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1.
Diabetes Res Clin Pract ; 158: 107916, 2019 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31682882

RESUMO

OBJECTIVE: Dulaglutide is an agonist of "glucagon-like peptide type 1″ receptors (arGLP1). The clinical efficacy of this molecule is based on reductions in glycosylated hemoglobin (HbA1c) and weight, data shown in the pivotal AWARD studies. METHODS: We propose a retrospective and multicenter study that allows evaluating the effectiveness of dulaglutide at 24 months after treatment began, under conditions of usual clinical practice, and comparing the results obtained with those that are reflected in the controlled trials. RESULTS: The results show a reduction in the HbA1c levels -1.4% at 6 M and this reduction were maintained throughout 12 M and 24 M (p < 0.001). Plasma glucose showed significant reductions around -30 mg / dL at 6 months (p < 0.001) that remained until the end of the follow-up at 12 and 24 M, respectively. The weight decreased significantly at 6 M (p < 0.001) but continued decreasing at 12 and 24 M, showing statistically significant differences (p: 0.001). CONCLUSIONS: Our results are similar to those obtained in pivotal clinical trials and confirm these benefits in real life.

2.
Atherosclerosis ; 285: 17-22, 2019 06.
Artigo em Inglês | MEDLINE | ID: mdl-30991288

RESUMO

BACKGROUND AND AIMS: Heterozygous familial hypercholesterolemia (FH) is a genetic disorder characterized by high levels of low-density lipoprotein cholesterol (LDL-C). The magnitude of atherosclerotic cardiovascular disease (ASCVD) risk in FH patients is highly variable, and this can result from genetic factors. The aim of our study was to characterize whether polymorphisms in VEGFR2 and OPG genes could influence the expression of ASCVD in FH patients. METHODS: We studied 318 FH patients from the SAFEHEART registry, without clinical diagnosis of ASCVD. A coronary tomographic angiography (CTA) was performed to determine and evaluate the presence of coronary stenosis and coronary artery calcium, as measured by coronary calcium score (CCS). Genotyping of OPG rs2073618 and VEGFR2 rs2071559 polymorphisms was performed using TaqMan 5'-exonuclease allelic discrimination assays. RESULTS: Homozygous GG genotype and G allele of VEGFR2 rs2071559 polymorphism were associated with decreased risk of developing coronary artery stenosis. In the analysis of OPG rs2073618 and VEGFR2 rs2071559 polymorphisms, according to the presence of coronary artery calcium, we found significant differences in both polymorphisms. Homozygous GG genotype and G allele of VEGFR2 rs2071559 polymorphism were associated with decreased risk of accumulation of coronary artery calcium measured by CCS in CTA. Moreover, being a carrier of the GG genotype and G allele of the OPG rs2073618 polymorphism increased the risk of the presence of coronary artery calcium measured by CCS in CTA. CONCLUSIONS: Polymorphisms in VEGFR2 and OPG genes modify the risk of ASCVD in FH patients.

3.
G3 (Bethesda) ; 9(5): 1545-1556, 2019 05 07.
Artigo em Inglês | MEDLINE | ID: mdl-30858235

RESUMO

Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes in order to improve prediction accuracy in the context of genomic selection (GS). For this reason, when breeders have mixed phenotypes, they usually analyze them using univariate models, and thus are not able to exploit the correlation between traits, which many times helps improve prediction accuracy. In this paper we propose applying deep learning for analyzing multiple traits with mixed phenotype data in terms of prediction accuracy. The prediction performance of multiple-trait deep learning with mixed phenotypes (MTDLMP) models was compared to the performance of univariate deep learning (UDL) models. Both models were evaluated using predictors with and without the genotype × environment (G×E) interaction term (I and WI, respectively). The metric used for evaluating prediction accuracy was Pearson's correlation for continuous traits and the percentage of cases correctly classified (PCCC) for binary and ordinal traits. We found that a modest gain in prediction accuracy was obtained only in the continuous trait under the MTDLMP model compared to the UDL model, whereas for the other traits (1 binary and 2 ordinal) we did not find any difference between the two models. In both models we observed that the prediction performance was better for WI than for I. The MTDLMP model is a good alternative for performing simultaneous predictions of mixed phenotypes (binary, ordinal and continuous) in the context of GS.


