Publications by authors named "Tony Kam-Thong"

15 Publications

  • Page 1 of 1

Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research.

NAR Genom Bioinform 2021 Dec 8;3(4):lqab102. Epub 2021 Nov 8.

Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, Basel, Switzerland.

Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit , which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.
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http://dx.doi.org/10.1093/nargab/lqab102DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8573822PMC
December 2021

Deep Phenotyping of CD11c B Cells in Systemic Autoimmunity and Controls.

Front Immunol 2021 12;12:635615. Epub 2021 Mar 12.

Department of Rheumatology and Clinical Immunology, Charité- Universitätsmedizin Berlin, Berlin, Germany.

Circulating CD11c B cells are a key phenomenon in certain types of autoimmunity but have also been described in the context of regular immune responses (i.e., infections, vaccination). Using mass cytometry to profile 46 different markers on individual immune cells, we systematically initially confirmed the presence of increased CD11c B cells in the blood of systemic lupus erythematosus (SLE) patients. Notably, significant differences in the expression of CD21, CD27, and CD38 became apparent between CD11c and CD11c B cells. We observed direct correlation of the frequency of CD21CD27 B cells and CD21CD38 B cells with CD11c B cells, which were most pronounced in SLE compared to primary Sjögren's syndrome patients (pSS) and healthy donors (HD). Thus, CD11c B cells resided mainly within memory subsets and were enriched in CD27IgD, CD21CD27, and CD21CD38 B cell phenotypes. CD11c B cells from all donor groups (SLE, pSS, and HD) showed enhanced CD69, Ki-67, CD45RO, CD45RA, and CD19 expression, whereas the membrane expression of CXCR5 and CD21 were diminished. Notably, SLE CD11c B cells showed enhanced expression of the checkpoint molecules CD86, PD1, PDL1, CD137, VISTA, and CTLA-4 compared to HD. The substantial increase of CD11c B cells with a CD21 phenotype co-expressing distinct activation and checkpoint markers, points to a quantitative increased alternate (extrafollicular) B cell activation route possibly related to abnormal immune regulation as seen under the striking inflammatory conditions of SLE which shows a characteristic PD-1/PD-L1 upregulation.
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http://dx.doi.org/10.3389/fimmu.2021.635615DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7994903PMC
September 2021

Transcriptome signatures from discordant sibling pairs reveal changes in peripheral blood immune cell composition in Autism Spectrum Disorder.

Transl Psychiatry 2020 04 14;10(1):106. Epub 2020 Apr 14.

Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento (TN), Italy.

Notwithstanding several research efforts in the past years, robust and replicable molecular signatures for autism spectrum disorders from peripheral blood remain elusive. The available literature on blood transcriptome in ASD suggests that through accurate experimental design it is possible to extract important information on the disease pathophysiology at the peripheral level. Here we exploit the availability of a resource for molecular biomarkers in ASD, the Italian Autism Network (ITAN) collection, for the investigation of transcriptomic signatures in ASD based on a discordant sibling pair design. Whole blood samples from 75 discordant sibling pairs selected from the ITAN network where submitted to RNASeq analysis and data analyzed by complementary approaches. Overall, differences in gene expression between affected and unaffected siblings were small. In order to assess the contribution of differences in the relative proportion of blood cells between discordant siblings, we have applied two different cell deconvolution algorithms, showing that the observed molecular signatures mainly reflect changes in peripheral blood immune cell composition, in particular NK cells. The results obtained by the cell deconvolution approach are supported by the analysis performed by WGCNA. Our report describes the largest differential gene expression profiling in peripheral blood of ASD subjects and controls conducted by RNASeq. The observed signatures are consistent with the hypothesis of immune alterations in autism and an increased risk of developing autism in subjects exposed to prenatal infections or stress. Our study also points to a potential role of NMUR1, HMGB3, and PTPRN2 in ASD.
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http://dx.doi.org/10.1038/s41398-020-0778-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7156413PMC
April 2020

An Introduction to Machine Learning.

Clin Pharmacol Ther 2020 04 3;107(4):871-885. Epub 2020 Mar 3.

Pharmaceutical Sciences, Roche Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland.

