ceftriaxona como PA mayor a 120 minutos previo a la incisión de la piel, estos pacientes se clasifican como ASA II y tenían un IMC superior a 30 kg/m2 y sin comorbilidades registradas. El tiempo de profilaxis antibiótica más frecuente en las pacientes llevadas a cesárea programada fue >120 minutos (34.08%) y se administró ceftriaxona en el 84.86% de la población que en su mayoría es ASA II (97.83%). El 100% de las heridas fueron superficiales. Conclusiones: En el presente estudio se encontró que la prevalencia de IHQ en cesáreas programadas en HRAEV fue de 2.16%, cifra que se encuentra por debajo de la prevalencia a nivel mundial, dado a que las pacientes seleccionadas no contaban con algunos de los factores de riesgo añadidos que aumentaran el riesgo de IHQ en comparación con otros estudios.

Abstract: Objective: To determine the prevalence of surgical wound infections in scheduled HRAEV cesarean sections. Materials and method: Retrospective, descriptive, observational cross-sectional study to determine the prevalence of IHC in patients undergoing scheduled cesarean section with antibiotic prophylaxis in HRAEV. Results: 185 records of patients undergoing scheduled cesarean section were evaluated, aged between 28 to 37 years (48.1%) with an average weight of 81 kg (SD = 10.1) with an average body mass index (BMI) of 30 (SD = 4.24) that is, a BMI between 25.76 and 34.24. Of these, 4 patients (2.16%) presented surgical wound infection during scheduled cesarean section, who received ceftriaxone as PA greater than 120 minutes prior to skin incision, these patients are classified as ASA II and had a BMI greater than 30 kg/m2 and without recorded comorbidities. The most frequent antibiotic prophylaxis time in patients undergoing scheduled cesarean section was >120 minutes (34.08%) and ceftriaxone was administered in 84.86% of the population, which is mostly ASA II (97.83%). 100% of the wounds were superficial. Conclusions: In the present study, it was found that the prevalence of IHC in cesarean sections scheduled in HRAEV was 2.16%, a figure that is below the worldwide prevalence, given that the selected patients did not have some of the risk factors. added risk that increased the risk of SSI compared to other studies.

Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean.

Increasing the rate of genetic gain for seed yield remains the primary breeding objective in both public and private soybean [Glycine max (L.) Merr.] breeding programs. Genomic selection (GS) has the potential to accelerate the rate of genetic gain for soybean seed yield. Limited studies to date have validated GS accuracy and directly compared GS with phenotypic selection (PS), and none have been reported in soybean. This study conducted the first empirical validation of GS for increasing seed yield using over 1,500 lines and over 7 yr (2010-2016) of replicated experiments in the University of Nebraska-Lincoln soybean breeding program. The study was designed to capture the varying genetic relatedness of the training population to three validation sets: two large biparental populations (TBP-1 and TBP-2) and a large validation set comprised of 457 preselected advanced lines derived from 45 biparental populations (TMP). We found that prediction accuracy (.54) realized in our validation experiments was comparable with what we obtained from a series of cross-validation experiments (.64). Both GS and PS were more effective for increasing the population mean performance compared with random selection (RS). We found a selection advantage of GS over PS, where higher genetic gain and identification of top-performing lines was maximized at 10-20% selected proportion. Genomic selection led to small increases in genetic similarity when compared with PS and RS presumably because of a significant shift on allelic frequencies toward the extremes, suggesting that it could erode genetic diversity more quickly. Overall, we found that GS can perform as effectively as PS but that measures should be considered to protect against loss of genetic variance when using GS.

Comparing a Mixed Model Approach to Traditional Stability Estimators for Mapping Genotype by Environment Interactions and Yield Stability in Soybean [Glycine max (L.) Merr.].

