Costos de la licencia de maternidad para apoyar la lactancia materna en Brasil, Ghana y México

Objective To develop a method to assess the cost of extending the duration of maternity leave for formally-employed women at the national level and apply it in Brazil, Ghana and Mexico. Methods We adapted a World Bank costing method into a five-step method to estimate the costs of extending the length of maternity leave mandates. Our method used the unit cost of maternity leave based on working women’s weekly wages; the number of additional weeks of maternity leave to be analysed for a given year; and the weighted population of women of reproductive and legal working age in a given country in that year. We weighted the population by the probability of having a baby that year among women in formal employment, according to individual characteristics. We applied nationally representative cross-sectional data from fertility, employment and population surveys to estimate the costs of maternity leave for mothers employed in the formal sector in Brazil, Ghana and Mexico for periods from 12 weeks up to 26 weeks, the WHO target for exclusive breastfeeding. Findings We estimated that 640 742 women in Brazil, 33 869 in Ghana and 288 655 in Mexico would require formal maternity leave annually. The median weekly cost of extending maternity leave for formally working women was purchasing power parity international dollars (PPP$) 195.07 per woman in Brazil, PPP$ 109.68 in Ghana and PPP$ 168.83 in Mexico. Conclusion Our costing method could facilitate evidence-based policy decisions across countries to improve maternity protection benefits and support breastfeeding

Hypertrophic Cardiomyopathy Diagnosis and Treatment in High- and Low-Income Countries: A Narrative Review

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac condition characterized by unexplained left ventricular hypertrophy without a hemodynamic cause. This condition is prevalent in the United States, resulting in various clinical manifestations, including diastolic dysfunction, left ventricular outflow obstruction, cardiac ischemia, and atrial fibrillation. HCM is associated with several genetic mutations, with sarcomeric mutations being the most common and contributing to a more complex disease course. Early diagnosis of HCM is essential for effective management, as late diagnosis often requires invasive treatments and creates a substantial financial burden. Disparities in HCM diagnosis and treatment exist between highincome and low-income countries. High-income countries have more resources to investigate and implement advanced diagnostic and treatment modalities. In contrast, low-income countries face challenges in accessing diagnostic equipment, trained personnel, and affordable medications, leading to a lower quality of life and life expectancy for affected individuals. Diagnostic tools for HCM include imaging studies such as 2D echocardiography, cardiovascular magnetic resonance (CMR), and electrocardiograms (ECGs). CMR is considered the gold standard but remains inaccessible to a significant portion of the world\u27s population, especially in low-income countries. Genetics plays a crucial role in HCM, with numerous mutations identified in various genes. Genetic counseling is essential but often limited in low-income countries due to resource constraints. Disparities in healthcare access and adherence to treatment recommendations exist between high-income and low-income countries, leading to differences in patient outcomes. Addressing these disparities is essential to improve the overall management of HCM on a global scale. In conclusion, this review highlights the complex nature of HCM, emphasizing the importance of early diagnosis, genetic counseling, and access to appropriate diagnostic and therapeutic interventions. Addressing healthcare disparities is crucial to ensure that all individuals with HCM receive timely and effective care, regardless of their geographic location or socioeconomic status

Factores determinantes del emprendimiento: Una mirada retrospectiva desde la producción científica

Desde la diversidad de aspectos que inciden en lo empresarial para hacerlo más competitivo y difundir la información que se produce bajo características específicas, el emprendimiento ha despertado el interés de la comunidad académica. En ese sentido, el objetivo de este artículo fue analizar los factores determinantes del espíritu empresarial, bajo un enfoque multidisciplinar (psicológico, organizacional, sectorial e institucional), a la vez que se detalla la evolución de la estructura conceptual. Se ha realizado una revisión exhaustiva de la literatura que abarca más de 60 años (1957-2020) en 4 etapas, utilizando un enfoque bibliométrico sobre 5.537 documentos WOS. Los resultados dan cuenta de que el emprendimiento ha sido abordado desde una perspectiva multidisciplinaria que fortalece o inhiben a lo empresarial debido a una variedad de factores internos y externos, por tal razón existe una producción científica que interpreta la realidad y destaca la necesidad. Se concluye que existe un interés integral en los estudios sobre el emprendimiento que destaquen metodologías para el abordaje del crecimiento empresarial

The genomic psychiatry cohort: Partners in discovery

The Genomic Psychiatry Cohort (GPC) is a longitudinal resource designed to provide the necessary population-based sample for large-scale genomic studies, studies focusing on Research Domain Criteria (RDoC) and/or other alternate phenotype constructs, clinical and interventional studies, nested case-control studies, long-term disease course studies, and genomic variant-to-phenotype studies. We provide and will continue to encourage access to the GPC as an international resource. DNA and other biological samples and diagnostic data are available through the National Institute of Mental Health (NIMH) Repository. After appropriate review and approval by an advisory board, investigators are able to collaborate in, propose, and co-lead studies involving cohort participants

A Rare Functional Noncoding Variant at the GWAS-Implicated MIR137/MIR2682 Locus Might Confer Risk to Schizophrenia and Bipolar Disorder

