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.

Eltoprazine counteracts l-DOPA-induced dyskinesias in Parkinson's disease: a dose-finding study.

Abstract: In advanced stages of Parkinson's disease, serotonergic terminals take up L-DOPA and convert it to dopamine. Abnormally released dopamine may participate in the development of L-DOPA-induced dyskinesias. Simultaneous activation of 5-HT1A and 5-HT1B receptors effectively blocks L-DOPA-induced dyskinesias in animal models of dopamine depletion, justifying a clinical study with eltoprazine, a 5-HT1A/B receptor agonist, against L-DOPA-induced dyskinesias in patients with Parkinson's disease. A double-blind, randomized, placebo-controlled and dose-finding phase I/IIa study was conducted. Single oral treatment with placebo or eltoprazine, at 2.5, 5 and 7.5 mg, was tested in combination with a suprathreshold dose of L-DOPA (Sinemet®) in 22 patients with Parkinson's disease (16 male/six female; 66.6 ± 8.8 years old) with L-DOPA-induced dyskinesias. A Wilcoxon Signed Ranked Test was used to compare each eltoprazine dose level to paired randomized placebo on the prespecified primary efficacy variables; area under the curve scores on Clinical Dyskinesia Rating Scale for 3 h post-dose and maximum change of Unified Parkinson's Disease Rating Scale part III for 3 h post-dose. Secondary objectives included effects on maximum Clinical Dyskinesia Rating Scale score, area under the curve of Rush Dyskinesia Rating Scale score for 3 h post-dose, mood parameters measured by Hospital Anxiety Depression Scale and Montgomery Asberg Depression Rating Scale along with the pharmacokinetics, safety and tolerability profile of eltoprazine. A mixed model repeated measures was used for post hoc analyses of the area under the curve and peak Clinical Dyskinesia Rating Scale scores. It was found that serum concentrations of eltoprazine increased in a dose-proportional manner. Following levodopa challenge, 5 mg eltoprazine caused a significant reduction of L-DOPA-induced dyskinesias on area under the curves of Clinical Dyskinesia Rating Scale [-1.02(1.49); P = 0.004] and Rush Dyskinesia Rating Scale [-0.15(0.23); P = 0.003]; and maximum Clinical Dyskinesia Rating Scale score [-1.14(1.59); P = 0.005]. The post hoc analysis confirmed these results and also showed an antidyskinetic effect of 7.5 mg eltoprazine. Unified Parkinson's Disease Rating Scale part III scores did not differ between the placebo and eltoprazine treatments. The most frequent adverse effects after eltoprazine were nausea and dizziness. It can be concluded that a single dose, oral treatment with eltoprazine has beneficial antidyskinetic effects without altering normal motor responses to L-DOPA. All doses of eltoprazine were well tolerated, with no major adverse effects. Eltoprazine has a favourable risk-benefit and pharmacokinetic profile in patients with Parkinson's disease. The data support further clinical studies with chronic oral eltoprazine to treat l-DOPA-induced-dyskinesias.

Transcription factor Nurr1 maintains fiber integrity and nuclear-encoded mitochondrial gene expression in dopamine neurons

Abstract: Developmental transcription factors important in early neuron specification and differentiation often remain expressed in the adult brain. However, how these transcription factors function to mantain appropriate neuronal identities in adult neurons and how transcription factor dysregulation may contribute to disease remain largely unknown. The transcription factor Nurr1 has been associated with Parkinson's disease and is essential for the development of ventral midbrain dopamine (DA) neurons. We used conditional Nurr1 gene-targeted mice in which Nurr1 is ablated selectively in mature DA neurons by treatment with tamoxifen. We show that Nurr1 ablation results in a progressive pathology associated with reduced striatal DA, impaired motor behaviors, and dystrophic axons and dendrites. We used laser-microdissected DA neurons for RNA extraction and next-generation mRNA sequencing to identify Nurr1-regulated genes. This analysis revealed that Nurr1 functions mainly in transcriptional activation to regulate a battery of genes expressed in DA neurons. Importantly, nuclear-encoded mitochondrial genes were identified as the major functional category of Nurr1-regulated target genes. These studies indicate that Nurr1 has a key function in sustaining high respiratory function in these cells, and that Nurr1 ablation in mice recapitulates early features of Parkinson's disease.

Unemployment and Mental Health: Some Evidence from Panel Data

Abstract: Microdata are used in this paper to analyze the effects of unemployment on mental health. The analysis is done in two steps. First, cross-section data of labor force participants are analyzed. It appears that the unemployed have worse mental health than the employed. Next, panel data are used to control for "fixed" effects, that is, unobserved omitted variables that are constant over time. The model is also specified to allow both the occurrence of and duration of unemployment to affect mental health. Then we cannot reject the hypothesis that there are no effects of unemployment on mental health. However, some sensitivity tests indicate that the precision of our estimates is rather low.

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.