In the last few years, machine learning (ML) and artificial intelligence have seen a new wave of publicity fueled by the huge and ever-increasing amount of data and computational power as well as the discovery of improved learning algorithms. However, the idea of a computer learning some abstract concept from data and applying them to yet unseen situations is not new and has been around at least since the 1950s. Many of these basic principles are very familiar to the pharmacometrics and clinical pharmacology community. In this paper, we want to introduce the foundational ideas of ML to this community such that readers obtain the essential tools they need to understand publications on the topic. Although we will not go into the very details and theoretical background, we aim to point readers to relevant literature and put applications of ML in molecular biology as well as the fields of pharmacometrics and clinical pharmacology into perspective.
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http://dx.doi.org/10.1002/cpt.1796DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7189875PMC
April 2020

Cellular Resistance Mechanisms to Targeted Protein Degradation Converge Toward Impairment of the Engaged Ubiquitin Transfer Pathway.

ACS Chem Biol 2019 10 8;14(10):2215-2223. Epub 2019 Oct 8.

Proteolysis targeting chimeras are bifunctional small molecules capable of recruiting a target protein of interest to an E3 ubiquitin ligase that facilitates target ubiquitination followed by proteasome-mediated degradation. The first molecules acting on this novel therapeutic paradigm have just entered clinical testing. Here, by using Bromodomain Containing 4 (BRD4) degraders engaging cereblon and Von Hippel-Lindau E3 ligases, we investigated key determinants of resistance to this new mode of action. A loss-of-function screen for genes required for BRD4 degradation revealed strong dependence on the E2 and E3 ubiquitin ligases as well as for members of the COP9 signalosome complex for both cereblon- and Von Hippel-Lindau-engaging BRD4 degraders. Cancer cell lines raised to resist BRD4 degraders manifested a degrader-specific mechanism of resistance, resulting from the loss of components of the ubiquitin proteasome system. In addition, degrader profiling in a cancer cell line panel revealed a differential pattern of activity of Von Hippel-Lindau- and cereblon-based degraders, highlighting the need for the identification of degradation-predictive biomarkers enabling effective patient stratification.
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http://dx.doi.org/10.1021/acschembio.9b00525DOI Listing
October 2019

The Italian autism network (ITAN): a resource for molecular genetics and biomarker investigations.

BMC Psychiatry 2018 11 21;18(1):369. Epub 2018 Nov 21.

Centre for Integrative Biology, University of Trento, Trento, Italy.

Background: A substantial genetic component accounts for Autism Spectrum Disorders (ASD) aetiology, with some rare and common genetic risk factors recently identified. Large collections of DNAs from thoroughly characterized ASD families are an essential step to confirm genetic risk factors, identify new variants and investigate genotype-phenotype correlations. The Italian Autism Network aimed at constituting a clinical database and a biorepository of samples derived from ASD subjects and first-degree relatives extensively and consistently characterized by child psychiatry centers in Italy.

Methods: The study was approved by the ethical committee of the University of Verona, the coordinating site, and by the local ethical committees of each recruiting site. Certified staff was specifically trained at each site for the overall study conduct, for clinical protocol administration and handling of biological material. A centralized database was developed to collect clinical assessment and medical records from each recruiting site. Children were eligible for recruitment based on the following inclusion criteria: age 4-18 years, at least one parent or legal guardian giving voluntary written consent, meeting DSM-IV criteria for Autistic Disorder or Asperger's Disorder or Pervasive Developmental Disorder NOS. Affected individuals were assessed by full psychiatric, neurological and physical examination, evaluation with ADI-R and ADOS scales, cognitive assessment with Wechsler Intelligence Scale for Children or Preschool and Primary, Leiter International Performance Scale or Griffiths Mental Developmental Scale. Additional evaluations included language assessment, the Krug Asperger's Disorder Index, and instrumental examination such as EEG and structural MRI. DNA, RNA and plasma were collected from eligible individuals and relatives. A central laboratory was established to host the biorepository, perform DNA and RNA extraction and lymphocytes immortalisation.

Discussion: The study has led to an extensive collection of biological samples associated with standardised clinical assessments from a network of expert clinicians and psychologists. Eighteen sites have received ADI/ADOS training, thirteen of which have been actively recruiting. The clinical database currently includes information on 812 individuals from 249 families, and the biorepository has samples for 98% of the subjects. This effort has generated a highly valuable resource for conducting clinical and genetic research of ASD, amenable to further expansion.
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http://dx.doi.org/10.1186/s12888-018-1937-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6247619PMC
November 2018

Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis.