Schizophrenia (SZ) genome-wide association studies (GWASs) have identified common risk variants in >100 susceptibility loci; however, the contribution of rare variants at these loci remains largely unexplored. One of the strongly associated loci spans MIR137 (miR137) and MIR2682 (miR2682), two microRNA genes important for neuronal function. We sequenced ∼6.9 kb MIR137/MIR2682 and upstream regulatory sequences in 2,610 SZ cases and 2,611 controls of European ancestry. We identified 133 rare variants with minor allele frequency (MAF) <0.5%. The rare variant burden in promoters and enhancers, but not insulators, was associated with SZ (p = 0.021 for MAF < 0.5%, p = 0.003 for MAF < 0.1%). A rare enhancer SNP, 1:g.98515539A>T, presented exclusively in 11 SZ cases (nominal p = 4.8 × 10−4). We further identified its risk allele T in 2 of 2,434 additional SZ cases, 11 of 4,339 bipolar (BP) cases, and 3 of 3,572 SZ/BP study controls and 1,688 population controls; yielding combined p values of 0.0007, 0.0013, and 0.0001 for SZ, BP, and SZ/BP, respectively. The risk allele T of 1:g.98515539A>T reduced enhancer activity of its flanking sequence by >50% in human neuroblastoma cells, predicting lower expression of MIR137/MIR2682. Both empirical and computational analyses showed weaker transcription factor (YY1) binding by the risk allele. Chromatin conformation capture (3C) assay further indicated that 1:g.98515539A>T influenced MIR137/MIR2682, but not the nearby DPYD or LOC729987. Our results suggest that rare noncoding risk variants are associated with SZ and BP at MIR137/MIR2682 locus, with risk alleles decreasing MIR137/MIR2682 expression

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources)

Genome-wide association study reveals GmFulb as candidate gene for maturity time and reproductive length in soybeans (Glycine max).

The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits

Development of Breeder-Friendly KASP Markers for Low Concentration of Kunitz Trypsin Inhibitor in Soybean Seeds

Trypsin inhibitors (TI), a common anti-nutritional factor in soybean, prevent animals’ protein digestibility reducing animal growth performance. No commercial soybean cultivars with low or null concentration of TI are available. The availability of a high throughput genotyping assay will be beneficial to incorporate the low TI trait into elite breeding lines. The aim of this study is to develop and validate a breeder friendly Kompetitive Allele Specific PCR (KASP) assay linked to low Kunitz trypsin inhibitor (KTI) in soybean seeds. A total of 200 F3:5 lines derived from PI 547656 (low KTI) X Glenn (normal KTI) were genotyped using the BARCSoySNP6K_v2 Beadchip. F3:4 and F3:5 lines were grown in Blacksburg and Orange, Virginia in three years, respectively, and were measured for KTI content using a quantitative HPLC method. We identified three SNP markers tightly linked to the major QTL associated to low KTI in the mapping population. Based on these SNPs, we developed and validated the KASP assays in a set of 93 diverse germplasm accessions. The marker Gm08_44814503 has 86% selection efficiency for the accessions with low KTI and could be used in marker assisted breeding to facilitate the incorporation of low KTI content in soybean seeds

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The ability of soybean [Glycine max (L.) Merr.] to adapt to different latitudes is attributed to genetic variation in major E genes and quantitative trait loci (QTLs) determining flowering time (R1), maturity (R8), and reproductive length (RL). Fully revealing the genetic basis of R1, R8, and RL in soybeans is necessary to enhance genetic gains in soybean yield improvement. Here, we performed a genome-wide association analysis (GWA) with 31,689 single nucleotide polymorphisms (SNPs) to detect novel loci for R1, R8, and RL using a soybean panel of 329 accessions with the same genotype for three major E genes (e1-as/E2/E3). The studied accessions were grown in nine environments and observed for R1, R8 and RL in all environments. This study identified two stable peaks on Chr 4, simultaneously controlling R8 and RL. In addition, we identified a third peak on Chr 10 controlling R1. Association peaks overlap with previously reported QTLs for R1, R8, and RL. Considering the alternative alleles, significant SNPs caused RL to be two days shorter, R1 two days later and R8 two days earlier, respectively. We identified association peaks acting independently over R1 and R8, suggesting that trait-specific minor effect loci are also involved in controlling R1 and R8. From the 111 genes highly associated with the three peaks detected in this study, we selected six candidate genes as the most likely cause of R1, R8, and RL variation. High correspondence was observed between a modifying variant SNP at position 04:39294836 in GmFulb and an association peak on Chr 4. Further studies using map-based cloning and fine mapping are necessary to elucidate the role of the candidates we identified for soybean maturity and adaptation to different latitudes and to be effectively used in the marker-assisted breeding of cultivars with optimal yield-related traits.</div

Manhattan plots of GWAS for flowering time (R1), maturity time (R8), and reproductive length (RL) in three hundred twenty-nine <i>G</i>. <i>max</i> accessions.

GWAS results correspond to the analysis across the nine environments. The horizontal dashed lines indicate the statistically significant cut-off of–log (p-value) = 4.16. Significant SNPs IDs correspond to the Wm82.a1.</p