Richard Batrla

4.1k total citations · 4 hit papers
24 papers, 2.4k citations indexed

About

Richard Batrla is a scholar working on Psychiatry and Mental health, Physiology and Epidemiology. According to data from OpenAlex, Richard Batrla has authored 24 papers receiving a total of 2.4k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Psychiatry and Mental health, 9 papers in Physiology and 6 papers in Epidemiology. Recurrent topics in Richard Batrla's work include Dementia and Cognitive Impairment Research (10 papers), Alzheimer's disease research and treatments (9 papers) and Immune Cell Function and Interaction (4 papers). Richard Batrla is often cited by papers focused on Dementia and Cognitive Impairment Research (10 papers), Alzheimer's disease research and treatments (9 papers) and Immune Cell Function and Interaction (4 papers). Richard Batrla collaborates with scholars based in Switzerland, Germany and United States. Richard Batrla's co-authors include Kaj Blennow, Henrik Zetterberg, Harald Hampel, Sid E. O’Bryant, José Luís Molinuevo, Tobias Bittner, Leslie M. Shaw, Simone Lista, Steven J. Kiddle and Colin L. Masters and has published in prestigious journals such as Neuron, Hepatology and Annals of the New York Academy of Sciences.

In The Last Decade

Richard Batrla

24 papers receiving 2.4k citations

Hit Papers

CSF biomarkers of Alzheimer's disease concord with amyloi... 2018 2026 2020 2023 2018 2018 2018 2023 100 200 300 400

Peers

Richard Batrla
Richard Batrla
Citations per year, relative to Richard Batrla Richard Batrla (= 1×) peers Christoffer Rosén

Countries citing papers authored by Richard Batrla

Since Specialization
Citations

This map shows the geographic impact of Richard Batrla's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Richard Batrla with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Batrla more than expected).

Fields of papers citing papers by Richard Batrla

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Richard Batrla. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Richard Batrla. The network helps show where Richard Batrla may publish in the future.

Co-authorship network of co-authors of Richard Batrla

This figure shows the co-authorship network connecting the top 25 collaborators of Richard Batrla. A scholar is included among the top collaborators of Richard Batrla based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Richard Batrla. Richard Batrla is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Frech, Feride, Li Gui, Yingying Ding, et al.. (2024). Economic Impact of Progression from Mild Cognitive Impairment to Alzheimer Disease in the United States. The Journal of Prevention of Alzheimer s Disease. 11(4). 983–991. 2 indexed citations
2.
Mattke, Soeren, Keith A. Betts, Sophie A. Kitchen, et al.. (2024). Real-world diagnostic, referral, and treatment patterns in early Alzheimer's disease among community-based practices in the United States. Journal of Alzheimer s Disease. 102(4). 1172–1182. 1 indexed citations
3.
Li, Gang, Nicola Toschi, Viswanath Devanarayan, et al.. (2023). The age-specific comorbidity burden of mild cognitive impairment: a US claims database study. Alzheimer s Research & Therapy. 15(1). 211–211. 11 indexed citations
4.
Hampel, Harald, Yan Hu, Jeffrey L. Cummings, et al.. (2023). Blood-based biomarkers for Alzheimer’s disease: Current state and future use in a transformed global healthcare landscape. Neuron. 111(18). 2781–2799. 121 indexed citations breakdown →
5.
Hulle, Carol Van, Erin M. Jonaitis, Tobey J. Betthauser, et al.. (2020). An examination of a novel multipanel of CSF biomarkers in the Alzheimer's disease clinical and pathological continuum. Alzheimer s & Dementia. 17(3). 431–445. 89 indexed citations
6.
Rózga, Małgorzata, Tobias Bittner, Richard Batrla, & Johann Karl. (2019). Preanalytical sample handling recommendations for Alzheimer's disease plasma biomarkers. Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring. 11(1). 291–300. 70 indexed citations
7.
Hampel, Harald, Sid E. O’Bryant, José Luís Molinuevo, et al.. (2018). Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nature Reviews Neurology. 14(11). 639–652. 464 indexed citations breakdown →
8.
Molinuevo, José Luís, Scott Ayton, Richard Batrla, et al.. (2018). Current state of Alzheimer’s fluid biomarkers. Acta Neuropathologica. 136(6). 821–853. 375 indexed citations breakdown →
9.
Hansson, Oskar, John Seibyl, Erik Stomrud, et al.. (2018). CSF biomarkers of Alzheimer's disease concord with amyloid‐β PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimer s & Dementia. 14(11). 1470–1481. 485 indexed citations breakdown →
10.
Hansson, Oskar, Alvydas Mikulskis, Anne M. Fagan, et al.. (2018). The impact of preanalytical variables on measuring cerebrospinal fluid biomarkers for Alzheimer's disease diagnosis: A review. Alzheimer s & Dementia. 14(10). 1313–1333. 91 indexed citations
11.
Mollenhauer, Brit, Richard Batrla, Omar M. A. El‐Agnaf, et al.. (2017). A user's guide for α‐synuclein biomarker studies in biological fluids: Perianalytical considerations. Movement Disorders. 32(8). 1117–1130. 51 indexed citations
12.
Jordan, Bruce W.M., et al.. (2015). The Clinical and Health Economic Value of Clinical Laboratory Diagnostics.. PubMed. 26(1). 47–62. 9 indexed citations
13.
Marcellin, Patrick, Michelle Martinot-Peignoux, Tarik Asselah, et al.. (2014). Serum Levels of Hepatitis B Surface Antigen Predict Severity of Fibrosis in Patients With E Antigen-Positive Chronic Hepatitis B. Clinical Gastroenterology and Hepatology. 13(8). 1532–1539.e1. 13 indexed citations
14.
Martinot-Peignoux, Michelle, Roberto José de Carvalho‐Filho, Martine Lapalus, et al.. (2013). Hepatitis B surface antigen serum level is associated with fibrosis severity in treatment-naïve, e antigen-positive patients. Journal of Hepatology. 58(6). 1089–1095. 86 indexed citations
15.
Brunetto, Maurizia Rossana, F. Moriconi, Ferruccio Bonino, et al.. (2008). Hepatitis B virus surface antigen levels: A guide to sustained response to peginterferon alfa-2a in HBeAg-negative chronic hepatitis B # †. Hepatology. 49(4). 1141–1150. 369 indexed citations
16.
Gückel, Brigitte, W. Rudy, Richard Batrla, et al.. (1999). Interleukin-12 requires initial CD80-mediated T-cell activation to support immune responses toward human breast and ovarian carcinoma. Cancer Gene Therapy. 6(3). 228–237. 13 indexed citations
18.
Moebius, Ulrich, W. Rudy, Richard Batrla, et al.. (1998). Induction of Antigen-Specific T Cells by Allogeneic CD80 Transfected Human Carcinoma Cells. Advances in experimental medicine and biology. 451. 195–202. 5 indexed citations
19.
Reinartz, Jeannette, Birgit Schäfer, Richard Batrla, Christophe Klein, & Michael D. Kramer. (1995). Plasmin Abrogates αvβ5-Mediated Adhesion of a Human Keratinocyte Cell Line (HaCaT) to Vitronectin. Experimental Cell Research. 220(2). 274–282. 41 indexed citations
20.
Reinartz, Jeannette, Richard Batrla, Petra Boukamp, Norbert E. Fusenig, & Michael D. Kramer. (1993). Binding and Activation of Plasminogen at the Surface of Human Keratinocytes. Experimental Cell Research. 208(1). 197–208. 45 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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