Nat Neurosci 2018 08 26;21(8):1117-1125. Epub 2018 Jul 26.

Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.

Genome-wide association studies have identified 108 schizophrenia risk loci, but biological mechanisms for individual loci are largely unknown. Using developmental, genetic and illness-based RNA sequencing expression analysis in human brain, we characterized the human brain transcriptome around these loci and found enrichment for developmentally regulated genes with novel examples of shifting isoform usage across pre- and postnatal life. We found widespread expression quantitative trait loci (eQTLs), including many with transcript specificity and previously unannotated sequence that were independently replicated. We leveraged this general eQTL database to show that 48.1% of risk variants for schizophrenia associate with nearby expression. We lastly found 237 genes significantly differentially expressed between patients and controls, which replicated in an independent dataset, implicated synaptic processes, and were strongly regulated in early development. These findings together offer genetics- and diagnosis-related targets for better modeling of schizophrenia risk. This resource is publicly available at http://eqtl.brainseq.org/phase1 .
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http://dx.doi.org/10.1038/s41593-018-0197-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6438700PMC
August 2018

HtrA1 Mediated Intracellular Effects on Tubulin Using a Polarized RPE Disease Model.

EBioMedicine 2018 Jan 13;27:258-274. Epub 2017 Dec 13.

Biozentrum and the Swiss Nanoscience Institute, University of Basel, Basel 4056, Switzerland. Electronic address:

Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss. The protein HtrA1 is enriched in retinal pigment epithelial (RPE) cells isolated from AMD patients and in drusen deposits. However, it is poorly understood how increased levels of HtrA1 affect the physiological function of the RPE at the intracellular level. Here, we developed hfRPE (human fetal retinal pigment epithelial) cell culture model where cells fully differentiated into a polarized functional monolayer. In this model, we fine-tuned the cellular levels of HtrA1 by targeted overexpression. Our data show that HtrA1 enzymatic activity leads to intracellular degradation of tubulin with a corresponding reduction in the number of microtubules, and consequently to an altered mechanical cell phenotype. HtrA1 overexpression further leads to impaired apical processes and decreased phagocytosis, an essential function for photoreceptor survival. These cellular alterations correlate with the AMD phenotype and thus highlight HtrA1 as an intracellular target for therapeutic interventions towards AMD treatment.
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http://dx.doi.org/10.1016/j.ebiom.2017.12.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5828370PMC
January 2018

Connecting Anxiety and Genomic Copy Number Variation: A Genome-Wide Analysis in CD-1 Mice.

PLoS One 2015 26;10(5):e0128465. Epub 2015 May 26.

Department of Behavioral Neuroendocrinology, Max Planck Institute of Psychiatry, Munich, Germany.

Genomic copy number variants (CNVs) have been implicated in multiple psychiatric disorders, but not much is known about their influence on anxiety disorders specifically. Using next-generation sequencing (NGS) and two additional array-based genotyping approaches, we detected CNVs in a mouse model consisting of two inbred mouse lines showing high (HAB) and low (LAB) anxiety-related behavior, respectively. An influence of CNVs on gene expression in the central (CeA) and basolateral (BLA) amygdala, paraventricular nucleus (PVN), and cingulate cortex (Cg) was shown by a two-proportion Z-test (p = 1.6 x 10-31), with a positive correlation in the CeA (p = 0.0062), PVN (p = 0.0046) and Cg (p = 0.0114), indicating a contribution of CNVs to the genetic predisposition to trait anxiety in the specific context of HAB/LAB mice. In order to confirm anxiety-relevant CNVs and corresponding genes in a second mouse model, we further examined CD-1 outbred mice. We revealed the distribution of CNVs by genotyping 64 CD 1 individuals using a high-density genotyping array (Jackson Laboratory). 78 genes within those CNVs were identified to show nominally significant association (48 genes), or a statistical trend in their association (30 genes) with the time animals spent on the open arms of the elevated plus-maze (EPM). Fifteen of them were considered promising candidate genes of anxiety-related behavior as we could show a significant overlap (permutation test, p = 0.0051) with genes within HAB/LAB CNVs. Thus, here we provide what is to our knowledge the first extensive catalogue of CNVs in CD-1 mice and potential corresponding candidate genes linked to anxiety-related behavior in mice.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0128465PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4444327PMC
April 2016

Disease modeling and phenotypic drug screening for diabetic cardiomyopathy using human induced pluripotent stem cells.

