Anders Björklund

Bio: Anders Björklund is an academic researcher from Lund University. The author has contributed to research in topics: Transplantation & Dopamine. The author has an hindex of 165, co-authored 769 publications receiving 84268 citations. Previous affiliations of Anders Björklund include University of Washington & Institute for the Study of Labor.

Cell therapy for Parkinson's disease: problems and prospects.

Abstract: Cell replacement therapy in Parkinson's disease (PD) has so far been based on the use of primary dopaminergic (DA) neuroblasts obtained from the brain of aborted human fetuses. Clinical trials show that intrastriatal DA neuron transplants can give substantial symptomatic relief in advanced PD patients. Two recent NIH-sponsored placebo-controlled trials, however, have given disappointing results and highlighted a number of critical issues that need to be resolved in order to turn cell transplantation into an acceptable clinical therapy. First, graft survival and clinical outcome has so far been too variable, suggesting that DA neuron grafts may not be equally effective in all PD patients. Secondly, it has become clear that immune mechanisms leading to slowly developing inflammatory responses may compromise long-term graft survival and function. Third, the problems associated with the use of tissue from aborted fetuses make it necessary to develop alternative sources of cells for transplantation. Recent progress in the generation of DA neuroblasts from neural progenitors and embryonic stem cells suggest that these kinds of cell may offer more accessible, defined and standardized sources of cells for clinical transplantation in PD.

Replacing Dopamine Neurons in Parkinson's Disease: How did it happen?

Abstract: The efforts to develop a dopamine cell replacement therapy for Parkinson's disease have spanned over more than three decades. Based on almost 10 years of transplantation studies in animal models, the first patients receiving grafts of fetal-derived dopamine neuroblasts were operated in Lund in 1987. Over the following two decades, a total of 18 patients were transplanted and followed closely by our team with mixed but also very encouraging results. In this article we tell the story of how the preclinical and clinical transplantation program in Lund evolved. We recall the excitement when we obtained the first evidence for survival and function of transplanted neurons in the diseased human brain. We also remember the setbacks that we have experienced during these 30 years and discuss the very interesting developments that are now taking place in this exciting field.

Brain-derived neurotrophic factor enhances striatal neuropeptide expression in both the intact and the dopamine-depleted rat striatum.

Abstract: In order to elucidate the importance of the striatal GABAergic neurones in mediating functional effects of exogenously applied brain-derived neurotrophic factor (BDNF) in the basal ganglia, we performed daily injections of BDNF or vehicle into the dopamine-depleted striatum of unilaterally 6-hydroxydopamine-lesioned rats for 1 week. In situ hybridization revealed that BDNF exacerbated the lesion-induced up-regulation of preproenkephalin (PPE) mRNA, and completely reversed the lesion-induced decrease of preprotachykinin (PPT) mRNA. In contrast, striatal levels of trkB mRNA were not significantly affected by BDNF administration. Up-regulation of PPE and PPT mRNA was also observed in unlesioned BDNF-injected animals. We conclude that exogenously applied BDNF increases neuropeptide mRNA expression in striatal neurones independently of the presence of a dopaminergic innervation.

Electrical stimulation of the lateral habenula increases hippocampal noradrenaline release as monitored by in vivo microdialysis.

Abstract: Hippocampal extracellular levels of noradrenaline (NA) were monitored with the microdialysis technique during electrical stimulation of the lateral habenula (LHb) in halothane anaesthetized rats. The steady state NA level was 20.8±4.6 fmole/15 min of perfusion (mean ± SEM). Electrical stimulation of the LHb for 15 min (15 Hz, 0.5 mA) induced an immediate 228±48% increase in hippocampal NA release, compared to the pre-stimulation baseline (p<0.05). A second stimulation 90 min later induced a similar increase. The effect of LHb stimulation was completely abolished by a knife cut transecting the dorsal NA bundle either immediately rostral to the locus coeruleus or at the level of the parafascicular nucleus. This suggests that the effect was dependent on nerve impulses flow in the coeruleo-hippocampal NA neurons, and was not mediated, e.g., by a local spread of electricity into the hippocampus. Since the LHb has previously been shown to be a powerful activator of the mesencephalic raphe nuclei we tested whether the effect was mediated via the serotonergic system. However, the effect of LHb stimulation on hippocampal NA release persisted after 5,7-di-hydroxytryptamine treatment and after complete radiofrequency lesions of the dorsal and central superior raphe nuclei. The present data suggest that electrical stimulation of the LHb can increase hippocampal NA release through an activation of the locus coeruleus, and that this effect is not dependent on the mesencephalic raphe nuclei. The results support the role of the LHb as a link for limbic and striatal forebrain activation of brain stem monoaminergic systems.

Econometric Analysis of Cross Section and Panel Data

Abstract: The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Econometric Analysis of Panel Data

Abstract: Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.