Assuntos
Aprendizado Profundo , Estudos de Associação Genética , Genoma , Genômica , Modelos Genéticos , Fenótipo , Característica Quantitativa Herdável , Algoritmos , Genoma de Planta , Genômica/métodos , Genótipo , Melhoramento Vegetal , Reprodutibilidade dos Testes , Seleção Genética
4.
G3 (Bethesda) ; 9(2): 601-618, 2019 02 07.
Artigo em Inglês | MEDLINE | ID: mdl-30593512

RESUMO

Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic based prediction performance of two popular machine learning methods: the Multi Layer Perceptron (MLP) and support vector machine (SVM) methods vs. the Bayesian threshold genomic best linear unbiased prediction (TGBLUP) model. We used the percentage of cases correctly classified (PCCC) as a metric to measure the prediction performance, and seven real data sets to evaluate the prediction accuracy, and found that the best predictions (in four out of the seven data sets) in terms of PCCC occurred under the TGLBUP model, while the worst occurred under the SVM method. Also, in general we found no statistical differences between using 1, 2 and 3 layers under the MLP models, which means that many times the conventional neuronal network model with only one layer is enough. However, although even that the TGBLUP model was better, we found that the predictions of MLP and SVM were very competitive with the advantage that the SVM was the most efficient in terms of the computational time required.


Assuntos
Melhoramento Vegetal/métodos , Máquina de Vetores de Suporte , Teorema de Bayes , Característica Quantitativa Herdável , Seleção Artificial
5.
G3 (Bethesda) ; 8(12): 3829-3840, 2018 12 10.
Artigo em Inglês | MEDLINE | ID: mdl-30291108

RESUMO

Multi-trait and multi-environment data are common in animal and plant breeding programs. However, what is lacking are more powerful statistical models that can exploit the correlation between traits to improve prediction accuracy in the context of genomic selection (GS). Multi-trait models are more complex than univariate models and usually require more computational resources, but they are preferred because they can exploit the correlation between traits, which many times helps improve prediction accuracy. For this reason, in this paper we explore the power of multi-trait deep learning (MTDL) models in terms of prediction accuracy. The prediction performance of MTDL models was compared to the performance of the Bayesian multi-trait and multi-environment (BMTME) model proposed by Montesinos-López et al. (2016), which is a multi-trait version of the genomic best linear unbiased prediction (GBLUP) univariate model. Both models were evaluated with predictors with and without the genotype×environment interaction term. The prediction performance of both models was evaluated in terms of Pearson's correlation using cross-validation. We found that the best predictions in two of the three data sets were found under the BMTME model, but in general the predictions of both models, BTMTE and MTDL, were similar. Among models without the genotype×environment interaction, the MTDL model was the best, while among models with genotype×environment interaction, the BMTME model was superior. These results indicate that the MTDL model is very competitive for performing predictions in the context of GS, with the important practical advantage that it requires less computational resources than the BMTME model.


Assuntos
Genoma de Planta , Aprendizado de Máquina , Modelos Genéticos , Análise de Sequência de DNA/métodos , Triticum/genética , Zea mays/genética , Interação Gene-Ambiente
6.
PLoS One ; 11(7): e0158624, 2016.
Artigo em Inglês | MEDLINE | ID: mdl-27409084

RESUMO

The study of the heterogeneity of effect sizes is a key aspect of ecological meta-analyses. Here we propose a meta-analytic methodology to study the influence of moderators in effect sizes by splitting heterogeneity: meta-partition. To introduce this methodology, we performed a meta-partition of published data about the traits that influence species sensitivity to habitat loss, that have been previously analyzed through meta-regression. Thus, here we aim to introduce meta-partition and to make an initial comparison with meta-regression. Meta-partition algorithm consists of three steps. Step 1 is to study the heterogeneity of effect sizes under the assumption of fixed effect model. If heterogeneity is found, we perform step 2, that is, to partition the heterogeneity by the moderator that minimizes heterogeneity within a subset while maximizing heterogeneity between subsets. Then, if effect sizes of the subset are still heterogeneous, we repeat step 1 and 2 until we reach final subsets. Finally, step 3 is to integrate effect sizes of final subsets, with fixed effect model if there is homogeneity, and with random effects model if there is heterogeneity. Results show that meta-partition is valuable to assess the importance of moderators in explaining heterogeneity of effect sizes, as well as to assess the directions of these relations and to detect possible interactions between moderators. With meta-partition we have been able to evaluate the importance of moderators in a more objective way than with meta-regression, and to visualize the complex relations that may exist between them. As ecological issues are often influenced by several factors interacting in complex ways, ranking the importance of possible moderators and detecting possible interactions would make meta-partition a useful exploration tool for ecological meta-analyses.


