52 research outputs found
Attitudes and burden in relatives of patients with schizophrenia in a middle income country
BACKGROUND: Most studies of family attitudes and burden have been conducted in developed countries. Thus it is important to test the generalizability of this research in other contexts where social conditions and extended family involvement may be different. The aim of this study was to assess the relationship between the attitudes of caregivers and the burden they experience in such a context, namely Arica, a town located in the northernmost region of Chile, close to the border with Peru and Bolivia. METHODS: We assessed attitudes towards schizophrenia (including affective, cognitive and behavioural components) and burden (including subjective distress, rejection and competence) in 41 main caregivers of patients with schizophrenia, all of whom were users of Public Mental Health Services in Arica. RESULTS: Attitude measures differed significantly according to socio-demographic variables, with parents (mainly mothers) exhibiting a more negative attitude towards the environment than the rest of the family (t = 4.04; p = 0.000).This was also the case for caregivers with a low educational level (t = 3.27; p < 0.003), for the oldest caregivers (r = 0.546; p = 0.000) and for those who had spent more time with the patient (r = 0.377; p = 0.015). Although attitudes had significant association with burden, their explanatory power was modest (R2 = .104, F = 4,55; p = .039). CONCLUSIONS: Similar to finding developed countries, the current study revealed a positive and significant relationship between the attitudes of caregivers and their burden. These findings emphasize the need to support the families of patients with schizophrenia in this social context
Genomic variation in tomato, from wild ancestors to contemporary breeding accessions
[EN] Background: Domestication modifies the genomic variation of species. Quantifying this variation provides insights
into the domestication process, facilitates the management of resources used by breeders and germplasm centers,
and enables the design of experiments to associate traits with genes. We described and analyzed the genetic
diversity of 1,008 tomato accessions including Solanum lycopersicum var. lycopersicum (SLL), S. lycopersicum var.
cerasiforme (SLC), and S. pimpinellifolium (SP) that were genotyped using 7,720 SNPs. Additionally, we explored the
allelic frequency of six loci affecting fruit weight and shape to infer patterns of selection.
Results: Our results revealed a pattern of variation that strongly supported a two-step domestication process, occasional
hybridization in the wild, and differentiation through human selection. These interpretations were consistent with the
observed allele frequencies for the six loci affecting fruit weight and shape. Fruit weight was strongly selected in SLC in
the Andean region of Ecuador and Northern Peru prior to the domestication of tomato in Mesoamerica. Alleles affecting
fruit shape were differentially selected among SLL genetic subgroups. Our results also clarified the biological status of SLC.
True SLC was phylogenetically positioned between SP and SLL and its fruit morphology was diverse. SLC and “cherry
tomato” are not synonymous terms. The morphologically-based term “cherry tomato” included some SLC, contemporary
varieties, as well as many admixtures between SP and SLL. Contemporary SLL showed a moderate increase in nucleotide
diversity, when compared with vintage groups.
Conclusions: This study presents a broad and detailed representation of the genomic variation in tomato. Tomato
domestication seems to have followed a two step-process; a first domestication in South America and a second step in
Mesoamerica. The distribution of fruit weight and shape alleles supports that domestication of SLC occurred in the
Andean region. Our results also clarify the biological status of SLC as true phylogenetic group within tomato. We detect
Ecuadorian and Peruvian accessions that may represent a pool of unexplored variation that could be of interest for crop
