Tag: pubmed

Acta Paediatr. 2021 Jun 2. doi: 10.1111/apa.15964. Online ahead of print.

ABSTRACT

AIM: Malnutrition is a major public health issue that has been associated with high susceptibility for impaired brain development and mental functioning. However, to date studies on this topic have not been collated and appraised. This systematic review and meta-analysis investigated the association between malnutrition and cognitive development.

METHODS: We searched the MEDLINE, Scopus, CINAHL, Embase PsycINFO and Cochrane Library databases in English up to 8 December 2020. All studies reporting an association between nutritional status and cognitive development were included. P values of less than 0.05 were considered statistically significant and the results are reported as standardised mean differences (SMD), 95% confidence intervals (95%) and I² statistics.

RESULTS: We included 12 studies comprising 7,607 participants aged 1 to 12 years. Children with malnutrition had worse scores than controls for the Wechsler Intelligence Scale (SMD -0.40; 95% CI -0.60 to -0.20; p<0.0001; I² 77.1%), the Raven’s Coloured Progressive Matrices (SMD -3.75; 95% CI -5.68 to -1.83; p<0.0001; I² 99.2%), visual processing (SMD -0.85; 95% CI -1.23 to -0.46; p 0.009; I² 11.0%) and short memory (SMD 0.85; 95% CI -1.21 to -0.49; p<0.0001; I² 0%) tests.

CONCLUSION: Normal cognitive development requires access to good and safe nutrition.

PMID:34077582 | DOI:10.1111/apa.15964

Plant Biotechnol J. 2021 Jun 2. doi: 10.1111/pbi.13645. Online ahead of print.

ABSTRACT

Starch is the most abundant storage carbohydrate in maize kernels and provides calories for humans and other animals as well as raw materials for various industrial applications. Decoding of the genetic basis of natural variation in kernel starch content is needed to manipulate starch quantity and quality via molecular breeding to meet future needs. Here, we identified 50 unique single quantitative trait loci (QTLs) for starch content with 18 novel QTLs via single linkage mapping, joint linkage mapping, and a genome-wide association study in a multi-parent population containing six recombinant inbred line populations. Only five QTLs explained over 10% of phenotypic variation in single populations. In addition to a few large-effect and many small-effect additive QTLs, limited pairs of epistatic QTLs also contributed to the genetic basis of the variation in kernel starch content. A regional association study identified five non-starch-pathway genes that were the causal candidate genes underlying the identified QTLs for starch content. The pathway-driven analysis identified ZmTPS9, which encodes a trehalose-6-phosphate synthase in the trehalose pathway, as the causal gene for the QTL qSTA4-2, which was detected by all three statistical analyses. Knockout of ZmTPS9 increased kernel starch content and, in turn, kernel weight in maize, suggesting potential applications for ZmTPS9 in maize starch and yield improvement. These findings extend our knowledge about the genetic basis of starch content in maize kernels and provide valuable information for maize genetic improvement of starch quantity and quality.

PMID:34077617 | DOI:10.1111/pbi.13645

Ann Bot. 2021 Jun 2:mcab065. doi: 10.1093/aob/mcab065. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Secondary metabolites are integral to multiple key plant processes: growth regulation, pollinator attraction, interactions with conspecifics, competitors and symbionts, yet their role in plant adaptation remains an underexplored area of research. Carnivorous plants use secondary metabolites to acquire nutrients from prey, but the extent of the role of secondary metabolites in plant carnivory is not known. We aimed to determine the extent of the role of secondary metabolites in facilitating carnivory of the Cape sundew, Drosera capensis.

METHODS: We conducted metabolomic analysis of 72 plants in a time-series experiment before and after simulated prey capture. We used UHPLC-MS/MS and retention time index to identify compounds in the leaf trap tissue which changed up to 72 hrs following simulated prey capture. We identified associated metabolic pathways, and cross-compared these compounds to metabolites previously known to be involved in carnivorous plants across taxa.

KEY RESULTS: For the first time in a carnivorous plant, we have profiled the whole-leaf metabolome response to prey capture. Reliance on secondary plant metabolites was higher than previously thought-2383 out of 3257 compounds in fed leaves had statistically significant concentration changes in comparison to unfed controls. Of these, ~34 compounds are also associated with carnivory in other species; 11 are unique to Nepenthales. At least 20 compounds had 10-fold changes in concentration, 12 of which had 30-fold changes and are typically associated with defence or attraction in non-carnivorous plants.