Assuntos
Algoritmos , Ecologia , Modelos Teóricos , Áreas Alagadas , Anfíbios , Animais , Aves , Humanos , Mamíferos , Metanálise como Assunto , Répteis
7.
Implant Dent ; 25(2): 272-80, 2016 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-26836129

RESUMO

BACKGROUND: Clinicians should be able to weigh the role of the main risk factors associated with early implant failure. PURPOSE: The aim of this meta-analysis was to assess the influence of different patient-related and implant-related risk factors on the occurrence of early implant failure. MATERIALS AND METHODS: In July, 2014 the main electronic databases were searched for studies reporting on early failures. Relevant papers were selected by 2 independent authors using predefined selection criteria. Three authors independently scored the included studies for quality assessment. The estimated odds ratios of the main risk factors from the selected papers were subjected to meta-analysis. RESULTS: Nine studies were included. A total of 18,171 implants were meta-analyzed, of which 10,921 were analyzed for smoking, 15,260 for implant diameter, 16,075 for implant length, and 16,711 for implant location (maxilla vs mandible). The main significant risk factors for early implant failures were the smoking habit (odds ratio [OR], 1.7; 95% confidence interval [CI], 1.3, 2.3), implants shorter than 10 mm (OR, 1.6; 95% CI, 1.2, 2.2) and implants placed in the maxilla (OR, 1.3; 95% CI, 1.0, 1.6). CONCLUSIONS: Clinicians should be aware of the increased risk of early failure in the presence of smokers, implants with reduced length, and implant-supported maxillary rehabilitation.


Assuntos
Implantes Dentários/efeitos adversos , Falha de Restauração Dentária/estatística & dados numéricos , Implantação Dentária/efeitos adversos , Humanos , Fatores de Risco , Fatores de Tempo
8.
An. psicol ; 29(3): 762-771, sept.-dic. 2013. tab, graf
Artigo em Espanhol | IBECS | ID: ibc-116918

RESUMO

La calidad de vida (CV) se define como la percepción personal que un individuo tiene de su situación vital. Dentro de los factores que pueden influir en la CV, se encuentra la Reserva Cognitiva (RC), que podría entenderse como la capacidad del cerebro para hacer frente al daño cerebral generado por la patología, mediante procesos cognitivos preexistentes o compensatorios. El objetivo principal de este estudio, consiste en analizar, como influye la RC en la auto-percepción subjetiva de la CV en sujetos diagnosticados de Enfermedad de Alzheimer (EA) y comprobar si existen perfiles diferenciales en función de la sintomatología depresiva y el estado cognitivo de los mismos. La muestra utilizada estaba formada por 112 sujetos que se distribuyeron en dos grupos: uno de 74 sujetos diagnosticados de EA, y otro de 38 sujetos sanos. Se ha utilizado el cuestionario SF-36 para evaluar la CV. En relación a la variable RC, destacar que los sujetos con mayor RC, puntuaron más alto en cada una de las dimensiones del SF-36. La RC podría ser una fuente de influencia en la percepción de la CV de las personas con EA, en la medida en que sus diversos componentes conducirían a la consecución de una capacidad funcional más óptima y una aceptación del estado cognitivo (AU)


Quality of life (QL) is defined as the personal perception an individual has of his or her own life situation. Among the factors that can affect QL is Cognitive Reserve (CR) which can be understood as the brain’s capacity to resist the brain damage caused by pathology through preexisting or compensatory cognitive processes. The main objective of this study is to analyze how CR affects the subjective self-perception of QL in patients diagnosed with Alzheimer’s Disease (AD) and to determine the existence of different profiles in terms of depressive symptoms and cognitive state. The sample comprised 112 individuals divided into two groups: one group with 74 patients diagnosed with AD, and the other with 38 healthy participants. The SF-36 questionnaire was used to assess QL. As regards cognitive reserve, it was found that subjects with greater CR scored higher in each of the dimensions of the SF-36. CR could be a source of influence on perception of QL in persons with AD, to the extent that its different components would lead to a more optimal functional capability and a better acceptance of one's cognitive state (AU)