improvement.We are grateful to the gene banks for their collections that made this study possible. We thank Syngenta Seeds for providing genotyping data for 42 accessions. We would like to thank the Supercomputing and Bioinnovation Center (Universidad de Malaga, Spain) for providing computational resources to process the SNAPP phylogenetic tree. This research was supported in part by the USDA/NIFA funded SolCAP project under contract number to DF and USDA AFRI 2013-67013-21229 to EvdK and DF.Blanca Postigo, JM.; Montero Pau, J.; Sauvage, C.; Bauchet, G.; Illa, E.; Díez Niclós, MJTDJ.; Francis, D.... (2015). Genomic variation in tomato, from wild ancestors to contemporary breeding accessions. BMC Genomics. 16(257):1-19. https://doi.org/10.1186/s12864-015-1444-1S11916257Tanksley SD, McCouch SR. Seed banks and molecular maps: unlocking genetic potential from the wild. Science (80-). 1997;277:1063–6.Doebley JF, Gaut BS, Smith BD. The molecular genetics of crop domestication. Cell. 2006;127:1309–21.Gepts P. A comparison between crop domestication, classical plant breeding, and genetic engineering. Crop Sci. 2002;42:1780.Weigel D, Nordborg M. Natural variation in Arabidopsis. How do we find the causal genes? Plant Physiol. 2005;138:567–8.Peralta IE, Spooner DM, Knapp S, Anderson C. Taxonomy of wild tomatoes and their relatives (Solanum sect. Lycopersicoides, sect. Juglandifolia, sect. Lycopersicon; Solanaceae). Syst Bot Monogr. 2008;84:1–186.Rick CM, Fobes JF. Allozyme variation in the cultivated tomato and closely related species. Bull Torrey Bot Club. 1975;102:376–84.Zuriaga E, Blanca J, Nuez F. Classification and phylogenetic relationships in Solanum section Lycopersicon based on AFLP and two nuclear gene sequences. Genet Resour Crop Evol. 2008;56:663–78.Zuriaga E, Blanca J, Cordero L, Sifres A, Blas-Cerdán WG, Morales R, et al. Genetic and bioclimatic variation in Solanum pimpinellifolium. Genet Resour Crop Evol. 2008;56:39–51.Blanca J, Cañizares J, Cordero L, Pascual L, Diez MJ, Nuez F. Variation revealed by SNP genotyping and morphology provides insight into the origin of the tomato. PLoS One. 2012;7:e48198.Rick CM. Natural variability in wild species of Lycopersicon and its bearing on tomato breeding. Genet Agrar. 1976;30:249–59.Rick CM, Holle M. Andean Lycopersicon esculentum var. cerasiforme: genetic variation and its evolutionary significance. Econ Bot. 1990;44:69–78.Nakazato T, Franklin RA, Kirk BC, Housworth EA. Population structure, demographic history, and evolutionary patterns of a green-fruited tomato, Solanum peruvianum (Solanaceae), revealed by spatial genetics analyses. Am J Bot. 2012;99:1207–16.Rick CM, Butler L. Cytogenetics of the Tomato. Adv Genet. 1956;8:267–382. Advances in Genetics.Jenkins JA. The origin of the cultivated tomato. Econ Bot. 1948;2:379–92.Nesbitt TC, Tanksley SD. Comparative sequencing in the genus lycopersicon: implications for the evolution of fruit size in the domestication of cultivated tomatoes. Genetics. 2002;162:365–79.Ranc N, Muños S, Santoni S, Causse M. A clarified position for Solanum lycopersicum var cerasiforme in the evolutionary history of tomatoes (solanaceae). BMC Plant Biol. 2008;8:130.De Candolle A. Origin of cultivated plants. 2nd ed. London: Trench, Paul; 1886.Miller JC, Tanksley SD. RFLP analysis of phylogenetic relationships and genetic variation in the genus Lycopersicon. Theor Appl Genet. 1990;80:437–48.Williams CE, Clair DAS. Phenetic relationships and levels of variability detected by restriction fragment length polymorphism and random amplified polymorphic DNA analysis of cultivated and wild accessions of Lycopersicon esculentum. Genome. 1993;36:619–30.Park YH, West MAL, St Clair DA. Evaluation of AFLPs for germplasm fingerprinting and assessment of genetic diversity in cultivars of tomato (Lycopersicon esculentum L). Genome. 2004;47:510–8.Sim S-C, Robbins MD, Van Deynze A, Michel AP, Francis DM. Population structure and genetic differentiation associated with breeding history and selection in tomato (Solanum lycopersicum L.). Heredity (Edinb). 2011;106:927–35.Sim S-C, Robbins MD, Chilcott C, Zhu T, Francis DM. Oligonucleotide array discovery of polymorphisms in cultivated tomato (Solanum lycopersicum L) reveals patterns of SNP variation associated with breeding. BMC Genomics. 2009;10:466.Sim S-C, Durstewitz G, Plieske J, Wieseke R, Ganal MW, Van Deynze A, et al. Development of a large SNP genotyping array and generation of high-density genetic maps in tomato. PLoS One. 2012;7:e40563.Frary A, Nesbitt TC, Grandillo S, Knaap E, Cong B, Liu J, et al. fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. Science. 2000;289:85–8.Liu J, Van Eck J, Cong B, Tanksley SD. A new class of regulatory genes underlying the cause of pear-shaped tomato fruit. Proc Natl Acad Sci U S A. 2002;99:13302–6.Xiao H, Jiang N, Schaffner E, Stockinger EJ, van der Knaap E. A retrotransposon-mediated gene duplication underlies morphological variation of tomato fruit. Science. 2008;319:1527–30.Cong B, Barrero LS, Tanksley SD. Regulatory change in YABBY-like transcription factor led to evolution of extreme fruit size during tomato domestication. Nat Genet. 