Cell Rep 2014 Nov 30;9(3):810-21. Epub 2014 Oct 30.

Roche Pharma Research & Early Development, Roche Innovation Center Basel, 4070 Basel, Switzerland. Electronic address:

Diabetic cardiomyopathy is a complication of type 2 diabetes, with known contributions of lifestyle and genetics. We develop environmentally and genetically driven in vitro models of the condition using human-induced-pluripotent-stem-cell-derived cardiomyocytes. First, we mimic diabetic clinical chemistry to induce a phenotypic surrogate of diabetic cardiomyopathy, observing structural and functional disarray. Next, we consider genetic effects by deriving cardiomyocytes from two diabetic patients with variable disease progression. The cardiomyopathic phenotype is recapitulated in the patient-specific cells basally, with a severity dependent on their original clinical status. These models are incorporated into successive levels of a screening platform, identifying drugs that preserve cardiomyocyte phenotype in vitro during diabetic stress. In this work, we present a patient-specific induced pluripotent stem cell (iPSC) model of a complex metabolic condition, showing the power of this technique for discovery and testing of therapeutic strategies for a disease with ever-increasing clinical significance.
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http://dx.doi.org/10.1016/j.celrep.2014.09.055DOI Listing
November 2014

The challenges of genome-wide interaction studies: lessons to learn from the analysis of HDL blood levels.

PLoS One 2014 20;9(10):e109290. Epub 2014 Oct 20.

Genetic Epidemiology Unit, Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.

Genome-wide association studies (GWAS) have revealed 74 single nucleotide polymorphisms (SNPs) associated with high-density lipoprotein cholesterol (HDL) blood levels. This study is, to our knowledge, the first genome-wide interaction study (GWIS) to identify SNP×SNP interactions associated with HDL levels. We performed a GWIS in the Rotterdam Study (RS) cohort I (RS-I) using the GLIDE tool which leverages the massively parallel computing power of Graphics Processing Units (GPUs) to perform linear regression on all genome-wide pairs of SNPs. By performing a meta-analysis together with Rotterdam Study cohorts II and III (RS-II and RS-III), we were able to filter 181 interaction terms with a p-value<1 · 10-8 that replicated in the two independent cohorts. We were not able to replicate any of these interaction term in the AGES, ARIC, CHS, ERF, FHS and NFBC-66 cohorts (Ntotal = 30,011) when adjusting for multiple testing. Our GWIS resulted in the consistent finding of a possible interaction between rs774801 in ARMC8 (ENSG00000114098) and rs12442098 in SPATA8 (ENSG00000185594) being associated with HDL levels. However, p-values do not reach the preset Bonferroni correction of the p-values. Our study suggest that even for highly genetically determined traits such as HDL the sample sizes needed to detect SNP×SNP interactions are large and the 2-step filtering approaches do not yield a solution. Here we present our analysis plan and our reservations concerning GWIS.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0109290PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4203717PMC
July 2015

GLIDE: GPU-based linear regression for detection of epistasis.

Hum Hered 2012 4;73(4):220-36. Epub 2012 Sep 4.

Machine Learning and Computational Biology Research Group, Max Planck Institutes Tübingen, Tübingen, Germany.

Due to recent advances in genotyping technologies, mapping phenotypes to single loci in the genome has become a standard technique in statistical genetics. However, one-locus mapping fails to explain much of the phenotypic variance in complex traits. Here, we present GLIDE, which maps phenotypes to pairs of genetic loci and systematically searches for the epistatic interactions expected to reveal part of this missing heritability. GLIDE makes use of the computational power of consumer-grade graphics cards to detect such interactions via linear regression. This enabled us to conduct a systematic two-locus mapping study on seven disease data sets from the Wellcome Trust Case Control Consortium and on in-house hippocampal volume data in 6 h per data set, while current single CPU-based approaches require more than a year's time to complete the same task.
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http://dx.doi.org/10.1159/000341885DOI Listing
January 2013

Epistasis detection on quantitative phenotypes by exhaustive enumeration using GPUs.

Bioinformatics 2011 Jul;27(13):i214-21

Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany.

