Use of antibiotics and factors associated with treatment failure among 152,245 patients with pneumonia treated in the community - a retrospective cohort study.

This study aims to evaluate risk factors associated with treatment failure and the antibiotics prescribed by primary care physicians in a large patient cohort treated for pneumonia in the community. A retrospective cohort study based on the databases of Maccabi Healthcare Services that provide healthcare to a quarter of the Israeli population. Included patients were > 12 years and diagnosed with pneumonia in the outpatient setting. Cohort 1 included patients with community-acquired pneumonia (CAP), whereas cohort 2 included patients with a documented pneumonia diagnosis following hospital discharge. Treatment failure (TF) was defined as either the use of a second line antibiotic OR hospital admission within 3-14 days OR death within 30 days of diagnosis. Risk factors for TF in the study cohorts were analyzed using multivariable logistic regression. During the study period, 148,376 patients were included in cohort 1 and 3,869 patients in cohort 2, with mean ages of 46.5 ± 20.3 and 63.8 ± 19.5 years, respectively. The most commonly used antibiotics were cephalosporins (36%) and macrolides (35.5%). TF occurred in 12% of cohort 1 and was associated with older age, comorbid conditions, use of non-respiratory fluoroquinolones, and penicillin. Atypical coverage (either macrolides or tetracyclines) was associated with a lower risk of failure. Among cohort 2, TF was higher (16.4%, p < 0.001) and was associated with older age, prior cancer, and congestive heart failure. Treatment failure was associated with comorbid conditions and increasing age. Among young patients with CAP and no comorbid conditions, macrolides or tetracyclines may suffice.

The root meristem is shaped by brassinosteroid control of cell geometry.

Growth extent and direction determine cell and whole-organ architecture. How they are spatio-temporally modulated to control size and shape is not well known. Here we tackled this question by studying the effect of brassinosteroid (BR) signalling on the structure of the root meristem. Quantification of the three-dimensional geometry of thousands of individual meristematic cells across different tissue types showed that the modulation of BR signalling yields distinct changes in growth rate and anisotropy, which affects the time that cells spend in the meristem and has a strong impact on the final root form. By contrast, the hormone effect on cell volume was minor, establishing cell volume as invariant to the effect of BR. Thus, BR has the highest effect on cell shape and growth anisotropy, regulating the overall longitudinal and radial growth of the meristem, while maintaining a coherent distribution of cell sizes. Moving from single-cell quantification to the whole organ, we developed a computational model of radial growth. The simulation demonstrates how differential BR-regulated growth between the inner and outer tissues shapes the meristem and thus explains the non-intuitive outcomes of tissue-specific perturbation of BR signalling. The combined experimental data and simulation suggest that the inner and outer tissues have distinct but coordinated roles in growth regulation.

Optimization of a genomic breeding program for a moderately sized dairy cattle population.

Although it now standard practice to genotype thousands of female calves, genotyping of bull calves is generally limited to progeny of elite cows. In addition to genotyping costs, increasing the pool of candidate sires requires purchase, isolation, and identification of calves until selection decisions are made. We economically optimized via simulation a genomic breeding program for a population of approximately 120,000 milk-recorded cows, corresponding to the Israeli Holstein population. All 30,000 heifers and 60,000 older cows of parities 1 to 3 were potential bull dams. Animals were assumed to have genetic evaluations for a trait with heritability of 0.25 derived by an animal model evaluation of the population. Only bull calves were assumed to be genotyped. A pseudo-phenotype corresponding to each animal's genetic evaluation was generated, consisting of the animal's genetic value plus a residual with variance set to obtain the assumed reliability for each group of animals. Between 4 and 15 bulls and between 200 and 27,000 cows with the highest pseudo-phenotypes were selected as candidate bull parents. For all progeny of the founder animals, genetic values were simulated as the mean of the parental values plus a Mendelian sampling effect with variance of 0.5. A probability of 0.3 for a healthy bull calf per mating, and a genomic reliability of 0.43 were assumed. The 40 bull calves with the highest genomic evaluations were selected for general service for 1 yr. Costs included genotyping of candidate bulls and their dams, purchase of the calves from the farmers, and identification. Costs of raising culled calves were partially recovered by resale for beef. Annual costs were estimated as $10,922 + $305 × candidate bulls. Nominal profit per cow per genetic standard deviation was $106. Economic optimum with a discount rate of 5%, first returns after 4 yr, and a profit horizon of 15 yr were obtained with genotyping 1,620 to 1,750 calves for all numbers of bull sires. However, 95% of the optimal profit can be achieved with only 240 to 300 calves. The higher reliabilities achieved through addition of genomic information to the selection process contribute not only in obtaining higher genetic gain, but also in obtaining higher absolute profits. In addition, the optimal profits are obtained for a lower number of calves born in each generation. Inbreeding, as allowed within genomic selection for the Israeli herd, had virtually no effect on genetic gain or on profits, when compared with the case of exclusion of all matings that generate inbreeding. Annual response to selection ranged from 0.35 to 0.4 genetic standard deviation for 4 to 15 bull sires, as compared with 0.25 to 0.3 for a comparable half-sib design without genomic selection.