CONCLUSIONS: Secondary plant metabolites are utilised in plant carnivory to an extent greater than previously thought-we found a whole metabolome response to prey capture. Plant carnivory, at the metabolic level, likely evolved from at least two distinct functions-attraction and defence. Findings of this study support the hypothesis that secondary metabolites play an important role in plant diversification and adaptation to new environments.

PMID:34077503 | DOI:10.1093/aob/mcab065

Int J Gynaecol Obstet. 2021 Jun 2. doi: 10.1002/ijgo.13775. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of this study is to assess the effect of cesarean section (CS) timing, elective versus unplanned, on the residual myometrial thickness (RMT) and CS scars.

METHODS: This is a prospective single-blinded observational cohort study with 186 observations. Patients indicated to undergo first singleton CS were preoperatively recruited. Exclusion criteria are history of repeated CS, vertical hysterotomy, diabetes and additional uterine surgeries. Sonographic examination was performed for assessing the RMT ratio, the presence of a niche, fibrosis, and the distance from the scar to the internal os (SO) one year after CS. Power analysis was performed with 0.05 α, 0.1 β, and all statistical analysis was conducted with Stata^® .

RESULTS: Wilcoxon rank-sum test for the association between CS timing, RMT ratio and SO showed -0.59 and -4.94 Z values (0.553, <0.001 p-values) respectively. There was no association between CS timing and niches and fibrosis (>0.99, 0.268 p-values). Linear regression between SO and the extent of cervical dilatation showed a -0.45 β (95% CI: -0.68 to -0.21) and a 10.22 mm intercept (< 0.001 p-value).

CONCLUSION: RMT is independent of the timing of cesarean section, but the SO distance shows a negative linear relationship with the cervical dilatation.

PMID:34077556 | DOI:10.1002/ijgo.13775

Int J Gynaecol Obstet. 2021 Jun 2. doi: 10.1002/ijgo.13777. Online ahead of print.

ABSTRACT

OBJECTIVE: To explore the indirect impact of the COVID-19 pandemic on patterns of pregnancy-related venous thromboembolism (VTE) events, mediated by population mobility restrictions during lockdown periods.

METHODS: Pregnancy-related VTE hospitalizations were identified through a code-targeted search of the Hadassah Medical Center’s computerized database. A manual analysis of relevant medical records was performed, and cases diagnosed throughout the year 2020 were compared to those diagnosed during 2019 and 2018. Statistical analyses studied obstetrical outcomes, as well as the extent and treatment of VTE events during the COVID-19 pandemic compared to those of preceding years, stratified by pre-, intra-, and post-lockdown periods.

RESULTS: The incidence of pregnancy-related thromboembolic events during 2020 was 0.16% of all deliveries, significantly higher than in 2018 and 2019 (0.06% and 0.1%, respectively; P<0.05). Higher rates of VTE events were found during post-lockdown periods in 2020, compared with corresponding time periods in 2019 and 2018.

CONCLUSION: The present data suggest that lockdown periods impact pregnancy-related VTE hospitalizations, possibly as a result of restricted population mobility. Increased awareness of this undesirable outcome may aid health policymakers in the continuing struggle with epidemics.

PMID:34077561 | DOI:10.1002/ijgo.13777

JMIRx Med. 2021 Mar 18;2(1):e22617. doi: 10.2196/22617. eCollection 2021 Jan-Mar.

ABSTRACT

BACKGROUND: Since the beginning of the COVID-19 pandemic, researchers and health authorities have sought to identify the different parameters that drive its local transmission cycles to make better decisions regarding prevention and control measures. Different modeling approaches have been proposed in an attempt to predict the behavior of these local cycles.

OBJECTIVE: This paper presents a framework to characterize the different variables that drive the local, or epidemic, cycles of the COVID-19 pandemic, in order to provide a set of relatively simple, yet efficient, statistical tools to be used by local health authorities to support decision making.