Identifying genetic loci associated with yield stability has helped plant breeders and geneticists begin to understand the role and influence of genotype by environment (GxE) interactions in soybean [Glycine max (L.) Merr.] productivity, as well as other crops. Quantifying a genotype's range of performance across testing locations has been developed over decades with dozens of methodologies available. This includes directly modeling GxE interactions as part of an overall model for yield, as well as methods which generate overall yield "stability" values from multi-environment trial data. Correspondence between these methods as it pertains to the outcomes of genome wide association studies (GWAS) has not been well defined. In this study, the GWAS results for yield and yield stability were compared in 213 soybean lines across 11 environments to determine their utility and potential intersection. Both univariate and multivariate conventional stability estimates were considered alongside a mixed model for yield that fit marker by environment interactions as a random effect. One-hundred and six total QTL were discovered across all mapping results, however, genetic loci that were significant in the mixed model for grain yield that fit marker by environment interactions were completely distinct from those that were significant when mapping using traditional stability measures as a phenotype. Furthermore, 73.21% of QTL discovered in the mixed model were determined to cause a crossover interaction effect which cause genotype rank changes between environments. Overall, the QTL discovered via explicitly mapping GxE interactions also explained more yield variance that those QTL associated with differences in traditional stability estimates making their theoretical impact on selection greater. A lack of intersecting results between mapping approaches highlights the importance of examining stability in multiple contexts when attempting to manipulate GxE interactions in soybean.

Group screening for rare events based on incomplete block designs.

Fields such as, diagnostic testing, biotherapeutics, drug development, and toxicology among others, center on the premise of searching through many specimens for a rare event. Scientists in the business of "searching for a needle in a haystack" may greatly benefit from the use of group screening design strategies. Group screening, where specimens are composited into pools with each pool being tested for the presence of the event, can be much more cost-efficient than testing each individual specimen. A number of group screening designs have been proposed in the literature. Incomplete block screening designs are described here and compared with other group screening designs. It is shown under certain conditions, that incomplete block screening designs can provide nearly a 90% cost saving compared to other group screening designs such as when prevalence is 0.001 and screening 3876 specimens with an ICB-sequential design vs. a Dorfman design. In other cases, previous group screening designs are shown to be most efficient. Overall, when prevalence is small (≤0.05) group screening designs are shown to be quite cost effective at screening a large number of specimens and in general there is no one design that is best in all situations. © 2018 American Institute of Chemical Engineers Biotechnol Progress, 35: e2770, 2019.

Genotyping by sequencing for genomic prediction in a soybean breeding population.

BACKGROUND: Advances in genotyping technology, such as genotyping by sequencing (GBS), are making genomic prediction more attractive to reduce breeding cycle times and costs associated with phenotyping. Genomic prediction and selection has been studied in several crop species, but no reports exist in soybean. The objectives of this study were (i) evaluate prospects for genomic selection using GBS in a typical soybean breeding program and (ii) evaluate the effect of GBS marker selection and imputation on genomic prediction accuracy. To achieve these objectives, a set of soybean lines sampled from the University of Nebraska Soybean Breeding Program were genotyped using GBS and evaluated for yield and other agronomic traits at multiple Nebraska locations. RESULTS: Genotyping by sequencing scored 16,502 single nucleotide polymorphisms (SNPs) with minor-allele frequency (MAF) > 0.05 and percentage of missing values ≤ 5% on 301 elite soybean breeding lines. When SNPs with up to 80% missing values were included, 52,349 SNPs were scored. Prediction accuracy for grain yield, assessed using cross validation, was estimated to be 0.64, indicating good potential for using genomic selection for grain yield in soybean. Filtering SNPs based on missing data percentage had little to no effect on prediction accuracy, especially when random forest imputation was used to impute missing values. The highest accuracies were observed when random forest imputation was used on all SNPs, but differences were not significant. A standard additive G-BLUP model was robust; modeling additive-by-additive epistasis did not provide any improvement in prediction accuracy. The effect of training population size on accuracy began to plateau around 100, but accuracy steadily climbed until the largest possible size was used in this analysis. Including only SNPs with MAF > 0.30 provided higher accuracies when training populations were smaller. CONCLUSIONS: Using GBS for genomic prediction in soybean holds good potential to expedite genetic gain. Our results suggest that standard additive G-BLUP models can be used on unfiltered, imputed GBS data without loss in accuracy.