2008;40:800–4.Muños S, Ranc N, Botton E, Bérard A, Rolland S, Duffé P, et al. Increase in tomato locule number is controlled by two single-nucleotide polymorphisms located near WUSCHEL. Plant Physiol. 2011;156:2244–54.Chakrabarti M, Zhang N, Sauvage C, Muños S, Blanca J, Cañizares J, et al. A cytochrome P450 regulates a domestication trait in cultivated tomato. Proc Natl Acad Sci U S A. 2013;110:17125–30.Rodríguez GR, Muños S, Anderson C, Sim S-C, Michel A, Causse M, et al. Distribution of SUN, OVATE, LC, and FAS in the tomato germplasm and the relationship to fruit shape diversity. Plant Physiol. 2011;156:275–85.Sim S-C, Van Deynze A, Stoffel K, Douches DS, Zarka D, Ganal MW, et al. High-density SNP genotyping of tomato (Solanum lycopersicum L) reveals patterns of genetic variation due to breeding. PLoS One. 2012;7:e45520.Sauvage C, Segura V, Bauchet G, Stevens R, Thi Do P, Nikoloski Z, et al. Genome Wide Association in tomato reveals 44 candidate loci for fruit metabolic traits. Plant Physiol. 2014;165:1120–32.Hamilton JP, Sim S-C, Stoffel K, Van Deynze A, Buell CR, Francis DM. Single nucleotide polymorphism discovery in cultivated tomato via sequencing by synthesis. Plant Genome J. 2012;5:17.Patterson NJ, Price AL, Reich D. Population structure and eigenanalysis. PLoS Genet. 2006;2:e190.Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904–9.Kosman E, Leonard KJ. Similarity coefficients for molecular markers in studies of genetic relationships between individuals for haploid, diploid, and polyploid species. Mol Ecol. 2005;14:415–24.Adler D. vioplot: Violin plot. 2005.Jost L. Gst and its relatives do not measure differentiation. Mol Ecol. 2008;17:4015–26.Excoffier L, Lischer H. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol Ecol Resour. 2010;10:564–7.Huson DH, Bryant D. Application of phylogenetic networks in evolutionary studies. Mol Ecol Evol. 2006;23:254–67.Knight R, Maxwell P, Birmingham A, Carnes J, Caporaso JG, Easton BC, et al. PyCogent: a toolkit for making sense from sequence. Genome Biol. 2007;8:R171.Szpiech ZA, Jakobsson M, Rosenberg NA. ADZE: a rarefaction approach for counting alleles private to combinations of populations. Bioinformatics. 2008;24:2498–504.Bradbury PJ, Zhang Z, Kroon DE, Casstevens TM, Ramdoss Y, Buckler ES. TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics. 2007;23:2633–5.Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc. 1979;74:829.R Core Team. R: A Language and Environment for Statistical Computing. 2013.Sinnot RS. Virtues of the haversine. Sky Telesc. 1984;68:159.Hijmans RJ, Etten JV. raster: Geographic data analysis and Modeling. 2013.Bryant D, Bouckaert R, Felsenstein J, Rosenberg NA, RoyChoudhury A. Inferring species trees directly from biallelic genetic markers: bypassing gene trees in a full coalescent analysis. Mol Biol Evol. 2012;29:1917–32.Drummond AJ, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007;7:214.Rambaut A. Tracer v.1.5. 2009.Huang Z, van der Knaap E. Tomato fruit weight 11.3 maps close to fasciated on the bottom of chromosome 11. Theor Appl Genet. 2011;123:465–74.Guo M, Rupe MA, Dieter JA, Zou J, Spielbauer D, Duncan KE, et al. Cell Number Regulator1 affects plant and organ size in maize: implications for crop yield enhancement and heterosis. Plant Cell. 2010;22:1057–73.Sambrook J, Fritsch EF, Maniatis T. Molecular cloning. New York: Cold Spring Harbor Laboratory Press; 1989.Lin T, Zhu G, Zhang J, Xu X, Yu Q, Zheng Z, et al. Genomic analyses provide insights into the history of tomato breeding. Nat Genet. 2014;46:1220–6.Platt A, Horton M, Huang YS, Li Y, Anastasio AE, Mulyati NW, et al. The scale of population structure in Arabidopsis thaliana. PLoS Genet. 2010;6:e1000843.Pressoir G, Berthaud J. Patterns of population structure in maize landraces from the Central Valleys of Oaxaca in Mexico. Heredity (Edinb). 2004;92:88–94.Koenig D, Jiménez-Gómez JM, Kimura S, Fulop D, Chitwood DH, Headland LR, et al. Comparative transcriptomics reveals patterns of selection in domesticated and wild tomato. Proc Natl Acad Sci U S A. 2013;110:e2655–62.Nakazato T, Housworth EA. Spatial genetics of wild tomato species reveals roles of the Andean geography on demographic history. Am J Bot. 2011;98:88–98.United States. Office of Experimental Stations. Experimental Station Recod, Volumen 39. Volume 39. Washington, DC, USA: United States. Office of Experimental Stations; 1918.Merk HL, Yames SC, Van Deynze A, Tong N, Menda N, Mueller LA, et al. Trait diversity and potential for selection indeces based on variation among regionally adapted processing tomato germplasm. J Am Soc Hortic Sci. 2012;137:427–37
A Preliminary Study of Left Ventricular Rotational Mechanics in Children with Noncompaction Cardiomyopathy: Do They Influence Ventricular Function?