METHODS: Virtually closed cycles were compared to cycles in progress from different locations that present similar patterns in the figures that describe them. With the aim to compare populations of different sizes at different periods of time and locations, the cycles were normalized, allowing an analysis based on the core behavior of the numerical series. A model for the reproduction number was derived from the experimental data, and its performance was presented, including the effect of subnotification (ie, underreporting). A variation of the logistic model was used together with an innovative inventory model to calculate the actual number of infected persons, analyze the incubation period, and determine the actual onset of local epidemic cycles.

RESULTS: The similarities among cycles were demonstrated. A pattern between the cycles studied, which took on a triangular shape, was identified and used to make predictions about the duration of future cycles. Analyses on effective reproduction number (R_t) and subnotification effects for Germany, Italy, and Sweden were presented to show the performance of the framework introduced here. After comparing data from the three countries, it was possible to determine the probable dates of the actual onset of the epidemic cycles for each country, the typical duration of the incubation period for the disease, and the total number of infected persons during each cycle. In general terms, a probable average incubation time of 5 days was found, and the method used here was able to estimate the end of the cycles up to 34 days in advance, while demonstrating that the impact of the subnotification level (ie, error) on the effective reproduction number was <5%.

CONCLUSIONS: It was demonstrated that, with relatively simple mathematical tools, it is possible to obtain a reliable understanding of the behavior of COVID-19 local epidemic cycles, by introducing an integrated framework for identifying cycle patterns and calculating the variables that drive it, namely: the R_t, the subnotification effects on estimations, the most probable actual cycles start dates, the total number of infected, and the most likely incubation period for SARS-CoV-2.

PMID:34077489 | PMC:PMC8078446 | DOI:10.2196/22617

Am J Health Syst Pharm. 2021 Jun 2:zxab232. doi: 10.1093/ajhp/zxab232. Online ahead of print.

ABSTRACT

PURPOSE: Our aim was to review key methodological concepts and provide a practical guide to employing mixed methods research to enhance pharmacy practice research.

SUMMARY: Mixed methods research provides multiple organized analytic perspectives to thoroughly investigate complex social and scientific problems in a methodologically rigorous manner. This research design incorporates collection and analysis of both qualitative and quantitative data components to create a thorough understanding of a complex question. The 5 most commonly identified reasons for conducting mixed methods research include triangulation, complementarity, development, initiation, and expansion of results. For research questions that benefit from mixed methods research, we review how to structure the study, including timing, sequencing, and prioritization of methods. Illustrative examples from the literature highlight the utility of this methodology for clinical and operational pharmacy research questions.

CONCLUSION: Mixed methods designs can enhance pharmacy research inquiry, provide a means to understand complicated issues, and uncover optimal interventions.

PMID:34077491 | DOI:10.1093/ajhp/zxab232

JAMA. 2021 Jun 2. doi: 10.1001/jama.2021.6530. Online ahead of print.

ABSTRACT

IMPORTANCE: Continuous glucose monitoring (CGM) is recommended for patients with type 1 diabetes; observational evidence for CGM in patients with insulin-treated type 2 diabetes is lacking.

OBJECTIVE: To estimate clinical outcomes of real-time CGM initiation.

DESIGN, SETTING, AND PARTICIPANTS: Exploratory retrospective cohort study of changes in outcomes associated with real-time CGM initiation, estimated using a difference-in-differences analysis. A total of 41 753 participants with insulin-treated diabetes (5673 type 1; 36 080 type 2) receiving care from a Northern California integrated health care delivery system (2014-2019), being treated with insulin, self-monitoring their blood glucose levels, and having no prior CGM use were included.

EXPOSURES: Initiation vs noninitiation of real-time CGM (reference group).

MAIN OUTCOMES AND MEASURES: Ten end points measured during the 12 months before and 12 months after baseline: hemoglobin A1c (HbA1c); hypoglycemia (emergency department or hospital utilization); hyperglycemia (emergency department or hospital utilization); HbA1c levels lower than 7%, lower than 8%, and higher than 9%; 1 emergency department encounter or more for any reason; 1 hospitalization or more for any reason; and number of outpatient visits and telephone visits.