Assuntos
Humanos , Reserva Cognitiva , Doença de Alzheimer/psicologia , Demência/psicologia , Qualidade de Vida/psicologia , Fatores de Risco , Nível de Saúde , Autoavaliação
9.
BMC Clin Pathol ; 13(1): 23, 2013 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-24079673

RESUMO

BACKGROUND: Malignant astrocytomas are the most common primary brain tumors and one of the most lethal among human cancers despite optimal treatment. Therefore, the characterization of molecular alterations underlying the aggressive behavior of these tumors and the identification of new markers are thus an important step towards a better patient stratification and management. METHODS AND RESULTS: VRK1 and VRK2 (Vaccinia-related kinase-1, -2) expression, as well as proliferation markers, were determined in a tissue microarray containing 105 primary astrocytoma biopsies. Kaplan Meier and Cox models were used to find clinical and/or molecular parameters related to overall survival. The effects of VRK protein levels on proliferation were determined in astrocytoma cell lines. High levels of both protein kinases, VRK1 or VRK2, correlated with proliferation markers, p63 or ki67. There was no correlation with p53, reflecting the disruption of the VRK-p53-DRAM autoregulatory loop as a consequence of p53 mutations. High VRK2 protein levels identified a subgroup of astrocytomas that had a significant improvement in survival. The potential effect of VRK2 was studied by analyzing the growth characteristics of astrocytoma cell lines with different EGFR/VRK2 protein ratios. CONCLUSION: High levels of VRK2 resulted in a lower growth rate suggesting these cells are more indolent. In high-grade astrocytomas, VRK2 expression constitutes a good prognostic marker for patient survival.

10.
PLoS One ; 8(9): e76401, 2013.
Artigo em Inglês | MEDLINE | ID: mdl-24073290

RESUMO

Malignant astrocytomas are the most aggressive primary brain tumors with a poor prognosis despite optimal treatment. Dysfunction of mismatch repair (MMR) system accelerates the accumulation of mutations throughout the genome causing uncontrolled cell growth. The aim of this study was to characterize the MMR system defects that could be involved in malignant astrocytoma pathogenesis. We analyzed protein expression and promoter methylation of MLH1, MSH2 and MSH6 as well as microsatellite instability (MSI) and MMR gene mutations in a set of 96 low- and high-grade astrocytomas. Forty-one astrocytomas failed to express at least one MMR protein. Loss of MSH2 expression was more frequent in low-grade astrocytomas. Loss of MLH1 expression was associated with MLH1 promoter hypermethylation and MLH1-93G>A promoter polymorphism. However, MSI was not related with MMR protein expression and only 5% of tumors were MSI-High. Furthermore, the incidence of tumors carrying germline mutations in MMR genes was low and only one glioblastoma was associated with Lynch syndrome. Interestingly, survival analysis identified that tumors lacking MSH6 expression presented longer overall survival in high-grade astrocytoma patients treated only with radiotherapy while MSH6 expression did not modify the prognosis of those patients treated with both radiotherapy and chemotherapy. Our findings suggest that MMR system alterations are a frequent event in malignant astrocytomas and might help to define a subgroup of patients with different outcome.


Assuntos
Astrocitoma/genética , Biomarcadores Tumorais/análise , Metilação de DNA , Reparo de Erro de Pareamento de DNA/genética , Enzimas Reparadoras do DNA/genética , Enzimas Reparadoras do DNA/metabolismo , Mutação/genética , Adulto , Idoso , Astrocitoma/metabolismo , Astrocitoma/mortalidade , Astrocitoma/patologia , Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Estudos de Coortes , Feminino , Humanos , Técnicas Imunoenzimáticas , Masculino , Instabilidade de Microssatélites , Pessoa de Meia-Idade , Gradação de Tumores , Prognóstico , Regiões Promotoras Genéticas/genética , Taxa de Sobrevida , Análise Serial de Tecidos
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