Adiponectin Deficiency Promotes Tumor Growth in Mice by Reducing Macrophage Infiltration

Adiponectin is an adipocyte-derived plasma protein that has been implicated in regulating angiogenesis, but the role of adiponectin in regulating this process is still controversial. In this study, in order to determine whether adiponectin affects tumor growth and tumor induced vascularization, we implanted B16F10 melanoma and Lewis Lung Carcinoma cells subcutaneously into adiponectin knockout and wild-type control mice, and found that adiponectin deficiency markedly promoted the growth of both tumors. Immunohistochemical analyses indicated that adiponectin deficiency reduced macrophage recruitment to the tumor, but did not affect cancer cell mitosis, apoptosis, or tumor-associated angiogenesis. In addition, treatment with recombinant adiponectin did not affect the proliferation of cultured B16F10 tumor cells. Importantly, the restoration of microphage infiltration at an early stage of tumorigenesis by means of co-injection of B16F10 cells and macrophages reversed the increased tumor growth in adiponectin knockout mice. Thus, we conclude that the enhanced tumor growth observed in adiponectin deficient mice is likely due to the reduction of macrophage infiltration rather than enhanced angiogenesis

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Modelling human choices: MADeM and decision‑making

Research supported by FAPESP 2015/50122-0 and DFG-GRTK 1740/2. RP and AR are also part of the Research, Innovation and Dissemination Center for Neuromathematics FAPESP grant (2013/07699-0). RP is supported by a FAPESP scholarship (2013/25667-8). ACR is partially supported by a CNPq fellowship (grant 306251/2014-0)

Efficient algorithms for training the parameters of hidden Markov models using stochastic expectation maximization (EM) training and Viterbi training

Background: Hidden Markov models are widely employed by numerous bioinformatics programs used today. Applications range widely from comparative gene prediction to time-series analyses of micro-array data. The parameters of the underlying models need to be adjusted for specific data sets, for example the genome of a particular species, in order to maximize the prediction accuracy. Computationally efficient algorithms for parameter training are thus key to maximizing the usability of a wide range of bioinformatics applications. Results: We introduce two computationally efficient training algorithms, one for Viterbi training and one for stochastic expectation maximization (EM) training, which render the memory requirements independent of the sequence length. Unlike the existing algorithms for Viterbi and stochastic EM training which require a two-step procedure, our two new algorithms require only one step and scan the input sequence in only one direction. We also implement these two new algorithms and the already published linear-memory algorithm for EM training into the hidden Markov model compiler HMM- CONVERTER and examine their respective practical merits for three small example models. Conclusions: Bioinformatics applications employing hidden Markov models can use the two algorithms in order to make Viterbi training and stochastic EM training more computationally efficient. Using these algorithms, parameter training can thus be attempted for more complex models and longer training sequences. The two new algorithms have the added advantage of being easier to implement than the corresponding default algorithms for Viterbi training and stochastic EM training.Computer Science, Department ofMedical Genetics, Department ofMedicine, Faculty ofScience, Faculty ofOther UBCReviewedFacult

Randomised controlled trial of clinical decision support tools to improve learning of evidence based medicine in medical students

Objective To assess the educational effectiveness on learning evidence based medicine of a handheld computer clinical decision support tool compared with a pocket card containing guidelines and a control. Design Randomised controlled trial. Setting University of Hong Kong, 2001. Participants 169 fourth year medical students. Main outcome measures Factor and individual item scores from a validated questionnaire on five key self reported measures: personal application and current use of evidence based medicine; future use of evidence based medicine; use of evidence during and after clerking patients; frequency of discussing the role of evidence during teaching rounds; and self perceived confidence in clinical decision making. Results The handheld computer improved participants' educational experience with evidence based medicine the most, with significant improvements in all outcome scores. More modest improvements were found with the pocket card, whereas the control group showed no appreciable changes in any of the key outcomes. No significant deterioration was observed in the improvements even after withdrawal of the handheld computer during an eight week washout period, suggesting at least short term sustainability of effects. Conclusions Rapid and convenient access to valid and relevant evidence on a portable computing device can improve learning in evidence based medicine, increase current and future use of evidence, and boost students' confidence in clinical decision making

Patterns of cancer-related risk behaviors among Hong Kong construction workers: a latent class analysis

Conference Theme: Translating Discoveries into Prevention and Cure

High-functioning autism patients share similar but more severe impairments in verbal theory of mind than schizophrenia patients

Background. Evidence suggests that autism and schizophrenia share similarities in genetic, neuropsychological and behavioural aspects. Although both disorders are associated with theory of mind (ToM) impairments, a few studies have directly compared ToM between autism patients and schizophrenia patients. This study aimed to investigate to what extent high-functioning autism patients and schizophrenia patients share and differ in ToM performance. Methods. Thirty high-functioning autism patients, 30 schizophrenia patients and 30 healthy individuals were recruited. Participants were matched in age, gender and estimated intelligence quotient. The verbal-based Faux Pas Task and the visual-based Yoni Task were utilised to examine first- and higher-order, affective and cognitive ToM. The task/item difficulty of two paradigms was examined using mixed model analyses of variance (ANOVAs). Multiple ANOVAs and mixed model ANOVAs were used to examine group differences in ToM. Results. The Faux Pas Task was more difficult than the Yoni Task. High-functioning autism patients showed more severely impaired verbal-based ToM in the Faux Pas Task, but shared similar visual-based ToM impairments in the Yoni Task with schizophrenia patients. Conclusions. The findings that individuals with high-functioning autism shared similar but more severe impairments in verbal ToM than individuals with schizophrenia support the autism-schizophrenia continuum. The finding that verbal-based but not visual-based ToM was more impaired in high-functioning autism patients than schizophrenia patients could be attributable to the varied task/item difficulty between the two paradigms