RESULTS: The real-time CGM initiators included 3806 patients (mean age, 42.4 years [SD, 19.9 years]; 51% female; 91% type 1, 9% type 2); the noninitiators included 37 947 patients (mean age, 63.4 years [SD, 13.4 years]; 49% female; 6% type 1, 94% type 2). The prebaseline mean HbA1c was lower among real-time CGM initiators than among noninitiators, but real-time CGM initiators had higher prebaseline rates of hypoglycemia and hyperglycemia. Mean HbA1c declined among real-time CGM initiators from 8.17% to 7.76% and from 8.28% to 8.19% among noninitiators (adjusted difference-in-differences estimate, -0.40%; 95% CI, -0.48% to -0.32%; P < .001). Hypoglycemia rates declined among real-time CGM initiators from 5.1% to 3.0% and increased among noninitiators from 1.9% to 2.3% (difference-in-differences estimate, -2.7%; 95% CI, -4.4% to -1.1%; P = .001). There were also statistically significant differences in the adjusted net changes in the proportion of patients with HbA1c lower than 7% (adjusted difference-in-differences estimate, 9.6%; 95% CI, 7.1% to 12.2%; P < .001), lower than 8% (adjusted difference-in-differences estimate, 13.1%; 95% CI, 10.2% to 16.1%; P < .001), and higher than 9% (adjusted difference-in-differences estimate, -7.1%; 95% CI, -9.5% to -4.6%; P < .001) and in the number of outpatient visits (adjusted difference-in-differences estimate, -0.4; 95% CI, -0.6 to -0.2; P < .001) and telephone visits (adjusted difference-in-differences estimate, 1.1; 95% CI, 0.8 to 1.4; P < .001). Initiation of real-time CGM was not associated with statistically significant changes in rates of hyperglycemia, emergency department visits for any reason, or hospitalizations for any reason.

CONCLUSIONS AND RELEVANCE: In this retrospective cohort study, insulin-treated patients with diabetes selected by physicians for real-time continuous glucose monitoring compared with noninitiators had significant improvements in hemoglobin A1c and reductions in emergency department visits and hospitalizations for hypoglycemia, but no significant change in emergency department visits or hospitalizations for hyperglycemia or for any reason. Because of the observational study design, findings may have been susceptible to selection bias.

PMID:34077502 | DOI:10.1001/jama.2021.6530

PLoS One. 2021 Jun 2;16(6):e0252582. doi: 10.1371/journal.pone.0252582. eCollection 2021.

ABSTRACT

Languages emerge and change over time at the population level though interactions between individual speakers. It is, however, hard to directly observe how a single speaker’s linguistic innovation precipitates a population-wide change in the language, and many theoretical proposals exist. We introduce a very general mathematical model that encompasses a wide variety of individual-level linguistic behaviours and provides statistical predictions for the population-level changes that result from them. This model allows us to compare the likelihood of empirically-attested changes in definite and indefinite articles in multiple languages under different assumptions on the way in which individuals learn and use language. We find that accounts of language change that appeal primarily to errors in childhood language acquisition are very weakly supported by the historical data, whereas those that allow speakers to change incrementally across the lifespan are more plausible, particularly when combined with social network effects.

PMID:34077472 | DOI:10.1371/journal.pone.0252582

PLoS One. 2021 Jun 2;16(6):e0252556. doi: 10.1371/journal.pone.0252556. eCollection 2021.

ABSTRACT

In this paper, we consider Dagum distribution which is capable of modeling various shapes of failure rates and aging criteria. Based on progressively type-I interval censoring data, we first obtain the maximum likelihood estimators and the approximate confidence intervals of the unknown parameters of the Dagum distribution. Next, we obtain the Bayes estimators of the parameters of Dagum distribution under the squared error loss (SEL) and balanced squared error loss (BSEL) functions using independent informative gamma and non informative uniform priors for both scale and two shape parameters. A Monte Carlo simulation study is performed to assess the performance of the proposed Bayes estimators with the maximum likelihood estimators. We also compute credible intervals and symmetric 100(1 – τ)% two-sided Bayes probability intervals under the respective approaches. Besides, based on observed samples, Bayes predictive estimates and intervals are obtained using one-and two-sample schemes. Simulation results reveal that the Bayes estimates based on SEL and BSEL performs better than maximum likelihood estimates in terms of bias and MSEs. Besides, credible intervals have smaller interval lengths than confidence interval. Further, predictive estimates based on SEL with informative prior performs better than non-informative prior for both one and two sample schemes. Further, the optimal censoring scheme has been suggested using a optimality criteria. Finally, we analyze a data set to illustrate the results derived.

PMID:34077455 | DOI:10.1371/journal.pone.0252556