Motivation: In recent years, numerous genome-wide association studies have been conducted to identify genetic makeup that explains phenotypic differences observed in human population. Analytical tests on single loci are readily available and embedded in common genome analysis software toolset. The search for significant epistasis (gene-gene interactions) still poses as a computational challenge for modern day computing systems, due to the large number of hypotheses that have to be tested.

Results: In this article, we present an approach to epistasis detection by exhaustive testing of all possible SNP pairs. The search strategy based on the Hilbert-Schmidt Independence Criterion can help delineate various forms of statistical dependence between the genetic markers and the phenotype. The actual implementation of this search is done on the highly parallelized architecture available on graphics processing units rendering the completion of the full search feasible within a day.

Availability: The program is available at http://www.mpipsykl.mpg.de/epigpuhsic/.

Contact: [email protected]
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http://dx.doi.org/10.1093/bioinformatics/btr218DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3117340PMC
July 2011

An examination of single nucleotide polymorphism selection prioritization strategies for tests of gene-gene interaction.

Biol Psychiatry 2011 Jul 9;70(2):198-203. Epub 2011 Apr 9.

MRC Centre for Neuropsychiatric Genetics and Genomics, Department of Psychological Medicine and Neurology, School of Medicine, Cardiff University, Cardiff, UK.

Background: Given that genome-wide association studies (GWAS) of psychiatric disorders have identified only a small number of convincingly associated variants (single nucleotide polymorphism [SNP]), there is interest in seeking additional evidence for associated variants with tests of gene-gene interaction. Comprehensive pair-wise single SNP-SNP interaction analysis is computationally intensive, and the penalty for multiple testing is severe, given the number of interactions possible. Aiming to minimize these statistical and computational burdens, we have explored approaches to prioritize SNPs for interaction analyses.

Methods: Primary interaction analyses were performed with the Wellcome Trust Case-Control Consortium bipolar disorder GWAS (1868 cases, 2938 control subjects). Replication analyses were performed with the Genetic Association Information Network bipolar disorder dataset (1001 cases, 1033 control subjects). The SNPs were prioritized for interaction analysis that showed evidence for association that surpassed a number of nominally significant thresholds, are within genome-wide significant genes, or are within genes that are functionally related.

Results: For no set of prioritized SNPs did we obtain evidence to support the hypothesis that the selection strategy identified pairs of variants that were enriched for true (statistical) interactions.

Conclusions: The SNPs prioritized according to a number of criteria do not have a raised prior probability for significant interaction that is detectable in samples of this size. We argue that the use of significance levels reflecting only the number of tests performed, as is now widely accepted for single SNP analysis, does not offer an appropriate degree of protection against the potential for GWAS studies to generate an enormous number of false positive interactions.
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http://dx.doi.org/10.1016/j.biopsych.2011.01.034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3125485PMC
July 2011

EPIBLASTER-fast exhaustive two-locus epistasis detection strategy using graphical processing units.

Eur J Hum Genet 2011 Apr 8;19(4):465-71. Epub 2010 Dec 8.

Max-Planck-Institute of Psychiatry, Munich, Germany.

Detection of epistatic interaction between loci has been postulated to provide a more in-depth understanding of the complex biological and biochemical pathways underlying human diseases. Studying the interaction between two loci is the natural progression following traditional and well-established single locus analysis. However, the added costs and time duration required for the computation involved have thus far deterred researchers from pursuing a genome-wide analysis of epistasis. In this paper, we propose a method allowing such analysis to be conducted very rapidly. The method, dubbed EPIBLASTER, is applicable to case-control studies and consists of a two-step process in which the difference in Pearson's correlation coefficients is computed between controls and cases across all possible SNP pairs as an indication of significant interaction warranting further analysis. For the subset of interactions deemed potentially significant, a second-stage analysis is performed using the likelihood ratio test from the logistic regression to obtain the P-value for the estimated coefficients of the individual effects and the interaction term. The algorithm is implemented using the parallel computational capability of commercially available graphical processing units to greatly reduce the computation time involved. In the current setup and example data sets (211 cases, 222 controls, 299468 SNPs; and 601 cases, 825 controls, 291095 SNPs), this coefficient evaluation stage can be completed in roughly 1 day. Our method allows for exhaustive and rapid detection of significant SNP pair interactions without imposing significant marginal effects of the single loci involved in the pair.
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http://dx.doi.org/10.1038/ejhg.2010.196DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3060319PMC
April 2011
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