BACKGROUND:Current diagnostic criteria for noncompaction cardiomyopathy (NCC) lack specificity, and the disease lacks prognostic indicators. Reverse apical rotation (RAR) with abnormal rotation of the cardiac apex in the same clockwise direction as the base has been described in adults with NCC. The aim of this study was to test the hypothesis that RAR might differentiate between symptomatic NCC and benign hypertrabeculations and might be associated with ventricular dysfunction. METHODS:Echocardiograms from 28 children with NCC without cardiac malformations were prospectively compared with those from 29 age-matched normal control subjects. A chart review was performed to identify the patients' histories and clinical characteristics. Speckle-tracking was used to measure longitudinal strain, circumferential strain, and rotation. RESULTS:RAR occurred in 39% of patients with NCC. History of left ventricular (LV) dysfunction or arrhythmia was universal in, but not exclusive to, patients with RAR. Patients with RAR had lower LV longitudinal strain but similar ejection fractions compared with patients without RAR (median, -15.6% [interquartile range, -12.9% to -19.3%] vs -19% [interquartile range, -14.5% to -21.9%], P < .01; 53% [interquartile range, 43% to 68%] vs 61% [interquartile range, 58% to 67%], P = .08). Only a pattern of contraction with RAR, early arrest of twisting by mid-systole, and premature untwisting was associated with lower ejection fraction (46%; interquartile range, 43% to 52%; P = .006). CONCLUSIONS:RAR is not a sensitive but is a specific indicator of complications in children with NCC. Therefore, RAR may have prognostic rather than diagnostic value. Premature untwisting of the left ventricle during ejection may be an even more worrisome indicator of LV dysfunction
Classical determinants of coronary artery disease as predictors of complexity of coronary lesions, assessed with the SYNTAX score
\u3cp\u3eBackground We need new biomarkers that can predict cardiovascular disease to improve both diagnosis and therapeutic strategies. The CIRCULATING CELLS study was designed to study the role of several cellular mediators of atherosclerosis as biomarkers of coronary artery disease (CAD). An objective and reproducible method for the quantification of CAD extension is required to establish relationships with these potential biomarkers. We sought to analyse the correlation of the SYNTAX score with known CAD risk factors to test it as a valid marker of CAD extension. Methods and results A subgroup of 279 patients (67.4% males) were included in our analysis. Main exclusion criteria were a history of previous percutaneous coronary intervention or surgical revascularisation that prevent an accurate assessment of the SS. Diabetes mellitus, smoking, renal insufficiency, body mass index and a history of CAD and myocardial infarction were all positively and strongly associated with a higher SYNTAX score after adjustment for the non-modifiable biological factors (age and sex). In the multivariate model, age and male sex, along with smok- ing and renal insufficiency, remain statistical significantly associated with the SYNTAX score. Conclusion In a selected cohort of revascularisation-naive patients with CAD undergoing coronary angiography, non-modifiable cardiovascular risk factors such as advanced age, male sex, as well as smoking and renal failure were independently associated with CAD complexity assessed by the SYNTAX score. The SYNTAX score may be a valid marker of CAD extension to establish relationships with potential novel biomarkers of coronary atherosclerosis.\u3c/p\u3
Bevacizumab in the Treatment of Metastatic Breast Cancer: Friend or Foe?
Metastatic breast cancer (MBC) is a major cause of death among women worldwide. Progress has been made in treating MBC with the advent of anti-estrogen therapies, potent cytotoxic agents, and monoclonal antibodies. Bevacizumab is a monoclonal antibody against circulating vascular endothelial growth factor (VEGF), which was approved in 2008 by the US Food and Drug Administration (FDA), for first-line treatment of HER-2 negative MBC in combination with paclitaxel. The FDA then reversed this decision in December 2010 by recommending removal of the MBC indication from bevacizumab, citing primarily safety concerns, and that these risks did not outweigh the ability of bevacizumab to significantly prolong progression-free survival. This decision was unexpected in the oncology community and remains controversial. This review looks at all available phase 3 data with bevacizumab in the MBC setting to determine whether the data support this decision by the FDA, and discusses the future of bevacizumab in breast cancer
- …